Electronics and communications engineering Books

Book SynopsisDiscover the latest research in path planning and robust path tracking control InAutonomous Road Vehicle Path Planning and Tracking Control, a team of distinguished researchers delivers a practical and insightful exploration of how to design robust path tracking control. The authors include easy to understand concepts that are immediately applicable to the work of practicing control engineers and graduate students working in autonomous driving applications. Controller parameters are presented graphically, and regions of guaranteed performance are simple to visualize and understand. The book discusses the limits of performance, as well as hardware-in-the-loop simulation and experimental results that are implementable in real-time. Concepts of collision and avoidance are explained within the same framework and a strong focus on the robustness of the introduced tracking controllers is maintained throughout. In addition to a continuous treatment of comTable of ContentsAuthor biographies Preface Abbreviations Chapter 1. Introduction 1 1.1 Motivation and Introduction 1 1.2 History of Automated Driving 4 1.3 ADAS to Autonomous Driving 13 1.4 Autonomous Driving Architectures 14 1.5 Cybersecurity Considerations 15 1.6 Organization and Scope of the Book 16 1.7 Chapter Summary and Concluding Remarks 16 References 16 Chapter 2. Vehicle, Path and Path Tracking Models 21 2.1 Tire Force Model 21 2.1.1 Introduction 21 2.1.2 Tire forces/moments and slip 22 2.1.3 Longitudinal tire force modeling 25 2.1.4 Lateral tire force modeling 28 2.1.5 Self-aligning moment model 30 2.1.6 Coupling of tire forces 32 2.2 Vehicle longitudinal dynamics model 37 2.3 Vehicle Lateral Dynamics Model 41 2.3.1 Geometry of cornering 41 2.3.2 Single track lateral vehicle model 43 2.3.3 Augmented single track lateral vehicle model 47 2.3.4 Linearized single track lateral vehicle model 48 2.4 Path Model 52 2.5 Pure Pursuit: Geometry Based Low Speed Path Tracking 58 2.6 Stanley Method for Path Tracking 59 2.7 Path Tracking in Reverse Driving and Parking 62 2.8 Chapter Summary and Concluding Remarks 63 References 63 Chapter 3. Simulation, Experimentation and Estimation Overview 65 3.1 Introduction to the Simulation Based Development and Evaluation Process 65 3.2 Model-in-the-Loop Simulation 68 3.2.1 Linear and Nonlinear Vehicle Simulation Models 68 3.2.2 Higher Fidelity Vehicle Simulation Models 69 3.3 Virtual Environments Used in Simulation 71 3.3.1 Road Network Creation 71 3.3.2 Driving Environment Construction 73 3.3.3 Capabilities 77 3.4 Hardware-in-the-Loop Simulation 82 3.5 Experimental Vehicle Testbeds 84 3.5.1 Unified Approach 84 3.5.2 Unified AV Functions and Sensors Library 87 3.6 Estimation 88 3.6.1 Estimation of the Effective Tire Radius 88 3.6.2 Slip Slope Method for Road Friction Coefficient Estimation 89 3.6.3 Results and Discussion 92 3.7 Chapter Summary and Concluding Remarks 97 References 97 Chapter 4. Path Description and Generation 100 4.1 Introduction 100 4.2 Discrete Waypoint Representation 100 4.3 Parametric Path Description 103 4.3.1 Clothoids 104 4.3.2 Bezier Curves 107 4.3.3 Polynomial Spline Description 108 4.4 Tracking Error Calculation 113 4.5 Conclusions 114 References 115 Chapter 5. Collision Free Path Planning 117 5.1 Introduction 117 5.2 Elastic Band Method 121 5.2.1 Path Structure 121 5.2.2 Calculation of Forces 121 5.2.3 Reaching Equilibrium Point 124 5.2.4 Selected Scenarios 125 5.2.5 Results 127 5.3 Path Planning with Minimum Curvature Variation 135 5.3.1 Optimization based on G2-quintic Splines Path Description 135 5.3.2 Reduction of Computation Cost using Lookup Tables 138 5.3.3 Geometry-based Collision-free Target Points Generation 142 5.3.4 Simulation Results 145 5.4 Model-based Trajectory Planning 148 5.4.1 Problem Formulation 148 5.4.2 Parameterized Vehicle Control 149 5.4.3 Constrained Optimization on Curvature Control 150 5.4.4 Sampling of the Longitudinal Movements 155 5.4.5 Trajectory Evaluation and Selection 157 5.4.6 Integration of Road Friction Coefficient Estimation for Safety Enhancement 159 5.4.7 Simulation Results in Complex Scenarios 162 5.5 Chapter Summary and Concluding Remarks 169 References 170 Chapter 6. Path Tracking Model Regulation 174 6.1 Introduction 174 6.2 DOB Design and Frequency Response Analysis 175 6.2.1 DOB Derivation and Loop Structure 175 6.2.2 Application Examples 178 6.2.3 Disturbance Rejection Comparison 188 6.3 Q Filter Design 188 6.4 Time Delay Performance 189 6.5 Chapter Summary and Concluding Remarks 193 References 193 Chapter 7. Robust Path Tracking Control 195 7.1 Model Predictive Control for Path Following 196 7.1.1 Formulation of linear adaptive MPC problem 196 7.1.2 Estimation of Lateral Velocity 198 7.1.3 Experimental Results 201 7.2 Design Methodology for Robust Gain-scheduling Law 204 7.2.1 Problem Formulation 204 7.2.2 Design via Optimization in Linear Matrix Inequalities form 205 7.2.3 Parameter-space Gain-scheduling Methodology 207 7.3 Robust Gain-scheduling Application to Path Tracking Control 213 7.3.1 Car Steering Model and Parameter Uncertainty 213 7.3.2 Controller Structure and Design Parameters 215 7.3.3 Application of Parameter-space Gain-scheduling 217 7.3.4 Comparative Study of LMI Design 222 7.3.5 Experimental Results and Discussions 223 7.4 Add-on Vehicle Stability Control for Autonomous Driving 227 7.4.1 Direct Yaw Moment Control Strategies 228 7.4.2 Direct Yaw Moment Distribution via Differential Braking 234 7.4.3 Simulation Results and Discussion 235 7.5 Chapter Summary and Concluding Remarks 238 References 238 Chapter 8. Summary and Conclusions 242 8.1 Summary 242 8.2 Conclusions 244

Read more less

Book SynopsisLearn to deploy novel algorithms to improve and secure smart city infrastructure In Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision Making, accomplished researchers Drs. M. Hadi Amini and Miadreza Shafie-Khah deliver a crucial exploration of new directions in the science and engineering of deploying novel and efficient computing algorithms to enhance the efficient operation of the networks and communication systems underlying smart city infrastructure. The book covers special issues on the deployment of these algorithms with an eye to helping readers improve the operation of smart cities. The editors present concise and accessible material from a collection of internationally renowned authors in areas as diverse as computer science, electrical engineering, operation research, civil engineering, and the social sciences. They also include discussions of the use of artificial intelligence to secure the operations of cyberphysicaTable of ContentsBiography xv List of Contributors xvii Ch 1. Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications 1Aaron Turransky and M. Hadi Amini Ch 2. Data Analytics for Smart cities: Challenges and Promises 13Ghareh Mohammadi, Farzan Shenavarmasouleh, M. Hadi Amini, and Hamid Reza Arabnia Ch 3. Embodied AI-driven Operation of Smart Cities: A Concise Review 29Farzan Shenavarmasouleh, Ghareh Mohammadi, M. Hadi Amini, and Hamid Reza Arabnia Ch 4. Analysis of Different Regression Techniques for Battery Capacity Prediction 47Param Khakhar and Rahul Kumar Dubey Ch 5. Smart Charging and Operation of Electric Fleet Vehicles in a Smart City 61Milad Kazemi, Samuel Bailey, Sadegh Soudjani, and Vahid Vahidinasab Ch 6. Risk-Aware Cyber-Physical Control for Resilient Smart Cities 95Eman Hammad and Abdallah Farraj Ch 7. Wind speed prediction using a robust possibilistic c-regression models: A case study of Tunisia 123Achraf J. Telmoudi, Moez Soltani, Lotfi Chaouech, and Abdelkader Chaari Ch 8. Intelligent Traffic: Formulating an Applied Research Methodology FOR Computer Vision and Vehicle Detection 139Gabrielle Bakker-Reynolds, Emre Erturk, and Istvan Lengyel Ch 9. Implementation And Evaluation of Computer Vision Prototype for Vehicle Detection 167Gabrielle Bakker-Reynolds, Emre Erturk, Istvan Lengyel, and Noor Alani Ch 10. A Review on Applications of the standard series IEC 61850 in Smart Grid applications 197Youcef Himri, S. M. Muyeen, Farhan Hameed Malik, Saliha Himri, Khairol Amali bin Ahmad, Nachida Kasbadji Merzouk, and Mustapha Merzouk Ch 11. Electric vehicles in smart cities 255Sahand G. Liasi and Mohammad T. Bina Author Index 287

Read more less

Book SynopsisIn Active Electronically Scanned Arrays: Fundamentals and Applications, electromagnetics expert Dr. Arik D. Brown delivers a foundational treatment of active electronically scanned arrays (AESAs) ideal for engineering students and professionals. The distinguished author provides an overview of the primary subsystems of an AESA and detailed explanations of key design concepts and fundamentals for subsystems, including antenna array elements, transmit/receive modules, and beamformers. Performance results for various AESA architectures often found in industry, including analog, subarrayed, and digital beamforming AESAs, are discussed. With a focus on practical knowledge and applications, Active Electronically Scanned Arrays: Fundamentals and Applications offers an accessible overview of a technology critical to the implementation of collision avoidance in cars, air surveillance radar, communication antennas, and defense technologies. The book also includes:<Table of ContentsPreface xiii Acknowledgments xv Acronyms xvii 1 AESA Overview 1 1.1 Introduction 1 1.2 AESA History 1 1.3 AESA Applications 3 1.3.1 RADAR 3 1.3.2 ElectronicWarfare 7 1.3.2.1 Electronic Attack 9 1.3.2.2 Electronic Support Measures 9 1.3.3 Communications 10 1.3.4 Signals Intelligence 10 1.4 AESA Point of Reference 11 1.5 Block Diagram 15 1.5.1 Antenna Array Elements 15 1.5.2 Transmit Receive Modules 16 1.5.3 Beamformer 16 1.6 AESA Cascaded Performance and Architecture Selection 16 References 17 2 AESA Theory 19 2.1 Introduction 19 2.2 General One-Dimensional Formulation 20 2.2.1 Pattern Expression without Electronic Scanning 20 2.2.2 Pattern Expression with Electronic Scanning 22 2.3 AESA Fundamental Topics 23 2.3.1 Beamwidth 23 2.3.2 Instantaneous Bandwidth 24 2.3.3 Grating Lobes 27 2.3.4 Error Effects 29 2.3.5 Quantization Effects 29 2.3.6 Random Error Effects (Amplitude and Phase) 30 2.4 One-Dimensional Pattern Synthesis 31 2.4.1 Varying Amplitude Distribution 33 2.4.2 Varying Frequency 39 2.4.3 Varying Scan Angle 39 2.5 Conformal Arrays 40 2.5.1 Array Pattern for a Linear Array 40 2.5.2 Array Pattern for a Conformal Array 42 2.5.3 Example 43 2.5.3.1 Conformal One-Dimensional Array 43 2.6 2D AESA Pattern Formulation 44 2.6.1 AESA Spatial Coordinate Definitions 45 2.6.2 Antenna Coordinates 46 2.6.3 Radar Coordinates 48 2.6.4 Antenna Cone Angle Coordinates 49 2.6.5 Sine Space Representation 50 2.6.6 AESA Element Grid 52 2.6.6.1 Rectangular Grid 52 2.6.6.2 Triangular Grid 55 2.6.7 Two-Dimensional Pattern Synthesis 56 2.6.7.1 Ideal Patterns 57 2.7 Circular Grid AESA Patterns 61 2.8 Tilted AESA Patterns 66 2.9 Integrated Gain 71 References 73 3 Array Elements 75 3.1 Introduction 75 3.2 Bandwidth 78 3.3 Polarization 81 3.3.1 Electromagnetic Polarization Fundamentals 82 3.3.2 Types of Polarization 83 3.3.2.1 Linear Polarization 83 3.3.2.2 Circular Polarization 84 3.3.2.3 Elliptical Polarization 85 3.3.3 Polarization States 87 3.3.4 Array Polarization 88 3.3.4.1 Key Requirements 90 3.4 Array Grid 91 3.5 Mismatch and Ohmic Loss 92 3.6 Active Match 95 3.7 Scan Loss 98 References 101 4 Transmit Receive Modules 103 4.1 Overview 103 4.1.1 TRM Baseline Topology 108 4.1.1.1 TR Switches 108 4.1.1.2 Amplifiers 109 4.1.1.3 Pre-Amplifier and HPA 109 4.1.1.4 LNA 110 4.1.1.5 Phase Shifter 110 4.1.1.6 Attenuator 110 4.1.1.7 Circulator 110 4.1.1.8 Receiver Protector 111 4.1.1.9 Filters 111 4.1.2 TRM Topology Types 111 4.1.2.1 Receive Only 111 4.1.2.2 Channelization 112 4.1.2.3 Simultaneous Beams 113 4.1.2.4 Multi-Channel TRMs 113 4.2 Transmit Operation 115 4.2.1 Efficiency and Amplifier Classes 116 4.2.2 P1dB 117 4.2.3 Linearity 118 4.2.3.1 Harmonics and Intermodulation Products 118 4.2.3.2 Intercept Point 121 4.2.4 Wideband Operation 123 4.2.4.1 Nonlinear Beams 123 4.2.5 Thermal Implications Due to Output Match 125 4.3 Receive Operation 127 4.4 Reliability 128 4.4.1 Probability of Failed Elements 129 4.4.2 MTBF 132 References 134 5 Beamformers 135 5.1 Introduction 135 5.1.1 Tile and Brick Architectures 136 5.1.2 Corporate and Noncorporate Beamforming 140 5.2 Lossless Beamformer 142 5.2.1 Transmit 142 5.2.2 Receive 143 5.3 BeamformerWeighting 145 5.4 DistributedWeighting 148 5.5 Beam Spoiling 149 5.6 Monopulse for Angle Estimation 153 5.6.1 Three Channel Monopulse with an AESA 154 5.6.1.1 Calibration for Monopulse Coupler Errors 159 5.6.2 Two-Channel Monopulse with an AESA 159 5.6.2.1 Low Sidelobe Delta Beams 162 References 163 6 AESA Cascaded Performance 165 6.1 Introduction 165 6.2 Fundamental Expressions for Cascade Calculations 168 6.2.1 Noise Model 168 6.2.1.1 Active Device 168 6.2.1.2 Resistive Device 169 6.2.1.3 Noise Factor Definition 169 6.2.2 Cascaded Noise Factor 170 6.3 AESA Cascaded Performance 174 6.3.1 AESA Output Signal Power 174 6.3.2 AESA Output Noise Power 175 6.3.3 AESA Signal/Noise Gain and Noise Factor 177 6.3.4 AESA nth-Order Intercept Point 179 6.3.5 AESA Spurious Free Dynamic Range 181 References 182 7 AESA Architectures 183 7.1 Introduction 183 7.2 Baseline Architecture 183 7.3 Subarray Architectures 186 7.4 Subarray Pattern Formulation 188 7.5 Subarray Beamforming 189 7.5.1 Subarray Phase Shifter Beamforming 189 7.5.2 Subarray Time Delay Beamforming 191 7.5.3 Subarray Digital Beamforming 194 7.6 Overlapped Subarrays 195 7.7 Elemental DBF Architecture 199 7.8 Adaptive Beamforming 201 References 207 Appendix A Array Factor (AF) Derivation 209 Appendix B Instantaneous Bandwidth Derivation 211 Reference 212 Appendix C Triangular Grid Grating Lobes Derivation 213 References 215 Appendix D General Expression for Intercept Point Derivation 217 Appendix E Impact of Failed Elements on AESA Performance 219 Appendix F Sidelobe Blanking with an AESA 223 Reference 227 Appendix G External Noise Considerations 229 Appendix H Important AESA Equations Reference 233 H.1 System Level Equations 233 H.1.1 Radar Range Equation 233 H.1.2 Signal and Noise Gain 233 H.1.3 Array Gain 234 H.2 AESA Theory 234 H.2.1 1D Pattern 234 H.2.1.1 Phase Shifter and Time Delay Steering 234 H.2.1.2 General Expression 234 H.2.1.3 Conformal Array 234 H.2.1.4 Alternate AF Expression 235 H.2.2 2D Pattern 235 H.2.3 Beamwidth 235 H.2.4 Instantaneous Bandwidth (IBW) 235 H.2.5 Grating Lobes 235 H.2.6 AESA Errors 236 H.2.7 Coordinate System Transformations 236 H.2.8 Sine Space 237 H.2.9 Roll, Pitch, and Yaw Formulas 237 H.2.10 Integrated Gain 237 H.3 Array Elements 237 H.3.1 Fractional Bandwidth 237 H.3.2 Polarization 238 H.3.3 Active Match 238 H.3.4 Scan Loss 238 H.4 Transmit Receive Modules 239 H.4.1 Amplifier Expressions 239 H.4.2 Reliability 239 H.5 Beamformer 240 H.5.1 General Beamformer Expressions 240 H.5.2 Beam Spoiling 240 H.5.3 Monopulse AOA 240 H.6 AESA Cascaded Performance 241 H.6.1 Fundamental Expressions 241 H.6.2 AESA Cascaded Expressions 241 H.7 Adaptive Beamforming 242 Reference 243 Index 245

Read more less

Book SynopsisINTEGRATION OF RENEWABLE ENERGY SOURCES WITH SMART GRID Provides comprehensive coverage of renewable energy and its integration with smart grid technologies. This book starts with an overview of renewable energy technologies, smart grid technologies, and energy storage systems and covers the details of renewable energy integration with smart grid and the corresponding controls. It also provides an enhanced perspective on the power scenario in developing countries. The requirement of the integration of smart grid along with the energy storage systems is deeply discussed to acknowledge the importance of sustainable development of a smart city. The methodologies are made quite possible with highly efficient power convertor topologies and intelligent control schemes. These control schemes are capable of providing better control with the help of machine intelligence techniques and artificial intelligence. The book also addresses modern power convertor topologies and theTable of ContentsPreface xv 1 Renewable Energy Technologies 1V. Chamundeswari, R. Niraimathi, M. Shanthi and A. Mahaboob Subahani 1. Introduction 1 1.1 Types of Renewable Energy 2 1.1.1 Solar Energy 3 1.1.2 Wind Energy 7 1.1.3 Fuel Cell 8 1.1.4 Biomass Energy 11 1.1.5 Hydro-Electric Energy 13 1.1.6 Geothermal Energy 14 References 17 2 Present Power Scenario in India 19Niraimathi R., Pradeep V., Shanthi M. and Kathiresh M. 2.1 Introduction 20 2.2 Thermal Power Plant 20 2.2.1 Components of Thermal Power Plant 21 2.2.2 Major Thermal Power Plants in India 23 2.3 Gas-Based Power Generation 24 2.3.1 Basics of Gas-Based Power Generation 24 2.3.2 Major Gas-Based Power Plants in India 25 2.4 Nuclear Power Plants 26 2.4.1 India’s Hold in Nuclear Power 27 2.4.2 Major Nuclear Power Plants 27 2.4.3 Currently Operational Nuclear Power Plants 28 2.4.4 Challenges of Nuclear Power Plants 28 2.5 Hydropower Generation 29 2.5.1 Pumped Storage Plants 29 2.6 Solar Power 30 2.6.1 Photovoltaic 30 2.6.2 Photovoltaic Solar Power System 30 2.6.3 Concentrated Solar Power System 31 2.6.4 Major Solar Parks in India 32 2.7 Wind Energy 32 2.8 The Inherited Structure 34 References 34 3 Introduction to Smart Grid 37G. R. Hemanth, S. Charles Raja and P. Venkatesh 3.1 Need for Smart Grid in India 38 3.2 Present Power Scenario in India 38 3.2.1 Performance of Generation From Conventional Sources 40 3.2.2 Status of Renewable Energy Sources 40 3.3 Electric Grid 43 3.3.1 Evolving Scenario of the Electric Grid 45 3.3.1.1 Integrated Grid 46 3.3.1.2 Prosumers 46 3.3.1.3 Transmission v/s Energy Storage 47 3.3.1.4 Changing Nature of Loads 47 3.3.1.5 Electric Vehicles 48 3.3.1.6 Microgrids 48 3.4 Overview of Smart Grids 49 3.4.1 Purpose of Smart Grid 49 3.5 Smart Grid Components for Transmission System 50 3.5.1 Supervisory Control and Data Acquisition System 50 3.5.1.1 SCADA Overview 51 3.5.1.2 Components of SCADA 51 3.5.2 Energy Management System 52 3.5.3 Wide-Area Monitoring System 52 3.6 Smart Grid Functions Used in Distribution System 53 3.6.1 Supervisory Control and Data Acquisition System 53 3.6.2 Distribution Management System 54 3.6.3 Distribution Automation 54 3.6.4 Substation Automation 55 3.6.5 Advanced Metering Infrastructure 55 3.6.6 Geographical Information System 57 3.6.7 Peak Load Management 58 3.6.8 Demand Response 58 3.6.9 Power Quality Management 59 3.6.10 Outage Management System 59 3.6.11 Distribution Transformer Monitoring System 59 3.6.12 Enterprise Application Integration 59 3.6.13 Smart Street Lights 60 3.6.14 Energy Storage 60 3.6.15 Cyber Security 60 3.6.16 Analytics 60 3.7 Case Study: Techno-Economic Analysis 61 3.7.1 Peak Load Shaving and Metering Efficiency 61 3.7.2 Outage Management System 63 3.7.3 Loss Detection 64 3.7.4 Tamper Analysis 66 3.8 Case Study: Solar PV Awareness of Puducherry SG Pilot Project 69 3.9 Recent Trends in Smart Grids 70 3.9.1 Smart GRIP Architecture 70 3.9.2 Implementation of Smart Meter With Prepaid Facility 74 References 74 4 Internet of Things–Based Advanced Metering Infrastructure (AMI) for Smart Grids 77V. Gomathy, V. Kavitha, C. Nayantara, J. Mohammed Feros Khan, Vimalarani G. and S. Sheeba Rani 4.1 Introduction 78 4.1.1 Smart Grids 78 4.1.2 Smart Meters 80 4.2 Advanced Metering Infrastructure 81 4.2.1 Smart Devices 82 4.2.2 Communication 83 4.2.3 Data Management System 85 4.2.4 Mathematical Modeling 87 4.2.5 Energy Theft Detection Techniques 89 4.3 IoT-Based Advanced Metering Infrastructure 89 4.3.1 Intrusion Detection System 90 4.4 Results 93 4.5 Discussion 94 4.6 Conclusion and Future Scope 97 References 97 5 Requirements for Integrating Renewables With Smart Grid 101Indrajit Sarkar 5.1 Introduction 102 5.1.1 Smart Grid 102 5.1.2 Renewable Energy Resources 105 5.1.3 How Smart Grids Enable Renewables 111 5.1.4 Smart Grid and Distributed Generation 111 5.1.5 Grid Integration Terminologies 112 5.2 Challenges in Integrating Renewables Into Smart Grid 112 5.2.1 The Power Flow Control of Distributed Energy Resources 113 5.2.2 Investments on New Renewable Energy Generations 113 5.2.3 Transmission Expansion 114 5.2.4 Improved Flexibility 114 5.2.5 High Penetration of Renewables in Future 115 5.2.6 Standardizing Control of ESS 115 5.2.7 Regulations 116 5.2.8 Standards 116 5.3 Conclusion 116 References 117 6 Grid Energy Storage Technologies 119Chandra Sekhar Nalamati 6.1 Introduction 120 6.1.1 Need of Energy Storage System 121 6.1.2 Services Provided by Energy Storage System 122 6.2 Grid Energy Storage Technologies: Classification 123 6.2.1 Pumped Hydro Storage System 123 6.2.2 Compressed Air Storage System 124 6.2.3 Flywheel Energy Storage System 125 6.2.4 Superconducting Magnet Storage System 125 6.2.5 Battery Storage System 127 6.2.6 Capacitors and Super Capacitor Storage System 129 6.2.7 Fuel Cell Energy Storage System 130 6.2.8 Thermal Storage System 131 6.3 Grid Energy Storage Technologies: Analogy 132 6.4 Applications of Energy Storage System 135 6.5 Power Conditioning of Energy Storage System 136 6.6 Conclusions 136 References 137 7 Multi-Mode Power Converter Topology for Renewable Energy Integration With Smart Grid 141M. Sathiyanathan, S. Jaganathan and R. L. Josephine 7.1 Introduction 142 7.2 Literature Survey 144 7.3 System Architecture 145 7.3.1 Solar PV Array 146 7.3.2 Wind Energy Generator 147 7.4 Modes of Operation of Multi-Mode Power Converter 149 7.4.1 Buck Mode 150 7.4.2 Boost Mode 152 7.4.3 Bi-Directional Mode 155 7.5 Control Scheme 158 7.5.1 Mode Selection 159 7.5.2 Maximum Power Point Tracking 159 7.5.3 Reconfigurable SPWM Generation 161 7.6 Results and Discussion 163 7.7 Conclusion 167 References 168 8 Decoupled Control With Constant DC Link Voltage for PV-Fed Single-Phase Grid Connected Systems 171C. Maria Jenisha 8.1 Introduction 171 8.2 Schematic of the Grid-Tied Solar PV System 173 8.2.1 DC Link Voltage Controller 175 8.2.2 MPPT Controller 176 8.2.3 SPWM-Based dq Controller 176 8.3 Simulation and Experimental Results of the Grid Tied Solar PV System 178 8.4 Conclusion 183 References 184 9 Wind Energy Conversion System Feeding Remote Microgrid 187K. Arthishri and N. Kumaresan 9.1 Introduction 188 9.2 Literature Review 189 9.3 Direct Grid Connected Configurations of Three-Phase WDIG Feeding Single-Phase Grid 191 9.4 Three-Phase WDIG Feeding Single-Phase Grid With Power Converters 191 9.5 Performance of the Three-Phase Wind Generator System Feeding Power to Single-Phase Grid 193 9.5.1 Wind Turbine Characteristics 193 9.5.2 Generator Analysis 194 9.6 Power Converter Configurations 198 9.6.1 Configuration 1: WDIG With Uncontrolled Rectifier–Line Commutated Inverter 198 9.6.2 Configuration 2: WDIG With Uncontrolled Rectifier–(DC-DC)–Line Commutated Inverter 200 9.6.2.1 Closed-Loop Operation of UR-DC/DC-LCI Configuration 200 9.6.3 Configuration 3: WDIG With Uncontrolled Rectifier–Voltage Source Inverter 201 9.6.3.1 Closed-Loop Operation of UR-VSI Configuration 202 9.7 Conclusion 204 References 204 10 Microgrid Protection 209Suman M., Srividhya S. and Padmagirisan P. 10.1 Introduction 209 10.2 Necessity of Distributed Energy Resources 210 10.3 Concept of Microgrid 210 10.4 Why the Protection With Microgrid is Different From the Conventional Distribution System Protection 211 10.4.1 Role of the Type of DER on Protection 212 10.5 Foremost Challenges in Microgrid Protection 212 10.5.1 Relay Blinding 212 10.5.2 Variations in Fault Current Level 213 10.5.3 Selectivity 214 10.5.4 False/Unnecessary Tripping 214 10.5.5 Loss of Mains (Islanding Condition) 214 10.6 Microgrid Protection 215 10.6.1 Overcurrent Protection 215 10.6.2 Distance Protection 216 10.6.2.1 Effect of Distributed Generator Inclusion in the Distribution System on Distance Relay 218 10.6.3 Differential Protection 219 10.6.3.1 Drawbacks in Differential Protection 220 10.6.4 Hybrid Tripping Relay Characteristic 220 10.6.5 Voltage-Based Methods 221 10.6.6 Adaptive Protection Methods 222 10.7 Literature Survey 223 10.8 Comparison of Various Existing Protection Schemes for Microgrids 225 10.9 Loss of Mains (Islanding) 225 10.10 Necessity to Detect the Unplanned Islanding 227 10.10.1 Health Hazards to Maintenance Personnel 227 10.10.2 Unsynchronized Reclosing 228 10.10.3 Ineffective Grounding 228 10.10.4 Inept Protection 229 10.10.5 Loss of Voltage and Frequency Control 229 10.11 Unplanned Islanding Identification Methods 229 10.11.1 Communication-Based Methods (Remote Method) 230 10.11.2 Non-Communication–Based Methods (Local Method) 230 10.11.2.1 Passive Method 230 10.11.2.2 Active Method 231 10.11.2.3 Hybrid Method 232 10.12 Comparison of Unplanned Islanding Identification Methods 234 10.13 Discussion 234 10.14 Conclusion 235 References 235 11 Microgrid Optimization and Integration of Renewable Energy Resources: Innovation, Challenges and Prospects 239Blesslin Sheeba T., G. Jims John Wessley, Kanagaraj V., Kamatchi S., A. Radhika and Janeera D.A. 11.1 Introduction 240 11.2 Microgrids 242 11.3 Renewable Energy Sources 245 11.3.1 Renewable Energy Technologies (RETs) 246 11.3.2 Distributed Storage Technologies 247 11.3.3 Combined Heat and Power 248 11.4 Integration of RES in Microgrid 248 11.5 Microgrid Optimization Schemes 250 11.5.1 Load Forecasting Schemes 251 11.5.2 Generation Unit Control 252 11.5.3 Storage Unit Control 252 11.5.4 Data Monitoring and Transmission 253 11.5.4.1 Communication Systems 254 11.5.5 Energy Management and Power Flow 256 11.6 Challenges in Implementation of Microgrids 257 11.7 Future Prospects of Microgrids 259 11.8 Conclusion 259 References 260 12 Challenges in Planning and Operation of Large-Scale Renewable Energy Resources Such as Solar and Wind 263J. Vishnupriyan and A. Dhanasekaran 12.1 Introduction 264 12.2 Solar Grid Integration 265 12.3 Wind Energy Grid Integration 267 12.4 Challenges in the Integration of Renewable Energy Systems with Grid 267 12.4.1 Disturbances in the Grid Side 269 12.4.2 Virtual Synchronous Machine Method 271 12.4.3 Frequency Control 272 12.4.4 Solar Photovoltaic Array in Frequency Regulation 275 12.4.5 Harmonics 275 12.5 Electrical Energy Storage (EES) 276 12.6 Conclusion 277 References 278 13 Mitigating Measures to Address Challenges of Renewable Integration—Forecasting, Scheduling, Dispatch, Balancing, Monitoring, and Control 281K. Latha Maheswari, B. Sathya and A. Maideen Abdhulkader Jeylani 13.1 Introduction 282 13.2 Microgrid 283 13.2.1 Types of Microgrid 284 13.2.1.1 DC Microgrid 284 13.2.1.2 AC Microgrid 285 13.2.1.3 Hybrid AC-DC Microgrid 286 13.3 Large-Scale Integration of Renewables: Issues and Challenges 287 13.4 A Review on Short-Term Load Forecasting Methods 288 13.4.1 Short-Term Load Forecasting Methods 290 13.4.1.1 Statistical Technique 290 13.5 Overview on Control of Microgrid 291 13.5.1 Need for Microgrid Control 291 13.5.2 Fully Centralized Control 292 13.5.3 Decentralized Control 292 13.5.4 Hierarchical Control 293 13.5.4.1 Primary Control 293 13.5.4.2 Secondary Control 295 13.5.4.3 Tertiary Control 295 13.6 Measures to Support Large-Scale Renewable Integration 296 13.6.1 Basic Idea of Preventive Control 297 13.6.1.1 Maximum Output Control Mode 297 13.6.1.2 Output Following Mode 298 References 298 14 Mitigation Measures for Power Quality Issues in Renewable Energy Integration and Impact of IoT in Grid Control 305Hepsiba D., L.D. Vijay Anand, Granty Regina Elwin J., J.B. Shajilin and D. Ruth Anita Shirley 14.1 Introduction 306 14.2 Impact of Power Quality Issues 308 14.2.1 Power Quality in Renewable Energy 314 14.2.2 Power Quality Issues in Wind and Solar Renewable Energy 316 14.2.2.1 Wind Renewable Energy 316 14.2.2.2 Solar Renewable Energy 317 14.3 Mitigation of Power Quality Issues 317 14.3.1 UPQC 317 14.3.2 DVR 318 14.3.3 D-STATCOM 319 14.3.4 UPS 319 14.3.5 TVSS 320 14.3.6 Internet of Things in Distributed Generations Systems 320 14.4 Discussions 321 14.5 Conclusion and Future Scope 322 References 323 15 Smart Grid Implementations and Feasibilities 327Suresh N. S., Padmavathy N. S., S. Arul Daniel and Ramakrishna Kappagantu 15.1 Introduction 328 15.1.1 Smart Grid Technologies—Literature Review 328 15.2 Need for Smart Grid 329 15.2.1 Smart Grid Description 330 15.3 Smart Grid Sensing, Measurement, Control, and Automation Technologies 331 15.3.1 Advanced Metering Infrastructure 332 15.3.2 Key Components of AMI 332 15.3.3 Smart Meter 332 15.3.4 Communication Infrastructure and Protocols for AMI 333 15.3.4.1 Data Concentrator Unit 334 15.3.5 Benefits of AMI 335 15.3.6 Peak Load Management 336 15.3.7 Distribution Management System 336 15.3.8 Distribution Automation System 337 15.4 Implementation of Smart Grid Project 339 15.4.1 Challenges and Issues of SG Implementation 339 15.4.2 Smart Grid Implementation in India: Puducherry Pilot Project 341 15.4.3 Power Quality of the Smart Grid 341 15.5 Solar PV System Implementation Barriers 342 15.6 Smart Grid and Microgrid in Other Areas 343 15.6.1 Maritime Power System 343 15.6.2 Space Electrical Grids 343 15.7 Conclusion 344 References 345 Index 347

Read more less

Book SynopsisAUTONOMOUS AIRBORNE WIRELESS NETWORKS Discover what lies beyond the bleeding-edge of autonomous airborne networks with this authoritative new resource Autonomous Airborne Wireless Networks delivers an insightful exploration on recent advances in the theory and practice of using airborne wireless networks to provide emergency communications, coverage and capacity expansion, information dissemination, and more. The distinguished engineers and editors have selected resources that cover the fundamentals of airborne networks, including channel models, recent regulation developments, self-organized networking, AI-enabled flying networks, and notable applications in a variety of industries. The book evaluates advances in the cutting-edge of unmanned aerial vehicle wireless network technology while offering readers new ideas on how airborne wireless networks can support various applications expected of future networks. The rapidly developing field is examined from a fresh perspective, one not Table of ContentsEditor biography Contributors list Chapter 1 Introduction Muhammad A Imran, Oluwakayode Onireti, Shuja S Ansari, Qammer H Abbasi Chapter 2 Channel Model for Airborne Networks Aziz Altaf Khuwaja and Yunfei Chen Chapter 3 Ultra-Wide Band Channel Measurements and Modelling for Unmanned Aerial Vehicle-to-Wearables (UAV2W) Systems Amit Kachroo, Surbhi Vishwakarma, Jacob N. Dixon, Hisham Abuella, Adithya Popuri, Qammer H. Abbasi, Charles F. Bunting, Jamey D. Jacob, Sabit Ekin, Chapter 4 A cooperative multi-agent approach for optimal drone deployment using reinforcement learning Rigoberto Acosta-González, Paulo Valente Klaine, Samuel Montejo-Sánchez, Richard Demo, Lei Zhang, Muhammad A. Imran Chapter 5 SWIPT-PS Enabled Cache-Aided Self-Energized UAV for Cooperative Communication Tharindu D. Ponnimbaduge Perera Chapter 6 Performance of mmWave UAV-Assisted 5G Hybrid Heterogeneous Networks Muhammad Karam Shehzad, Muhammad Waseem Akhtar, Syed Ali Hassan Chapter 7 UAV-Enabled Cooperative Jamming for Physical Layer Security in Cognitive Radio Network Phu Xuan Nguyen, Hieu Van Nguyen, Van-Dinh Nguyen, Oh-Soon Shin Chapter 8 IRS assisted Localization for Airborne Mobile Networks Olaoluwa Popoola, Shuja Ansari, Rafay Iqbal Ansari, Lina Mohjazi, Syed Ali Hassan, Nauman Aslam, Qammer Hussain Abbasi, Muhammad Ali Imran Chapter 9 Performance Analysis of UAV Enabled Disaster Recovery Networks Rabeea Basir, Naveed Ahmad Chughtai, Saad Qaisar, Mudassar Ali, Muhammad Ali Imran Chapter 10 Network-assisted Unmanned Aerial Vehicle Communication for Smart Monitoring of Lock-down Navuday Sharma, Muhammad Awais, Haris Pervaiz, Hassan Malik, Qiang Ni Chapter 11 Unmanned Aerial Vehicles for Agriculture: an overview of IoT-based scenarios Bacco Manlio, Barsocchi Paolo, Gotta Alberto, Ruggeri Massimiliano Chapter 12 Airborne Systems and Underwater Monitoring Elizabeth Basha, Jason To-Tran, Davis Young, Sean Thalken, Christopher Uramoto Chapter 13 Demystifying Futuristic Satellite Networks: Requirements, Security Threats, and Issues Muhammad Usman, Muhammad Rizwan Asghar, Imran Shafique Ansari, Marwa Qaraqe Chapter 14 Conclusions and future outlook Muhammad Imran, Oluwakayode, Shuja Ansari and Qammer Abbasi

Read more less

Book SynopsisMODERN FORENSIC TOOLS AND DEVICES The book offers a comprehensive overview of the latest technologies and techniques used in forensic investigations and highlights the potential impact of these advancements on the field. Technology has played a pivotal role in advancing forensic science over the years, particularly in modern-day criminal investigations. In recent years, significant advancements in forensic tools and devices have enabled investigators to gather and analyze evidence more efficiently than ever. Modern Forensic Tools and Devices: Trends in Criminal Investigation is a comprehensive guide to the latest technologies and techniques used in forensic science. This book covers a wide range of topics, from computer forensics and personal digital assistants to emerging analytical techniques for forensic samples. A section of the book provides detailed explanations of each technology and its applications in forensic investigations, along with case studiTable of ContentsPreface xix 1 Computer Forensics and Personal Digital Assistants 1 Muhammad Qadeer, Chaudhery Ghazanfer Hussain and Chaudhery Mustansar Hussain 1.1 Introduction 2 1.1.1 Computer and Digital Forensics 2 1.2 Digital Forensics Classification 3 1.3 Digital Evidence 8 1.4 Information Used in Investigation to Find Digital Evidence 8 1.5 Short History of Digital/Computer Forensics 10 1.6 The World of Crimes 12 1.6.1 Cybercrimes vs. Traditional Crimes 12 1.7 Computer Forensics Investigation Steps 15 1.8 Report Generation of Forensic Findings Through Software Tools 17 1.9 Importance of Forensics Report 18 1.10 Guidelines for Report Writing 18 1.11 Objectives of Computer Forensics 19 1.12 Challenges Faced by Computer Forensics 20 References 20 2 Network and Data Analysis Tools for Forensic Science 23 Shrutika Singla, Shruthi Subhash and Amarnath Mishra 2.1 Introduction 23 2.2 Necessity for Data Analysis 25 2.2.1 Operational Troubleshooting 25 2.2.2 Log Monitoring 25 2.2.3 Data Recovery 25 2.2.4 Data Acquisition 25 2.3 Data Analysis Process 26 2.3.1 Acquisition 26 2.3.2 Examination 26 2.3.3 Utilization 26 2.3.4 Review 26 2.4 Network Security and Forensics 26 2.5 Digital Forensic Investigation Process 27 2.5.1 Data Identification 28 2.5.2 Project Planning 28 2.5.3 Data Capture 29 2.5.4 Data Processing 29 2.5.5 Data Analysis 29 2.5.6 Report Generation 29 2.6 Tools for Network and Data Analysis 29 2.6.1 EnCase Forensic Imager Tool 30 2.6.2 Cellebrite UFED 31 2.6.3 FTK Imager Tool 31 2.6.4 Paladin Forensic Suite 32 2.6.5 Digital Forensic Framework (DFF) 32 2.6.6 Forensic Imager Tx 1 32 2.6.7 Tableau TD2U Forensic Duplicator 32 2.6.8 Oxygen Forensics Detective 33 2.6.9 SANS Investigative Forensic Toolkit (SIFT) 33 2.6.10 Win Hex 33 2.6.11 Computer Online Forensic Evidence Extractor (COFEE) 34 2.6.12 WindowsSCOPE Toolkit 34 2.6.13 ProDiscover Forensics 34 2.6.14 Sleuth Kit 35 2.6.15 Caine 35 2.6.16 Magnet RAM Capture 35 2.6.17 X-Ways Forensics 36 2.6.18 WireShark Tool 36 2.6.19 Xplico 36 2.6.20 e-Fensee 36 2.7 Evolution of Network Data Analysis Tools Over the Years 37 2.8 Conclusion 37 References 38 3 Cloud and Social Media Forensics 41 Nilay Mistry and Sureel Vora 3.1 Introduction 42 3.2 Background Study 42 3.2.1 Social Networking Trend Among Users 42 3.2.2 Pros and Cons of Social Networking and Chat Apps 43 3.2.3 Privacy Issues in Social Networking and Chat Apps 44 3.2.4 Usefulness of Personal Information for Law Enforcements 45 3.2.5 Cloud Computing and Social Media Applications 45 3.2.5.1 SaaS Model 45 3.2.5.2 PaaS Model 46 3.2.5.3 IaaS Model 46 3.3 Technical Study 46 3.3.1 User-Agent and Its Working 46 3.3.2 Automated Agents and Their User-Agent String 47 3.3.3 User Agent Spoofing and Sniffing 47 3.3.4 Link Forwarding and Rich Preview 47 3.3.5 WebView and its User Agent 48 3.3.6 HTTP Referrer and Referring Page 48 3.3.7 Application ID 48 3.4 Methodology 49 3.4.1 Testing Environment 49 3.4.2 Research and Analysis 49 3.4.2.1 Activities Performed 51 3.4.2.2 Information Gathered 52 3.4.2.3 Analysis of Gathered Information 53 3.4.3 Activity Performed - Opening the Forwarded Link 59 3.5 Protection Against Leakage 60 3.6 Conclusion 60 3.7 Future Work 61 References 61 4 Vehicle Forensics 65 Disha Bhatnagar and Piyush K. Rao 4.1 Introduction 65 4.1.1 Motives Behind Vehicular Theft 67 4.1.1.1 Insurance Fraud 67 4.1.1.2 Resale and Export 67 4.1.1.3 Temporary Transportation 68 4.1.1.4 Commitment of Another Crime 68 4.2 Intervehicle Communication and Vehicle Internal Networks 68 4.3 Classification of Vehicular Forensics 70 4.3.1 Automative Vehicle Forensics 71 4.3.1.1 Live Forensics 71 4.3.1.2 Post-Mortem Forensics 71 4.3.1.3 Physical Tools for Forensic Investigation 73 4.3.2 Unmanned Aerial Vehicle Forensics (UAV)/Drone Forensics 74 4.3.2.1 Methodology 74 4.3.2.2 Steps Involved in Drone Forensics 75 4.3.2.3 Challenges in UAV Forensics 76 4.4 Vehicle Identification Number 76 4.4.1 Placement in a Vehicle and Usage of a VIN 77 4.4.2 Vehicle Identification 78 4.4.2.1 Federal Motor Vehicle Safety Certification Label 79 4.4.2.2 Anti-Theft Label 79 4.4.2.3 Stamping on Vehicle Parts 79 4.4.2.4 Secondary and Confidential VIN 79 4.5 Serial Number Restoration 79 4.5.1 Restoration Methods 80 4.5.1.1 Chemical Etching 80 4.5.1.2 Electrolytic Etching 81 4.5.1.3 Heat Treatment 81 4.5.1.4 Magnetic Particle Method 81 4.5.1.5 Electron Channeling Contrast 81 4.6 Conclusion 81 References 82 5 Facial Recognition and Reconstruction 85 Payal V. Bhatt, Piyush K. Rao and Deepak Rawtani 5.1 Introduction 86 5.2 Facial Recognition 86 5.3 Facial Reconstruction 87 5.4 Techniques for Facial Recognition 88 5.4.1 Image-Based Facial Recognition 89 5.4.1.1 Appearance-Based Method 89 5.4.1.2 Model-Based Method 90 5.4.1.3 Texture-Based Method 90 5.4.2 Video-Based Facial Recognition 91 5.4.2.1 Sequence-Based Method 91 5.4.2.2 Set-Based Method 92 5.5 Techniques for Facial Reconstruction 92 5.5.1 Manual Method 93 5.5.2 Graphical Method 94 5.5.3 Computerized Method 94 5.6 Challenges in Forensic Face Recognition 95 5.6.1 Facial Aging 96 5.6.2 Face Marks 97 5.6.3 Forensic Sketch Recognition 97 5.6.4 Face Recognition in Video 98 5.6.5 Near Infrared (NIR) Face Recognition 99 5.7 Soft Biometrics 99 5.8 Application Areas of Facial Recognition 100 5.9 Application of Facial Reconstruction 101 5.10 Conclusion 102 References 102 6 Automated Fingerprint Identification System 107 Piyush K. Rao, Shreya Singh, Aayush Dey, Deepak Rawtani and Garvita Parikh Abbreviations 108 6.1 Introduction 108 6.2 Ten-Digit Fingerprint Classification 110 6.3 Henry Faulds Classification System 110 6.4 Manual Method for the Identification of Latent Fingerprint 111 6.5 Need for Automation 112 6.6 Automated Fingerprint Identification System 112 6.7 History of Automatic Fingerprint Identification System 113 6.8 Automated Method of Analysis 113 6.9 Segmentation 114 6.10 Enhancement and Quality Assessment 115 6.11 Feature Extraction 117 6.12 Latent Fingerprint Matching 118 6.13 Latent Fingerprint Database 120 6.14 Conclusion 120 References 121 7 Forensic Sampling and Sample Preparation 125 Disha Bhatnagar, Piyush K. Rao and Deepak Rawtani 7.1 Introduction 126 7.2 Advancement in Technologies Used in Forensic Science 126 7.3 Evidences 127 7.3.1 Classification of Evidences 127 7.3.1.1 Direct Evidence 127 7.2.1.2 Circumstantial Evidence 127 7.4 Collection of Evidences 129 7.4.1 Sampling Methods 130 7.5 Sample Preparation Techniques for Analytical Instruments 133 7.5.1 Conventional Methods of Sample Preparation 134 7.5.2 Solvent Extraction 134 7.5.2.1 Distillation 135 7.5.2.2 Acid Digestion 135 7.5.2.3 Solid Phase Extraction 136 7.5.2.4 Soxhlet Extraction 137 7.5.3 Modern Methods of Sample Preparation 138 7.5.3.1 Accelerated Solvent Extraction 138 7.5.3.2 Microwave Digestion 138 7.5.3.3 Ultrasonication-Assisted Extraction 139 7.5.3.4 Microextraction 139 7.5.3.5 Supercritical Fluid Extraction 142 7.5.3.6 QuEChERS 143 7.5.3.7 Membrane Extraction 143 7.6 Conclusion 144 7.7 Future Perspective 144 References 145 8 Spectroscopic Analysis Techniques in Forensic Science 149 Payal V. Bhatt and Deepak Rawtani 8.1 Introduction 150 8.2 Spectroscopy 150 8.2.1 Spectroscopy and its Applications 153 8.3 Spectroscopy and Forensics 155 8.4 Spectroscopic Techniques and their Forensic Applications 156 8.4.1 X-Ray Absorption Spectroscopy 156 8.4.1.1 Application of X-Ray Absorption Spectroscopy in Forensics 157 8.4.2 UV/Visible Spectroscopy 159 8.4.2.1 Application of UV/Vis Spectroscopy in Forensics 160 8.4.3 Atomic Absorption Spectroscopy 162 8.4.3.1 Application of Atomic Absorption Spectroscopy in Forensics 163 8.4.4 Infrared Spectroscopy 165 8.4.4.1 Application of Infrared Spectroscopy in Forensics 166 8.4.5 Raman Spectroscopy 167 8.4.5.1 Application of Raman Spectroscopy in Forensics 168 8.4.6 Electron Spin Resonance Spectroscopy 171 8.4.6.1 Application of Electron Spin Resonance Spectroscopy in Forensics 172 8.4.7 Nuclear Magnetic Resonance Spectroscopy 173 8.4.7.1 Application of Nuclear Magnetic Resonance Spectroscopy in Forensics 174 8.4.8 Atomic Emission Spectroscopy 176 8.4.8.1 Application of Atomic Emission Spectroscopy in Forensics 177 8.4.9 X-Ray Fluorescence Spectroscopy 178 8.4.9.1 Application of X-Ray Fluorescence Spectroscopy in Forensics 179 8.4.10 Fluorescence Spectroscopy 181 8.4.10.1 Application of Fluorescence Spectroscopy in Forensics 182 8.4.11 Phosphorescence Spectroscopy 183 8.4.11.1 Application of Phosphorescence Spectroscopy in Forensics 184 8.4.12 Atomic Fluorescence Spectroscopy 186 8.4.12.1 Application of Atomic Fluorescence Spectroscopy in Forensics 187 8.4.13 Chemiluminescence Spectroscopy 188 8.4.13.1 Application of Chemiluminescence Spectroscopy in Forensics 189 8.5 Conclusion 190 References 190 9 Emerging Analytical Techniques in Forensic Samples 199 Disha Bhatnagar and Piyush K. Rao 9.1 Introduction 199 9.2 Separation Techniques 200 9.2.1 Chromatography 200 9.2.1.1 Gas Chromatography 202 9.2.2 Liquid Chromatography 208 9.2.3 Capillary Electrophoresis 211 9.3 Mass Spectrometry 213 9.4 Tandem Mass (MS/MS) 219 9.5 Inductively Coupled Plasma-Mass Spectrometry 220 9.6 Laser Ablation–Inductively Coupled Plasma-Mass Spectrometry 221 9.7 Conclusion 222 References 223 10 DNA Sequencing and Rapid DNA Tests 225 Archana Singh and Deepak Rawtani 10.1 Introduction 226 10.1.1 DNA Sequencing 226 10.1.2 DNA Profiling Analysis Methods 228 10.1.3 The Rapid DNA Test 228 10.2 DNA – The Hereditary Material 230 10.2.1 DNA – Structure and Genetic Information 230 10.3 DNA Sequencing 231 10.3.1 Maxam and Gilbert Method 232 10.3.2 Chain Termination Method or Sanger’s Sequencing 233 10.3.3 Automated Method 235 10.3.4 Semiautomated Method 235 10.3.5 Pyrosequencing Method 236 10.3.6 Clone by Clone Sequencing Method 237 10.3.7 The Whole-Genome Shotgun Sequencing Method 237 10.3.8 Next-Generation DNA Sequencing 238 10.4 Laboratory Processing and DNA Evidence Analysis 238 10.4.1 Restriction Fragment Length Polymorphism 239 10.4.2 Polymerase Chain Reaction (PCR) 239 10.4.3 Short Tandem Repeats (STR) 241 10.4.4 Mitochondrial DNA (mt-DNA) 241 10.4.5 Amplified Fragment Length Polymorphism (aflp) 242 10.4.6 Y-Chromosome 242 10.5 Rapid DNA Test 243 10.5.1 The Evolution of the Rapid DNA Test 244 10.5.2 Rapid DNA Instrument 245 10.5.3 Methodology of Rapid DNA 250 10.6 Conclusion and Future Aspects 250 References 251 11 Sensor-Based Devices for Trace Evidence 265 Aayush Dey, Piyush K. Rao and Deepak Rawtani 11.1 Introduction 266 11.2 Immunosensors in Forensic Science 267 11.2.1 Direct Immunosensing Strategies 268 11.2.1.1 Surface Plasmon Resonance 268 11.2.1.2 Electrochemical Impedance Spectroscopy 274 11.2.1.3 Piezoelectric Immunosensors 275 11.2.2 Indirect Immunosensing Strategies 276 11.2.2.1 Optical Immunosensors 276 11.2.2.2 Electrochemical Immunosensors 280 11.3 Genosensors and Cell-Based Biosensors in Forensic Science 282 11.4 Aptasensors in Forensic Science 283 11.4.1 Forensic Applications of Aptasensors 287 11.5 Enzymatic Biosensors in Forensic Science 288 11.5.1 Applications of Enzymatic Biosensors for Trace Evidence Analysis 289 11.6 Conclusion 289 References 290 12 Biomimetic Devices for Trace Evidence Detection 299 Manika and Astha Pandey 12.1 Introduction 300 12.2 Tools or Machines for Biomimetics 301 12.3 Methods of Biomimetics 302 12.4 Applications 302 12.4.1 Detection of Trace Evidences 302 12.4.1.1 Biomimetic Sniffing 302 12.4.1.2 L-Nicotine Detection 307 12.4.1.3 TNT Detection 307 12.4.2 Hybrid Materials to Medical Devices 309 12.4.2.1 Smart Drug Delivery Micro and Nanodevices 309 12.4.2.2 Nanodevices for Combination of Therapy and Theranostics 310 12.4.2.3 Continuous Biosensors for Glucose 310 12.4.2.4 Electro-Active Lenses 311 12.4.2.5 Smart Tattoos 311 12.5 Challenges for Biomimetics in Practice 311 12.6 Conclusion 312 References 314 13 Forensic Photography 315 Aayush Dey, Piyush K. Rao and Deepak Rawtani 13.1 Introduction 316 13.2 Forensic Photography and Its Purpose 316 13.3 Modern Principles of Forensic Photography 318 13.4 Fundamental Rules of Forensic Photography 319 13.4.1 Rule Number 1. Filling the Frame Space 319 13.4.2 Rule Number 2. Expansion of Depth of Field 320 13.4.3 Rule Number 3. Positioning the Film Plane 321 13.5 Camera Setup and Apparatus for Forensic Photography 321 13.6 The Dynamics of a Digital Camera 322 13.6.1 Types of Digital Cameras 323 13.6.2 Sensor Architecture 324 13.6.2.1 Full Frame 324 13.6.2.2 Frame Transfer 325 13.6.2.3 Interline Architecture 325 13.6.3 Spectral Response 325 13.6.4 Light Sensitivity and Noise Cancellation 326 13.6.5 Dynamic Range 326 13.6.6 Blooming and Anti-Blooming 326 13.6.7 Signal to Noise Ratio 326 13.6.8 Spatial Resolution 327 13.6.9 Frame Rate 327 13.7 Common Crime Scenarios and How They Must be Photographed 327 13.7.1 Photography of Road Traffic Accidents 328 13.7.2 Photography of Homicides 329 13.7.3 Arson Crime Scenes 330 13.7.4 Photography of Print Impressions at a Crime Scene 330 13.7.5 Tire Marks and Their Photography 331 13.7.6 Photography of Skin Wounds 331 13.8 Conclusion 332 References 332 14 Scanners and Microscopes 335 Aayush Dey, Piyush K. Rao and Deepak Rawtani 14.1 Introduction 336 14.2 Scanners in Forensic Science 337 14.2.1 Three-Dimensional Laser Scanners 338 14.2.1.1 Benefits of Three-Dimensional Laser Scanners 338 14.2.1.2 Drawbacks of Three-Dimensional Laser Scanners 338 14.2.1.3 Applications in Forensic Science 339 14.2.2 Structured Light Scanners 341 14.2.2.1 Applications in Forensic Science 341 14.2.3 Intraoral Optical Scanners 342 14.2.3.1 Applications in Forensic Science 342 14.2.4 Computerized Tomography Scanner 343 14.2.4.1 Applications in Forensic Science 343 14.3 Microscopes in Forensic Science 344 14.3.1 Light Microscopes 345 14.3.1.1 Compound Microscope 345 14.3.1.2 Comparison Microscope 347 14.3.1.3 Polarizing Microscope 348 14.3.1.4 Stereoscopic Microscope 348 14.3.2 Electron Microscopes 349 14.3.2.1 Scanning Electron Microscope 349 14.3.2.2 Transmission Electron Microscope 350 14.3.3 Probing Microscopes 350 14.3.3.1 Atomic Force Microscope 350 14.4 Conclusion 355 References 356 15 Recent Advances in Forensic Tools 361 Tatenda Justice Gunda, Charles Muchabaiwa, Piyush K. Rao, Aayush Dey and Deepak Rawtani 15.1 Introduction 362 15.1.1 Recent Forensic Tool: Trends in Crime Investigations 363 15.1.2 Recent Forensic Device 364 15.2 Classification of Forensic Tools and Devices 364 15.2.1 Forensic Chemistry 365 15.2.1.1 Sensors 365 15.2.1.2 Chromatographic Techniques 368 15.2.1.3 Gas Chromatography–Mass Spectrometer (GC-MS) 369 15.2.1.4 High-Performance Liquid Chromatography (HPLC) 370 15.2.1.5 Liquid Chromatography (LC/MS/MS) Rapid Toxicology Screening System 370 15.2.1.6 Fourier Transform Infrared (FTIR) Spectroscopy 372 15.2.1.7 Drug Testing Toxicology of Hair 372 15.2.2 Question Document and Fingerprinting 373 15.2.2.1 Electrostatic Detection Analysis (esda) 374 15.2.2.2 Video Spectral Comparator 375 15.2.2.3 Fingerprinting 376 15.2.3 Forensic Physics 377 15.2.3.1 Facial Recognition 377 15.2.3.2 3D Facial Reconstruction 378 15.2.3.3 Arsenal Automated Ballistic Identification System (ABIS) 378 15.2.3.4 Audio Video Aided Forensic Analysis 379 15.2.3.5 Brain Electrical Oscillations Signature (beos) 379 15.2.3.6 Phenom Desktop Scanning Electron Microscope (SEM) 379 15.2.3.7 X-Ray Spectroscopy EDX 380 15.2.3.8 Drones/UAVs 380 15.2.4 Forensic Biology 382 15.2.4.1 Massive Parallel Sequencing (MPS) 384 15.2.4.2 Virtopsy 384 15.2.4.3 Three-Dimensional Imaging System 385 15.3 Conclusion and Future Perspectives 385 References 386 16 Future Aspects of Modern Forensic Tools and Devices 393 Swathi Satish, Gargi Phadke and Deepak Rawtani 16.1 Introduction 394 16.2 Forensic Tools 395 16.2.1 Emerging Trends in Forensic Tools 396 16.2.2 Future Facets of Forensic Tools 397 16.2.2.1 Analytical Forensic Tools 397 16.2.2.2 Digital Forensic Tools 399 16.3 Forensic Devices 403 16.3.1 Emerging Trends in Forensic Devices 403 16.3.2 Future Aspects of Forensic Devices 404 16.4 Conclusion 409 References 410 Index 415

Read more less

Book SynopsisMACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT's potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning anTable of ContentsPreface xiii 1 Machine Learning Concept–Based IoT Platforms for Smart Cities’ Implementation and Requirements 1M. Saravanan, J. Ajayan, R. Maheswar, Eswaran Parthasarathy and K. Sumathi 1.1 Introduction 2 1.2 Smart City Structure in India 3 1.2.1 Bhubaneswar City 3 1.2.1.1 Specifications 3 1.2.1.2 Healthcare and Mobility Services 3 1.2.1.3 Productivity 4 1.2.2 Smart City in Pune 4 1.2.2.1 Specifications 5 1.2.2.2 Transport and Mobility 5 1.2.2.3 Water and Sewage Management 5 1.3 Status of Smart Cities in India 5 1.3.1 Funding Process by Government 6 1.4 Analysis of Smart City Setup 7 1.4.1 Physical Infrastructure-Based 7 1.4.2 Social Infrastructure-Based 7 1.4.3 Urban Mobility 8 1.4.4 Solid Waste Management System 8 1.4.5 Economical-Based Infrastructure 9 1.4.6 Infrastructure-Based Development 9 1.4.7 Water Supply System 10 1.4.8 Sewage Networking 10 1.5 Ideal Planning for the Sewage Networking Systems 10 1.5.1 Availability and Ideal Consumption of Resources 10 1.5.2 Anticipating Future Demand 11 1.5.3 Transporting Networks to Facilitate 11 1.5.4 Control Centers for Governing the City 12 1.5.5 Integrated Command and Control Center 12 1.6 Heritage of Culture Based on Modern Advancement 13 1.7 Funding and Business Models to Leverage 14 1.7.1 Fundings 15 1.8 Community-Based Development 16 1.8.1 Smart Medical Care 16 1.8.2 Smart Safety for The IT 16 1.8.3 IoT Communication Interface With ML 17 1.8.4 Machine Learning Algorithms 17 1.8.5 Smart Community 18 1.9 Revolutionary Impact With Other Locations 18 1.10 Finding Balanced City Development 20 1.11 E-Industry With Enhanced Resources 20 1.12 Strategy for Development of Smart Cities 21 1.12.1 Stakeholder Benefits 21 1.12.2 Urban Integration 22 1.12.3 Future Scope of City Innovations 22 1.12.4 Conclusion 23 References 24 2 An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan 27W. H. Rankothge 2.1 Introduction 28 2.2 Background 29 2.2.1 Prediction of Future Paddy Harvest and Rice Consumption Demand 29 2.2.2 Rice Distribution 31 2.3 Methodology 31 2.3.1 Requirements of the Proposed Platform 32 2.3.2 Data to Evaluate the ‘isRice” Platform 34 2.3.3 Implementation of Prediction Modules 34 2.3.3.1 Recurrent Neural Network 35 2.3.3.2 Long Short-Term Memory 36 2.3.3.3 Paddy Harvest Prediction Function 37 2.3.3.4 Rice Demand Prediction Function 39 2.3.4 Implementation of Rice Distribution Planning Module 40 2.3.4.1 Genetic Algorithm–Based Rice Distribution Planning 41 2.3.5 Front-End Implementation 44 2.4 Results and Discussion 45 2.4.1 Paddy Harvest Prediction Function 45 2.4.2 Rice Demand Prediction Function 46 2.4.3 Rice Distribution Planning Module 46 2.5 Conclusion 49 References 49 3 A Collaborative Data Publishing Model with Privacy Preservation Using Group-Based Classification and Anonymity 53Carmel Mary Belinda M. J., K. Antonykumar, S. Ravikumar and Yogesh R. Kulkarni 3.1 Introduction 54 3.2 Literature Survey 56 3.3 Proposed Model 58 3.4 Results 61 3.5 Conclusion 64 References 64 4 Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC) 67Dineshbabu V., Arul Kumar V. P. and Gowtham M. S. 4.1 Introduction 68 4.2 Related Works 69 4.3 Industry 4.0 Production and Dashboard Design 69 4.4 Results and Discussion 70 4.5 Conclusion 73 References 73 5 Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation 75S. Pradeep Kumar and G. Kalpana 5.1 Introduction 75 5.2 Types of CAPTCHAs 78 5.2.1 Text-Based CAPTCHA 78 5.2.2 Image-Based CAPTCHA 80 5.2.3 Audio-Based CAPTCHA 80 5.2.4 Video-Based CAPTCHA 81 5.2.5 Puzzle-Based CAPTCHA 82 5.3 Related Work 82 5.4 Proposed Technique 82 5.5 Text-Based CAPTCHA Scheme 83 5.6 Breaking Text-Based CAPTCHA’s Scheme 85 5.6.1 Individual Character-Based Segmentation Method 85 5.6.2 Character Width-Based Segmentation Method 86 5.7 Implementation of Text-Based CAPTCHA Using Graphical Operation 87 5.7.1 Graphical Operation 87 5.7.2 Two-Dimensional Composite Transformation Calculation 89 5.8 Graphical Text-Based CAPTCHA in Online Application 91 5.9 Conclusion and Future Enhancement 93 References 94 6 Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning 97Pradeep Kumar S., Jayanthi K. and Selvakumari S. 6.1 Introduction 98 6.1.1 Internet of Things 98 6.1.2 Deep Learning 98 6.1.3 Detecting the Traffic Sign With the Mask R-CNN 99 6.1.3.1 Mask R-Convolutional Neural Network 99 6.1.3.2 Color Space Conversion 100 6.2 Experimental Evaluation 101 6.2.1 Implementation Details 101 6.2.2 Traffic Sign Classification 101 6.2.3 Traffic Sign Detection 102 6.2.4 Sample Outputs 103 6.2.5 Raspberry Pi 4 Controls Vehicle Using OpenCV 103 6.2.5.1 Smart IoT-Enabled Traffic Signs Recognizing With High Accuracy Using Deep Learning 103 6.2.6 Python Code 108 6.3 Conclusion 109 References 110 7 Offline and Online Performance Evaluation Metrics of Recommender System: A Bird’s Eye View 113R. Bhuvanya and M. Kavitha 7.1 Introduction 114 7.1.1 Modules of Recommender System 114 7.1.2 Evaluation Structure 115 7.1.3 Contribution of the Paper 115 7.1.4 Organization of the Paper 116 7.2 Evaluation Metrics 116 7.2.1 Offline Analytics 116 7.2.1.1 Prediction Accuracy Metrics 116 7.2.1.2 Decision Support Metrics 118 7.2.1.3 Rank Aware Top-N Metrics 120 7.2.2 Item and List-Based Metrics 122 7.2.2.1 Coverage 122 7.2.2.2 Popularity 123 7.2.2.3 Personalization 123 7.2.2.4 Serendipity 123 7.2.2.5 Diversity 123 7.2.2.6 Churn 124 7.2.2.7 Responsiveness 124 7.2.3 User Studies and Online Evaluation 125 7.2.3.1 Usage Log 125 7.2.3.2 Polls 126 7.2.3.3 Lab Experiments 126 7.2.3.4 Online A/B Test 126 7.3 Related Works 127 7.3.1 Categories of Recommendation 129 7.3.2 Data Mining Methods of Recommender System 129 7.3.2.1 Data Pre-Processing 129 7.3.2.2 Data Analysis 131 7.4 Experimental Setup 135 7.5 Summary and Conclusions 142 References 143 8 Deep Learning–Enabled Smart Safety Precautions and Measures in Public Gathering Places for COVID-19 Using IoT 147Pradeep Kumar S., Pushpakumar R. and Selvakumari S. 8.1 Introduction 148 8.2 Prelims 148 8.2.1 Digital Image Processing 148 8.2.2 Deep Learning 149 8.2.3 WSN 149 8.2.4 Raspberry Pi 152 8.2.5 Thermal Sensor 152 8.2.6 Relay 152 8.2.7 TensorFlow 153 8.2.8 Convolution Neural Network (CNN) 153 8.3 Proposed System 154 8.4 Math Model 156 8.5 Results 158 8.6 Conclusion 161 References 161 9 Route Optimization for Perishable Goods Transportation System 167Kowsalyadevi A. K., Megala M. and Manivannan C. 9.1 Introduction 167 9.2 Related Works 168 9.2.1 Need for Route Optimization 170 9.3 Proposed Methodology 171 9.4 Proposed Work Implementation 174 9.5 Conclusion 178 References 178 10 Fake News Detection Using Machine Learning Algorithms 181M. Kavitha, R. Srinivasan and R. Bhuvanya 10.1 Introduction 181 10.2 Literature Survey 183 10.3 Methodology 193 10.3.1 Data Retrieval 195 10.3.2 Data Pre-Processing 195 10.3.3 Data Visualization 196 10.3.4 Tokenization 196 10.3.5 Feature Extraction 196 10.3.6 Machine Learning Algorithms 197 10.3.6.1 Logistic Regression 197 10.3.6.2 Naïve Bayes 198 10.3.6.3 Random Forest 200 10.3.6.4 XGBoost 200 10.4 Experimental Results 202 10.5 Conclusion 203 References 203 11 Opportunities and Challenges in Machine Learning With IoT 209Sarvesh Tanwar, Jatin Garg, Medini Gupta and Ajay Rana 11.1 Introduction 209 11.2 Literature Review 210 11.2.1 A Designed Architecture of ML on Big Data 210 11.2.2 Machine Learning 211 11.2.3 Types of Machine Learning 212 11.2.3.1 Supervised Learning 212 11.2.3.2 Unsupervised Learning 215 11.3 Why Should We Care About Learning Representations? 217 11.4 Big Data 218 11.5 Data Processing Opportunities and Challenges 219 11.5.1 Data Redundancy 219 11.5.2 Data Noise 220 11.5.3 Heterogeneity of Data 220 11.5.4 Discretization of Data 220 11.5.5 Data Labeling 221 11.5.6 Imbalanced Data 221 11.6 Learning Opportunities and Challenges 221 11.7 Enabling Machine Learning With IoT 223 11.8 Conclusion 224 References 225 12 Machine Learning Effects on Underwater Applications and IoUT 229Mamta Nain, Nitin Goyal and Manni Kumar 12.1 Introduction 229 12.2 Characteristics of IoUT 231 12.3 Architecture of IoUT 232 12.3.1 Perceptron Layer 233 12.3.2 Network Layer 234 12.3.3 Application Layer 234 12.4 Challenges in IoUT 234 12.5 Applications of IoUT 235 12.6 Machine Learning 240 12.7 Simulation and Analysis 241 12.8 Conclusion 242 References 242 13 Internet of Underwater Things: Challenges, Routing Protocols, and ML Algorithms 247Monika Chaudhary, Nitin Goyal and Aadil Mushtaq 13.1 Introduction 248 13.2 Internet of Underwater Things 248 13.2.1 Challenges in IoUT 249 13.3 Routing Protocols of IoUT 250 13.4 Machine Learning in IoUT 255 13.4.1 Types of Machine Learning Algorithms 258 13.5 Performance Evaluation 259 13.6 Conclusion 260 References 260 14 Chest X-Ray for Pneumonia Detection 265Sarang Sharma, Sheifali Gupta and Deepali Gupta 14.1 Introduction 266 14.2 Background 267 14.3 Research Methodology 268 14.4 Results and Discussion 271 14.4.1 Results 271 14.4.2 Discussion 271 14.5 Conclusion 273 Acknowledgment 273 References 274 Index 275

Read more less

Book SynopsisLike most industries around the world, the energy industry has also made, and continues to make, a long march toward green energy. The science has come a long way since the 1970s, and renewable energy and other green technologies are becoming more and more common, replacing fossil fuels. It is, however, still a struggle, both in terms of energy sources keeping up with demand, and the development of useful technologies in this area. To maintain the supply for electrical energy, researchers, engineers and other professionals in industry are continuously exploring new eco-friendly energy technologies and power electronics, such as solar, wind, tidal, wave, bioenergy, and fuel cells. These technologies have changed the concepts of thermal, hydro and nuclear energy resources by the adaption of power electronics advancement and revolutionary development in lower manufacturing cost for semiconductors with long time reliability. The latest developments in renewable resources have proTable of ContentsPreface xix 1 Fabrication and Manufacturing Process of Solar Cell: Part I 1S. Dwivedi 1.1 Introduction 2 1.1.1 Introduction to Si-Based Fabrication Technology 2 1.1.2 Introduction to Si Wafer 4 1.1.3 Introduction to Diode Physics 5 1.1.3.1 Equilibrium Fermi Energy (EF) 10 1.2 Fabrication Technology of Diode 19 1.3 Energy Production by Equivalent Cell Circuitry 27 1.4 Conclusion 30 References 31 2 Fabrication and Manufacturing Process of Solar Cell: Part II 39Prabhansu and Nayan Kumar 2.1 Introduction 39 2.2 Silicon Solar Cell Technologies 41 2.2.1 Crystalline Structured Silicon (c-Si) 41 2.2.2 Silicon-Based Thin-Film PV Cell 43 2.3 Homojunction Silicon Solar Cells 44 2.3.1 Classic Structure and Manufacture Process 44 2.3.2 Plans for High Productivity 45 2.4 Solar Si-Heterojunction Cell 46 2.5 Si Thin-Film PV Cells 48 2.5.1 PV Cell Development Based on p-I-n and n-I-p 49 2.5.2 Light-Based Trapping Methodologies 49 2.5.3 Approach to Tandem 51 2.5.4 Current Trends 51 2.6 Perovskite Solar Cells 52 2.6.1 Introduction 52 2.6.2 Specific Properties with Perovskites-Based Metaldhalide for Photovoltaics 53 2.6.3 Crystallization of Perovskite 55 2.6.4 Current Trends 56 2.7 Future Possibility and Difficulties 56 2.8 Conclusions 57 References 58 3 Fabrication and Manufacturing Process of Perovskite Solar Cell 67Nandhakumar Eswaramoorthy and Kamatchi R 3.1 Introduction 67 3.2 Architectures of Perovskite Solar Cells 68 3.3 Working Principle of Perovskite Solar Cell 70 3.4 Components of Perovskite Solar Cell 73 3.4.1 Transparent Conducting Metal Oxide (TCO) Layer 73 3.4.2 Electron Transport Layer (ETL) 74 3.4.3 Perovskite Layer 74 3.4.4 Hole Transport Layer (HTL) 75 3.4.5 Electrodes 75 3.5 Fabrication of Perovskite Films 76 3.5.1 One-Step Method 77 3.5.2 Two-Step Method 77 3.5.3 Solid-State Method 78 3.5.4 Bifacial Stamping Method 78 3.5.5 Solvent-Solvent Extraction Method 78 3.5.6 Pulse Laser Deposition Method 78 3.5.7 Vapor Deposition Method 79 3.5.8 Solvent Engineering 79 3.5.9 Additive Engineering 79 3.6 Manufacturing Techniques of Perovskite Solar Cells 79 3.6.1 Solution-Based Manufacturing Technique 80 3.6.1.1 Spin Coating 80 3.6.1.2 Dip Coating 81 3.6.2 Roll-to-Roll (R2R) Process 82 3.6.2.1 Knife-Over-Roll Coating 82 3.6.2.2 Slot-Die Coating 83 3.6.2.3 Flexographic Printing 84 3.6.2.4 Gravure Printing 85 3.6.2.5 Screen Printing 85 3.6.2.6 Inkjet Printing 86 3.6.2.7 Spray Coating 87 3.6.2.8 Brush Painting 88 3.6.2.9 Doctor Blade Coating 88 3.7 Encapsulation 89 3.8 Conclusions 90 References 90 4 Parameter Estimation of Solar Cells: A State-of-the-Art Review with Metaheuristic Approaches and Future Recommendations 103Shilpy Goyal, Parag Nijhawan and Souvik Ganguli 4.1 Introduction 104 4.2 Related Works 106 4.3 Problem Formulation 107 4.3.1 Single-Diode Model (SDM) 113 4.3.2 Double-Diode Model (DDM) 115 4.3.3 Three-Diode Model (TDM) 117 4.4 Salient Simulations and Discussions for Future Work 121 4.5 Conclusions 134 References 134 5 Power Electronics and Solar Panel: Solar Panel Design and Implementation 139Nayan Kumar, Tapas Kumar Saha and Jayati Dey 5.1 Chapter Overview 139 5.2 Challenges in Solar Power 141 5.3 Solar PV Cell Design and Implementation 141 5.3.1 Solar PV Cell Basics 145 5.3.2 Single-Diode-Based PV Cells (SDPVCs) 148 5.3.3 Determination of the Parameters 151 5.3.4 Double-Diode-Based PV Cell (DDPVC) 152 5.3.5 Solar PV System Configuration 153 5.4 MPPT Scheme for PV Panels 154 5.4.1 Operation and Modeling of MPPT Schemes for Solar PV Panels 155 5.4.2 Comparisons of Existing Solar MPPT Schemes 156 5.4.2.1 Perturbation and Observation (P&O)-MPPT Algorithms 156 5.4.2.2 Incremental-Conductance MPPT Algorithm 158 5.5 Way for Utilization of PV Schemes 159 5.5.1 Stand-Alone (SA) Based PV System 159 5.5.2 Grid-Integration–Based PV System 161 5.6 Future Trends 161 5.7 Conclusion 162 References 162 6 An Effective Li-Ion Battery State of Health Estimation Based on Event-Driven Processing 167Saeed Mian Qaisar and Maram Alguthami 6.1 Introduction 168 6.2 Background and Literature Review 169 6.2.1 Rechargeable Batteries 169 6.2.2 Applications of Li-Ion Batteries 171 6.2.3 Battery Management Systems 171 6.2.4 State of Health Estimation Methods 173 6.2.4.1 Direct Assessment Approaches 173 6.2.4.2 Adaptive Model–Based Approaches 173 6.2.4.3 Data-Driven Approaches 174 6.3 The Proposed Approach 175 6.3.1 The Li-Ion Battery Model 175 6.3.2 The Event-Driven Sensing 176 6.3.3 The Event-Driven State of Health Estimation 177 6.3.3.1 The Conventional Coulomb Counting Based SoH Estimation 178 6.3.3.2 The Event-Driven Coulomb Counting Based SoH Estimation 178 6.3.4 The Evaluation Measures 179 6.3.4.1 The Compression Ratio 179 6.3.4.2 The Computational Complexity 179 6.3.4.3 The SoH Estimation Error 181 6.4 Experimental Results and Discussion 181 6.4.1 Experimental Results 181 6.4.2 Discussion 185 6.5 Conclusion 187 Acknowledgement 187 References 188 7 Effective Power Quality Disturbances Identification Based on Event-Driven Processing and Machine Learning 191Saeed Mian Qaisar and Raheef Aljefri 7.1 Introduction 192 7.2 Background and Literature Review 194 7.2.1 Types of PQ Disturbances 195 7.2.1.1 Transient 196 7.2.1.2 Voltage Fluctuation 196 7.2.1.3 Long Duration Voltage Interruption 196 7.2.1.4 Noise 196 7.2.1.5 Flicker 196 7.2.1.6 Waveform Distortion 196 7.2.2 Reasons for Generation of the PQ Disturbances 196 7.2.3 PQ Disturbances Monitoring Techniques 197 7.2.4 Facilities Effected by Power Quality Disturbances 198 7.2.5 Power Quality (PQ) Disturbances Model 198 7.2.6 Extraction of Features 199 7.2.7 Classification Techniques 200 7.3 Proposed Solution 201 7.3.1 Power Quality (PQ) Disturbances Model 201 7.3.1.1 The Pure Signal 202 7.3.1.2 The Sag 203 7.3.1.3 The Interruption 203 7.3.1.4 The Swell 203 7.3.2 The Signal Reconstruction 204 7.3.3 The Event-Driven Sensing 206 7.3.4 The Event-Driven Segmentation 207 7.3.5 Extraction of Features 207 7.3.6 Classification Techniques 208 7.3.6.1 k-Nearest Neighbor (KNN) 208 7.3.6.2 Naïve Bayes 209 7.3.7 Evaluation Measures 209 7.4 Results 210 7.5 Discussion 213 7.6 Conclusion 215 Acknowledgement 215 References 215 8 Sr2SnO4 Ruddlesden Popper Oxide: Future Material for Renewable Energy Applications 221Upendra Kumar and Shail Upadhya 8.1 Introduction 222 8.1.1 Needs of Renewable Energy 222 8.1.2 Ruddlesden Popper Oxide Phase 224 8.1.3 Application of Ruddlesden Popper Phase 227 8.1.4 Motivation of Present Work 229 8.2 Experimental Work 230 8.2.1 Preparation of Materials 230 8.2.2 Characterizations of Materials 231 8.3 Experimental Results 231 8.3.1 Thermogravimetric and Differential Scanning Calorimetry Analysis 231 8.3.2 Characterization of Sr2-xBaxSnO4 232 8.3.2.1 Phase Determination using XRD 232 8.3.2.2 Optical Properties 234 8.3.2.3 Dielectric Analysis of Samples 236 8.3.3 Characterization of Sr2-xLaxSnO4 239 8.3.3.1 Structural Analysis using XRD 239 8.3.3.2 UV-Vis. Spectroscopy 242 8.3.3.3 Electrical Analysis 244 8.4 Conclusions 245 Acknowledgement 246 References 246 9 A Universal Approach to Solar Photovoltaic Panel Modeling 251Chitra A., M. Manimozhi, Sanjeevikumar P, Nirupama Nambiar and Saransh Chhawchharia 9.1 Introduction 251 9.2 PV Panel Modeling: A Brief Overview 252 9.3 Proposed Model 254 9.4 Current Model 259 9.5 Voltage Model 260 9.6 Simulation Results 260 9.7 Conclusion 265 Acknowledgement 265 References 266 10 Stepped DC Link Converters for Solar Power Applications 271Dr. R. Uthirasamy, Dr. V. Kumar Chinnaiyan, Dr. J. Karpagam and Dr. V. J.Vijayalakshmi 10.1 Introduction 272 10.1.1 Photovoltaic Cell 272 10.1.2 Photovoltaic Module 272 10.1.3 Photovoltaic Array 273 10.1.4 Working of Solar Cell 273 10.1.5 Modeling of Solar Cell 273 10.1.6 Effect of Irradiance 277 10.1.7 Effect of Temperature 279 10.1.8 Maximum Efficiency 280 10.1.9 Fill Factor 280 10.1.10 Modeling of Solar Panel 281 10.1.11 Simulation Model of PV Interfaced Boost Chopper Unit 282 10.2 Power Converters for Solar Power Applications 283 10.2.1 Introduction 283 10.2.2 DC-DC Converters 284 10.2.2.1 Boost Converter 285 10.2.2.2 Buck-Boost Converter 286 10.2.3 DC-AC Converters 288 10.2.3.1 Structure of Boost Cascaded Multilevel Inverter 288 10.2.3.2 Analysis of DC Sources in BCMLI System 298 10.2.4 Structure of Single-Phase Seven-Level BCDCLHBI 298 10.2.4.1 Operation of Boost Cascaded DC Link Configuration 300 10.2.4.2 Operation of H-Bridge Inverter Configuration 309 10.2.4.3 Calculation of Losses in BCDCLHBI 310 10.2.5 Realization of Boost Cascaded Dc Link H-Bridge Inverter 312 10.2.5.1 Peripheral Interface Controller 312 10.2.5.2 Features of PIC16F877A Microcontroller 312 10.2.5.3 Equivalent Circuit of Boost Cascaded DC Link H-Bridge Inverter 313 10.2.5.4 Design of Boost Chopper Parameters 314 10.2.6 Conclusion 315 References 315 11 A Harris Hawks Optimization (HHO)–Based Parameter Assessment for Modified Two-Diode Model of Solar Cells 319Shilpy Goyal, Parag Nijhawan and Souvik Ganguli 11.1 Introduction 320 11.2 Problem Formulation 322 11.3 Proposed Methodology of Work 325 11.3.1 Exploration Phase 326 11.3.2 Switching from Exploration to Exploitation 327 11.3.3 Exploitation Phase 327 11.4 Simulation Results 327 11.5 Conclusions 340 References 341 12 A Large-Gain Continuous Input-Current DC-DC Converter Applicable for Solar Energy Systems 345Tohid Taghiloo, Kazem Varesi and Sanjeevikumar Padmanaban 12.1 Introduction 345 12.2 Proposed Configuration 348 12.3 Steady-State Analysis 351 12.4 Component Design 354 12.5 Real Gain Relation 355 12.6 Comparative Analysis 356 12.7 Simulation Outcomes 360 12.8 Conclusions 364 References 364 13 Stability Issues in Microgrids: A Review 369Sonam Khurana and Sheela Tiwari 13.1 Introduction 370 13.2 Stability Issues 373 13.2.1 Control System Stability 375 13.2.2 Power Supply and Balance Stability 376 13.3 Analysis Techniques 378 13.3.1 Large-Perturbation Stability 379 13.3.2 Small-Perturbation Stability 381 13.4 Microgrid Control System 382 13.4.1 Control Methods for AC Microgrids 384 13.4.1.1 Primary Control 384 13.4.1.2 Secondary Control 389 13.4.1.3 Tertiary Control 391 13.4.2 Control Methods for DC Microgrid 392 13.4.2.1 Primary Control 392 13.4.2.2 Secondary Control 394 13.4.2.3 Tertiary Control 396 13.5 Conclusion 396 References 396 14 Theoretical Analysis of Torque Ripple Reduction in the SPMSM Drives Using PWM Control-Based Variable Switching Frequency 411Mohamed G. Hussien and Sanjeevikumar Padmanaban 14.1 Introduction 411 14.2 Prediction of Current and Torque Ripples 413 14.2.1 Current Ripple Prediction 413 14.2.2 Torque Ripple Prediction 416 14.3 Variable Switching Frequency PWM (VSFPWM) Method for Torque Ripple Control 418 14.4 Conclusion 422 References 422 Appendix: Simulation Model Circuits 424 Main Model 424 Speed & Current Loop Controllers 425 VSFPWM for Torque Ripple Control 426 15 Energy-Efficient System for Smart Cities 427Dushyant Kumar Singh, Ashish Kumar Singh and Himani Jerath 15.1 Introduction 428 15.2 Factors Promoting Energy-Efficient System 429 15.2.1 Smart and Clean Energy 429 15.2.2 Smart Grid 430 15.2.3 Smart Infrastructure 431 15.2.4 Smart Home 431 15.2.4.1 Home Automation 432 15.2.5 Smart Surveillance 437 15.2.6 Smart Roads and Traffic Management 438 15.2.7 Smart Agriculture and Water Distribution 439 References 440 16 Assessment of Economic and Environmental Impacts of Energy Conservation Strategies in a University Campus 441Sunday O. Oyedepo, Emmanuel G. Anifowose, Elizabeth O. Obembe, Joseph O. Dirisu, Shoaib Khanmohamadi, Kilanko O., Babalola P.O., Ohunakin O.S., Leramo R.O. and Olawole O.C. 16.1 Introduction 442 16.2 Materials and Methods 444 16.2.1 Study Location 445 16.2.2 Instrumentation 446 16.2.2.1 Building Energy Simulation Tool – eQUEST Software 446 16.2.3 Procedure for Data Collection and Analysis 446 16.2.4 Analysis of Electrical Energy Consumption 447 16.2.5 Economic Analysis 448 16.2.6 Environmental Impacts Analysis 449 16.3 Electricity Consumption Pattern in Covenant University 449 16.3.1 Result of Electricity Demand in Covenant University for Various End Uses 450 16.3.1.1 Results of Energy Audit in Cafeterias 1 & 2 450 16.3.1.2 Results of Energy Audit in Academic Buildings (Mechanical Engineering Building) 453 16.3.1.3 Results of Energy Audit in University Library 455 16.3.1.4 Results of Energy Audit in Health Center 457 16.3.1.5 Results of Energy Audit in the Student Halls of Residence (Daniel Hall) 459 16.3.2 Comparison of Energy Use Among the University Buildings 461 16.3.3 Results of Greenhouse Gas Emissions 462 16.3.4 Qualitative Recommendation Analysis 463 16.3.4.1 Replacement of Lighting Fixtures with LED Bulbs 463 16.3.4.2 Installation of Solar Panels on the Roofs of Selected Buildings 464 16.4 Conclusion 465 References 466 17 A Solar Energy–Based Multi-Level Inverter Structure with Enhanced Output-Voltage Quality and Increased Levels per Components 469Fatemeh Esmaeili, Kazem Varesi and Sanjeevikumar Padmanaban 17.1 Introduction 470 17.2 Proposed Basic Topology 471 17.2.1 Topology of Basic Unit 471 17.2.2 Operation of Basic Configuration 472 17.2.3 Switching of Basic Unit for Different Magnitudes of Input Sources 473 17.2.3.1 Symmetric Value of Input DC Supplies (P1) 473 17.2.3.2 DC Sources with Binary Order Magnitudes (P2 ) 475 17.2.3.3 DC Sources with Trinary Manner Magnitudes (P3) 476 17.3 Proposed Extended Structure 478 17.3.1 Structure 478 17.3.2 Determination of Values of DC Supplies 478 17.3.3 Blocking Voltage (BV) on Switches 479 17.4 Efficiency and Losses Analysis in Suggested Structure 480 17.4.1 Conduction Power Loss 480 17.4.2 Switching Power Loss 481 17.5 Comparison Results 483 17.6 Nearest Level Technique 485 17.7 Simulation Results 485 17.8 Conclusions 490 References 490 18 Operations of Doubly Fed Induction Generators Applied in Green Energy Systems 495Bhagwan Shree Ram and Suman Lata Tripathi 18.1 Introduction 496 18.2 Doubly Fed Induction Generators (DFIG) Systems Operated by Wind Turbines 496 18.3 Control Scheme of Direct Current Controller 497 18.4 Simulation Studies of Direct Current Control of DFIG System 498 18.5 Characteristics of DFIG at Transient and After Transient Situation 499 18.6 Pulsation of DFIG Parameters with DCC Control Technique 501 18.7 Effects of 5th and 7th Harmonics of IS and VGRID 502 18.8 Load Contribution of DFIG in Grid with DCC Control Technique 503 18.9 Speed Control Scheme of Generators 505 18.10 DFIG Control Scheme 506 18.11 General Description About PI Controller Design 507 18.12 GSC Controller 508 18.13 Characteristics of DFIG with Wind Speed Variations 509 18.14 Conclusion 511 References 512 19 A Developed Large Boosting Factor DC-DC Converter Feasible for Photovoltaic Applications 515Hussein Mostafapour, Kazem Varesi and Sanjeevikumar Padmanaban 19.1 Introduction 515 19.2 Suggested Topology 518 19.2.1 Configuration 518 19.2.2 Operating Modes during CCM 520 19.2.3 Operating Modes during DCM 521 19.3 Steady State Analyses 524 19.3.1 Gain Calculation 524 19.3.2 Average Currents and Current Ripple of Inductors 527 19.3.3 Stress on Semiconductors 528 19.3.4 Efficiency 529 19.4 Design Consideration 531 19.4.1 Design Consideration of Capacitors 531 19.4.2 Design Consideration of Inductors 531 19.5 Comparison 532 19.6 Simulation 539 19.7 Conclusion 544 References 545 20 Photovoltaic-Based Switched-Capacitor Multi-Level Inverters with Self-Voltage Balancing and Step-Up Capabilities 549Saeid Deliri Khatoonabad, Kazem Varesi and Sanjeevikumar Padmanaban 20.1 Introduction 550 20.2 Suggested First (13-Level) Basic Configuration 551 20.3 Suggested Second Basic Configuration 556 20.4 Modulation Method 561 20.5 Design Consideration of Capacitors 562 20.6 Efficiency and Losses Analysis 563 20.7 Simulation Results 567 20.7.1 First Structure 567 20.7.2 Second Structure 571 20.8 Comparative Analysis 575 20.9 Conclusions 578 References 579 Index 583

Read more less

Book Synopsis

Read more less

Book SynopsisUNDERSTANDING INFRASTRUCTURE EDGE COMPUTING A comprehensive review of the key emerging technologies that will directly impact areas of computer technology over the next five yearsInfrastructure edge computing is the model of data center and network infrastructure deployment which distributes a large number of physically small data centers around an area to deliver better performance and to enable new economical applications. It is vital for those operating at business or technical levels to be positioned to capitalize on the changes that will occur as a result of infrastructure edge computing.This book provides a thorough understanding of the growth of internet infrastructure from its inception to the emergence of infrastructure edge computing. Author Alex Marcham, an acknowledged leader in the field who coined the term infrastructure edge computing,' presents an accessible, accurate, and expansive view of the next generation of internet infrastructure. The book Table of ContentsPreface xv About the Author xvii Acknowledgements xix 1 Introduction 1 2 What Is Edge Computing? 3 2.1 Overview 3 2.2 Defining the Terminology 3 2.3 Where Is the Edge? 4 2.3.1 A Tale of Many Edges 5 2.3.2 Infrastructure Edge 6 2.3.3 Device Edge 6 2.4 A Brief History 8 2.4.1 Third Act of the Internet 8 2.4.2 Network Regionalisation 10 2.4.3 CDNs and Early Examples 10 2.5 Why Edge Computing? 12 2.5.1 Latency 12 2.5.2 Data Gravity 13 2.5.3 Data Velocity 13 2.5.4 Transport Cost 14 2.5.5 Locality 14 2.6 Basic Edge Computing Operation 15 2.7 Summary 18 References 18 3 Introduction to Network Technology 21 3.1 Overview 21 3.2 Structure of the Internet 21 3.2.1 1970s 22 3.2.2 1990s 22 3.2.3 2010s 23 3.2.4 2020s 23 3.2.5 Change over Time 23 3.3 The OSI Model 24 3.3.1 Layer 1 25 3.3.2 Layer 2 25 3.3.3 Layer 3 26 3.3.4 Layer 4 26 3.3.5 Layers 5, 6, and 7 27 3.4 Ethernet 28 3.5 IPv4 and IPv6 29 3.6 Routing and Switching 29 3.6.1 Routing 30 3.6.2 Routing Protocols 31 3.6.3 Routing Process 34 3.7 LAN, MAN, and WAN 41 3.8 Interconnection and Exchange 42 3.9 Fronthaul, Backhaul, and Midhaul 44 3.10 Last Mile or Access Networks 45 3.11 Network Transport and Transit 46 3.12 Serve Transit Fail (STF) Metric 48 3.13 Summary 51 References 52 4 Introduction to Data Centre Technology 53 4.1 Overview 53 4.2 Physical Size and Design 53 4.3 Cooling and Power Efficiency 54 4.4 Airflow Design 56 4.5 Power Distribution 57 4.6 Redundancy and Resiliency 58 4.7 Environmental Control 61 4.8 Data Centre Network Design 61 4.9 Information Technology (IT) Equipment Capacity 65 4.10 Data Centre Operation 66 4.10.1 Notification 67 4.10.2 Security 67 4.10.3 Equipment Deployment 67 4.10.4 Service Offerings 68 4.10.5 Managed Colocation 68 4.11 Data Centre Deployment 69 4.11.1 Deployment Costing 69 4.11.2 Brownfield and Greenfield Sites 69 4.11.3 Other Factors 70 4.12 Summary 70 References 70 5 Infrastructure Edge Computing Networks 71 5.1 Overview 71 5.2 Network Connectivity and Coverage Area 71 5.3 Network Topology 72 5.3.1 Full Mesh 74 5.3.2 Partial Mesh 74 5.3.3 Hub and Spoke 75 5.3.4 Ring 76 5.3.5 Tree 76 5.3.6 Optimal Topology 76 5.3.7 Inter-area Connectivity 77 5.4 Transmission Medium 78 5.4.1 Fibre 78 5.4.2 Copper 78 5.4.3 Wireless 79 5.5 Scaling and Tiered Network Architecture 80 5.6 Other Considerations 81 5.7 Summary 82 6 Infrastructure Edge Data Centres 83 6.1 Overview 83 6.2 Physical Size and Design 83 6.2.1 Defining an Infrastructure Edge Data Centre 84 6.2.2 Size Categories 84 6.3 Heating and Cooling 102 6.4 Airflow Design 105 6.4.1 Traditional Designs 107 6.4.2 Non-traditional Designs 109 6.5 Power Distribution 113 6.6 Redundancy and Resiliency 114 6.6.1 Electrical Power Delivery and Generation 116 6.6.2 Network Connectivity 118 6.6.3 Cooling Systems 120 6.6.4 Market Design 122 6.6.5 Redundancy Certification 124 6.6.6 Software Service Resiliency 125 6.6.7 Physical Redundancy 126 6.6.8 System Resiliency Example 127 6.7 Environmental Control 128 6.8 Data Centre Network Design 131 6.9 Information Technology (IT) Equipment Capacity 134 6.9.1 Operational Headroom 135 6.10 Data Centre Operation 135 6.10.1 Site Automation 136 6.10.2 Single or Multi-tenant 142 6.10.3 Neutral Host 144 6.10.4 Network Operations Centre (NOC) 145 6.11 Brownfield and Greenfield Sites 147 6.12 Summary 151 7 Interconnection and Edge Exchange 153 7.1 Overview 153 7.2 Access or Last Mile Network Interconnection 153 7.3 Backhaul and Midhaul Network Interconnection 158 7.4 Internet Exchange 160 7.5 Edge Exchange 164 7.6 Interconnection Network Technology 167 7.6.1 5G Networks 168 7.6.2 4G Networks 169 7.6.3 Cable Networks 170 7.6.4 Fibre Networks 172 7.6.5 Other Networks 173 7.6.6 Meet Me Room (MMR) 173 7.6.7 Cross Connection 174 7.6.8 Virtual Cross Connection 176 7.6.9 Interconnection as a Resource 179 7.7 Peering 180 7.8 Cloud On-ramps 181 7.9 Beneficial Impact 183 7.9.1 Latency 183 7.9.2 Data Transport Cost 184 7.9.3 Platform Benefit 185 7.10 Alternatives to Interconnection 186 7.11 Business Arrangements 187 7.12 Summary 188 8 Infrastructure Edge Computing Deployment 189 8.1 Overview 189 8.2 Physical Facilities 189 8.3 Site Locations 191 8.3.1 kW per kM2 192 8.3.2 Customer Facility Selection 193 8.3.3 Site Characteristics 194 8.4 Coverage Areas 195 8.5 Points of Interest 197 8.6 Codes and Regulations 198 8.7 Summary 200 9 Computing Systems at the Infrastructure Edge 203 9.1 Overview 203 9.2 What Is Suitable? 203 9.3 Equipment Hardening 204 9.4 Rack Densification 205 9.4.1 Heterogenous Servers 207 9.4.2 Processor Densification 208 9.4.3 Supporting Equipment 210 9.5 Parallel Accelerators 211 9.5.1 Field Programmable Gate Arrays (FPGAs) 213 9.5.2 Tensor Processing Units (TPUs) 213 9.5.3 Graphics Processing Units (GPUs) 214 9.5.4 Smart Network Interface Cards (NICs) 215 9.5.5 Cryptographic Accelerators 216 9.5.6 Other Accelerators 217 9.5.7 FPGA, TPU, or GPU? 217 9.6 Ideal Infrastructure 218 9.6.1 Network Compute Utilisation 218 9.7 Adapting Legacy Infrastructure 221 9.8 Summary 221 References 222 10 Multi-tier Device, Data Centre, and Network Resources 223 10.1 Overview 223 10.2 Multi-tier Resources 223 10.3 Multi-tier Applications 226 10.4 Core to Edge Applications 228 10.5 Edge to Core Applications 230 10.6 Infrastructure Edge and Device Edge Interoperation 231 10.7 Summary 234 11 Distributed Application Workload Operation 235 11.1 Overview 235 11.2 Microservices 235 11.3 Redundancy and Resiliency 236 11.4 Multi-site Operation 237 11.5 Workload Orchestration 238 11.5.1 Processing Requirements 240 11.5.2 Data Storage Requirements 240 11.5.3 Network Performance Requirements 241 11.5.4 Application Workload Cost Profile 241 11.5.5 Redundancy and Resiliency Requirements 242 11.5.6 Resource Marketplaces 243 11.5.7 Workload Requirement Declaration 243 11.6 Infrastructure Visibility 244 11.7 Summary 245 12 Infrastructure and Application Security 247 12.1 Overview 247 12.2 Threat Modelling 247 12.3 Physical Security 249 12.4 Logical Security 250 12.5 Common Security Issues 251 12.5.1 Staff 251 12.5.2 Visitors 252 12.5.3 Network Attacks 252 12.6 Application Security 253 12.7 Security Policy 254 12.8 Summary 255 13 Related Technologies 257 13.1 Overview 257 13.2 Multi-access Edge Computing (MEC) 257 13.3 Internet of Things (IoT) and Industrial Internet of Things (IIoT) 258 13.4 Fog and Mist Computing 259 13.5 Summary 260 Reference 260 14 Use Case Example: 5G 261 14.1 Overview 261 14.2 What Is 5G? 261 14.2.1 5G New Radio (NR) 262 14.2.2 5G Core Network (CN) 263 14.3 5G at the Infrastructure Edge 264 14.3.1 Benefits 264 14.3.2 Architecture 264 14.3.3 Considerations 265 14.4 Summary 266 15 Use Case Example: Distributed AI 267 15.1 Overview 267 15.2 What Is AI? 268 15.2.1 Machine Learning (ML) 268 15.2.2 Deep Learning (DL) 269 15.3 AI at the Infrastructure Edge 270 15.3.1 Benefits 270 15.3.2 Architecture 271 15.3.3 Considerations 272 15.4 Summary 273 16 Use Case Example: Cyber-physical Systems 275 16.1 Overview 275 16.2 What Are Cyber-physical Systems? 275 16.2.1 Autonomous Vehicles 276 16.2.2 Drones 278 16.2.3 Robotics 280 16.2.4 Other Use Cases 280 16.3 Cyber-physical Systems at the Infrastructure Edge 280 16.3.1 Benefits 280 16.3.2 Architecture 281 16.3.3 Considerations 282 16.4 Summary 282 Reference 283 17 Use Case Example: Public or Private Cloud 285 17.1 Overview 285 17.2 What Is Cloud Computing? 286 17.2.1 Public Clouds 286 17.2.2 Private Clouds 287 17.2.3 Hybrid Clouds 287 17.2.4 Edge Cloud 288 17.3 Cloud Computing at the Infrastructure Edge 288 17.3.1 Benefits 288 17.3.2 Architecture 289 17.3.3 Considerations 290 17.4 Summary 290 18 Other Infrastructure Edge Computing Use Cases 291 18.1 Overview 291 18.2 Near Premises Services 291 18.3 Video Surveillance 293 18.4 SD-WAN 294 18.5 Security Services 295 18.6 Video Conferencing 296 18.7 Content Delivery 297 18.8 Other Use Cases 298 18.9 Summary 299 19 End to End: An Infrastructure Edge Project Example 301 19.1 Overview 301 19.2 Defining Requirements 301 19.2.1 Deciding on a Use Case 302 19.2.2 Determining Deployment Locations 304 19.2.3 Identifying Required Equipment 306 19.2.4 Choosing an Infrastructure Edge Computing Network Operator 307 19.2.5 Regional or National Data Centres 307 19.3 Success Criteria 307 19.4 Comparing Costs 308 19.5 Alternative Options 309 19.6 Initial Deployment 310 19.7 Ongoing Operation 311 19.7.1 SLA Breaches 312 19.8 Project Conclusion 312 19.9 Summary 314 20 The Future of Infrastructure Edge Computing 315 20.1 Overview 315 20.2 Today and Tomorrow 315 20.3 The Next Five Years 316 20.4 The Next 10 Years 316 20.5 Summary 316 21 Conclusion 317 Appendix A: Acronyms and Abbreviations 319 Index 323

Read more less

Book SynopsisTable of ContentsPreface xv List of Contributors xix Acronyms List xx 1 Hands-on Wireless Communication Experience 1Hüseyin Arslan 1.1 Importance of Laboratory-Based Learning of Wireless Communications 1 1.2 Model for a Practical Lab Bench 3 1.3 Examples of Co-simulation with Hardware 6 1.4 A Sample Model for a Laboratory Course 8 1.4.1 Introduction to the SDR and Testbed Platform 11 1.4.2 Basic Simulation 11 1.4.3 Measurements and Multidimensional Signal Analysis 11 1.4.4 Digital Modulation 12 1.4.5 Pulse Shaping 13 1.4.6 RF Front-end and RF Impairments 13 1.4.7 Wireless Channel and Interference 14 1.4.8 Synchronization and Channel Estimation 15 1.4.9 OFDM Signal Analysis and Performance Evaluation 15 1.4.10 Multiple Accessing 16 1.4.11 Independent Project Development Phase 16 1.4.11.1 Software Defined Radio 17 1.4.11.2 Dynamic Spectrum Access and CR Experiment 17 1.4.11.3 Wireless Channel 17 1.4.11.4 Wireless Channel Counteractions 18 1.4.11.5 Antenna Project 18 1.4.11.6 Signal Intelligence 18 1.4.11.7 Channel, User, and Context Awareness Project 19 1.4.11.8 Combination of DSP Lab with RF and Microwave Lab 19 1.4.11.9 Multiple Access and Interference Management 19 1.4.11.10 Standards 20 1.5 Conclusions 20 References 20 2 Performance Metrics and Measurements 23Hüseyin Arslan 2.1 Signal Quality Measurements 23 2.1.1 Measurements Before Demodulation 24 2.1.2 Measurements During and After Demodulation 25 2.1.2.1 Noise Figure 26 2.1.2.2 Channel Frequency Response Estimation 26 2.1.3 Measurements After Channel Decoding 26 2.1.3.1 Relation of SNR with BER 27 2.1.4 Error Vector Magnitude 27 2.1.4.1 Error-Vector-Time and Error-Vector-Frequency 29 2.1.4.2 Relation of EVM with Other Metrics 30 2.1.4.3 Rho 31 2.1.5 Measures After Speech or Video Decoding 31 2.2 Visual Inspections and Useful Plots 32 2.2.1 Advanced Scatter Plot 39 2.3 Cognitive Radio and SDR Measurements 40 2.4 Other Measurements 42 2.5 Clarifying dB and dBm 44 2.6 Conclusions 45 References 45 3 Multidimensional Signal Analysis 49Hüseyin Arslan 3.1 Why Multiple Dimensions in a Radio Signal? 49 3.2 Time Domain Analysis 52 3.2.1 CCDF and PAPR 53 3.2.2 Time Selectivity Measure 56 3.3 Frequency Domain Analysis 57 3.3.1 Adjacent Channel Power Ratio 59 3.3.2 Frequency Selectivity Measure 61 3.4 Joint Time-Frequency Analysis 62 3.5 Code Domain Analysis 64 3.5.1 Code Selectivity 66 3.6 Correlation Analysis 67 3.7 Modulation Domain Analysis 68 3.8 Angular Domain Analysis 68 3.8.1 Direction Finding 68 3.8.2 Angular Spread 70 3.9 MIMO Measurements 71 3.9.1 Antenna Correlation 72 3.9.2 RF Cross-Coupling 72 3.9.3 EVM Versus Antenna Branches 73 3.9.4 Channel Parameters 73 3.10 Conclusions 73 References 74 4 Simulating a Communication System 77Muhammad Sohaib J. Solaija and Hüseyin Arslan 4.1 Simulation: What,Why? 77 4.2 Approaching a Simulation 78 4.2.1 Strategy 78 4.2.2 General Methodology 80 4.3 Basic Modeling Concepts 81 4.3.1 System Modeling 81 4.3.2 Subsystem Modeling 81 4.3.3 Stochastic Modeling 82 4.4 What is a Link/Link-level Simulation? 82 4.4.1 Source and Source Coding 82 4.4.2 Channel Coding 83 4.4.3 Symbol Mapping/Modulation 83 4.4.4 Upsampling 84 4.4.5 Digital Filtering 84 4.4.6 RF Front-end 85 4.4.7 Channel 86 4.4.8 Synchronization and Equalization 87 4.4.9 Performance Evaluation and Signal Analysis 87 4.5 Communication in AWGN – A Simple Case Study 88 4.5.1 Receiver Design 88 4.6 Multi-link vs. Network-level Simulations 88 4.6.1 Network Layout Generation 90 4.6.1.1 Hexagonal Grid 90 4.6.1.2 PPP-based Network Layout 91 4.7 Practical Issues 93 4.7.1 Monte Carlo Simulations 93 4.7.2 Random Number Generation 94 4.7.2.1 White Noise Generation 94 4.7.2.2 Random Binary Sequence 94 4.7.3 Values of Simulation Parameters 95 4.7.4 Confidence Interval 95 4.7.5 Convergence/Stopping Criterion 95 4.8 Issues/Limitations of Simulations 95 4.8.1 Modeling Errors 96 4.8.1.1 Errors in System Model 96 4.8.1.2 Errors in Subsystem Model 96 4.8.1.3 Errors in Random Process Modeling 96 4.8.2 Processing Errors 96 4.9 Conclusions 97 References 97 5 RF Impairments 99Hüseyin Arslan 5.1 Radio Impairment Sources 99 5.2 IQ Modulation Impairments 102 5.3 PA Nonlinearities 106 5.4 Phase Noise and Time Jitter 110 5.5 Frequency Offset 112 5.6 ADC/DAC Impairments 113 5.7 Thermal Noise 114 5.8 RF Impairments and Interference 114 5.8.1 Harmonics and Intermodulation Products 114 5.8.2 Multiple Access Interference 116 5.9 Conclusions 118 References 118 6 Digital Modulation and Pulse Shaping 121Hüseyin Arslan 6.1 Digital Modulation Basics 121 6.2 Popularly Used Digital Modulation Schemes 123 6.2.1 PSK 123 6.2.2 FSK 125 6.2.2.1 GMSK and Approximate Representation of GSM GMSK Signal 127 6.2.3 QAM 129 6.2.4 Differential Modulation 132 6.3 Adaptive Modulation 133 6.3.1 Gray Mapping 135 6.3.2 Calculation of Error 135 6.3.3 Relation of Eb No with SNR at the receiver 138 6.4 Pulse-Shaping Filtering 138 6.5 Conclusions 146 References 146 7 OFDM Signal Analysis and Performance Evaluation 147Hüseyin Arslan 7.1 Why OFDM? 147 7.2 Generic OFDM System Design and Its Evaluation 149 7.2.1 Basic CP-OFDM Transceiver Design 150 7.2.2 Spectrum of the OFDM Signal 151 7.2.3 PAPR of the OFDM Signal 155 7.2.4 Performance in Multipath Channel 157 7.2.4.1 Time-Dispersive Multipath Channel 157 7.2.4.2 Frequency-Dispersive Multipath Channel 161 7.2.5 Performance with Impairments 162 7.2.5.1 Frequency Offset 163 7.2.5.2 Symbol Timing Error 167 7.2.5.3 Sampling Clock Offset 170 7.2.5.4 Phase Noise 171 7.2.5.5 PA Nonlinearities 172 7.2.5.6 I/Q Impairments 175 7.2.6 Summary of the OFDM Design Considerations 177 7.2.7 Coherent versus Differential OFDM 178 7.3 OFDM-like Signaling 180 7.3.1 OFDM Versus SC-FDE 180 7.3.2 Multi-user OFDM and OFDMA 181 7.3.3 SC-FDMA and DFT-S-OFDM 182 7.4 Case Study: Measurement-Based OFDM Receiver 185 7.4.1 System Model 185 7.4.1.1 Frame Format 186 7.4.1.2 OFDM Symbol Format 186 7.4.1.3 Baseband Transmitter Blocks and Transmitted Signal Model 186 7.4.1.4 Received Signal Model 188 7.4.2 Receiver Structure and Algorithms 189 7.4.2.1 Packet Detection 191 7.4.2.2 Frequency Offset Estimation and Compensation 191 7.4.2.3 Symbol Timing Estimation 192 7.4.2.4 Packet-end Detection and Packet Extraction 193 7.4.2.5 Channel Estimation and Equalization 194 7.4.2.6 Pilot Tracking 195 7.4.2.7 Auto-modulation Detection 195 7.4.3 FCH Decoding 196 7.4.4 Test and Measurements 196 7.5 Conclusions 197 References 198 8 Analysis of Single-Carrier Communication Systems 201Hüseyin Arslan 8.1 A Simple System in AWGN Channel 201 8.2 Flat Fading (Non-Dispersive) Multipath Channel 210 8.3 Frequency-Selective (Dispersive) Multipath Channel 215 8.3.1 Time-Domain Equalization 219 8.3.2 Channel Estimation 223 8.3.3 Frequency-Domain Equalization 226 8.4 Extension of Dispersive Multipath Channel to DS-CDMA-based Wideband Systems 229 8.5 Conclusions 232 References 232 9 Multiple Accessing, Multi-Numerology, Hybrid Waveforms 235Mehmet Mert ¸Sahin and Hüseyin Arslan 9.1 Preliminaries 235 9.1.1 Duplexing 236 9.1.2 Downlink Communication 237 9.1.3 Uplink Communication 238 9.1.4 Traffic Theory and Trunking Gain 238 9.2 Orthogonal Design 241 9.2.1 TDMA 241 9.2.2 FDMA 242 9.2.3 Code Division Multiple Access (CDMA) 243 9.2.4 Frequency Hopped Multiple Access (FHMA) 245 9.2.5 Space Division Multiple Access (SDMA) 246 9.2.5.1 Multiuser Multiple-input Multiple-output (MIMO) 247 9.3 Non-orthogonal Design 249 9.3.1 Power-domain Non-orthogonal Multiple Access (PD-NOMA) 250 9.3.2 Code-domain Non-orthogonal Multiple Access 251 9.4 Random Access 253 9.4.1 ALOHA 253 9.4.2 Carrier Sense Multiple Accessing (CSMA) 254 9.4.3 Multiple Access Collision Avoidance (MACA) 254 9.4.4 Random Access Channel (RACH) 255 9.4.5 Grant-free Random Access 255 9.5 Multiple Accessing with Application-Based Hybrid Waveform Design 256 9.5.1 Multi-numerology Orthogonal Frequency Division Multiple Access (OFDMA) 256 9.5.2 Radar-Sensing and Communication (RSC) Coexistence 258 9.5.3 Coexistence of Different Waveforms in Multidimensional Hyperspace for 6G and Beyond Networks 260 9.6 Case Study 261 Appendix: Erlang B table 263 References 263 10 Wireless Channel and Interference 267Abuu B. Kihero, Armed Tusha, and Hüseyin Arslan 10.1 Fundamental Propagation Phenomena 267 10.2 Multipath Propagation 269 10.2.1 Large-Scale Fading 269 10.2.1.1 Path Loss 270 10.2.1.2 Shadowing 271 10.2.2 Small-Scale Fading 272 10.2.2.1 Characterization of Time-Varying Channels 273 10.2.2.2 Rayleigh and Rician Fading Distributions 274 10.2.3 Time, Frequency and Angular Domains Characteristics of Multipath Channel 276 10.2.3.1 Delay Spread 276 10.2.3.2 Angular Spread 279 10.2.3.3 Doppler Spread 281 10.2.4 Novel Channel Characteristics in the 5G Technology 284 10.3 Channel as a Source of Interference 288 10.3.1 Interference due to Large-Scale Fading 288 10.3.1.1 Cellular Systems and CoChannel Interference 288 10.3.1.2 Cochannel Interference Control via Resource Assignment 289 10.3.2 Interference due to Small-Scale Fading 292 10.4 Channel Modeling 293 10.4.1 Analytical Channel Models 294 10.4.1.1 Correlation-based Models 294 10.4.1.2 Propagation-Motivated Models 294 10.4.2 Physical Models 295 10.4.2.1 Deterministic Model 295 10.4.2.2 Geometry-based Stochastic Model 295 10.4.2.3 Nongeometry-based Stochastic Models 296 10.4.3 3GPP 5G Channel Models 297 10.4.3.1 Tapped Delay Line (TDL) Model 297 10.4.3.2 Clustered Delay Line (CDL) Model 298 10.4.3.3 Generating Channel Coefficients Using CDL Model 299 10.4.4 Role of Artificial Intelligence (AI) in Channel Modeling 300 10.5 Channel Measurement 301 10.5.1 Frequency Domain Channel Sounder 303 10.5.1.1 Swept Frequency/Chirp Sounder 303 10.5.2 Time Domain Channel Sounder 304 10.5.2.1 Periodic Pulse/Impulse Sounder 304 10.5.2.2 Correlative/Pulse Compression Sounders 305 10.5.3 Challenges of Practical Channel Measurement 308 10.6 Channel Emulation 308 10.6.1 Baseband and RF Domain Channel Emulators 309 10.6.2 Reverberation Chambers as Channel Emulator 309 10.6.2.1 General Principles 309 10.6.2.2 Emulating Multipath Effects Using RVC 311 10.6.3 Commercial Wireless Channel Emulators 318 10.7 Wireless Channel Control 319 10.8 Conclusion 321 References 321 11 Carrier and Time Synchronization 325Musab Alayasra and Hüseyin Arslan 11.1 Signal Modeling 325 11.2 Synchronization Approaches 327 11.3 Carrier Synchronization 329 11.3.1 Coarse Frequency Offset Compensation 331 11.3.1.1 DFT-based Coarse Frequency Offset Compensation 331 11.3.1.2 Phase-based Coarse Frequency Offset Compensation 333 11.3.2 Fine Frequency Offset Compensation 335 11.3.2.1 Feedforward MLE-Based Frequency Offset Compensation 335 11.3.2.2 Feedback Heuristic-Based Frequency Offset Compensation 340 11.3.3 Carrier Phase Offset Compensation 344 11.4 Time Synchronization 345 11.4.1 Frame Synchronization 346 11.4.2 Symbol Timing Synchronization 347 11.4.2.1 Feedforward MLE-based Symbol Timing Synchronization 348 11.4.2.2 Feedback Heuristic-based Symbol Timing Synchronization 349 11.5 Conclusion 352 References 353 12 Blind Signal Analysis 355Mehmet Ali Aygül, Ahmed Naeem, and Hüseyin Arslan 12.1 What is Blind Signal Analysis? 355 12.2 Applications of Blind Signal Analysis 355 12.2.1 Spectrum Sensing 356 12.2.2 Parameter Estimation and Signal Identification 357 12.2.2.1 Parameter Estimation 357 12.2.2.2 Signal Identification 357 12.2.3 Radio Environment Map 358 12.2.4 Equalization 360 12.2.5 Modulation Identification 361 12.2.6 Multi-carrier (OFDM) Parameters Estimation 362 12.3 Case Study: Blind Receiver 363 12.3.1 Bandwidth Estimation 364 12.3.2 Carrier Frequency Estimation 365 12.3.3 Symbol Rate Estimation 366 12.3.4 Pulse-Shaping and Roll-off Factor Estimation 366 12.3.5 Optimum Sampling Phase Estimation 368 12.3.6 Timing Recovery 369 12.3.7 Frequency Offset and Phase Offset Estimation 371 12.4 Machine Learning for Blind Signal Analysis 372 12.4.1 Deep Learning 374 12.4.2 Applications of Machine Learning 375 12.4.2.1 Signal and Interference Identification 375 12.4.2.2 Multi-RF Impairments Identification, Separation, and Classification 375 12.4.2.3 Channel Modeling and Estimation 376 12.4.2.4 Spectrum Occupancy Prediction 377 12.5 Challenges and Potential Study Items 378 12.5.1 Challenges 378 12.5.2 Potential Study Items 379 12.6 Conclusions 379 References 380 13 Radio Environment Monitoring 383Halise Türkmen, Saira Rafique, and Hüseyin Arslan 13.1 Radio Environment Map 384 13.2 Generalized Radio Environment Monitoring 385 13.2.1 Radio Environment Monitoring with the G-REM Framework 387 13.3 Node Types 388 13.4 Sensing Modes 388 13.5 Observable Data, Derivable Information and Other Sources 389 13.6 Sensing Methods 389 13.6.1 Sensing Configurations 390 13.6.2 Processing Data and Control Signal 391 13.6.2.1 Channel State Information (CSI) 391 13.6.2.2 Channel Impulse Response (CIR) 393 13.6.2.3 Channel Frequency Response (CFR) 393 13.6.3 Blind Signal Analysis 393 13.6.4 Radio Detection and Ranging 394 13.6.4.1 Radar Test-bed 401 13.6.5 Joint Radar and Communication 402 13.6.5.1 Coexistence 403 13.6.5.2 Co-Design 403 13.6.5.3 RadComm 405 13.6.5.4 CommRad 406 13.7 Mapping Methods 407 13.7.1 Signal Processing Algorithms 407 13.7.2 Interpolation Techniques 408 13.7.2.1 Inverse Distance Weighted Interpolation 408 13.7.2.2 Kriging’s Interpolation 409 13.7.3 Model-Based Techniques 410 13.7.4 Learning-Based Techniques 410 13.7.5 Hybrid Techniques 410 13.7.6 Case Study: Radio Frequency Map Construction 410 13.7.6.1 Radio Frequency Map Construction Test-bed for CR 411 13.7.7 Case Study: Wireless Local Area Network/Wi-Fi Sensing 413 13.7.7.1 WLAN Sensing Test-bed for Gesture Detection 415 13.8 Applications of G-REM 416 13.8.1 Cognitive Radios 417 13.8.2 Security 417 13.8.2.1 PHY Layer Security 417 13.8.2.2 Cross-Layer Security 417 13.8.3 Multi-Antenna Communication Systems 418 13.8.3.1 UE and Obstacle Tracking for Beam Management 418 13.8.3.2 No-Feedback Channel Estimation for FDD MIMO and mMIMO Systems 418 13.8.4 Formation and Management of Ad Hoc Networks and Device-to-Device Communication 418 13.8.5 Content Caching 419 13.8.6 Enabling Flexible Radios for 6G and Beyond Networks 419 13.8.7 Non-Communication Applications 419 13.9 Challenges and Future Directions 420 13.9.1 Security 420 13.9.2 Scheduling 421 13.9.3 Integration of (New) Technologies 421 13.9.3.1 Re-configurable Intelligent Surfaces 421 13.9.3.2 Quantum Radar 421 13.10 Conclusion 422 References 422 Index 425

Read more less

Book SynopsisElectrical and instrumentation engineering is changing rapidly, and it is important for the veteran engineer in the field not only to have a valuable and reliable reference work which he or she can consult for basic concepts, but also to be up to date on any changes to basic equipment or processes that might have occurred in the field. Covering all of the basic concepts, from three-phase power supply and its various types of connection and conversion, to power equation and discussions of the protection of power system, to transformers, voltage regulation, and many other concepts, this volume is the one-stop, go to for all of the engineer's questions on basic electrical and instrumentation engineering. There are chapters covering the construction and working principle of the DC machine, all varieties of motors, fundamental concepts and operating principles of measuring, and instrumentation, both from a high end point of view and the point of view of developing countries, emphasizing Table of ContentsForeword xi Acknowledgements xiii 1 Introduction to Electric Power Systems 1 1.1 Introduction 1 1.1.1 Electrical Parameters 3 1.1.1.1 Voltage 3 1.1.1.2 Current 11 1.1.1.3 Time Period and Frequency 15 1.1.1.4 Phase Angle (ɸ) 16 1.2 Three-Phase Supply Connections 17 1.2.1 Star Connection 17 1.2.2 Delta Connection 19 1.2.3 Balanced Load 21 1.2.4 Unbalanced Load 23 1.2.5 Star – Delta Conversion 23 1.2.6 Delta to Star Conversion 24 1.3 Power 25 1.3.1 Real Power or Active Power (P) 25 1.3.2 Reactive Power (Q) 28 1.3.3 Apparent Power (S) 31 1.4 Power Factor (PF) 35 1.4.1 Classification Based on Load Characteristics 35 1.4.2 Classification Based on Harmonics Producing Loads 46 1.4.3 The Need for Power Factor Improvement 47 1.4.4 Methods of Power Factor Improvement 48 1.5 Types of Loads 49 1.5.1 Linear Loads 50 1.5.2 Non-Linear Loads 50 1.6 Three-Phase Power Measurement 50 1.7 Overview of Power Systems 56 1.7.1 Components of an Electric Power System 58 1.8 Protection of Power System 63 References 75 2 Transformers 79 2.1 Introduction 79 2.2 Transformer Magnetics 82 2.3 Construction of Transformer 85 2.4 EMF Equation of a Transformer 88 2.5 Ideal Transformer 91 2.6 Transformation Ratio (K) 95 2.7 Circuit Model or Equivalent Circuit of Transformer 96 2.8 Voltage Regulation of Transformer 100 2.9 Name Plate Rating 101 2.10 Efficiency of Transformer 102 2.11 Three-Phase Transformer 104 2.12 Components of the Transformer 113 2.13 Standards for Transformers 116 References 123 3 DC Machines 125 3.1 Introduction 125 3.1.1 DC Generators 125 3.1.2 DC Motors 125 3.1.3 Construction of DC Machines 125 3.2 Operation of DC Machines 132 3.2.1 Principle of DC Generators 132 3.2.2 Operating Principle of Motors 133 3.3 EMF Equation of DC Generator 136 3.4 Torque Equation of a DC Motor 138 3.5 Circuit Model 139 3.5.1 Generator Mode 140 3.5.2 Motor Mode 141 3.5.3 Symbolic Representation of DC Generator 141 3.6 Methods of Excitation 142 3.7 Characteristics of DC Generator 148 3.7.1 Characteristics of Separately Excited DC Generator 150 3.7.2 Load Characteristics of DC Shunt Generator 152 3.7.3 Load Characteristics of DC Series Generator 154 3.7.4 Load Characteristics of DC Compound Generator 155 3.8 Types of DC Motor 156 3.9 DC Motor Characteristics 160 3.10 Necessity for Starters 165 3.11 Speed Control of DC Motors 170 3.12 Universal Motor 179 References 183 4 AC Machines 185 4.1 Introduction 185 4.2 Three-Phase Induction Motor 185 4.2.1 Rotating Magnetic Field 186 4.2.2 Construction 186 4.2.3 Working Principle 189 4.2.4 Slip of an Induction Motor 192 4.2.5 Torque Equation 193 4.2.6 Torque–Slip Characteristics 195 4.2.7 Induction Motor as a Transformer 197 4.2.8 Equivalent Circuit of Induction Motor 198 4.3 Single-Phase Induction Motor 201 4.3.1 Introduction 201 4.3.2 Working Principle 203 4.3.3 Types of Single-Phase Induction Motor 203 4.4 Starting Methods of Induction Motor 209 4.4.1 Need for Starters 209 4.4.2 Types of Starters 209 4.5 Speed Control of Three-Phase Induction Motor 215 4.6 Synchronous Motor 220 4.6.1 Construction 220 4.6.2 Features of a Synchronous Motor 220 4.6.3 Working Principle 221 4.6.4 Starting Methods of Synchronous Motor 221 4.6.5 Torque Equation of Synchronous Motor 222 4.7 Stepper Motor 223 4.8 Brushless DC (BLDC) Motor 225 4.8.1 Construction 225 4.8.2 Working Principle 226 4.9 Alternator 226 4.9.1 Construction 226 4.9.2 Working Principle 229 4.9.3 EMF Equation of an Alternator 232 4.9.4 Voltage Regulation of an Alternator 234 4.10 Standards for Electric Machines 235 References 241 5 Measurement and Instrumentation 243 5.1 Electrical and Electronic Instruments 243 5.1.1 Classification of Instruments 243 5.1.2 Basic Requirements for Measurement 250 5.1.3 Types of Indicating Instruments 259 5.1.4 AC Indicating Instruments 270 5.1.5 Electrical Instruments 275 5.2 Cathode Ray Oscilloscope (CRO) 278 5.3 Digital Storage Oscilloscope 283 5.4 Static and Dynamic Characteristics of Measurements 289 5.4.1 Static Characteristics 289 5.4.2 Dynamic Characteristics 296 5.5 Measurement of Errors 297 5.5.1 Types of Errors 298 5.6 Transducer 300 5.6.1 Classification of Transducers 302 References 338 Index 341

Read more less

Book SynopsisA cutting-edge, advanced level, exploration of optical sensing application in power transformers Optical Sensing in Power Transformers is filled with the critical information and knowledge on the optical techniques applied in power transformers, which are important and expensive components in the electric power system. Effective monitoring of systems has proven to decrease the transformer lifecycle cost and increase a high level of availability and reliability. It is commonly held that optical sensing techniques will play an increasingly significant role in online monitoring of power transformers. In this comprehensive text, the authorsnoted experts on the topicpresent a scholarly review of the various cutting-edge optical principles and methodologies adopted for online monitoring of power transformers. Grounded in the authors' extensive research, the book examines optical techniques and high-voltage equipment testing and provides the foundation for further application, prototype, aTable of ContentsForeword ix Preface xi Acknowledgments xiii About the Authors xv Acronyms xvii List of Figures xxi List of Tables xxix 1 Power Transformer in a Power Grid 1 1.1 Typical Structure of a Power Transformer 2 1.2 Insulation Oil in a Power Transformer 3 1.3 Condition Monitoring of an Oil-Immersed Power Transformer 7 1.3.1 Temperature 7 1.3.2 Moisture 8 1.3.3 Dissolved Gases Analysis 9 1.3.4 Partial Discharge 10 1.3.5 Combined Online Monitoring 11 1.4 Conclusion 11 References 12 2 Temperature Detection with Optical Methods 15 2.1 Thermal Analysis in a Power Transformer 15 2.1.1 Heat Source in a Power Transformer 15 2.1.2 Heat Transfer in a Power Transformer 16 2.2 Fluorescence-Based Temperature Detection 18 2.2.1 Detection Principle 18 2.2.2 Fabrication and Application 20 2.2.3 Merits and Drawbacks 21 2.3 FBG-Based Temperature Detection 22 2.3.1 Detection Principle 22 2.3.2 Fabrication and Application 24 2.3.3 Merits and Drawbacks 25 2.4 Distribution Measurement 27 2.4.1 Quasi-Distributed Temperature Sensing 27 2.4.2 Distribute Temperature Sensing 28 2.4.2.1 Light Scattering 28 2.4.2.2 Raman Based Distributed Temperature Sensing 28 2.4.2.3 Rayleigh-Based Distributed Temperature Sensing 32 2.4.3 Merits and Drawbacks 33 2.5 Conclusion 33 References 34 3 Moisture Detection with Optical Methods 37 3.1 Online Monitoring of Moisture in a Transformer 37 3.1.1 Distribution of Moisture in the Power Transformer 38 3.1.2 Typical Moisture Detection Techniques 40 3.2 FBG-Based Moisture Detection 42 3.2.1 Detection Principle 42 3.2.2 Fabrication and Application 45 3.2.3 Merits and Drawbacks 48 3.3 Evanescent Wave-Based Moisture Detection 49 3.3.1 Detection Principle 49 3.3.2 Fabrication of MNF 53 3.3.2.1 Chemical Etching Method 53 3.3.2.2 Fused Biconical Taper Method 54 3.3.3 MNF Moisture Detection 56 3.3.4 Merits and Drawbacks 57 3.4 Fabry–Perot-Based Moisture Detection 58 3.4.1 Detection Principle 58 3.4.2 Fabrication and Application 59 3.4.3 Merits and Drawbacks 61 3.5 Conclusion 61 References 62 4 Dissolved Gases Detection with Optical Methods 65 4.1 Online Dissolved Gases Analysis 65 4.1.1 General Quantitive Requirements of Online DGA 67 4.1.2 Advantages of Optical Techniques in DGA 70 4.2 Photoacoustic Spectrum Technique 70 4.2.1 Detection Principle of PAS 70 4.2.2 Application of a PAS-Based Technique 73 4.2.3 Merits and Drawbacks 74 4.3 Fourier Transform Infrared Spectroscopy (FTIR) Technique 76 4.3.1 Detection Principle of FTIR 76 4.3.2 Application of the FTIR-Based Techniques 80 4.3.2.1 FTIR Technique 80 4.3.2.2 Online FTIR Application 85 4.3.2.3 Combination of FTIR and PAS 86 4.3.3 Merits and Drawbacks 88 4.4 TDLAS-Based Technique 89 4.4.1 Detection Principle of TDLAS 89 4.4.2 Application of the TDLAS-Based Technique 92 4.4.2.1 Optical Lasers 94 4.4.2.2 Multi-pass Gas Cell 95 4.4.2.3 Topology of Multi-gas Detection 96 4.4.2.4 Laboratory Tests 99 4.4.2.5 Field Application 103 4.4.3 Merits and Drawbacks 105 4.5 Laser Raman Spectroscopy Technique 106 4.5.1 Detection Principle of Raman Spectroscopy 106 4.5.2 Application of Laser Raman Spectroscopy 107 4.5.3 Merits and Drawbacks 109 4.6 Fiber Bragg Grating (FBG) Technique 110 4.6.1 Detection Principle of FBG 110 4.6.2 Application of the FBG Technique 110 4.6.2.1 Standard FBG Sensor 110 4.6.2.2 Etched FBG Sensor 114 4.6.2.3 Side-Polished FBG Sensor 118 4.6.3 Merits and Drawbacks 121 4.7 Discussion and Prediction 123 4.7.1 Comparison of Optical Fiber Techniques 123 4.7.2 Future Prospects of Optic-Based Diagnosis 125 4.8 Conclusions 127 References 128 5 Partial Discharge Detection with Optical Methods 137 5.1 PD Activities in Power Transformers 137 5.1.1 Online PD Detection Techniques 138 5.1.2 PD Induced Acoustic Emission 139 5.2 FBG-Based Detection 142 5.2.1 FBG PD Detection Principle 142 5.2.2 PS-FBG PD Detection 144 5.2.3 High Resolution FBG PD Detection 148 5.2.4 Merits and Drawbacks 149 5.3 FP-Based PD Detection 150 5.3.1 FP-Based Principle 150 5.3.2 Application of FP PD Detection 152 5.3.3 Sensitivity of an FP-Based Sensor 155 5.3.3.1 The Diaphragm Thickness 155 5.3.3.2 The Diaphragm Material 156 5.3.3.3 The Diaphragm Shape 156 5.3.4 Merits and Drawbacks 157 5.4 Dual-Beam Interference-Based PD Detection 158 5.4.1 Principle of Different Interference Structures 158 5.4.1.1 Mach-Zehnder Interference 158 5.4.1.2 Michelson Interference 159 5.4.1.3 Sagnac Interference 160 5.4.2 Application Cases 162 5.4.2.1 PD Detection Based on Mach-Zehnder 162 5.4.2.2 PD Detection Based on Michelson 162 5.4.2.3 PD Detection Based on Sagnac 163 5.4.3 Sensitivity of an Interference-Based Sensor 166 5.4.3.1 Sensor Parameter Variation 166 5.4.3.2 Phase Modulation and Demodulation Techniques 168 5.4.4 Merits and Drawbacks 171 5.5 Multiplexing Technology of an Optical Sensor 171 5.5.1 Multiplexing Technique with the Same Structure 171 5.5.2 Multiplexing Technique with the Different Structures 175 5.5.3 Distributed Optical Sensing Technique 176 5.6 Conclusion 179 References 182 6 Other Parameters with Optical Methods 189 6.1 Winding Deformation and Vibration Detection in Optical Techniques 189 6.1.1 Winding Deformation Detection 189 6.1.1.1 Winding Deformation in Power Transformer 189 6.1.1.2 Winding Deformation Detection with an Optical Technique 190 6.1.2 Vibration Detection 192 6.1.2.1 Vibration in Power Transformers 192 6.1.2.2 Vibration Detection with Optical Techniques 194 6.1.3 Merits and Drawbacks 197 6.2 Voltage and Current Measurement with Optical Techniques 198 6.2.1 Current Measurement with Optical Technique 199 6.2.1.1 Principle of Optical Current Transducer 199 6.2.1.2 All-Fiber Optical Current Transducer 200 6.2.2 Voltage Measurement with the Optical Technique 200 6.2.2.1 Principle of the Optical Voltage Transducer 200 6.2.2.2 All-Fiber Optical Voltage Transducer 202 6.2.3 Merits and Drawbacks 202 6.3 Electric Field Measurement 203 6.4 Conclusion 205 6.5 Outlook 207 6.5.1 Profound and Extensive Interdisciplinary Combinations 208 6.5.2 Mature Scheme and Low Cost Manufacturing 208 6.5.3 Reliable Measurement and Long-Term Stability 208 6.5.4 Pre-factory Installation and Integration into a Monitoring System 209 6.5.5 Rapid Expansion and Development 209 6.5.6 Advanced Algorithms and Novel Diagnosis 210 References 210 Index 213

Read more less

Book SynopsisAUTOMATED VEHICLES AND MaaS A topical overview of the issues facing automated driving systems and Mobility as a Service, identifies the obstacles to implementation and offers potential solutionsAdvances in cooperative and automated vehicle (CAV) technologies, cultural and socio-economic shifts, measures to combat climate change, social pressures to reduce road deaths and injuries, and changing attitudes toward self-driving cars, are creating new and exciting mobility scenarios worldwide. However, many obstacles remain and are compounded by the consequences of COVID-19. Mobility as a Service (MaaS) integrates various forms of public and private transport services into a single on-demand mobility service. Combining trains, cars, buses, bicycles, and other forms of transport, MaaS promises a convenient, cost-effective, and eco-friendly alternative to private automobiles.Automated Vehicles and MaaS: Removing the Barriers is an up-to-date overview of the contemTable of Contents1. The promise and hype regarding automated driving and MaaS 6 1.1 The promise 6 1.2 What do we mean by the term ‘automated driving’? 9 1.3 The hype 11 2 Automated Driving levels 27 2.1 SAE J3016 27 2.2 The Significance of Operational Design Domain (ODD 38 2.3 Deprecated terms 39 2.4 No relative merit 40 2.5 Mutually Exclusive Levels 40 2.6 J3016 Limitations 41 2.7 Actors in the automated vehicle paradigm 42 2.8 Other functions 49 2.8.1 Regulation data access 49 3 The current reality 51 3.1 UNECE WP 29 51 3.2 Social acceptance 53 3.3 SMMT 53 3.4 Other observations 54 3.5 The European Commission 55 3.6 Legislation 56 3.7 Subsidiarity 57 3.8 Viewpoints 57 4 Automated Driving Paradigms 60 4.1 OECD 60 4.4 Communications evolution 60 4.2 Cooperative ITS 62 4.3 The C-ITS Platform 65 4.5 Holistic approach 67 4.6 It won’t happen quickly 68 4.7 Implications of fully automated vehicles 69 5 The MaaS Paradigm 81 5.1 Purist definition for MaaS 81 5.2 Vehicle manufacturer perspective for MaaS 81 5.3 Traditional transport service provider perspective for MaaS 82 5.4 MaaS from the perspective of the MaaS Broker 82 5.5 MaaS as a tool for Social Engineering 87 5.6 MaaS experience to date 89 5.7 MaaS and Covid-19 89 6 Challenges facing automated driving 93 7 Potential problems hindering the instantiation of MaaS 98 7.1 Root causes of obstacles 98 7.2 Level of community readiness 98 7.3 Level of Social Engineering readiness 99 7.4 Perception of risks 101 7.5 Level of market readiness 101 7.6 Level of Software solution readiness 103 7.7 Training 103 7.8 Timing 103 7.9 Institutional & Governance 103 8 Potential solutions to overcoming barriers to automated driving 106 8.1 Vehicle manufacturers flawed paradigm of the automated vehicle 106 8.2 Vehicle manufacturers using different paradigms for competitive advantage 107 8.3 Road operator’s responsibilities 110 8.4 New modes of transport and new mobility services must be safe and secure by design 118 8.5 How other road users interact with AVs 119 8.6 Automated vehicles will have to be able to identify and consistently respond to different forms of communication 119 8.7 AV’s by themselves will not necessarily be smarter than conventional vehicles 122 8.8 Congestion levels will not drop significantly 124 8.9 Automated vehicles will release unsatiated demand 125 8.10 Safety and some operational data must be freely shared 128 8.11 Mixed AV and conventional traffic 128 8.12 AV Acceptability 129 8.13 Low latency communication 130 8.14 Roads could be allocated exclusively to AVs 133 8.15 Automated and connected vehicles bring new requirements 135 8.16 Cybersecurity 136 8.17 Changing speed limits and even getting signs put up can take years 141 8.18 Political decisions needed 142 8.19 Role of government 143 8.20 Fallback to driver 149 8.21 Range of services supported 156 8.21.1 Services that can be instantiated without the support of the local infrastructure 157 8.21.2 Services that can only be provided using data/information from the local infrastructure 158 8.21.3 Services that can be enhanced/improved/extended by using data/information from the local infrastructure 158 8.21.4 The HARTS architecture with reference to C-ITS platform Day/Day 1.5 services 160 8.22 Young drivers and experience 197 8.23 Liability 198 8.24 Level 5 may take a long time to instantiate 203 9 Potential solutions to overcoming barriers to MaaS 205 9.1 Addressing General issues 205 9.2 Essentials to enable MaaS 206 9.2.1 Trust 207 9.2.2 Impartiality 207 9.2.3 Cooperation 208 9.2.4 Integration services 208 9.2.5 Commercial agreements 209 9.2.6 Data protection 210 9.2.7 Solid Governance model 211 9.3 Removing Obstacles to MaaS 217 9.3 Innovative enablers for MaaS 218 10 The C-ART innovation 220 10.1 Overview 220 10.2 Policy context 221 10.3 Key conclusions 222 10.4 C-ART scenarios 223 10.4.1 Short to medium term scenario (2020-2030): C-ART 2030 223 10.4.2 Medium to long term scenario (2030-2050): C-ART 2050 224 10.4.3 Town planning as a consequence of C-ART 224 10.4.4 An assessment of C-ART 225 10.4.5 Technology principles and architecture behind C-ART 225 10. 4.6 The C-ART framework 228 10.4.7 Some observations on Project C-ART 231 11 Potential solutions to instantiate AVs and MaaS: Managed Architecture for Transportation Optimisation (MOAT) 233 11.1 Managed not controlled 233 11.2 High level Actors in the MOAT architecture 235 11.2.1 Traveller Group (Traveller) 235 11.2.2 Subscriber (Subscriber) 235 11.2.3 Travel Service Provider (TSP) 236 11.2.4 AV operator (AVO) 236 11.2.6 Travel Information Provider (TIP) 236 11.2.7 Traffic Management Centre (TMC) 236 11.2.8 Travel Optimisation Service (TOS) 236 11.3 MOAT from the subscriber / user perspective 237 11.4 MOAT from the Travel Service Provider perspective 239 11.4.1 Operate user interface (UI) 239 11.4.2 Receive request from subscriber 239 11.4.3 Characterise request options 239 11.4.4 Calculate viable travel options 239 11.4.5 Confirm options to subscriber 239 11.4.6 Receive subscriber selection 240 11.4.7 Fulfil travel arrangements 240 11.4.8 Provide confirmation to subscriber 240 11.4.9 Monitor/Manage progress of journey 240 11.4.10 Acknowledge end of journey 240 11.4.11 Process administration requirement 240 11.4.12 Delete personal data 240 11.5 MOAT from the road operator perspective 240 11.6 MOAT from the AV operator (AVO) perspective 241 11.7 MOAT from the Travel Optimisation Service (TOS) perspective 242 11.8 MOAT from the Traffic Management Centre (TMC) perspective 243 11.9 MOAT from the Travel Information Provider (TIP) perspective 243 11.10 MOAT and privacy 243 11.11 The MOAT overview architecture 243 11.12 The MOAT systems architecture 244 12 The Business Case for MaaS 247 12.1 The Challenge 247 12.3 The Solution 247 12.4 The Outlook 248 13 The Business Case for Automated Vehicles 248 13.1 The Challenge 248 13.3 The Solution 249 13.4 The Outlook 250 14 Timescales to successful implementation 251 14.1 Caveat 251 14.2 Phased MOAT 252 14.3 Timescales MaaS 253 14.4 Timescales for Automated Vehicles 253 14.5 The first half of the Twentieth Century 255 14.6 The second half of the twentieth Century 255 14.7 2000 - 2009 256 14.8 2010-2019 257 14.9 2020 – 2029 259 14.10 2030 - 2039 260 14.11 2040 – 2050 260 14.12 2050-2060 261 14.13 In summary 261 Bibliography 262

Read more less

Book SynopsisADVANCED HEALTHCARE SYSTEMS This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists. The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployedTable of ContentsPreface xvii 1 Internet of Medical Things—State-of-the-Art 1Kishor Joshi and Ruchi Mehrotra 1.1 Introduction 2 1.2 Historical Evolution of IoT to IoMT 2 1.2.1 IoT and IoMT—Market Size 4 1.3 Smart Wearable Technology 4 1.3.1 Consumer Fitness Smart Wearables 4 1.3.2 Clinical-Grade Wearables 5 1.4 Smart Pills 7 1.5 Reduction of Hospital-Acquired Infections 8 1.5.1 Navigation Apps for Hospitals 8 1.6 In-Home Segment 8 1.7 Community Segment 9 1.8 Telehealth and Remote Patient Monitoring 9 1.9 IoMT in Healthcare Logistics and Asset Management 12 1.10 IoMT Use in Monitoring During COVID-19 13 1.11 Conclusion 14 References 15 2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 21Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma 2.1 Introduction 22 2.2 Related Works 23 2.3 Architecture 25 2.3.1 Device Layer 25 2.3.2 Fog Layer 26 2.3.3 Cloud Layer 26 2.4 Issues and Challenges 26 2.5 Conclusion 29 References 30 3 Study of Thyroid Disease Using Machine Learning 33Shanu Verma, Rashmi Popli and Harish Kumar 3.1 Introduction 34 3.2 Related Works 34 3.3 Thyroid Functioning 35 3.4 Category of Thyroid Cancer 36 3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 37 3.5.1 Decision Tree Algorithm 38 3.5.2 Support Vector Machines 39 3.5.3 Random Forest 39 3.5.4 Logistic Regression 39 3.5.5 Naïve Bayes 40 3.6 Conclusion 41 References 41 4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 43Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi 4.1 Introduction 44 4.1.1 Introduction to IoT 44 4.1.2 Introduction to Vulnerability, Attack, and Threat 45 4.2 IoT in Healthcare 46 4.2.1 Confidentiality 46 4.2.2 Integrity 46 4.2.3 Authorization 46 4.2.4 Availability 47 4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 48 4.4 Conclusion 54 References 54 5 Methods of Lung Segmentation Based on CT Images 59Amit Verma and Thipendra P. Singh 5.1 Introduction 59 5.2 Semi-Automated Algorithm for Lung Segmentation 60 5.2.1 Algorithm for Tracking to Lung Edge 60 5.2.2 Outlining the Region of Interest in CT Images 62 5.2.2.1 Locating the Region of Interest 62 5.2.2.2 Seed Pixels and Searching Outline 62 5.3 Automated Method for Lung Segmentation 63 5.3.1 Knowledge-Based Automatic Model for Segmentation 63 5.3.2 Automatic Method for Segmenting the Lung CT Image 64 5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 64 5.5 Conclusion 65 References 65 6 Handling Unbalanced Data in Clinical Images 69Amit Verma 6.1 Introduction 70 6.2 Handling Imbalance Data 71 6.2.1 Cluster-Based Under-Sampling Technique 72 6.2.2 Bootstrap Aggregation (Bagging) 75 6.3 Conclusion 76 References 76 7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer 81Ishita Banerjee and Madhumathy P. 7.1 Introduction 82 7.2 Literature Survey 84 7.3 Procedure 86 7.4 Results 93 7.5 Conclusion 97 References 97 8 Smart IoT Devices for the Elderly and People with Disabilities 101K. N. D. Saile and Kolisetti Navatha 8.1 Introduction 101 8.2 Need for IoT Devices 102 8.3 Where Are the IoT Devices Used? 103 8.3.1 Home Automation 103 8.3.2 Smart Appliances 104 8.3.3 Healthcare 104 8.4 Devices in Home Automation 104 8.4.1 Automatic Lights Control 104 8.4.2 Automated Home Safety and Security 104 8.5 Smart Appliances 105 8.5.1 Smart Oven 105 8.5.2 Smart Assistant 105 8.5.3 Smart Washers and Dryers 106 8.5.4 Smart Coffee Machines 106 8.5.5 Smart Refrigerator 106 8.6 Healthcare 106 8.6.1 Smart Watches 107 8.6.2 Smart Thermometer 107 8.6.3 Smart Blood Pressure Monitor 107 8.6.4 Smart Glucose Monitors 107 8.6.5 Smart Insulin Pump 108 8.6.6 Smart Wearable Asthma Monitor 108 8.6.7 Assisted Vision Smart Glasses 109 8.6.8 Finger Reader 109 8.6.9 Braille Smart Watch 109 8.6.10 Smart Wand 109 8.6.11 Taptilo Braille Device 110 8.6.12 Smart Hearing Aid 110 8.6.13 E-Alarm 110 8.6.14 Spoon Feeding Robot 110 8.6.15 Automated Wheel Chair 110 8.7 Conclusion 112 References 112 9 IoT-Based Health Monitoring and Tracking System for Soldiers 115Kavitha N. and Madhumathy P. 9.1 Introduction 116 9.2 Literature Survey 117 9.3 System Requirements 118 9.3.1 Software Requirement Specification 119 9.3.2 Functional Requirements 119 9.4 System Design 119 9.4.1 Features 121 9.4.1.1 On-Chip Flash Memory 122 9.4.1.2 On-Chip Static RAM 122 9.4.2 Pin Control Block 122 9.4.3 UARTs 123 9.4.3.1 Features 123 9.4.4 System Control 123 9.4.4.1 Crystal Oscillator 123 9.4.4.2 Phase-Locked Loop 124 9.4.4.3 Reset and Wake-Up Timer 124 9.4.4.4 Brown Out Detector 125 9.4.4.5 Code Security 125 9.4.4.6 External Interrupt Inputs 125 9.4.4.7 Memory Mapping Control 125 9.4.4.8 Power Control 126 9.4.5 Real Monitor 126 9.4.5.1 GPS Module 126 9.4.6 Temperature Sensor 127 9.4.7 Power Supply 128 9.4.8 Regulator 128 9.4.9 LCD 128 9.4.10 Heart Rate Sensor 129 9.5 Implementation 129 9.5.1 Algorithm 130 9.5.2 Hardware Implementation 130 9.5.3 Software Implementation 131 9.6 Results and Discussions 133 9.6.1 Heart Rate 133 9.6.2 Temperature Sensor 135 9.6.3 Panic Button 135 9.6.4 GPS Receiver 135 9.7 Conclusion 136 References 136 10 Cloud-IoT Secured Prediction System for Processing and Analysis of Healthcare Data Using Machine Learning Techniques 137G. K. Kamalam and S. Anitha 10.1 Introduction 138 10.2 Literature Survey 139 10.3 Medical Data Classification 141 10.3.1 Structured Data 142 10.3.2 Semi-Structured Data 142 10.4 Data Analysis 142 10.4.1 Descriptive Analysis 142 10.4.2 Diagnostic Analysis 143 10.4.3 Predictive Analysis 143 10.4.4 Prescriptive Analysis 143 10.5 ML Methods Used in Healthcare 144 10.5.1 Supervised Learning Technique 144 10.5.2 Unsupervised Learning 145 10.5.3 Semi-Supervised Learning 145 10.5.4 Reinforcement Learning 145 10.6 Probability Distributions 145 10.6.1 Discrete Probability Distributions 146 10.6.1.1 Bernoulli Distribution 146 10.6.1.2 Uniform Distribution 147 10.6.1.3 Binomial Distribution 147 10.6.1.4 Normal Distribution 148 10.6.1.5 Poisson Distribution 148 10.6.1.6 Exponential Distribution 149 10.7 Evaluation Metrics 150 10.7.1 Classification Accuracy 150 10.7.2 Confusion Matrix 150 10.7.3 Logarithmic Loss 151 10.7.4 Receiver Operating Characteristic Curve, or ROC Curve 152 10.7.5 Area Under Curve (AUC) 152 10.7.6 Precision 153 10.7.7 Recall 153 10.7.8 F1 Score 153 10.7.9 Mean Absolute Error 154 10.7.10 Mean Squared Error 154 10.7.11 Root Mean Squared Error 155 10.7.12 Root Mean Squared Logarithmic Error 155 10.7.13 R-Squared/Adjusted R-Squared 156 10.7.14 Adjusted R-Squared 156 10.8 Proposed Methodology 156 10.8.1 Neural Network 158 10.8.2 Triangular Membership Function 158 10.8.3 Data Collection 159 10.8.4 Secured Data Storage 159 10.8.5 Data Retrieval and Merging 161 10.8.6 Data Aggregation 162 10.8.7 Data Partition 162 10.8.8 Fuzzy Rules for Prediction of Heart Disease 163 10.8.9 Fuzzy Rules for Prediction of Diabetes 164 10.8.10 Disease Prediction With Severity and Diagnosis 165 10.9 Experimental Results 166 10.10 Conclusion 169 References 169 11 CloudIoT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues 173Junaid Latief Shah, Heena Farooq Bhat and Asif Iqbal Khan 11.1 Introduction 174 11.2 Background Elements 180 11.2.1 Security Comparison Between Traditional and IoT Networks 185 11.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 187 11.3.1 Security Protocols 187 11.3.2 Enabling Technologies 188 11.4 CloudIoT Health System Framework 191 11.4.1 Data Perception/Acquisition 192 11.4.2 Data Transmission/Communication 193 11.4.3 Cloud Storage and Warehouse 194 11.4.4 Data Flow in Healthcare Architecture - A Conceptual Framework 194 11.4.5 Design Considerations 197 11.5 Security Challenges and Vulnerabilities 199 11.5.1 Security Characteristics and Objectives 200 11.5.1.1 Confidentiality 202 11.5.1.2 Integrity 202 11.5.1.3 Availability 202 11.5.1.4 Identification and Authentication 202 11.5.1.5 Privacy 203 11.5.1.6 Light Weight Solutions 203 11.5.1.7 Heterogeneity 203 11.5.1.8 Policies 203 11.5.2 Security Vulnerabilities 203 11.5.2.1 IoT Threats and Vulnerabilities 205 11.5.2.2 Cloud-Based Threats 208 11.6 Security Countermeasures and Considerations 214 11.6.1 Security Countermeasures 214 11.6.1.1 Security Awareness and Survey 214 11.6.1.2 Security Architecture and Framework 215 11.6.1.3 Key Management 216 11.6.1.4 Authentication 217 11.6.1.5 Trust 218 11.6.1.6 Cryptography 219 11.6.1.7 Device Security 219 11.6.1.8 Identity Management 220 11.6.1.9 Risk-Based Security/Risk Assessment 220 11.6.1.10 Block Chain–Based Security 220 11.6.1.11 Automata-Based Security 220 11.6.2 Security Considerations 234 11.7 Open Research Issues and Security Challenges 237 11.7.1 Security Architecture 237 11.7.2 Resource Constraints 238 11.7.3 Heterogeneous Data and Devices 238 11.7.4 Protocol Interoperability 238 11.7.5 Trust Management and Governance 239 11.7.6 Fault Tolerance 239 11.7.7 Next-Generation 5G Protocol 240 11.8 Discussion and Analysis 240 11.9 Conclusion 241 References 242 12 A Novel Usage of Artificial Intelligence and Internet of Things in Remote-Based Healthcare Applications 255V. Arulkumar, D. Mansoor Hussain, S. Sridhar and P. Vivekanandan 12.1 Introduction Machine Learning 256 12.2 Importance of Machine Learning 256 12.2.1 ML vs. Classical Algorithms 258 12.2.2 Learning Supervised 259 12.2.3 Unsupervised Learning 261 12.2.4 Network for Neuralism 263 12.2.4.1 Definition of the Neural Network 263 12.2.4.2 Neural Network Elements 263 12.3 Procedure 265 12.3.1 Dataset and Seizure Identification 265 12.3.2 System 265 12.4 Feature Extraction 266 12.5 Experimental Methods 266 12.5.1 Stepwise Feature Optimization 266 12.5.2 Post-Classification Validation 268 12.5.3 Fusion of Classification Methods 268 12.6 Experiments 269 12.7 Framework for EEG Signal Classification 269 12.8 Detection of the Preictal State 270 12.9 Determination of the Seizure Prediction Horizon 271 12.10 Dynamic Classification Over Time 272 12.11 Conclusion 273 References 273 13 Use of Machine Learning in Healthcare 275V. Lakshman Narayana, R. S. M. Lakshmi Patibandla, B. Tarakeswara Rao and Arepalli Peda Gopi 13.1 Introduction 276 13.2 Uses of Machine Learning in Pharma and Medicine 276 13.2.1 Distinguish Illnesses and Examination 277 13.2.2 Drug Discovery and Manufacturing 277 13.2.3 Scientific Imaging Analysis 278 13.2.4 Twisted Therapy 278 13.2.5 AI to Know-Based Social Change 278 13.2.6 Perception Wellness Realisms 279 13.2.7 Logical Preliminary and Exploration 279 13.2.8 Publicly Supported Perceptions Collection 279 13.2.9 Better Radiotherapy 280 13.2.10 Incidence Forecast 280 13.3 The Ongoing Preferences of ML in Human Services 281 13.4 The Morals of the Use of Calculations in Medicinal Services 284 13.5 Opportunities in Healthcare Quality Improvement 288 13.5.1 Variation in Care 288 13.5.2 Inappropriate Care 289 13.5.3 Prevents Care–Associated Injurious and Death for Carefrontation 289 13.5.4 The Fact That People Are Unable to do What They Know Works 289 13.5.5 A Waste 290 13.6 A Team-Based Care Approach Reduces Waste 290 13.7 Conclusion 291 References 292 14 Methods of MRI Brain Tumor Segmentation 295Amit Verma 14.1 Introduction 295 14.2 Generative and Descriptive Models 296 14.2.1 Region-Based Segmentation 300 14.2.2 Generative Model With Weighted Aggregation 300 14.3 Conclusion 302 References 303 15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network–Based Model 305Varun Sapra and Luxmi Sapra 15.1 Introduction 306 15.2 Data Set 307 15.2.1 Data Insights 308 15.3 Feature Engineering 310 15.4 Framework for Early Detection of Disease 312 15.4.1 Deep Neural Network 313 15.5 Result 314 15.6 Conclusion 315 References 315 16 A Comprehensive Analysis on Masked Face Detection Algorithms 319Pranjali Singh, Amitesh Garg and Amritpal Singh 16.1 Introduction 320 16.2 Literature Review 321 16.3 Implementation Approach 325 16.3.1 Feature Extraction 325 16.3.2 Image Processing 325 16.3.3 Image Acquisition 325 16.3.4 Classification 325 16.3.5 MobileNetV2 326 16.3.6 Deep Learning Architecture 326 16.3.7 LeNet-5, AlexNet, and ResNet-50 326 16.3.8 Data Collection 326 16.3.9 Development of Model 327 16.3.10 Training of Model 328 16.3.11 Model Testing 328 16.4 Observation and Analysis 328 16.4.1 CNN Algorithm 328 16.4.2 SSDNETV2 Algorithm 330 16.4.3 SVM 331 16.5 Conclusion 332 References 333 17 IoT-Based Automated Healthcare System 335Darpan Anand and Aashish Kumar 17.1 Introduction 335 17.1.1 Software-Defined Network 336 17.1.2 Network Function Virtualization 337 17.1.3 Sensor Used in IoT Devices 338 17.2 SDN-Based IoT Framework 341 17.3 Literature Survey 343 17.4 Architecture of SDN-IoT for Healthcare System 344 17.5 Challenges 345 17.6 Conclusion 347 References 347 Index 351

Read more less

Book SynopsisELECTRICAL EQUIPMENT A FIELD GUIDE A comprehensive guide for all the electrical equipment in plants to understand their basic theories, relevant standards, operation and maintenance, challenges, and scope for future research. This valuable new volume is a must-have for any engineer. Covering almost all electrical equipment, such as generators, motors, transformers, cables, batteries, meters, relays, fuses, lamps, lightning arresters, circuit breakers, and so much more, it covers not only the basic theory, but also mathematical equations, selection guidelines, installation, commissioning, operation and maintenance, and many other practical applications. Equally as importantly, also covered here are all the applicable international standards, such as IEC and IEEE. This book is written in a simple language for easy understanding by field engineers. The rating plate of all the equipment is described in detail. The relevant details of the equipment have been taken frTable of ContentsForeword xiii Preface xv 1 Introduction 1 1.1 Introduction 1 1.2 Electrical Power Supply 1 1.3 Classification of Voltages or Voltage Bands 2 1.4 Standards Agencies 2 1.5 Electrical Standards 12 1.6 Abbreviations 15 1.7 Constants 19 1.8 Types of Maintenance 19 1.9 Useful Life of Equipment 22 2 Transformers 25 2.1 Introduction 25 2.2 Types of Transformers 27 2.3 Transformer on No Load 27 2.4 Transformer on Load 28 2.5 Total Equivalent Circuit of Transformer (Referred to Primary Side) 29 2.6 Losses in a Transformer 29 2.7 Efficiency of Transformer 30 2.8 Parallel Operation of Transformers 31 2.9 Rating Plate of Transformer 32 2.10 Information to Be Given to Purchase a Transformer 49 2.11 Tests on Transformer 52 2.12 Maintenance of Transformers 54 2.13 Troubleshooting Chart for Transformers 65 2.14 Latest Trends Opportunities in Transformer Technology 65 3 Generators 73 3.1 Introduction 73 3.2 Alternator 73 3.3 Field Poles 74 3.4 Construction of Field Poles 74 3.5 EMF Equation of Alternator 75 3.6 Capability Curve 76 3.7 Design of Alternator 80 3.8 Rating Plates 80 3.9 Voltage Regulation of Synchronous Generator 85 3.10 Excitation 86 3.11 Connections 87 3.12 Neutral Grounding 88 3.13 Cooling 88 3.14 Short-Circuit Ratio (SCR) 89 3.15 Pitch Factor (Kp) or Chording Factor (Kc) 90 3.16 Distribution Factor (Kd) 91 3.17 Leakage Reactance (Xl) 92 3.18 Armature Reaction 92 3.19 Operation of Generator When Connected to an Infinite Bus 93 3.20 Load Sharing of Grid-Connected Alternator 94 3.21 Typical Values of Various Reactances and Time-Constants 94 3.22 Load Characteristics of Alternators 94 3.23 Salient Pole Machine with Two Reaction Theory 97 3.24 Hunting 98 3.25 Stability and Swing Equation 98 3.26 Prime-Mover Rating Plates 99 3.27 Effect of Unbalanced Loads and External Faults 100 3.28 Voltage Regulators 101 3.29 Parallel Operation of Alternators Under Different Conditions 101 3.30 Induction Generator 103 3.31 Doubly Fed Induction Generator 105 3.32 Latest Trends in TG Technology 105 3.33 Maintenance 107 3.34 Fault Finding 107 3.35 Generator Failure Modes 107 3.36 Tests on a Turbo-Generator 107 3.37 Tests on Engine-Driven Generator 110 3.38 Gaps and Research Opportunities 111 4 Induction Motors 113 4.1 Introduction 113 4.2 Comparison Between Various Types of Motors 113 4.3 Working Principle of 3-Phase Induction Motor 114 4.4 Construction of SCIM 115 4.5 Equivalent Circuit of SCIM 118 4.6 Torque-Speed Curve of SCIM 119 4.7 T-S Curve for SRIM 121 4.8 Torque-Speed Curve of Single-Phase Motor (Split Phase) 122 4.9 Name Plate or Rating Plate of SCIM 122 4.10 Power Stages of Induction Motor 140 4.11 Abnormal Conditions 140 4.12 Starting of Induction Motors 143 4.13 Speed Control of Induction Motors 143 4.14 Deep Cage Induction Motor 143 4.15 Double Cage SCIM 145 4.16 Selection of Motor Power for an Application 146 4.17 Design of Induction Motors 146 4.18 Characteristics of Loads 149 4.19 Circle Diagram 149 4.20 Alignment of Motor with Driven Equipment 152 4.21 Shaft and Bearing Currents in Large Motors 153 4.22 Special Motors for Hazardous/Explosive Areas 154 4.23 Identification of 3-Phase Winding Leads 155 4.24 Tests on Induction Motor 156 4.25 Maintenance 158 4.26 Trouble-Shooting 170 4.27 Heating and Cooling Curves of Induction Motor 170 4.28 Smart Motors 178 4.29 Single-Phase Induction Motors 179 4.30 Information to Be Given to Purchase a 3-Phase Induction Motor 181 4.31 Protection Against Faults 183 4.32 Motors for Electrical Vehicles 183 4.33 Future Scope 184 5 Circuit Breakers and Contactors 185 5.1 Introduction 185 5.2 Arcing Phenomenon 185 5.3 Types of Circuit Breakers 186 5.4 AC and DC CBs 186 5.5 DCCB 186 5.6 CB Contacts 189 5.7 Selection of CB 189 5.8 Operation of CBs 192 5.9 Name Plate of CBs 199 5.10 Tests on CB 203 5.11 Information to Be Given to Purchase a CB 204 5.12 Maintenance of CB 205 5.13 Contactors 207 5.14 MCB, MCCB and RCCB 215 6 Protection and Measurement Systems 225 6.1 Introduction 225 6.2 Desirable Characteristics of Protective Device 225 6.3 Current Transformer 226 6.4 Voltage Transformer 242 6.5 Measuring Instruments 249 6.6 Multi-Function Meter 253 6.7 Desirable Characteristics of Meters 253 6.8 Meter Symbols and Codes 254 6.9 AVO or Multimeter 254 6.10 Meter Calibration Reports 255 7 Earthing and Lightning 257 7.1 Earthing 257 7.2 Earthing, Grounding and Bonding 257 7.3 System Neutral Grounding 259 7.4 LV Neutral Earthing 260 7.5 Types of Earth Electrodes 264 7.6 Measurement of Earth Resistance 264 7.7 General Guidelines on Earthing 269 7.8 Lightning Arrester 269 7.9 Protection Against Lightning 269 7.10 Definitions 271 7.11 Name Plate of LA 273 7.12 Protective Devices Against Lightning Surges 274 7.13 Surge Protective Device (SPD) 274 7.14 Lightning Conductor Size 277 7.15 Inspection and Maintenance of Lightning Protection System 277 7.16 Testing of LA 278 8 Fuses 281 8.1 Introduction 281 8.2 Terms Used in the Fuse Field 281 8.3 Cut-Off Characteristic of Fuse 282 8.4 Fuse Law (Prece’s Law) 283 8.5 Types of Fuses 284 8.6 Application Categories and TCC of Fuses 288 8.7 Discrimination between an Over Current Relay and Fuse 289 8.8 Semi-Conductor Fuse 290 8.9 Examples of Selection of Fuse 291 8.10 Symbols of Fuse Letter Code 293 8.11 Desirable Characteristics of Fuse 293 8.12 Tests Recommended on Fuses 294 8.13 Market Models of Fuses 294 9 Protective Relays 301 9.1 Introduction 301 9.2 Terms Used in Relaying 301 9.3 Types of Protection 302 9.4 Types of Relays 302 9.5 Relay Block Diagrams of Three Generations 304 9.6 IDMT Relay Calculations 305 9.7 Inverse – Time Over-Current Relays 306 9.8 Comparison between Three Generation Relays 308 9.9 Thermal Overload Relays 308 9.10 Protections of Various Electrical Equipment 310 9.11 Relay Settings 312 9.12 Protection System Failure Modes 313 9.13 Maintenance of Relays 315 9.14 Field Testing 315 9.15 Relay Co-Ordination 317 9.16 Protective Device Numbers 323 9.17 Challenges and Opportunities 323 10 Cables and Overhead Conductors 329 10.1 Introduction 329 10.2 Conducting Materials 329 10.3 Cable Insulating Material 330 10.4 Construction of Cables 330 10.5 Overhead Conductor versus Cable 332 10.6 Comparison between PVC and XLPE Cables 332 10.7 De-Rating Factors 333 10.8 Special Cables 333 10.9 FRLS Cable Properties 335 10.10 Methods of Cable Laying 336 10.11 Identification Codes of Cables 336 10.12 Selection of Cable 337 10.13 Rule of Thumb for Industrial Work 338 10.14 Fault Location Methods 339 10.15 Maintenance on Cables 340 10.16 Cable Jointing 341 10.17 Tests on PVC Cables 341 10.18 Tests on XLPE Cables 343 10.19 Overhead Lines 345 10.20 FACTS 347 11 Solar Photovoltaics 353 11.1 Introduction 353 11.2 Solar Energy 353 11.3 Forms of Energy Resources 354 11.4 Solar Spectrum 354 11.5 Solar Energy Fundamentals 357 11.6 I-V and P-V Curves 362 11.7 Solar Photovoltaic Power Plants 363 11.8 Modelling of PV Modules 365 11.9 Performance Indicators 366 11.10 Maximum Power Point Tracking 368 11.11 Rating Plates of SPP 369 11.12 Opportunities and Future Scope 369 12 Storage Batteries 375 12.1 Introduction 375 12.2 Faraday’s Law of Electrolysis 375 12.3 Types of Batteries 376 12.4 Dry Cell 376 12.5 Technical Terms 379 12.6 Secondary Batteries 381 12.7 Lead – Acid Battery 383 12.8 Nickel-Cadmium Batteries 391 12.9 Lithium Batteries 393 12.10 Latest Trends in Energy Storage Field 395 12.11 Maintenance 397 12.12 Some Other Notable Points on Battery 399 12.13 Batteries for Electric Vehicles 400 12.14 Smart Battery 402 12.15 Future Outlook 402 13 Computer Aided Studies in Power Systems (CASiPS) 405 13.1 Introduction 405 13.2 E-TAP 406 13.3 EDSA 408 13.4 PV Syst 409 13.5 Power Factory 410 13.6 Matlab-Simulink 412 14 Lighting 413 14.1 Introduction 413 14.2 Definitions 414 14.3 Type of Lighting Technologies 419 14.4 Estimation of Illumination 419 14.5 Recommended Illumination Levels 421 14.6 Lamps Rating Plate 421 14.7 Fluorescent Lamp Colour Temperatures 430 15 Electrical Safety 439 15.1 Introduction 439 15.2 Hazards and Effects of Electric Current 439 15.3 Electric Shock 440 15.4 Permit to Work System and Qualification and Training 442 15.5 Personnel Protective Equipment and Devices 446 Index 449

Read more less

Book SynopsisDiscover the utility of four popular electromagnetic geophysical techniques In GeoRadar, FDEM, TDEM, and AEM Methods, accomplished researchers Fabio Giannino and Giovanni Leucci deliver an in-depth exploration of the theory and application of four different electromagnetic geophysical techniques: ground penetrating radar, the frequency domain electromagnetic method, the time domain electromagnetic method, and the airborne electromagnetic method. The authors offer a full description of each technique as they relate to the economics, planning, and logistics of deploying each of them on-site. The book also discusses the potential output of each method and how it can be combined with other sources of below- and above-ground information to create a digitized common point cloud containing a wide variety of data. Giannino and Leucci rely on 25 years of professional experience in over 40 countries around the world to provide readers with a fulsome descriptTable of ContentsPreface SECTION I: INTRODUCTION TO ELECTROMAGNETIC THEORY 1. Introduction 2. Electromagnetic (EM) Theory: an outline 2.1. Ground Penetrating Radar (GPR): operative principles and theory 2.2. Frequency Domain Electro Magnetic Method (FDEM): operative principles and theory 2.3. Time Domain Electro Magnetic (TDEM) Method: operative principle and theory 2.4. Airborne Electro Magnetic (AEM) Method: operative principle and theory SECTION II: HARDWARE ARCHITECTURE AND SURVEYING 3. GPR surveying 3.1. GPR: Systems architecture 3.2. Survey Design 3.3. Data acquisition 3.4. Data Analysis 3.5. Data Interpretation 4. FDEM surveying 4.1. FDEM: Systems architecture 4.2. Survey Design 4.3. Data Acquisition 4.4. Data Analysis 4.5. Data Interpretation 5. TDEM surveying 5.1. TDEM: Systems architecture 5.2. Survey Design 5.3. Data acquisition 5.4. Data Analysis 5.5. Data Interpretation 6. AEM surveying 6.1. AEM: Systems architecture 6.2. Survey Design 6.3. Data acquisition 6.4. Data Analysis 6.5. Data Interpretation SECTION III: APPLICATIONS 7. Case study 7.1. GPR: multiple-geophysical Archaeological survey in Turkey 7.2. GPR: Massive array Archaeological survey in Italy 7.3. GPR: Archaeological and Monumental application at a Cathedral in Italy 7.4. GPR: Archaeological application in Peru 7.5. GPR: Monumental Heritage conservation at a hypogeal site in Italy 7.6. GPR: Concrete rebars detection in South-Europe 7.7. GPR: Concrete rebars detection and water content estimate in Italy 7.8. GPR: Large area underground utility mapping in Italy 7.9. GPR: Utility mapping and Fibre Optics reconnaissance in Scandinavia 7.10. GPR: Utility and cavity mapping in Taiwan 7.11. GPR: Pipes leakage detection experimental test and application in Italy 7.12. GPR: Pipes leakage detection in Italy 7.13. GPR: Bridge deck study in Japan 7.14. FDEM: UXO search in building area in Italy 7.15. FDEM: Search of various object in a test site in Italy 7.16. FDEM: Pollutants search in Italy 7.17. FDEM: Forensic search in South Europe 7.18. TDEM: Geologic modelling for a reference site in Italy 7.19. AEM: Geologic modelling of buried valleys for hydrogeological purposes 7.20. AEM: Effect of Induced Polarization over AEM data 8. General on planning and logistic 8.1. Planning a campaign and mobilization aspects 8.2. Shipment and clearance of survey equipment 8.3. Managing the operative aspects of the field activity 8.4. De-mobilization 8.5. Reporting Index

Read more less

Book SynopsisAn incisive discussion of biofuel production from an economically informed technical perspective that addresses sustainability and commercialization together In Biodiesel Production: Feedstocks, Catalysts and Technologies, renowned chemists Drs Rokhum, Halder, Ngaosuwan and Assabumrungrat present an up-to-date account of the most recent developments, challenges, and trends in biodiesel production. The book addresses select feedstocks, including edible and non-edible oils, waste cooking oil, microalgae, and animal fats, and highlights their advantages and disadvantages from a variety of perspectives. It also discusses several catalysts used in each of their methods of preparation, as well as their synthesis, reactivity, recycling techniques, and stability. The contributions explore recently developed technologies for sustainable production of biodiesel and provides robust treatments of their sustainability, commercialization, and their prospects for future biodiesTable of ContentsPreface xv List of Contributors xvii An Overview of Biodiesel Production xxi Part 1 Biodiesel Feedstocks 1 1 Advances in Production of Biodiesel from Vegetable Oils and Animal Fats 3 Umer Rashid and Balkis Hazmi 1.1 Introduction 3 1.2 History of the Use of Vegetable Oil in Biodiesel 6 1.3 Feedstocks for Biodiesel Production 6 1.3.1 Generations of Biodiesel 7 1.3.2 First-Generation Biodiesel 7 1.3.3 Second-Generation Biodiesel 8 1.3.4 Third-Generation Biodiesel 8 1.4 Basics of the Transesterification Reaction 8 1.5 Variables Affecting Transesterification Reaction 10 1.6 Alkaline-Catalyzed Transesterification 10 1.7 Acid-Catalyzed Transesterification 15 1.8 Enzymatic-Catalyzed Transesterification 16 1.9 Fuel Properties and Quality Specifications for Biodiesel 19 1.10 Conclusion 20 References 21 2 Green Technologies in Valorization of Waste Cooking Oil to Biodiesel 33 Bisheswar Karmakar and Gopinath Halder 2.1 Introduction 33 2.1.1 The Necessity for Biodiesel 33 2.1.2 Sourcing the Correct Precursor 33 2.2 Importance of Valorization 35 2.3 Purification and Characterization 35 2.4 Transesterification: A Comprehensive Look 36 2.5 Conversion Techniques 37 2.5.1 Traditional Conversion Approaches 38 2.5.1.1 Acid Catalysis 38 2.5.1.2 Alkali Catalysis 38 2.5.1.3 Enzyme Catalysis 40 2.5.1.4 Other Novel Heterogeneous Catalysts 40 2.5.1.5 Two-Step Catalyzed Process 41 2.5.2 Modern Conversion Approaches 41 2.5.2.1 Supercritical Fluids 41 2.5.2.2 Microwave Irradiation 43 2.5.2.3 Ultrasonication 43 2.6 Economics and Environmental Impact 44 2.7 Conclusion and Perspectives 45 References 45 3 Non-edible Oils for Biodiesel Production: State of the Art and Future Perspectives 49 Valeria D’Ambrosio, Enrico Scelsi, and Carlo Pastore 3.1 Introduction 49 3.2 Vegetable Non-edible Oils 50 3.2.1 General Cultivation Data 50 3.2.2 Composition and Chemical–Physical Properties of Biodiesel Obtained from Non-edible Vegetable Oils 50 3.2.3 Biodiesel Production from Non-edible Vegetable Oil 54 3.2.3.1 Extraction Methods 54 3.2.3.2 Biodiesel Production 57 3.2.4 Criticisms Related to Non-edible Oils 57 3.3 Future Perspectives of Non-edible Oils: Oils from Waste 58 3.4 Conclusion 60 Acknowledgments 61 References 61 4 Algal Oil as a Low-Cost Feedstock for Biodiesel Production 67 Michael Van Lal Chhandama, Kumudini Belur Satyan, and Samuel Lalthazuala Rokhum 4.1 Introduction 67 4.1.1 Microalgae for Biodiesel Production 68 4.2 Lipid and Biosynthesis of Lipid in Microalgae 70 4.2.1 Lipid Biosynthesis 71 4.2.2 Lipid Extraction 72 4.3 Optimization of Lipid Production in Microalgae 73 4.3.1 Nitrogen Stress 73 4.3.2 Phosphorous Stress 73 4.3.3 pH Stress 74 4.3.4 Temperature Stress 74 4.3.5 Light 75 4.4 Conclusion 75 References 76 Part 2 Different Catalysts Used in Biodiesel Production 83 5 Homogeneous Catalysts Used in Biodiesel Production 85 Bidangshri Basumatary, Biswajit Nath, and Sanjay Basumatary 5.1 Introduction 85 5.2 Transesterification in Biodiesel Synthesis 86 5.3 Homogeneous Catalyst in Biodiesel Synthesis 88 5.3.1 Homogeneous Acid Catalyst 88 5.3.2 Homogeneous Base Catalyst 90 5.4 Properties of Biodiesel Produced by Homogeneous Acid and Base-Catalyzed Reactions 93 5.5 Relevance of Homogeneous Acid and Base Catalysts in Biodiesel Synthesis 96 5.6 Conclusion 96 References 97 6 Application of Metal Oxides Catalyst in Production of Biodiesel 103 Hui li 6.1 Basic Metal Oxide 103 6.1.1 Monobasic Metal Oxide 103 6.1.1.1 Alkaline Earth Metal Oxide 103 6.1.1.2 Transition Metal Oxide 105 6.1.2 Multibasic Metal Oxide 105 6.1.2.1 Supported on Metal Oxide 106 6.1.2.2 Supported on Activated Carbon 106 6.1.2.3 Supported on Metal Organic Framework 107 6.1.3 Active Site-Doped Basic Metal Oxide 107 6.1.3.1 Alkali Metal Doped 107 6.1.3.2 Active Metal Oxide Doped 107 6.1.4 Mechanism of Transesterification Catalyzed by Basic Metal Oxide 108 6.2 Acid Metal Oxide 108 6.2.1 Monoacid Metal Oxide 109 6.2.2 Multiacid Metal Oxide 109 6.2.3 Supported on Metal Organic Framework 112 6.2.4 Mechanism of Transesterification/Esterification Catalyzed by Acid Metal Oxide 112 6.3 Deactivation of Metal Oxide 113 References 114 7 Supported Metal/Metal Oxide Catalysts in Biodiesel Production 119 Pratibha Agrawal and Samuel Lalthazuala Rokhum 7.1 Introduction 119 7.2 Supported Catalyst 120 7.3 Metals and Metal Oxide Supported on Alumina 120 7.4 Metals and Metal Oxide Supported on Zeolite 123 7.5 Metals and Metal Oxide Supported on ZnO 125 7.6 Metals and Metal Oxide Supported on Silica 127 7.7 Metals and Metal Oxide Supported on Biochar 128 7.7.1 Solid Acid Catalysts 129 7.7.2 Solid Alkali Catalysts 129 7.8 Metals and Metal Oxide Supported on Metal Organic Frameworks 131 7.9 Metal/Metal Oxide Supported on Magnetic Nanoparticles 134 7.10 Summary 135 References 136 8 Mixed Metal Oxide Catalysts in Biodiesel Production 143 Brandon Lowe, Jabbar Gardy, Kejun Wu, and Ali Hassanpour 8.1 Introduction 143 8.2 Previous Research 144 8.3 State of the Art 150 8.3.1 Solid Acid MMO Catalysts 150 8.3.2 Solid Base MMO Catalysts 150 8.3.3 Solid Bifunctional MMO Catalysts 156 8.4 Discussion 157 8.5 Conclusion 161 8.6 Symbols and Nomenclature 162 References 162 9 Nanocatalysts in Biodiesel Production 167 Avinash P. Ingle, Rahul Bhagat, Mangesh P. Moharil, Samuel Lalthazuala Rokhum, Shreshtha Saxena, and S. R. Kalbande 9.1 Introduction 167 9.2 Transesterification of Vegetable Oils 169 9.3 Conventional Catalysts Used in Biodiesel Production: Advantages and Limitations 171 9.3.1 Homogeneous Catalysts 171 9.3.2 Heterogeneous Catalysts 172 9.3.3 Biocatalysts 173 9.4 Role of Nanotechnology in Biodiesel Production 173 9.5 Different Nanocatalysts in Biodiesel Production 173 9.5.1 Metal-Based Nanocatalysts 174 9.5.2 Carbon-Based Nanocatalysts 175 9.5.3 Zeolites/Nanozeolites 180 9.5.4 Magnetic Nanocatalysts 182 9.5.5 Nanoclays 184 9.5.6 Other Nanocatalysts 184 9.6 Conclusion 185 Acknowledgment 185 References 185 10 Sustainable Production of Biodiesel Using Ion-Exchange Resin Catalysts 193 Naomi Shibasaki-Kitakawa and Kousuke Hiromori 10.1 Introduction 193 10.2 Features of Ion-Exchange Resin Catalysts 194 10.3 Cation-Exchange Resin Catalyst 194 10.3.1 Notes of Caution When Comparing the Activity of Resins with Different Properties 194 10.3.2 Reversible Reduction of Resin Catalytic Activity by Water 196 10.3.3 Search for Operating Conditions for Maximum Productivity Rather than Maximum Catalytic Activity 198 10.3.4 Challenges Regarding One-Step Reaction with Simultaneous Esterification and Transesterification Catalyzed by Cation-Exchange Resin 198 10.4 Anion-Exchange Resin Catalysts 199 10.4.1 Requirements for High Catalytic Activity in the Transesterification of Triglycerides 199 10.4.2 Analysis of Previous Studies 201 10.4.3 Decreased Catalytic Activity and Regeneration Method 203 10.4.4 Additional Functions Unique to Anion-Exchange Resins 204 10.5 Summary 204 References 205 11 Advances in Bifunctional Solid Catalysts for Biodiesel Production 209 Bishwajit Changmai, Michael Van Lal Chhandama, Chhangte Vanlalveni, Andrew E.H. Wheatley, and Samuel Lalthazuala Rokhum 11.1 Introduction 209 11.2 Application of Solid Bifunctional Catalyst in Biodiesel Production 210 11.2.1 Acid–Base Bifunctional Catalysts 210 11.2.1.1 Oxides of Acid–Base 211 11.2.1.2 Acid–Base Hydrides 213 11.2.2 Bifunctional Acid Catalyst 217 11.2.2.1 Bifunctional Brønsted–Lewis Acid Oxides 217 11.2.2.2 Heteropolyacid-Based Bifunctional Catalyst 220 11.2.3 Biowaste-Derived Bifunctional Catalyst 222 11.3 Summary and Concluding Remarks 224 Acknowledgment 225 References 225 12 Application of Catalysts Derived from Renewable Resources in Production of Biodiesel 229 Kanokwan Ngaosuwan, Apiluck Eiad-ua, Atthapon Srifa, Worapon Kiatkittipong, Weerinda Appamana, Doonyapong Wongsawaeng, Armando T. Quitain, and Suttichai Assabumrungrat 12.1 Introduction 229 12.2 Potential Renewable Resources for Production of Biodiesel Catalysts 230 12.2.1 Animal Resources 230 12.2.1.1 Eggshells (Chicken and Ostrich) 231 12.2.1.2 Seashells (Snail, Mussel, Oyster, and Capiz) 231 12.2.1.3 Bones 233 12.2.2 Plant Resources 233 12.2.2.1 Carbon-Supported Catalysts 233 12.2.2.2 Silica-Supported Catalysts 236 12.2.2.3 Other Potential Elements from Plant Residues 236 12.2.3 Natural Resources 236 12.2.3.1 Dolomitic Rock (Calcined Dolomite and Modified Dolomite) 236 12.2.3.2 Lime 237 12.2.3.3 Natural Clays 237 12.2.3.4 Zeolites 238 12.2.4 Industrial Waste Resources 240 12.2.4.1 Food Industry Wastes 240 12.2.4.2 Mining Industry Wastes 240 12.3 Advantages, Disadvantages, and Challenges of These Types of Catalyst for Biodiesel Production 242 Acknowledgment 243 References 243 13 Biodiesel Production Using Ionic Liquid-Based Catalysts 249 B. Sangeetha and G. Baskar 13.1 Introduction 249 13.2 Mechanism of IL-Catalyzed Biodiesel Production 250 13.3 Acidic and Basic Ionic Liquids (AILs/BILs) as Catalyst in Biodiesel Production 250 13.4 Supported Ionic Liquids in Biodiesel Production 251 13.5 IL Lipase Cocatalysts 255 13.6 Optimization and Novel Biodiesel Production Technologies Using ILs 257 13.7 Recyclability of the Ionic Liquids on Biodiesel Production 259 13.7.1 Recovery of ILs 259 13.7.2 Reuse of Ionic Liquids 260 13.8 Kinetics of IL-Catalyzed Biodiesel Production 260 13.9 Techno-Economic Analysis and Environmental Impact Analysisof Biodiesel Production Using Ionic Liquid as Catalyst 261 13.10 Conclusion 262 References 263 14 Metal–Organic Frameworks (MOFs) as Versatile Catalysts for Biodiesel Synthesis 269 Vasudeva Rao Bakuru, Marilyn Esclance DMello, and Suresh Babu Kalidindi 14.1 Introduction 269 14.1.1 Metal-Containing Secondary Building Units 271 14.1.2 Organic Linker 272 14.1.3 Pore Volume 272 14.2 Biodiesel Synthesis Over MOF Catalysts 273 14.2.1 Transesterification Reaction 274 14.2.1.1 Transesterification at SBUs of MOFs 274 14.2.1.2 Transesterification at Linker Active Sites 276 14.2.2 Esterification of Carboxylic Acids 277 14.2.2.1 Esterification of Carboxylic Acids at SBUs of MOFs 277 14.2.2.2 Esterification of Carboxylic Acids at Linker Active Sites 279 14.2.2.3 Esterification at Pore Volume (Guest Incorporation) 280 14.3 Conclusion 281 References 281 Part 3 Technologies, By-product Valorization and Prospects of Biodiesel Production 285 15 Upstream Strategies (Waste Oil Feedstocks, Nonedible Oils, and Unicellular Oil Feedstocks like Microalgae) 287 Aleksandra Sander and Ana Petračić 15.1 Introduction 287 15.1.1 Classification of Biodiesel 287 15.1.2 Commercial Production of Biodiesel 288 15.2 Biodiesel Feedstocks 290 15.2.1 Edible Oils as Feedstock for Biodiesel Production 291 15.2.2 Nonedible Oils as Feedstocks for Biodiesel Production 292 15.2.3 Waste Feedstocks (Waste Cooking Oils, Waste Animal Fats, Waste Coffee Ground Oil, Olive Pomace) 292 15.2.4 Unicellular Oil Feedstocks (Microalgae, Yeasts, Cyanobacteria) 293 15.3 Composition of Oils and Fats 293 15.4 Methods for Oil Extraction 294 15.4.1 Mechanical Extraction 294 15.4.2 Solvent Extraction 295 15.4.3 Enzymatic Extraction 296 15.5 Purification of Oils and Fats 297 15.5.1 Deacidification 297 15.5.2 Winterization 298 15.5.3 Demetallization 298 15.5.4 Degumming 298 15.6 Production of Biodiesel 299 15.6.1 Catalysts for Biodiesel Production 300 15.6.2 Homogeneous Catalysts 300 15.6.3 Heterogeneous Catalysts 301 15.7 Future Prospects 302 References 302 16 Mainstream Strategies for Biodiesel Production 311 Narita Chanthon, Nattawat Petchsoongsakul, Kanokwan Ngaosuwan, Worapon Kiatkittipong, Doonyapong Wongsawaeng, Weerinda Appamana, and Suttichai Assabumrungrat 16.1 Introduction 311 16.2 Mainstream Strategies and Technology for Biodiesel Production 312 16.2.1 Current Mainstream Operation 312 16.2.1.1 Batch Mode 312 16.2.1.2 Continuous Mode 312 16.2.2 Process Mainstream for Biodiesel Production Based on the Reactor Types 313 16.2.2.1 Rotating Reactor 313 16.2.2.2 Tubular Flow Reactor 315 16.2.2.3 Cavitational Reactor 317 16.2.2.4 Microwave Reactor 318 16.2.2.5 Multifunctional Reactor (Reactive Distillation, Membrane, Centrifugal Reactors) 319 16.2.2.6 Other Process Intensification 322 16.3 Future Prospects and Challenges 323 Acknowledgment 327 References 327 17 Downstream Strategies for Separation, Washing, Purification, and Alcohol Recovery in Biodiesel Production 331 Ramón Piloto-Rodríguez and Yosvany Díaz-Domínguez 17.1 Introduction 331 17.1.1 Factors Affecting Biodiesel Yield 332 17.1.2 Transesterification Reaction Conditions 332 17.1.3 Separation After FAME Conversion 332 17.1.4 Washing 334 17.2 Glycerol Separation and Refining 336 17.3 Membrane Reactors 337 17.4 Methanol Recovery 339 17.5 Additization 339 17.6 Conclusion 342 References 343 18 Heterogeneous Catalytic Routes for Bio-glycerol-Based Acrylic Acid Synthesis 345 Nittan Singh, Pavan Narayan Kalbande, and Putla Sudarsanam 18.1 Introduction 345 18.2 Acrylic Acid Synthesis from Propylene 346 18.3 Acrylic Acid Synthesis from Glycerol 346 18.3.1 Glycerol Dehydration to Acrolein 347 18.3.2 Acrylic Acid Synthesis from Glycerol 349 18.4 Conclusion 351 Acknowledgments 353 References 353 19 Sustainability, Commercialization, and Future Prospects of Biodiesel Production 355 Pothiappan Vairaprakash, and Arumugam Arumugam 19.1 Introduction 355 19.2 Biodiesel as a Promising Renewable Energy Carrier 356 19.3 Overview of the Biodiesel Production Process 358 19.4 Evolution in the Feedstocks Used for the Sustainable Production of Biodiesel 359 19.5 First-Generation Biodiesel and the Challenges in Its Sustainability 359 19.6 Development of Second-Generation Biodiesel to Address the Sustainability 361 19.7 Algae-Based Biodiesel 362 19.8 Waste Oils, Grease, and Animal Fats in Biodiesel Production 363 19.9 Technical Impact by the Biodiesel Usage 363 19.10 Socioeconomic Impacts 364 19.11 Toxicological Impact 364 19.12 Sustainability Challenges in the Biodiesel Production and Use 365 19.13 Concluding Remarks 366 References 366 20 Advanced Practices in Biodiesel Production 377 Trinath Biswal, Krushna Prasad Shadangi, and Rupam Kataki 20.1 Introduction 377 20.2 Mechanism of Transesterification 378 20.3 Advanced Biodiesel Production Technologies 379 20.3.1 Production of Biodiesel Using Membrane Reactor 379 20.3.1.1 Principle 379 20.3.2 Microwave-Assisted Transesterification Technology 381 20.3.2.1 Principle 381 20.3.3 Ultrasonic-Assisted Transesterification Techniques 382 20.3.4 Production of Biodiesel Using Cosolvent Method 385 20.3.4.1 Principle 385 20.3.5 In Situ Biodiesel Production Technology 385 20.3.5.1 Principle 385 20.3.6 Production of Biodiesel Through Reactive Distillation Process 387 20.3.6.1 Principle 387 20.4 Conclusion 389 20.5 Future Perspectives 390 References 390 Index 397

Read more less

Book SynopsisTable of ContentsAbout the Editor xvii About the Contributors xix List of Reviewers xxiii Foreword by Albert H. Zimmerman and Ralph E. White xxv Preface xxvii Acronyms and Abbreviations xxix 1 Introduction 1Thomas P. Barrera 1.1 Introduction 1 1.2 Purpose 1 1.2.1 Background 2 1.2.2 Knowledge Management 2 1.3 History of Spacecraft Batteries 3 1.3.1 The Early Years – 1957 to 1975 3 1.3.1.1 Silver- Zinc 4 1.3.1.2 Silver- Cadmium 4 1.3.1.3 Nickel- Cadmium 5 1.3.2 The Next Generation – 1975 to 2000 5 1.3.2.1 Nickel- Hydrogen 6 1.3.2.2 Sodium- Sulfur 7 1.3.2.3 Transition to Lithium- Ion 7 1.3.3 The Li- ion Revolution – 2000 to Present 8 1.3.3.1 First Space Applications 8 1.3.3.2 Advantages and Disadvantages 10 1.4 State of Practice 11 1.4.1 Raw Materials Supply Chain 11 1.4.2 COTS and Custom Li- ion Cells 12 1.4.3 Hazard Safety and Controls 12 1.4.4 Acquisition Strategies 13 1.5 About the Book 13 1.5.1 Organization 14 1.5.2 Li- ion Cells and Batteries 14 1.5.3 Electrical Power System 14 1.5.4 On- Orbit LIB Experience 15 1.5.5 Safety and Reliability 15 1.5.6 Life Cycle Testing 15 1.5.7 Ground Processing and Mission Operations 15 1.6 Summary 16 References 16 2 Space Lithium- Ion Cells 19Yannick Borthomieu, Marshall C. Smart, Sara Thwaite, Ratnakumar V. Bugga, and Thomas P. Barrera 2.1 Introduction 19 2.1.1 Types of Space Battery Cells 19 2.1.2 Rechargeable Space Cells 20 2.1.3 Non- Rechargeable Space Cells 20 2.1.4 Specialty Reserve Space Cells 21 2.2 Definitions 22 2.2.1 Capacity 22 2.2.2 Energy 23 2.2.3 Depth- of- Discharge 23 2.3 Cell Components 24 2.3.1 Positive Electrode 24 2.3.1.1 Lithium Cobalt Oxide 25 2.3.1.2 Lithium Nickel Cobalt Aluminum Oxide 25 2.3.1.3 Lithium Nickel Manganese Cobalt Oxide 25 2.3.1.4 Lithium Manganese Oxide 25 2.3.1.5 Lithium Iron Phosphate 26 2.3.2 Negative Electrode 26 2.3.2.1 Solid Electrolyte Interphase 26 2.3.2.2 Coke 27 2.3.2.3 Hard Carbon 27 2.3.2.4 Graphite 27 2.3.2.5 Mesocarbon Microbead 27 2.3.2.6 Si- C Composites 28 2.3.2.7 Low- Voltage Resilience 28 2.3.3 Electrolytes 28 2.3.3.1 Room Temperature Electrolytes 28 2.3.3.2 Low- Temperature Electrolytes 29 2.3.4 Separators 30 2.3.5 Safety Devices 31 2.3.5.1 Pressure Vents 31 2.3.5.2 Current Interrupt Devices 32 2.3.5.3 Positive Temperature Coefficient 33 2.3.5.4 Shutdown Separator 33 2.4 Cell Geometry 33 2.4.1 Standardization 34 2.4.2 Cylindrical 34 2.4.3 Prismatic 35 2.4.4 Elliptical–Cylindrical 35 2.4.5 Pouch 35 2.5 Cell Requirements 36 2.5.1 Specification 36 2.5.2 Capacity and Energy 36 2.5.3 Operating Voltage 37 2.5.4 Mass and Volume 37 2.5.5 dc Resistance 37 2.5.6 Self- Discharge Rate 37 2.5.7 Environments 38 2.5.7.1 Operating and Storage Temperature 38 2.5.7.2 Vibration, Shock, and Acceleration 38 2.5.7.3 Thermal Vacuum 39 2.5.7.4 Radiation 39 2.5.8 Lifetime 39 2.5.9 Cycle Life 39 2.5.10 Safety and Reliability 40 2.6 Cell Performance Characteristics 40 2.6.1 Charge and Discharge Voltage 40 2.6.2 Capacity 41 2.6.3 Energy 42 2.6.4 Internal Resistance 42 2.6.5 Depth of Discharge 43 2.6.6 Life Cycle 44 2.7 Cell Qualification Testing 46 2.7.1 Test Descriptions 46 2.7.1.1 Electrical 46 2.7.1.2 Environmental 47 2.7.1.3 Safety 48 2.7.1.4 Life- Cycle Testing 48 2.8 Cell Screening and Acceptance Testing 49 2.8.1 Screening 49 2.8.2 Lot Definition 50 2.8.3 Acceptance Testing 50 2.9 Summary 52 Acknowledgments 52 References 53 3 Space Lithium- Ion Batteries 59Sara Thwaite, Marshall C. Smart, Eloi Klein, Ratnakumar V. Bugga, Aakesh Datta, Yannick Borthomieu, and Thomas P. Barrera 3.1 Introduction 59 3.2 Requirements 59 3.2.1 Battery Requirements Specification 60 3.2.2 Statement of Work 61 3.2.3 Voltage 62 3.2.4 Capacity 62 3.2.5 Mass and Volume 62 3.2.6 Cycle Life 63 3.2.7 Environments 63 3.3 Cell Selection and Matching 63 3.3.1 Selection Methodologies 64 3.3.2 Matching Process 64 3.4 Mission- Specific Characteristics 64 3.4.1 LIB Sizing 65 3.4.2 GEO Missions 65 3.4.3 LEO Missions 67 3.4.4 MEO and HEO Missions 69 3.4.5 Lagrange Orbit Missions 69 3.5 Interfaces 70 3.5.1 Electrical 70 3.5.2 Mechanical 70 3.5.3 Thermal 70 3.6 Battery Design 71 3.6.1 Electrical 71 3.6.1.1 S- P and P- S Design 72 3.6.1.2 Analysis 75 3.6.2 Mechanical 75 3.6.2.1 Packaging 76 3.6.2.2 Structural Mechanical Analysis 76 3.6.3 Thermal 77 3.6.3.1 Design 78 3.6.3.2 Analysis 79 3.6.4 Materials, Parts, and Processes 80 3.6.4.1 Parts 81 3.6.4.2 Cleanliness 81 3.6.5 Safety and Reliability 82 3.6.5.1 Human- Rated and Unmanned Missions 82 3.6.5.2 Safety Features and Devices 83 3.7 Battery Testing 84 3.7.1 Test Requirements and Planning 84 3.7.2 Test Articles and Events 85 3.7.3 Qualification Test Descriptions 86 3.7.3.1 Capacity 86 3.7.3.2 Resistance 87 3.7.3.3 Charge Retention 88 3.7.3.4 Vibration 88 3.7.3.5 Shock 89 3.7.3.6 Thermal Cycle 89 3.7.3.7 Thermal Vacuum 90 3.7.3.8 Electromagnetic Compatibility 91 3.7.3.9 Life Cycle 92 3.7.3.10 Safety 93 3.7.4 Acceptance Test Descriptions 93 3.8 Supply Chain 94 3.8.1 Battery Parts and Materials 94 3.8.2 Space LIB Suppliers 94 3.9 Summary 94 References 95 4 Spacecraft Electrical Power Systems 99Thomas P. Barrera 4.1 Introduction 99 4.2 EPS Functional Description 101 4.2.1 Power Generation 101 4.2.2 Energy Storage 102 4.2.3 Power Management and Distribution 102 4.2.4 Harness 103 4.3 EPS Requirements 103 4.3.1 Requirements Specification 104 4.3.2 Orbital Mission Profile 105 4.3.3 Power Capability 106 4.3.4 Mission Lifetime 106 4.4 EPS Architecture 106 4.4.1 Bus Voltage 107 4.4.2 Direct Energy Transfer 108 4.4.2.1 Unregulated Bus 108 4.4.2.2 Partially- Regulated Bus 108 4.4.2.3 Fully- Regulated Bus 109 4.4.3 Peak- Power Tracker 109 4.4.4 Direct Energy Transfer and Peak- Power Tracker Trades 110 4.5 Battery Management Systems 111 4.5.1 Autonomy 111 4.5.2 Battery Charge Management 111 4.5.3 Battery Cell Voltage Balancing 112 4.5.3.1 Passive Cell Balancing 113 4.5.3.2 Active Cell Balancing 114 4.5.4 EPS Telemetry 114 4.6 Dead Bus Events 114 4.6.1 Orbital Considerations 115 4.6.2 Survival Fundamentals 115 4.7 EPS Analysis 115 4.7.1 Energy Balance 116 4.7.2 Power Budget 116 4.7.2.1 Inputs 118 4.7.2.2 Outputs 118 4.8 EPS Testing 119 4.8.1 Assembly, Integration, and Test 119 4.8.2 Bus Integration 120 4.8.3 Functional Test 121 4.9 Summary 122 References 122 5 Earth- Orbiting Satellite Batteries 125Penni J. Dalton, Eloi Klein, David Curzon, Samuel P. Russell, Keith Chin, David J. Reuter, and Thomas P. Barrera 5.1 Introduction 125 5.2 Earth Orbit Battery Requirements 126 5.3 NASA International Space Station – LEO 127 5.3.1 Introduction 127 5.3.2 Electrical Power System 127 5.3.3 Ni- H 2 Battery Heritage 128 5.3.4 Transition to Lithium- Ion Battery Power Systems 129 5.4 NASA Goddard Space Flight Center Spacecraft 130 5.4.1 Introduction 130 5.4.2 Solar Dynamics Observatory – GEO 131 5.4.3 Lunar Reconnaissance Orbiter – Lunar 133 5.4.4 Global Precipitation Measurement – LEO 133 5.5 Van Allen Probes – HEO 134 5.5.1 Mission Objectives 134 5.5.2 Electrical Power System 134 5.5.3 LIB Architecture 135 5.6 GOES Communication Satellites – GEO 136 5.6.1 Mission Objectives 136 5.6.2 Battery Heritage 136 5.6.3 LIB and Power System Architecture 136 5.7 James Webb Space Telescope – Earth–Sun Lagrange Point 2 137 5.7.1 Mission Objectives 137 5.7.2 Lagrange Orbit 138 5.7.3 Electrical Power System 138 5.7.4 LIB Architecture 139 5.8 CubeSats – LEO 140 5.8.1 Introduction 140 5.8.2 Electrical Power System and Battery Architecture 141 5.8.3 Advanced Hybrid EPS Systems 142 5.9 European Space Agency Spacecraft 143 5.9.1 Introduction 143 5.9.2 Sentinel- 1 Mission Objectives 143 5.9.3 Galileo Mission Objectives – MEO 144 5.10 NASA Astronaut Battery Systems 146 5.10.1 Introduction 146 5.10.2 EMU Long- Life Battery 146 5.10.3 Lithium- Ion Rechargeable EVA Battery Assembly 147 5.10.4 Lithium- Ion Pistol- Grip Tool Battery 148 5.10.5 Simplified Aid for EVA Rescue 149 5.11 Summary 151 Acknowledgment 151 References 151 6 Planetary Spacecraft Batteries 155Marshall C. Smart and Ratnakumar V. Bugga 6.1 Introduction 155 6.2 Planetary Mission Battery Requirements 155 6.2.1 Service Life and Reliability 156 6.2.2 Radiation Tolerance 156 6.2.3 Extreme Temperature 156 6.2.4 Low Magnetic Signature 157 6.2.5 Mechanical Environments 157 6.2.6 Planetary Protection 157 6.3 Planetary and Space Exploration Missions 158 6.3.1 Earth Orbiters 158 6.3.2 Lunar Missions 158 6.3.2.1 Gravity Recovery and Interior Laboratory 159 6.3.2.2 Lunar Crater Observation and Sensing Satellite 159 6.3.3 Mars Missions 159 6.3.3.1 Mars Orbiters 160 6.3.3.2 Mars Landers 161 6.3.3.3 Mars Rovers 166 6.3.3.4 Mars Helicopters, CubeSats, and Penetrators 174 6.3.4 Missions to Jupiter 177 6.3.4.1 NASA Juno Mission 177 6.3.5 Missions to Comets and Asteroids 179 6.3.5.1 Hayabusa (MUSES- C) 179 6.3.5.2 ESA Rosetta Lander Philae 180 6.3.5.3 NASA OSIRIS- REx Mission 180 6.3.6 Missions to Deep Space and Outer Planets 180 6.4 Future Missions 180 6.4.1 The Planned NASA Europa Clipper Mission 181 6.4.2 ESA JUICE Mission 183 6.5 Mars Sample Return Missions 183 6.6 Summary 184 Acknowledgment 184 References 184 7 Space Battery Safety and Reliability 189Thomas P. Barrera and Eric C. Darcy 7.1 Introduction 189 7.1.1 Space Battery Safety 189 7.1.2 Industry Lessons Learned 190 7.2 Space LIB Safety Requirements 191 7.2.1 Nasa Jsc- 20793 192 7.2.2 Range Safety 192 7.2.3 Design for Minimum Risk 193 7.3 Safety Hazards, Controls, and Testing 193 7.3.1 Electrical 194 7.3.1.1 Overcharge 194 7.3.1.2 Overdischarge 194 7.3.1.3 External Short Circuit 195 7.3.1.4 Internal Short Circuit 195 7.3.2 Mechanical 196 7.3.3 Thermal 196 7.3.3.1 Overtemperature 197 7.3.3.2 Low Temperature 198 7.3.4 Chemical 198 7.3.5 Safety Testing 199 7.4 Thermal Runaway 200 7.4.1 Likelihood and Severity 200 7.4.2 Characterization 201 7.4.3 Testing 202 7.4.3.1 Single Cell 202 7.4.3.2 Module and Battery 204 7.5 Principles of Safe- by- Design 204 7.5.1 Field Failures Due to ISCs 204 7.5.2 Cell Design 205 7.5.3 Cell Manufacturing and Quality Audits 205 7.5.4 Cell Testing and Operation 206 7.6 Passive Propagation Resistant LIB Design 207 7.6.1 PPR Design Guidelines 207 7.6.1.1 Control of Side Wall Rupture 207 7.6.1.2 Cell Spacing and Heat Dissipation 208 7.6.1.3 Current- Limiting Cells 208 7.6.1.4 Ejecta Path 208 7.6.1.5 Flame Suppression 208 7.6.2 PPR Verification 209 7.6.2.1 Trigger Cell Selection 209 7.6.2.2 PPR LIB Unit Design and Manufacturing 210 7.6.2.3 PPR LIB Test Execution 210 7.6.2.4 Post- Test Analysis and Reporting 211 7.6.3 Case Study – NASA US Astronaut Spacesuit LIB Redesign 211 7.7 Battery Reliability 215 7.7.1 Requirements 215 7.7.1.1 Battery Reliability Analysis 215 7.7.1.2 Hazard Analysis 216 7.7.2 Battery Failure Rates 217 7.7.2.1 Failure Rate in Time 217 7.7.2.2 Failure Rate Characteristics 218 7.8 Summary 218 References 219 8 Life- Cycle Testing and Analysis 225Samuel Stuart, Shriram Santhanagopalan, and Lloyd Zilch 8.1 Introduction 225 8.1.1 Test- Like- You- Fly 225 8.1.2 Design of Test 226 8.1.3 Test Article Selection 226 8.1.4 Personnel, Equipment, and Facilities 227 8.2 LCT Planning 228 8.2.1 Test Plan 228 8.2.2 Test Procedures 228 8.2.3 Test Readiness Review 229 8.2.4 Sample Size Statistics 229 8.3 Charge and Discharge Test Conditions 229 8.3.1 Charge and Discharge Rates 229 8.3.2 Capacity and DOD 230 8.3.3 Voltage Limits 230 8.3.4 Charge and Discharge Control 230 8.3.5 Parameter Margin 231 8.4 Test Configuration and Environments 231 8.4.1 Test Article Configuration 231 8.4.2 Test Environments 232 8.4.2.1 Temperature Controlled Chambers 232 8.4.2.2 Thermal Vacuum Chambers 232 8.4.2.3 Cold Plates 233 8.5 Test Equipment and Safety Hazards 233 8.5.1 Test Equipment Configuration 234 8.5.1.1 Hardware 234 8.5.1.2 Software 235 8.5.2 Test Safety Hazards 236 8.5.2.1 Test Articles 237 8.5.2.2 Equipment Induced 238 8.5.2.3 Laboratory Induced 238 8.5.2.4 Test Control Mitigations 239 8.5.2.5 Physical Mitigations 239 8.6 Real- Time Life- Cycle Testing 239 8.6.1 Test Article Selection 240 8.6.2 Test Execution and Monitoring 240 8.6.3 LCT End- of- Life Management 240 8.7 Calendar and Storage Life Testing 241 8.7.1 Calendar Life 241 8.7.2 Storage Life 241 8.7.3 Test Methodology 242 8.8 Accelerated Life- Cycle Testing 242 8.8.1 Accelerated Life Test Methodologies 242 8.8.2 Lessons Learned 243 8.9 Data Analysis 244 8.9.1 LCT Data Analysis 244 8.9.2 Trend Analysis and Reporting 245 8.10 Modeling and Simulation 246 8.10.1 Modeling and Simulation in Battery- Life Testing 247 8.10.2 Empirical Approaches 248 8.10.3 First Principles of Physics- Based Models 249 8.10.4 Systems Engineering Models 249 8.10.5 Models for Tracking Test Progress 250 8.10.6 Parameterization Approaches 252 8.10.7 Data Requirements 252 8.10.8 Lifetime and Performance Prediction 253 8.11 Summary 255 References 255 9 Ground Processing and Mission Operations 257Steven E. Core, Scott Hull, and Thomas P. Barrera 9.1 Introduction 257 9.1.1 Satellite Systems Engineering 257 9.1.2 Ground and Space Satellite EPS Requirements 258 9.2 Ground Processing 258 9.2.1 Storage 258 9.2.2 Transportation and Handling 259 9.3 Launch Site Operations 260 9.3.1 Launch Site Processing 260 9.3.2 Pre- Launch Operations 263 9.3.3 Launch Operations 264 9.4 Mission Operations 264 9.4.1 GEO Transfer Orbit 265 9.4.2 GEO On- Station Operations 266 9.4.3 On- Orbit Maintenance Operations 267 9.4.4 Contingency Operations 269 9.4.4.1 Safe Mode 269 9.4.4.2 Dead Bus Survival 270 9.4.4.3 Dead Bus Recovery 270 9.4.5 End- of- Life Operations 271 9.5 End- of- Mission Operations 272 9.5.1 Satellite Disposal Operations 273 9.5.1.1 LEO Disposal Operations 273 9.5.1.2 GEO Disposal Operations 274 9.5.2 Passivation Requirements 274 9.5.2.1 United States Passivation Guidance 275 9.5.2.2 International Passivation Guidance 276 9.5.3 Satellite EPS Passivation Operations 276 9.5.3.1 Hard Passivation Operations 277 9.5.3.2 Soft Passivation Operations 278 9.5.3.3 lv Orbital Stage EPS Passivation Operations 279 9.6 Summary 279 References 280 Appendix A: Terms and Definitions 283 Index 293

Read more less

Book SynopsisResilient Power Electronic Systems Discover an advanced reference offering a powerful novel approach to the design and use of reliable and fault-tolerant power electronic systems In Resilient Power Electronic Systems, a team of accomplished researchers deliver an insightful treatment of the challenges faced by practitioners and researchers working with power electronic converters and attempting to analyze internal and external failure mechanisms. The authors expertly present advanced techniques for reducing noise effects on fault detection and prognosis. Comprised of thirteen chapters, the authors discuss the concepts of resilience and effective operative life in the context of power electronics. The differences between reliable and efficient systems are discussed, as well as the nature of these differences in complex systems. Finally, the book explores various methods to improve the resilience of power converters. Resilient Power Electronic Systems is packed with features, including Table of ContentsPreface Part 1: Resilient Power Electronic Systems Chapter 1 Resilient Systems Chapter 2 Mission Critical Power Electronic Systems Chapter 3 Resilience in Power Electronics Chapter 4 State of the Art of Resilience Part 2: Useful Life of the Power Electronic Systems Chapter 5 Useful Life Modeling Chapter 6 Internal Faults: Converter Level Chapter 7 Internal Faults: Element Level Chapter 8 External Faults Chapter 9 Malfunctioning: Influence of Noise and Disturbance Part 3: Health Estimation of the Power Electronic Systems Chapter 10 Condition Monitoring Chapter 11 Fault Prognosis Chapter 12 Fault Diagnosis Part 4: Methods of Resilience in Power Electronic Systems Chapter 13 Resilience Against Internal Faults Chapter 14 Resilience Against External Faults Chapter 15 Inherently Resilient Power Electronic Converters

Read more less

Book SynopsisSensing Technologies for Real Time Monitoring of Water Quality A comprehensive guide to the development and application of smart sensing technologies for water quality monitoring With contributions from a panel of experts on the topic, Sensing Technologies for Real Time Monitoring of Water Quality offers an authoritative resource that explores a complete set of sensing technologies designed to monitor, in real-time, water quality including agriculture. The contributing authors explore the fundamentals of sensing technologies and review the most recent advances of various materials and sensors for water quality??monitoring. This comprehensive resource includes information on a range of designs of smart electronics, communication systems, packaging, and innovative implementation approaches used for remote monitoring of water quality in various atmospheres. The book explores a variety of techniques for online water quality monitoring including internet of Things (IoT), communication systTable of ContentsAbout the Editors xiii List of Contributors xv Preface xix Section I Materials and Sensors Development Including Case Study 1 1 Smart Sensors for Monitoring pH, Dissolved Oxygen, Electrical Conductivity, and Temperature in Water 3 Kiranmai Uppuluri 1.1 Introduction 3 1.2 Water Quality Parameters and Their Importance 4 1.2.1 Impact of pH on Water Quality 4 1.2.2 Impact of Dissolved Oxygen on Water Quality 5 1.2.3 Impact of Electrical Conductivity on Water Quality 5 1.2.4 Impact of Temperature on Water Quality 5 1.3 Water Quality Sensors 6 1.3.1 pH 7 1.3.1.1 pH Sensors: Principles, Materials, and Designs 7 1.3.1.2 Glass Electrode 7 1.3.1.3 Solid- State Ion- Selective Electrodes 8 1.3.1.4 Metal Oxide pH Sensors 8 1.3.2 Dissolved Oxygen 10 1.3.2.1 DO Sensors: Principles, Materials, and Designs 10 1.3.2.2 Chemical Sensors 10 1.3.2.3 Electrochemical Sensors 11 1.3.2.4 Optical or Photochemical Sensors 12 1.3.3 Electrical Conductivity 13 1.3.3.1 Conductivity Sensors: Principles, Materials, and Designs 13 1.3.4 Temperature 15 1.3.4.1 Temperature Sensors: Principles, Materials, and Designs 16 1.3.4.2 Thermocouples 17 1.3.4.3 Resistance Temperature Detector 17 1.3.4.4 Thermistor 17 1.3.4.5 Integrated Circuit 18 1.4 Smart Sensors 18 1.5 Conclusion 18 Acknowledgment 19 References 19 2 Dissolved Heavy Metal Ions Monitoring Sensors for Water Quality Analysis 25 Tarun Narayan, Pierre Lovera, and Alan O’Riordan 2.1 Introduction 25 2.2 Sources and Effects of Heavy Metals 26 2.3 Detection Techniques 26 2.3.1 Analytical Detection: Conventional Detection Techniques of Heavy Metals 26 2.3.2 Electrochemical Detection Techniques of Heavy Metals 26 2.3.2.1 Nanomaterial- Modified Electrodes 29 2.3.2.2 Metal Nanoparticle- Based Modification 29 2.3.2.3 Metal Oxide Nanoparticle- Based Modification 33 2.3.2.4 Carbon Nanomaterials- Based Modification 34 2.3.3 Biomolecules Modification for Heavy Metal Detection 35 2.3.3.1 Antibody- Based Detection 35 2.3.3.2 Nucleic Acid- Based Detection 37 2.3.3.3 Cell- Based Sensor 38 2.4 Future Direction 40 2.5 Conclusions 40 Acknowledgment 41 References 42 3 Ammonia, Nitrate, and Urea Sensors in Aquatic Environments 51 Fabiane Fantinelli Franco 3.1 Introduction 51 3.2 Detection Techniques for Ammonia, Nitrate, and Urea in Water 53 3.2.1 Spectrophotometry 53 3.2.2 Fluorometry 54 3.2.3 Electrochemical Sensors 54 3.3 Ammonia 59 3.3.1 Ammonia in Aquatic Environments 59 3.3.2 Ammonia Detection Techniques 62 3.4 Nitrate 65 3.4.1 Nitrate in Aquatic Environments 65 3.4.2 Nitrate Detection Techniques 65 3.5 Urea 67 3.5.1 Urea in Aquatic Environment 67 3.5.2 Urea Detection Techniques 69 3.6 Conclusion and Future Perspectives 71 Acknowledgment 71 References 71 4 Monitoring of Pesticides Presence in Aqueous Environment 77 Yuqing Yang, Pierre Lovera, and Alan O’Riordan 4.1 Introduction: Background on Pesticides 77 4.1.1 Types and Properties 77 4.1.2 Risks 78 4.1.3 Regulation and Legislation 79 4.1.4 Occurrence of Pesticide Exceedance 80 4.2 Current Pesticides Detection Methods 80 4.2.1 Detection of Pesticides Based on Electrochemical Methods 82 4.2.1.1 Brief Overview of Electrochemical Methods 82 4.2.1.2 Detection of Pesticides by Electrochemistry 82 4.2.2 Detection of Pesticides Based on Optical Methods 83 4.2.2.1 Detection of Pesticides Based on Fluorescence 87 4.2.3 Detection of Pesticides Based on Raman Spectroscopy 89 4.2.3.1 Introduction to SERS 89 4.2.3.2 Fabrication of SERS Substrates 91 4.2.3.3 Detection of Pesticide by SERS 92 4.2.3.4 Challenges and Future Perspectives 95 4.3 Conclusion 96 Acknowledgment 96 References 96 5 Waterborne Bacteria Detection Based on Electrochemical Transducer 107 Nasrin Razmi, Magnus Willander, and Omer Nur 5.1 Introduction 107 5.2 Typical Waterborne Pathogens 108 5.3 Traditional Diagnostic Tools 108 5.4 Biosensors for Bacteria Detection in Water 110 5.4.1 Common Bioreceptors for Electrochemical Sensing of Foodborne and Waterborne Pathogenic Bacteria 110 5.4.1.1 Antibodies 111 5.4.1.2 Enzymes 111 5.4.1.3 DNA and Aptamers 111 5.4.1.4 Phages 112 5.4.1.5 Cell and Molecularly Imprinted Polymers 112 5.4.2 Nanomaterials for Electrochemical Sensing of Waterborne Pathogenic Bacteria 112 5.4.2.1 Metal and Metal Oxide Nanoparticles 113 5.4.2.2 Conducting Polymeric Nanoparticles 114 5.4.2.3 Carbon Nanomaterials 114 5.4.2.4 Silica Nanoparticles 114 5.5 Various Electrochemical Biosensors Available for Pathogenic Bacteria Detection in Water 115 5.5.1 Amperometric Detection 115 5.5.2 Impedimetric Detection 121 5.5.3 Conductometric Detection 123 5.5.4 Potentiometric Detection 124 5.6 Conclusion and Future Prospective 126 Acknowledgment 127 References 127 6 Zinc Oxide- Based Miniature Sensor Networks for Continuous Monitoring of Aqueous pH in Smart Agriculture 139 Akshaya Kumar Aliyana, Aiswarya Baburaj, Naveen Kumar S. K., and Renny Edwin Fernandez 6.1 Introduction 139 6.2 Metal Oxide- Based Sensors and Detection Methods 140 6.3 pH Sensor Fabrication 141 6.3.1 Detection of pH: Materials and Method 141 6.3.2 Detection of pH: Surface Morphology of the Nanostructured ZnO and IDEs 144 6.3.3 Detection of pH: Electrochemical Sensing Performance 145 6.3.4 Detection of Real- Time pH Level in Smart Agriculture: Wireless Sensor Networks and Embedded System 149 6.4 Conclusion 151 Acknowledgment 152 References 152 Section II Readout Electronic and Packaging 161 7 Integration and Packaging for Water Monitoring Systems 163 Muhammad Hassan Malik and Ali Roshanghias 7.1 Introduction 163 7.2 Advanced Water Quality Monitoring Systems 167 7.2.1 Multi- sensing on a Single Chip 167 7.2.2 Heterogeneous Integration 169 7.2.3 Case Study: MoboSens 169 7.3 Basics of Packaging 171 7.4 Hybrid Flexible Packaging 173 7.4.1 Interconnects 174 7.4.2 Thin Die Embedding 176 7.4.3 Encapsulation and Hermeticity 178 7.4.4 Roll to Roll Assembly 180 7.5 Conclusion 181 References 181 8 A Survey on Transmit and Receive Circuits in Underwater Communication for Sensor Nodes 185 Noushin Ghaderi and Leandro Lorenzelli 8.1 Introduction 185 8.2 Sensor Networks in an Underwater Environment 186 8.2.1 Acoustic Sensor Network 186 8.2.1.1 Energy Sink- Hole Problem 187 8.2.1.2 Acoustic Sensor Design Problems 188 8.2.1.3 The Underwater Transducer 189 8.2.1.4 Amplifier Design 190 8.2.1.5 Analog- to- Digital Converter 194 8.2.2 Electromagnetic (EM) Waves Underwater Sensors 197 8.2.2.1 Antenna Design 198 8.2.2.2 Multipath Propagation 198 8.3 Conclusion 199 Acknowledgment 199 References 200 Section III Sensing Data Assessment and Deployment Including Extreme Environment and Advanced Pollutants 203 9 An Introduction to Microplastics, and Its Sampling Processes and Assessment Techniques 205 Bappa Mitra, Andrea Adami, Ravinder Dahiya, and Leandro Lorenzelli 9.1 Introduction 205 9.1.1 Properties of Microplastics 208 9.1.2 Microplastics in Food Chain 209 9.1.3 Human Consumption of Microplastics and Possible Health Effects 209 9.1.4 Overview 210 9.2 Microplastic Sampling Tools 212 9.2.1 Non- Discrete Sampling Devices 212 9.2.1.1 Nets 212 9.2.1.2 Pump Tools 213 9.2.2 Discrete Sampling Devices 215 9.2.3 Surface Microlayer Sampling Devices 215 9.3 Microplastics Separation 215 9.3.1 Separating Microplastics from Liquid Samples 215 9.3.1.1 Filtration 215 9.3.1.2 Sieving 216 9.3.2 Separating Microplastics from Sediments 218 9.3.2.1 Density Separation 218 9.3.2.2 Elutriation 218 9.3.2.3 Froth Floatation 219 9.4 Microplastic Sample Digestion Process 220 9.4.1 Acidic Digestion 221 9.4.2 Alkaline Digestion 221 9.4.3 Oxidizing Digestion 221 9.4.4 Enzymatic Degradation 222 9.5 Microplastic Identification and Classification 222 9.5.1 Visual Counting 222 9.5.2 Fluorescence 223 9.5.3 Destructive Analysis 223 9.5.3.1 Thermoanalytical Methods 224 9.5.3.2 High- Performance Liquid Chromatography 225 9.5.4 Nondestructive Analysis 225 9.5.4.1 Fourier Transform Infrared Spectroscopy 225 9.5.4.2 Raman Spectroscopy 226 9.6 Conclusions 228 Acknowledgment 229 References 229 10 Advancements in Drone Applications for Water Quality Monitoring and the Need for Multispectral and Multi- Sensor Approaches 235 Joao L. E. Simon, Robert J. W. Brewin, Peter E. Land, and Jamie D. Shutler 10.1 Introduction 235 10.2 Airborne Drones for Environmental Remote Sensing 237 10.3 Drone Multispectral Remote Sensing 239 10.4 Integrating Multiple Complementary Sensor Strategies with a Single Drone 241 10.5 Conclusion 242 Acknowledgment 243 References 243 11 Sensors for Water Quality Assessment in Extreme Environmental Conditions 253 Priyanka Ganguly 11.1 Introduction 253 11.2 Physical Parameters 255 11.2.1 Electrical Conductivity 255 11.2.2 Temperature 258 11.2.3 Pressure 260 11.3 Chemical Parameters 262 11.3.1 pH 262 11.3.2 Dissolved Oxygen and Chemical Oxygen Demand 265 11.3.3 Inorganic Content 268 11.4 Biological Parameters 271 11.5 Sensing in Extreme Water Environments 273 11.6 Discussion and Outlook 276 11.7 Conclusion 278 References 278 Section IV Sensing Data Analysis and Internet of Things with a Case Study 283 12 Toward Real- Time Water Quality Monitoring Using Wireless Sensor Networks 285 Sohail Sarang, Goran M. Stojanović, and Stevan Stankovski 12.1 Introduction 285 12.2 Water Quality Monitoring Systems 286 12.2.1 Laboratory- Based WQM (LB- WQM) 286 12.2.2 Wireless Sensor Networks- Based WQM (WSNs- WQM) 287 12.2.2.1 Solar- Powered Water Quality Monitoring 289 12.2.2.2 Battery- Powered Water Quality Monitoring 291 12.3 The Use of Industry 4.0 Technologies for Real- Time WQM 296 12.4 Conclusion 297 References 298 13 An Internet of Things- Enabled System for Monitoring Multiple Water Quality Parameters 305 Fowzia Akhter, H. R. Siddiquei, Md. E. E. Alahi, and S. C. Mukhopadhyay 13.1 Introduction 305 13.2 Water Quality Parameters and Related Sensors 306 13.3 Design and Fabrication of the Proposed Sensor 310 13.3.1 Sensor’s Working Principle 312 13.4 Experimental Process 312 13.5 Autonomous System Development 313 13.5.1 Algorithm for Data Classification 315 13.6 Experimental Results 318 13.6.1 Sensor Characterization for Temperature, pH, Nitrate, Phosphate, Calcium, and Magnesium Measurement 319 13.6.2 Repeatability 323 13.6.3 Reproducibility 325 13.6.4 Real Sample Measurement and Validation 327 13.6.5 Data Collection 330 13.6.6 Power Consumption 330 13.7 Conclusion 333 Acknowledgment 333 References 333 Index 339

Read more less

Book SynopsisDC MICROGRIDS Written and edited by a team of well-known and respected experts in the field, this new volume on DC microgrids presents the state-of-the-art developments and challenges in the field of microgrids for sustainability and scalability for engineers, researchers, academicians, industry professionals, consultants, and designers. The electric grid is on the threshold of a paradigm shift. In the past few years, the picture of the grid has changed dramatically due to the introduction of renewable energy sources, advancements in power electronics, digitalization, and other factors. All these megatrends are pointing toward a new electrical system based on Direct Current (DC). DC power systems have inherent advantages of no harmonics, no reactive power, high efficiency, over the conventional AC power systems. Hence, DC power systems have become an emerging and promising alternative in various emerging applications, which include distributed energy sources like wTable of ContentsPreface xv 1 On the DC Microgrids Protection Challenges, Schemes, and Devices – A Review 1Mohammed H. Ibrahim, Ebrahim A. Badran and Mansour H. Abdel-Rahman 1.1 Introduction 2 1.2 Fault Characteristics and Analysis in DC Microgrid 4 1.3 DC Microgrid Protection Challenges 7 1.3.1 Low Inductance of DC System 7 1.3.2 Fast Rise Rate of DC Fault Current 7 1.3.3 Difficulties of Overcurrent (O/C) Relays Coordination 7 1.3.4 Fault Detection and Location 8 1.3.5 Arcing Fault Detection and Clearing 10 1.3.6 Short-Circuit (SC) Analysis and Change of Its Level 13 1.3.7 Non-Suitability of AC Circuit Breakers (ACCBs) 16 1.3.8 Inverters Low Fault Current Capacity 17 1.3.9 Constant Power Load (CPL) Impact 17 1.3.10 Grounding 18 1.4 DC Microgrid Protection Schemes 21 1.4.1 The Differential Protection-Based Strategies 25 1.4.2 The Voltage-Based Protection Strategies 27 1.4.3 The Adaptive Overcurrent Protection Schemes 28 1.4.4 Impedance-Based Protection Strategy (Distance Protection) 29 1.4.5 Non-Conventional Protection Schemes (Data-Based Protection Scheme) 32 1.5 DC Microgrid Protective Devices (PDs) 34 1.5.1 Z-Source DC Circuit Breakers (ZSB) 35 1.5.2 Hybrid DC Circuit Breakers (HCB) 38 1.5.3 Solid State Circuit Breakers (SSCBs) 42 1.5.4 Arc Fault Current Interrupter (AFCI) 45 1.5.5 Fuses 47 1.6 Conclusions 48 References 50 2 Control Strategies for DC Microgrids 63Bhabani Kumari Choudhury and Premalata Jena 2.1 Introduction: The Concept of Microgrids 63 2.1.1 DC Microgrids 64 2.2 Introduction: The Concept of Control Strategies 65 2.2.1 Basic Control Schemes for DC MGs 66 2.2.1.1 Centralized Control Strategy 66 2.2.1.2 Decentralized Controller 67 2.2.1.3 Distributed Control 68 2.2.2 Multilevel Control 68 2.2.2.1 Primary Control 69 2.2.2.2 Secondary Control 73 2.2.2.3 Tertiary Control 74 2.2.2.4 Current Sharing Loop 74 2.2.2.5 Microgrid Central Controller (MGCC) 74 2.3 Control Strategies for DGs in DC MGs 76 2.3.1 Control Strategy for Solar Cell in DC MGs 76 2.3.1.1 Control Strategy for Wind Energy in DC MGs 77 2.3.1.2 Control Strategy for Fuel Cell in DC MGs 77 2.3.1.3 Control Strategy for Energy Storage System in DC MGs 78 2.4 Conclusions and Future Scopes 79 References 80 3 Protection Issues in DC Microgrids 83Bhabani Kumari Choudhury and Premalata Jena 3.1 Introduction 83 3.1.1 Protection Challenge 84 3.1.1.1 Arcing and Fault Clearing Time 84 3.1.1.2 Stability 85 3.1.1.3 Multiterminal Protections 85 3.1.1.4 Ground Fault Challenges 85 3.1.1.5 Communication Challenges 86 3.1.2 Effect of Constant Power Loads (CPLs) 86 3.2 Fault Detection in DC MGs 87 3.2.1 Principles and Methods of Fault Detection 87 3.2.1.1 Voltage Magnitude-Based Detection 87 3.2.1.2 Current Magnitude-Based Detection 88 3.2.1.3 Impedance Estimation Method 88 3.2.1.4 Power Probe Unit (PPU) Method 88 3.3 Fault Location 92 3.3.1 Passive Approach 92 3.3.1.1 Traveling Wave-Based Scheme 92 3.3.1.2 Differential Fault Location 93 3.3.1.3 Local Measurement-Based Fault Location 93 3.3.2 Active Approach for Fault Location 94 3.3.2.1 Injection-Based Fault Location 94 3.4 Islanding Detection (ID) 94 3.4.1 Types of IDSs 95 3.4.2 Passive Detection Schemes (PDSs) for DC MGs 96 3.4.3 Active Detection Schemes (ADS) for DC MGs 96 3.5 Protection Coordination Strategy 97 3.6 Conclusion and Future Research Scopes 97 References 97 4 Dynamic Energy Management System of Microgrid Using AI Techniques: A Comprehensive & Comparative Study 101Priyadarshini Balasubramanyam and Vijay K. Sood Nomenclature 102 4.1 Introduction 103 4.1.1 Background and Motivation 103 4.1.2 Prior Work 103 4.1.3 Contributions 104 4.1.4 Layout of the Chapter 104 4.2 Problem Statement 104 4.3 Mathematical Modelling of Microgrid 105 4.3.1 Cost Functions 106 4.3.1.1 Diesel Generator 106 4.3.1.2 Solar Generation 106 4.3.1.3 Wind Generation Unit 106 4.3.1.4 Energy Storage System (ESS) 107 4.3.1.5 Transaction with Utility 108 4.3.2 Objective Function 109 4.3.3 Constraints 109 4.4 Optimization Algorithm 110 4.4.1 Heuristic-Based Genetic Algorithm (GA) 110 4.4.2 Pattern Search Algorithm (PSA) 111 4.5 Results 113 4.6 Conclusion 118 References 118 5 Energy Management Strategies Involving Energy Storage in DC Microgrid 121S. K. Rai, H. D. Mathur and Sanjeevikumar Padmanaban 5.1 Introduction 121 5.2 Literature Review 123 5.2.1 Classic Approaches of EMS 124 5.2.2 Meta-Heuristic Approach of EMS 129 5.2.3 Artificial Intelligence Approach of EMS 134 5.2.4 Model Predictive, Stochastic and Robust Programming Approach of EMS 139 5.3 Case Study 142 5.3.1 Energy Management System 144 5.3.2 Objective Functions 144 5.3.3 Result and Discussion 145 5.4 Conclusion 151 References 151 6 A Systematic Approach for Solar and Hydro Resource Assessment for DC Microgrid Applications 159Sanjay Kumar, Nikita Gupta, Vineet Kumar and Tarlochan Kaur 6.1 Introduction 160 6.1.1 Micro Hydro and Solar PV 162 6.1.2 Renewable Energy for Rural Electrification in Indian Perspective 162 6.1.3 Solar Resource Assessment 163 6.1.4 Hydro Resource Assessment 166 6.1.5 Demand Assessment 167 6.2 Methodology 168 6.2.1 Data Collection 168 6.2.1.1 Meteorological and Geographical Data 168 6.2.1.2 Discharge Data for Hydro Potential Estimation 168 6.3 Result and Discussion 172 6.3.1 ANN Architecture 172 6.3.2 Hydro Resource Estimation 176 6.4 Conclusion 178 References 179 7 Secondary Control Based on the Droop Technique for Power Sharing 183Waner W.A.G. Silva, Thiago R. de Oliveira, Rhonei P. Santos and Danilo I. Brandao 7.1 Introduction 184 7.2 Voltage Deviation and Power Sharing Issues in Droop Technique 186 7.2.1 Approaches for Correcting Power and Current Sharing 190 7.2.2 Hybrid Secondary Control: Distributed Power Sharing and Decentralized Voltage Restoration 197 7.2.2.1 Dynamics and Convergence of the Power Sharing Correction 200 7.2.2.2 Communication Delays in Consensus-Based Algorithm 203 7.2.2.3 Secondary Control Modeling 204 7.2.2.4 Computational and Experimental Validation 208 7.2.3 Secondary Level Control Based on Unique Voltage-Shifting (vs) 215 7.2.3.1 Power Sharing and Average Voltage Convergence Analysis 218 7.2.3.2 Secondary Control Level Modeling 223 7.2.3.3 Computational and Experimental Validation 226 7.3 Design and Implementation of the Communication System 230 7.4 Conclusions 234 References 235 8 Dynamic Analysis and Reduced-Order Modeling Techniques for Power Converters in DC Microgrid 241Divya Navamani J., Lavanya A., Jagabar Sathik, M.S. Bhaskar and Vijayakumar K. 8.1 Introduction 242 8.2 Need of Dynamic Analysis for Power Converters 243 8.3 Various Modeling Techniques 245 8.3.1 Analysis from Modeling Method 249 8.4 Reduce-Order Modeling 253 8.4.1 Faddeev Leverrier Algorithm 253 8.4.1.1 Procedure for Faddeev Leverrier Algorithm 253 8.4.1.2 Illustrative Example with Switched- Inductor-Based Quadratic Boost Converter 254 8.4.2 Order Reduction of Transfer Function 257 8.4.3 Techniques for Model Order Reduction 257 8.4.4 Pole Clustering Method 258 8.4.5 Procedure for Improved Pole Clustering Technique 258 8.4.5.1 Computation of Denominator Polynomial of Lower-Dimensional Model 259 8.4.5.2 Computation of Numerator Polynomial of Lower-Dimensional Model 261 8.4.5.3 Design of Controller 261 8.5 Illustrative Example with the Power Converter 262 8.5.1 Derivation of the Denominator 263 8.5.2 Derivation of the Numerator 264 8.6 Controllers for Power Converter 265 8.6.1 Need of Controller 265 8.6.2 Types of Controller 265 8.7 Conclusion 267 References 267 9 Matrix Converter and Its Probable Applications 273Khaliqur Rahman 9.1 Introduction 274 9.2 Classification of Matrix Converter 275 9.2.1 Classical Matrix Converter 277 9.2.2 Sparse Matrix Converter 277 9.2.3 Very Sparse Matrix Converter 277 9.2.4 Ultra-Sparse Matrix Converter 278 9.3 Problems Associated with the MC and the Drives 280 9.3.1 Commutation Issues 280 9.3.2 Modulation Issues 280 9.3.3 Common-Mode Voltage and Common-Mode Current Issues 280 9.3.4 Protection Issues 281 9.4 Control Techniques 282 9.5 Basic Components of the Matrix Converter Fed Drive System 283 9.6 Industrial Applications of Matrix Converter 289 9.7 Summary 294 References 294 10 Multilevel Converters and Applications 299P. Prem, Jagabar Sathik and K.T. Maheswari 10.1 Introduction 300 10.2 Multilevel Inverters 301 10.2.1 Multilevel Inverters vs. Two-Level Inverters 301 10.2.2 Advantages of Multilevel Converters Based on Waveforms 303 10.2.3 Advantages of Multilevel Converters Based on Topology 304 10.3 Traditional Multilevel Inverter Topologies 305 10.3.1 Diode Clamped Multilevel Inverter 305 10.3.1.1 Features of DCMLI 308 10.3.1.2 Advantages of DCMLI 308 10.3.1.3 Disadvantages of DCMLI 308 10.3.1.4 Applications of DCMLI 309 10.3.2 Flying Capacitor Multilevel Inverter 309 10.3.2.1 Features of FCMLI 312 10.3.2.2 Advantages of FCMLI 312 10.3.2.3 Disadvantages of FCMLI 312 10.3.2.4 Applications of FCMLI 313 10.3.3 Cascaded H Bridge Multilevel Inverter 313 10.3.3.1 Features of CHBMLI 315 10.3.3.2 Advantages of CHBMLI 315 10.3.3.3 Disadvantages of CHBMLI 316 10.3.3.4 Applications of CHBMLI 316 10.4 Advent of Active Neutral Point Clamped Converter 316 10.4.1 Comparison with Traditional Topologies 319 10.4.2 Advantages of ANPC MLI 320 10.4.3 Disadvantages of ANPC MLI 320 10.5 Conclusion 322 References 322 11 A Quasi Z-Source (QZS) Network-Based Quadratic Boost Converter Suitable for Photovoltaic-Based DC Microgrids 325Amir Ghorbani Esfahlan and Kazem Varesi 11.1 Introduction 326 11.2 Proposed Converter 328 11.3 Steady-State Analyses 331 11.4 Comparison with Other Structures 335 11.5 Converter Analyzes in Discontinuous Conduction Mode (DCM) 335 11.6 Simulation Results 342 11.7 Real Voltage Gain and Losses Analyzes 346 11.8 Dynamic Behavior of the Proposed Converter 352 11.9 The Maximum Power Point Tracking (MPPT) 354 11.10 Conclusions 356 11.11 Appendix 357 References 358 12 Research on Protection Strategy Utilizing Full-Scale Transient Fault Information for DC Microgrid Based on Integrated Control and Protection Platform 361Shi Bonian and Sun Gang 12.1 Introduction 362 12.2 Topological Structure and Grounding Model of Studied Microgrid 363 12.2.1 Proposed DC Distribution Network Topology 363 12.2.2 Neutral Grounding Model 366 12.2.2.1 Grounding Position Selection 366 12.2.2.2 Grounding Mode Selection 366 12.3 Fault Characteristics of DC Microgrid 367 12.3.1 DC Unipolar Fault Characteristics 368 12.3.2 DC Bipolar Fault Characteristics 370 12.4 DC Microgrid Protection Strategy 373 12.4.1 Protection Zone Division and Protection Configuration 373 12.4.1.1 Protection Zone Division 373 12.4.1.2 Protection Configuration 375 12.4.2 Integrated Control and Protection Platform 376 12.4.3 Fault Isolation and Recovery Strategy Utilizing Full-Scale Transient Fault Information 378 12.4.3.1 Unipolar Fault Isolation and Recovery of DC Line/Bus 378 12.4.3.2 Bipolar Fault Isolation and Recovery of DC Line/Bus 380 12.5 Simulation Verification 384 12.5.1 Verification under DC Unipolar Fault 386 12.5.1.1 Metal Short Circuit Fault of DC Line 386 12.5.1.2 Unipolar Fault with High Transition Resistance 386 12.5.1.3 High Resistance Unipolar Fault with Parallel Resistance Switching Strategy 386 12.5.2 Verification under DC Bipolar Fault 390 12.6 Conclusion 394 References 395 13 A Decision Tree-Based Algorithm for Fault Detection and Section Identification of DC Microgrid 397Shankarshan Prasad Tiwari and Ebha Koley Acronyms 398 Symbols 398 13.1 Introduction 398 13.2 DC Test Microgrid System 400 13.3 Overview of Decision Tree-Based Proposed Scheme 401 13.4 DC Microgrid Protection Using Decision Tree Classifier 403 13.5 Performance Evaluation 404 13.5.1 Mode Detection Module 408 13.5.2 Fault Detection/Classification 409 13.5.3 Section Identification 409 13.5.4 Comparative Analysis of the Proposed Scheme with other DC Microgrid Protection Techniques 412 13.6 Conclusion 416 References 417 14 Passive Islanding Detection Method Using Static Transfer Switch for Multi-DGs Microgrid 421Rahul S. Somalwar and S. G. Kadwane 14.1 Introduction 422 14.1.1 Technical Challenges of Microgrid and Benefits 424 14.1.2 System with Multi-DGs 425 14.1.3 Power Sharing Methods 426 14.1.3.1 Conventional Droop Control Method 426 14.2 Islanding 427 14.2.1 Challenges with Islanding 427 14.2.2 Different Standards for Microgrid 428 14.2.3 Islanding Detection Methods 428 14.3 Static Transfer Switch (STS) 431 14.3.1 Simulation Results of STS 432 14.4 Proposed Scheme of Islanding 435 14.4.1 Proposed PV System 435 14.4.2 Mathematical Analysis of Harmonic Extraction 436 14.5 Flow Chart 437 14.6 Simulation Results 438 14.7 Experimental Results 441 14.8 Conclusion 445 References 446 Index 449

Read more less

Book SynopsisProvides a thorough introduction to the development, operation, maintenance, and troubleshooting of mobile communications systems Mobile Communications Systems Development: A Practical Introduction for System Understanding, Implementation, and Deployment is a comprehensive how to manual for mobile communications system design, deployment, and support. Providing a detailed overview of end-to-end system development, the book encompasses operation, maintenance, and troubleshooting of currently available mobile communication technologies and systems. Readers are introduced to different network architectures, standardization, protocols, and functions including 2G, 3G, 4G, and 5G networks, and the 3GPP standard. In-depth chapters cover the entire protocol stack from the Physical (PHY) to the Application layer, discuss theoretical and practical considerations, and describe software implementation based on the 3GPP standardized technical specifications. The book includes figures, tables, and sample computer code to help readers thoroughly comprehend the functions and underlying concepts of a mobile communications network. Each chapter includes an introduction to the topic and a chapter summary. A full list of references, and a set of exercises are also provided at the end of the book to test comprehension and strengthen understanding of the material. Written by a respected professional with more than 20 years' experience in the field, this highly practical guide: Provides detailed introductory information on GSM, GPRS, UMTS, and LTE mobile communications systems and networksDescribes the various aspects and areas of the LTE system air interface and its protocol layersCovers troubleshooting and resolution of mobile communications systems and networks issuesDiscusses the software and hardware platforms used for the development of mobile communications systems network elementsIncludes 5G use cases, enablers, and architectures that cover the 5G NR (New Radio) and 5G Core Network Mobile Communications Systems Development is perfect for graduate and postdoctoral students studying mobile communications and telecom design, electronic engineering undergraduate students in their final year, research and development engineers, and network operation and maintenance personnel.Trade Review"The author provides a comprehensive summary on the mobile communications systems covering 2G, 3G, 4G and 5G. The great addition to the theoretical foundations are practical elements including system operation and development aspects, with multitude practical examples and self-assessment. This handbook shall be useful for telecom practitioners including radio and core network engineers. It’s also a good source for software engineers from a different domain who would like to enter the telco domain. It shall be of interest to those, especially in present times where IT, software development and mobile communications are closer to each other than ever before."- Marcin Dryjański, Ph.D., PRINCIPAL CONSULTANT / CEOTable of ContentsAbout the Author xiv Preface xv Acknowledgments xviii List of Abbreviations xix 1 Introduction 1 Part I Network Architectures, Standardization, Protocols, and Functions 3 2 Network Architectures, Standardizations Process 5 2.1 Network Elements and Basic Networks Architectures 5 2.1.1 GSM (2G) Network Architecture 6 2.1.2 General Packet Radio Service (GPRS-2.5G) Network Architecture 7 2.1.3 Universal Mobile Telecommunications System (3G) Network Architecture 7 2.1.4 LTE (4G) Network Architecture 8 2.1.5 GSM, UMTS, LTE, and 5G Network Elements: A Comparison 9 2.1.6 Circuit Switched (CS) vs Packet Switched (PS) 9 2.2 Mobile Communication Network Domains 10 2.2.1 AN Domain 10 2.2.2 Core Network (CN) Domain 11 2.2.3 Network Domains and Its Elements 11 2.2.4 Example: End-to-End Mobile Network Information Flow 12 2.2.5 Example: GSM MO Call 13 2.3 Mobile Communications Systems Evolutions 14 2.3.1 Evolutions of Air Interface 14 2.3.2 Evolutions of 3GPP Networks Architectures 16 2.4 Mobile Communications Network System Engineering 19 2.4.1 Mobility Management 19 2.4.2 Air Interface Management 20 2.4.3 Subscribers and Services Management 20 2.4.4 Security Management 20 2.4.5 Network Maintenance 20 2.5 Standardizations of Mobile Communications Networks 21 2.5.1 3rd Generation Partnership Project (3GPP) 21 2.5.2 3GPP Working Groups 21 2.5.3 3GPP Technical Specification and Technical Report 22 2.5.4 Stages of a 3GPP Technical Specification 22 2.5.5 Release Number of 3GPP Technical Specification 22 2.5.6 3GPP Technical Specification Numbering Nomenclature 23 2.5.7 Vocabulary of 3GPP Specifications 24 2.5.8 Examples in a 3GPP Technical Specification 24 2.5.9 Standardization of Technical Specifications by 3GPP 24 2.5.10 Scope of 3GPP Technical Specification (TS) 24 2.5.11 3GPP TS for General Description of a Protocol Layer 25 2.5.12 3GPP TS Drafting Rules: Deriving Requirements 25 2.5.13 Download 3GPP Technical Specifications 25 2.5.14 3GPP Change Requests 26 2.5.15 Learnings from 3GPP Meetings TDocs 26 2.6 3GPP Releases and Its Features 26 Chapter Summary 27 3 Protocols, Interfaces, and Architectures 29 3.1 Protocol Interface and Its Stack 29 3.1.1 Physical Interface 30 3.1.2 Logical Interface 30 3.1.3 Logical Interfaces’ Names and Their Protocol Stack 33 3.1.4 Examples of Logical Interface and Its Protocol Layers 35 3.2 Classifications of Protocol Layers 36 3.2.1 Control Plane or Signaling Protocols 36 3.2.2 User Plane Protocols 38 3.3 Grouping of UMTS, LTE, and 5G Air Interface Protocol Layers 39 3.3.1 Access Stratum (AS): UMTS UE – UTRAN; LTE UE – E-UTRAN;5G UE - NG-RAN 39 3.3.2 Non-Access Stratum: UMTS UE – CN, LTE UE – EPC; 5G UE-Core 41 3.4 Initialization of a Logical Interface 42 3.5 Protocol Layer Termination 43 3.6 Protocol Sublayers 43 3.7 Protocol Conversion 44 3.8 Working Model of a 3GPP Protocol Layer: Services and Functions 45 3.9 General Protocol Model Between RAN and CN (UMTS, LTE, 5G) 46 3.10 Multiple Transport Networks, Protocols, and Physical Layer Interfaces 47 3.11 How to Identify and Understand Protocol Architectures 49 3.11.1 Identifying a Logical Interface, Protocol Stack, and Its Layers 49 3.11.2 Identification of Technical Requirements Using Interface Name 51 3.12 Protocol Layer Procedures over CN Interfaces 51 3.12.1 Similar Functions and Procedures over the CN Interfaces 52 3.12.2 Specific Functions and Procedures over the CN Interfaces 53 Chapter Summary 54 4 Encoding and Decoding of Messages 55 4.1 Description and Encoding/Decoding of Air Interface Messages 55 4.1.1 Encoding/Decoding: Air Interface Layer 3 Messages 56 4.1.2 Encoding/Decoding: LTE and 5G NR Layer 2: RLC Protocol 60 4.1.3 Encoding/Decoding: LTE and 5G NR Layer 2: MAC Protocol 60 4.1.4 CSN.1 Encoding/Decoding: GPRS Layer 2 Protocol (RLC/MAC) 60 4.1.5 ASN.1 Encoding/Decoding: UMTS, LTE, and 5G NR Layer 3 Protocol 61 4.1.6 Direct/Indirect Encoding Method 62 4.1.7 Segmented Messages over the Air Interface 63 4.1.8 Piggybacking a Signaling Message 63 4.2 Encoding/Decoding of Signaling Messages: RAN and CN 64 Chapter Summary 65 5 Network Elements: Identities and Its Addressing 67 5.1 Network Elements and Their Identities 67 5.2 Permanent Identities 68 5.3 Temporary Identities Assigned by CN 69 5.3.1 GSM System Temporary Identities 69 5.3.2 GPRS System Temporary Identities 69 5.3.3 LTE/EPS System Temporary Identities 70 5.4 Temporary Identities Assigned by RAN: RNTI 72 5.5 Usages of Network Identities 73 5.6 Native and Mapped Network Identities 73 5.7 LTE UE Application Protocol Identity 75 Chapter Summary 76 6 Interworking and Interoperations of Mobile Communications Networks 77 6.1 Requirements and Types of Interworking 77 6.2 Interworking Through Enhanced Network Elements 78 6.2.1 Interworking for Voice Call Through IMS: VoLTE 79 6.2.1.1 IP Multimedia Subsystem (IMS) 80 6.2.1.2 UE Registration and Authentication 81 6.2.2 Interworking for VoLTE Call Through LTE/EPS: SRVCC 83 6.2.3 Interworking for Voice Call Through LTE/EPS: CSFB 85 6.3 Interworking Through Legacy Network Elements 88 6.4 Interworking Between LTE/EPS and 5G Systems 89 6.5 Interoperations of Networks: LTE/EPS Roaming 90 6.5.1 Roaming Through Interoperations of Enhanced Networks Elements 90 6.5.2 Roaming Through Interoperations of Legacy Networks Elements 92 6.6 UE Mode of Operation 92 6.7 Function of E-UTRAN in a VoLTE Call 95 Chapter Summary 95 7 Load Balancing and Network Sharing 97 7.1 Core Network Elements Load Balancing 97 7.1.1 Identification of NAS Node: NRI and Its Source 99 7.1.2 NAS Node Selection Function 99 7.2 Network Sharing 102 7.2.1 GSM/GPRS/LTE RAN Sharing Through MOCN Feature 103 7.2.2 5G NG‐RAN Sharing Through MOCN Feature (Release 16) 109 Chapter Summary 110 8 Packets Encapsulations and Their Routing 111 8.1 User Data Packets Encapsulations 111 8.1.1 Packets Encapsulations at the CN and RAN 112 8.1.1.1 GPRS Tunneling Protocol ( GTP) 112 8.1.1.2 GTP Functions 112 8.1.1.3 GTP User Plane PDU: G-PDU 113 8.1.1.4 GTP Control Plane PDU 114 8.1.1.5 Example: GTP and Packet Encapsulations at LTE EPC 115 8.1.2 Packet Encapsulations over Air Interface 115 8.2 IP Packet Routing in Mobile Communications Networks 116 8.3 IP Header Compression and Decompression 117 Chapter Summary 119 9 Security Features in Mobile Communications Networks 121 9.1 A Brief on the Security Architecture: Features and Mechanisms 121 9.2 Security Features and Its Mechanisms 123 9.3 GSM Security Procedures 126 9.4 UMTS, LTE, and 5G: AS and NAS Layer Security Procedures 127 9.5 Security Contexts 130 9.6 Security Interworking 130 Chapter Summary 132 Part II Operations and Maintenances 133 10 Alarms and Faults Managements 135 10.1 Network Elements Alarm and Its Classifications 135 10.2 Sources of Abnormal Events and Alarms 136 10.3 Identifying Sources of Alarms from 3GPP TSs 136 10.3.1 Abnormal Conditions 136 10.3.2 Protocol Layer Error Handling 137 10.3.3 Abnormal Conditions Due to Local Errors 138 10.4 Design and Implementation of an Alarm Management System 138 10.4.1 Design and Components of an Alarm 139 10.4.2 Alarm Application Programming Interfaces (APIs) 139 10.4.3 Alarm Database 139 10.5 Alarm Due to Protocol Error 140 10.5.1 Sample Protocol Error Alarm Description 142 10.6 Alarm Due to Abnormal Conditions 142 10.6.1 Normal Scenario 143 10.6.2 Abnormal Scenario 143 10.6.3 Sample Alarm Description 144 10.6.4 Sample Alarm Generation 145 10.6.5 Sample Protocol Error Alarm Generation 145 10.7 How to Troubleshoot Protocol Error Using the Alarm Data 146 Chapter Summary 146 11 Performance Measurements and Optimizations of Mobile Communications Networks 147 11.1 Counters for Performance Measurements and Optimizations 147 11.2 Performance and Optimizations Management System 149 11.3 Key Performance Indicator (KPI) 150 11.3.1 What Is a KPI? 150 11.3.2 KPI Domains 150 11.3.3 KPI for Signaling or Control Plane 152 11.3.4 KPI for User or Data Plane 153 11.3.5 KPI Categories 154 11.3.6 KPI Evaluation Steps 155 11.3.7 Troubleshooting and Improving KPI 156 11.3.8 Components of a KPI Definition 157 Chapter Summary 157 12 Troubleshooting of Mobile Communications Networks Issues 159 12.1 Air Interface-Related Issues 159 12.1.1 Drive Test, Data Collection, and Its Analysis 160 12.2 Debugging Issues with IP-Based Logical Interface 160 12.2.1 IP Protocol Analyzer 161 12.2.2 Network/Application Throughput Issue 161 12.2.3 Switch Port Mirroring 161 12.3 Conformance Testing Issues 162 12.3.1 Example: Mobile Device (MS)/User Equipment (UE) Conformance Test 163 12.3.2 Example: Location Area Update Request 163 12.4 Interoperability Testing (IOT) Issues 164 12.5 Interworking Issues 165 12.6 Importance of Log/Traces and Its Collections 166 12.7 Steps for Troubleshooting 167 Chapter Summary 170 Part III Mobile Communications Systems Development 171 13 Core Software Development Fundamentals 173 13.1 A Brief on Software Development Fundamentals 173 13.1.1 Requirements Phase 174 13.1.2 Design 174 13.1.3 Implementation 175 13.1.4 Integration and Testing 175 13.1.5 Operation and Maintenance 175 13.2 Hardware Platforms: Embedded System, Linux Versus PC 176 13.2.1 System Development Using Embedded System Board 176 13.2.2 System Development Using Multicore Hardware Platform 177 13.2.2.1 What Is a Core? 178 13.2.2.2 Network Element Development Using Multicore Platform 178 13.2.2.3 Runtime Choices of Multicore Processor 178 13.2.2.4 Software Programming Model for Multicore Processor 179 13.3 Selecting Software Platforms and Features 179 13.3.1 Selecting Available Data/Logical Structures 180 13.3.1.1 Advanced Data Structures 180 13.3.1.2 How Data Structure Affects the Application’s Performance 180 13.3.2 Selecting an Operating System Services/Facilities 181 13.3.2.1 Advance Features of Operating System: IPC 181 13.4 Software Simulators for a Mobile Communications Network 184 13.5 Software Root Causes and Their Debugging 185 13.5.1 Incorrect Usages of Software Library System Calls/APIs 185 13.5.2 Incorrect Usages of System Resources 185 13.5.3 Bad Software Programming Practices 185 13.6 Static Code Analysis of Software 186 13.7 Software Architecture and Software Organization 186 13.8 System and Software Requirements Analysis 188 13.9 Software Quality: Reliability, Scalability, and Availability 188 13.9.1 Reliability 188 13.9.2 Scalability 188 13.9.3 Availability 188 Chapter Summary 189 14 Protocols, Protocol Stack Developments, and Testing 191 14.1 Components of a 3GPP Protocol TS 191 14.2 3GPP Protocol Layer Structured Procedure Description 193 14.3 Protocol Layer Communications 194 14.3.1 Layer-to-Layer Communication Using Service Primitives 195 14.3.2 Layer-to-Layer Communication: SAP 196 14.3.3 Peer-to-Peer Layer Communication: PDU and Service Data Unit (SDU) 197 14.3.4 Types of PDU 198 14.3.5 Formats of PDU 198 14.4 Air Interface Message Format: Signaling Layer 3 198 14.4.1 A Brief on the Air Interface Layer 3 Protocol Stack 198 14.4.2 Classification of Layer 3 Messages 199 14.4.3 Layer 3 Protocol Header: Signaling Message Format 200 14.4.4 Layer 3 Protocol Header: Protocol Discriminator 202 14.4.5 Layer 3 Protocol Header: GSM, GPRS Skip Indicator 202 14.4.6 Layer 3 Protocol Header: GSM, GPRS Transaction Identifier 204 14.4.7 Layer 3 Protocol Header: LTE/EPS Bearer Identity 204 14.4.8 Layer 3 Protocol Header: 5GSM PDU Session Identity 204 14.4.9 Constructing a Layer 3 Message 204 14.4.10 Security Protected LTE/EPS and 5G NAS Layer MM Messages 205 14.4.11 Layer 3 Protocol Layer’s Message Dump 207 14.5 Air Interface Message Format: Layer 2 207 14.6 RAN – CN Signaling Messages 208 14.6.1 Protocol Layer Elementary Procedure 208 14.6.2 Types and Classes of EPs 210 14.6.3 EPs Code 210 14.6.4 Criticality of IE 211 14.6.5 Types of Protocol Errors and Its Handling 211 14.6.6 Choices of Triggering Message 212 14.6.7 Message Type 212 14.6.8 Message Description 212 14.6.9 Example: LTE/EPS S1 Interface: S1 Setup Procedure 213 14.7 Modes Operation of a Protocol Layer 213 14.8 Example of a Protocol Primitive and PDU Definition 215 14.9 Example of a Protocol Layer Frame Header Definition 216 14.10 Examples of System Parameters 216 14.11 Examples of Protocol Information Elements and Its Identifier 217 14.12 3GPP Release Specific Changes Implementation 218 14.13 Examples of Protocol Messages Types 219 14.14 Protocol Layer Timer Handling 219 14.15 Protocol Layer Development Using State Machine 222 14.16 Protocol Layer Development Using Message Passing 224 14.17 Protocol Layer Data and its Types 225 14.18 Protocol Layer Control and Configuration 226 14.19 Protocol Context Information 227 14.20 Protocol Layer Message Padding 228 14.21 Device Driver Development 229 14.22 Guidelines for Protocol Stack/Layer Development 230 14.23 Software Profiling, Tools and Performance Improvement 231 14.24 Protocol Stack Testing and Validation 231 Chapter Summary 233 15 Deriving Requirements Specifications from a TS 235 15.1 3GPP Protocol Layer Procedures 235 15.1.1 LTE UE Mode of Operation Requirements 236 15.1.2 LTE UE ATTACH Procedure Requirements 236 15.1.3 LTE UE DETACH Procedure Requirements 237 15.1.4 LTE UE Tracking Area Update Procedure Requirements 237 15.2 3GPP System Feature Development Requirements 238 15.2.1 Identification of System/Network Elements Interfaces Changes 238 15.2.2 Identifications of Impacts on Performance 238 15.2.3 Identifications of Impacts on Feature Management 239 15.2.4 Identification of Interworking Requirements with Existing Features 239 15.2.5 Charging and Accounting Aspects 239 15.3 Example Feature: Radio Access Network Sharing 239 15.3.1 Effects on Network Elements 239 15.3.2 Effects on Logical Interfaces 240 15.3.3 Selection of Core Network Operator: PLMN Id 241 15.4 Example: Interworking/Interoperations 242 15.4.1 Circuit-Switched Fall Back (CSFB) 242 15.4.2 Single Radio Voice Call Continuity (SRVCC) 243 15.5 3GPP System Feature and High-Level Design 244 Chapter Summary 245 Part IV 5G System and Network 247 16 5G Network: Use Cases and Architecture 249 16.1 5G System (5GS) Use Cases 249 16.1.1 Enablers and Key Principles of 5GS Use Cases 250 16.1.2 Other Enablers in 5G System 253 16.2 Support of Legacy Services by 5G System 253 16.3 5G System Network Architecture 254 16.3.1 3GPP Access Architecture 254 16.3.2 Non-3GPP Access Architecture 256 16.4 5G System NG–RAN/gNB Logical Architecture 256 16.5 5GC System Architecture Elements 259 16.6 5G System Deployment Solutions 260 16.6.1 E–UTRA–NR Dual Connectivity (EN–DC) for NSA Deployment 261 16.7 5G System and LTE/EPS Interworking 265 16.7.1 RAN-Level Interworking 265 16.7.2 Core Network (CN) Level Interworking: N26 Interface 265 16.7.2.1 Single Registration Mode with N26 Interface 266 16.7.2.2 Dual Registration Mode: Without N26 Interface 266 16.8 5G System Native and Mapped Network Identities 268 16.8.1 Mobility Area Identifiers 268 16.8.2 UE/Subscriber Permanent Identifiers 269 16.8.3 Core Network Identifiers 269 16.8.4 RAN Identifiers 269 16.8.5 Core Network Temporary Identities 270 16.9 5G System Network Slicing 270 16.9.1 Identities for a Network Slice 271 16.9.2 Impacts of Network Slicing Feature 273 16.10 Management and Orchestration (MANO) of 5G Network 278 16.11 5G System Security 280 16.11.1 UE Authentication Frameworks and Methods 280 16.11.2 Primary Authentication and Secondary Authentication 282 16.11.3 Key Hierarchy and Authentication Vector 282 16.11.4 New Security Requirements in 5G System 283 16.11.5 Subscriber Identities/Privacy Protection 286 Chapter Summary 287 17 Introduction to GSM, UMTS, and LTE Systems Air Interface 289 17.1 Air Interfaces Protocol Architectures 289 17.2 Protocol Sublayers 290 17.3 Control Plane and User Plane Protocols 291 17.4 Protocols Domains Classifications 291 17.5 Access Stratum and Non-access Stratum 291 17.6 Message Formats 292 17.7 Security Over the Air Interface 293 17.8 Piggybacking for Reduction of Signaling Overhead 293 17.8.1 Examples Piggybacking of Signaling Messages 293 Chapter Summary 294 18 5G NR Air Interface: Control Plane Protocols 295 18.1 NR Control Plane Protocol Layers 295 18.2 Session Management (5G SM) Layer 296 18.2.1 Procedures of 5G SM Layer 297 18.2.2 PDU Session Types 298 18.2.3 PDU Session Service Continuity (SSC) 299 18.2.4 PDU Sessions for Network Slices 300 18.2.5 Session Management (SM) Layer States 301 18.3 Quality of Service (5G QoS) 301 18.3.1 LTE/EPS QoS Model: EPS Bearer 301 18.3.2 5GS QoS Model: QoS Flow 301 18.3.3 GTP-U Plane Tunnel for PDU Session 302 18.3.4 Service Data Flow and PCC Rule 302 18.3.5 Binding of Service Data Flow 303 18.3.6 QoS Profile and QFI 303 18.3.7 QoS Rule and QRI 305 18.3.8 Mapping QoS Flow to Data Radio Bearer 305 18.3.9 Downlink Data Flow Through GTP-U Plane Tunnels 307 18.4 Mobility Management (5G MM) Layer 308 18.4.1 Mobility Area Concepts and Identifiers 308 18.4.2 Requirements of Mobility Management Functions 313 18.4.3 Functions and Procedures of 5G MM Layer 314 18.4.4 Mobility Management Layer States 315 18.4.5 Connection Management (CM) and Service Request 316 18.4.6 Mobility Pattern of UE 317 18.5 RRC Layer 317 18.5.1 Functions and Procedures of RRC Layer 317 18.5.2 System Information (SI) Broadcast 318 18.5.3 RRC Layer States 319 18.5.4 RRC INACTIVE State 320 18.5.5 Mobility of UE 326 18.5.5.1 UE Mobility in RRC IDLE State 326 18.5.5.2 UE Mobility in RRC INACTIVE State 326 18.5.5.3 UE Mobility in RRC CONNECTED State 327 18.5.6 Admission Control 332 Chapter Summary 334 19 5G NR Air Interface 335 19.1 NR User Plane Protocol Layers 335 19.2 SDAP Layer 336 19.3 PDCP Layer 336 19.4 RLC Layer 340 19.5 MAC Layer 342 19.5.1 Functions and Procedures 342 19.5.2 Scheduling Procedure 344 19.5.3 Random Access Procedure 346 19.5.4 Error Correction Through HARQ Procedure 351 19.5.5 Buffer Status Reporting (BSR) Procedure 352 19.5.6 Scheduling Request (SR) Procedure 353 19.5.7 Low Latency in the NR Due to Configured Scheduling 353 19.5.8 MAC Layer PDU and Header Structures 354 19.5.9 How MAC Layer Ensures Low‐Latency Requirements 356 19.5.10 Channel Structures in NR 357 19.6 Physical Layer 359 19.6.1 Principles of Transmissions and Its Directions 360 19.6.2 Physical Layer Functions, Procedures, and Services 360 19.6.3 OFDM Symbol 363 19.6.4 NR Frame and Slot Format 364 19.6.4.1 Subcarrier Spacing (SCS)/Numerologies (μ) 364 19.6.4.2 Slots per NR Frame and Subframe 364 19.6.4.3 Slot Formats in TDD Mode 366 19.6.4.4 Dynamic TDD 367 19.6.5 Resource Grid and Resource Block 368 19.6.5.1 Control Resource Set (CORESET) 369 19.6.5.2 Common Resource Blocks (CRB) 370 19.6.5.3 Physical Resource Block (PRB) 370 19.6.5.4 Virtual Resource Block (VRB) 370 19.6.5.5 Interleaved and Non‐interleaved PRB Allocation 370 19.6.5.6 PRB Bundling and VRB to PRB Mapping 371 19.6.5.7 Reference Point “A” 371 19.6.6 Channel and Transmission Bandwidths 371 19.6.7 Bandwidth Part (BWP) 373 19.6.7.1 Types of BWP 374 19.6.7.2 BWP Configuration 375 19.6.7.3 BWP Switching and Associated Delay 376 19.6.8 NR Resource Allocations 377 19.6.8.1 Frequency Domain Resource Allocation for FDD Transmission 377 19.6.8.2 Time‐Domain Resources Allocation for FDD Transmission 380 19.6.8.3 Time‐Domain Resources Allocation for TDD 383 19.6.9 Transport Channels and Their Processing Chain 384 19.6.9.1 CRC Calculation and its Attachment to a Transport Block 385 19.6.9.2 Code Block Segmentation 385 19.6.9.3 Channel Encoding with LDPC 386 19.6.9.4 Rate Matching and Concatenation 387 19.6.9.5 Multiplexing of UL‐SCH Data and Uplink Control Information 388 19.6.9.6 LDPC Encoding Examples 388 19.6.10 Physical Channels and Their Processing Chain 390 19.6.10.1 Physical Channels 390 19.6.10.2 Channel Mappings 391 19.6.10.3 Multiple Physical Antenna Transmissions 392 19.6.10.4 Physical Channel Processing Chain 395 19.6.10.5 Physical Downlink Control Channel (PDCCH) 397 19.6.10.6 Physical Uplink Control Channel (PUCCH) and Information (UCI) 404 19.6.11 Code Block Group‐Based Transmission and Reception 405 19.6.12 Physical Signals 409 19.6.12.1 Reference Signals Transmitted as Part of Physical Channels 410 19.6.12.2 Sounding Reference Signals 412 19.6.13 Downlink Synchronization 414 19.6.14 Millimeter Wave Transmission, Beamforming, and Its Management 419 19.6.15 Cell‐Level Radio Link Monitoring (RLM) 424 19.6.16 RRM Measurements for UE Mobility 426 19.6.16.1 RRM Measurement Signals and Their Quantities 426 19.6.16.2 RRM Measurements Framework 427 19.6.16.3 Overall RRM Process 429 19.6.17 Channel State Information (CSI) 430 19.6.18 Modulation and Coding Schemes (MCSs) 433 19.6.19 Link Adaptation Procedure 434 19.6.20 Random Access (RACH) Procedure 435 19.6.21 NR Radio Resources Management (RRM) Procedure 439 19.6.22 UE Transmit Power Control 444 19.6.22.1 Types of Power Control Procedure in NR 444 19.6.22.2 UE Transmit Power Determination Procedure in NR 445 19.6.23 Effect of Physical Layer on Data Throughputs 445 Chapter Summary 446 20 5G Core Network Architecture 447 20.1 Control Plane and User Plane Separation – CUPS 447 20.1.1 Impacts of CUPS Feature 448 20.1.2 CUPS in the LTE/EPC Network 449 20.1.3 CUPS Feature in 5G Core Network 450 20.2 Service-Based Architecture (SBA) 452 20.2.1 Network Functions and Its Instances 453 20.2.2 Network Functions (NFs) and Their Services Interfaces 454 20.2.3 5G System Architecture with NF 456 20.2.4 Network Functions and Their Services and Operations 457 20.2.5 Network Functions Services Framework 458 20.2.6 Services API for Network Functions 462 20.2.7 Network Function Selection 468 20.3 Network Function Virtualization (NFV) 469 Chapter Summary 472 21 5G System: Low-level Design 473 21.1 Design of 5GC Service Interface and Its Operations 473 21.2 Design of 5GC NF Service Interface Using UML and C++ Class Diagram 474 21.3 Usages of C++ Standard Template Library (STL) 475 21.4 Software Architecture for 5G System 476 21.4.1 NG-RAN Logical Nodes Software Architecture 476 21.4.2 5GC Software Architecture 479 21.5 Data Types Used in 5GC SBI Communications 479 Chapter Summary 491 22 3GPP Release 16 and Beyond 493 22.1 5GS Enhancements as Part of Release 16 493 22.2 5GS New Features as Part of Release 16 494 22.3 3GPP Release 17 496 Chapter Summary 496 Appendix 497 References 503 Index 507

Read more less

Book SynopsisTable of ContentsAbout the Author xi List of Figures xii List of Tables xxvi Preface xxvii Acknowledgments xxx Abbreviations xxxi Syllabus xxxv 1 The Art of Embedded Computers 1 Overview of Embedded Computers and Their Interdisciplinarity 1 Computer vs. Embedded Computer Programming and Application Development 2 Group 1: Programmable Logic Devices 3 Group 2: Reconfigurable Computers 4 Group 3: Microcomputers 4 Group 4: Single-Board Computers 6 Group 5: Mobile Computing Devices 6 TPACK Analysis Toward Teaching and Learning Microcomputers 7 TPACK Analysis of the Interdisciplinary Microcontroller Technology 7 Content Knowledge (The What) 8 Technology Knowledge (The Why) 9 Pedagogical Knowledge (The How) 11 From Computational Thinking (CT) to Micro-CT (μCT) 12 CT Requirement and Embedded Computers 13 Microcomputers and Abstraction Process 14 The μCT Concept: An Onion Learning Framework 15 “Transparent” Teaching Methods 17 The Impact of Microcontroller Technology on the Maker Industry 19 Hardware Advancement in μC Technology 20 Software Advancement in μC Technology 23 The Impact of Arduino on the μC Community 23 Where Is Creativity in Embedded Computing Devices Hidden? 26 Creativity in Mobile Computing Devices: Travel Light, Innovate Readily! 26 Communication with the Outside World: Sensors, Actuators, and Interfaces 28 Conclusion 30 2 Embedded Programming with Arduino 31 Number Representation and Special-Function Codes 31 Arduino and C Common Language Reference 34 Working with Data (Variables, Constants, and Arrays) 36 Arduino UART Interface to the Outside World (Printing Data) 39 Arduino Ex.2–1 40 Arduino Ex.2–2 44 Program Flow of Control (Arithmetic and Bitwise Operations) 47 Arduino UART Interface (Flow of Control and Arithmetic/Bitwise Examples) 52 Arduino Ex.2–3 52 Arduino Ex.2–4 53 Arduino Ex.2–5 54 Arduino Ex.2–6 59 Arduino Ex.2–7 63 Code Decomposition (Functions and Directives) 69 Arduino Ex.2–8 69 Conclusion 72 Problem 2–1 (Data Output from the μC Device: Datatypes and Bytes Reserved by the hw) 73 Problem 2–2 (Data Output from the μC Device: Logical Operators in Control Flow) 73 Problem 2–3 (Data Input to the μC Device: Arithmetic and Bitwise Operations) 73 Problem 2–4 (Code Decomposition) 73 3 Hardware Interface with the Outside World 75 Digital Pin Interface 75 Arduino Ex.3.1 76 Arduino Ex.3.2 77 Arduino Ex.3.3 81 Arduino Ex.3.4 82 Arduino Ex.3.5 84 Analog Pin Interface 86 Arduino Ex.3.6 87 Arduino Ex.3.7 91 Interrupt Pin Interface 91 Arduino Ex.3.8 94 UART Serial Interface 96 Arduino Ex.3.9 97 Arduino Ex.3.10 98 Arduino Ex.3.11 99 SPI Serial Interface 101 Arduino Ex.3.12 103 Arduino Ex.3.13 110 Arduino Ex.3.14 115 Arduino Ex.3.15 121 I2C Serial Interface 122 Arduino Ex.3.16 125 Arduino Ex.3.17 130 Arduino Ex.3.18 135 Arduino Ex.3.19 142 Conclusion 146 Problem 3.1 (Data Input and Output to/from the μC Using Push-Button and LED IO Units) 147 Problem 3.2 (PWM) 147 Problem 3.3 (UART, SPI, I2C) 147 4 Sensors and Data Acquisition 149 Environmental Measurements with Arduino Uno 149 Arduino Ex.4–1 150 DAQ Accompanying Software of the Ex.4–1 157 DAQ Accompanying Software with Graphical Monitoring Feature Via gnuplot 166 Arduino Ex.4–2 169 Orientation, Motion, and Gesture Detection with Teensy 3.2 171 Arduino Ex.4–3 173 Arduino Ex.4–4 174 Arduino Ex.4–5 177 Arduino Ex.4–6 184 DAQ Accompanying Software for Orientation, Motion, and Gesture Detection with gnuplot 191 Real Time Monitoring with Open GL 193 Distance Detection and 1D Gesture Recognition with TinyZero 200 Arduino Ex.4–7 201 Arduino Ex.4–8 205 DAQ Accompanying Software for Distance Measurements 209 Color Sensing and Wireless Monitoring with Micro:bit 211 Arduino Ex.4–9 212 Arduino Ex.4–10 216 Open GL Example Applying to RGB Sensing 220 Arduino Ex.4–11 222 Conclusion 226 Problem 4–1 (Data Acquisition of Atmospheric Pressure) 226 Problem 4–2 (Fusion of Linear Acceleration and Barometric Altitude) 226 Problem 4–3 (1D Gesture Recognition) 226 Problem 4–4 (Color Sensing) 226 5 Tinkering and Prototyping with 3D Printing Technology 227 Tinkering with a Low-cost RC Car 227 Arduino Ex.5.1 231 Arduino Ex.5.2 236 A Prototype Interactive Game for Sensory Play 237 Hardware Boards of the Prototype System 238 Assembly Process of the 3D Printed Parts of the System’s Enclosure 243 Firmware Code Design and User Instructions 249 Arduino Ex.5.3 250 Arduino Ex.5.4 253 Arduino Ex.5.5 256 Arduino Ex.5.6 260 3D Printing 262 Modeling 3D Objects with FreeCAD Software 262 Preparing the 3D Prints with Ultimaker Cura Software 269 3D Printing with Prima Creator P120 272 Presentation of the Rest 3D Models of the Prototype Interactive Game 276 PrototypeB (Modeling the battery.stl Part) 276 PrototypeC (Modeling the booster.stl Part) 278 PrototypeD (Modeling the speaker.stl Part) 283 PrototypeE (Modeling the cover.stl Part) 284 PrototypeF (Modeling the button.stl Part) 287 PrototypeG (Modeling the sensor.stl Part) 290 PrototypeH (Modeling the front.stl Part) 290 Conclusion 294 Problem 5.1 (Tinkering with a Low-cost RC Car) 294 Problem 5.2 (A Prototype Interactive Game for Sensory Play) 294 Problem 5.3 (A Prototype Interactive Game for Sensory Play) 295 Problem 5.4 (A Prototype Interactive Game for Sensory Play) 296 Problem 5.5 (3D Printing) 296 References 297 Index 301

Read more less

Book SynopsisDIGITAL SYSTEM DESIGN USING FSMS Explore this concise guide perfect for digital designers and students of electronic engineering who work in or study embedded systemsDigital System Design using FSMs: A Practical Learning Approach delivers a thorough update on the author's earlier work, FSM-Based Digital Design using Verilog HDL. The new book retains the foundational content from the first book while including refreshed content to cover the design of Finite State Machines delivered in a linear programmed learning format. The author describes a different form of State Machines based on ToggleFlip Flops and Data Flip Flops.The book includes many figures of which 15 are Verilog HDL simulations that readers can use to test out the design methods described in the book, as well as 19 Logisim simulation files with figures. Additional circuits are also contained within the Wiley web folder. It has tutorials and exercises, including comprehensive coverage of real-woTable of ContentsPreface viii Acknowledgements x About the Companion Website xi Guide to Supplementary Resources xii 1 Introduction to Finite State Machines 1 1.1 Some Notes on Style 1 2 Using FSMs to Control External Devices 25 2.1 Introduction 25 3 Introduction to FSM Synthesis 45 3.1 Introduction 45 3.2 Tutorials Covering Chapters 1, 2, and 3 71 3.2.1 Binary data serial transmitter FSM 71 3.2.2 The high low FSM system 76 3.2.3 The clocked watchdog timer FSM 80 3.2.3.1 FSM equations 81 3.2.4 The asynchronous receiver system clocked FSM 84 3.2.4.1 Brief note on the development of the test bench generator 86 3.2.4.2 The state diagram 86 3.2.4.3 The state diagram equations 87 3.2.4.4 The outputs 87 3.2.4.5 Verilog HDL simulation of the completed system 95 4 Asynchronous FSM Methods 97 4.1 Introduction to Asynchronous FSM 97 4.2 Summary 144 4.3 Tutorials 144 4.3.1 FSM motor with fault detection 144 4.3.2 The mower in four and two states 148 5 Clocked One Hot Method of FSM Design 153 5.1 Introduction 153 5.2 Tutorials on the Clocked One Hot FSM Method 168 5.2.1 Seven-state system clocked one hot method 168 5.2.2 Memory tester FSM 170 5.2.3 Eight-bit sequence detector FSM 174 6 Further Event-Driven FSM Design 179 6.1 Introduction 179 6.2 Conclusions 195 7 Petri Net FSM Design 197 7.1 Introduction 197 7.2 Tutorials Using Petri Net FSM 234 7.2.1 Controlled shared resource Petri nets 234 7.2.2 Serial clock-driven Petri net FSM 240 7.2.3 Using asynchronous (event-driven) design with Petri nets 247 7.3 Conclusions 249 Appendix A1: Boolean Algebra 251 A1.1 Basic Gate Symbols 251 A1.2 The Exclusive OR and Exclusive NOR 252 A1.3 Laws of Boolean Algebra 252 A1.3.1 Basic OR rules 252 A1.3.2 Basic AND rules 253 A1.3.3 Associative and commutative laws 253 A1.3.4 Distributive laws 253 A1.3.5 Auxiliary rule for static 1 hazard removal 254 A1.3.5.1 Proof of the Auxiliary Rule 254 A1.3.6 Consensus theorem 254 A1.3.7 The effect of signal delay in logic gates 255 A1.3.8 De-Morgan’s theorem 256 A1.4 Examples of Applying the Laws of Boolean Algebra 257 A1.4.1 Converting AND–OR to NAND 257 A1.4.2 Converting AND–OR to NOR 257 A1.4.3 Logical adjacency rule 258 A1.5 Summary 258 Appendix A2: Use of Verilog HDL and Logisim to FSM 261 A2.1 The Single-Pulse Generator with Memory Clock-Driven FSM 261 A2.2 Test Bench Module and its Purpose 267 A2.3 Using Synapticad Software 268 A2.4 More Direct Method 270 A2.5 A Very Simple Guide to Using the Logisim Simulator 271 A2.5.1 The Logisim top level menu items 271 A2.6 Using Flip-Flops in a Circuit 273 A2.7 Example Single-Pulse FSM 275 A2.8 How to Use the Simulator to Simulate the Single-Pulse FSM 278 A2.8.1 Using Logisim with the truth table approach 278 A2.9 Using Logisim with the Truth Table Approach 279 A2.9.1 Useful note 281 A2.10 Summary 281 Appendix A3: Counters, Shift Registers, Input, and Output with an FSM 285 A3.1 Basic Down Synchronous Binary Counter Development 285 A3.2 Example of a Four-Bit Synchronous Up Counter with T Type Flip-Flops 288 A3.3 Parallel Loading Counters – Using T Flip-Flops 291 A3.4 Using D Flip-Flops To Build Parallel Loading Counters 292 A3.5 Simple Binary Up Counter with Parallel Inputs 293 A3.6 Clock Circuit to Drive the Counter (and FSM) 294 A3.7 Counter Design Using Don’t Care States 295 A3.8 Shift Registers 296 A3.9 Dealing with Input and Output Signals Using FSM 298 A3.10 Using Logisim to Work with Larger FSM Systems 301 A3.10.1 The equations 302 A3.11 Summary 305 Appendix A4: Finite State Machines Using Verilog Behavioural Mode 307 A4.1 Introduction 307 A4.2 The Single-Pulse/Multiple-Pulse Generator with Memory FSM 307 A4.3 The Memory Tester FSM Revisited 313 A4.4 Summary 315 Appendix A5: Programming a Finite State Machine 317 A5.1 Introduction 317 A5.2 The Parallel Loading Counter 317 A5.3 The Multiplexer 319 A5.4 The Micro Instruction 320 A5.5 The Memory 320 A5.6 The Instruction Set 321 A5.7 Simple Example: Single-Pulse FSM 323 A5.8 The Final Example 325 A5.9 The Program Code 328 A5.10 Returning Unused States via Other Transition Paths 328 A5.11 Summary 328 Appendix A6: The Rotational Detector Using Logisim Simulator with Sub-Circuits 329 A6.1 Using the Two-State Diagram Arrangement 333 Bibliography 335 Index 337

Read more less

Book SynopsisCYBER-PHYSICAL SYSTEMS Provides a unique general theory of cyber-physical systems, focusing on how physical, data, and decision processes are articulated as a complex whole Cyber-physical systems (CPS) operate in complex environments systems with integrated physical and computational capabilities. With the ability to interact with humans through variety of modalities, cyber-physical systems are applied across areas such as Internet of Things (IoT)-enabled devices, smart grids, autonomous automotive systems, medical monitoring, and distributed robotics. Existing engineering methods are capable of solving technical problems, yet the deployment of CPS in a net-enabled society requires a general theory of cyber-physical systems that goes beyond specific study cases and their associated technological development. Cyber-physical Systems: Theory, Methodology, and Applications is a unique theoretical-methodological guide to assessing systems where complex informaTable of ContentsPreface xi 1 Introduction 1 1.1 Cyber-Physical Systems in 2020 1 1.2 Need for a General Theory 3 1.3 Historical Highlights: Control Theory, Information Theory, and Cybernetics 6 1.4 Philosophical Background 9 1.5 Book Structure 14 1.6 Summary 15 Exercises 15 References 16 Part I 19 2 System 21 2.1 Introduction 21 2.2 Systems Engineering 22 2.3 Demarcation of Specific Systems 24 2.4 Classification of Systems 28 2.4.1 Natural and Human-Made Systems 29 2.4.2 Material and Conceptual Systems 29 2.4.3 Static and Dynamic Systems 30 2.4.4 Closed and Open Systems 31 2.5 Maxwell’s Demon as a System 31 2.5.1 System Demarcation 33 2.5.2 Classification 33 2.5.3 Discussions 34 2.6 Summary 35 Exercises 36 References 37 3 Uncertainty 39 3.1 Introduction 39 3.2 Games and Uncertainty 40 3.3 Uncertainty and Probability Theory 45 3.4 Random Variables: Dependence and Stochastic Processes 56 3.5 Summary 63 Exercises 63 References 64 4 Information 67 4.1 Introduction 67 4.2 Data and Information 68 4.3 Information and Its Different Forms 75 4.3.1 Mathematical Information and Communication 76 4.3.2 Semantic Information 77 4.3.3 Biological Information 78 4.3.4 Physical Information 79 4.4 Physical and Symbolic Realities 79 4.5 Summary 82 Exercises 82 References 84 5 Network 87 5.1 Introduction 87 5.2 Network Types 92 5.2.1 Peer-to-Peer Networks 93 5.2.2 One-to-Many, Many-to-One, and Star Networks 93 5.2.3 Complete and Erdös–Rényi Networks 94 5.2.4 Line, Ring, and Regular Networks 94 5.2.5 Watts–Strogatz, Barabási–Albert and Other Networks 95 5.3 Processes on Networks and Applications 96 5.3.1 Communication Systems 97 5.3.2 Transportation in Cities 98 5.3.3 Virus Propagation and Epidemiology 99 5.4 Limitations 101 5.4.1 From (Big) Data to Mathematical Abstractions 101 5.4.2 From Mathematical Abstractions to Models of Physical Processes 103 5.4.3 Universality and Cross-Domain Issues 103 5.5 Summary 105 Exercises 105 References 106 6 Decisions and Actions 109 6.1 Introduction 109 6.2 Forms of Decision-Making 110 6.3 Optimization 113 6.4 Game Theory 117 6.5 Rule-Based Decisions 123 6.6 Limitations 124 6.7 Summary 126 Exercises 126 References 129 Part II 131 7 The Three Layers of Cyber-Physical Systems 133 7.1 Introduction 133 7.2 Physical Layer, Measuring, and Sensing Processes 137 7.3 Data Layer and Informing Processes 139 7.4 Decision Layer and Acting Processes 144 7.5 Self-developing Reflexive–Active System and Cyber-Physical Systems 145 7.6 Layer-Based Protocols and Cyber-Physical Systems Design 147 7.7 Summary 152 Exercises 152 References 153 8 Dynamics of Cyber-Physical Systems 155 8.1 Introduction 155 8.2 Dynamics of Cyber-Physical Systems 159 8.2.1 Elementary Cellular Automaton 159 8.2.2 Example of a Cyber-Physical System 163 8.2.3 Observable Attributes and Performance Metrics 164 8.2.4 Optimization 167 8.3 Failures and Layer-Based Attacks 170 8.4 Summary 174 Exercises 174 References 174 Part III 177 9 Enabling Information and Communication Technologies 179 9.1 Introduction 179 9.2 Data Networks and Wireless Communications 180 9.2.1 Network Layers and Their Protocols 181 9.2.2 Network: Edge and Core 185 9.2.3 IoT, Machine-Type Communications, and 5G 187 9.3 Artificial Intelligence and Machine Learning 189 9.3.1 Machine Learning: Data, Model, and Loss Function 191 9.3.2 Formalizing and Solving a ML Problem 191 9.3.3 ml Methods 193 9.4 Decentralized Computing and Distributed Ledger Technology 194 9.4.1 Federated Learning and Decentralized Machine Learning 194 9.4.2 Blockchain and Distributed Ledger Technology 196 9.5 Future Technologies: A Look at the Unknown Future 198 9.5.1 Quantum Internet 198 9.5.2 Internet of Bio-Nano Things 199 9.5.3 After Moore’s Law 200 9.6 Summary 202 Exercises 202 References 204 10 Applications 207 10.1 Introduction 207 10.2 Cyber-Physical Industrial System 209 10.2.1 Tennessee Eastman Process 209 10.2.2 Tennessee Eastman Process as a Cyber-Physical System 211 10.2.3 Example of Fault Detection in the TEP 214 10.3 Cyber-Physical Energy System 215 10.3.1 Electricity Power Grid as a System 216 10.3.2 Frequency Regulation by Smart Fridges 218 10.3.3 Challenges in Demand-Side Management in Cyber-Physical Energy Systems 222 10.4 Other Examples 223 10.4.1 Cyber-Physical Public Health Surveillance System 223 10.4.2 Mobile Application for Real-Time Traffic Routes 224 10.5 Summary 226 Exercises 227 References 230 11 Beyond Technology 233 11.1 Introduction 233 11.2 Governance Models 235 11.2.1 Markets 235 11.2.2 Central Planning 238 11.2.3 Commons 240 11.2.4 Final Remarks About Governance Models 245 11.3 Social Implications of the Cyber Reality 245 11.3.1 Data Ownership 245 11.3.2 Global Platforms 246 11.3.3 Fake News 247 11.3.4 Hybrid Warfare 248 11.4 The Cybersyn Project 251 11.5 Summary 253 Exercises 253 References 254 12 Closing Words 259 12.1 Strong Theory Leads to Informed Practices 260 12.2 Open Challenges in CPSs 261 12.3 CPSs and the Fourth Industrial Revolution 262 12.4 Building the Future 263 Exercises 263 Index 265

Read more less

Book SynopsisRENEWABLE ENERGY FOR SUSTAINABLE GROWTH ASSESSMENT Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of renewable energy for sustainable growth assessment and provides practical solutions for engineers and scientists. Renewable energy resources (RERs) are gaining more attention in academia and industry as one of the preferred choices of sustainable energy conversion. Due to global energy demand, environmental impacts, economic needs and social issues, RERs are encouraged and even funded by many governments around the world. Today, researchers are facing numerous challenges as this field emerges and develops, but, at the same time, new opportunities are waiting for RERs utilization in sustainable development all over the globe. Efficient energy conversion of solar, wind, biomass, fuel cells, and other techniques are gaining more popularity and are the future of energy. The present book cross-poTable of ContentsPreface xix 1 Biomass as Emerging Renewable: Challenges and Opportunities 1Prabhansu and Nayan Kumar 1.1 Introduction 1 1.2 Bioenergy Chemical Characterization 5 1.2.1 Cellulose [C6(H2O)5]n 5 1.2.2 Hemicellulose [C5(H2O)4]n 5 1.2.3 Lignin [C10H12O3]n 5 1.2.4 Starch 5 1.2.5 Other Minor Components of Organic Matter 5 1.2.6 Inorganic Matter 6 1.3 Technologies Available for Conversion of Bioenergy 6 1.4 Progress in Scientific Study 7 1.4.1 Combustion Technology 7 1.4.2 Hybrid Systems 8 1.4.3 Circular Bio-Economy 8 1.4.4 Other Notable Developments 9 1.5 Status of Biomass Utilization in India 9 1.6 Major Issues in Biomass Energy Projects 11 1.6.1 Large Task Costs 11 1.6.2 Lower Proficiency of Advancements 11 1.6.3 Immature Innovations 11 1.6.4 Lack of Subsidizing Alternatives 11 1.6.5 Non-Transparent Exchange Markets 11 1.6.6 High Dangers/Low Compensations 12 1.6.7 Resource Value Acceleration 12 1.7 Challenges in Commercialization 12 1.7.1 Financial Dangers 12 1.7.2 Technological Dangers 12 1.7.3 Principal Specialist Hazard 13 1.7.4 Market Acknowledgement Chances 13 1.7.5 Environmental Dangers 13 1.7.6 COVID-19: The Impact on Bioenergy 13 1.8 Concluding Remarks 14 References 14 2 Assessment of Renewable Energy Technologies Based on Sustainability Indicators for Indian Scenario 25Anuja Shaktawat and Shelly Vadhera Nomenclature 25 2.1 Introduction 26 2.2 RE Scenario in India 27 2.2.1 Large Hydropower 28 2.2.2 Small Hydropower 28 2.2.3 Onshore Wind Power 29 2.2.4 Solar Power 29 2.2.5 Bioenergy 29 2.3 Impact of COVID-19 on RE Sector in India 30 2.4 Sustainability Assessment of RE Technologies 30 2.4.1 RE Technologies Selection 31 2.4.2 Sustainability Indicators Selection and Their Weightage 31 2.4.3 Methodology 32 2.4.3.1 The TOPSIS Method 32 2.4.3.2 The Fuzzy-TOPSIS 34 2.5 Ranking of RE Technologies 36 2.5.1 The TOPSIS 36 2.5.2 The Fuzzy-TOPSIS 36 2.5.3 Monte Carlo Simulations–Based Probabilistic Ranking 38 2.6 Results and Discussion 42 2.7 Conclusion 43 References 43 3 A Review of Biomass Impact and Energy Conversion 49Dhanasekaran Subashri and Pambayan Ulagan Mahalingam 3.1 Introduction 49 3.2 Non-Renewable Energy Resources: Crisis and Demand 50 3.3 Environmental Impacts and Control by Biomass Conversion 52 3.3.1 Biomass and Its Various Sources for Energy Conversion 52 3.3.1.1 Sugar and Starch-Based Biomass (First-Generation - 1G) 53 3.3.1.2 Lignocellulosic Biomass (Second-Generation - 2G) 53 3.3.1.3 Micro and Macroalgal Biomass (Third-Generation - 3G) 58 3.3.1.4 Genetically Engineered Biomass (Fourth-Generation) 60 3.3.1.5 Waste Biomass Resources 60 3.3.2 Biomass Conversion Process 66 3.3.2.1 Thermochemical Conversion 66 3.3.2.2 Biological Conversion 67 3.3.2.3 Advanced Technology for Biomass Conversion 68 3.3.3 Biofuel as Renewable Energy for the Future 70 3.3.3.1 Solid Fuel 70 3.3.3.2 Gaseous Fuel 71 3.3.3.3 Liquid Biofuel 71 3.4 Future Trends 72 3.5 Conclusion 72 Acknowledgment 73 References 73 4 Power Electronics for Renewable Energy Systems 81Vishal Anand, Varsha Singh and Saad Mekhlief 4.1 Introduction: Need of Renewable Energy System 81 4.1.1 Financial Aspects 83 4.1.2 Environmental Aspects 83 4.1.3 Economic Feasibility 84 4.1.4 Present Scenario of Renewable Energy Sources 86 4.2 Power Electronics Technologies 87 4.2.1 AC-DC Converters 87 4.2.2 DC-AC Converters 88 4.2.3 DC-DC Converters 90 4.2.4 AC-AC Converter 91 4.3 Energy Conversion Controller Design Using Power Electronics 92 4.4 Carbon Emission Reduction Using Power Electronics 95 4.4.1 Renewable Power Generation 97 4.5 Efficient Transmission of Power 100 4.6 Issues and Challenges of Power Electronics 100 4.7 Energy Storage Utilized by Power Electronics for Power System 101 4.8 Application of Power Electronics 101 4.8.1 VSC-Based HVDC 101 4.8.2 Power Electronics in Electric Drives 102 4.8.3 Power Electronics in Electric Vehicles 103 4.8.4 Power Electronics in More Electric Effect (MEE) 105 4.8.4.1 More Electric Aircraft 105 4.8.4.2 More Electric Ships 105 4.8.5 Advanced Applications of Power Converters in Wireless Power Transfer (WPT) 106 4.9 Case Study on PV Farm and Wind Farm Using Converter Modelling 106 4.9.1 A 400KW 4 PV Farm 106 4.9.2 Wind Generation Using DFIG 109 4.10 Reliability of Renewable Energy System 110 4.10.1 Reliability of Photovolatic-Based Power System 110 4.10.2 Reliability of Wind-Turbine-Based Power System 110 4.10.3 Reliability of Power Electronics Converters in Renewable Energy System 111 4.11 Conclusion 111 References 112 5 Thermal Performance Studies of an Artificially Roughened Corrugated Aluminium Alloy (AlMn1Cu) Plate Solar Air Heater (SAH) at a Moderate Air Flow Rate 119Dutta P. P., Goswami P.., Das A., Chutia L., Borbara M., Das V., Bania K., Rai S. and Bardalai M. Nomenclature 119 5.1 Introduction 120 5.2 Methodology 124 5.2.1 Experimental Setup 124 5.2.2 Mathematical Modelling 125 5.3 Results and Discussion 128 5.4 Conclusions 131 Acknowledgement 132 References 132 6 An Overview of Partial Shading on PV Systems 135Siddharth Mathur, Gautam Raina, Pulkit Jain and Sunanda Sinha Nomenclature 135 6.1 Introduction 136 6.2 Basics of Partial Shading 139 6.2.1 Types & Occurrence of Partial Shading 142 6.2.2 Problem Associated with Partial Shading 143 6.2.3 Details About Partial Shading Mitigation Techniques 146 6.2.3.1 Maximum Power Point Tracking Techniques 146 6.2.3.2 PV System Architecture 147 6.2.3.3 Converter Topologies 148 6.3 Mitigation of Partial Shading Using Array Reconfiguration Techniques 149 6.3.1 Conventional 151 6.3.2 Hybrid 155 6.3.3 Reconfigured/Modified Configurations 157 6.3.4 Puzzle-Based Configuration 157 6.3.5 Metaheuristic-Based PV Array Configurations 168 6.4 Case Study on Different Techniques of Array Reconfiguration According to its Classification – (2015-2020) 172 6.5 Future Directions 172 6.6 Discussion & Conclusion 173 References 174 7 Optical Modeling Techniques for Bifacial PV 181Pulkit Jain, Gautam Raina, Siddharth Mathur and Sunanda Sinha Nomenclature 181 7.1 Introduction 182 7.2 Background 183 7.2.1 Bifacial Cells and Modules 183 7.2.2 Cell Technologies 185 7.2.3 Geometric Parameters and Metrics 186 7.2.3.1 Bifaciality Factor 187 7.2.3.2 Bifacial Gain (BG) 187 7.3 Bifacial PV System and Modelling 188 7.3.1 Need for Optical Modeling of Bifacial PV 188 7.3.2 Bifacial PV Modeling Challenges 189 7.3.3 Bifacial Irradiance Models 192 7.3.3.1 Ray-Tracing Model 192 7.3.3.2 Empirical Models 195 7.3.3.3 View Factor Model 196 7.3.4 Optical Modelling of Bifacial PV 198 7.3.4.1 Frontside Irradiance 198 7.3.4.2 Rear-Side Irradiance 202 7.3.5 Comparison of Different Models/Software 205 7.4 Effect of Installation and Weather Parameters on Energy Yield 208 7.4.1 Effect of Installation Parameters 208 7.4.2 Effect of Albedo 208 7.4.3 Effect of Tilt Angle 208 7.4.4 Effect of Elevation 209 7.4.5 Effect of Weather Parameters 210 7.5 Conclusion 211 References 212 8 Intervention of Microorganisms for the Pretreatment of Lignocellulosic Biomass to Extract the Fermentable Sugars for Biofuel Production 217M. Naveen Kumar, A. Gangagni Rao, Sudharshan Juntupally, Vijayalakshmi Arelli and Sameena Begum 8.1 Introduction 217 8.2 Lignocellulosic Biomass 218 8.2.1 Types of Lignocellulosic Biomass 219 8.2.1.1 Virgin Biomass 219 8.2.1.2 Agricultural and Energy Crops 220 8.2.1.3 Waste Biomass 220 8.3 Role of Pretreatment in Biofuel Generations 220 8.3.1 Non-Biological Pretreatment 222 8.3.1.1 Physical Pretreatment 223 8.3.1.2 Chemical Pretreatment 223 8.3.1.3 Physico-Chemical (Hybrid) Pretreatment 224 8.4 Biological Pretreatment and its Significance 227 8.4.1 Role of Fungi in Pretreatment 228 8.4.1.1 Biological Mechanisms of Delignification in Fungi 228 8.4.2 Role of Prokaryotic Pretreatment 232 8.4.2.1 Bacterial Enzymes Involved in Lignin De-Polymerization 232 8.4.2.2 Types of Bacteria and their Role in Delignification 233 8.5 Combined Biological Pretreatment Case Studies and Opportunities 234 8.6 Future Prospects 236 8.6.1 Role of Biotechnology and Genetic Engineering 236 8.7 Conclusion 236 Acknowledgement 237 Conflicts of Interest 237 References 237 9 Biomass and Bioenergy: Resources, Conversion and Application 243Dr. Sunita Barot 9.1 Introduction to Biomass 243 9.2 Classification of Biomass Resources 244 9.3 Biomass to Bioenergy Conversion 247 9.4 Environmental Impacts of Biomass & Bioenergy 253 9.5 Solutions to the Environmental Impacts 254 9.6 Case Study of US – Conversion of MSW to Energy 255 9.7 Bioenergy Products 256 9.8 Effects of Covid-19 on Bioenergy Sector 258 References 258 10 Renewable Energy Development in Africa: Lessons and Policy Recommendations from South Africa, Egypt, and Nigeria 263Adedoyin Adeleke, Fabio Inzoli and Emanuela Colombo 10.1 Introduction 263 10.2 Existing Knowledge and Contributions to Literature 265 10.3 Renewable Energy Development in South Africa 269 10.3.1 Policies and Strategies 269 10.3.2 Policy Impact on Renewable Energy Development 272 10.4 Renewable Energy Development in Egypt 275 10.4.1 Policies and Strategies 275 10.4.2 Policy Impact on Renewable Energy Development 277 10.5 Renewable Energy Development in Nigeria 284 10.5.1 Policies and Strategies 285 10.5.2 Policy Impact on Renewable Energy Development 288 10.6 Conclusion and Policy Implications 291 10.6.1 Policy Implications from South Africa and Egypt 291 10.6.2 Barriers to Renewable Energy Development in Africa: The Case of Nigeria 293 10.7 Conclusion 297 References 298 11 Sustainable Development of Pine Biocarbon Derived Thermally Stable and Electrically Conducting Polymer Nanocomposite Films 305Rehnuma Saleheen, MGH Zaidi, Sameena Mehtab and Kavita Singhal 11.1 Introduction 305 11.1.1 Biomass Resources 307 11.1.2 Biomass Utilization 308 11.1.2.1 Production of BC from Biomass 308 11.1.2.2 Production of CF 309 11.1.3 Applications of BC 310 11.1.3.1 BC as CI 310 11.1.3.2 BC for ESDs 311 11.1.3.3 BC as Filler for Polymer Composites 311 11.1.3.4 BC-Derived Sustainable OP 313 11.2 Experimental Procedures 314 11.2.1 Starting Materials 314 11.2.2 Development of Pine Cone–Derived BC and Nano Pine–Derived BC 314 11.2.3 Development of OP 314 11.2.4 Development of ECF 316 11.3 Characterization 316 11.4 Results and Discussion 316 11.4.1 Spectra of ECF 316 11.4.2 Microstructure of ECF 318 11.4.3 Thermal Stability of ECF 318 11.5 Electrical Behaviour of ECF 320 11.6 Conclusion and Future Aspects 321 Acknowledgement 322 References 322 12 Power Electronics for Renewable Energy Systems 327Nandhini Gayathri M. and Kannbhiran A. 12.1 Introduction 327 12.2 Power Electronics on Energy Systems and its Impact 328 12.3 The Power Electronics Contribution and its Challenges in the Current Energy Scenario 330 12.4 Recent Growth in Power Semiconductor Technology 335 12.5 A New Class of Power Converters for Renewable Energy Systems: AC-Link Universal Power Converters 337 12.6 Power Converters for Wind Turbines and Power Semiconductors for Wind Power Converter 340 12.7 Recent Developments in Multilevel Inverter Based PV Systems 342 12.8 AC-DC-AC Converters for Distributed Power Generation Systems 345 12.9 Multilevel Converter/Inverter Topologies and Applications 345 12.10 Multiphase Matrix Converter Topologies 349 12.11 Boost Pre-Regulators for Power Factor Correction in Single-Phase Rectifiers 350 12.12 Active Power Filter 350 12.13 Common-Mode Voltage and Bearing Currents in PWM Inverters: Causes, Effects and Prevention 351 12.14 Single-Phase Grid-Side Converters 352 12.15 Impedance Source Inverters 353 12.16 Conclusion 354 References 354 13 Fuel Cells for Alternative and Sustainable Energy Systems 363N. V. Raghavaiah and Dr. G. Naga Srinivasulu 13.1 Introduction to Fuel Cell Systems 363 13.1.1 Brief History 363 13.2 Overview of Fuel Technology 364 13.2.1 Introduction to Fuel Cell Working 365 13.2.2 Classification of Fuel Cells 366 13.2.3 Fuel Cell Performance 368 13.2.4 Fuel Cell Power Density 371 13.3 Energy Storage Applications of Fuel Cells 371 13.4 Environmental Impact of Fuel Cell System 372 13.5 Latest Developments in Fuel Cell Technology 372 13.5.1 Electrode Design – as a Function of Catalyst 374 13.5.2 Efficient Structure Design: Fuel Cell Mass Transportation 375 13.5.3 Design of Flow Patterns 375 13.5.4 Environmental Impact of Fuel Cells 376 13.6 Future Perspective of Fuel Cell 376 13.6.1 Research and Technological Factors 376 13.6.2 Perspective View 377 13.6.3 Environmental Crisis 377 13.6.4 Fuel EVs Infrastructure 378 13.6.5 Renewables: A Window of Opportunity for Fuel Cells 378 13.6.6 Energy Storage: A Big, Challenging Issue 380 13.6.7 Future Predictions: On Fuel Cell Systems 380 13.6.8 Hydrogen Economy 383 13.7 Case Studies 384 13.7.1 Case Study-1 384 13.7.2 Case Study-2 385 13.7.3 Case Study-3 386 13.8 Summary 387 References 387 14 Fuel Cell Utilization for Energy Storage 389Archit Rai and Sumit Pramanik 14.1 Introduction to Fuel Cells 389 14.2 Fuel Cell Mechanism 391 14.3 Efficiency of Fuel Cell 391 14.3.1 Efficiency Calculations 392 14.3.2 Co-Generation of Heat and Power 393 14.4 Types of Fuel Cells 393 14.4.1 Polymer Electrolyte Membrane Fuel Cell (PEMFC) 394 14.4.2 Phosphoric Acid Fuel Cell (PAFC) 394 14.4.3 Alkaline Fuel Cell (AFC) 398 14.4.4 Molten Carbonate Fuel Cell (MCFC) 398 14.4.5 Solid Oxide Fuel Cell (SOFC) 398 14.5 Hydrogen Production 399 14.5.1 Steam Methane Reforming or SMR (Natural Gas Reforming) 400 14.5.2 Coal Gasification Process 400 14.5.3 Biomass Gasification 400 14.5.4 Biomass Derived Fuel Reforming 401 14.5.5 Thermochemical Water Splitting 401 14.5.6 Electrolytic Process 401 14.5.7 Direct Solar Water Splitting Process 402 14.5.8 Biological Processes 402 14.5.9 Microbial Biomass Conversion 402 14.5.10 Microbial Electrolysis Cells (MECs) 403 14.6 Fuel Cells Applications and Advancements 403 14.6.1 Applications 403 14.6.2 Advancements 404 14.6.3 Applications and Advancements of Fuel Cells in Automobile Sector 405 14.6 Conclusions 405 References 406 15 Miniature Hydel Energy Harvesting Unit to Power Auto Faucet and Lighting Systems for Domestic Applications 409Farid Ullah Khan, Adil Ahmad Taj, Umar Safi Ullah Jan and Gule Saman 15.1 Introduction 409 15.2 Literature Review 412 15.3 Data Collection and Theoretical Hydraulic Power Calculations 414 15.4 Architecture and Working of Prototypes 414 15.5 Design and Simulation 416 15.6 Fabrication of Prototypes 420 15.6.1 Fabrication of Prototype-1 420 15.6.2 Fabrication of Prototype-2 422 15.6.3 Fabrication of Prototype-3 423 15.7 Experimentation of Prototypes 424 15.8 Experimentation for Auto Faucet System 428 15.9 Conclusions 432 References 432 16 Modeling, Performance Analysis, Impact Study and Operational Paradigms of Solar Photovoltaic Power Plant 435B. Koti Reddy and Dr. Amit Kumar Singh 16.1 Introduction 435 16.2 Solar Energy 436 16.2.1 Forms of Energy Resources 436 16.2.2 Solar Spectrum 437 16.2.3 Sun Tracking and Location 438 16.2.4 Solar Energy Fundamentals 439 16.2.5 Solar Photovoltaic Power Plants (SPP) 444 16.3 Modeling of PV Modules 445 16.3.1 Simulation Model 447 16.3.2 Simulation Results 448 16.4 Design of 12 MWp SPP 452 16.4.1 Selection of Site 452 16.4.2 Equipment Sizing 453 16.4.3 Cost Estimates 454 16.4.4 Shadow Analysis 454 16.4.5 Power Output Estimates 457 16.5 Field Equipment Details 457 16.6 Performance Analysis 458 16.6.1 Performance Indicators 458 16.6.2 Field Data and Analysis 459 16.6.3 Intangible Benefits Realised in Past Three Years 459 16.7 Technical Issues and New Paradigms 459 16.7.1 Technical Issues 461 16.7.2 Paradigm Shift 467 16.8 Opportunities and Future Scope 470 16.8.1 Opportunities 471 16.8.2 Latest Trends 471 16.8.3 Future Scope 471 16.9 Conclusions 473 References 473 17 A Review on Control Technologies and Islanding Issues in Microgrids 475Anup Kumar Nanda, Babita Panda and Chinmoy Kumar Panigrahi 17.1 Introduction 475 17.2 Importance of Microgrid 476 17.3 Microgrid Types 477 17.4 Problems in Islanded Mode of Operation 478 17.5 Features of Microgrid Control System 479 17.6 Microgrid Islanding 480 17.7 Control Techniques 481 17.7.1 Primary Level 481 17.7.2 Secondary Level 482 17.7.2.1 Centralized Control Strategy 483 17.7.2.2 Decentralized Control Strategy 483 17.7.3 Tertiary Level 484 17.8 Autonomous Control Architecture 486 17.9 Optimization of Control in Microgrids 487 17.9.1 Linear Programming 487 17.9.2 Non-Linear Programming 488 17.10 Inverter Control in Microgrids 488 17.10.1 PQ Control 488 17.10.2 Voltage Source Inverter Control 489 17.10.2.1 Power Control Mode (PCM) 489 17.10.2.2 Voltage Control Mode (VCM) 489 17.11 Droop Control 489 17.11.1 V/f Control 491 17.12 Modern Prospects of Microgrid Research 492 17.12.1 Multi Microgrid Control 492 17.12.2 Energy Storage Management 492 17.12.3 Management of Loads 492 17.12.4 Hybrid Energy Mangement System 492 17.12.5 Implementation of Soft Switches 492 17.12.6 Protection and Stability Analysis 493 17.12.7 Metaheuristic Optimization Techniques 493 17.12.7.1 Grey Wolf Optimization (GWO) 494 17.12.7.2 Hybrid GWO and P&O Algorithm (Hyb.) 495 17.12.7.3 Whale Optimization Algorithm (WOA) 495 17.12.7.4 Communication Technologies 498 17.13 Conclusion 498 References 499 18 A Review of Microgrid Protection Schemes Resilient to Weather Intermittency and DER Faults 503Goyal R. Awagan Ebha Koley and Subhojit Ghosh 18.1 Introduction 503 18.2 Islanding Detection 506 18.2.1 Central Islanding Detection 506 18.2.2 Local Islanding Detection 507 18.2.3 Feature Extraction-Based Islanding Detection 507 18.2.4 Machine Learning-Based Islanding Detection 508 18.3 Protection Challenges Due to Weather Intermittency 508 18.3.1 Solar Irradiance Intermittency 509 18.3.2 Wind Speed Intermittency 510 18.3.3 Solar-Wind Combined Intermittency 511 18.4 Protection Challenges Due to Converter Faults 511 18.5 Protection Challenges Due to PV Array Faults 513 18.5.1 LG Fault 513 18.5.2 LL Fault 513 18.5.3 Arc Fault 513 18.5.4 Faults Due to Partial Shading 514 18.6 Conclusion 517 References 517 19 Theories of Finance for Generation Portfolio Optimization 523Arjun C. Unni, Weerakorn Ongsakul and Nimal Madhu M. Acronyms 523 19.1 Introduction 524 19.2 Introduction to Portfolio Optimization 526 19.3 Using Fuzzy Logic to Create Risk and Reward Index 527 19.4 Markovitz Mean-Variance Theory 530 19.5 Black-Litterman Model 531 19.6 Mean Absolute Deviation (MAD) 532 19.7 Conditional Value at Risk (CVaR) 532 19.8 Results and Discussion 534 19.9 Conclusion 540 References 540 20 Variable Speed Permanent Magnet Synchronous Generator-Wind Energy Systems 543Vijaya Priya R., Raja Pichamuthu and M.P. Selvan 20.1 PMSG-Based WECS 543 20.1.1 Configurations of WECS 544 20.1.2 General Control Requirements of WECS 544 20.1.3 Insights from Literature Review 545 20.1.4 Objectives and Scope of the Present Research Work 546 20.1.5 Contributions of the Chapter 546 20.2 System Modelling 547 20.2.1 Wind Turbine Modelling 547 20.2.2 PMSG Modelling 548 20.2.3 Drive-Train Shaft Modelling 549 20.2.4 DC-Link Modelling 549 20.2.5 GSC Filter Design 550 20.2.6 Grid Modelling 550 20.2.7 Dynamic Operating Conditions 551 20.2.7.1 Grid Disturbances 551 20.2.7.2 Converter Non-Linearities 554 20.2.8 SRF-PLL Modelling 554 20.3 Rotor Speed and Position Estimation Based on Stator SRF-PLL 555 20.3.1 PMSG Angular Speed Reference Signal Computation 556 20.3.2 Rotor Speed and Position Estimation 556 20.3.3 Vector Control 558 20.3.4 Analytical Validations 559 20.3.4.1 Starting Characteristics 559 20.3.4.2 Wind Velocity Variation 559 20.3.4.3 Converter Non-Linearities 560 20.3.4.4 Utility Harmonics 561 20.3.4.5 Sensitivity Study 562 20.3.5 Summary 564 20.4 Active Power and Current Reference Generation Scheme 564 20.4.1 System Modeling 565 20.4.1.1 MSC Controller Design 565 20.4.1.2 GSC and Controller Design 567 20.4.2 MSC Reference Power Generation Scheme 570 20.4.3 GSC Current Oscillation Component Computation 573 20.4.4 Analytical Validation 574 20.4.4.1 Symmetrical Voltage Sag 574 20.4.4.2 Distorted Utility 575 20.4.5 Summary 577 20.5 Torsional Oscillation Damping 577 20.5.1 Dynamic Effects under MPPT and PLMs 578 20.5.1.1 Fast DC Link Voltage Control 579 20.5.1.2 Slow DC-Link Voltage Control 581 20.5.2 Proposed Active Damping Scheme for Torsional Mode Operation 583 20.5.3 Proposed Control for GSC Control 585 20.5.3.1 DPC Scheme 586 20.5.3.2 Power Oscillation Term Computation 586 20.5.4 Simulation Validation 587 20.5.4.1 Turbulent and Gust Wind Speed 587 20.5.4.2 Unsymmetrical Voltage Sag 588 20.5.5 Summary 590 20.6 Conclusions 590 Appendices and Nomenclature 591 References 592 21 Study of Radiant Cooling System with Parallel Desiccant Based Dedicated Outdoor Air System with Solar Regeneration 595Prateek Srivastava and Gaurav Singh 21.1 Introduction 595 21.2 Dedicated Outdoor Air System 598 21.3 Desiccant 599 21.4 Radiant Cooling System with DOAS 602 21.5 Methodology 604 21.6 Building Description 605 21.7 System and Model Description 606 21.8 Result and Discussion 609 21.9 Primary Energy Consumption and Coefficient of Performance (COP) Analysis 610 21.10 Solar Energy Performance 613 21.11 Conclusions 614 References 614 Index 619

Read more less

Book SynopsisAdvances in Biofeedstocks and Biofuels PRODUCTION TECHNOLOGIES FOR SOLIDS AND GASEOUS BIOFUELS This latest volume in the series, Advances in Biofeedstocks and Biofuels, offers the most up-to-date and comprehensive coverage available for the production technologies for solid and gaseous biofuels. Biofuel production is one of the most extensively studied recent fields of innovation that can provide the world an alternative energy source. Biomass-based fuel production, or renewable fuels, are becoming increasingly important as a remedy for the increasing greenhouse effect, depleting oil reserves, and rising oil prices. Therefore, research on the production of various biofuels is gaining very much importance among scientists and researchers all over the globe. The book, Production Technologies for Solid and Gaseous Biofuels, is the fourth volume of the book series entitled Advances in Biofeedstocks and Biofuels. The first volume, Biofeedstocks and Their PTable of ContentsPreface xv 1 Biogas, Biomethane and BioCNG: Definitions, Technologies and Solutions 1Alessandra Lee Barbosa Firmo, Fabrícia Maria Santana Silva, Ingrid Roberta de F.S. Alves, Ericka Patrícia Lima de Brito and Leandro Cesar Santos da Silva 1.1 Definitions and Sources of Production of Biogas, Biomethane and BioCNG 2 1.2 Production Chains, Utilization and Valorization of Biogas 5 1.2.1 Anaerobic Digesters 8 1.2.1.1 Techniques for Optimization of Anaerobic Digestion 12 1.2.1.2 Biogas Recovery Plants 14 1.2.1.3 Biofertilizers – Material Valorization 15 1.2.2 Landfills: Final Disposal and Biogasvalorization 16 1.3 Uses of Biomethane: Practice Examples 20 1.4 Challenges and Opportunities 21 References 25 2 Biomethanisation: Biogas Production Technologies 33Gabor Z. Szelenyi 2.1 Relevance 34 2.2 Oxidation without Oxygen – Anaerobic Biodegradation of the Organic Matter 35 2.3 Bifurcating Metabolic Pathways 35 2.4 Methanogenesis 37 2.5 Imitation of Nature – Improvement through Controlled Environment 40 2.6 Operational Challenges 44 2.7 Post-Treatment 47 2.8 Outlook – Fields of Further Research and Technological Development 49 2.9 Conclusion – Development Goals 55 Acknowledgments 60 References 60 3 Effect of Process Parameters on Biogas Yield: A Systematic Review 65H.O. Omoregbee, M. O. Okwu, L.K. Tartibu, A.E. Ivbanikaro, M.U. Olanipekun and A.B. Edward 3.1 Introduction 66 3.2 Effect of Process Parameters on Biogas Yield 67 3.2.1 Temperature Effect on Biogas Yield 67 3.2.2 Effect of pH on Biogas Yield 69 3.2.3 Effect of Hydraulic Retention Time (HRT) on Biogas Yield 70 3.2.4 Effect of Agitation or Stirring on Biogas Yield 71 3.3 Pre-Treatment Process 72 3.3.1 Mechanical Treatment 73 3.3.2 Microwave Irradiation 73 3.3.3 Thermal Pre-Treatment Process 73 3.3.4 Chemical Treatment 74 3.3.4.1 Acid 74 3.3.4.2 Alkali 74 3.3.5 Biological Treatment 75 3.3.6 Biochemical Methane Potential 76 3.4 Effect of Co-Digestion of Two or More Substrates 76 3.5 Effect of Total Solid ContenT (TSC) 78 3.5.1 Acidogenesis 79 3.5.2 Hydrolysis 79 3.5.3 Methanogenesis 80 3.5.4 Acetogenesis 80 3.6 Addressing AD Bottlenecks Caused by the Physicochemical Properties of Substrate 80 3.6.1 Carbon Dioxide Removal Technologies for Upgrading Biogas 81 3.7 Conclusion 83 References 84 4 Biogas for Electricity Generation in Nigeria: A Systematic Review of the Prospects, Efforts and Contemporary Challenges 91Victor M. Mbachu, Modestus O. Okwu, Celine C. Chiabuotu and Lagouge K. Tartibu 4.1 Introduction 92 4.2 Bioenergy and Biogas Technology 93 4.3 Chronicle of Research Efforts in Biogas Technology 94 4.3.1 Assessment of Biomass Potential for Biogas and Electricity Generation 94 4.3.2 Use of Co-Digestion for Enhanced Production 95 4.3.3 Enhancement of Biogas Production Using Pre-Treatment of Feedstock 96 4.3.4 Inoculation of Substrate for Biogas Production 96 4.3.5 Optimization of Biogas Production Process Parameters 97 4.3.6 Digester Design 97 4.3.7 Upgrading and Purification of Biogas 98 4.3.8 Modeling of Biogas Production 99 4.4 Current Research and Developmental Trend in Biogas Technology 100 4.5 Conclusion 101 References 101 5 Biohydrogen Production Technologies: Current Status, Challenges, and Future Perspectives 115Akanksha Jain, Eeshita Das, Venkata Giridhar Poosarla and Gobinath Rajagopalan 5.1 Introduction 116 5.2 Hydrogen vs. Biohydrogen 116 5.3 Biohydrogen from Light Dependent Processes 119 5.3.1 Photo-Fermentation (PF) 119 5.3.1.1 Biocatalysts Involved in PF 120 5.3.1.2 General Mechanism of Biohydrogen Production from PF 123 5.3.1.3 Current Status of PF 124 5.3.1.4 Major Factors that Influence the PF Process 124 5.3.1.5 Challenges Reported 134 5.3.2 Biophotolysis (BP) 134 5.3.2.1 General Mechanism of Hydrogen Production from Biophotolysis 136 5.3.2.2 Current Status of BP 136 5.3.2.3 Major Factors Influence BP 137 5.3.2.4 Challenges Reported 141 5.4 Biohydrogen Production from Dark Fermentation 141 5.4.1 Dark Fermentation (DF) 141 5.4.2 Biocatalysts Involved in DF 143 5.4.2.1 Formate Lyase Complex 144 5.4.3 General Mechanism and Biochemistry of Biohydrogen Production from DF 144 5.4.3.1 Clostridia 144 5.4.3.2 Non-Clostridia 146 5.4.4 Current Status 146 5.4.4.1 Feedstock 146 5.4.4.2 Process Design 148 5.4.4.3 Factors Influencing DF 150 5.4.4.4 DF by Mixed Consortia 152 5.4.4.5 Biohydrogen Production by Using Pure Culture 154 5.4.5 Challenges Reported 154 5.5 Other Methods of Biohydrogen Production 154 5.5.1 Bioelectrolysis 154 5.6 Future Perspectives of Biohydrogen Production 157 Acknowledgment 158 References 158 6 Biomass Gasification, Some Theory, and Practical Examples 169Eduardo C. M. Loureiro, Isabella A. Garrett, Clériston Vieira Junior and Sérgio Peres 6.1 Introduction 170 6.2 Fixed-Bed Reactors 171 6.3 Fluidized-Bed Reactors 173 6.4 Biomass Characterization 175 6.5 Production of Syngas from Wood in a Downdraft Fixed Bed 176 6.5.1 Methodology 176 6.5.2 Results 183 6.6 Construction and Hydrodynamic Characterization of a Bubbling Fluidized-Bed Gasifier 184 6.6.1 Introduction 184 6.6.2 Methodology 185 6.6.2.1 Bed Characterization 185 6.6.2.2 Cold Flow Model – CFM 186 6.6.2.3 Experimental Vmf 187 6.6.2.4 Theoretical Vmf 189 6.6.3 Results and Discussions 190 6.6.3.1 Velocity of Minimal Fluidization - Vmf 191 6.6.3.2 Gasifier Construction 200 6.6.3.3 Gasification Experiments 201 References 204 7 Experimental Investigation on Producer Gas Generation Through Briquettes Using Agricultural Wastes 207Senthil Ramlingam, Balamurugan Rajendiran,Thendral T. and Sudagar S. 7.1 Introduction 208 7.2 Materials for Present Work 210 7.2.1 Feedstock 210 7.2.1.1 Sesame Plant 210 7.2.1.2 Maize Cob (MC) 211 7.2.2 Binder Material 211 7.2.3 Briquette Preparation 212 7.2.4 Physical Properties of Briquette 213 7.2.4.1 Proximate Analysis 213 7.2.4.2 Bulk Density 215 7.2.5 Ultimate Analysis 215 7.2.6 Calorific Value of Feedstock 215 7.2.7 Mechanical Properties of Briquette 216 7.2.7.1 Compressive Strength 216 7.2.7.2 Shatter Index 216 7.3 Result and Discussion 216 7.3.1 Proximate Analysis 217 7.3.1.1 Ash 217 7.3.1.2 Moisture 217 7.3.1.3 Fixed Carbon 217 7.3.1.4 Volatile Matter 218 7.3.2 Ultimate Analysis 218 7.3.3 Density 219 7.3.4 Compressive Strength of Briquette 219 7.3.5 Calorific Value 220 7.3.6 Comparative Analysis of Properties 221 7.4 Generation of Producer Gas 222 7.4.1 Effect of Temperature on Producer Gas 223 7.5 Producer Gas Suitability in Engines 224 7.6 Conclusion 224 Bibliography 225 8 Biomass Gasification for Distributed Generation and Biochar Production: An Application to the Olive Oil Supply Chain 229Roque Aguado, Antonio Escámez, David Vera, Dolores Eliche-Quesada and Luis Pérez-Villarejo 8.1 Introduction 230 8.1.1 By-Products of the Olive Oil Industry 230 8.1.2 Gasification for Distributed Generation 232 8.1.3 Gasification for Biochar Production 236 8.2 Methodology 237 8.2.1 Description of the Experimental Gasification Plant 237 8.2.2 Physicochemical Properties of the By-Products from the Olive Oil Industry 239 8.2.3 Experimental Procedure 243 8.2.4 Biochar Physicochemical Characterization 245 8.3 Results 245 8.3.1 Assembly and Installation of the Gasification Plant 245 8.3.2 Experimental Results 246 8.3.3 Biochar Characterization and Potential for the Olive Oil Industry 250 8.4 Economic Impact of Gasification in the Olive Oil Industry 252 8.5 Conclusions 256 Acknowledgements 257 References 258 9 Conversion of Agro Wastes to Solid and Gaseous Biofuels through Thermal Cracking Technique 263Senthil Ramlingam, Sudagar Subramanian and Pranesh Ganesan 9.1 Introduction 264 9.1.2 Energy Resources 264 9.2 Biomass 266 9.3 Biomass Energy Conversion Technologies 267 9.3.1 Thermal Cracking Process 268 9.3.1.1 Gasification 268 9.3.1.2 Pyrolysis Process 268 9.4 Types of Pyrolysis Process 269 9.4.1 Conventional or Slow Pyrolysis 269 9.4.2 Fast Pyrolysis 270 9.4.3 Flash Pyrolysis 270 9.5 Mechanism Involved During Pyrolysis 270 9.5.1 Mechanism in Hemicelluloses 270 9.5.2 Mechanism in Cellulose 272 9.5.3 Mechanism in Lignin 272 9.6 Pyrolysis Products 272 9.6.1 Bio-Oil 273 9.6.2 Residue 273 9.6.3 Syngas 274 9.7 Present Investigation 274 9.7.1 Materials and Methods 275 9.7.1.1 Cashew Nut Shell 275 9.7.1.2 Sawdust 275 9.7.1.3 Sugarcane Bagasse 276 9.7.1.4 Binder 277 9.7.2 Preparation of Briquetting 278 9.7.3 Sources for Briquetting 278 9.8 Methodology 278 9.8.1 Bio-Oil Extraction Process 281 9.9 Result and Discussion 281 9.9.1 Analysis of Briquette 281 9.9.2 Thermo Gravimetric Analysis 282 9.9.3 Products of Pyrolysis Process 283 9.9.4 Fuel Properties 284 9.9.4.1 FTIR 284 9.9.4.2 Biochar and Syngas Analysis 285 9.9.4.3 Biochar 285 9.9.4.4 Syngas 286 9.10 Conclusion 286 Bibliography 287 10 Insights Into the Production of Biochar from Organic Waste 291Jaskiran Kaur and Gaurav Chaudhary 10.1 Introduction 292 10.2 Organic Waste as Feedstocks for Biochar Production 293 10.3 Thermochemical Conversion of Organic Waste into Biochar 294 10.4 Factors Affecting Biochar Yield and Properties 295 10.4.1 Feedstock Type and Composition 295 10.4.2 Pyrolysis Temperature 296 10.5 Utilization of Biochar 310 10.5.1 As a Soil Amendment 310 10.5.2 Carbon Sequestration 310 10.5.3 Remediation of Pollutants from Soil 311 10.5.4 Water and Wastewater Treatment 311 10.6 Conclusion 312 References 313 11 Thermo-Economic Study of öNORM M7 133 Chips in a Pilot Scale Reactor 321Alok Dhaundiya and Divine Atsu Notation 321 11.1 Introduction 322 11.2 Material and Methods 324 11.2.1 Installation of the Experimental Unit 324 11.2.2 Physical Exergy of the System 327 11.2.3 Sinking Fund Method 329 11.3 Results and Discussion 331 11.3.1 Exergy Analysis 331 11.3.2 Valuation of Pyrolysis Unit 337 11.4 Conclusion 338 References 338 12 Production and Characterization of Briquettes Produced from Blend of Rice Husk and Water-Hyacinth 341Modestus O. Okwu, Omonigho B. Otanocha, Olusegun D. Samuel and E. E. Akporhonor 12.1 Background of the Study 342 12.2 Review of Literature 343 12.2.1 Renewable Energy Demand 343 12.2.2 Briquette Production 344 12.2.3 Feedstock for Briquette Production 344 12.2.4 Proximate Analysis of Briquettes 345 12.3 Materials and Method 345 12.3.1 Material Processing, Measurement and Blending 345 12.3.2 Proximate Analysis of Sample Materials 346 12.3.3 Moisture Content MC (%) 347 12.3.4 Ash Content AC (%) 347 12.3.5 Volatile Matter (VM) Content 348 12.3.6 Fixed Carbon Content FC (%) 348 12.3.7 Calorific Value 348 12.4 Results and Analysis 349 12.4.1 Moisture Content 349 12.4.2 Volatile Matter Content 349 12.4.3 Ash Content 350 12.4.4 Fixed Carbon Content 350 12.5 Discussion 351 12.6 Conclusion 352 Acknowledgement 352 References 352 13 Torrefaction and Pelletization of Lignocellulosic Biomass for Energy Intensified Fuel Substitute 357Chitra Devi Venkatachalam, Mothil Sengottian and Sathish Raam Ravichandran 13.1 Introduction – Biomass as Fuel 358 13.2 Torrefaction 359 13.2.1 Reaction Mechanism 359 13.2.2 Characterization of Torrefied Biomass 360 13.2.2.1 Moisture Content 360 13.2.2.2 Bulk Density 360 13.2.2.3 Grindability 361 13.2.2.4 High Heating Value 361 13.2.2.5 Mass Yield, Energy Yield and Enhancement Factor 362 13.2.2.6 Particle Size Distribution 363 13.2.3 Reactors for Torrefaction 364 13.2.3.1 Fixed Bed Reactor 364 13.2.3.2 Moving Bed Reactor 364 13.2.3.3 Entrained Flow Reactor 364 13.2.3.4 Fluidized Bed Reactor 364 13.2.3.5 Rotary Drum Reactor 365 13.2.3.6 Microwave Reactor 365 13.2.3.7 Hydrothermal Reactor 365 13.3. Pelletization 365 13.3.1 Pelletization of Torrefied Biomass 365 13.3.2 Types of Pelletizers 367 13.3.2.1 Flat Die Pellet Mill 367 13.3.2.2 Round Die Pellet Mill 367 13.3.3 Influence of Process Parameters during the Pelletization 368 13.3.3.1 Moisture Content 368 13.3.3.2 Pelletization Temperature 368 13.3.3.3 Particle Size 368 13.3.3.4 Press Channel Dimensions 368 13.3.3.5 Pelletization Pressure 368 13.3.3.6 Torrefaction Temperature 369 13.4 Application of Torrefaction Process 369 13.4.1 Using Torrefaction as Pre-Treatment Step for Biomass Gasification 369 13.4.2 Blending Torrefied Biomass with Coal and Co-Firing for Energy Production 369 13.4.3 Fuel for Steel Making in Blast Furnace 370 13.5 Conclusion 370 References 370 Index 375

Read more less

Book SynopsisIoT Product Design and Development Learn to incorporate IoT products into the process of building a product Internet of Things (or IoT) is currently one of the central building blocks of industry. It is the driving technology of the connected worldbe it smart cars, smart homes, smart factories, or smart cities. Industrial IoT (IIoT) is one of the most impactful areas of the global market, where it has fundamentally altered industries as varied as manufacturing, electronics, automotive, consumer goods, healthcare, and process industries like oil and gas, among others. As such, it is essential that engineers working in these fields improve their IoT knowledge to keep pace with this growing demand. IoT Product Design and Development offers an accessible entry point to the methods, techniques, and best practices necessary to add IoT onto an existing product or to build new IoT products wholesale. To accomplish this, the volume examines product design requiremeTable of ContentsAcknowledgments ix Preface xi Who Is This Book For? xii What Is Covered? xiii Chapter 1 Introduction to IoT 1 What Is IoT? 1 Why Is IoT Important? 3 Why Now? 5 Chapter 2 IoT and Digital Transformation 9 The Role of IoT in Modern Businesses 11 It’s an Integration Process: The Human Aspects of Digital Transformation 17 Chapter 3 Business Models and Market Analysis 23 Business and Industrial Market 23 Consumer Market 37 Connectivity Choice and Its Impact on ROI 49 Climate Change and Performance Per Watt 50 Data By-Products Beyond the Original IoT Application 51 Chapter 4 Security 57 Encryption Techniques 59 Software and Firmware Update Techniques 69 Key Management and Protection Against Known Threats 70 Security Attacks and Chain of Trust 71 Blockchain 77 Summary of Best Security Practices for IoT Systems 79 Networking Concepts 80 IoT Security Standards and Certificates 86 Chapter 5 IoT System Design Process and Main Components 95 Design and Deployment Process 95 Sensors 107 File Systems 114 Machine Learning 119 Networking and Communication System, Product Lifecycle, Device Management 136 Wireless Communication Protocols 144 Examples 155 IoT Systems Lifecycle 160 Chapter 6 The Process of Building a Data Product 163 What Personas Do You Need on Your Team? 165 Business Understanding 166 Data Understanding 167 Data Preparation 168 Modeling 169 Evaluation 171 Deployment 172 The Cycle Repeats – Crisp-DM 172 Chapter 7 Concluding Remarks 175 Further Reading 177 Index 179

Read more less

Book SynopsisAdvances in Remote Sensing for Forest Monitoring An expert overview of remote sensing as applied to forests and other vegetation In Advances in Remote Sensing for Forest Monitoring, a team of distinguished researchers delivers an expansive and insightful discussion of the latest research on remote sensing technologies as they relate to the monitoring of forests, plantations, and other vegetation. The authors also explore the use of unmanned aerial vehicles and drones, as well as multisource and multi-sensor data such as optical, SAR, LIDAR, and hyperspectral data. The book draws on the latest data and research to show how remote sensing solutions are being used in real-world settings. It offers contributions from researchers and practitioners from a wide variety of backgrounds and geographical regions to provide a diverse and global set of perspectives on the subject. Readers will also find: A thorough introduction to forest monitoring using remote sensing including recent advancesTable of Contents1. Introduction to Forest Monitoring Using Advanced Remote Sensing Technology 2. Geospatial Perspectives of Sustainable Forests Management to Enhance Ecosystem Services and Livelihood Security 3. Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data 4. Modelling of Abiotic Stress of Conifers with Remote Sensing Data 5. Retrieval of Mangrove Forest Properties Using Synthetic Aperture Radar 6. Photosynthetic Variables Estimation in a Mangrove Forest 7. Quantifying Carbon Stock Variability of Species within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery 8. Mapping Oil Palm Plantations in the Fringe of Sebangau National Park, Central Kalimantan, Indonesia 9. Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms 10. Leveraging Google Earth Engine (GEE) and Landsat Images to Assess Bushfire Severity and Postfire Short-Term Vegetation Recovery: a Case Study of Victoria, Australia. 11. Recent Advancement and Role of Drones in Forest monitoring: Research and Practices 12. Applications of Multi-Source and Multi-Sensor Data Fusion of Remote Sensing for Forest Species Mapping 13. Challenges and Monitoring Methods of Forest Management through Geospatial Application 14. Challenges and Future Possibilities Towards Himalayan Forest Monitoring

Read more less

Book SynopsisCYBERSECURITY AND LOCAL GOVERNMENT Learn to secure your local government's networks with this one-of-a-kind resource In Cybersecurity and Local Government, a distinguished team of researchers delivers an insightful exploration of cybersecurity at the level of local government. The book makes a compelling argument that every local government official, elected or otherwise, must be reasonably knowledgeable about cybersecurity concepts and provide appropriate support for it within their governments. It also lays out a straightforward roadmap to achieving those objectives, from an overview of cybersecurity definitions to descriptions of the most common security challenges faced by local governments. The accomplished authors specifically address the recent surge in ransomware attacks and how they might affect local governments, along with advice as to how to avoid and respond to these threats. They also discuss the cybersecurity law, cybersecurity policies that local government should adopt, the future of cybersecurity, challenges posed by Internet of Things, and much more. Throughout, the authors provide relevant field examples, case studies of actual local governments, and examples of policies to guide readers in their own application of the concepts discussed within. Cybersecurity and Local Government also offers: A thorough introduction to cybersecurity generally, including definitions of key cybersecurity terms and a high-level overview of the subject for non-technologists. A comprehensive exploration of critical information for local elected and top appointed officials, including the typical frequencies and types of cyberattacks. Practical discussions of the current state of local government cybersecurity, with a review of relevant literature from 2000 to 2021. In-depth examinations of operational cybersecurity policies, procedures and practices, with recommended best practices. Perfect for local elected and top appointed officials and staff as well as local citizens, Cybersecurity and Local Government will also earn a place in the libraries of those studying or working in local government with an interest in cybersecurity.Table of ContentsPreface ix About the Authors xi 1 Why Local Government Cybersecurity? 1 2 What is Cybersecurity? 17 3 Cybersecurity 101 for Local Governments 27 4 What the Literature Says About Local Government Cybersecurity 47 5 Cyberattacks: Targetting Local Government 67 6 Managing Local Government Cybersecurity 85 7 Cybersecurity Policies for Local Government 113 8 People: The Root of The Problem 143 9 The NIST Cybersecurity Framework Demystified 151 10 Cybersecurity Law and Regulation for Local Government 167 11 Important Questions to Ask 187 12 The Future of Local Government Cybersecurity 201 13 Summary and Recommendations 227 Index 235

Read more less

Book SynopsisExplore this insightful foundational resource for academics and industry professionals dealing with the move toward intelligent devices and networks Interference Mitigation in Device-to-Device Communications delivers a thorough discussion of device-to-device (D2D) and machine-to-machine (M2M) communications as solutions to the proliferation of ever more data hungry devices being attached to wireless networks. The book explores the use of D2D and M2M technologies as a key enabling component of 5G networks. It brings together a multidisciplinary team of contributors in fields like wireless communications, signal processing, and antenna design. The distinguished editors have compiled a collection of resources that practically and accessibly address issues in the development, integration, and enhancement of D2D systems to create an interference-free network. This book explores the complications posed by the restriction of device form-factors and the co-location of seTable of ContentsPreface xiii Acknowledgments xv About the Editors xvii List of Contributors xix 1 Introduction to D2D Communications 1Ghazanfar Ali Safdar, Masood Ur Rehman, and Mohammad Asad Rehman Chaudhry 1.1 D2D Communication 1 1.2 Evolution of D2D Communication 3 1.3 D2D Communication in Cellular Spectrum 6 1.4 Classification of D2D Communication 9 1.5 Challenges in D2D Implementation 10 1.6 Summary 11 References 11 2 Interference Mitigation in D2D Communication Underlaying LTE-A Network 13Ghazanfar Ali Safdar, Masood Ur Rehman, Mujahid Muhammad, and Muhammad A. Imran 2.1 Applicability of D2D Communication 15 2.2 Interference – The Compelling Issue in D2D 17 2.3 Types of D2D Communication 17 2.3.1 In-Band D2D Communication 18 2.3.1.1 Underlay In-Band 18 2.3.1.2 Overlay In-Band 19 2.3.2 Out-Band D2D Communication 19 2.3.2.1 Network-Assisted D2D Communication 21 2.3.2.2 Autonomous D2D Communication 22 2.4 D2D Communication Underlaying Cellular Network – The Challenges 23 2.4.1 Device Discovery 24 2.4.2 Mode Selection 25 2.4.3 Radio Resource Management 25 2.4.4 Modification to LTE-A Architecture 27 2.4.5 Security in D2D 27 2.4.6 Mobility Management 28 2.5 Interference in D2D 28 2.5.1 Power Control Techniques 31 2.5.2 Radio Resource Allocation Techniques 32 2.5.3 Joint Power Control and Radio Resource Allocation Techniques 33 2.5.4 Spectrum Splitting Techniques 34 2.5.5 Other Interference Mitigation Techniques 34 2.5.6 Multiple-Input Multiple-Output Techniques 35 2.5.7 Comparative Analysis of D2D Interference Mitigation Techniques 39 2.6 Summary 42 References 42 3 Rethinking D2D Interference: Beyond the Past 49Mohammad Asad Rehman Chaudhry and Zakia Asad 3.1 Interference Manipulation 49 3.1.1 Example 50 3.2 Formulation of Interference Manipulation Problem 52 3.3 Matrix Rank Minimization: A Way to Manipulate Interference 53 3.3.1 Reduction of Interference Manipulation to Matrix Rank Minimization 53 3.3.2 Minimum Rank Matrix to Transmission Scheme 54 3.3.3 Does the Field Size Matter? 55 3.4 Interference Manipulation: A Boolean Satisfiability Approach 55 3.5 Interference Manipulation: Index Coding Perspective 56 3.5.1 Interference Manipulation Is NP-hard 57 3.5.2 An Efficient Solution for Interference Manipulation 58 3.6 Summary 60 References 60 4 User Pairing Scheme for Efficient D2D Content Delivery in Cellular Networks 63Yanli Xu 4.1 D2D Content Delivery 63 4.2 D2D Content Delivery Architecture 65 4.2.1 Network Model 65 4.2.2 Channel Model 66 4.2.3 Content Delivery Model 66 4.3 D2D Content Delivery Strategies 67 4.3.1 Pairing Range 67 4.3.2 Energy Efficiency for Multicast and Unicast 72 4.3.3 Caching and Delivery 73 4.4 D2D Delivery Mode Selection 75 4.5 Performance Evaluation 77 4.6 Summary 84 References 84 5 Resource Allocation for NOMA-based D2D Systems Coexisting with Cellular Networks 89Tien H. Nguyen, Taehyun Yoon, Xuan T. Nguyen, Daeseung Yoo, Byungtae Jang, and Van D. Nguyen 5.1 NOMA-based D2D Systems 90 5.2 System Model and Performance Analysis 91 5.2.1 System Model and Assumptions 91 5.2.2 Capacity Analysis of D2D and Cellular Networks 92 5.2.2.1 Uplink Cellular Networks Transmission 92 5.2.2.2 Downlink NOMA-D2D Transmission 93 5.3 Joint Subchannel Assignment and Power Control for D2D Communication 95 5.3.1 Subchannel Assignment Scheme 96 5.3.2 Power Control Scheme 97 5.4 Optimization of D2D Device Pairing 99 5.5 Results and Discussion 100 5.5.1 Channel Model 101 5.5.2 Performance Evaluation 102 5.6 Summary 105 References 105 6 Distributed Multi-Agent RL-Based Autonomous Spectrum Allocation in D2D-Enabled Multi-Tier HetNets 109Kamran Zia, Nauman Javed, Muhammad N. Sial, Sohail Ahmed, Asad A. Pirzada, and Farrukh Pervez 6.1 D2D Resource Allocation Methods 110 6.2 Reinforcement Q-Learning 113 6.3 System Model 114 6.4 Resource Allocation in Multi-tier D2D Communication 116 6.4.1 Autonomous Spectrum Allocation Scheme 118 6.5 Performance Evaluation 119 6.5.1 Performance of D2D Users 121 6.5.2 Performance of Cellular Users 122 6.5.3 Coverage Analysis 125 6.5.4 Computational Time Analysis 125 6.5.5 Memory Requirements 127 6.5.6 Effect of Base Stations Density 128 6.5.7 Effect of Network Tiers 129 6.6 Summary 130 References 130 7 Adaptive Interference Aware Device-to-Device-Enabled Unmanned Aerial Vehicle Communications 133Navuday Sharma, Rafay I. Ansari, Rida Khan, Hassan Malik, and Haris Pervaiz 7.1 Key Elements in D2D Communication 134 7.1.1 D2D Network Discovery 135 7.1.2 SWIPT for D2D 135 7.1.3 Resource Allocation 136 7.1.4 3GPP Standardization 136 7.2 Unmanned Aerial Vehicles in D2D 137 7.2.1 Key Challenges in UAV-based D2D 139 7.2.2 Transmission over PC5 Interface for UAV-based D2D Discovery 140 7.2.3 Interference in UAV-based D2D 141 7.3 Summary 144 References 144 8 Emergency Device-to-Device Communication: Applicability, Case Studies and Interference Mitigation 149Imran Haider, Mohsin Raza, Kamran Ali, Muhammad Awais, Vishnu V. Paranthaman, and Muhammad Y. M. Mirza 8.1 Emergency D2D Communication 150 8.2 Approaches for Efficient Emergency D2D Communication 152 8.3 Emergency D2D Communication: Case Studies 155 8.4 Interference Mitigation in Emergency D2D Communication 158 8.4.1 Radiated Power Management 159 8.4.2 Frequency Allocation 160 8.4.2.1 Hybrid Schemes for Power Control and Intelligent Frequency Allocation 160 8.4.3 Time Division Multiplexing (TDM) 161 8.4.4 Adjacent Channel Interference Cancellation in DSRC 161 8.4.5 Interference Mitigation through Multiple Antennas (MIMO) 162 8.4.5.1 Beam Steering in 3GPP 5G NR Supported Vehicular Systems 162 8.5 Summary 164 References 164 9 Disaster Management Using D2D Communication With Power Transfer and Clustering Techniques 167Kamran Ali, Huan Nguyen, Aboubaker Lasebae, Anum Tanveer, Purav Shah, Mohsin Raza, Bushrah Naeem, and Tahera Kalsoom 9.1 D2D Communication in Disaster Management 168 9.2 D2D Communication in Disaster Management: Key Considerations 169 9.3 D2D Disaster Management System Architecture 171 9.3.1 Time Switching-Based Protocol 172 9.3.2 Network Configuration 173 9.3.3 Outage Probability for Mode Selection 174 9.4 Power Transfer Using Relaying and Clustering in D2D Disaster Management 178 9.4.1 System Model 178 9.4.2 Performance Evaluation 179 9.4.2.1 Energy Calculation 179 9.5 Results and Discussion 182 9.6 Summary 187 References 188 10 Road Ahead for D2D Communications 191Masood Ur Rehman, Ghazanfar Ali Safdar, and Mohammad Asad Rehman Chaudhry 10.1 Future Prospects and Challenges 191 10.1.1 Spectrum Sharing and Coexistence 192 10.1.2 Standardization 192 10.1.3 Secure Communication 193 10.1.4 Energy Consumption and Energy Harvesting 194 10.1.5 Interference Management 195 10.1.6 Resource Allocation 196 10.1.7 Device Discovery 197 10.1.8 Handover 198 10.1.9 Network Slicing 198 10.1.10 D2D in Vehicular Communications 199 10.1.11 D2D in Disaster Management 199 10.1.12 D2D at Millimeter Wave Frequencies 199 10.1.13 D2D and Social Networks 200 10.1.14 D2D and Visible Light Communication (VLC) 200 References 201 Index 207

Read more less

Book SynopsisSmart Energy for Transportation and Health in a Smart City A comprehensive review of the advances of smart cities' smart energy, transportation, infrastructure, and health Smart Energy for Transportation and Health in a Smart City offers an essential guide to the functions, characteristics, and domains of smart cities and the energy technology necessary to sustain them. The authorsnoted experts on the topicinclude theoretical underpinnings, practical information, and potential benefits for the development of smart cities. The book includes information on various financial models of energy storage, the management of networked micro-grids, coordination of virtual energy storage systems, reliability modeling and assessment of cyber space, and the development of a vehicle-to-grid voltage support. The authors review smart transportation elements such as advanced metering infrastructure for electric vehicle charging, power system dispatching with plug-in hybrid electric vehicles, and best Table of ContentsForeword xv Preface xvii Authors’ Biography xxi Acknowledgments xxiii 1 What Is Smart City? 1 1.1 Introduction 1 1.2 Characteristics, Functions, and Applications 4 1.2.1 Sensors and Intelligent Electronic Devices 4 1.2.2 Information Technology, Communication Networks, and Cyber Security 5 1.2.3 Systems Integration 6 1.2.4 Intelligence and Data Analytics 6 1.2.5 Management and Control Platforms 7 1.3 Smart Energy 7 1.4 Smart Transportation 11 1.4.1 Data Processing 11 1.5 Smart Health 12 1.6 Impact of COVID-19 Pandemic 12 1.7 Standards 14 1.7.1 International Standards for Smart City 14 1.7.2 Smart City Pilot Projects 19 1.8 Challenges and Opportunities 26 1.9 Conclusions 29 Acknowledgements 29 References 29 2 Lithium-Ion Storage Financial Model 37 2.1 Introduction 37 2.2 Literature Review 38 2.2.1 Techno-economic Studies of Biogas, PV, and EES Hybrid Energy Systems 38 2.2.2 EES Degradation 39 2.2.3 Techno-Economic Analysis for EES 41 2.2.4 Financing for Renewable Energy Systems and EES 42 2.3 Research Background: Hybrid Energy System in Kenya 46 2.3.1 Hybrid System Sizing and Operation 46 2.3.2 Solar and Retail Electricity Price Data 47 v ftoc.3d 5 8/10/2022 8:29:08 PM 2.4 A Case Study on the Degradation Effect on LCOE 49 2.4.1 Sensitivity Analysis on the SOCThreshold 49 2.4.2 Sensitivity Analysis on PV and EES Rated Capacities 50 2.5 Financial Modeling for EES 52 2.5.1 Model Description 53 2.5.2 Case Studies Context 55 2.6 Case Studies on Financing EES in Kenya 57 2.6.1 Influence of WACC on Equity NPV and LCOS 57 2.6.2 Equity and Firm Cash Flows 58 2.6.2.1 Cash Flows for EES Capital Cost at 1500 $/kWh 58 2.6.2.2 Cash Flows for EES Capital Cost at 200 $/kWh 58 2.6.3 LCOS and Project Lifecycle Cost Composition 61 2.6.4 EES Finance Under Different Electricity Prices 63 2.6.4.1 Study on the Retail Electricity Price 63 2.7 Sensitivity Analysis of Technical and Economic Parameters 64 2.8 Discussion and Future Work 66 2.9 Conclusions 68 Acknowledgments 68 References 68 3 Levelized Cost of Electricity for Photovoltaic with Energy Storage 73 Nomenclature 73 3.1 Introduction 75 3.2 Literature Review 76 3.3 Data Analysis and Operating Regime 78 3.3.1 Solar and Load Data Analysis 78 3.3.2 Problem Context 79 3.3.3 Operating Regime 81 3.3.4 Case Study 84 3.4 Economic Analysis 86 3.4.1 AD Operational Cost Model 86 3.4.2 LiCoO2 Degradation Cost Model and Number of Replacements 86 3.4.3 Levelized Cost of Electricity Derivation 90 3.4.3.1 LCOE for PV 91 3.4.3.2 LCOE for AD 92 3.4.3.3 Levelized Cost of Storage (LCOS) 92 3.4.3.4 Levelized Cost of Delivery (LCOD) 93 3.4.3.5 LCOE for System 94 3.4.4 LCOE Analyses and Discussion 94 3.5 Conclusions 96 Acknowledgment 97 References 97 4 Electricity Plan Recommender System 101 Nomenclature 101 4.1 Introduction 102 4.2 Proposed Matrix Recovery Methods 105 4.2.1 Previous Matrix Recovery Methods 105 vi Contents ftoc.3d 6 8/10/2022 8:29:09 PM 4.2.2 Matrix Recovery Methods with Electrical Instructions 106 4.2.3 Solution 107 4.2.4 Convergence Analysis and Complexity Analysis 111 4.3 Proposed Electricity Plan Recommender System 112 4.3.1 Feature Formulation Stage 112 4.3.2 Recommender Stage 112 4.3.3 Algorithm and Complexity Analysis 113 4.4 Simulations and Discussions 115 4.4.1 Recovery Simulation 115 4.4.2 Recovery Result Discussions 119 4.4.3 Application Study 121 4.4.4 Application Result Discussions 125 4.5 Conclusion and Future Work 126 Acknowledgments 127 References 127 5 Classifier Economics of Semi-intrusive Load Monitoring 131 5.1 Introduction 131 5.1.1 Technical Background 131 5.1.2 Original Contribution 132 5.2 Typical Feature Space of SILM 132 5.3 Modeling of SILM Classifier Network 134 5.3.1 Problem Definition 134 5.3.2 SILM Classifier Network Construction 135 5.4 Classifier Locating Optimization with Ensuring on Accuracy and Classifier Economics 137 5.4.1 Objective of SILM Construction 137 5.4.2 Constraint of Devices Covering Completeness and Over Covering 137 5.4.3 Constraint of Bottom Accuracy and Accuracy Measurement 138 5.4.4 Constraint of Sampling Computation Requirements 138 5.4.5 Optimization Algorithm 139 5.5 Numerical Study 140 5.5.1 Devices Operational Datasets for Numerical Study 140 5.5.2 Feature Space Set for Numerical Study 140 5.5.3 Numerical Study 1: Classifier Economics via Different Meter Price and Different Accuracy Constraints 141 5.5.3.1 Result Analysis via Row Variation in Table 5.5 143 5.5.3.2 Result Analysis via Column Variation in Table 5.5 143 5.5.3.3 Result Converging via Price Variation 144 5.5.4 Numerical Study 2: Classifier Economics via different Classifiers Models 146 5.6 Conclusion 147 Acknowledgements 147 References 147 6 Residential Demand Response Shifting Boundary 151 6.1 Introduction 151 6.2 Residential Customer Behavior Modeling 153 6.2.1 Multi-Agent System Modeling 153 Contents vii ftoc.3d 7 8/10/2022 8:29:09 PM 6.2.2 Multi-agent System Structure for PBP Demand Response 153 6.2.3 Agent of Residential Consumer 155 6.3 Residential Customer Shifting Boundary 157 6.3.1 Consumer Behavior Decision-Making 157 6.3.2 Shifting Boundary 157 6.3.3 Target Function and Constraints 158 6.4 Case Study 160 6.4.1 Case Study Description 160 6.4.2 Residential Shifting Boundary Simulation under TOU 164 6.4.3 Residential Shifting Boundary Simulation Under RTP 169 6.5 Case Study on Residential Customer TOU Time Zone Planning 173 6.5.1 Case Study Description 173 6.5.2 Result and Analysis 173 6.6 Case Study on Smart Meter Installation Scale Analysis 178 6.6.1 Case Study Description 178 6.6.2 Analysis on Multiple Smart Meter Installation Scale under TOU and RTP 179 6.7 Conclusions and Future Work 181 Acknowledgements 181 References 182 7 Residential PV Panels Planning-Based Game-Theoretic Method 185 Nomenclature 185 7.1 Introduction 186 7.2 System Modeling 188 7.2.1 Network Branch Flow Model 188 7.2.2 Energy Sharing Agent Model 189 7.3 Bi-level Energy Sharing Model for Determining Optimal PV Panels Installation Capacity 191 7.3.1 Uncertainty Characterization 191 7.3.2 Stackelberg Game Model 191 7.3.3 Bi-level Energy Sharing Model 192 7.3.4 Linearization of Bi-level Energy Sharing Model 194 7.3.5 Descend Search-Based Solution Algorithm 195 7.4 Stochastic Optimal PV Panels Allocation in the Coalition of Prosumer Agents 197 7.5 Numerical Results 199 7.5.1 Implementation on IEEE 33-Node Distribution System 199 7.5.2 Implementation on IEEE 123-Node Distribution System 205 7.6 Conclusion 206 Acknowledgements 207 References 207 8 Networked Microgrids Energy Management Under High Renewable Penetration 211 Nomenclature 211 8.1 Introduction 212 8.2 Problem Description 215 8.2.1 Components and Configuration of Networked MGs 215 8.2.2 Proposed Strategy 216 8.3 Components Modeling 216 viii Contents ftoc.3d 8 8/10/2022 8:29:09 PM 8.3.1 CDGs 216 8.3.2 BESSs 217 8.3.3 Controllable Load 218 8.3.4 Uncertain Sets of RESs, Load, and Electricity Prices 218 8.3.5 Market Model 218 8.4 Proposed Two-Stage Operation Model 219 8.4.1 Hourly Day-Ahead Optimal Scheduling Model 219 8.4.1.1 Lower Level EMS 219 8.4.1.2 Upper Level EMS 220 8.4.2 5-Minute Real-Time Dispatch Model 221 8.5 Case Studies 222 8.5.1 Set Up 222 8.5.2 Results and Discussion 222 8.6 Conclusions 230 Acknowledgements 231 References 231 9 A Multi-agent Reinforcement Learning for Home Energy Management 233 Nomenclature 233 9.1 Introduction 233 9.2 Problem Modeling 236 9.2.1 State 238 9.2.2 Action 238 9.2.3 Reward 239 9.2.4 Total Reward of HEM System 239 9.2.5 Action-value Function 240 9.3 Proposed Data-Driven-Based Solution Method 240 9.3.1 ELM-Based Feedforward NN for Uncertainty Prediction 241 9.3.2 Multi-Agent Q-Learning Algorithm for Decision-Making 241 9.3.3 Implementation Process of Proposed Solution Method 241 9.4 Test Results 244 9.4.1 Case Study Setup 244 9.4.2 Performance of the Proposed Feedforward NN 244 9.4.3 Performance of Multi-Agent Q-Learning Algorithm 246 9.4.4 Numerical Comparison with Genetic Algorithm 249 9.5 Conclusion 251 Acknowledgements 251 References 251 10 Virtual Energy Storage Systems Smart Coordination 255 10.1 Introduction 255 10.1.1 Related Work 255 10.1.2 Main Contributions 257 10.2 VESS Modeling, Aggregation, and Coordination Strategy 257 10.2.1 VESS Modeling 257 10.2.2 VESS Aggregation 259 10.2.3 VESS Coordination Strategies 260 10.3 Proposed Approach for Network Loading and Voltage Management by VESSs 261 Contents ix ftoc.3d 9 8/10/2022 8:29:09 PM 10.3.1 Network Loading Management Strategy 261 10.3.2 Voltage Regulation Strategy 264 10.4 Case Studies 267 10.4.1 Case 1 269 10.4.2 Case 2 269 10.5 Conclusions and Future Work 276 Acknowledgements 276 References 276 11 Reliability Modeling and Assessment of Cyber-Physical Power Systems 279 Nomenclature 279 11.1 Introduction 279 11.2 Composite Markov Model 282 11.2.1 Multistate Markov Chain of Information Layer 282 11.2.2 Two-state Markov Chain of Physical Layer 284 11.2.3 Coupling Model of Physical and Information Layers 285 11.3 Linear Programming Model for Maximum Flow 286 11.3.1 Node Classification and Flow Constraint Model 286 11.3.2 Programming Model for Network Flow 288 11.4 Reliability Analysis Method 289 11.4.1 Definition and Measures of System Reliability 289 11.4.2 Sequential Monte-Carlo Simulation 289 11.4.2.1 System State Sampling 289 11.4.2.2 Reliability Computing Procedure 290 11.5 Case Analysis 291 11.5.1 Case Description 291 11.5.2 Calculation Results and Analysis 293 11.5.2.1 Effect of Demand Flow on Reliability 293 11.5.2.2 Effect of Node Capacity on Reliability 295 11.5.2.3 Effect of the Information Flow Level on Reliability 297 11.6 Conclusion 298 Acknowledgements 299 References 299 12 A Vehicle-To-Grid Voltage Support Co-simulation Platform 301 12.1 Introduction 301 12.2 Related Works 303 12.2.1 Simulation of Power Systems 303 12.2.2 Simulation of Communication Network 304 12.2.3 Simulation of Distributed Software 305 12.2.4 Time Synchronization 305 12.2.5 Co-Simulation Interface 306 12.3 Direct-Execution Simulation 306 12.3.1 Operation of a Direct-Execution Simulation 307 12.3.1.1 Simulation Metadata 307 12.3.1.2 Enforcing Simulated Thread Scheduling 308 12.3.1.3 Tracking Action Timestamps 308 x Contents ftoc.3d 10 8/10/2022 8:29:09 PM 12.3.1.4 Enforcing Timestamp Order 308 12.3.1.5 Handling External Events 308 12.3.2 DecompositionJ Framework 309 12.4 Co-Simulation Platform for Agent-Based Smart Grid Applications 310 12.4.1 Co-Simulation Message Exchange 311 12.4.2 Co-Simulation Time Synchronization 312 12.5 Agent-Based FLISR Case Study 312 12.5.1 The Restoration Problem 312 12.5.2 Reconfiguration Algorithm 314 12.5.3 Restoration Agents 315 12.5.4 Communication Network Configurations 316 12.6 Simulation Results 316 12.6.1 Agent Actions and Events 317 12.6.1.1 Phase 1 – Fault Detection 317 12.6.1.2 Phase 2 – Fault Location 317 12.6.1.3 Phase 3 – Enquire DERs 317 12.6.1.4 Phase 4 – Reconfiguration 320 12.6.1.5 Phase 5 – Transient 320 12.6.2 Effects of Background Traffics and Link Failure 321 12.6.3 Effects of Link Failure Time 322 12.6.4 Effects of Main-Container Location Configuration 323 12.6.5 Summary on Simulation Results 324 12.7 Case Study on V2G for Voltage Support 324 12.7.1 Modeling of Electrical Grid and EVs 324 12.7.2 Modeling of Communication Network 326 12.7.3 Simulation Events 327 12.7.4 Co-simulation Results 327 12.8 Conclusions 330 Acknowledgements 331 References 331 13 Advanced Metering Infrastructure for Electric Vehicle Charging 335 13.1 Introduction 335 13.2 EVAMI Overview 338 13.2.1 Advantage of Adopting EVAMI 338 13.2.2 Choice of Signal Transmission Platform 338 13.2.3 Onsite Charging System 340 13.2.4 EV Charging Station 340 13.2.5 Utility Information Management System 340 13.2.6 Third Party Customer Service Platform 341 13.3 System Architecture, Protocol Design, and Implementation 341 13.3.1 Communication Protocol 342 13.3.1.1 Charging Service Session Management 343 13.3.1.2 Device Management 344 13.3.1.3 Demand Response Management 346 13.3.2 Web Portal 347 13.4 Performance Evaluation 348 Contents xi ftoc.3d 11 8/10/2022 8:29:09 PM 13.4.1 Network Performance of OCS 348 13.4.2 Effectiveness of EVAMI on Demand Response 348 13.5 Conclusion 351 Acknowledgements 352 References 352 14 Power System Dispatching with Plug-In Hybrid Electric Vehicles 355 Nomenclature 355 14.1 Introduction 357 14.1.1 Model Decoupling 357 14.1.2 Security Reinforcement 358 14.1.3 Potential for Practical Application 358 14.2 Framework of PHEVs Dispatching 358 14.3 Framework for the Two-Stage Model 359 14.4 The Charging and Discharging Mode 360 14.4.1 PHEV Charging Mode 360 14.4.2 PHEV Discharging Mode 360 14.4.3 PHEV Charging and Discharging Power 361 14.5 The Optimal Dispatching Model with PHEVs 361 14.5.1 Sub-Model 1 361 14.5.2 Sub-Model 2 363 14.6 Numerical Examples 364 14.7 Practical Application – The Impact of Electric Vehicles on Distribution Network 370 14.7.1 Modeling of Electric Vehicles 370 14.7.2 Uncontrolled Charging 374 14.7.3 Results 376 14.8 Conclusions 376 Acknowledgements 377 References 377 15 Machine Learning for Electric Bus Fast-Charging Stations Deployment 381 Nomenclature 381 15.1 Introduction 383 15.2 Problem Description and Assumptions 387 15.2.1 Operating Characteristics of Electric Buses 388 15.2.2 Affinity Propagation Algorithm 388 15.3 Model Formulation 389 15.3.1 Capacity Model of Electric Bus Fast-Charging Station 389 15.3.2 Deployment Model of Electric Bus Fast-Charging Station 392 15.3.3 Constraints 393 15.4 Results and Discussion 394 15.4.1 Spatio-temporal Distribution of Buses 394 15.4.2 Optimized Deployment of EB Fast-Charging Stations 394 15.4.3 Comparison of Different Planning Methods 395 xii Contents ftoc.3d 12 8/10/2022 8:29:09 PM 15.4.4 Comparison Under Different Time Headways 399 15.4.5 Comparison Under Different Battery Size and Charging Power 399 15.4.6 Policy and Business Model Implications 402 15.5 Conclusions 403 Acknowledgements 403 References 404 16 Best Practice for Parking Vehicles with Low-power Wide-Area Network 407 16.1 Introduction 407 16.2 Related Work 413 16.2.1 LoRaWAN 414 16.2.2 NB-IoT 415 16.2.3 Sigfox 416 16.3 LP-INDEX for Best Practices of LPWAN Technologies 416 16.3.1 Latency 417 16.3.2 Data Capacity 417 16.3.3 Power and Cost 418 16.3.4 Coverage 418 16.3.5 Scalability 419 16.3.6 Security 419 16.4 Case Study 419 16.4.1 Experimental Setup 419 16.4.2 Depolyment of Car Park Sensors 419 16.4.3 Evaluation Matrices and Results 419 16.5 Conclusion and Future Work 421 Acknowledgements 421 References 421 17 Smart Health Based on Internet of Things (IoT) and Smart Devices 425 17.1 Introduction 425 17.2 Technology Used in Healthcare 430 17.2.1 Internet of Things 434 17.2.2 Smart Meters 438 17.3 Case Study 443 17.3.1 Continuous Glucose Monitoring 443 17.3.2 Smart Pet 445 17.3.3 Smart Meters for Healthcare 448 17.3.4 Other Case Studies 453 17.3.4.1 Cancer Treatment 453 17.3.4.2 Connected Inhalers 454 17.3.4.3 Ingestible Sensors 454 17.3.4.4 Elderly People 454 17.4 Conclusions 455 References 456 Contents xiii ftoc.3d 13 8/10/2022 8:29:09 PM 18 Criteria Decision Analysis Based Cardiovascular Diseases Classifier for Drunk Driver Detection 463 18.1 Introduction 463 18.2 Cardiovascular Diseases Classifier 465 18.2.1 Design of the Optimal CDC 466 18.2.2 Data Pre-Processing and Features Construction 466 18.2.3 Cardiovascular Diseases Classifier Construction 467 18.3 Multiple Criteria Decision Analysis of the Optimal CDC 468 18.4 Analytic Hierarchy Process Scores and Analysis 470 18.5 Development of EDG-Based Drunk Driver Detection 471 18.5.1 ECG Sensors Implementations 472 18.5.2 Drunk Driving Detection Algorithm 473 18.6 ECG-Based Drunk Driver Detection Scheme Design 473 18.7 Result Comparisons 475 18.8 Conclusions 476 Acknowledgements 477 References 477 19 Bioinformatics and Telemedicine for Healthcare 481 19.1 Introduction 481 19.2 Bioinformatics 483 19.3 Top-Level Design for Integration of Bioinformatics to Smart Health 486 19.4 Artificial Intelligence Roadmap 488 19.5 Intelligence Techniques for Data Analysis Examples 492 19.6 Decision Support System 497 19.7 Conclusions 501 References 501 20 Concluding Remark and the Future 507 20.1 The Relationship 507 20.2 Roadmap 508 20.3 The Future 509 20.3.1 Smart Energy 509 20.3.2 Healthcare 513 20.3.3 Smart Transportation 516 20.3.4 Smart Buildings 517 References 518 Index 000

Read more less

Book SynopsisTable of ContentsList of Contributors xiii Preface xvii Abbreviations xxiii 1 What is Blockchain Radio Access Network? 1 Xintong Ling, Yuwei Le, Jiaheng Wang, Zhi Ding, and Xiqi Gao 1.1 Introduction 1 1.2 What is B-RAN 3 1.2.1 B-RAN Framework 3 1.2.2 Consensus Mechanism 6 1.2.3 Implementation 6 1.3 Mining Model 7 1.3.1 Hash-Based Mining 7 1.3.2 Modeling of Hash Trials 7 1.3.3 Threat Model 10 1.4 B-RAN Queuing Model 10 1.5 Latency Analysis of B-RAN 12 1.5.1 Steady-State Analysis 12 1.5.2 Average Service Latency 16 1.6 Security Considerations 18 1.6.1 Alternative History Attack 18 1.6.2 Probability of a Successful Attack 19 1.7 Latency-Security Trade-off 20 1.8 Conclusions and Future Works 22 1.8.1 Network Effect and Congest Effect 22 1.8.2 Chicken and Eggs 22 1.8.3 Decentralization and Centralization 22 1.8.4 Beyond Bitcoin Blockchain 22 References 23 2 Consensus Algorithm Analysis in Blockchain: PoW and Raft 27 Taotao Wang, Dongyan Huang, and Shengli Zhang 2.1 Introduction 27 2.2 Mining Strategy Analysis for the PoWConsensus-Based Blockchain 30 2.2.1 Blockchain Preliminaries 30 2.2.2 Proof ofWork and Mining 30 2.2.3 Honest Mining Strategy 31 2.2.4 PoW Blockchain Mining Model 32 2.2.4.1 State 33 2.2.4.2 Action 33 2.2.4.3 Transition and Reward 34 2.2.4.4 Objective Function 39 2.2.4.5 Honest Mining 40 2.2.4.6 Selfish Mining 40 2.2.4.7 Lead Stubborn Mining 40 2.2.4.8 Optimal Mining 41 2.2.5 Mining Through RL 41 2.2.5.1 Preliminaries for Original Reinforcement Learning Algorithm 41 2.2.5.2 New Reinforcement Learning Algorithm for Mining 42 2.2.6 Performance Evaluations 44 2.3 Performance Analysis of the Raft Consensus Algorithm 52 2.3.1 Review of Raft Algorithm 52 2.3.2 System Model 53 2.3.3 Network Model 53 2.3.4 Network Split Probability 55 2.3.5 Average Number of Replies 57 2.3.6 Expected Number of Received Heartbeats for a Follower 57 2.3.7 Time to Transition to Candidate 58 2.3.8 Time to Elect a New Leader 59 2.3.9 Simulation Results 60 2.3.10 Discussion 67 2.3.10.1 Extended Model 67 2.3.10.2 System Availability and Consensus Efficiency 68 2.4 Conclusion 69 Appendix A.2 69 References 70 3 A Low Communication Complexity Double-layer PBFT Consensus 73 Chenglin Feng, Wenyu Li, Bowen Yang, Yao Sun, and Lei Zhang 3.1 Introduction 73 3.1.1 PBFT Applied to Blockchain 74 3.1.2 From CFT to BFT 74 3.1.2.1 State Machine Replication 74 3.1.2.2 Primary Copy 75 3.1.2.3 Quorum Voting 75 3.1.3 Byzantine Generals Problem 76 3.1.4 Byzantine Consensus Protocols 76 3.1.4.1 Two-Phase Commit 76 3.1.4.2 View Stamp 76 3.1.4.3 PBFT Protocol 76 3.1.5 Motivations 78 3.1.6 Chapter Organizations 78 3.2 Double-Layer PBFT-Based Protocol 79 3.2.1 Consensus Flow 79 3.2.1.1 The Client 79 3.2.1.2 First-Layer Protocol 81 3.2.1.3 Second-Layer Protocol 81 3.2.2 Faulty Primary Elimination 82 3.2.2.1 Faulty Primary Detection 82 3.2.2.2 View Change 83 3.2.3 Garbage Cleaning 84 3.3 Communication Reduction 84 3.3.1 Operation Synchronization 85 3.3.2 Safety and Liveness 85 3.4 Communication Complexity of Double-Layer PBFT 85 3.5 Security Threshold Analysis 86 3.5.1 Faulty Probability Determined 87 3.5.2 Faulty Number Determined 89 3.6 Conclusion 90 References 90 4 Blockchain-Driven Internet of Things 93 Bin Cao, Weikang Liu, and Mugen Peng 4.1 Introduction 93 4.1.1 Challenges and Issues in IoT 93 4.1.2 Advantages of Blockchain for IoT 94 4.1.3 Integration of IoT and Blockchain 94 4.2 Consensus Mechanism in Blockchain 96 4.2.1 PoW 96 4.2.2 PoS 97 4.2.3 Limitations of PoWand PoS for IoT 98 4.2.3.1 Resource Consumption 98 4.2.3.2 Transaction Fee 98 4.2.3.3 Throughput Limitation 98 4.2.3.4 Confirmation Delay 98 4.2.4 PBFT 98 4.2.5 DAG 100 4.2.5.1 Tangle 101 4.2.5.2 Hashgraph 102 4.3 Applications of Blockchain in IoT 102 4.3.1 Supply Chain 102 4.3.1.1 Introduction 102 4.3.1.2 Modified Blockchain 103 4.3.1.3 Integrated Architecture 104 4.3.1.4 Security Analysis 105 4.3.2 Smart City 106 4.3.2.1 Introduction 106 4.3.2.2 Smart Contract System 107 4.3.2.3 Main Functions of the Framework 109 4.3.2.4 Discussion 110 4.4 Issues and Challenges of Blockchain in IoT 111 4.4.1 Resource Constraints 111 4.4.2 Security Vulnerability 111 4.4.3 Privacy Leakage 112 4.4.4 Incentive Mechanism 112 4.5 Conclusion 112 References 112 5 Hyperledger Blockchain-Based Distributed Marketplaces for 5G Networks 117 Nima Afraz, Marco Ruffini, and Hamed Ahmadi 5.1 Introduction 117 5.2 Marketplaces in Telecommunications 118 5.2.1 Wireless Spectrum Allocation 119 5.2.2 Network Slicing 119 5.2.3 Passive optical networks (PON) Sharing 120 5.2.4 Enterprise Blockchain: Hyperledger Fabric 121 5.2.4.1 Shared Ledger 122 5.2.4.2 Organizations 122 5.2.4.3 Consensus Protocol 122 5.2.4.4 Network Peers 122 5.2.4.5 Smart Contracts (chaincodes) 123 5.2.4.6 Channels 123 5.3 Distributed Resource Sharing Market 123 5.3.1 Market Mechanism (Auction) 125 5.3.2 Preliminaries 125 5.4 Experimental Design and Results 126 5.4.1 Experimental Blockchain Deployment 127 5.4.1.1 Cloud Infrastructure 127 5.4.1.2 Container Orchestration: Docker Swarm 127 5.4.2 Blockchain Performance Evaluation 127 5.4.3 Benchmark Apparatus 128 5.4.3.1 Hyperledger Caliper 130 5.4.3.2 Data Collection: Prometheus Monitor 130 5.4.4 Experimental Results 131 5.4.4.1 Maximum Transaction Throughput 131 5.4.4.2 Block Size 131 5.4.4.3 Network Size 131 5.5 Conclusions 133 References 133 6 Blockchain for Spectrum Management in 6G Networks 137 Asuquo A. Okon, Olusegun S. Sholiyi, Jaafar M. H. Elmirghani, and Kumudu Munasighe 6.1 Introduction 137 6.2 Background 139 6.2.1 Rise of Micro-operators 139 6.2.2 Case for Novel Spectrum Sharing Models 140 6.2.2.1 Blockchain for Spectrum Sharing 141 6.2.2.2 Blockchain in 6G Networks 142 6.3 Architecture of an Integrated SDN and Blockchain Model 143 6.3.1 SDN Platform Design 143 6.3.2 Blockchain Network Layer Design 144 6.3.3 Network Operation and Spectrum Management 146 6.4 Simulation Design 149 6.5 Results and Analysis 152 6.5.1 Radio Access Network and Throughput 152 6.5.2 Blockchain Performance 154 6.5.3 Blockchain Scalability Performance 155 6.6 Conclusion 156 Acknowledgments 156 References 157 7 Integration of MEC and Blockchain 161 Bin Cao, Weikang Liu, and Mugen Peng 7.1 Introduction 161 7.2 Typical Framework 162 7.2.1 Blockchain-Enabled MEC 162 7.2.1.1 Background 162 7.2.1.2 Framework Description 162 7.2.2 MEC-Based Blockchain 164 7.2.2.1 Background 164 7.2.2.2 Framework Description 164 7.3 Use Cases 166 7.3.1 Security Federated Learning via MEC-Enabled Blockchain Network 166 7.3.1.1 Background 166 7.3.1.2 Blockchain-Driven Federated Learning 167 7.3.1.3 Experimental Results 168 7.3.2 Blockchain-Assisted Secure Authentication for Cross-Domain Industrial IoT 170 7.3.2.1 Background 170 7.3.2.2 Blockchain-Driven Cross-Domain Authentication 170 7.3.2.3 Experimental Results 172 7.4 Conclusion 174 References 174 8 Performance Analysis on Wireless Blockchain IoT System 179 Yao Sun, Lei Zhang, Paulo Klaine, Bin Cao, and Muhammad Ali Imran 8.1 Introduction 179 8.2 System Model 181 8.2.1 Blockchain-Enabled IoT Network Model 181 8.2.2 Wireless Communication Model 183 8.3 Performance Analysis in Blockchain-Enabled Wireless IoT Networks 184 8.3.1 Probability Density Function of SINR 185 8.3.2 TDP Transmission Successful Rate 187 8.3.3 Overall Communication Throughput 189 8.4 Optimal FN Deployment 189 8.5 Security Performance Analysis 190 8.5.1 Eclipse Attacks 190 8.5.2 Random Link Attacks 192 8.5.3 Random FN Attacks 192 8.6 Numerical Results and Discussion 192 8.6.1 Simulation Settings 193 8.6.2 Performance Evaluation without Attacks 193 8.7 Chapter Summary 197 References 197 9 Utilizing Blockchain as a Citizen-Utility for Future Smart Grids 201 Samuel Karumba, Volkan Dedeoglu, Ali Dorri, Raja Jurdak, and Salil S. Kanhere 9.1 Introduction 201 9.2 DET Using Citizen-Utilities 204 9.2.1 Prosumer Community Groups 204 9.2.1.1 Microgrids 205 9.2.1.2 Virtual Power Plants (VPP) 206 9.2.1.3 Vehicular Energy Networks (VEN) 206 9.2.2 Demand Side Management 207 9.2.2.1 Energy Efficiency 208 9.2.2.2 Demand Response 209 9.2.2.3 Spinning Reserves 210 9.2.3 Open Research Challenges 211 9.2.3.1 Scalability and IoT Overhead Issues 211 9.2.3.2 Privacy Leakage Issues 212 9.2.3.3 Standardization and Interoperability Issues 212 9.3 Improved Citizen-Utilities 213 9.3.1 Toward Scalable Citizen-Utilities 213 9.3.1.1 Challenges 213 9.3.1.2 HARB Framework-Based Citizen-Utility 214 9.3.2 Toward Privacy-Preserving Citizen-Utilities 216 9.3.2.1 Threat Model 217 9.3.2.2 PDCH System 219 9.4 Conclusions 220 References 221 10 Blockchain-enabled COVID-19 Contact Tracing Solutions 225 Hong Kang, Zaixin Zhang, Junyi Dong, Hao Xu, Paulo Valente Klaine, and Lei Zhang 10.1 Introduction 225 10.2 Preliminaries of BeepTrace 228 10.2.1 Motivation 228 10.2.1.1 Comprehensive Privacy Protection 229 10.2.1.2 Performance is Uncompromising 229 10.2.1.3 Broad Community Participation 229 10.2.1.4 Inclusiveness and Openness 230 10.2.2 Two Implementations are Based on Different Matching Protocols 230 10.3 Modes of BeepTrace 231 10.3.1 BeepTrace-Active 231 10.3.1.1 Active Mode Workflow 231 10.3.1.2 Privacy Protection of BeepTrace-Active 232 10.3.2 BeepTrace-Passive 233 10.3.2.1 Two-Chain Architecture and Workflow 233 10.3.2.2 Privacy Protection in BeepTrace-Passive 235 10.4 Future Opportunity and Conclusions 237 10.4.1 Preliminary Approach 237 10.4.2 Future Directions 238 10.4.2.1 Network Throughput and Scalability 238 10.4.2.2 Technology for Elders and Minors 239 10.4.2.3 Battery Drainage and Storage Optimization 240 10.4.2.4 Social and Economic Aspects 240 10.4.3 Concluding Remarks 240 References 241 11 Blockchain Medical Data Sharing 245 Qi Xia, Jianbin Gao, and Sandro Amofa 11.1 Introduction 245 11.1.1 General Overview 248 11.1.2 Defining Challenges 248 11.1.2.1 Data Security 248 11.1.2.2 Data Privacy 248 11.1.2.3 Source Identity 248 11.1.2.4 Data Utility 249 11.1.2.5 Data Interoperability 249 11.1.2.6 Trust 249 11.1.2.7 Data Provenance 249 11.1.2.8 Authenticity 250 11.1.3 Sharing Paradigms 250 11.1.3.1 Institution-to-Institution Data Sharing 251 11.1.3.2 Patient-to-Institution Data Sharing 256 11.1.3.3 Patient-to-Patient Data Sharing 257 11.1.4 Special Use Cases 260 11.1.4.1 Precision Medicine 261 11.1.4.2 Monetization of Medical Data 263 11.1.4.3 Patient Record Regeneration 264 11.1.5 Conclusion 266 Acknowledgments 266 References 266 12 Decentralized Content Vetting in Social Network with Blockchain 269 Subhasis Thakur and John G. Breslin 12.1 Introduction 269 12.2 Related Literature 270 12.3 Content Propagation Models in Social Network 271 12.4 Content Vetting with Blockchains 273 12.4.1 Overview of the Solution 273 12.4.2 Unidirectional Offline Channel 273 12.4.3 Content Vetting with Blockchains 275 12.5 Optimized Channel Networks 278 12.6 Simulations of Content Propagation 280 12.7 Evaluation with Simulations of Social Network 286 12.8 Conclusion 293 Acknowledgment 293 References 294 Index 297

Read more less

Book SynopsisNanotechnology Applications for Solar Energy Systems Understand the latest developments in solar nanotechnology with this comprehensive guide Solar energy has never seemed a more critical component of humanity's future. As global researchers and industries work to develop sustainable technologies and energy sources worldwide, the need to increase efficiency and decrease costs becomes paramount. Nanotechnology has the potential to play a considerable role in meeting these challenges, leading to the development of solar energy systems that overcome the limitations of existing technologies. Nanotechnology Applications for Solar Energy Systems is a comprehensive guide to the latest technological advancements and applications of nanotechnology in the field of solar energy. It analyzes nanotechnology applications across a full range of solar energy systems, reviewing feasible technological advancements for enhanced performance of solar energy devices, and discussing emerging nanomaterials Table of ContentsAbout the Editor xiii List of Contributors xv Preface xix 1 Solar Energy Applications 1 Swati Singh, Punit Singh, and Zafar Said 1.1 Introduction and Recent Advances 1 1.2 Solar Energy Applications 5 1.2.1 Electricity Production Using Photovoltaics at Large Scale 5 1.2.2 Small-Scale Electricity Production for Houses and Commercial Buildings 6 1.2.3 Off-Grid Applications Using Photovoltaics 6 1.2.4 Concentrating Solar Thermal Electricity 7 1.2.5 Solar Thermochemical Processes 7 1.2.6 Solar Water Heating 8 1.2.7 Heating of Solar Architecture 8 1.2.8 Air Conditioning Through Water Evaporation 8 1.2.9 Artificial Photosynthesis 9 1.2.10 Decomposing Waste and Biofuels Production 9 1.3 Classification of Solar Energy Devices 10 1.3.1 Concentrating Solar Power 10 1.3.2 Building Integrated Solar Systems 10 1.3.3 Solar-Thermal Collectors 11 1.3.4 Solar Thermochemistry 11 1.3.5 Solar Thermal Energy Storage 12 1.3.6 Solar-Driven Water Distillation 12 1.4 Benefits and Opportunities 13 1.5 Challenges 16 1.6 Future Aspects 18 1.7 Conclusion 18 References 19 2 Application of Nanofluid for Solar Stills 25 Mohammad Javad Raji Asadabadi , Mohsen Sheikholeslami, and Ladan Momayez 2.1 Introduction 25 2.2 Desalination Technology 25 2.2.1 What is a Solar Still? 26 2.2.2 Parameters Affecting Pure Water Yield of Basin Type SSs 27 2.2.3 Pure Water Augmentation of Solar Still Units 28 2.3 Nanofluid 33 2.3.1 Nanofluid Basics 34 2.3.2 Nanofluid Characteristics 35 2.3.3 Nanofluid Application in Solar Desalination 35 References 43 3 Classification of Concentrating Solar Collectors Based on Focusing Shape and Studying on Their Performance, Financial Evaluation, and Industrial Adoption 49 Z. Ebrahimpour and Mark Mba-Wright 3.1 Introduction 49 3.1.1 Overview of Concentrating Solar Collectors 49 3.1.2 Some of the Applications of Concentrating Solar Collectors 50 3.2 Line Focus Concentrating Solar Collectors 51 3.2.1 Linear Fresnel Reflector 51 3.2.2 Parabolic Trough Collector 53 3.2.3 Compound Parabolic 55 3.3 Point Focus and Other Concentrating Solar Collectors 57 3.3.1 Central Receiver System 57 3.3.2 Solar Dish 59 3.3.3 Fresnel Lens 60 3.4 Improving the Thermal Performance of Solar Concentrating Collectors 62 3.5 Industrial Adoption and Costs of Solar Concentrating Collectors 63 3.6 Conclusions and Recommendations 63 References 66 4 Nanotechnology for Heat Transfer 71 Zafar Said , Maham Aslam Sohail, and Evangelos Bellos 4.1 Introduction 71 4.2 Classification of Nanomaterials 72 4.2.1 Zero-dimensional (0D) 72 4.2.2 One-dimensional (1D) 72 4.2.3 Two-dimensional (2D) 72 4.2.4 Three-dimensional (3D) 73 4.3 Heat Transfer Characteristics and Applications of Nanotechnology on the Heat Transfer Enhancement 73 4.3.1 Convective Heat Transfer 75 4.3.2 Boiling Heat Transfer 77 4.3.3 Thermal Conductivity 77 4.3.4 Viscosity 78 4.4 A Review of Studies and Recent Advances Using Nanomaterials in Energy Conversion, Energy Storage, and Heat Transfer Development 79 4.5 Recent Advances 79 4.6 Challenges and Future Scope 86 4.7 Conclusion 87 References 87 5 Nanofluids in Linear Fresnel Reflector 99 Evangelos Bellos, Zafar Said, and Christos Tzivanidis 5.1 Introduction and Recent Advances of Linear Fresnel Reflectors 99 5.2 The Idea of Using Nanofluids in Solar Collectors 108 5.3 A Review of Studies with Nanofluid-based Linear Fresnel Reflector 112 5.4 Remarks and Future Scope 118 5.4.1 Advantages of LFR 118 5.4.2 Disadvantages of LFR 118 5.5 Conclusions 121 References 121 6 Thermal Management and Performance Enhancement of Parabolic Trough Concentrators Using Nanofluids 125 Muhammed A. Hassan 6.1 Introduction 125 6.2 Recent Advances of Parabolic Trough Collectors 127 6.3 Application of Nanofluids in PTCs 131 6.4 State-of-Art Studies on Using Nanofluids in Parabolic Trough Collectors 136 6.5 Conclusions and Future Scope 139 References 142 7 Developing Innovations in Parabolic Trough Collectors (PTCs) Based on Numerical Studies 145 Sanaz Akbarzadeh, Maziar Dehghan, Mohammad Sadegh Valipour, and Huijin Xu 7.1 Introduction 145 7.2 An Introduction to Simulation Software 148 7.3 Numerical Studies 148 7.3.1 Design Parameters and Working Conditions in PTCs 150 7.3.2 Using Inserts in PTCs 154 7.3.3 Using Surface Modification Methods in PTCs 157 7.3.4 Using Nanofluids in PTCs 160 7.3.5 Using Nanofluids and Other Passive Methods in PTCs 162 7.3.6 PTCs Integrated into Cooling Systems 165 7.3.7 PTCs Integrated into Concentrated Solar Power Plants 166 7.3.8 PTCs Integrated into Solar-powered Cycles 168 7.3.9 PTCs Integrated into Solar Industrial Process Heat Plants 170 7.3.10 PTCs Integrated into Photovoltaic/Thermal (PV/T) System 175 7.3.11 PTCs Integrated into Desalination Systems 175 7.4 Challenges 179 7.5 Conclusion 179 7.6 Future Directions 183 References 183 8 Nanofluids in Solar Thermal Parabolic Trough Collectors (PTCs) 191 Maziar Dehghan, Sanaz Akbarzadeh, Mohammad Sadegh Valipour, and Hafiz Muhammad Ali 8.1 Introduction 191 8.2 Fundamentals of PTCs 194 8.2.1 Components of a PTC 194 8.2.2 Mathematical Formulations of PTCs 195 8.2.3 Experimental Analysis (Standard Test Methods) 203 8.3 Heat Transfer Fluids (HTFs) in PTCs 203 8.3.1 Thermal Oils 204 8.3.2 Liquid-water Steam 204 8.3.3 Pressurized Gasses 204 8.3.4 Molten Salts 204 8.3.5 Nanofluids 204 8.4 Heat Transfer Improvement Methods in PTCs 206 8.4.1 Design Parameters 206 8.4.2 The Application of Nanofluids in PTCs 208 8.4.3 Combination of Nanofluids and Other Thermal Efficiency Enhancement Methods 219 8.5 Economic Analysis 225 8.6 Challenges 228 8.7 Conclusion 228 8.8 Future Directions 229 Acknowledgment 230 References 230 9 Applications of Nanotechnology in the Harvesting of Solar Energy 239 Seyede Mohaddese Mousavi, Zahra Sayah Alborzi, Saba Raveshiyan, and Younes Amini 9.1 Introduction 239 9.1.1 Overview of the Status of the Solar Energy 239 9.1.2 Nanotechnology Overview 240 9.2 Solar Harvesting Technology Using Nanomaterials 242 9.3 Various Modern Solar Harvesting Technologies 242 9.3.1 Solar Collectors 242 9.3.2 Fuel Cells 243 9.3.3 Photocatalysis 243 9.3.4 Solar Photovoltaics 246 9.4 Production Methods of Solar Cell Technology 247 9.4.1 First Generation Solar Cell: Silicon Solar Cells 247 9.4.2 Second Generation Solar Cells: Thin-film Solar Cell 248 9.4.3 Third Generation Solar Cells 250 9.5 Challenges in Using Nanotechnology 251 9.6 Conclusion 252 References 253 10 Tubular Solar Thermal System: Recent Development and Its Utilization 257 Arun Kumar Tiwari and Amit Kumar 10.1 Introduction 257 10.2 Different Tubular Solar System 258 10.2.1 Evacuated Tubular Collector 258 10.2.2 Tubular Solar Still 259 10.2.3 Tubular System for Concentrating Solar Power 262 10.3 Heat Transfer Fluid for the Tubular System 264 10.3.1 Nanofluid 264 10.3.2 Nano-enhanced Molten Salt 264 10.3.3 Liquid Metal 265 10.4 Conclusion 266 References 266 11 Nanofluids in Flat Plate Solar Collectors 273 L. Syam Sundar and Zafar Said 11.1 Nanofluid in Flat Plate Collector 273 11.2 Introduction and Recent Advances of Flat Plate Collectors 273 11.3 Application of Nanofluids in the Flat Plate Collector 276 11.4 A Review of Studies Using Nanomaterials in Flat Pale Collector 281 11.5 Remarks and Future Scope 284 11.6 Conclusion 284 References 285 12 Recent Advances in the Simulation of Solar Photovoltaic Cell Cooling Systems Using Nanofluids 289 Javad Mohammadpour and Fatemeh Salehi 12.1 Introduction 289 12.2 Photovoltaic Thermal (PVT) System 291 12.3 Performance Parameters 291 12.4 An Overview of Numerical Approaches 292 12.5 Previous Research on PVT Systems 294 12.5.1 PVT Nanofluid-Based Systems 294 12.5.2 PVT Multiple-Nanofluid-Based Systems 295 12.5.3 PVT/ PCM Nanofluid-Based Systems 298 12.5.4 Economic Analysis in PVT Studies 299 12.6 Future Works 304 12.7 Conclusions 306 References 306 13 Multiphase Modeling of Powder Flow in an Ejector of Solar-driven Refrigeration System by Eulerian-Lagrangian Approach 313 Mohit Biglarian, Ahmadreza Najafi, Morsal Momeni Larimi, and Masih Parhizkari 13.1 Introduction 313 13.2 Governing Equations 314 13.2.1 Continuity Equation 314 13.2.2 Momentum Equation 314 13.3 Geometry Design and Meshing 315 13.3.1 Generation of the Model 315 13.3.2 Mesh Generation and Study 315 13.3.3 Grid Independency 318 13.3.4 Validation 319 13.4 Results 319 13.4.1 Optimization of the Nozzle 319 13.4.2 Investigation of the Relation between Outlet Velocity and Entrainment Parameter (N) 326 13.4.3 Unsteady Case 327 13.5 Conclusion 335 Declaration of interests 335 References 335 14 Radiative Non-Newtonian Nanofluid Flow through Stretchable Disks: An Application to Solar Thermal Systems 337 S. A. Shehzad, A. Rauf, and M. Omar 14.1 Introduction 337 14.2 Problem Formulation 339 14.3 Numerical Solution 343 14.4 Results and Discussion 344 14.5 Conclusions 351 References 352 15 Cooling of PV/ T System with Nanofluid and PCM 355 Mohit Barthwal, Dibakar Rakshit, and Sujit Kr. Verma 15.1 Introduction 355 15.1.1 Overview 355 15.1.2 Need for Cooling of Photovoltaics 356 15.2 Application of Nanofluid and PCM for Cooling of PV/T System 359 15.2.1 Nanofluids 359 15.2.2 Phase Change Materials 360 15.3 A Review of Studies Using Nanofluid and PCM for Cooling of PV/T System 361 15.4 Remarks and Future Scope 374 15.5 Conclusion 376 Acknowledgment 376 References 377 16 Revival of Functional Nanofluid Photothermal Materials for Solar Still Applications 381 Muhammad Sultan Irshad, Naila Arshad, and Xianbao Wang 16.1 Nanofluid Based Solar Stills 381 16.2 General Factors for Efficient Solar Still 384 16.2.1 Environmental Factors 384 16.2.2 Physical Factors 385 16.3 Development and Modifications 386 16.3.1 Conventional Single-effect Solar Still 386 16.3.2 Solar Reflectors 387 16.3.3 Wicked Type Solar Stills 388 16.4 Application of Nanofluids in Solar Still 388 16.4.1 Methodologies for the Fabrication of Nanofluids 389 16.4.2 Optical Properties of Nanofluids 389 16.4.3 Photothermal of Nanofluids 391 16.5 Carbon-based Nanofluid 391 16.6 Metallic/ Metal Oxide Nanofluids 392 16.7 Magnetic Nanofluids 394 16.8 Solar Thermal Collectors 395 16.9 Solar-driven Steam Generators 397 16.10 Remarks and Future Scope 398 16.11 Conclusion 399 References 400 17 Nanotechnology in Solar Lighting 403 Chao Shen, Changyun Ruan, and Guoquan lv 17.1 Optical Fiber Lighting Based on Sunlight 403 17.2 Radiation Properties of Nanoparticles 405 17.3 Spectral Control of Nanofluid 406 17.3.1 Full Spectrum Absorption Based on Nanofluids 406 17.3.2 Thermal/Electrical Decoupling Control Based on Nanofluids 407 17.4 Design of a Solar Lighting/Heating System 408 17.5 Selection of Nanofluids for the Solar Lighting/Heating System 409 17.6 System Efficiency of the Solar Lighting/Heating System 410 17.7 Spectral Characteristics of Output Light of the Solar Lighting/Heating System 411 17.8 Future Research 413 17.9 Conclusion 414 References 415 Index 421

Read more less

Book SynopsisSince the first edition in 1948, Patty's Industrial Hygiene and Toxicology has become a flagship publication for Wiley. During its nearly seven decades in print, it has become a standard reference for the fields of occupational health and toxicology. The volumes on industrial hygiene are cornerstone reference works for not only industrial hygienists but also chemists, engineers, toxicologists, lawyers, and occupational safety personnel. Volume 1 covers Introduction of Industrial Hygiene and Recognition of Chemical Agents. In addition to revised and updated chapters, a number of new chapters reflect current technology and concerns. The chapters include Ethics in Industrial Hygiene, Prevention through Design, Risk Communication, Managing Workplace Demographics, and Mastering Digital Media for Workers, Employers and Community Practice.Table of ContentsContributors viiPreface ixUseful Equivalents and Conversion Factors xi Part I Introduction To Industrial Hygiene 1Occupational and Industrial Hygiene as a Profession: Yesterday, Today, and Tomorrow 3Barbara J. Dawson, Kyle B. Dotson, Faye Grimsley, Thomas Grumbles, Zack Mansdorf, David Roskelley, Jennifer Sahmel, Noel Tresider, and Candace Tsai Ethics in Industrial Hygiene 19Nina Townsend, Garrett Brown, and Mark Katchen Prevention through Design 31Georgi Popov, Bruce Lyon, and Tsvetan Popov Risk Communication 51David M. Zalk Health Risk Assessment in the Workplace 67Chris Laszcz-Davis, Fred W. Boelter, Michael Jayjock, Frank Hearl, Perry Logan, Cristina Ford McLaughlin, Mary V. O’Reilly, R. Thomas Radcliffe Jr., Esquire, and Mark Stenzel Decision Making in Managing Risk 103Charles F. Redinger, Fred W. Boelter, Mary V. O’Reilly, John Howard and Glenn J. Barbi Managing Workplace Demographics 127John Howard Mastering Digital Media for Workers, Employers, and Our Community of Practice 139Max Lum Part II Chemical Agents 159The History and Biological Basis of Occupational Exposure Limits for Chemical Agents 161Dennis J. Paustenbach and William D. Cyrs The Mode of Absorption, Distribution, and Elimination of Toxic Materials 213Franklin E. Mirer Symptomatic Responses to Low-Level Occupational and Environmental Exposures 241Brian Linde and Carrie A. Redlich Basic Aerosol Science 255Parker C. Reist and Yifang Zhu Pulmonary Effects of Inhaled Mineral Dusts 283David Fishwick and Chris M. Barber Engineered Nanomaterials 311Thomas M. Peters and Peter C. Raynor Gases and Vapors Affecting the Respiratory System 333Philip Harber, William S. Beckett, and Marion J. Fedoruk Dermal Effects of Chemical Exposures 361Katherine J. Allnutt and Rosemary L. Nixon Analytical Methods 383Robert G. Lieckfield Jr. Index 401

Read more less

Book Synopsis Since the first edition in 1948, Patty's Industrial Hygiene and Toxicology has become a flagship publication for Wiley. During its nearly seven decades in print, it has become a standard reference for the fields of occupational health and toxicology. The volumes on industrial hygiene are cornerstone reference works for not only industrial hygienists but also chemists, engineers, toxicologists, lawyers, and occupational safety personnel. Volume 2 covers Chemical Exposure Evaluation and Control. Along with the updated and revised chapters from the prior edition, this volume has two new chapters: Sensor Technology and Control Banding. Table of ContentsContributors vii Preface ix Useful Equivalents and Conversion Factors xi Part III Chemical Exposure Evaluation 1 Biological Monitoring of Exposure to Industrial Chemicals 3 Nancy B. Hopf and Silvia Fustinoni Real-Time Assessment of Air Contaminants Using Video Exposure Monitoring (VEM) Methods and Techniques 65 James D. McGlothlin, Fan Xu, Sandra S. Cole, and Dave Huizen Computed Tomography in Industrial Hygiene 95 Lori A. Todd Mathematical Modeling of Indoor Air Contaminant Concentrations 121 Mark Nicas and Thomas W. Armstrong Sensor 145 Misti L. Zamora, Christopher Zuidema, and Kirsten Koehler Part IV Chemical Exposure Control 161 Characterizing Air Contaminant Emission Sources 163 D. Jeff Burton, Robert L. Harris, and Earl W. Arp Engineering Control of Airborne Contaminants: History, Philosophy, and the Development of Primary Approaches 177 D. Jeff Burton and William A. Burgess Industrial Ventilation 199 D. Jeff Burton and Robert D. Soule Respiratory Protective Equipment 225 Craig E. Colton Chemical Protective Clothing 251 Krister Forsberg and James P. Zeigler Control Banding: Background, Evolution, and Application 269 David M. Zalk, Elaine West, and Deborah I. Nelson Occupational Safety and Health Law 307 John Howard and Steven Smith Index 357

Read more less

Book SynopsisAdvanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. DeeTable of ContentsPreface xix Part 1: Introduction to Computer Vision 1 1 Artificial Intelligence in Language Learning: Practices and Prospects 3Khushboo Kuddus 1.1 Introduction 4 1.2 Evolution of CALL 5 1.3 Defining Artificial Intelligence 7 1.4 Historical Overview of AI in Education and Language Learning 7 1.5 Implication of Artificial Intelligence in Education 8 1.5.1 Machine Translation 9 1.5.2 Chatbots 9 1.5.3 Automatic Speech Recognition Tools 9 1.5.4 Autocorrect/Automatic Text Evaluator 11 1.5.5 Vocabulary Training Applications 12 1.5.6 Google Docs Speech Recognition 12 1.5.7 Language MuseTM Activity Palette 13 1.6 Artificial Intelligence Tools Enhance the Teaching and Learning Processes 13 1.6.1 Autonomous Learning 13 1.6.2 Produce Smart Content 13 1.6.3 Task Automation 13 1.6.4 Access to Education for Students with Physical Disabilities 14 1.7 Conclusion 14 References 15 2 Real Estate Price Prediction Using Machine Learning Algorithms 19Palak Furia and Anand Khandare 2.1 Introduction 20 2.2 Literature Review 20 2.3 Proposed Work 21 2.3.1 Methodology 21 2.3.2 Work Flow 22 2.3.3 The Dataset 22 2.3.4 Data Handling 23 2.3.4.1 Missing Values and Data Cleaning 23 2.3.4.2 Feature Engineering 24 2.3.4.3 Removing Outliers 25 2.4 Algorithms 27 2.4.1 Linear Regression 27 2.4.2 LASSO Regression 27 2.4.3 Decision Tree 28 2.4.4 Support Vector Machine 28 2.4.5 Random Forest Regressor 28 2.4.6 XGBoost 29 2.5 Evaluation Metrics 29 2.6 Result of Prediction 30 References 31 3 Multi-Criteria–Based Entertainment Recommender System Using Clustering Approach 33Chandramouli Das, Abhaya Kumar Sahoo and Chittaranjan Pradhan 3.1 Introduction 34 3.2 Work Related Multi-Criteria Recommender System 35 3.3 Working Principle 38 3.3.1 Modeling Phase 39 3.3.2 Prediction Phase 39 3.3.3 Recommendation Phase 40 3.3.4 Content-Based Approach 40 3.3.5 Collaborative Filtering Approach 41 3.3.6 Knowledge-Based Filtering Approach 41 3.4 Comparison Among Different Methods 42 3.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis 42 3.4.1.1 Discussion and Result 43 3.4.2 User Preference Learning in Multi-Criteria Recommendation Using Stacked Autoencoders by Tallapally et al. 46 3.4.2.1 Dataset and Evaluation Matrix 46 3.4.2.2 Training Setting 49 3.4.2.3 Result 49 3.4.3 Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts by Zheng 49 3.4.3.1 Evaluation Setting 50 3.4.3.2 Experimental Result 50 3.4.4 Utility-Based Multi-Criteria Recommender Systems by Zheng 51 3.4.4.1 Experimental Dataset 51 3.4.4.2 Experimental Result 52 3.4.5 Multi-Criteria Clustering Approach by Wasid and Ali 53 3.4.5.1 Experimental Evaluation 53 3.4.5.2 Result and Analysis 53 3.5 Advantages of Multi-Criteria Recommender System 54 3.5.1 Revenue 57 3.5.2 Customer Satisfaction 57 3.5.3 Personalization 57 3.5.4 Discovery 58 3.5.5 Provide Reports 58 3.6 Challenges of Multi-Criteria Recommender System 58 3.6.1 Cold Start Problem 58 3.6.2 Sparsity Problem 59 3.6.3 Scalability 59 3.6.4 Over Specialization Problem 59 3.6.5 Diversity 59 3.6.6 Serendipity 59 3.6.7 Privacy 60 3.6.8 Shilling Attacks 60 3.6.9 Gray Sheep 60 3.7 Conclusion 60 References 61 4 Adoption of Machine/Deep Learning in Cloud With a Case Study on Discernment of Cervical Cancer65Jyothi A. P., S. Usha and Archana H. R. 4.1 Introduction 66 4.2 Background Study 69 4.3 Overview of Machine Learning/Deep Learning 72 4.4 Connection Between Machine Learning/Deep Learning and Cloud Computing 74 4.5 Machine Learning/Deep Learning Algorithm 74 4.5.1 Supervised Learning 74 4.5.2 Unsupervised Learning 77 4.5.3 Reinforcement or Semi-Supervised Learning 77 4.5.3.1 Outline of ML Algorithms 77 4.6 A Project Implementation on Discernment of Cervical Cancer by Using Machine/Deep Learning in Cloud 93 4.6.1 Proposed Work 94 4.6.1.1 MRI Dataset 94 4.6.1.2 Pre Processing 95 4.6.1.3 Feature Extraction 96 4.6.2 Design Methodology and Implementation 97 4.6.3 Results 100 4.7 Applications 101 4.7.1 Cognitive Cloud 102 4.7.2 Chatbots and Smart Personal Assistants 103 4.7.3 IoT Cloud 103 4.7.4 Business Intelligence 103 4.7.5 AI-as-a-Service 104 4.8 Advantages of Adoption of Cloud in Machine Learning/ Deep Learning 104 4.9 Conclusion 105 References 106 5 Machine Learning and Internet of Things–Based Models for Healthcare Monitoring 111Shruti Kute, Amit Kumar Tyagi, Aswathy S.U. and Shaveta Malik 5.1 Introduction 112 5.2 Literature Survey 113 5.3 Interpretable Machine Learning in Healthcare 114 5.4 Opportunities in Machine Learning for Healthcare 116 5.5 Why Combining IoT and ML? 119 5.5.1 ML-IoT Models for Healthcare Monitoring 119 5.6 Applications of Machine Learning in Medical and Pharma 121 5.7 Challenges and Future Research Direction 122 5.8 Conclusion 123 References 123 6 Machine Learning–Based Disease Diagnosis and Prediction for E-Healthcare System 127Shruti Suhas Kute, Shreyas Madhav A. V., Shabnam Kumari and Aswathy S. U. 6.1 Introduction 128 6.2 Literature Survey 129 6.3 Machine Learning Applications in Biomedical Imaging 132 6.4 Brain Tumor Classification Using Machine Learning and IoT 134 6.5 Early Detection of Dementia Disease Using Machine Learning and IoT-Based Applications 135 6.6 IoT and Machine Learning-Based Diseases Prediction and Diagnosis System for EHRs 137 6.7 Machine Learning Applications for a Real-Time Monitoring of Arrhythmia Patients Using IoT 140 6.8 IoT and Machine Learning–Based System for Medical Data Mining 141 6.9 Conclusion and Future Works 143 References 144 Part 2: Introduction to Deep Learning and its Models 149 7 Deep Learning Methods for Data Science 151K. Indira, Kusumika Krori Dutta, S. Poornima and Sunny Arokia Swamy Bellary 7.1 Introduction 152 7.2 Convolutional Neural Network 152 7.2.1 Architecture 154 7.2.2 Implementation of CNN 154 7.2.3 Simulation Results 157 7.2.4 Merits and Demerits 158 7.2.5 Applications 159 7.3 Recurrent Neural Network 159 7.3.1 Architecture 160 7.3.2 Types of Recurrent Neural Networks 161 7.3.2.1 Simple Recurrent Neural Networks 161 7.3.2.2 Long Short-Term Memory Networks 162 7.3.2.3 Gated Recurrent Units (GRUs) 164 7.3.3 Merits and Demerits 167 7.3.3.1 Merits 167 7.3.3.2 Demerits 167 7.3.4 Applications 167 7.4 Denoising Autoencoder 168 7.4.1 Architecture 169 7.4.2 Merits and Demerits 169 7.4.3 Applications 170 7.5 Recursive Neural Network (RCNN) 170 7.5.1 Architecture 170 7.5.2 Merits and Demerits 172 7.5.3 Applications 172 7.6 Deep Reinforcement Learning 173 7.6.1 Architecture 174 7.6.2 Merits and Demerits 174 7.6.3 Applications 174 7.7 Deep Belief Networks (DBNS) 175 7.7.1 Architecture 176 7.7.2 Merits and Demerits 176 7.7.3 Applications 176 7.8 Conclusion 177 References 177 8 A Proposed LSTM-Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG 181Rupali Gill and Jaiteg Singh 8.1 Introduction 182 8.2 Background and Motivation 183 8.2.1 Emotion Model 183 8.2.2 Neuromarketing and BCI 184 8.2.3 EEG Signal 185 8.3 Related Work 185 8.3.1 Machine Learning 186 8.3.2 Deep Learning 191 8.3.2.1 Fast Feed Neural Networks 193 8.3.2.2 Recurrent Neural Networks 193 8.3.2.3 Convolutional Neural Networks 194 8.4 Methodology of Proposed System 195 8.4.1 DEAP Dataset 196 8.4.2 Analyzing the Dataset 196 8.4.3 Long Short-Term Memory 197 8.4.4 Experimental Setup 197 8.4.5 Data Set Collection 197 8.5 Results and Discussions 198 8.5.1 LSTM Model Training and Accuracy 198 8.6 Conclusion 199 References 199 9 An Extensive Survey of Applications of Advanced Deep Learning Algorithms on Detection of Neurodegenerative Diseases and the Tackling Procedure in Their Treatment Protocol 207Vignesh Baalaji S., Vergin Raja Sarobin M., L. Jani Anbarasi, Graceline Jasmine S. and Rukmani P. 9.1 Introduction 208 9.2 Story of Alzheimer’s Disease 208 9.3 Datasets 210 9.3.1 ADNI 210 9.3.2 OASIS 210 9.4 Story of Parkinson’s Disease 211 9.5 A Review on Learning Algorithms 212 9.5.1 Convolutional Neural Network (CNN) 212 9.5.2 Restricted Boltzmann Machine 213 9.5.3 Siamese Neural Networks 213 9.5.4 Residual Network (ResNet) 214 9.5.5 U-Net 214 9.5.6 LSTM 214 9.5.7 Support Vector Machine 215 9.6 A Review on Methodologies 215 9.6.1 Prediction of Alzheimer’s Disease 215 9.6.2 Prediction of Parkinson’s Disease 221 9.6.3 Detection of Attacks on Deep Brain Stimulation 223 9.7 Results and Discussion 224 9.8 Conclusion 224 References 227 10 Emerging Innovations in the Near Future Using Deep Learning Techniques 231Akshara Pramod, Harsh Sankar Naicker and Amit Kumar Tyagi 10.1 Introduction 232 10.2 Related Work 234 10.3 Motivation 235 10.4 Future With Deep Learning/Emerging Innovations in Near Future With Deep Learning 236 10.4.1 Deep Learning for Image Classification and Processing 237 10.4.2 Deep Learning for Medical Image Recognition 237 10.4.3 Computational Intelligence for Facial Recognition 238 10.4.4 Deep Learning for Clinical and Health Informatics 238 10.4.5 Fuzzy Logic for Medical Applications 239 10.4.6 Other Intelligent-Based Methods for Biomedical and Healthcare 239 10.4.7 Other Applications 239 10.5 Open Issues and Future Research Directions 244 10.5.1 Joint Representation Learning From User and Item Content Information 244 10.5.2 Explainable Recommendation With Deep Learning 245 10.5.3 Going Deeper for Recommendation 245 10.5.4 Machine Reasoning for Recommendation 246 10.5.5 Cross Domain Recommendation With Deep Neural Networks 246 10.5.6 Deep Multi-Task Learning for Recommendation 247 10.5.7 Scalability of Deep Neural Networks for Recommendation 247 10.5.8 Urge for a Better and Unified Evaluation 248 10.6 Deep Learning: Opportunities and Challenges 249 10.7 Argument with Machine Learning and Other Available Techniques 250 10.8 Conclusion With Future Work 251 Acknowledgement 252 References 252 11 Optimization Techniques in Deep Learning Scenarios: An Empirical Comparison 255Ajeet K. Jain, PVRD Prasad Rao and K. Venkatesh Sharma 11.1 Introduction 256 11.1.1 Background and Related Work 256 11.2 Optimization and Role of Optimizer in DL 258 11.2.1 Deep Network Architecture 259 11.2.2 Proper Initialization 260 11.2.3 Representation, Optimization, and Generalization 261 11.2.4 Optimization Issues 261 11.2.5 Stochastic GD Optimization 262 11.2.6 Stochastic Gradient Descent with Momentum 263 11.2.7 SGD With Nesterov Momentum 264 11.3 Various Optimizers in DL Practitioner Scenario 265 11.3.1 AdaGrad Optimizer 265 11.3.2 RMSProp 267 11.3.3 Adam 267 11.3.4 AdaMax 269 11.3.5 AMSGrad 269 11.4 Recent Optimizers in the Pipeline 270 11.4.1 EVE 270 11.4.2 RAdam 271 11.4.3 MAS (Mixing ADAM and SGD) 271 11.4.4 Lottery Ticket Hypothesis 272 11.5 Experiment and Results 273 11.5.1 Web Resource 273 11.5.2 Resource 277 11.6 Discussion and Conclusion 278 References 279 Part 3: Introduction to Advanced Analytics 283 12 Big Data Platforms 285Sharmila Gaikwad and Jignesh Patil 12.1 Visualization in Big Data 286 12.1.1 Introduction to Big Data 286 12.1.2 Techniques of Visualization 287 12.1.3 Case Study on Data Visualization 302 12.2 Security in Big Data 305 12.2.1 Introduction of Data Breach 305 12.2.2 Data Security Challenges 306 12.2.3 Data Breaches 307 12.2.4 Data Security Achieved 307 12.2.5 Findings: Case Study of Data Breach 309 12.3 Conclusion 309 References 309 13 Smart City Governance Using Big Data Technologies 311K. Raghava Rao and D. Sateesh Kumar 13.1 Objective 312 13.2 Introduction 312 13.3 Literature Survey 314 13.4 Smart Governance Status 314 13.4.1 International 314 13.4.2 National 316 13.5 Methodology and Implementation Approach 318 13.5.1 Data Generation 319 13.5.2 Data Acquisition 319 13.5.3 Data Analytics 319 13.6 Outcome of the Smart Governance 322 13.7 Conclusion 323 References 323 14 Big Data Analytics With Cloud, Fog, and Edge Computing 325Deepti Goyal, Amit Kumar Tyagi and Aswathy S. U. 14.1 Introduction to Cloud, Fog, and Edge Computing 326 14.2 Evolution of Computing Terms and Its Related Works 330 14.3 Motivation 332 14.4 Importance of Cloud, Fog, and Edge Computing in Various Applications 333 14.5 Requirement and Importance of Analytics (General) in Cloud, Fog, and Edge Computing 334 14.6 Existing Tools for Making a Reliable Communication and Discussion of a Use Case (with Respect to Cloud, Fog, and Edge Computing) 335 14.6.1 CloudSim 335 14.6.2 SPECI 336 14.6.3 Green Cloud 336 14.6.4 OCT (Open Cloud Testbed) 337 14.6.5 Open Cirrus 337 14.6.6 GroudSim 338 14.6.7 Network CloudSim 338 14.7 Tools Available for Advanced Analytics (for Big Data Stored in Cloud, Fog, and Edge Computing Environment) 338 14.7.1 Microsoft HDInsight 338 14.7.2 Skytree 339 14.7.3 Splice Machine 339 14.7.4 Spark 339 14.7.5 Apache SAMOA 339 14.7.6 Elastic Search 339 14.7.7 R-Programming 339 14.8 Importance of Big Data Analytics for Cyber-Security and Privacy for Cloud-IoT Systems 340 14.8.1 Risk Management 340 14.8.2 Predictive Models 340 14.8.3 Secure With Penetration Testing 340 14.8.4 Bottom Line 341 14.8.5 Others: Internet of Things-Based Intelligent Applications 341 14.9 An Use Case with Real World Applications (with Respect to Big Data Analytics) Related to Cloud, Fog, and Edge Computing 341 14.10 Issues and Challenges Faced by Big Data Analytics (in Cloud, Fog, and Edge Computing Environments) 342 14.10.1 Cloud Issues 343 14.11 Opportunities for the Future in Cloud, Fog, and Edge Computing Environments (or Research Gaps) 344 14.12 Conclusion 345 References 346 15 Big Data in Healthcare: Applications and Challenges 351V. Shyamala Susan, K. Juliana Gnana Selvi and Ir. Bambang Sugiyono Agus Purwono 15.1 Introduction 352 15.1.1 Big Data in Healthcare 352 15.1.2 The 5V’s Healthcare Big Data Characteristics 353 15.1.2.1 Volume 353 15.1.2.2 Velocity 353 15.1.2.3 Variety 353 15.1.2.4 Veracity 353 15.1.2.5 Value 353 15.1.3 Various Varieties of Big Data Analytical (BDA) in Healthcare 353 15.1.4 Application of Big Data Analytics in Healthcare 354 15.1.5 Benefits of Big Data in the Health Industry 355 15.2 Analytical Techniques for Big Data in Healthcare 356 15.2.1 Platforms and Tools for Healthcare Data 357 15.3 Challenges 357 15.3.1 Storage Challenges 357 15.3.2 Cleaning 358 15.3.3 Data Quality 358 15.3.4 Data Security 358 15.3.5 Missing or Incomplete Data 358 15.3.6 Information Sharing 358 15.3.7 Overcoming the Big Data Talent and Cost Limitations 359 15.3.8 Financial Obstructions 359 15.3.9 Volume 359 15.3.10 Technology Adoption 360 15.4 What is the Eventual Fate of Big Data in Healthcare Services? 360 15.5 Conclusion 361 References 361 16 The Fog/Edge Computing: Challenges, Serious Concerns, and the Road Ahead 365Varsha. R., Siddharth M. Nair and Amit Kumar Tyagi 16.1 Introduction 366 16.1.1 Organization of the Work 368 16.2 Motivation 368 16.3 Background 369 16.4 Fog and Edge Computing–Based Applications 371 16.5 Machine Learning and Internet of Things–Based Cloud, Fog, and Edge Computing Applications 374 16.6 Threats Mitigated in Fog and Edge Computing–Based Applications 376 16.7 Critical Challenges and Serious Concerns Toward Fog/Edge Computing and Its Applications 378 16.8 Possible Countermeasures 381 16.9 Opportunities for 21st Century Toward Fog and Edge Computing 383 16.9.1 5G and Edge Computing as Vehicles for Transformation of Mobility in Smart Cities 383 16.9.2 Artificial Intelligence for Cloud Computing and Edge Computing 384 16.10 Conclusion 387 References 387 Index 391

Read more less

Book SynopsisDATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today's world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward Table of ContentsPreface xvii 1 Introduction to Data Mining 1Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Sandeep Kumar 1.1. Introduction 1 1.1.1 Data Mining 1 1.2 Knowledge Discovery in Database (KDD) 2 1.2.1 Importance of Data Mining 3 1.2.2 Applications of Data Mining 3 1.2.3 Databases 4 1.3 Issues in Data Mining 6 1.4 Data Mining Algorithms 7 1.5 Data Warehouse 9 1.6 Data Mining Techniques 10 1.7 Data Mining Tools 11 1.7.1 Python for Data Mining 12 1.7.2 KNIME 13 1.7.3 Rapid Miner 17 References 18 2 Classification and Mining Behavior of Data 21Srinivas Konda, Kavitarani Balmuri and Kishore Kumar Mamidala 2.1 Introduction 22 2.2 Main Characteristics of Mining Behavioral Data 23 2.2.1 Mining Dynamic/Streaming Data 23 2.2.2 Mining Graph & Network Data 24 2.2.3 Mining Heterogeneous/Multi-Source Information 25 2.2.3.1 Multi-Source and Multidimensional Information 26 2.2.3.2 Multi-Relational Data 26 2.2.3.3 Background and Connected Data 27 2.2.3.4 Complex Data, Sequences, and Events 27 2.2.3.5 Data Protection and Morals 27 2.2.4 Mining High Dimensional Data 28 2.2.5 Mining Imbalanced Data 29 2.2.5.1 The Class Imbalance Issue 29 2.2.6 Mining Multimedia Data 30 2.2.6.1 Common Applications Multimedia Data Mining 31 2.2.6.2 Multimedia Data Mining Utilizations 31 2.2.6.3 Multimedia Database Management 32 2.2.7 Mining Scientific Data 34 2.2.8 Mining Sequential Data 35 2.2.9 Mining Social Networks 36 2.2.9.1 Social-Media Data Mining Reasons 39 2.2.10 Mining Spatial and Temporal Data 40 2.2.10.1 Utilizations of Spatial and Temporal Data Mining 41 2.3 Research Method 44 2.4 Results 48 2.5 Discussion 49 2.6 Conclusion 50 References 51 3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects 57Rakhi Seth and Aakanksha Sharaff 3.1 Introduction 58 3.2 Related Work on Different Recommender System 60 3.2.1 Challenges in RS 65 3.2.2 Research Questions and Architecture of This Paper 66 3.2.3 Background 68 3.2.3.1 The Architecture of Hybrid Approach 69 3.2.4 Analysis 78 3.2.4.1 Evaluation Measures 78 3.2.5 Materials and Methods 81 3.2.6 Comparative Analysis With Traditional Recommender System 85 3.2.7 Practical Implications 85 3.2.8 Conclusion & Future Work 94 References 94 4 Stream Mining: Introduction, Tools & Techniques and Applications 99Naresh Kumar Nagwani 4.1 Introduction 100 4.2 Data Reduction: Sampling and Sketching 101 4.2.1 Sampling 101 4.2.2 Sketching 102 4.3 Concept Drift 103 4.4 Stream Mining Operations 105 4.4.1 Clustering 105 4.4.2 Classification 106 4.4.3 Outlier Detection 107 4.4.4 Frequent Itemsets Mining 108 4.5 Tools & Techniques 109 4.5.1 Implementation in Java 110 4.5.2 Implementation in Python 116 4.5.3 Implementation in R 118 4.6 Applications 120 4.6.1 Stock Prediction in Share Market 120 4.6.2 Weather Forecasting System 121 4.6.3 Finding Trending News and Events 121 4.6.4 Analyzing User Behavior in Electronic Commerce Site (Click Stream) 121 4.6.5 Pollution Control Systems 122 4.7 Conclusion 122 References 122 5 Data Mining Tools and Techniques: Clustering Analysis 125Rohit Miri, Amit Kumar Dewangan, S.R. Tandan, Priya Bhatnagar and Hiral Raja 5.1 Introduction 126 5.2 Data Mining Task 129 5.2.1 Data Summarization 129 5.2.2 Data Clustering 129 5.2.3 Classification of Data 129 5.2.4 Data Regression 130 5.2.5 Data Association 130 5.3 Data Mining Algorithms and Methodologies 131 5.3.1 Data Classification Algorithm 131 5.3.2 Predication 132 5.3.3 Association Rule 132 5.3.4 Neural Network 132 5.3.4.1 Data Clustering Algorithm 133 5.3.5 In-Depth Study of Gathering Techniques 134 5.3.6 Data Partitioning Method 134 5.3.7 Hierarchical Method 134 5.3.8 Framework-Based Method 136 5.3.9 Model-Based Method 136 5.3.10 Thickness-Based Method 136 5.4 Clustering the Nearest Neighbor 136 5.4.1 Fuzzy Clustering 137 5.4.2 K-Algorithm Means 137 5.5 Data Mining Applications 138 5.6 Materials and Strategies for Document Clustering 140 5.6.1 Features Generation 142 5.7 Discussion and Results 143 5.7.1 Discussion 146 5.7.2 Conclusion 149 References 149 6 Data Mining Implementation Process 151Kamal K. Mehta, Rajesh Tiwari and Nishant Behar 6.1 Introduction 151 6.2 Data Mining Historical Trends 152 6.3 Processes of Data Analysis 153 6.3.1 Data Attack 153 6.3.2 Data Mixing 153 6.3.3 Data Collection 153 6.3.4 Data Conversion 154 6.3.4.1 Data Mining 154 6.3.4.2 Design Evaluation 154 6.3.4.3 Data Illustration 154 6.3.4.4 Implementation of Data Mining in the Cross-Industry Standard Process 154 6.3.5 Business Understanding 155 6.3.6 Data Understanding 156 6.3.7 Data Preparation 158 6.3.8 Modeling 159 6.3.9 Evaluation 160 6.3.10 Deployment 161 6.3.11 Contemporary Developments 162 6.3.12 An Assortment of Data Mining 162 6.3.12.1 Using Computational & Connectivity Tools 163 6.3.12.2 Web Mining 163 6.3.12.3 Comparative Statement 163 6.3.13 Advantages of Data Mining 163 6.3.14 Drawbacks of Data Mining 165 6.3.15 Data Mining Applications 165 6.3.16 Methodology 167 6.3.17 Results 169 6.3.18 Conclusion and Future Scope 171 References 172 7 Predictive Analytics in IT Service Management (ITSM) 175Sharon Christa I.L. and Suma V. 7.1 Introduction 176 7.2 Analytics: An Overview 178 7.2.1 Predictive Analytics 180 7.3 Significance of Predictive Analytics in ITSM 181 7.4 Ticket Analytics: A Case Study 186 7.4.1 Input Parameters 188 7.4.2 Predictive Modeling 188 7.4.3 Random Forest Model 189 7.4.4 Performance of the Predictive Model 191 7.5 Conclusion 191 References 192 8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques 195K. Ramya Laxmi, Sumit Srivastava, K. Madhuravani, S. Pallavi and Omprakash Dewangan 8.1 Introduction 196 8.2 Literature Review 198 8.3 Methodology and Implementation 200 8.3.1 Selection of the Independent Variables 200 8.4 Data Partitioning 203 8.4.1 Interpreting the Results of Logistic Regression Model 203 8.5 Conclusions 204 References 205 9 Inductive Learning Including Decision Tree and Rule Induction Learning 209Raj Kumar Patra, A. Mahendar and G. Madhukar 9.1 Introduction 210 9.2 The Inductive Learning Algorithm (ILA) 212 9.3 Proposed Algorithms 213 9.4 Divide & Conquer Algorithm 214 9.4.1 Decision Tree 214 9.5 Decision Tree Algorithms 215 9.5.1 ID3 Algorithm 215 9.5.2 Separate and Conquer Algorithm 217 9.5.3 RULE EXTRACTOR-1 226 9.5.4 Inductive Learning Applications 226 9.5.4.1 Education 226 9.5.4.2 Making Credit Decisions 227 9.5.5 Multidimensional Databases and OLAP 228 9.5.6 Fuzzy Choice Trees 228 9.5.7 Fuzzy Choice Tree Development From a Multidimensional Database 229 9.5.8 Execution and Results 230 9.6 Conclusion and Future Work 231 References 232 10 Data Mining for Cyber-Physical Systems 235M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi and Shaik Munawar 10.1 Introduction 236 10.1.1 Models of Cyber-Physical System 238 10.1.2 Statistical Model-Based Methodologies 239 10.1.3 Spatial-and-Transient Closeness-Based Methodologies 240 10.2 Feature Recovering Methodologies 240 10.3 CPS vs. IT Systems 241 10.4 Collections, Sources, and Generations of Big Data for CPS 242 10.4.1 Establishing Conscious Computation and Information Systems 243 10.5 Spatial Prediction 243 10.5.1 Global Optimization 244 10.5.2 Big Data Analysis CPS 245 10.5.3 Analysis of Cloud Data 245 10.5.4 Analysis of Multi-Cloud Data 247 10.6 Clustering of Big Data 248 10.7 NoSQL 251 10.8 Cyber Security and Privacy Big Data 251 10.8.1 Protection of Big Computing and Storage 252 10.8.2 Big Data Analytics Protection 252 10.8.3 Big Data CPS Applications 256 10.9 Smart Grids 256 10.10 Military Applications 258 10.11 City Management 259 10.12 Clinical Applications 261 10.13 Calamity Events 262 10.14 Data Streams Clustering by Sensors 263 10.15 The Flocking Model 263 10.16 Calculation Depiction 264 10.17 Initialization 265 10.18 Representative Maintenance and Clustering 266 10.19 Results 267 10.20 Conclusion 268 References 269 11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining 281Vivek Parganiha, Soorya Prakash Shukla and Lokesh Kumar Sharma 11.1 Introduction 282 11.2 Background 283 11.3 Methodology of CRISP-DM 284 11.4 Stage One—Determine Business Objectives 286 11.4.1 What Are the Ideal Yields of the Venture? 287 11.4.2 Evaluate the Current Circumstance 288 11.4.3 Realizes Data Mining Goals 289 11.5 Stage Two—Data Sympathetic 290 11.5.1 Portray Data 291 11.5.2 Investigate Facts 291 11.5.3 Confirm Data Quality 292 11.5.4 Data Excellence Description 292 11.6 Stage Three—Data Preparation 292 11.6.1 Select Your Data 294 11.6.2 The Data Is Processed 294 11.6.3 Data Needed to Build 294 11.6.4 Combine Information 295 11.7 Stage Four—Modeling 295 11.7.1 Select Displaying Strategy 296 11.7.2 Produce an Investigation Plan 297 11.7.3 Fabricate Ideal 297 11.7.4 Evaluation Model 297 11.8 Stage Five—Evaluation 298 11.8.1 Assess Your Outcomes 299 11.8.2 Survey Measure 299 11.8.3 Decide on the Subsequent Stages 300 11.9 Stage Six—Deployment 300 11.9.1 Plan Arrangement 301 11.9.2 Plan Observing and Support 301 11.9.3 Produce the Last Report 302 11.9.4 Audit Venture 302 11.10 Data on ERP Systems 302 11.11 Usage of CRISP-DM Methodology 304 11.12 Modeling 306 11.12.1 Association Rule Mining (ARM) or Association Analysis 307 11.12.2 Classification Algorithms 307 11.12.3 Regression Algorithms 308 11.12.4 Clustering Algorithms 308 11.13 Assessment 310 11.14 Distribution 310 11.15 Results and Discussion 310 11.16 Conclusion 311 References 314 12 Human–Machine Interaction and Visual Data Mining 317Upasana Sinha, Akanksha Gupta, Samera Khan, Shilpa Rani and Swati Jain 12.1 Introduction 318 12.2 Related Researches 320 12.2.1 Data Mining 323 12.2.2 Data Visualization 323 12.2.3 Visual Learning 324 12.3 Visual Genes 325 12.4 Visual Hypotheses 326 12.5 Visual Strength and Conditioning 326 12.6 Visual Optimization 327 12.7 The Vis 09 Model 327 12.8 Graphic Monitoring and Contact With Human–Computer 328 12.9 Mining HCI Information Using Inductive Deduction Viewpoint 332 12.10 Visual Data Mining Methodology 334 12.11 Machine Learning Algorithms for Hand Gesture Recognition 338 12.12 Learning 338 12.13 Detection 339 12.14 Recognition 340 12.15 Proposed Methodology for Hand Gesture Recognition 340 12.16 Result 343 12.17 Conclusion 343 References 344 13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection 349Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu 13.1 Introduction 349 13.2 Literature Survey 352 13.3 Methods and Material 353 13.3.1 Proposed Methodology: Multi Source Dynamic TrAdaBoost Algorithm 355 13.4 Experimental Results 357 13.5 Libraries Used 357 13.6 Comparing Algorithms Based on Decision Boundaries 357 13.7 Evaluating Results 358 13.8 Conclusion 361 References 361 14 New Algorithms and Technologies for Data Mining 365Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla and Maguluri Sudeep Joel 14.1 Introduction 366 14.2 Machine Learning Algorithms 368 14.3 Supervised Learning 368 14.4 Unsupervised Learning 369 14.5 Semi-Supervised Learning 369 14.6 Regression Algorithms 371 14.7 Case-Based Algorithms 371 14.8 Regularization Algorithms 372 14.9 Decision Tree Algorithms 372 14.10 Bayesian Algorithms 373 14.11 Clustering Algorithms 374 14.12 Association Rule Learning Algorithms 375 14.13 Artificial Neural Network Algorithms 375 14.14 Deep Learning Algorithms 376 14.15 Dimensionality Reduction Algorithms 377 14.16 Ensemble Algorithms 377 14.17 Other Machine Learning Algorithms 378 14.18 Data Mining Assignments 378 14.19 Data Mining Models 381 14.20 Non-Parametric & Parametric Models 381 14.21 Flexible vs. Restrictive Methods 382 14.22 Unsupervised vs. Supervised Learning 382 14.23 Data Mining Methods 384 14.24 Proposed Algorithm 387 14.24.1 Organization Formation Procedure 387 14.25 The Regret of Learning Phase 388 14.26 Conclusion 392 References 392 15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier 397Sudesh Kumar, Rekh Ram Janghel and Satya Prakash Sahu 15.1 Introduction 398 15.2 Related Work 400 15.3 Material and Methods 401 15.3.1 Dataset Description 401 15.3.2 Proposed Methodology 403 15.3.3 Normalization 404 15.3.4 Preprocessing Using PCA 404 15.3.5 Restricted Boltzmann Machine (RBM) 406 15.3.6 Stochastic Binary Units (Bernoulli Variables) 407 15.3.7 Training 408 15.3.7.1 Gibbs Sampling 409 15.3.7.2 Contrastive Divergence (CD) 409 15.4 Experimental Framework 410 15.5 Experimental Results and Discussion 412 15.5.1 Performance Measurement Criteria 412 15.5.2 Experimental Results 412 15.6 Discussion 414 15.7 Conclusion 418 References 419 16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques 423Nanda R. Wagh and Sanjay R. Sutar 16.1 Introduction 424 16.2 Related Work 424 16.2.1 WoSApp 424 16.2.2 Abhaya 425 16.2.3 Women Empowerment 425 16.2.4 Nirbhaya 425 16.2.5 Glympse 426 16.2.6 Fightback 426 16.2.7 Versatile-Based 426 16.2.8 RFID 426 16.2.9 Self-Preservation Framework for WomenBWith Area Following and SMS Alarming Through GSM Network 426 16.2.10 Safe: A Women Security Framework 427 16.2.11 Intelligent Safety System For Women Security 427 16.2.12 A Mobile-Based Women Safety Application 427 16.2.13 Self-Salvation—The Women’s Security Module 427 16.3 Issue and Solution 427 16.3.1 Inspiration 427 16.3.2 Issue Statement and Choice of Solution 428 16.4 Selection of Data 428 16.5 Pre-Preparation Data 430 16.5.1 Simulation 431 16.5.2 Assessment 431 16.5.3 Forecast 434 16.6 Application Development 436 16.6.1 Methodology 436 16.6.2 AI Model 437 16.6.3 Innovations Used The Proposed Application Has Utilized After Technologies 437 16.7 Use Case For The Application 437 16.7.1 Application Icon 437 16.7.2 Enlistment Form 438 16.7.3 Login Form 439 16.7.4 Misconduct Place Detector 439 16.7.5 Help Button 440 16.8 Conclusion 443 References 443 17 Conclusion and Future Direction in Data Mining and Machine Learning 447Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Ramakant Chandrakar 17.1 Introduction 448 17.2 Machine Learning 451 17.2.1 Neural Network 452 17.2.2 Deep Learning 452 17.2.3 Three Activities for Object Recognition 453 17.3 Conclusion 457 References 457 Index 461

Read more less

Book SynopsisHANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer empTable of ContentsForeword xxxi Preface xxxv Acknowledgment xlv Part I: Intelligent Computing and Applications 1 1 Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models 3Souvik Das, Kintada Prudhvi and J. Maiti 1.1 Introduction 3 1.2 Data Acquisition Method 4 1.3 Feature Extraction 4 1.4 Deep Learning Models 5 1.5 Results 8 1.6 Discussion 10 1.7 Advantages and Disadvantages of the Study 11 1.8 Limitations of the Study 11 1.9 Conclusion 11 References 12 2 Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates 13Mandrita Mondal and Kumar S. Ray 2.1 Introduction 13 2.2 Biological Neurons 15 2.3 Artificial Neural Networks 17 2.4 DNA Neural Networks 22 2.5 DNA Logic Gates 28 2.6 Advantages and Limitations 45 2.7 Conclusion 47 Acknowledgment 47 References 47 3 Intelligent Garment Detection Using Deep Learning 49Aniruddha Srinivas Joshi, Savyasachi Gupta, Goutham Kanahasabai and Earnest Paul Ijjina 3.1 Introduction 49 3.2 Literature 50 3.3 Methodology 52 3.4 Experimental Results 59 3.5 Highlights 64 3.6 Conclusion and Future Works 65 Acknowledgements 65 References 66 4 Intelligent Computing on Complex Numbers for Cryptographic Applications 69Ni Ni Hla and Tun Myat Aung 4.1 Introduction 69 4.2 Modular Arithmetic 70 4.3 Complex Plane 71 4.4 Matrix Algebra 71 4.5 Elliptic Curve Arithmetic 73 4.6 Cryptographic Applications 74 4.7 Conclusion 78 References 79 5 Application of Machine Learning Framework for Next-Generation Wireless Networks: Challenges and Case Studies 81Satyendra Singh Yadav, Shrishail Hiremath, Pravallika Surisetti, Vijay Kumar and Sarat Kumar Patra 5.1 Introduction 82 5.2 Machine/Deep Learning for Future Wireless Communication 83 5.3 Case Studies 87 5.4 Major Findings 95 5.5 Future Research Directions 95 5.6 Conclusion 96 References 96 6 Designing of Routing Protocol for Crowd Associated Networks (CrANs) 101Rabia Bilal and Bilal Muhammad Khan 6.1 Introduction 101 6.2 Background Study 103 6.3 CrANs 117 6.4 Simulation of MANET Network 123 6.5 Simulation of VANET Network 126 6.6 CrANs 130 6.7 Conclusion 132 References 132 7 Application of Group Method of Data Handling–Based Neural Network (GMDH-NN) for Forecasting Permeate Flux (%) of Disc-Shaped Membrane 135Anirban Banik, Mrinmoy Majumder, Sushant Kumar Biswal and Tarun Kanti Bandyopadhyay 7.1 Introduction 135 7.2 Experimental Procedure 138 7.3 Methodology 139 7.4 Results and Discussions 142 7.5 Conclusions 146 Acknowledgements 147 References 147 8 Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach 149M. Sunil Kumar, A. Harika, C. Sushama and P. Neelima 8.1 Introduction 149 8.2 Literature Survey 153 8.3 Methodology 156 8.4 Dataset 165 8.5 Evaluation 166 8.6 Conclusion 169 References 170 9 Image Classification by Reinforcement Learning With Two-State Q-Learning 171Abdul Mueed Hafiz 9.1 Introduction 171 9.2 Proposed Approach 173 9.3 Datasets Used 174 9.4 Experimentation 176 9.5 Conclusion 178 References 178 10 Design and Development of Neural-Fuzzy Control Model for Computer-Based Control Systems in a Multivariable Chemical Process 183Pankaj Mohindru, Pooja and Vishwesh Akre 10.1 Introduction 184 10.2 Distributed Control System 187 10.3 Fuzzy Logic 192 10.4 Artificial Neural Network 193 10.5 Neuro-Fuzzy 194 10.6 Case Study 197 10.7 Software Implementation on Graphical User Interface 203 10.8 Results and Discussion 212 10.9 Discussion 214 10.10 Conclusion 214 10.11 Scope for Future Work 215 References 215 Appendix 10.1 MATLAB Simulation Configuration Using Sugeno 217 Appendix 10.2 MATLAB Window Displaying Desired Training-Data Fed to Neuro-Fuzzy Model 218 Appendix 10.3 MATLAB Window Displaying Checking-Data Fed to Neuro-Fuzzy Model 218 11 Artificial Neural Network in the Manufacturing Sector 219Navriti Gupta 11.1 Introduction 219 11.2 Optimization 221 11.3 Artificial Neural Network: Optimization of Mechanical Systems 223 11.4 ANN vs. Human Brain 228 11.5 Architecture of Artificial Neural Networks 229 11.6 Learning Algorithm(s) 235 11.7 Different Type of Data 237 11.8 Case Study: Hard Machining of EN 31 Steel 238 11.9 Advantages of Using ANN in Manufacturing Sectors 242 11.10 Disadvantages of Using ANN in Manufacturing Sectors 242 11.11 Applications 242 11.12 Conclusions 243 11.13 Future Scope of ANN in Manufacturing Sectors 244 References 245 12 Speech-Based Multilingual Translation Framework 249Saloni and Williamjeet Singh 12.1 Introduction 249 12.2 Literature Survey 250 12.3 Phases of ASR 252 12.4 Modules of ASR 253 12.5 Speech Database for ASR 253 12.6 Developing ASR 255 12.7 Performance of ASR 256 12.8 Application Areas 257 12.9 Conclusion and Future Work 258 References 258 13 Text Summarization: A Technical Overview and Research Perspectives 261Korrapati Sindhu and Karthick Seshadri 13.1 Introduction 262 13.2 Summarization Techniques 263 13.3 Evaluating Summaries 279 13.4 Datasets and Results 281 13.5 Future Research Directions 281 13.6 Conclusion 282 References 282 14 Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery 287Sitendra Tamrakar, B. K. Madhavi and V. Mohan 14.1 Introduction 287 14.2 Literature Review 289 14.3 Understanding the Google Cloud Platform 291 14.4 Using BigQuery in the Google Cloud Console 294 14.5 Sentiment Analysis 294 14.6 Turning to Google BigQuery Analysis 295 14.7 Proposed Method 297 Streaming API 298 14.8 Experimental Setup and Results 300 14.9 Conclusion 302 References 303 15 A Review of Topic Modeling and Its Application 305R. Sandhiya, A. M. Boopika, M. Akshatha, S. V. Swetha and N. M. Hariharan 15.1 Introduction 305 15.2 Objective of Topic Modeling 306 15.3 Motivations and Contributions 307 15.4 Detailed Survey of Research Articles 308 Information Extraction Systems by Gibbs Sampling 316 Monte Carlo Algorithm 316 15.5 Comparison Table of Previous Research 319 15.6 Expected Future Work 320 15.7 Conclusion 320 References 321 Part II: Optimization 323 16 ROC Method for Identifying the Optimal Threshold With an Application to Email Classification 325Fasanya, Oluwafunmibi O., Adediran, Adetola A., Ewemooje, Olusegun S. and Adebola, Femi B. 16.1 Introduction 325 16.2 Related Works 326 16.3 Methodology 328 16.4 Results and Discussion 334 16.5 Conclusion 337 References 338 17 Optimal Inventory System in a Urea Bagging Industry 339C. Vijayalakshmi, R. Subramani and N. Anitha 17.1 Introduction 339 17.2 Continuous Review Policy 345 17.3 Inventory Optimization Techniques 345 17.5 Numerical Calculations 353 17.6 Conclusion 354 References 354 18 Design of a Mixed Integer Linear Programming Model for Optimization of Supply Chain of a Single Product With Disruption Scenario 357C. Vijayalakshmi 18.1 Introduction 357 18.2 Mixed Integer Programming Methods 359 18.3 Introduction to Supply Chain Management System 359 18.4 Mathematical Model Formulation 362 18.5 Conclusion 368 References 368 19 Development of Base Tax Liability Insurance Premium Calculator for the South African Construction Industry—A Machine Learning Approach 371Blanche Mabusela-Motsosi, Senzosenkosi Myeni and Elias Munapo 19.1 Introduction 372 19.2 Literature Review 373 19.3 The Aim and Objectives of the Study 374 19.4 Research Methodology 374 19.5 Study Results and Discussions 376 19.6 Conclusions 381 References 382 20 A 90-Degree Schiffman Phase Shifter and Study of Tunability Using Varactor Diode 385Partha Kumar Deb, Tamasi Moyra and Bidyut Kumar Bhattacharyya 20.1 Introduction 385 20.2 Designing of 90° SPS 386 20.3 Designing of Tunable Schiffman Phase Shifter 391 20.4 Major Finding and Limitation 398 20.5 Conclusion 398 References 399 21 Optimizing Manufacturing Performance Through Fuzzy Techniques 401Chandan Deep Singh, Harleen Kaur and Rajdeep Singh 21.1 Introduction 401 21.2 Literature Review 403 21.3 Performance Optimization through Fuzzy Techniques 408 21.4 Conclusions 441 References 443 22 Implementation of Non-Linear Inventory Optimization Model for Multiple Products 447Thiripura Sundari P.R. and Vijayalakshmi C. 22.1 Introduction 447 22.2 Literature Review 448 22.3 Symbols and Assumptions 449 22.4 Model Formulation 451 22.5 Conclusion 459 References 459 Part III: Meta-Heuristics: Applications and Innovations 461 23 Pufferfish Optimization Algorithm: A Bioinspired Optimizer 463Mehmet Cem Catalbas and Arif Gulten 23.1 An Introduction to Optimization 463 23.2 Optimization and Engineering 465 23.3 Meta-Heuristic Optimization 469 23.4 Torquigener Albomaculosus 471 23.5 Pufferfish and Circular Structures 471 23.6 Results 475 23.7 Conclusion 483 References 483 24 A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization 487Hisham A. Shehadeh and Nura Modi Shagari 24.1 Introduction 487 24.2 Background on Sperm Swarm Optimization (SSO) and Grey Wolf Optimizer (GWO) 489 24.3 Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization (HGWOSSO) 493 24.4 Experimental and Results 494 24.5 Discussion 504 24.6 Conclusion 505 References 505 25 State-of-the-Art Optimization and Metaheuristic Algorithms 509Vineet Kumar, R. Naresh, Veena Sharma and Vineet Kumar 25.1 Introduction 509 25.2 An Overview of Traditional Optimization Approaches 511 25.3 Properties of Metaheuristics 512 25.4 Classification of Single Objective Metaheuristic Algorithms 514 25.5 Applications of Single Objective Metaheuristic Approaches 519 25.6 Classification of Multi-Objective Optimization Algorithms 519 25.7 Hybridization of MOPs Algorithms 521 25.8 Parallel Multi-Objective Optimization 521 25.9 Applications of Multi-Objective Optimization 525 25.10 Significant Contributions of Researchers in Various Metaheuristic Approaches 526 25.11 Conclusion 528 25.12 Major Findings, Future Scope of Metaheuristics and Its Applications 529 25.13 Limitations and Motivation of Metaheuristics 529 Acknowledgements 530 References 530 26 Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta Domain Using a Hybrid Firefly Technique 537Souvik Ganguli, Tanya Srivastava, Gagandeep Kaur and Prasanta Sarkar 26.1 Introduction 538 26.2 Proposed Methodology 541 26.3 Simulation Results 542 26.4 Conclusions 545 References 546 27 A New Parameter Estimation Technique of Three-Diode PV Cells 549Shilpy Goyal, Parag Nijhawan, Yashonidhi Srivastava and Souvik Ganguli 27.1 Introduction 549 27.2 Problem Statement 551 27.3 Proposed Method 553 27.4 Simulation Results and Discussions 555 27.5 Conclusions 603 References 603 Part IV: Sustainable Computing 605 28 Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network 607Saikat Majumder and Mukhdeep Singh Manshahia 28.1 Introduction 607 28.2 System Model and Preliminaries 610 28.3 Machine Learning Techniques of Decision Fusion 613 28.4 Optimum Quantization of Decision Statistic and Fusion 618 28.5 Measurement Setup 621 28.6 Performance Evaluation 623 28.7 Conclusion 633 28.8 Limitations and Scope for Future Work 633 References 634 29 Green IoT for Smart Agricultural Monitoring: Prediction Intelligence With Machine Learning Algorithms, Analysis of Prototype, and Review of Emerging Technologies 637Parijata Majumdar, Sanjoy Mitra and Diptendu Bhattacharya 29.1 Introduction 638 29.2 Green Approaches: Significance and Motivation 638 29.3 Machine Learning Algorithms for Prediction Intelligence in Smart Irrigation Control 639 29.4 Green IoT–Based Smart Irrigation Monitoring 639 29.5 Technology Enablers for GIoT–Based Irrigation Monitoring 642 29.6 Prototype of the Layered GIoT Framework for Intelligent Irrigation 642 29.7 Other Recent Developments on GIoT–Based Smart Agriculture 643 29.8 Literature Review of Edge Computing–Based Irrigation Monitoring 645 29.9 LPWAN for GIoT–Based Smart Agriculture 646 29.10 Analysis and Discussion 647 29.11 Research Gap in GIoT–Based Precision Agriculture 649 29.12 Analysis of Merits and Shortcomings 650 29.13 Future Research Scope 651 29.14 Conclusion 651 References 652 30 Prominence of Sentiment Analysis in Web-Based Data Using Semi-Supervised Classification 655B. Bazeer Ahamed and Z. A. Feroze Ahamed 30.1 Introduction 655 30.2 Related Works 656 30.3 Proposed Approach 657 30.4 Experimental Details and Results 660 30.5 Conclusion 662 References 662 31 A Three-Phase Fuzzy and A* Approach to Sensor Deployment and Transmission 665R. Deepa, Revathi Venkataraman and Soumya Snigdha Kundu 31.1 Introduction 665 31.2 Related Work 666 31.3 Proposed Model 667 31.4 Complexity Analysis of Algorithms for Data Transmission 671 31.5 Experimental Analysis 672 31.6 Motivation and Limitations of Research 675 31.7 Conclusion 675 31.8 Future Work 675 References 675 32 Intelligent Computing for Precision Agriculture 677Priyanka Gupta, Kavita Jhajharia and Pratistha Mathur 32.1 Introduction 677 32.2 Technology in Agriculture 684 References 691 33 Intelligent Computing for Green Sustainability 693Chandan Deep Singh and Harleen Kaur 33.1 Introduction 693 33.2 Modified DEMATEL 697 33.3 Weighted Sum Model 706 33.4 Weighted Product Model 708 33.5 Weighted Aggregated Sum Product Assessment 709 33.6 Grey Relational Analysis 712 33.7 Simple Multi-Attribute Rating Technique 717 33.8 Criteria Importance Through Inter-Criteria Correlation 721 33.9 Entropy 726 33.10 Evaluation Based on Distance From Average Solution 731 33.11 MOORA 739 33.12 Interpretive Structural Modeling 739 33.13 Conclusions 748 33.14 Limitations of the Study 749 33.15 Suggestions for Future Research 749 References 750 Part V: AI in Healthcare 753 34 Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease 755Vivek Verma, Anita Verma, Ashwani Kumar Mishra, Hafiz T.A. Khan, Dilip C. Nath and Rajiv Narang 34.1 Introduction 756 34.2 Methods 757 34.3 Statistical Analysis 757 34.4 Results 759 34.5 Discussion 761 34.6 Conclusion 767 Acknowledgements 767 References 767 35 Reconstruction of Dynamic MRI Using Convolutional LSTM Technique 771Shashidhar V. Yakkundi and Subha D. Puthankattil 35.1 Introduction 771 35.2 Methodologies 773 35.3 Problem Formulation 774 35.4 Network Architecture 776 35.5 Results 778 35.6 Discussion 780 35.7 Conclusion 782 References 784 36 Gender Classification Using Multispectral Imaging: A Comparative Performance Analysis Between Affine Hull and Wavelet Fusion 785Narayan Vetrekar, Aparajita Naik and R. S. Gad 36.1 Introduction 785 36.2 Literature Review 787 36.3 Multispectral Face Database 791 36.4 Methodology 792 36.5 Experiments 794 36.6 Results and Discussion 794 36.7 Conclusions 796 Acknowledgments 797 References 797 37 Polyp Detection Using Deep Neural Networks 801Nancy Rani, Rupali Verma and Alka Jindal 37.1 Introduction 801 37.2 Literature Survey 803 37.3 Proposed Methodology 806 37.4 Implementation and Results 810 37.5 Conclusion and Future Work 812 References 813 38 Boundary Exon Prediction in Humans Sequences Using External Information Sources 815Neelam Goel, Shailendra Singh and Trilok Chand Aseri 38.1 Introduction 815 38.2 Proposed Exon Prediction Model 817 38.3 Homology-Based Exon Prediction 819 38.4 Results and Discussion 827 38.5 Conclusion 830 38.6 Motivation and Limitations of the Research 831 38.7 Major Findings of the Research 831 References 832 39 Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform 835Jivan Parab, M. Sequeira, M. Lanjewar, C. Pinto and G.M. Naik 39.1 Introduction 835 39.2 Sample Preparation 837 39.3 Methodology 839 39.4 Results and Discussion 842 39.5 Discussion 845 39.6 Conclusion 846 39.7 Future Scope 846 Acknowledgement 847 References 847 40 GIS-Based Geospatial Assessment of Novel Corona Virus (COVID-19) in One of the Promising Industrial States of India—A Case of Gujarat 849Azazkhan I. Pathan, Pankaj J. Gandhi , P.G. Agnihotri and Dhruvesh Patel 40.1 Introduction 849 40.2 The Rationale of the Study 852 40.3 Materials and Methodology 854 40.4 GIS and COVID-19 (Corona) Mapping 859 40.5 Results and Discussion 860 40.6 Conclusion 865 References 866 41 Mobile-Based Medical Alert System for COVID-19 Based on ZigBee and WiFi 869Munish Manas and Shivam Kumar 41.1 Introduction 869 41.2 Hardware Design of Monitoring System 870 41.3 Software Design of Monitoring System 873 41.4 Working of ZigBee Module 874 41.5 Developed App for the Monitoring of Health 874 41.6 Google Fusion Table—Online Database 875 41.7 Application Developed for Health Monitoring System 876 41.8 Conclusion and Future Work 877 References 877 Index 879

Read more less

Book SynopsisNEXT-GENERATION ANTENNAS: ADVANCES AND CHALLENGES The first book in this exciting new series, written and edited by a group of international experts in the field, this exciting new volume covers the latest advances and challenges in the next generation of antennas. Antenna design and wireless communication has recently witnessed their fastest growth period ever in history, and these trends are likely to continue for the foreseeable future. Due to recent advances in industrial applications as well as antenna, wireless communication, and 5G technology, we are witnessing a variety of developing and expanding new technologies. Compact and low-cost antennas are increasing the demand for ultra-wide bandwidth in next-generation (5G) wireless communication systems and the Internet of Things (IoT). Enabling the next generation of high-frequency communication, various methods have been introduced to achieve reliable high data rate communication links and enhance the directivTable of ContentsPreface xiii 1 Different Types of Microstrip Filters for UWB Communication 1Prashant Ranjan, Krishna Kumar, Sachin Kumar Pal and Rachna Shah 1.1 Introduction 1 1.2 Previous Work 2 1.2.1 Multiband Microwave Filter for a Wireless Communication System 2 1.2.2 Ultra-Wideband (UWB) Bandpass Filter 5 1.2.3 Ultra-Wideband Filter with Notch Band Characteristic 10 1.3 Conclusions 16 References 17 2 Design, Isolation Analysis, and Characterization of 2×2/4×4 MIMO Antennas for High-Speed Wireless Applications 23Manish Sharma, Rajeev Kumar and Preet Kaur 2.1 Introduction 24 2.2 Understanding 2×2 MIMO Antenna Configuration 25 2.3 Diversity Performance Analysis of 2×2 UWB-MIMO/Dual-Polarization/UWB: Single, Dual, Triple, and Four Notched Bands 32 2.4 4×4 MIMO Antenna 39 2.5 Conclusions 40 References 41 3 Various Antenna Array Designs Using Scilab Software: An Exploratory Study 49V. A. Sankar Ponnapalli and Praveena A 3.1 Introduction 49 3.2 Scilab: An Open-Source Software Solution 51 3.3 Antenna Array Design Using Scilab: Codes and Results 52 3.4 Conclusions 57 References 58 4 Conformal Wearable Antenna Design, Implementation and Challenges 61Brajlata Chauhan, Vivek Kumar Srivastava, Amrindra Pal and Sandip Vijay 4.1 Introduction 62 4.2 Conformal Antenna 63 4.2.1 Singly Curved Surfaces 63 4.3 Characteristics of Conformal Antenna 64 4.3.1 Radiation Pattern 64 4.3.2 Scan-Invariant Pattern 65 4.3.3 Phase-Scanned Pattern 65 4.3.4 Polarization 65 4.4 Design Methodology - Antenna Modeling 66 4.4.1 Overview 66 4.4.2 Geometry and Calculation of Planar MSA 66 4.4.3 Calculated and Optimized Value of Antenna 69 4.5 Wearable Conformal Antenna 69 4.5.1 Wearable Technology 71 4.5.2 Wearable Devices for Medical Systems 72 4.5.3 Wearable Medical Devices Applications 72 4.5.4 Measurement of Human Body Temperature 73 4.5.5 Measurement of Blood Pressure 73 4.5.6 Measurement of Heart Rate 73 4.5.7 Measurement of Respiration Rate 74 4.5.8 Measurement of Sweat Rate 74 4.5.9 Measurement of Human Gait 74 4.6 Textile and Cloth Fabric Wearable Antennas 76 4.6.1 Specific Absorption Rate (SAR) 76 4.6.2 Interaction with Human Body 77 4.6.3 Wearable Devices Tracking and Monitoring Doctors 77 4.6.4 Wireless Body Area Networks (WBANs) 78 4.7 Design of Liquid Crystalline Polymer (LCP) Based Wearable Antenna 79 4.7.1 Dimensions of the Proposed Model 80 4.7.2 Slot Loaded Ground: (Defective Ground Structure - DGS) 80 4.7.3 Radiation Characteristics 81 4.8 Result Discussion and Analysis 82 4.9 Challenges and Future Needs 83 4.10 Conclusion 83 References 85 5 Design and Analysis of On-Body Wearable Antenna with AMC Backing for ISM Band Applications 91B Prudhvi Nadh and B T P Madhav 5.1 Introduction 92 5.2 Design of Star-Shape with AMC Backed Structure 92 5.2.1 Characterization of AMC Unit Cell 94 5.3 Discussion of Results of Star-Shaped Antenna with AMC Structure 95 5.3.1 Bending Analysis of Star-Shaped Antenna with AMC Backed Structure 96 5.4 On-body Placement Analysis of Proposed Antenna with AMC Structure 97 5.4.1 Specific Absorption Rate Analysis 97 5.4.2 On-Body Gain of the Star-Shaped Antenna With and Without AMC 98 5.4.3 Far-Field Characteristics of An Antenna 99 5.5 Transmitting Signal Strength 100 5.6 Conclusion 101 References 101 6 Antenna Miniaturization for IoT Applications 105Sandip Vijay and Brajlata Chauhan 6.1 Introduction 106 6.2 Issues in Antenna Miniaturization 108 6.3 Antenna for IoT Applications 109 6.4 Miniaturize Reconfigurable Antenna for IoT 112 6.5 Conclusion & Future Work 114 References 114 7 Modified Circular-Shaped Wideband Microstrip Patch Antenna for Wireless Communication Utilities 119Manpreet Kaur, Jagtar Singh Sivia and Navneet Kaur 7.1 Overview of Wireless Communication 120 7.2 Introduction to Microstrip Patch Antenna 120 7.3 Literature Review 122 7.4 Design and Implementation of Projected Antenna 124 7.5 Results and Discussion 126 7.5.1 Scattering Parameters (S11) 126 7.5.2 Voltage Standing Wave Ratio 127 7.5.3 Bandwidth 128 7.5.4 Gain 128 7.5.5 Radiation Pattern 130 7.5.6 Surface Current Distribution 132 7.5.7 Axial Ratio 132 7.5.8 Group Delay 132 7.6 Parametric Analysis 132 7.6.1 Effect of Parameter ‘RP’ 134 7.6.2 Effect of Parameter ‘Fw’ 135 7.6.3 Effect of Parameter ‘LPG’ 135 7.6.4 Effect of Different Substrate Materials 135 7.7 Summary 138 References 138 8 Reconfigurable Antenna for Cognitive Radio System 143Dr. Swapnil Srivastava, Vinay Singh and Dr. Sanjeev Kumar Gupta 8.1 Introduction 143 8.2 Antenna 144 8.3 Antenna Reconfigurations 146 8.4 Uses and Drawbacks of Reconfigurable Antenna 146 8.5 Spectrum Access and Cognitive Radio 147 8.6 Cognitive Radio 147 8.7 Spectrum Sensing and Allocation 147 8.8 Results and Discussion 149 8.9 Conclusions 153 References 154 9 Ultra-Wideband Filtering Antenna: Advancement and Challenges 155Prashant Ranjan, Krishna Kumar, Sachin Kumar Pal and Rachna Shah 9.1 Introduction 155 9.2 Ultra-Wideband Filtering Antenna 156 9.3 Ultra-Wideband Filtering Antenna with Notch Band Characteristic 159 9.4 Conclusions 162 References 163 10 UWB and Multiband Reconfigurable Antennas 165Manish Sharma, Rajeev Kumar and Preet Kaur 10.1 Introduction 166 10.2 Need for Reconfigurable Antennas 167 10.3 RF PIN Diode and MEMS Switch as Switching Devices 168 10.4 Triple Notched Band Reconfigurable Antenna 171 10.5 Tri-Band Reconfigurable Monopole Antenna 180 10.6 Conclusions 180 References 181 11 IoT World Communication through Antenna Propagation with Emerging Design Analysis Features 185E.B. Priyanka and S. Thangavel 11.1 Introduction 186 11.2 Design and Parameter Analysis of Multi-Input Multi-Output Antennas 188 11.3 Measurement Analysis in 3D Pattern with IoT Module 190 11.4 Comparison of Antenna Design Concerning the IoT Data Transmission 193 11.5 Conclusions 196 Acknowledgement 197 References 197 12 Reconfigurable Antennas 203Dr. K Suman 12.1 Introduction 203 12.2 Reconfigurability of Antenna 205 12.2.1 Frequency Reconfigurable Antennas (FRAs) 205 12.2.1.1 Continuous Tuning 206 12.2.1.2 Discrete Tuning 206 12.3 Polarization Reconfigurable Antenna (RA) 207 12.3.1 Polarization RA with Single Band 207 12.3.2 Dual-Band Polarization RA 208 12.3.3 Pattern Reconfigurable Antenna (RA) 209 12.3.4 Main-Beam Shape 209 12.3.5 Main Beam Scanning 209 12.4 Compound Reconfigurable Antennas (RAs) 210 12.5 Reconfigurable Leaky Wave Antennas 211 12.6 Reconfigurable Antennas - Applications in Wireless Communication 212 12.6.1 Reconfigurable Antennas – MIMO Communication Systems 212 12.6.2 Reconfigurable Antennas - Mobile Terminals 213 12.6.3 Reconfigurable Antennas for Cognitive Radio Applications 213 12.6.4 Reconfigurable Antennas - MIMO-Based Cognitive Radio Applications 214 12.6.5 Reconfigurable Antennas - WLAN Band Rejection 215 12.6.6 Reconfigurable Antennas - Wireless Sensing 215 12.6.7 Reconfigurable Antennas - Terahertz (THz) Communication Applications 216 12.6.8 Reconfigurable Antennas - Millimeter-Wave Communication Applications 216 12.7 Optimization, Control, and Modeling of Reconfigurable Antennas 217 12.8 Conclusions 218 References 219 13 Design of Compact Ultra-Wideband (UWB) Antennas for Microwave Imaging Applications 221Dr. J. Vijayalakshmi and Dr. V. Dinesh 13.1 Introduction 222 13.1.1 Ultra-Wideband Antennas (UWB) 222 13.2 Microwave Imaging 224 13.3 Antenna Design Implementation 226 13.3.1 Design of Reflector-Based Antipodal Bowtie Antenna 226 13.3.2 Fabrication of Slotted Bowtie Antenna with Reflector Prototype 228 13.3.3 Parametric Study on the Effect of Slot in the Bowtie Antenna 229 13.3.4 Radiation Pattern 232 13.4 Design of a UWB-Based Compact Rectangular Antenna 232 13.4.1 Parametric Results of the Strip Attached at the Top of the Antenna 233 13.4.2 Effect of Inserting Slot L1 xW1 and Location of the Slot ds 236 13.4.3 Effect of Varying the Length of Slot L2 and L3 237 13.4.4 Performance Comparison of the Measured and Simulated Results of the Miniaturized UWB Antenna 238 13.4.5 Radiation Characteristic of the Proposed Miniaturized UWB Antenna 240 13.5 Validation of the Miniaturized UWB Antenna with the Human Breast Model Developed 240 13.5.1 Validation of the Staircase UWB Antenna with the Human Breast Model Developed 242 13.5.2 MUSIC Beamforming Algorithm 243 13.5.3 Estimation of DOA Using MUSIC Algorithm 246 13.6 Conclusions 247 References 248 14 Joint Transmit and Receive MIMO Beamforming in Multiuser MIMO Communications 251Muhammad Moinuddin, Jawwad Ahmad, Muhammad Zubair and Syed Sajjad Hussain Rizvi 14.1 Introduction 252 14.2 System Model: Proposed Mimo Beamforming Architecture 253 14.3 Mimo Beamforming Based on Generalized Least Mean (GLM) Algorithm 254 14.3.1 Update of the Receive Weight Vector 255 14.3.2 Update of Transmit Weight Vector 255 14.4 Mean and Mean Square Stability of the GLM 255 14.5 Simulation Results 256 14.5.1 Effect of K on the MSE 257 14.5.2 Effect of μ on the MSE 257 14.5.3 Effect of M and N on the MSE 258 14.5.4 Effect of SNR on the MSE 258 14.5.5 Effect of SNR on Bit Error Rate 259 14.6 Summary 260 References 260 15 Adaptive Stochastic Gradient Equalizer Design for Multiuser MIMO System 263Muhammad Moinuddin, Jawwad Ahmad, Muhammad Zubair and Syed Sajjad Hussain Rizvi 15.1 Introduction 264 15.2 Related Literature Review 264 15.3 System Model 265 15.4 Derivation for the Probability of Error 267 15.5 Design of Adaptive Equalizer by Minimizing BER 270 15.5.1 Interior Point Approach 270 15.5.2 Stochastic Gradient Approach 270 15.6 Simulation Results 273 15.7 Summary 274 References 275 Index 279

Read more less

Book SynopsisPOWER CONVERTERS, DRIVES AND CONTROLS FOR SUSTAINABLE OPERATIONS Written and edited by a group of experts in the field, this groundbreaking reference work sets the standard for engineers, students, and professionals working with power converters, drives, and controls, offering the scientific community a way towards combating sustainable operations. The future of energy and power generation is complex. Demand is increasing, and the demand for cleaner energy and electric vehicles (EVs) is increasing with it. With this increase in demand comes an increase in the demand for power converters. Part one of this book is on switched-mode converters and deals with the need for power converters, their topologies, principles of operation, their steady-state performance, and applications. Conventional topologies like buck, boost, buck-boost converters, inverters, multilevel inverters, and derived topologies are covered in part one with their applications in fuel cells, photovolTable of ContentsPreface xxi Part I: Power Converter Topologies for Sustainable Applications 1 1 DC-DC Power Converter Topologies for Sustainable Applications 3 Nandish B. M., Pushparajesh V. and Marulasiddappa H. B. 1.1 Introduction 4 1.2 Classifications of DC-DC Converters 4 1.2.1 Classification of Linear Mode DC-DC Converters 5 1.2.1.1 Series Regulators 5 1.2.1.2 Parallel Regulators 6 1.2.2 Classification of Hard Switching DC-DC Converter 6 1.2.2.1 List of Isolated DC-DC Topologies 6 1.2.2.2 Classification of Non-Isolated DC-DC Converters 10 1.2.3 Classification of Soft Switching DC-DC Converter 16 1.2.3.1 Zero Current Switching (ZCS) 16 1.2.3.2 Zero Voltage Switching (ZVS) 16 1.3 Applications of DC-DC Converters in Real World 16 1.4 Conclusion 18 References 18 2 DC-DC Converters for Fuel Cell Power Sources 21 M. Venkatesh Naik, Paulson Samuel and Srinivasan Pradabane 2.1 DC-DC Boost Converter in Fuel Cell (FC) Applications 22 2.2 DC-DC Buck Converter 26 2.3 DC-DC Buck-Boost Converter 27 2.4 DC-DC Cuk-Converter 29 2.5 DC-DC Sepic Converter 30 2.6 Multi-Phase and Multi-Device Techniques for Ripple Current Reduction 32 2.6.1 Multi-Device Boost Converter 33 2.6.2 Multi-Phase Interleaved Boost Converter 35 2.6.3 Multi-Device Multi-Phase Interleaved Boost Converter 37 2.7 The Proposed High Gain Multi-Device Multi-Phase Interleaved Boost Converter 42 2.7.1 Operating Principle of HGMDMPIBC 44 2.8 Non-Inverting Buck-Boost Converters for Low Voltage FC Applications 48 2.8.1 Single Switch Non-Inverting Buck-Boost Converter 49 2.8.2 Interleaved Buck-Boost Converter 52 2.9 Proposed Multi-Device Buck-Boost Converter for Low Voltage FC Applications 57 2.10 The Proposed Multi-Device Multi-Phase Interleaved Buck-Boost Converter for Low Voltage FC Applications 59 2.11 Converter Configurations for Integrating FC with 400 V Grid Voltages 62 2.11.1 Series Configuration 62 2.11.2 DC-Distributed Configuration 64 2.12 Conclusions 65 References 66 3 High Gain DC-DC Converters for Photovoltaic Applications 71 M. Prabhakar and B. Sri Revathi 3.1 Introduction 71 3.1.1 Role of DC-DC Converter in Renewable Energy System 72 3.1.2 Classical Boost Converter (CBC) 75 3.2 Gain Extension Mechanisms 77 3.2.1 Voltage-Lift Capacitor (Clift ) 77 3.2.2 Coupled Inductor (CI) 78 3.2.3 Voltage Multiplier Cells (VMC) 79 3.3 Synthesis of High Gain DC-DC Converters 80 3.3.1 Concept of Interleaving 80 3.3.2 Interleaving Mechanism with Coupled Inductors (CIs) 83 3.3.3 VMCs at Secondary Side of CIs 84 3.4 Development of High Gain DC-DC Converters (HGCs) 84 3.4.1 HGC with 3 CIs, Clift , and VMC 85 3.4.1.1 Design Details of HGC- 1 90 3.4.1.2 Experimental Results of Prototype HGC- 1 and Discussion 95 3.4.2 3-Phase Interleaved HGC with 1 CI, Clift , and VMC 101 3.4.3 Modular HGC with 3 CIs, Clift , and 3 VMCs 104 3.4.4 Compact HGC Based on Multi-Winding CI, Clift , and VMC 107 3.4.4.1 Voltage Stress on Devices 109 3.4.4.2 Current Stress on Devices 109 3.5 Operating Capabilities of the Proposed HGCs – A Comparison 111 3.5.1 Electrical Characteristics 111 3.5.1.1 Ideal Voltage Gain 111 3.5.1.2 Loss Distribution Profile 113 3.5.2 Stress on Switches 115 3.5.2.1 Peak Voltage Stress 116 3.5.2.2 Peak Current Stress 117 3.5.3 Structural Parameters 117 3.5.3.1 Coefficient of Coupling (k) 117 3.5.3.2 Component Count (CC) and Component Utilisation Ratio (CUR) 118 3.6 Salient Features of the Presented High Gain Converters 119 3.7 Summary and Outlook 120 References 122 4 Design of DC-DC Converters for Electric Vehicle Wireless Charging Energy Storage System 127 T. Kripalakshmi and T. Deepa 4.1 Introduction 128 4.2 Isolated Converters 130 4.2.1 Bridge Type 130 4.2.2 Z-Source Type 131 4.2.3 Sinusoidal Amplitude High Voltage Bus Converter (sahvc) 131 4.2.4 Multiport Converter 133 4.3 Non-Isolated Converter 133 4.3.1 Conventional Converters 133 4.3.2 Interleaved Converter 134 4.3.3 Multi-Device Interleaved 135 4.4 Design of DC-DC Converter with Integration of ICPT and Battery Implementation with Digital Control Loop 136 4.4.1 Design of DC-DC for BEV with the Integration of ICPT 136 4.4.2 Digital Control with Sliding Mode Control Approach 139 4.5 Design of Converter with Hybrid Energy Storage System and Bidirectional Converter 143 4.6 Conclusion 145 References 145 5 Performance Analysis of Series Load Resonant (SLR) DC–DC Converter 149 A. Mitra, S. Bhowmik, A. Halder, S. Karmakar and T. Paul 5.1 Introduction 149 5.2 Theoretical Background 151 5.3 Simulation Results 155 5.4 Conclusion 157 References 158 6 Review on Different Methodologies of DC-AC Converter 159 Pushparajesh V., Marulasiddappa H. B. and Nandish B. M. 6.1 Introduction 160 6.2 Different Multilevel Inverter Topologies 162 6.2.1 Diode Clamped MLI (DCMLI) 162 6.2.2 Flying Capacitor mli 164 6.2.3 Cascaded H-Bridge mli 165 6.2.4 New Hybrid Cascaded mli 167 6.2.4.1 Stepped Wave Modulation Topology (swmt) 167 6.2.4.2 Fourier Series of Proposed Waveform 168 6.2.4.3 Proposed Topology (New Hybrid MLI) 169 6.3 Comparison between Various mli 172 6.4 Conclusion 173 References 173 7 Grid Connected Inverter for Solar Photovoltaic Power Generation 175 K.K. Saravanan and M. Durairasan 7.1 Single Phase Seven Level Inverter Fed Grid Connected PV System 176 7.1.1 Seven Level Inverter Topology 176 7.1.2 PWM Technique for Seven Level Inverter 177 7.1.3 Modelling and Simulation Analysis of Seven Level Inverter 180 7.2 Simlink Model of Nine Level H-Bridge Inverter 181 7.3 Three Phase Fifteen Level Inverter Fed Grid Connected System 182 7.3.1 Modified System of Fifteen Level Inverter 182 7.3.2 Modelling of Cascaded H-Bridge Fifteen Level Inverter 183 7.3.3 Evaluation of THD 184 7.4 Fesability Analysis of Photovoltaic System in Grid Connected Inverter 185 7.4.1 Modified PV-DVR System 185 7.4.1.1 Dynamic Voltage Restorer (DVR) Mode 187 7.4.1.2 Uninterruptable Power Supply (UPS) Mode 187 7.4.1.3 Energy Conservation Mode 187 7.4.1.4 Idle Mode 187 7.4.2 Photovoltaic DC-DC Converter 188 7.4.3 Maximum Power Point Tracking of PV System 191 7.4.4 Methods of Maximum Power Point Tracking 192 7.4.4.1 Perturb and Observe Method 192 7.4.4.2 Incremental Conductance Method 193 7.4.4.3 Current Sweep Method 193 7.4.4.4 Constant Voltage Method 194 7.4.5 Comparison of MPPT Methods 194 7.4.6 Operating Principle of P&O MPPT 195 7.4.7 Simulation Results of PV-DVR System 195 7.4.8 Grid Connected System Using PV Syst Tool 197 7.4.8.1 PV System Simulation Result Analysis 199 7.5 Conclusion 199 7.6 Future Scope of Work 200 References 200 8 A Novel Fusion Switching Pattern Generation Algorithm for “N-Level” Switching Angle Algorithm Based Trinary Cascaded Hybrid Multi-Level Inverter 203 Joseph Anthony Prathap and T.S. Anandhi 8.1 Introduction 204 8.2 Trinary Cascaded Hybrid MLI Circuitry 206 8.3 Switching Angle Algorithm 208 8.3.1 Equal Phase Switching Angle Algorithm (EP-SAA) 209 8.3.2 Half Equal Phase Switching Angle Algorithm (hep-saa) 209 8.3.3 Feed Forward Switching Angle Algorithm (FF-SAA) 209 8.3.4 Half Height Switching Angle Algorithm (HH-SAA) 209 8.4 9-Level Trinary Cascaded Hybrid Multi-Level Inverter 210 8.4.1 SAA for 9-Level TCHMLI 210 8.4.2 Generation of Switching Function for the 9-Level Trinary Cascaded Hybrid mli 215 8.4.3 Generation of DPWM for the 9-Level Trinary Cascaded Hybrid mli 215 8.4.4 Simulation Results of 9-Level Trinary Cascaded Hybrid mli 216 8.5 27-Level Trinary Cascaded Hybrid mli 222 8.5.1 SAA for 27-Level TCHMLI 223 8.5.2 Generation of Switching Function for the 27-Level Trinary Cascaded Hybrid mli 225 8.5.3 Generation of DPWM for the 27-Level Trinary Cascaded Hybrid mli 231 8.5.4 Simulation Results of 27-Level Trinary Cascaded Hybrid mli 231 8.6 81-Level Trinary Cascaded Hybrid mli 240 8.6.1 SAA for 81-Level Trinary Cascaded Hybrid mli 240 8.6.2 Generation of Switching Function for the 81-Level Trinary Cascaded Hybrid mli 248 8.6.3 Generation of DPWM for 81-Level Trinary Cascaded Hybrid mli 265 8.6.4 Flow Diagram of 81-Level Trinary Cascaded Hybrid mli 266 8.6.5 5 Roles of Design Resolution in Trinary Cascaded Hybrid mli 266 8.6.6 Simulation Results of 81-Level Trinary Cascaded Hybrid mli 268 8.7 FPGA Experimental Validation with Specification 279 8.8 Hardware Results and Discussion 279 8.9 Conclusion 280 References 290 9 An Inspection on Multilevel Inverters Based on Sustainable Applications 293 L. Vijayaraja, R. Dhanasekar and S. Ganesh Kumar 9.1 Introduction 293 9.2 Multilevel Inverters in Sustainable Applications 294 9.3 Development of Multilevel Inverter 299 9.3.1 Diode-Clamped 299 9.3.2 Flying Capacitor 300 9.3.3 Cascaded H-Bridge mli 301 9.4 Symmetric mli 301 9.5 Asymmetric mli 305 9.6 An Examination on Current MLI’s 307 9.7 Summary 311 Acknowledgement 311 References 311 Part II: Electric Machines and Drives for Sustainable Applications 315 10 Technical Study of Electric Vehicle Charging Infrastructure and Standards 317 R. Seyezhai and S. Harika 10.1 Introduction 317 10.2 Background 318 10.3 Review of EV Charging Infrastructure 320 10.4 Review of DC-DC Converters for EVCs 323 10.5 Standards for EV and EVSE 327 10.5.1 Description of EV Connector 330 10.6 Charging Stations in India 331 10.7 Conclusion 332 References 332 11 Implementation of Model Predictive Control for Reduced Torque Ripple in Orthopaedic Surgical Drilling Applications with Permanent Magnet Synchronous Machine 337 Ramya L. N. and Sivaprakasam A. 11.1 Introduction 338 11.2 Role of Motor in Orthopaedic Drilling Applications 341 11.2.1 BLDC Motors 341 11.2.2 Permanent Magnet Synchronous Motors 341 11.2.2.1 PMSM Machine Equations 342 11.2.3 Control Methods of PMSM 343 11.3 Model Predictive Control 347 11.3.1 Structure of MPC 348 11.3.2 Cost Function 349 11.4 Predictive Control Techniques for PMSM 350 11.4.1 Conventional Model Predictive Torque Control (MPC) 350 11.4.2 Proposed MPC Technique 352 11.5 Implementation and Results 354 11.5.1 Comparative Study of Steady State Performance of Proposed MPC and Conventional MPC under Loaded Condition 355 11.5.2 Steady State Performance at 50% Rated Speed 356 11.5.3 Steady State Performance at 100% Rated Speed 357 11.5.4 Real-Time Simulation Result Analysis with OPAL-RT Lab 357 11.5.4.1 Steady-State Response 358 11.5.4.2 Start-Up Response 359 11.6 Implementation Analysis 359 11.7 Conclusion 362 References 362 12 High Precision Drives for Piezoelectric Actuators Based Motion Control Microsystems 367 D. V. Sabarianand and P. Karthikeyan 12.1 Introduction 368 12.2 Driving Methods of PEA 369 12.3 Driver Circuits for Driving PEA in High Voltage Applications 369 12.4 Different Types of Power Supply Used for Driving the Piezo Driver 377 12.5 Different Types of Voltage Regulator Used for Driving the Piezo Driver 380 12.6 Conclusions 385 References 386 13 Design and Analysis of 31-Level Asymmetrical Multilevel Inverter Topology for R, RL, & Motor Load 391 E. Duraimurugan, R. S. Jeevitha, S. Dillirani, L. Vijayaraja and S. Ganesh Kumar 13.1 Introduction 391 13.2 Incorporation of Multilevel Inverters in Various Applications 392 13.3 Modeling of 31-Level Asymmetric Inverter 394 13.3.1 Mathematical Modeling of 31-Level Inverter 395 13.3.2 Modes of Operation 396 13.3.3 Switching Principle of 31-Level Inverter 398 13.4 Simulation Circuit and Result Discussions 400 13.4.1 Block Diagram for Pulse Generation 400 13.4.2 Simulation of 31-Level Inverter with R Load 400 13.4.3 Simulation of 31-Level Inverter with RL Load 402 13.4.4 Simulation of 31-Level Inverter Fed with 1φ Induction Motor 405 13.5 Conclusion 407 Acknowledgement 407 References 407 14 Permanent Magnet Assisted Synchronous Reluctance Motor: Analysis and Design with Rare Earth Free Hybrid Magnets 411 P. Ramesh, D. Pradhap and N. C. Lenin 14.1 Introduction 411 14.2 Literature Survey 413 14.3 Construction and Torque Equation 415 14.4 Design Specifications and Machine Topologies 417 14.5 No-Load Characteristics 421 14.6 Performance at Various Operating Regions 424 14.7 Conclusion 429 Acknowledgment 433 References 433 15 Design of Bidirectional DC – DC Converters and Controllers for Hybrid Energy Sources in Electric Vehicles 437 R. Chandrasekaran, M. Satish Kumar Reddy, K. Selvajyothi and B. Raja 15.1 Introduction 437 15.2 Need For Hybrid Energy Management Systems in EV 439 15.3 Hybrid Energy Storage System (HESS) 440 15.3.1 Passive Parallel HESS 441 15.3.2 Parallel Converter HESS 441 15.4 Bidirectional DC-DC Converters (BDC) 442 15.5 Specifications of DC-DC Converters 446 15.6 Control Strategy 447 15.7 Results and Discussion 449 15.8 Conclusions 459 References 460 16 Design of Rare Earth Magnet Free Traction Motor 463 Akhila K. and K. Selvajyothi 16.1 Introduction 464 16.2 Comparison Among Traction Motor Choices 468 16.3 Motor Peak Power Calculation Based on Vehicle Dynamics 473 16.4 Operating Principle of SynRM & Basic Terminologies 475 16.5 SynRM Design Concepts: Effect of Design Parameters on Performance 482 16.6 Analytical Design of SynRM 486 16.6.1 Stator & Winding Design 486 16.6.2 Rotor Design 490 16.6.2.1 Determining Barrier End Angle, αm 491 16.6.2.2 Determining Segment Width, SI 491 16.6.2.3 Determining Barrier Width, W1I 493 16.7 Electromagnetic Analysis –Results & Discussion 496 16.8 Investigation on Impact of Different Parameters 500 16.8.1 Torque-Speed Curve 506 16.9 Summary 510 16.10 Future Work 513 References 513 17 Implementation of Automatic Unmanned Battery Charging System for Electric Cars 517 Shefali Jagwani 17.1 Introduction 518 17.2 Proposed System 521 17.3 MATLAB Simulation 523 17.3.1 Mathematical Modelling 523 17.3.2 Simulation and Analysis of Battery Discharging at EV Charging Station 526 17.4 Conclusion 529 References 529 18 Improved Dual Output DC-DC Converter for Electric Vehicle Charging Application 533 R. Latha 18.1 Introduction 534 18.2 Proposed Dual Output Quadratic Boost Converter 537 18.2.1 Solar PV System 537 18.2.1.1 Mathematical Modeling of PV System 537 18.2.2 Switching Methodology 538 18.2.2.1 Topology of Proposed Converter 539 18.2.3 Estimation of Parameters of Proposed SIDO Converter 543 18.2.3.1 Design Example 544 18.3 Simulation of the Proposed Converter 545 18.4 Experimental Results 545 18.5 Conclusion 550 References 551 19 DFIG Based Wind Energy Conversion Using Direct Matrix Converter 553 Vineet Dahiya Chapter-i 554 Introduction 554 19.1 Introduction to Matrix Converters 558 19.2 Introduction to Control and Modulation Techniques in Matrix Convertor 559 19.3 Introduction to Predictive Control Techniques 562 Chapter-ii 562 Concept and System Description: Doubly Fed Induction Generator (DFIG) in Wind Energy Conversion System 562 Chapter-iii 571 Modeling and Simulation of DFIG in MATLAB 571 Chapter-iv 574 The Matrix Converter and Predictive Control Technique 574 19.4 Topologies of Matrix Converters and Use of Predictive Control 583 19.5 Conclusion 588 19.6 Scope for Future Work 589 References 590 Part III: Trends in Control Methods for Sustainable Applications 595 20 Microgrid: Recent Trends and Control 597 S. Monesha and S. Ganesh Kumar 20.1 Introduction 598 20.2 MG Concept 599 20.2.1 Different Structures of MG 600 20.2.1.1 Ac Mg 600 20.2.1.2 dc Mg 601 20.2.1.3 Hybrid AC/DC MG 602 20.2.1.4 Urban DC MG 602 20.2.1.5 Ceiling DC MG 602 20.3 MG Control Layer 603 20.4 Functional Requirements of MG Management 604 20.4.1 Forecast 604 20.4.2 Real-Time Optimization 604 20.4.3 Data Analysis and Communication 604 20.4.4 Human Machine Interface 605 20.5 Energy Management Schemes 605 20.5.1 Communication-Based Energy Management 605 20.5.2 The Communication-Less Energy Management System 608 20.6 Overview of MG Control 611 20.6.1 Power Flow Control by Current Regulation 611 20.6.2 Power Flow Control by Voltage Regulation 612 20.6.3 Agent-Based Control 613 20.6.4 Multi-Agent System (MAS) Based Distributed Control 613 20.6.5 PQ Control 614 20.6.6 VSI Control 614 20.6.7 Central Control 614 20.6.8 Master/Slave Control 615 20.6.9 Distributed Control 615 20.6.10 Droop Control 616 20.6.11 Control Design Based on Transfer Function 616 20.6.12 Direct Lyapunov Control (DLC) 617 20.6.13 Passivity Based Control (PBC) 617 20.6.14 Model Predictive Control (MPC) 618 20.7 IEEE and IEC Standards 621 20.8 Challenges of MG Controls 623 20.8.1 Future Trends 624 Acknowledgement 624 References 624 21 Control Techniques in Sustainable Applications 631 R. Dhanasekar, L. Vijayaraja and S. Ganesh Kumar 21.1 Introduction 632 21.2 Sliding Mode Control Techniques in Sustainable Applications 634 21.3 Passivity-Based Control in Sustainable Applications 644 21.4 Model Predictive Control in Sustainable Applications 650 21.5 Conclusion 655 Acknowledgement 655 References 655 22 Optimization Techniques for Minimizing Power Loss in Radial Distribution Systems by Placing Wind and Solar Systems 659 S. Angalaeswari, D. Subbulekshmi and T. Deepa I. Introduction 660 22.1 Distribution Systems 660 22.2 Radial Distribution Network 661 22.3 Power Loss Minimization 662 22.4 Optimization Techniques 664 22.5 MATLAB Tools for Optimization Techniques 670 22.6 Conclusion 674 References 675 Appendix 679 23 Passivity Based Control for DC-DC Converters 681 Arathy Rajeev V.K. and Ganesh Kumar S. 23.1 Introduction 681 23.2 Passivity Based Control 683 23.3 Control Law Generation Using ESDI, ESEDPOF, Etedpof 686 23.3.1 Energy Shaping and Damping Injection (ESDI) 686 23.3.2 Exact Tracking Error Dynamics Passive Output Feedback (ETEDPOF) 687 23.3.3 Exact Static Error Dynamics Passive Output Feedback 692 23.4 Control Law Generation Using ETEDPOF Method for DC Drives 692 23.4.1 Buck Converter Fed DC Motor 692 23.4.2 Boost Converter Fed DC Motor 697 23.4.3 Luo Converter Fed DC Motor 701 23.5 Sensitivity Analysis 706 23.5.1 Sensitivity Analysis of Buck Converter 707 23.5.2 Sensitivity Analysis of Boost Converter 709 23.5.3 Sensitivity Analysis of a Luo Converter 710 23.6 Reference Profile Generation 713 23.6.1 Boost Converter Fed DC Motor 713 23.6.2 Luo Converter Fed DC Motor 715 23.7 Load Torque Estimation 719 23.7.1 Reduced-Order Observer for Load Torque Estimation 719 23.7.2 SROO Approach for Load Torque Estimation 720 23.7.3 Load Torque Estimation Using Online Algebraic Approach 721 23.7.4 Sensorless Online Algebraic Approach (SAA) for Load Torque Estimation 723 23.8 Applications of PBC 724 23.9 Conclusion 726 References 728 24 Modeling, Analysis, and Design of a Fuzzy Logic Controller for Sustainable System Using MATLAB 731 T. Deepa, D. Subbulekshmi and S. Angalaeswari 24.1 Introduction 732 24.2 Modeling of MIMO System 734 24.3 Analysis of MIMO System Using MATLAB 734 24.4 Optimization Techniques for PID Parameter 742 24.4.1 Controller Design 742 24.4.1.1 PID Controller Design 742 24.4.2 Optimization of PID Controller Parameter 743 24.5 Fuzzy Logic Controller Using MATLAB/Simulink 744 24.6 Conclusion 745 References 746 25 Development of Backstepping Controller for Buck Converter 749 R. Sureshkumar and S. Ganesh Kumar 25.1 Introduction 749 25.2 Buck Converter With R-Load 751 25.2.1 Mathematical Model 752 25.2.2 Buck Converter with PMDC Motor 752 25.2.3 Mathematical Model 753 25.3 Controller Design 754 25.3.1 Basic Block Diagram for PI/Backstepping Controller 754 25.3.2 Conventional PI Controller Design 754 25.3.3 Backstepping Controller Design 756 25.3.4 Backstepping Control Algorithm 757 25.3.5 Controller Design for Buck Converter with R-Load 757 25.4 Simulation Results 766 25.5 Hardware Details 768 25.5.1 Buck Converter Specifications 771 25.5.2 Advanced Regulating Pulse Width Modulator 773 25.5.3 Principles of Operation 774 25.6 Hardware Results 775 25.7 Conclusion 777 References 778 26 Analysing Control Algorithms for Controlling the Speed of BLDC Motors Using Green IoT 779 V. Evelyn Brindha and X. Anitha Mary 26.1 Introduction 779 26.2 Working of BLDC Motor 780 26.3 Speed Control of Motor 781 26.4 Speed Control of BLDC Motor with FPGA 786 26.5 Advancements in Green IoT for BLDC Motors 786 26.6 Conclusion 787 References 787 Index 789

Read more less

Book SynopsisCompiled by a well-known expert in the field, Liquid Biofuels provides a profound knowledge to researchers about biofuel technologies, selection of raw materials, conversion of various biomass to biofuel pathways, selection of suitable methods of conversion, design of equipment, selection of operating parameters, determination of chemical kinetics, reaction mechanism, preparation of bio-catalyst: its application in bio-fuel industry and characterization techniques, use of nanotechnology in the production of biofuels from the root level to its application and many other exclusive topics for conducting research in this area. Written with the objective of offering both theoretical concepts and practical applications of those concepts, Liquid Biofuels can be both a first-time learning experience for the student facing these issues in a classroom and a valuable reference work for the veteran engineer or scientist. The description of the detailed characterization methTable of ContentsPreface xxi 1 Introduction to Biomass to Biofuels Technologies 1Ezgi Rojda Taymaz, Mehmet Emin Uslu and Irem Deniz 1.1 Introduction 1 1.2 Lignocellulosic Biomass and Its Composition 2 1.2.1 Cellulose 3 1.2.2 Hemicellulose 4 1.2.3 Lignin 5 1.3 Types and Category of the Biomass 6 1.3.1 Marine Biomass 6 1.3.2 Forestry Residue and Crops 7 1.3.3 Animal Manure 7 1.3.4 Industrial Waste 8 1.4 Methods of Conversion of Biomass to Liquid Biofuels 8 1.4.1 Pyrolysis and Types of the Pyrolysis Processes 9 1.4.2 Types of Reactors Used in Pyrolysis 12 1.4.2.1 Bubble Fluidized Bed Reactor 12 1.4.2.2 Circulating Fluidized Bed and Transport Bed Reactor 12 1.4.2.3 Ablative Pyrolysis Reactor 14 1.4.2.4 Rotary Cone Reactor 14 1.4.3 Chemical Conversion 14 1.4.4 Electrochemical Conversion 14 1.4.5 Biochemical Methods 16 1.4.6 Co-Conversion Methods of Pyrolysis (Copyrolysis) 16 1.5 Bioethanol and Biobutanol Conversion Techniques 16 1.6 Biogas and Syngas Conversion Techniques 20 1.7 Advantages and Drawbacks of Biofuels 23 1.8 Applications of Biofuels 25 1.9 Future Prospects 26 1.10 Conclusion 27 References 29 2 Advancements of Cavitation Technology in Biodiesel Production – from Fundamental Concept to Commercial Scale-Up 39Ritesh S. Malani, Vijayanand S. Moholkar, Nimir O. Elbashir and Hanif A. Choudhury 2.1 Introduction 40 2.2 Principles of Ultrasound and Cavitation 43 2.3 Intensification of Biodiesel Production Processes Through Cavitational Reactors 45 2.3.1 Acoustic Cavitation (or Ultrasound Irradiation) Assisted Processes 46 2.3.2 Acoustic or Ultrasonic Cavitation Assisted Processes 46 2.4 Designing the Cavitation Reactors 59 2.5 Scale-Up of Cavitational Reactors 63 2.6 Application of Cavitational Reactors for Large-Scale Biodiesel Production 66 2.7 Future Prospects and Challenges 67 References 67 3 Heterogeneous Catalyst for Pyrolysis and Biodiesel Production 77Anjana P Anantharaman and Niju Subramania Pillai 3.1 Biodiesel Production 78 3.1.1 Homogeneous Catalyst 79 3.1.2 Heterogeneous Catalyst 80 3.1.3 Natural Catalyst 84 3.1.4 Catalyst Characterization 88 3.1.4.1 Morphology and Surface Property 88 3.1.4.2 X-Ray Diffraction (XRD) 88 3.1.4.3 Fourier Transform Infrared (FTIR) Spectroscopy 90 3.1.4.4 Thermogravimetric Analysis (TGA) 91 3.1.4.5 Temperature Programmed Desorption (TPD) 91 3.1.4.6 X-Ray Photoemission Spectroscopy (XPS) 92 3.1.5 Kinetics of Biodiesel 93 3.2 Plastic Pyrolysis 97 3.2.1 Zeolite 99 3.2.2 Activated Carbon (AC) 103 3.2.3 Natural Catalyst 104 3.2.4 Characterization of Catalyst 107 3.2.4.1 Fourier Transform Infrared Spectroscopy (FTIR) 107 3.2.4.2 Surface Characteristics 107 3.2.4.3 NH3-Temperature Programmed Desorption (NH3-TPD) 107 3.2.5 Pyrolysis Kinetics 111 3.3 Conclusion 113 References 114 4 Algal Biofuel: Emergent Applications in Next-Generation Biofuel Technology 119Bidhu Bhusan Makut 4.1 Introduction 120 4.2 Burgeoning of Biofuel Resources 120 4.2.1 Potential Role of Microalgae Towards Biofuel Production 121 4.3 Common Steps Adopted for Microalgal Biofuel Production 122 4.3.1 Screening and Development of Robust Microalgal Strain 122 4.3.2 Cultivation for Algal Biomass Production 123 4.3.3 Harvesting of Microalgae Biomass 127 4.3.4 Dewatering and Drying Process 127 4.3.5 Extraction and Purification of Lipids from Microalgal Biomass for Biodiesel Production 130 4.3.6 Microalgal Biomass Conversion Technology Towards Different Types of Biofuel Production 130 4.3.6.1 Chemical Conversion 131 4.3.6.2 Biochemical Conversion 132 4.3.6.3 Thermochemical Conversion 134 4.3.6.4 Direct Conversion 136 4.4 Types of Microalgal Biofuels and their Emerging Applications 137 4.4.1 Biodiesel 137 4.4.2 Bioethanol 139 4.4.3 Biogas 140 4.4.4 Bio-Oil 140 4.5 Conclusion 141 References 141 5 Co-Liquefaction of Biomass to Biofuels 145Gerardo Martínez-Narro and Anh N. Phan 5.1 Introduction 145 5.2 Hydrothermal Liquefaction (HTL) 147 5.2.1 Background 147 5.2.2 Operating Parameters Affecting HTL Process 149 5.3 Co-Liquefaction of Biomass 151 5.3.1 Food Waste with Others 151 5.3.2 Lignocellulosic Biomass with Others 162 5.3.3 Biomass with Crude Glycerol 163 5.3.4 Algal Biomass with Others 164 5.3.5 Sludge with Others 168 5.3.6 Biomass with Plastic Waste 169 5.4 Current Development, Challenges and Future Perspectives 171 5.5 Conclusions 174 Acknowledgments 174 References 174 6 Biomass to Bio Jet Fuels: A Take Off to the Aviation Industry 183Anjani R K Gollakota, Anil Kumar Thandlam and Chi-Min Shu 6.1 Introduction 184 6.2 The Transition of Biomass to Biofuels 185 6.3 Properties of Aviation Jet Fuel (Bio-Jet Fuel) 187 6.4 Fuel Specification for Civil Aviation 188 6.5 Choice of Feedstock (Renewable Sources) 192 6.5.1 Camelina 192 6.5.2 Jatropha 192 6.5.3 Wastes 193 6.5.4 Algae 193 6.5.5 Halophytes 193 6.5.6 Fiber Feedstock 193 6.6 Pathways of Biomass to Bio-Jet Fuels 194 6.6.1 Hydrogenated Esters and Fatty Acids (HEFA) 194 6.6.2 Catalytic Hydrothermolysis (CH) 195 6.6.3 Hydro Processed Depolymerized Cellulosic Jet (HDCJ) 195 6.6.4 Fischer-Tropsch Process (FT) 196 6.6.5 Lignin to Jet 197 6.6.6 Direct Sugars to Hydrocarbons (DSHC) 202 6.6.7 Aqueous Phase Reforming (APR) 203 6.6.8 Alcohol to Bio-Jet 203 6.7 Challenges Associates with the Future of Bio-Jet Fuel Development 204 6.7.1 Ecological Challenges 204 6.7.2 Feedstock Availability and Sustainability 205 6.7.3 Production Challenge 205 6.7.4 Distribution Challenge 205 6.7.5 Compatibility Issues 206 6.8 Future Perspective 206 6.9 Conclusion 207 Acknowledgements 209 References 209 7 Advance in Bioethanol Technology: Production and Characterization 215Soumya Sasmal and Kaustubha Mohanty 7.1 Introduction 216 7.2 Production Technology of Ethanol and Global Players 218 7.3 Microbiology of Bioethanol Production 220 7.4 Fermentation Technology 222 7.5 Downstream Process 224 7.5.1 Distillation 224 7.5.2 Molecular Sieves 225 7.6 Ethanol Analysis 225 7.6.1 Gas Chromatography 225 7.6.2 High-Performance Liquid Chromatography 226 7.6.3 Infrared Spectroscopy 226 7.6.4 Olfactometry 226 7.7 Conclusion 227 References 228 8 Effect of Process Parameters on the Production of Pyrolytic Products from Biomass Through Pyrolysis 231Ranjeet Kumar Mishra and Kaustubha Mohanty 8.1 Introduction 232 8.2 Biomass to Energy Conversion Technologies 233 8.2.1 Biochemical Conversion of Biomass 233 8.2.2 Thermochemical Conversion (TCC) of Biomass 234 8.2.2.1 Combustion 235 8.2.2.2 Gasification 235 8.2.2.3 Pyrolysis 236 8.2.2.4 Liquefaction 236 8.2.2.5 Carbonization and Co-Firing 240 8.2.3 Comparison of Thermochemical Conversion Techniques 240 8.3 Advantages of Pyrolysis 241 8.4 Effect of Processing Parameters on Liquid Oil Yield 242 8.4.1 Temperature 242 8.4.2 Effect of Catalysts on Pyrolytic End Products 243 8.4.3 Vapour Residence Times 249 8.4.4 Size of Feed Particles 255 8.4.5 Effect of Heating Rates 256 8.4.6 Effect of Atmospheric Gas 257 8.4.7 Effect of Biomass Type 262 8.4.8 Effect of Mineral 262 8.4.9 Effect of Moisture Contents 264 8.4.10 Effect of Bed Height and Bed Thickness 264 8.5 Types of Reactors 266 8.5.1 Fixed Bed Reactor 266 8.5.2 Fluidized Bed Reactor 266 8.5.3 Bubbling Fluidized Bed (BFB) Reactor 267 8.5.4 Circulating Fluidized Bed (CFB) Reactors 267 8.5.5 Ablative Reactor 268 8.5.6 Vacuum Pyrolysis Reactor 268 8.5.7 Rotating Cone Reactor 269 8.5.8 PyRos Reactor 270 8.5.9 Auger Reactor 270 8.5.10 Plasma Reactor 271 8.5.11 Microwave Reactor 272 8.5.12 Solar Reactor 272 8.6 Advantages and Disadvantages of Different Types of Reactors 272 8.7 Conclusion 274 Acknowledgements 275 References 275 9 Thermo-Catalytic Conversion of Non-Edible Seeds (Extractive-Rich Biomass) to Fuel Oil 285Nilutpal Bhuyan, Neelam Bora, Rumi Narzari, Kabita Boruah and Rupam Kataki 9.1 Introduction 286 9.2 Thermochemical Technologies for Liquid Biofuel Production 289 9.2.1 Hydrothermal Liquefaction 289 9.2.2 Pyrolysis and Its Classification 292 9.3 Feedstock Classification for Biofuel Production 293 9.3.1 Agricultural Crops and Residues 294 9.3.2 Municipal and Industrial Wastes 294 9.3.3 Animal Wastes 295 9.3.4 Undesirable Plants or Weeds 295 9.3.5 Forest Wood and Residues 296 9.3.5.1 Non-Edible Oil Seeds: A Potential Feedstock for Liquid Fuel Production 296 9.3.5.2 Non-Edible Oil Seeds and Worldwide Availability 297 9.4 Characterization of Non-Edible Oil Seeds 310 9.5 Thermal Degradation Profile of Different Non-Edible Seeds 320 9.6 Preparation of Raw Materials for Pyrolysis 322 9.7 Catalytic and Non-Catalytic Thermal Conversion for Liquid Fuel Production 323 9.7.1 Non-Catalytic Pyrolysis 323 9.7.1.1 CHNSO Analysis of Seed Pyrolytic Oil 326 9.7.1.2 FTIR Analysis of Seed Pyrolytic Oil 326 9.8 Need for Up-Gradation of Pyrolytic Oil 329 9.8.1 Catalytic Pyrolysis 329 9.9 Application of Catalyst in Pyrolysis of Non-Edible Biomass 330 9.10 Effect of Parameters on Liquid Fuel Production 330 9.10.1 Effect of Operating Parameters on Yield 330 9.10.2 Effect of Temperature 339 9.10.3 Heating Rates 340 9.10.4 Effect of Flow of Sweeping Gas 340 9.10.5 Effect of Particle Size 341 9.10.6 Effect of Catalyst on Yield 341 9.10.7 Influence of Catalysts on Oil Composition 342 9.10.8 Effect of Catalyst Bed on Yield 343 9.10.9 Effect of Catalyst on Fuel Properties of Pyrolytic Oil 343 9.11 Fuel Properties of Thermal and Catalytic Pyrolytic Oil 343 9.12 Challenges in Utilization of Nonedible Oil Seed in Themocatalytic Conversion Process 345 9.13 Advantages and Drawbacks of Seed Pyrolytic Oils 346 9.14 Precautions Associated with the Application of Biofuel 347 9.15 Conclusion and Future Perspectives 348 References 350 10 Suitability of Oil Seed Residues as a Potential Source of Bio-Fuels and Bioenergy 361Vikranth Volli, Randeep Singh, Krushna Prasad Shadangi and Chi-Min Shu 10.1 Introduction 362 10.2 Biomass Conversion Processes 363 10.3 Biomass to Bioenergy via Thermal Pyrolysis 367 10.3.1 Thermogravimetric Analysis 367 10.3.2 Thermal Pyrolysis 368 10.4 Physicochemical Characterization of Bio-Oil 370 10.4.1 Physical Properties 370 10.4.2 FTIR Analysis 371 10.4.3 GC-MS Analysis 372 10.5 Engine Performance Analysis 384 10.5.1 Break Thermal Efficiency (BTE) 384 10.5.2 Brake Specific Fuel Consumption (BSFC) 384 10.5.3 Exhaust Gas Temperature (EGT) 385 10.6 Future Prospects and Recommendations 386 10.7 Conclusion 387 Acknowledgments 387 References 387 11 Co-Conversion of Algal Biomass to Biofuel 391Abhishek Walia, Chayanika Putatunda, Preeti Solanki, Shruti Pathania and Ravi Kant Bhatia 11.1 Introduction 392 11.2 Mechanism of Co-Pyrolysis Process 394 11.2.1 Major Types of Pyrolysis and Co-Pyrolysis 396 11.3 Factors Impacting Co-Pyrolysis 398 11.3.1 Composition of Co-Pyrolysis Substrates and the Products Obtained in Co-Pyrolysis 398 11.3.2 Main Reactor Types Used During Biomass Co-Pyrolysis and the Process Conditions/Parameters 399 11.3.2.1 Classification of Biomass (Co) Pyrolysis Bioreactors 401 11.3.3 The Role of Catalysts in Biomass Co-Pyrolysis 405 11.3.3.1 Catalytic Hydrotreating 405 11.3.3.2 Types of Catalysts Available 407 11.3.3.3 Factors Affecting the Performance of Catalysts 409 11.3.3.4 Mechanisms of Deactivation of Catalysts 410 11.3.3.5 Catalytic Upgradation of Bio-Oil with Hydrodeoxygenation (HDO) 410 11.4 Recent Advances and Studies on Co-Pyrolysis of Biomass and Different Substrates 411 11.5 Effect between Biomass and Different Substrates in Co-Pyrolysis 412 11.5.1 Increased Bio-Oil Yield 413 11.5.1.1 Type of Substrate 413 11.5.1.2 Particle Size 414 11.5.1.3 Temperature 415 11.5.1.4 Substrate to Biomass Ratio 416 11.5.1.5 Residence Time 417 11.5.2 Improved Oil Quality 417 11.5.2.1 Influence of Bioreactor 417 11.5.2.2 Influence of Catalyst 418 11.5.3 Effect of Biomass-Different Substrates Co-Pyrolysis on By-Products 420 11.5.3.1 Microalgae and Plastic Waste 420 11.5.3.2 Microalgae and Coal 423 11.5.3.3 Microalgae and Tires 424 11.6 Future Perspectives 425 11.7 Conclusion 427 References 428 12 Pyrolysis of Caryota Urens Seeds: Fuel Properties and Compositional Analysis 441Midhun Prasad Kothandaraman and Murugavelh Somasundaram 12.1 Introduction 442 12.2 Types of Pyrolysis Reactor 443 12.2.1 Fluidized Bed Reactor 443 12.2.2 Fixed Bed Reactor 444 12.2.3 Auger Reactor 445 12.2.4 Rotating Cone Pyrolysis Reactor 446 12.3 Materials and Methods 447 12.3.1 Feedstock Preparation and Collection 447 12.3.2 Tubular Reactor for Conversion of Caryota Ures Seeds to Bio Oil 447 12.4 Product Analysis 448 12.4.1 Characterization of Feedstock and Oil Yield 448 12.5 Kinetic Modelling 449 12.5.1 Kissinger Method for Activation Energy Calculation 450 12.5.2 Kissinger-Akahira-Sunose (KAS) Method for Activation Energy Calculation 450 12.5.3 Ozawa-Flynn-Wall (OFW) Method for Activation Energy Calculation 450 12.6 Result and Discussion 451 12.6.1 Characterization of Feedstock 451 12.6.2 Product Yield 452 12.6.3 FTIR of Bio Oil 452 12.6.4 GCMS of Bio Oil 453 12.6.5 Thermogravimetric Analysis of Caryota Urens 456 12.6.6 Activation Energy Calculation Using Isoconversional Models 459 12.6.6.1 Kissinger Method for Estimation of Activation Energy 459 12.6.6.2 KAS Method for Estimation of Activation Energy 460 12.6.6.3 The OFW Method 460 12.7 Conclusion 462 Acknowledgements 463 Nomenclature 463 References 463 13 Bio-Butanol as Biofuels: The Present and Future Scope 467Seim Timung, Harsimranpreet Singh and Anshika Annu 13.1 Introduction 467 13.2 Butanol Global Market 469 13.3 History of ABE Fermentation 469 13.4 Feedstocks 470 13.4.1 Non-Lignocellulosic Feedstock 470 13.4.2 Lignocellulosic Biomass 471 13.4.3 Algae 472 13.4.4 Waste Sources 474 13.4.5 Glycerol 475 13.5 Pretreatment Techniques 476 13.5.1 Acid Pretreatment 476 13.5.2 Alkali Pretreatment 477 13.5.3 Organosolvent Pretreatment 477 13.5.4 Other Pretreatment 478 13.6 Fermentation Techniques 478 13.7 Conclusion 479 References 480 14 Application of Nanotechnology in the Production of Biofuel 487Trinath Biswal and Krushna Prasad Shadangi 14.1 Introduction 488 14.2 Various Nanoparticles Used for Production of Biofuel 489 14.2.1 Magnetic Nanoparticles 489 14.2.2 Carbon Nanotubes (CNTs) 491 14.2.3 Graphene and Graphene Derived Nanomaterial for Biofuel 493 14.2.4 Other Nanoparticles Applied in Heterogeneous Catalysis for Biofuel Production 495 14.3 Factors Affecting the Performance of Nanoparticles in the Manufacturing Process of Biofuel 495 14.3.1 Nanoparticle Synthesis Temperature 496 14.3.2 Pressure During Synthesis of Nanoparticle 496 14.3.3 pH Influencing Synthesis of Nanoparticles 496 14.3.4 Size of Nanoparticles 496 14.4 Role of Nanomaterials in the Synthesis of Biofuels 496 14.5 Utilization of Nanomaterials for the Production of Biofuel 497 14.5.1 Production of Biodiesel Using Nanocatalysts 497 14.5.2 Application of Nanomaterials for the Pretreatment of Lignocellulosic Biomass 500 14.5.3 Application of Nanomaterials in Synthesis of Cellulase and Stability 501 14.5.4 Application of Nano-Materials in the Hydrolysis of Lignocellulosic Biomass 501 14.5.5 Bio-Ethanol Production by Using Nanotechnology 502 14.5.6 Application of Nanotechnology in the Production of Bio-Ethanol or Cellulosic Ethanol 506 14.5.7 Up-Gradation of Biofuel by Using Nanotechnology 508 14.5.8 Use of Nanoparticles in Biorefinery 509 14.6 Conclusion 510 References 511 15 Experimental Investigation of Long Run Viability of Engine Oil Properties in DI Diesel Engine Fuelled with Diesel, Bioethanol and Biodiesel Blend 517Dulari Hansdah and S. Murugan 15.1 Introduction 518 15.2 Materials and Method 519 15.2.1 Fuel Properties 520 15.3 Test Procedure 522 15.3.1 Engine Experimental Set Up 522 15.3.2 Methodology 525 15.4 Result Analysis 528 15.4.1 Wear Measurements of Different Components 528 15.4.2 Deposits of Carbon on the Various Engine Components 532 15.4.2.1 Cylinder Head and Piston Crown 532 15.4.2.2 Analysis Deposits on Fuel Injector 533 15.4.3 Analysis of Lubricating Oil 533 15.4.3.1 Effect of Crankcase Dilution 533 15.4.3.2 Analysis of Wear of Metals from Different Components 537 15.5 Conclusion 541 References 541 16 Studies on the Diesel Blends Oxidative Stability in Mixture with TBHQ Antioxidant and Soft Computation Approach Using ANN and RSM at Varying Blend Ratio 543Ramesh Kasimani 16.1 Introduction 544 16.2 Materials and Methodology 545 16.2.1 Bio-Diesel Preparation and its Properties 545 16.2.2 Antioxidant Reagent 547 16.2.3 GC-MS Analysis 547 16.2.4 Oxidation Stability Determination 547 16.2.5 Uncertainty Analysis 548 16.2.6 Experimental Setup and Test Procedure 552 16.2.7 Response Surface Methodology 552 16.2.8 Artificial Neural Network 554 16.3 Results and Discussion 555 16.3.1 Oxidation Stability Analysis 555 16.3.2 Performance and Emission Characteristics of CIB Diesel Blends 556 16.3.3 Brake-Specific Fuel Consumption 556 16.3.4 Brake Thermal Efficiency 559 16.3.5 Carbon Monoxide 560 16.3.6 Hydrocarbon 561 16.3.7 Nitrogen Oxides 561 16.3.8 Carbon Dioxide 562 16.3.9 Performance and Emission Characteristics of CIB Diesel Blends + TBHQ 563 16.3.10 Brake Specific Fuel Consumption 563 16.3.11 Brake Thermal Efficiency 567 16.3.12 Carbon Monoxide 567 16.3.13 Hydrocarbon 568 16.3.14 Nitrogen Oxides 568 16.3.15 Carbon Dioxide 569 16.4 Response Surface Methodology for Performance Parameter 570 16.4.1 Non-Linear Regression Model for Performance Parameter 570 16.4.2 Fit Summary for BSFC 571 16.4.3 ANOVA for Performance Parameters 571 16.4.4 Response Surface Plot and Contour Plot for BSFC 571 16.4.5 Response Surface Plot and Contour Plot for BTE 576 16.4.6 Non-Linear Regression Model for Emission Parameter 578 16.4.7 Fit Summary for Emission Parameters 578 16.4.8 ANOVA for Emission Parameters 580 16.4.9 Response Surface Plot and Contour Plot for CO 586 16.4.10 Response Surface Plot and Contour Plot for HC 591 16.4.11 Response Surface Plot and Contour Plot for NOx 591 16.4.12 Response Surface Plot and Contour Plot for CO2 592 16.5 Modelling of ANN 593 16.5.1 Prediction of Performance Characteristics 596 16.5.2 Prediction of Emission Characteristics 597 16.6 Validation of RSM and ANN 599 16.7 Conclusion 606 References 608 17 Effect of Nanoparticles in Bio-Oil on the Performance, Combustion and Emission Characteristics of a Diesel Engine 613V.Dhana Raju, S.Rami Reddy, Harish Venu, Lingesan Subramani and Manzoore Elahi M. Soudagar 17.1 Introduction 614 17.2 Materials and Methods 618 17.2.1 Waste Mango Seed Oil Extraction 618 17.2.2 Transesterification Process 619 17.2.3 Preparation of Alumina Nanoparticles 621 17.3 Experimental Setup 621 17.3.1 Error and Uncertainty Analysis 622 17.4 Results and Discussion 623 17.4.1 Mango Seed Biodiesel Yield 623 17.4.2 Characterization of Alumina Nanoparticles 624 17.4.3 Diverse Characteristics of Diesel Engine 625 17.4.3.1 Brake Thermal Efficiency (BTE) 626 17.4.3.2 Brake Specific Fuel Consumption (BSFC) 627 17.4.3.3 Cylinder Pressure (CP) 628 17.4.3.4 Heat Release Rate (HRR) 629 17.4.3.5 Carbon Monoxide Emissions (CO) 629 17.4.3.6 Carbon Dioxide Emissions (CO2) 630 17.4.3.7 Hydrocarbons Emissions (HC) 630 17.4.3.8 Nitrogen Oxides Emissions (NOX) 632 17.4.3.9 Smoke Opacity (SO) 632 17.5 Conclusions 633 Abbreviations 634 Nomenclature 634 References 635 18 Use of Optimization Techniques to Study the Engine Performance and Emission Analysis of Diesel Engine 639Sakthivel R, Mohanraj T, Abbhijith H and Ganesh Kumar P 18.1 Introduction 640 18.1.1 Engine Performance Optimization 644 18.2 Engine Parameter Optimization Using Taguchi’s S/N 645 18.3 Engine Parameter Optimization Using Response Surface Methodology 649 18.3.1 Analysis of Variance 652 18.4 Artificial Neural Networks 653 18.5 Genetic Algorithm 659 18.6 TOPSIS Algorithm 662 18.6.1 TOPSIS Method for Optimizing Engine Parameters 666 18.7 Grey Relational Analysis 669 18.8 Fuzzy Optimization 674 18.9 Conclusion 675 Abbreviations 676 References 676 19 Engine Performance and Emission Analysis of Biodiesel-Diesel and Biomass Pyrolytic Oil-Diesel Blended Oil: A Comparative Study 681K. Adithya, C.M Jagadesh Kumar, C.G. Mohan, R. Prakash and N. Gunasekar 19.1 Introduction 682 19.2 Experimental Analysis 683 19.2.1 Production of Coconut Shell Pyrolysis Oil 683 19.2.2 Production of JME 685 19.3 Experimental Set-Up 685 19.3.1 Engine Specifications 686 19.3.2 Error Analysis 686 19.4 Results and Discussion 687 19.4.1 Performance Parameters 687 19.4.1.1 Brake Thermal Efficiency 687 19.4.1.2 BSFC 688 19.4.1.3 Exhaust Gas Temperature 688 19.4.2 Emission Parameters 689 19.4.2.1 Carbon Monoxide 689 19.4.2.2 Hydrocarbons 689 19.4.2.3 NOx Emissions 691 19.4.2.4 Smoke Opacity 691 19.5 Conclusion 692 References 693 20 Agro-Waste for Second-Generation Biofuels 697Prakash Kumar Sarangi and Mousumi Meghamala Nayak 20.1 Introduction 697 20.2 Agro-Wastes 699 20.3 Value-Addition of Agro-Wastes 700 20.4 Production of Second-Generation Biofuels 702 20.4.1 Biogas 702 20.4.2 Biohydrogen 702 20.4.3 Bioethanol 703 20.4.4 Biobutanol 703 20.4.5 Biomethanol 704 20.4.6 Conclusion 705 References 706 Index 711

Read more less

Book SynopsisModern Electricity SystemsAwarded The Best Book for Energy Engineers by The American Energy Society 2023 A welcome textbook instructing on many current aspects of energy generation, transmission, distribution, and consumption The importance of a well-informed group of individuals in charge of energy production and use is essential to create a sustainable and greener tomorrow. Technologies and costs are rapidly changing, and environmental goals widely debated in this book. The future of energy is at a crossroads. In addition, energy and technology poverty affects as much as 25% of the world's population. Having the correct set of toolsa basic understanding of modern electrical systemsis essential, not just for engineers but for our leaders and decision-makers. With decades of experience in industry and academia behind them, the team of authors in Modern Electricity Systems offers a toolbox from which the reader will learn what is essential to make informed decisions. As such, this teTable of ContentsPreface Acknowledgments About the Authors 1 Essentials of Power and Control Abstract 2 Keywords 2 1.1 Introduction 3 1.2 Basic Principles of Power and Control 4 1.2.1 Energy and Power 5 1.2.2 Voltage, Current, and Impedance 8 1.2.3 Alternating Current Vs. Direct Current 10 1.2.4 Single Phase vs. Multiphase 13 1.2.5 Active, Reactive, Apparent Power and Power Factor 15 1.3 Control Overview 19 1.4 Power Generation and Grid: Operation and Control 21 1.5 Generation Dispatch and Balancing the System 23 1.6 Transmission and Distribution Network 24 1.6.1 Transmission Network 25 1.6.2 Distribution System 26 1.6.3 One Line or Single Line Diagram 27 1.7 Wholesale and Retails Markets 28 1.7.1 Wholesale Market 29 1.7.2 Retail Market 34 1.8 Smart Meters 40 1.9 Distributed Generation and Grid Edge 43 1.9.1 Microgrids in Kenya and Other Locations 43 1.9.2 Microgrids in the United States 44 1.9.3 Flexibility Services in Europe 45 1.9.4 Virtual Power Plants in Australia 46 1.10 Changes in the Grid 46 1.11 Visioning 47 Index 48 2 Basic Discounting and Levelized Costs Concepts Abstract 3 Keywords 3 2.1 Introduction 3 2.2 Fundamentals 6 2.2.1 Cashflow and Discount Rate 6 2.2.2 Market Failures and Externalities 9 2.2.3 Tax and Subsidy 11 2.2.3.1 Carbon Tax 12 2.2.3.2 Subsidy 14 2.2.4 Present Value and Future Value 17 2.2.5 Risk and Risk Management 19 2.2.5.1 Identification 20 2.2.5.2 Assessment 21 2.2.5.3 Mitigation 21 2.3 Simple Applications 23 2.3.1 Simple Payback 24 2.3.2 Return on Investment 24 2.3.3 Gross Margin 25 2.3.4 Net Present Value 26 2.3.5 Levelized Costs 26 2.3.7 Lifecycle cost 28 2.3.8 Supply and Demand 29 2.4 Extended Applications 32 2.4.1 Wholesale market 32 2.4.2 Retail Market 39 2.4.3 Local Electricity Market 41 2.5 Visioning 42 Index 43 3 Modern Electrical Engineering Systems, Current Events," Crises," and Tradeoffs Abstract: 2 Keywords: 2 3.1 Introduction: Tradeoffs, Crises, and Notable Current Events 2 3.2 Current Events, Crises, and Tradeoffs 5 3.2.1 Extreme weather and Climate events need Resilient and Diverse grid - Texas Power Crisis 2021 6 3.2.2 Wholesale Electricity Markets and their manipulations – big banks to wall street darlings 9 3.2.3 Systematic Energy Crisis – Nepal's Energy Poverty 12 3.2.4 Europe and Natural Gas: Increasing dependence on single resource –. Policies to achieve clean energy targets are not simple. 16 3.2.5 Pandemic's Impacts on electrical systems – Energy supply crunch and sudden change in electrical demand. 18 3.3 Tradeoffs 19 3.3.1 Green Energy Choices vs. Conventional Energy Choices 20 3.3.2 Regulation vs. Deregulation 24 3.3.3 Reliability vs. Costs 27 3.4 Crises and Tradeoffs mapping 29 3.5 Visioning 33 Index 36 4 Introduction to Influence of Wholesale Energy Markets in policy and pricing discussions Abstract 3 Keywords 3 4.1. Introduction 3 4.1.1. True market proponents believe in market-based solutions to enable energy transformation 4 4.1.2. It is energy markets, not electricity markets 4 4.1.3. The United States Regional Transmission Organization developments 5 4.1.4. International energy market developments 5 4.1.5. Don't expect a policy to lead energy markets 6 4.1.6. Finally, energy markets are fascinating and complex 6 4.2. Do energy markets influence policy? 7 4.3. How does policy benefit market operations? 8 4.4. Joining an energy market is a decision not to be taken lightly ("On-Ramp" of the market setup costs) 8 4.4.1. The benefit to cost studies 9 4.4.2. Energy Imbalance Markets 9 4.4.3. Value Proposition studies 11 4.4.4. Regulatory Compliance and Audits 12 4.5. States with multiple RTOs 13 4.5.1. Texas has 3 RTOs - Electric Reliability Council Of Texas, Southwest Power Pool, and Midcontinent Independent System Operator 13 4.5.2. Missouri – Southwest Power Pool and Midcontinent Independent System Operator 14 4.5.3. Illinois – Pennsylvania-New Jersey-Maryland Interconnection and Midcontinent Independent System Operator 14 4.5.4. States with multiple RTOs creates "Seams" issues 15 4.5.5. Joint and Common Market – Pennsylvania-New Jersey-Maryland Interconnection and Midcontinent Independent System Operator effort 15 4.6. Other organized wholesale markets 15 4.6.1. Australia 16 4.6.2. Germany 18 4.6.3. Vietnam 19 4.6.4. Nepal (Potential) 20 4.6.5. Africa (Potential) 21 4.7. Leaving energy markets is a decision not to be taken lightly ("Off-Ramp") 23 4.7.1. First Energy and Duke Energy Ohio left MISO 23 4.7.2. New Jersey threatened to leave PJM’s Capacity Market 24 4.8. States or Countries without RTOs 24 4.8.1. Who maintains reliability? 25 4.8.2. How are capacity needs assessed? 25 4.8.3. How are transmission needs assessed? 26 4.8.4. Capacity Benefit Margin is relevant in states without RTOs similar to locational capacity needs in states with RTOs 27 4.8.5. Some transmission planning concepts continue to be relevant for the market to non-market regional purposes 28 4.8.6. And energy markets added new metrics to continue to show the importance of transmission investments 29 4.8.7. Transmission planning and capacity markets are intertwined 30 4.9. Cost Allocation of Transmission projects 31 4.9.1. Reliability Project Cost Calculation 33 4.9.2. Economic Project Cost Calculation 33 4.9.3. Adjusted Production Cost Calculation 35 4.9.4. Public Policy Project Cost Calculation 36 4.10. Visioning – 37 INDEX 39 5 How to put together a regulatory policy by following a process 5.1. Introduction 3 5.2. What is a regulatory policy? 4 5.2.2. What about the influence on energy policy and regulatory actions in developing countries and fragile economies? 7 5.3. Different flavors of regulatory policy in the electric utility industry 8 5.3.1. A utility's regulatory policy for emerging technology is going to look different than the utility's strategy for an existing technology 8 5.3.2. The regulatory policy is going to look different in regions that have organized markets than the regions that don't 10 5.3.3. Regulatory policy for an IOU is different compared to a CCA 10 5.3.4. Regulatory policy for consumer advocates is going to be different than utilities 11 5.3.5. A utility's regulatory policy for industrial customers is different than consumer advocates or residential Customers 11 5.3.6. The regulatory policy for an Independent Transmission Company (ITC) is different than a Transmission Owner (TO) 11 5.3.7. Regulatory policy for a specific supply-side fuel such as Nuclear fuel is in a class by itself 12 5.3.8. A demand-side regulatory policy such as Demand Response 12 5.3.9. A regulatory policy with a compliance purpose 13 5.3.10. A technology provider's regulatory policy is going to look different than a national laboratory's policy 13 5.3.11. Regulatory policy drives partnerships 14 5.3.12. So, how do we know the regulatory policy is working? 14 5.4. There are five steps in any regulatory policy process 15 5.4.1. First, understand the customers of this process 16 5.4.2. Second, understand the output from the regulatory process. 16 5.4.3. Third, understand the regulatory process. 18 5.4.4. Fourth, understand the Inputs to the process. 19 5.4.5. Fifth, understand the Stakeholders in this regulatory process. 20 5.4.6. Applying the five steps to the Dynamic Line Rating (DLR) policy context 20 5.4.7. Applying the five steps to the Australian policy context 21 5.5. How does regulatory policy drive legislative affairs? 21 5.6. Additional examples of regulatory policy driving regulatory success 22 5.6.1. Salvation Army's Heat program 23 5.6.2. Example of IOU listening to stakeholder comments in Integrated Resource Planning (IRP) proceedings 23 5.6.3. Narrative about Citizen and Industry group influence on renewable standards 24 5.7. How does regulatory policy drive individual participation in industry communications? 26 5.7.1. How do you know you had a successful event? 27 5.8. Visioning 29 INDEX 30 6 How institutions shape energy policy Abstract 3 Keywords 3 6.1. Introduction 3 6.2. Strategic Action Field Framework for Policy 7 6.3. What are the major institutions in US energy policy? 10 6.3.1. US Congress 12 6.3.2. Department of Energy (DOE) 16 6.3.3. Federal Energy Regulatory Commission (FERC) and Independent System Operator (ISO) 18 6.3.4. Independent Market Monitors 20 6.3.5. Energy Information Administration (EIA) 21 6.3.6. North American Electric Reliability Corporation (NERC) 22 6.3.7. Federal Bureau of Ocean Energy Management (BOEM) 23 6.3.8. State Legislatures (Senate and House) 24 6.3.9. Public Utility Commissions (PUC) 24 6.3.10. National Association of Utility Regulatory Commissioners (NARUC) 26 6.3.11. The role of local city governments 26 6.3.12. Energy advocates role in US policy 27 6.3.13. Stakeholder working group's role in setting US energy policy 28 6.3.14. Associations & Alliances role in policy 30 6.3.15. Summary of US institutions 31 6.4. What are the major institutions in international energy policy? 32 6.4.1. Examples from strong economies 33 6.4.1.1 European Union (EU) 33 6.4.1.2. European Commission (EC) 33 6.4.1.3. International Energy Agency (IEA) 33 6.4.1.4. World Energy Council (WEC) 34 6.4.1.5. European Network of Transmission System Operators for Electricity (ENTSO-E) 34 6.4.1.6. Australia 35 6.4.1.7 Energy Regulators Regional Association (ERRA) 39 6.4.1.8. China 39 6.4.2. Examples from Fragile Economies 41 6.4.2.1 Nepal 41 6.4.2.2 Democratic Republic of the Congo 44 6.4.3. Examples from the private sector 45 6.4.12. Summary of International institutions 46 6.5. The role of Climate Change/Low Carbon/Renewable Energy regulations, goals, and pledges in setting policy 47 6.6. The role of courts 48 6.7. Visioning 50 INDEX 53 7 How does the power system work? Abstract: 3 Keywords: 3 7.1 Introduction 3 7.2 Guiding Principles for a Power System 5 7.3 Schematic of the modern energy system 6 7.4 Governing bodies and actors 8 7.5 Power System 8 7.5.1 Energy Management Systems 9 7.5.1.1 Generation Management 9 7.5.1.2 Transmission 13 7.5.1.3 Distribution 15 7.5.1.3.1 Distributed Energy Resource Management (DERMS 16 7.5.1.3.2 Head-End Systems (HES 18 7.5.1.3.3 Meter Data Management 19 7.5.1.3.4 Customer Information Systems (CIS) 20 7.5.1.3.5 Virtual Power Plant 21 7.5.1.3.6 Peer to Peer Trading and Flexibilities System 24 7.5.2 Market Management 25 7.6 High-level architecture and redundancies of the systems above 26 7.6.1 Cybersecurity 27 7.6.2 Change Management of Software Changes 28 7.7 Advanced Concepts of Power and Control 30 7.7.1 Power Flow 30 7.7.1.1 Transmission 30 7.7.1.2 Distribution 31 7.7.2 State Estimation 32 7.7.2.1 Transmission 32 7.7.2.2 Distribution 32 7.7.3 Contingency Analysis 33 7.7.4 Fault Management 34 7.7.4.1 Transmission 34 7.7.4.2 Distribution 35 7.7.5 Volt-Var-Watt control 37 7.7.6 Optimal Network Reconfiguration 37 7.7.6.1 Transmission 38 7.7.6.2 Distribution 38 7.7.7 Supervisory Control 38 7.7.8 Outage Management 39 7.7.8.1 Unplanned Outage 40 7.7.8.2 Planned Outage/Work 40 7.7.9 Asset Management 41 7.7.10 Automatic Generation Control 41 7.7.10.2 Unit Commitment 42 7.7.10.3 Reserve Calculations 42 7.7.11 Market Operations 43 7.7.12 Model Management and Digital Twin 43 7.7.13 Dynamic Line Rating 44 7.7.14 Other Basic Control 45 7.8 Power System 46 7.8.1 Long Term Planning 47 7.8.2 Medium Term Planning 47 7.8.3 Short Term Planning 47 7.8.4 Operational 47 7.9 Visioning 49 Index 51 8 How are changes to Power generation operation and control relevant today Abstract 2 Keywords: 2 8.1 Introduction 2 8.1.1 What is happening in the current power systems? What shall drive the future changes? 6 8.1.1.1 The costs of renewables are declining 6 8.1.1.2 The sectors are more coupled 11 8.1.1.3 Energy security, reliability, and resiliency goals are more important 13 8.1.1.4 Innovations and the Internet of Things (IoT) are opening newer doors 15 8.1.1.5 The customers are becoming more aware 17 8.1.1.6 New actors like the aggregators are emerging 17 8.1.2 What did we learn? How is this relevant today and for the future of the power systems? 27 8.1.3 Pathways to make informed decisions for the future of the power system 29 8.1.3.1 Transformation into an unleased Distribution System Operator (DSO) 29 8.1.3.2 Encouraging (re) innovation for cleaner restructuring 32 8.1.4 Newer Elements of the Power System 33 8.1.4.1 Mini and Microgrid and their roles in Top-down and bottom-up electrifications 33 8.1.4.2 The aggregator is the new actor 34 8.1.4.3 Peer to Peer (P2P) Trading and Localized Energy Markets (LEMs) 36 8.1.5 Innovation and the Power System 37 8.2 Visioning 40 Index 42 9 Influence of Wholesale Energy Markets in policy and pricing discussions 9.1. Introduction 3 9.2. How do energy markets coordinate reliability? 7 9.2.1. What past reliability issues from energy markets have influenced policy? 8 9.2.2. Balancing inverter-based resources is the future for operations in energy markets 9 9.3. How do energy markets facilitate grid investments? 11 9.3.1. What major events have influenced transmission policies? 12 9.3.2. DERs, Energy Storage and Off-Shore Wind, drive the future grid investments in energy markets 17 9.3.2.1. Modeling Energy Storage is increasingly relevant in transmission planning 20 9.4. An introduction to capacity markets 21 9.5. How do capacity markets ensure reliability? 23 9.5.1. How do reliability assessments inform capacity markets? 25 9.5.2. The future role of operations in capacity markets depends on how well DERs and other emerging technologies perform in the next 5-10 years 28 9.6. How do capacity markets facilitate grid investments? 29 9.6.1. Past transmission planning experience may not be relevant for the future capacity markets. 29 9.6.2. Generator Interconnection reform is the future for transmission planning in capacity markets 29 9.6.2.1. Multiple engineering studies 30 9.6.2.2. Negotiation 32 9.6.2.3. Construction 33 9.7. An introduction to Ancillary Services Markets 36 9.7.1. Operating Reserve Demand Curve (ORDC) 41 9.8. How Ancillary Services markets ensure reliability? 44 9.8.1. A single entity administrating ancillary services provides benefits to consumers. 46 9.8.2. Real-Time Co-Optimization is the future for operations in A/S markets 48 9.9. How do A/S Markets facilitate grid investments? 50 9.9.1. Past transmission planning experience may not be relevant for the future A/S markets. 50 9.9.2. More A/S market products would be needed in the future 51 9.10. Visioning 54 10 Energy policy should include consideration of Energy poverty Abstract 3 Keywords 3 10.1. Introduction 3 10.2. Energy Poverty definition 5 10.2.1. Energy Accessibility 5 10.2.2. Energy Quality Attributes 6 10.2.3. Multiple definitions of energy poverty 8 10.2.4. Developed and partially developed countries with Energy Poverty and Social Justice Issues 9 10.3. Hierarchy Model of Energy Attributes and Access 11 10.4. Importance of Energy Poverty mitigation as a priority in the eyes of international Non-Governmental Organizations (NGOs) 14 10.4.1. The World Bank definition 15 10.4.2. Energy poverty progress 15 10.5. Significant drivers for energy poverty 16 10.5.1. Energy Poverty links with basic needs 16 10.5.2. No one driver for energy poverty 17 10.5.3. Historical and current socioeconomic drivers for energy poverty 18 10.6. Energy poverty and ties to Thermal and, Cooking and Food energy 18 10.6.1. The need for multiple cooking and heating fuels 19 10.6.2. Energy Poverty and access to basic human and infrastructure needs 20 10.7. Why is energy poverty a significant issue now, more than ever? 20 10.7.1. Specific Experiences in Fragile Economies and the Global South 20 10.7.2. Need for Ongoing Data 22 10.7.3. The role of social media 22 10.7.4. Energy and safety, disproportionate effect on women, entrepreneurship, and energy 22 10.7.5. Energy Choice and Growth 23 10.8. Can wholesale energy markets help solve energy poverty? 23 10.8.1. Wholesale markets provide price transparency and non-discriminatory access to transmission 24 10.8.2. Market operators forecast future needs 25 10.8.3. Phased manner of market adoption and market startup costs 25 10.8.4. Power pool members in the global south are ideal candidates 26 10.8.5. Role of an independent board of directors 26 10.9. If there is no political will or economic driver for the wholesale markets, can proper retail reforms be the solution? 27 10.9.1. Industrial and Commercial customers guaranteed tariff 27 10.9.2. Residential customers tariff 28 10.9.3. Natural gas is the bridge fuel 28 10.9.4. Smart Meters role in reducing energy demand and consumption 29 10.9.5. Energy Subsidy should make way for Distribution System Operator 30 10.10. Can we get rid of energy poverty in our lifetime? 30 10.10.1. Energy Access – focus on power generation need 30 10.10.2. Energy Access - focus on the transmission system 32 10.10.3. Energy Access - focus on the Distribution system 33 10.1.1. Energy Quality - focus on the Data Institution Model (DIM) framework 34 10.1.1. Innovation for Energy Access and Quality – Examples 35 10.2. Visioning 37 INDEX 39

Read more less

Book SynopsisDiscover an up-to-date exploration of Embedded and Fan-Out Waver and Panel Level technologies In Embedded and Fan-Out Wafer and Panel Level Packaging Technologies for Advanced Application Spaces: High Performance Compute and System-in-Package, a team of accomplished semiconductor experts delivers an in-depth treatment of various fan-out and embedded die approaches. The book begins with a market analysis of the latest technology trends in Fan-Out and Wafer Level Packaging before moving on to a cost analysis of these solutions. The contributors discuss the new package types for advanced application spaces being created by companies like TSMC, Deca Technologies, and ASE Group. Finally, emerging technologies from academia are explored. Embedded and Fan-Out Wafer and Panel Level Packaging Technologies for Advanced Application Spaces is an indispensable resource for microelectronic package engineers, managers, and decision makers working with OEMs and IDMs. ITable of ContentsPreface xv 1 Fan-Out Wafer and Panel Level Packaging Market and Technology Trends 1Santosh Kumar, Favier Shoo, and Stephane Elisabeth 1.1 Introduction to Fan-Out Packaging 1 1.1.1 Historical Perspective 1 1.1.2 Key Drivers: Why Fan-Out Packaging? 6 1.1.3 FO-WLP vs. FO-PLP 8 1.1.4 Future of Fan-Out Packaging for Heterogeneous Integration 8 1.2 Market Overview and Applications 10 1.2.1 Fan-Out Packaging Definition 10 1.2.2 Market Segmentation: Core FO vs. HD FO vs. UHD FO 11 1.2.3 Market Valuation: Forecast of Revenue and Volume 12 1.2.4 Current and Future Target Markets 12 1.2.5 Applications of Fan-Out Packaging 14 1.3 Technology Trends and Supply Chain 19 1.3.1 Fan-Out Packaging Technology Roadmaps 19 1.3.2 Fan-Out Packaging Technology by Manufacturer 19 1.3.2.1 Amkor 19 1.3.2.2 JCET 20 1.3.2.3 NXP 21 1.3.2.4 DECA Technologies 21 1.3.2.5 ASE 22 1.3.2.6 TSMC 22 1.3.2.7 PTI 24 1.3.2.8 Samsung Electronics 25 1.3.2.9 Huatian 25 1.3.3 Supply Chain Overview 25 1.3.4 Analysis of the Latest Developments in the Supply Chain 26 1.4 Fan-Out Panel-Level Packaging (FO-PLP) 29 1.4.1 Motivation and Challenges for FO-PLP 29 1.4.2 FO-PLP Market and Applications 30 1.4.3 FO-PLP Supplier Overview 31 1.5 SystemDevice Teardowns 34 1.5.1 Teardown of End-Systems with Fan-Out Packaging 34 1.5.2 Technology Comparison 38 1.5.2.1 Radar IC: eWLB vs. RCP 38 1.5.2.2 MCM/SiP: RCP-SiP vs. eWLB 39 1.5.2.3 PMIC: eWLB vs. M-Series 40 1.5.3 Cost Comparison 41 1.6 Conclusion 42 References 45 2 Cost Comparison of FO-WLP with Other Technologies 47Amy Palesko Lujan 2.1 Introduction 47 2.2 Activity-Based Cost Modeling 47 2.3 Cost Analysis of FO-WLP Variations 49 2.3.1 Process Segment Costs 50 2.3.1.1 Die Preparation 50 2.3.1.2 Carrier 50 2.3.1.3 Die Bond 51 2.3.1.4 Mold 51 2.3.1.5 Backgrinding 51 2.3.1.6 RDL 51 2.3.1.7 UBM 52 2.3.1.8 Flux and Ball Attach 52 2.3.1.9 Singulation 52 2.3.2 FO-WLP Variations 52 2.3.2.1 Carrier 54 2.3.2.2 Die Cost and Preparation 54 2.3.2.3 Die Bond 54 2.3.2.4 Mold/Mold+CUF 54 2.3.2.5 Backgrind/Post-mold Grind 54 2.3.2.6 Scrap 55 2.4 Cost of FO-WLP versus Wire Bond and Flip Chip 55 2.5 Package-on-Package Cost Analysis 61 2.5.1.1 Substrate/RDLs 63 2.5.1.2 Die Bond 63 2.5.1.3 CUF and Mold Cost 63 2.5.1.4 Ball Attach 64 2.5.1.5 Singulation 64 2.5.1.6 TMV 64 2.5.1.7 Die Bond 66 2.5.1.8 CUF and Mold Cost 66 2.5.1.9 TMV/Large Copper Pillars 66 2.6 Conclusions 66 References 67 3 Integrated Fan-Out (InFO) for Mobile Computing 69Doug C.H. Yu, John Yeh, Kuo-Chung Yee, and Chih Hang Tung 3.1 Introduction 69 3.2 Fan-InWafer-Level Packaging 70 3.2.1 Dielectric and Redistribution Layers (RDL) 71 3.2.2 Under Bump Metallization (UBM) 71 3.2.3 Reliability and Challenges 72 3.2.4 Large Die WLP 72 3.3 Fan-OutWafer-Level System Integration 73 3.3.1 Chip-First vs. Chip-Last 74 3.3.2 Molding and Planarization 75 3.3.3 Redistribution Layer (RDL) 77 3.3.4 Through Via and Vertical Interconnection 80 3.4 Integrated Passive Devices (IPDs) 81 3.4.1 High Q-Factor 3D Solenoid Inductor 81 3.4.2 Antenna in Package (AiP) and 5G Communication 81 3.4.3 Passive Devices for MillimeterWave System Integration 82 3.5 Power, Performance, Form Factor, and Cost 85 3.5.1 Signal and Power Integrity 87 3.5.2 Heat Dissipation and Thermal Performance 88 3.5.3 Form Factor and Thickness 91 3.5.4 Cycle Time to Market and Cost 91 3.6 Summary 91 References 92 4 Integrated Fan-Out (InFO) for High Performance Computing 95Doug C.H. Yu, John Yeh, Kuo-Chung Yee, and Chih Hang Tung 4.1 Introduction 95 4.2 3DFabric and System-on-Integrated-Chip (SoIC) 97 4.3 CoWoS-R, CoWoS-S, and CoWoS-L 99 4.4 InFO-L and InFO-R 100 4.5 Info Ultra-High-Density Interconnect (InFO-UHD) 100 4.6 Multi-Stack System Integration (MUST) and Must-in-Must (MiM) 106 4.7 InFO on Substate (InFO-oS) and InFO Local Silicon Interconnect (InFO-L) 108 4.8 InFO with Memory on Substrate (InFO-MS) 110 4.9 InFO 3D Multi-Silicon (InFO-3DMS) and CoWoS-L 111 4.10 InFO System onWafer (InFO_SoW) 112 4.11 System on Integrated Substrate (SoIS) 116 4.12 Immersion Memory Compute (ImMC) 116 4.13 Summary 121 References 122 5 Adaptive Patterning and M-Series for High Density Integration 125Benedict San Jose, Cliff Sandstrom, Jan Kellar, Craig Bishop, and Tim Olson 5.1 Technology Description 125 5.2 Applications and Markets 127 5.3 Basic Package Construction 127 5.4 Manufacturing Process Flow and BOM 131 5.5 Design Features and System Integration Capability 134 5.6 Adaptive Patterning 137 5.7 Manufacturing Format and Scalability 144 5.8 Package Performance 149 5.9 Robustness and Reliability Data 151 5.10 Electrical Test Considerations 152 5.11 Summary 153 References 153 6 Panel-Level Packaging for Heterogenous Integration 155M. Töpper, T. Braun, M. Billaud, and L. Stobbe 6.1 Introduction 155 6.2 Fan-Out Panel-Level Packaging 157 6.3 Economic Efficiency Analysis of PLP 161 6.4 Summary 165 References 166 7 Next Generation Chip Embedding Technology for High Efficiency Power Modules and Power SiPs 169Vikas Gupta, Kay Essig, C.T. Chiu, and Mark Gerber 7.1 Technology Description 169 7.2 Basic Package Construction 172 7.3 Applications and Markets (HPC, SiP) 176 7.4 Manufacturing Process Flow and BOM 177 7.5 Design Features 180 7.6 System Integration Capability 182 7.7 Package Performance 183 7.8 Robustness and Reliability Data 186 7.9 Electrical Test Considerations 190 7.10 Summary 191 References 192 8 Die Integration Technologies on Advanced Substrates Including Embedding and Cavities 193Markus Leitgeb and Christian Vockenberger 8.1 Introduction 193 8.2 Heterogeneous Integration by Use of Embedded Chip Packaging (ECP®) 194 8.3 Embedding Process 196 8.4 Component Selection 198 8.5 Design Technology 199 8.6 Testing 200 8.7 Applications for ECP Technology 201 8.8 Heterogeneous Integration Using Cavities in PCB 206 8.9 Package Performance, Robustness, and Reliability 208 8.10 Conclusion 215 References 215 9 Advanced Embedded Trace Substrate – A Flexible Alternative to Fan-Out Wafer Level Packaging 217Shih Ping Hsu, Byron Hsu, and Adan Chou 9.1 Technology Description 217 9.1.1 C2iM Technology 217 9.1.2 C2iM-PLP Technology 218 9.2 Applications and Markets 219 9.3 Basic Package Construction 219 9.3.1 C2iM-PLP Experience 219 9.3.2 C2iM-PLP Advantages and Disadvantages Compared to Wirebond Quad Flat No Lead (WB-QFN) and Flip-Chip QFN (FC-QFN) Packages 219 9.3.3 C2iM-PLP Advantages and Disadvantages Compared to WLP and FO-WLP 220 9.3.4 Future Applications 222 9.3.5 Limitations of C2iM-PLP 222 9.4 Manufacturing Process Flow and BOM 223 9.5 Design Features 224 9.5.1 Package Design Rules 224 9.5.2 Design Rules for Die UBM 224 9.5.3 Design Rules for Die Side by Side 225 9.5.4 Design Rules for Cu Pillar 226 9.6 System Integration Capability 227 9.7 Manufacturing Format and Scalability 228 9.8 Package Performance 228 9.8.1 Electrical Performance 228 9.8.2 Thermal Performance 229 9.9 Robustness and Reliability Data 229 9.9.1 Automotive Reliability Certification Pass 229 9.9.2 Board Level Reliability Verification Pass 230 9.10 Electrical Test Considerations 230 9.11 Summary 231 References 231 10 Flexible Hybrid Electronics Using Fan-Out Wafer-Level Packaging 233Subramanian S. Iyer and Arsalan Alam 10.1 Introduction 233 10.2 Recent Trends in Packaging 239 10.3 FHE Using FO-WLP – FlexTrateTM 242 10.4 Applications on FlexTrateTM 250 Acknowledgments 256 References 256 11 Polylithic Integrated Circuits using 2.5D and 3D Heterogeneous Integration: Electrical and Thermal Design Considerations and Demonstrations 261Ting Zheng, Ankit Kaul, Sreejith Kochupurackal Rajan, and Muhannad S. Bakir 11.1 Introduction 261 11.2 Heterogeneous Interconnect Stitching Technology (HIST) 262 11.3 Thermal Evaluation of 2.5D Integration Using Bridge-Chip Technology 270 11.3.1 2.5D and 3D Benchmark Architectures 270 11.3.1.1 2.5D Integration 270 11.3.1.2 3D Integration 271 11.3.2 Thermal Modeling and Specifications 272 11.3.3 Comparison of Different 2.5D Integration Schemes 273 11.3.4 Thermal Comparison between 2.5D and 3D Integration 273 11.3.5 Thermal Study of Bridge-Chip 2.5D Integration 274 11.3.5.1 Impact of TIM conductivity 274 11.3.5.2 Die Thickness 275 11.3.5.3 Die Spacing 275 11.3.6 Polylithic 3D Integration 275 11.4 Monolithic Microfluidic Cooling of High-Power Electronics 276 11.4.1 Experimental Demonstration and Characterization on Single Die Systems 277 11.4.2 Microfluidic Cooling of 2.5D Devices: Experimental Demonstration 279 11.4.3 Monolithic Microfluidic Cooling of 3D Integration: Modelling Electrical Implications for I/Os 281 11.5 Conclusion 283 Acknowledgments 283 References 283 Index 289

Read more less

Book SynopsisOptimal Coordination of Power Protective Devices with Illustrative Examples Provides practical guidance on the coordination issue of power protective relays and fuses Protecting electrical power systems requires devices that isolate the components that are under fault while keeping the rest of the system stable. Optimal Coordination of Power Protective Devices with Illustrative Examples provides a thorough introduction to the optimal coordination of power systems protection using fuses and protective relays. Integrating fundamental theory and real-world practice, the text begins with an overview of power system protection and optimization, followed by a systematic description of the essential steps in designing optimal coordinators using only directional overcurrent relays. Subsequent chapters present mathematical formulations for solving many standard test systems, and cover a variety of popular hybrid optimization schemes and their mechanisms. The author also discusses a selection ofTable of ContentsAuthor Biography xvi Preface xvii Acknowledgments xviii Acronyms xix About The Companion Website xxiii Introduction xxv 1 Fundamental Steps in Optimization Algorithms 1 1.1 Overview 1 1.1.1 Design Variables 4 1.1.2 Design Parameters 4 1.1.3 Design Function 5 1.1.4 Objective Function(s) 5 1.1.5 Design Constraints 7 1.1.5.1 Mathematical Constraints 8 1.1.5.2 Inequality Constraints 8 1.1.5.3 Side Constraints 9 1.1.6 General Principles 10 1.1.6.1 Feasible Space vs. Search Space 10 1.1.6.2 Global Optimum vs. Local Optimum 11 1.1.6.3 Types of Problem 12 1.1.7 Standard Format 12 1.1.8 Constraint-Handling Techniques 13 1.1.8.1 Random Search Method 17 1.1.8.2 Constant Penalty Function 17 1.1.8.3 Binary Static Penalty Function 18 1.1.8.4 Superiority of Feasible Points (SFPs) – Type I 18 1.1.8.5 Superiority of Feasible Points (sfp) – Type II 18 1.1.8.6 Eclectic Evolutionary Algorithm 18 1.1.8.7 Typical Dynamic Penalty Function 19 1.1.8.8 Exponential Dynamic Penalty Function 19 1.1.8.9 Adaptive Multiplication Penalty Function 19 1.1.8.10 Self-Adaptive Penalty Function (SAPF) 20 1.1.9 Performance Criteria Used to Evaluate Algorithms 21 1.1.10 Types of Optimization Techniques 23 1.2 Classical Optimization Algorithms 23 1.2.1 Linear Programming 25 1.2.1.1 Historical Time-Line 25 1.2.1.2 Mathematical Formulation of LP Problems 26 1.2.1.3 Linear Programming Solvers 26 1.2.2 Global-Local Optimization Strategy 28 1.2.2.1 Multi-Start Linear Programming 29 1.2.2.2 Hybridizing LP with Meta-Heuristic Optimization Algorithms as a Fine-Tuning Unit 31 1.3 Meta-Heuristic Algorithms 33 1.3.1 Biogeography-Based Optimization 34 1.3.1.1 Migration Stage 40 1.3.1.2 Mutation Stage 41 1.3.1.3 Clear Duplication Stage 43 1.3.1.4 Elitism Stage 44 1.3.1.5 The Overall BBO Algorithm 45 1.3.2 Differential Evolution 45 1.4 Hybrid Optimization Algorithms 46 1.4.1 Bbo-lp 48 1.4.2 Bbo/de 51 Problems 51 Written Exercises 51 Computer Exercises 53 2 Fundamentals of Power System Protection 57 2.1 Faults Classification 57 2.2 Protection System 61 2.3 Zones of Protection 65 2.4 Primary and Backup Protection 66 2.5 Performance and Design Criteria 66 2.5.1 Reliability 66 2.5.1.1 Dependability 66 2.5.1.2 Security 66 2.5.2 Sensitivity 67 2.5.3 Speed 67 2.5.4 Selectivity 67 2.5.5 Performance versus Economics 67 2.5.6 Adequateness 67 2.5.7 Simplicity 67 2.6 Overcurrent Protective Devices 67 2.6.1 Fuses 68 2.6.2 Bimetallic Relays 69 2.6.3 Overcurrent Protective Relays 69 2.6.4 Instantaneous OCR (IOCR) 70 2.6.5 Definite Time OCR (DTOCR) 71 2.6.6 Inverse Time OCR (ITOCR) 72 2.6.7 Mixed Characteristic Curves 73 2.6.7.1 Definite-Time Plus Instantaneous 73 2.6.7.2 Inverse-Time Plus Instantaneous 74 2.6.7.3 Inverse-Time Plus Definite-Time Plus Instantaneous 74 2.6.7.4 Inverse-Time Plus Definite-Time 75 2.6.7.5 Inverse Definite Minimum Time (IDMT) 76 Problems 76 Written Exercises 76 Computer Exercises 77 3 Mathematical Modeling of Inverse-Time Overcurrent Relay Characteristics 79 3.1 Computer Representation of Inverse-Time Overcurrent Relay Characteristics 79 3.1.1 Direct Data Storage 79 3.1.2 Curve Fitting Formulas 82 3.1.2.1 Polynomial Equations 82 3.1.2.2 Exponential Equations 89 3.1.2.3 Artificial Intelligence 93 3.1.3 Special Models 94 3.1.3.1 RI-Type Characteristic 94 3.1.3.2 RD-Type Characteristic 95 3.1.3.3 FR Short Time Inverse 95 3.1.3.4 UK Rectifier Protection 95 3.1.3.5 BNP-Type Characteristic 95 3.1.3.6 Standard CO Series Characteristics 95 3.1.3.7 IAC and ANSI Special Equations 96 3.1.4 User-Defined Curves 98 3.2 Dealing with All the Standard Characteristic Curves Together 99 3.2.1 Differentiating Between Time Dial Setting and Time Multiplier Setting 99 3.2.2 Dealing with Time Dial Setting and Time Multiplier Setting as One Variable 104 3.2.2.1 Fixed Divisor 106 3.2.2.2 Linear Interpolation 108 3.2.3 General Guidelines Before Conducting Researches and Studies 111 Problems 113 Written Exercises 113 Computer Exercises 114 4 Upper Limit of Relay Operating Time 117 4.1 Do We Need to Define T max ? 117 4.2 How to Define T max ? 118 4.2.1 Thermal Equations 118 4.2.1.1 Thermal Overload Protection for 3φ Overhead Lines and Cables 118 4.2.1.2 Thermal Overload Protection for Motors 122 4.2.1.3 Thermal Overload Protection for Transformers 124 4.2.2 Stability Analysis 126 Problems 136 Written Exercises 136 Computer Exercises 138 5 Directional Overcurrent Relays and the Importance of Relay Coordination 139 5.1 Relay Grading in Radial Systems 139 5.1.1 Time Grading 140 5.1.2 Current Grading 140 5.1.3 Inverse-Time Grading 143 5.2 Directional Overcurrent Relays 146 5.3 Coordination of DOCRs 148 5.4 Is the Coordination of DOCRs an Iterative Problem? 148 5.5 Minimum Break-Point Set 161 5.6 Summary 163 Problems 164 Written Exercises 164 Computer Exercises 166 6 General Mechanism to Optimally Coordinate Directional Overcurrent Relays 169 6.1 Constructing Power Network 169 6.2 Power Flow Analysis 170 6.2.1 Per-Unit System and Three-to-One-Phase Conversion 172 6.2.2 Power Flow Solvers 173 6.2.3 How to Apply the Newton–Raphson Method 175 6.2.4 Sparsity Effect 179 6.3 P/B Pairs Identification 186 6.3.1 Inspection Method 186 6.3.2 Graph Theory Methods 186 6.3.3 Special Software 188 6.4 Short-Circuit Analysis 189 6.4.1 Short-Circuit Calculations 189 6.4.2 Electric Power Engineering Software Tools 190 6.4.2.1 Minimum Short-Circuit Current 190 6.4.2.2 Maximum Short-Circuit Current 192 6.4.3 Most Popular Standards 193 6.4.3.1 ANSI/IEEE Standards C37 & UL 489 193 6.4.3.2 IEC 61363 Standard 194 6.4.3.3 IEC 60909 Standard 194 6.5 Applying Optimization Techniques 201 Problems 202 Written Exercises 202 Computer Exercises 205 7 Optimal Coordination of Inverse-Time DOCRs with Unified TCCC 207 7.1 Mathematical Problem Formulation 207 7.1.1 Objective Function 208 7.1.1.1 Other Possible Objective Functions 210 7.1.2 Inequality Constraints on Relay Operating Times 211 7.1.3 Side Constraints on Relay Time Multiplier Settings 211 7.1.4 Side Constraints on Relay Plug Settings 211 7.1.5 Selectivity Constraint Among Primary and Backup Relay Pairs 212 7.1.5.1 Transient Selectivity Constraint 213 7.1.6 Standard Optimization Model 216 7.2 Optimal Coordination of DOCRs Using Meta-Heuristic Optimization Algorithms 217 7.2.1 Algorithm Implementation 217 7.2.2 Constraint-Handling Techniques 218 7.2.3 Solving the Infeasibility Condition 222 7.3 Results Tester 228 Problems 229 Written Exercises 229 Computer Exercises 231 8 Incorporating LP and Hybridizing It with Meta-heuristic Algorithms 235 8.1 Model Linearization 235 8.1.1 Classical Linearization Approach 236 8.1.1.1 IEC Curves: Fixing Plug Settings and Varying Time Multiplier Settings 236 8.1.1.2 IEEE Curves: Fixing Current Tap Settings and Varying Time Dial Settings 237 8.1.2 Transformation-Based Linearization Approach 237 8.1.2.1 IEC Curves: Fixing Time Multiplier Settings and Varying Plug Settings 238 8.1.2.2 IEEE Curves: Fixing Time Dial Settings and Varying Current Tap Settings 238 8.2 Multi-start Linear Programming 242 8.3 Hybridizing Linear Programming with Population-Based Meta-heuristic Optimization Algorithms 245 8.3.1 Classical Linearization Approach: Fixing PS/CTS and Varying TMS/TDS 245 8.3.2 Transformation-Based Linearization Approach: Fixing TMS/TDS and Varying Ps/cts 245 8.3.3 Innovative Linearization Approach: Fixing/Varying TMS/TDS and PS/CTS 250 Problems 250 Written Exercises 250 Computer Exercises 251 9 Optimal Coordination of DOCRs With OCRs and Fuses 253 9.1 Simple Networks 253 9.1.1 Protecting Radial Networks by Just OCRs 253 9.1.2 Protecting Double-Line Networks by OCRs and DOCRs 255 9.2 Little Harder Networks 257 9.2.1 Combination of OCRs and DOCRs 258 9.2.2 Combination of Fuses, OCRs, and DOCRs 261 9.3 Complex Networks 264 Problems 265 Written Exercises 265 Computer Exercises 266 10 Optimal Coordination with Considering Multiple Characteristic Curves 271 10.1 Introduction 271 10.2 Optimal Coordination of DOCRs with Multiple TCCCs 273 10.3 Optimal Coordination of OCRs/DOCRs with Multiple TCCCs 278 10.4 Inherent Weaknesses of the Multi-TCCCs Approach 279 Problems 280 Written Exercises 280 Computer Exercises 281 11 Optimal Coordination with Considering the Best TCCC 283 11.1 Introduction 283 11.2 Possible Structures of the Optimizer 284 11.3 Technical Issue 287 Problems 290 Written Exercises 290 Computer Exercises 291 12 Considering the Actual Settings of Different Relay Technologies in the Same Network 293 12.1 Introduction 293 12.2 Mathematical Formulation 294 12.2.1 Objective Function 294 12.2.2 Selectivity Constraint Among Primary and Backup Relay Pairs 295 12.2.3 Inequality Constraints on Relay Operating Times 296 12.2.4 Side Constraints on Relay Time Multiplier Settings 296 12.2.5 Side Constraints on Relay Plug Settings 296 12.3 Biogeography-Based Optimization Algorithm 297 12.3.1 Clear Duplication Stage 297 12.3.2 Avoiding Facing Infeasible Selectivity Constraints 297 12.3.2.1 Linear Programming Stage 297 12.3.3 Linking PS i YiAnd TMS iYiWith Yi 298 12.4 Further Discussion 299 Problems 300 Written Exercises 300 Computer Exercises 301 13 Considering Double Primary Relay Strategy 303 13.1 Introduction 303 13.2 Mathematical Formulation 306 13.2.1 Objective Function 307 13.2.2 Selectivity Constraint 308 13.2.3 Inequality Constraints on Relay Operating Times 308 13.2.4 Side Constraints on Relay Time Multiplier Settings 308 13.2.5 Side Constraints on Relay Plug Settings 309 13.3 Possible Configurations of Double Primary ORC Problems 309 Problems 315 Written Exercises 315 Computer Exercises 316 14 Adaptive ORC Solver 319 14.1 Introduction 319 14.2 Types of Network Changes 320 14.2.1 Operational Changes 321 14.2.2 Topological Changes 321 14.3 AI-Based Adaptive ORC Solver 322 14.3.1 Generating Datasets 323 14.3.2 Applying ANN to Solve ORC Problems 324 Problems 328 Written Exercises 328 Computer Exercises 329 15 Multi-objective Coordination 333 15.1 Basic Principles 333 15.1.1 Conventional Aggregation Method 334 15.2 Multi-objective Formulation of ORC Problems 335 15.2.1 Operating Time vs. System Reliability 336 15.2.2 Operating Time vs. System Cost 336 15.2.3 Operating Time vs. System Reliability vs. System Cost 342 15.3 Further Discussions 342 Problems 345 Written Exercises 345 Computer Exercises 345 16 Optimal Coordination of Distance and Overcurrent Relays 347 16.1 Introduction 347 16.2 Basic Mathematical Modeling 348 16.3 Mathematical Modeling with Considering Multiple TCCCs 350 16.3.1 Inequality Constraints 351 16.3.2 Objective Function 352 16.4 Mathematical Modeling with Considering Different Fault Locations 353 16.4.1 Objective Function 353 16.4.2 Inequality Constraints 354 16.4.2.1 Near-End Faults 354 16.4.2.2 Middle-Point Faults 354 16.4.2.3 Far-End Faults 355 17 Trending Topics and Existing Issues 357 17.1 New Inverse-Time Characteristics 357 17.1.1 Scaled Standard TCCC Models 357 17.1.2 Stepwise TCCCs 358 17.1.3 New Customized TCCCs 359 17.2 Smart Grid 359 17.2.1 Distributed Generation 359 17.2.2 Series Compensation and Flexible Alternating Current Transmission System 360 17.2.3 Fault Current Limiters 360 17.3 Economic Operation 360 17.4 Power System Realization 361 17.4.1 Power Lines 361 17.4.2 Economic Operation 363 17.4.2.1 Combined-Cycle Power Plants 364 17.4.2.2 Degraded Efficiency Phenomenon 364 17.4.2.3 Unaccounted Losses in Power Stations 365 17.5 Locating Faults in Mesh Networks by DOCRs 367 17.5.1 Mechanism of the Proposed Fault Location Algorithm 370 17.5.1.1 Approach No. 1: Classical Linear Interpolation 373 17.5.1.2 Approach No. 2: Logarithmic/Nonlinear Interpolation 374 17.5.1.3 Approach No. 3: Polynomial Regression 375 17.5.1.4 Approach No. 4: Asymptotic Regression 375 17.5.1.5 Approach No. 5: DTCC-Based Regression 375 17.5.2 Final Structure of the Proposed Fault Locator 377 17.5.3 Overall Accuracy vs. Uncertainty 379 17.5.4 Further Discussion 380 Appendix A Some Important Data Used in Power System Protection 381 A. 1 Standard Current Transformer Ratios 381 A. 2 Standard Device/Function Number and Function Acronym Descriptions 382 A.2. 1 Standard Device/Function Numbers 382 A.. 2 Device/Function Acronyms 383 A.2. 3 Suffix Letters 383 A.2.3. 1 Auxiliary Devices 383 A..3. 2 Actuating Quantities 383 A.2.. 3 Main Device 384 A.2.3. 4 Main Device Parts 384 A.2.3. 5 Other Suffix Letters 384 Appendix B How to Install PowerWorld Simulator (Education Version) 387 Appendix C Single-Machine Infinite Bus 391 Appendix D Linearizing Relay Operating Time Models 393 D.1 Linearizing the IEC/BS Model of DOCRs by Fixing Time Multiplier Settings 393 D.2 Linearizing the ANSI/IEEE Model of DOCRs by Fixing Time Multiplier Settings 394 Appendix E Derivation of the First Order Thermal Differential Equation 397 Appendix F List of ORC Test Systems 399 F. 1 Three-Bus Test Systems 399 F.. 1 System No. 1 399 F.1. 2 System No. 2 399 F. 2 Four-Bus Test Systems 403 F.2. 1 System No. 1 403 F.. 2 System No. 2 403 F. 3 Five-Bus Test System 408 F. 4 Six-Bus Test Systems 410 F.4. 1 System No. 1 410 F.4. 2 System No. 2 410 F.4. 3 System No. 3 411 F.. 4 System No. 4 413 F. 5 Eight-Bus Test Systems 418 F.5. 1 System No. 1 418 F.5. 2 System No. 2 422 F.5. 3 System No. 3 423 F.5. 4 System No. 4 424 F.. 5 System No. 5 425 F. 6 Nine-Bus Test System 427 F. 7 14-Bus Test Systems 430 F.7. 1 System No. 1 431 F.7. 2 System No. 2 433 F. 8 15-Bus Test System 437 F. 9 30-Bus Test Systems 441 F.9. 1 System No. 1 441 F.9. 2 System No. 2 444 F. 10 42-Bus Test System 448 F. 11 118-Bus Test System 453 References 457 Index 479

Read more less

Book SynopsisSAR IMAGE ANALYSIS A COMPUTATIONAL STATISTICS APPROACH Discover how to use statistics to extract information from SAR imagery In SAR Image Analysis A Computational Statistics Approach, an accomplished team of researchers delivers a practical exploration of how to use statistics to extract information from SAR imagery. The authors discuss various models, supply sample data and code, and explain theoretical aspects of SAR image analysis that are highly relevant to practitioners and students. The book offers the theoretical properties of models, estimators, interpretation, data visualization, and advanced techniques, along with the data and code samples, that students require to learn effectively and efficiently. SAR Image Analysis A Computational Statistics Approach provides various exercises throughout the book to help readers reinforce and retain the extensive information on parameter estimation, applications, reproducibility, replicability, aTable of ContentsForeword xiii Preface xvii Acknowledgments xxvii Acronyms xxxi Introduction xxxiii I.1 SAR xxxiii I.2 Statistics for SAR xxxiv I.3 The Book xxxv I.4 Commitment to Reproducibility and Replicability xxxix 1 Data Acquisition 1 1.1 Introduction 1 1.2 SAR 3 1.2.1 The radar 4 1.2.2 What is SAR? 6 1.2.3 SAR systems 10 1.2.4 The synthetic antenna 16 1.3 Spatial resolution 20 1.4 SAR Imaging Techniques 23 1.5 The Return Signal: backscatter and speckle 28 1.5.1 Backscatter 28 1.5.2 Speckle 31 1.5.3 SAR geometric distortions 39 1.6 SAR Satellites 44 1.7 Preprocessing SAR data 53 1.8 Copernicus Open Access Hub 53 1.9 NASA Earth Data Open Data 56 1.10 Actual SAR Data Examples 57 1.10.1 Hawaii’s Big Island 57 1.10.2 Other examples 60 Exercises 60 2 Elements of Data Analysis and Image Processing with R 73 2.1 Useful R Packages 73 2.1.1 Data loading 74 2.1.2 Data manipulation 76 2.2 Descriptive Statistics 78 2.2.1 Center tendency of data 78 2.2.2 Dispersion of data 81 2.2.3 Shape of data 84 2.3 Visualization 86 2.3.1 Rug and box plots 87 2.3.2 Histogram 88 2.3.3 Scattering Diagram 92 2.4 Statistics and Image Processing 94 2.4.1 Histogram based Image Transformation 94 2.4.2 Scattering based Analysis 98 2.5 The imagematrix package 101 3 Intensity SAR Data and the Multiplicative Model 105 3.1 The K distribution 115 3.2 The G0 distribution 117 3.3 The GH distribution 121 3.4 Connection between Models 122 Exercises 123 4 Parameter Estimation 127 4.1 Models 128 4.1.1 The Bernoulli distribution 128 4.1.2 The Binomial distribution 128 4.1.3 The Negative Binomial distribution 129 4.1.4 The Uniform distribution 129 4.1.5 Beta distribution 130 4.1.6 The Gaussian distribution 131 4.1.7 Mixture of Gaussian distributions 131 4.1.8 The (SAR) Gamma distribution 132 4.1.9 The Reciprocal Gamma distribution 132 4.1.10 The G0I distribution 133 4.2 Inference by analogy 134 4.2.1 The Uniform distribution 134 4.2.2 The Gaussian distribution 135 4.2.3 Mixture of Gaussian distributions 135 4.2.4 The (SAR) Gamma distribution 136 4.3 Inference by maximum likelihood 136 4.3.1 The Uniform distribution 137 4.3.2 The Gaussian distribution 137 4.3.3 Mixture of Gaussian distributions 138 4.3.4 The (SAR) Gamma distribution 139 4.3.5 The G0 distribution 140 4.4 Analogy vs. Maximum Likelihood 141 4.5 Improvement by bootstrap 142 4.6 Comparison of estimators 143 4.7 An example 144 4.8 The same example, revisited 150 4.9 Another example 152 Exercises 157 5 Applications 159 5.1 Statistical filters: Mean, Median, Lee 160 5.1.1 Mean filter 160 5.1.2 Median filter 164 5.1.3 Lee filter 167 5.2 Advanced filters: MAP and Nonlocal Means 175 5.2.1 MAP Filters 175 5.2.2 Nonlocal Means Filter 177 5.2.3 Statistical NLM filters 183 5.2.4 The statistical test 189 5.3 Implementation Details 191 5.4 Results 193 5.5 Classification 198 5.5.1 The image space of the SAR data 205 5.5.2 The feature space 207 5.5.3 Similarity criterion 210 5.6 Supervised Image Classification of SAR Data 212 5.6.1 The nearest neighbor classifier 214 5.6.2 The K-nn method 219 5.7 Maximum Likelihood Classifier 223 5.8 Unsupervised Image Classification of SAR Data: The K-means classifier 232 5.9 Assessment of Classification Results 236 Exercises 242 6 Advanced Topics 249 6.1 Assessment of Despeckling Filters 249 6.2 Standard Metrics 249 6.2.1 Advanced Metrics for SAR Despeckling Assessment 253 6.2.2 Completing the Assessment 259 6.3 Robustness 259 6.3.1 Robust inference 260 6.3.2 The mean and the median 261 6.3.3 Empirical Stylized Influence Function 266 6.4 Rejoinder and Recommendations 269 7 Reproducibility and Replicability 273 7.1 What Is Reproducibility? 273 7.2 What Is Replicability? 274 7.3 Reproducibility and Replicability: Benefits for the Remote Sensing Community 277 7.4 Recommendations for making “good science” 278 7.5 Conclusions 283 Index 301

Read more less

Book SynopsisA one-stop, comprehensive source for the latest research in joint radar-communications In Signal Processing for Joint Radar-Communications, a trio of eminent electrical engineers delivers a practical and informative contribution to the diffusion of newly developed joint radar-communications (JRC) tools into the radar and communications communities and to illustrate recent successes in applying modern signal processing theories to core problems in JRC. The book offers new results on algorithmic methods and applications of JRC in diverse areas, including autonomous vehicles, waveform design, information theory, privacy, security, beamforming, estimation theory, and sampling. The distinguished editors bring together contributions from leading JRC researchers working in radar systems, remote sensing, electromagnetics, optimization, and signal processing. The included resources provide an in-depth mathematical treatment of relevant signal processing tools and computational methods allowi

Read more less