Electronics and communications engineering Books

Book SynopsisPresents an overview of CubeSat antennas designed at the Jet Propulsion Laboratory (JPL) CubeSatsnanosatellites built to standard dimensions of 10cm x 10 cm x cmare making space-based Earth science observation and interplanetary space science affordable, accessible, and rapidly deployable for institutions such as universities and smaller space agencies around the world. CubeSat Antenna Design is an up-to-date overview of CubeSat antennas designed at NASA's Jet Propulsion Laboratory (JPL), covering the systems engineering knowledge required to design these antennas from a radio frequency and mechanical perspective. This authoritative volume features contributions by leading experts in the field, providing insights on mission-critical design requirements for state-of-the-art CubeSat antennas and discussing their development, capabilities, and applications. The text begins with a brief introduction to CubeSats, followed by a detailed survey of low-gain, medium-gain, and high-gain antennas. Subsequent chapters cover topics including the telecommunication subsystem of Mars Cube One (MarCO), the enabling technology of Radar in a CubeSat (RainCube), the development of a one-meter mesh reflector for telecommunication at X- and Ka-band for deep space missions, and the design of multiple metasurface antennas. Written to help antenna engineers to enable new CubeSate NASA missions, this volume: Describes the selection of high-gain CubeSat antennas to address specific mission requirements and constraints for instruments or telecommunicationHelps readers learn how to develop antennas for future CubeSat missionsProvides key information on the effect of space environment on antennas to inform design stepsCovers patch and patch array antennas, deployable reflectarray antennas, deployable mesh reflector, inflatable antennas, and metasurface antennas CubeSat Antenna Design is an important resource for antenna/microwave engineers, aerospace systems engineers, and advanced graduate and postdoctoral students wanting to learn how to design and fabricate their own antennas to address clear mission requirements.Table of ContentsPreface xi Editor Biography xiii Notes on Contributors xv 1 Introduction 1 1.1 Description of CubeSats 1 1.1.1 Introduction 1 1.1.2 Form Factors 3 1.1.3 Brief Introduction to CubeSat Subsystems 3 1.1.3.1 Attitude Control 3 1.1.3.2 Propulsion 6 1.1.3.3 Power 8 1.1.3.4 Telecommunication 9 1.1.4 CubeSat Antennas 11 1.1.4.1 Low Gain Antennas 11 1.1.4.2 Medium Gain Antennas 14 1.1.4.3 High Gain Antennas 15 1.1.5 Effect of Space Environment on Antennas 26 1.1.5.1 Radiation 26 1.1.5.2 Material Outgassing 27 1.1.5.3 Temperature Change 28 1.1.5.4 Multipaction Breakdown 29 1.2 Conclusion 30 2 Mars Cube One 35 2.1 Mission Description 35 2.2 Iris Radio 38 2.3 X-Band Subsystem 43 2.3.1 Frequency Allocation 43 2.3.2 Near Earth Communications Using Low Gain Antennas 43 2.3.2.1 Antenna Requirements 43 2.3.2.2 Antenna Solution and Performance 44 2.3.3 Mars-to-Earth Communications 46 2.3.3.1 Telecommunication Description: Uplink and Downlink from Mars 46 2.3.3.2 Mars Low Gain Antennas 48 2.3.3.3 High Gain Antenna 49 2.4 Entry, Descent, and Landing UHF Link 67 2.4.1 State-of-the-Art of UHF Deployable CubeSat Antennas 68 2.4.1.1 Four Monopole Antenna 68 2.4.1.2 Helical Antenna 68 2.4.1.3 Patch Antenna 70 2.4.2 Circularly Polarized Loop Antenna Concept 70 2.4.2.1 Loop Antenna Radiation and Polarization 70 2.4.2.2 Infinite Baluns Design and Shielded Loop 72 2.4.2.3 Feeding Structure 73 2.4.3 Mechanical Configuration and Deployment Scheme 74 2.4.4 Simulations and Measurements 78 2.4.5 In-Flight Performance 82 2.5 Conclusions 84 3 Radar in a CubeSat: RainCube 91 3.1 Mission Description 91 3.2 Deployable High-Gain Antenna 94 3.2.1 State of the Art 94 3.2.1.1 Inflatable Antennas 95 3.2.1.2 Deployable Reflectarray Antennas 95 3.2.1.3 Deployable Mesh Reflector Antennas 96 3.2.2 Parabolic Reflector Antenna Design 101 3.2.2.1 Paraboloidal Reflector 101 3.2.2.2 Dual-Reflector Antennas 102 3.2.3 RainCube High-Gain Antenna 104 3.2.3.1 Antenna Choice: Cassegrain Reflector 104 3.2.3.2 Antenna Description 104 3.2.3.3 Perfect Paraboloid Antenna 105 3.2.3.4 Unfurlable Paraboloid with Ribs and Mesh Structures 110 3.2.3.5 Antenna Measurement Results 119 3.2.4 Mechanical Deployment 122 3.2.5 Design and Testing for the Space Environment 127 3.2.6 In-Flight Performance 131 3.3 Telecommunication Challenge 131 3.4 Conclusion 134 4 One Meter Reflectarray Antenna: OMERA 139 4.1 Introduction 139 4.2 Reflectarray Antennas 141 4.2.1 Introductions to Reflectarray 141 4.2.2 Advantages of Reflectarray 141 4.2.3 Drawbacks of Reflectarray 142 4.2.4 State of the Art 142 4.3 OMERA 143 4.3.1 Antenna Description 143 4.3.2 Deployable Feed 146 4.3.3 Reflectarray Design 147 4.3.4 Deployment Accuracy 153 4.3.5 Effect of Struts 156 4.3.6 Predicted Gain and Efficiency 157 4.3.7 Prototype and Measurements 158 4.4 Conclusion 161 5 X/Ka-Band One Meter Mesh Reflector for 12U-Class CubeSat 163 5.1 Introduction 163 5.2 Mechanical Design 167 5.2.1 Trade Studies 167 5.2.1.1 Design Goals 167 5.2.1.2 Rigid 167 5.2.1.3 Elastic Composite 167 5.2.1.4 Mesh 168 5.2.2 Structural Design of the Reflector 168 5.2.2.1 Ribs 170 5.2.2.2 Hub 171 5.2.2.3 Battens 171 5.2.2.4 Nets 171 5.2.2.5 Perimeter Truss 174 5.2.3 Deployment 174 5.2.3.1 Boom Design and Deployment 174 5.2.3.2 Reflector Deployment 176 5.2.3.3 Deployment Issues 177 5.3 X/Ka RF Design 177 5.3.1 Antenna Configuration and Simulation Model 177 5.3.2 X-Band Feed and Mesh Reflector 179 5.3.3 Ka-Band Mesh Reflector 187 5.3.4 X/Ka-band Mesh Reflector 193 5.4 Conclusion 194 6 Inflatable Antenna for CubeSat 197 6.1 Introduction 197 6.2 Inflatable High Gain Antenna 199 6.2.1 State of the Art 199 6.2.1.1 History of Inflatable Antennas Research and Experiments 199 6.2.1.2 History of the Inflatable Antenna for CubeSat Concept 201 6.2.2 Inflatable Antenna Design at X-Band 207 6.2.2.1 Inflatable Antenna at X-Band: Initial Design and Lessons Learned 207 6.2.2.2 Inflatable Antenna at X-Band Final Design: Reflector and Feed Placement 208 6.2.2.3 Antenna Measurements 212 6.2.3 Structural Design 215 6.2.4 Inflation and On-Orbit Rigidization 220 6.3 Spacecraft Design Challenges 226 6.4 Conclusion 229 7 High Aperture Efficiency All-Metal Patch Array 233 7.1 Introduction 233 7.2 State of the Art 235 7.3 Dual-Band Circularly Polarized 8 × 8 Patch Array 240 7.3.1 Requirements 240 7.3.2 Unit Cell Optimization 240 7.3.3 8 × 8 Patch Array 244 7.3.4 Comparison With State-of-the-Art 247 7.3.5 Other Array Configurations 249 7.4 Conclusion 251 8 Metasurface Antennas: Flat Antennas for Small Satellites 255 8.1 Introduction 255 8.2 Modulated Metasurface Antennas 256 8.2.1 State of the Art: Pros and Cons 256 8.2.2 Design of Modulated Metasurface Antennas 260 8.2.3 300 GHz Silicon Micro-Machined MTS Antenna 269 8.2.3.1 Objective 269 8.2.3.2 Design Methodology: Modulation 270 8.2.3.3 MTS Element 270 8.2.3.4 Antenna Design, Fabrication, and Test 271 8.2.3.5 Improvement Using Anisotropic Surface 274 8.2.3.6 Conclusion 275 8.2.4 Ka-band Metal-Only Telecommunication Antenna 276 8.2.4.1 Objective 276 8.2.4.2 Synthesis of the Modulated Metasurface Antenna 277 8.2.4.3 Metallic Metasurface Elements 278 8.2.4.4 Antenna Design 279 8.2.4.5 Fabrication 280 8.2.4.6 Measurements 281 8.2.4.7 Toward a 20 cm Diameter Antenna 284 8.3 Beam Synthesis Using Holographic Metasurface Antennas 286 8.3.1 Introduction 286 8.3.2 Examples Holographic Metasurface Antennas 290 8.3.3 W-Band Pillbox Beam Steering Metasurface Antenna 294 8.3.4 Toward an Active Beam Steering Antenna 302 8.4 Conclusion 304 Acknowledgments 308 References 308 Index 315

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Book SynopsisCELLULAR V2X FOR CONNECTED AUTOMATED DRIVING A unique examination of cellular communication technologies for connected automated driving, combining expert insights from telecom and automotive industries as well as technical and scientific knowledge from industry and academia Cellular vehicle-to-everything (C-V2X) technologies enable vehicles to communicate both with the network, with each other, and with other road users using reliable, responsive, secure, and high-capacity communication links. Cellular V2X for Connected Automated Driving provides an up-to-date view of the role of C-V2X technologies in connected automated driving (CAD) and connected road user (CRU) services, such as advanced driving support, improved road safety, infotainment, over-the-air software updates, remote driving, and traffic efficiency services enabling the future large-scale transition to self-driving vehicles. This timely book discusses where C-V2X technology is situated within the increasingly interconnected ecosystems of the mobile communications and automotive industries. An expert contributor team from both industry and academia explore potential applications, business models, standardization, spectrum and channel modelling, network enhancements, security and privacy, and more. Broadly divided into two partsintroductory and advanced materialthe text first introduces C-V2X technology and introduces a variety of use cases and opportunities, requiring no prerequisite technical knowledge. The second part of the book assumes a basic understanding of the field of telecommunications, presenting technical descriptions of the radio, system aspects, and network design for the previously discussed applications. This up-to-date resource: Provides technical details from the finding of the European Commission H2020 5G PPP 5GCAR project, a collaborative research initiative between the telecommunications and automotive industries and academic researchersElaborates on use cases, business models, and a technology roadmap for those seeking to shape a start-up in the area of automated and autonomous drivingProvides up to date descriptions of standard specifications, standardization and industry organizations and important regulatory aspects for connected vehiclesProvides technical insights and solutions for the air interface, network architecture, positioning and security to support vehicles at different automation levelsIncludes detailed tables, plots, and equations to clarify concepts, accompanied by online tutorial slides for use in teaching and seminars Thanks to its mix of introductory content and technical information, Cellular V2X for Connected Automated Driving is a must-have for industry and academic researchers, telecom and automotive industry practitioners, leaders, policymakers, and regulators, and university-level instructors and students. Additional resources available at the following site:Cellular V2X for Connected Automated Driving 5GCARTable of ContentsList of Contributors xiii Forewords xvii Preface xxv List of Abbreviations xxix 1 Introduction 1 1.1 Background and Motivation for C-V2X 2 1.1.1 Intelligent Transport Systems 2 1.1.2 Connected Automated Driving 3 1.1.3 Connected Road User Services 4 1.2 Toward a Joint Telecom and Automotive Roadmap for CAD 4 1.2.1 Telecom’s Ambitions for Connected Driving 4 1.2.2 Automotive’s Ambitions for Automated Driving 6 1.2.3 Joint Roadmap for CAD 7 1.3 Communication Technologies for CAD 8 1.3.1 Standardization of IEEE V2X 10 1.3.2 Standardization and Regulation Aspects of C-V2X 12 1.3.2.1 Available C-V2X Releases and Regulations 12 1.3.2.2 Future Requirements for C-V2X Releases and Regulations 13 1.4 Structure of this Book 14 References 18 2 Business Models 21 2.1 Current Market Analysis 22 2.2 Services Definition for CAD and CRU 23 2.2.1 Existing CAD and CRU Services 24 2.2.1.1 Emergency Call 24 2.2.1.2 Remote Diagnostics 24 2.2.1.3 Car Sharing 25 2.2.1.4 OTA Software Updates 25 2.2.1.5 Predictive Maintenance 25 2.2.1.6 Real-Time Road Traffic Management and Vehicle Guidance 25 2.2.2 Emerging CAD Services 25 2.2.2.1 Perception by Wireless Connectivity and Sensor Sharing 26 2.2.2.2 High-Definition Maps 26 2.2.3 Emerging CRU Services 26 2.2.3.1 Video Streaming and Gaming 26 2.2.3.2 Parking Reservations and Payment 26 2.3 Technical Components 27 2.4 Practicalities 28 2.4.1 Profile and SIM Card Provisioning 28 2.4.2 Routing Strategy 28 2.4.3 Roaming and Inter-operator Cooperation 29 2.4.4 Possible Business Model Evolution 29 2.4.4.1 OTA Software Updates 30 2.4.4.2 CAD Services and Related Automation Levels 31 2.5 Business Market Opportunities for V2X 34 2.5.1 CAD Business Model Enabled by 5G 34 2.5.1.1 Passive Infrastructure Sharing 37 2.5.1.2 Active Infrastructure Sharing, Excluding Spectrum Sharing 37 2.5.1.3 Active Infrastructure Sharing, Including Spectrum Sharing 37 2.5.2 Security Provision 38 2.5.2.1 The PKI Workflow 38 2.5.2.2 Enrollment of an ITS Station 39 2.5.2.3 Use of Authorizations Tokens 40 2.5.2.4 The Cost Hypothesis 40 2.5.3 OTA Software Updates 41 2.6 Business Model Analysis of 5G V2X Technical Components 44 2.6.1 Positioning 45 2.6.2 V2X Radio Design 46 2.6.2.1 Predictor Antenna 46 2.6.2.2 Beam-Forming 46 2.6.2.3 Efficiency 49 2.6.2.4 Reliability 49 2.6.2.5 Sidelink Out of Coverage 49 2.6.2.6 Sidelink in Coverage 49 2.6.3 Network Procedures 49 2.6.3.1 Local Standalone Network Procedures 51 2.6.3.2 Network Service Relationship Enhancement 51 2.6.3.3 Multi-Operator Solutions for V2X Communications 53 2.6.3.4 Network Orchestration and Management 53 2.6.4 End-to-End Security 54 2.6.5 Edge Computing Enhancements 55 2.6.6 Summary 58 2.7 Conclusions 58 References 60 3 Standardization and Regulation 63 3.1 Standardization Process Overview 64 3.1.1 General Aspects 64 3.1.2 Standardization and Regulation Bodies Relevant to ITS Specifications 64 3.1.2.1 International Telecommunication Union 65 3.1.2.2 Regional Standards Developing Organizations 66 3.1.2.3 3GPP, IEEE, and SAE 67 3.1.2.4 5G PPP and EATA 67 3.1.2.5 5GAA 68 3.1.3 3GPP Structure and Standardization Process 69 3.2 Regulatory Aspects and Spectrum Allocation 70 3.2.1 C-V2X Policy and Regulations in Europe 71 3.2.2 Radio Frequency Spectrum Allocation for V2X Communications 71 3.2.2.1 Spectrum Allocation for IMT Systems and 3GPP Technologies 71 3.2.2.2 Dedicated Spectrum for ITS Applications 72 3.2.2.3 Worldwide Spectrum Harmonization 73 3.3 Standardization of V2X Communication Technology Solutions 73 3.3.1 A Brief History of V2X Communication 74 3.3.2 Overview of DSRC/C-V2X Specifications Around the Globe 75 3.3.2.1 Europe 75 3.3.2.2 The Americas 76 3.3.2.3 Asia 77 3.3.3 C-V2X Standardization in 3GPP: Toward and Within 5G 79 3.3.3.1 C-V2X in 4G 80 3.3.3.2 C-V2X Supported by 5G 82 3.3.3.3 Future Plans 83 3.4 Application Aspects 84 3.4.1 EU Standardization 86 3.4.2 US Standardization 87 3.5 Summary 87 References 88 4 Spectrum and Channel Modeling 91 4.1 Spectrum and Regulations for V2X Communications 91 4.1.1 Spectrum Bands in Europe 92 4.1.1.1 ITS Spectrum at 5.9 GHz 92 4.1.1.2 5.8 GHz Frequency for Toll Collection 93 4.1.1.3 60 GHz ITS Band 93 4.1.1.4 IMT Bands in Europe 93 4.1.2 Spectrum Bands in Other Regions 94 4.1.2.1 United States 94 4.1.2.2 China 95 4.1.2.3 Other Regions of the World 96 4.1.3 Spectrum Auctions Worldwide 96 4.1.3.1 Europe 96 4.1.3.2 United States 104 4.1.3.3 Asia 105 4.1.3.4 Summary of Auctions and Cost Comparison Worldwide 108 4.1.4 Spectrum Harmonization Worldwide 111 4.1.4.1 Europe and Digital Single Market 111 4.1.4.2 World Radiocommunication Conference 2019 111 4.1.5 Summary 112 4.2 Channel Modeling 113 4.2.1 Propagation Environments 114 4.2.1.1 Link Types 114 4.2.1.2 Environments 114 4.2.2 Channel-Modeling Framework and Gap Analysis 116 4.2.3 Path-Loss Models 116 4.2.3.1 Path-Loss for V2V LOS Links 116 4.2.3.2 Shadow-Fading Models 121 4.2.3.3 Fast-Fading Parameters 122 4.2.3.4 Summary 123 4.2.4 Recent V2X Channel Measurements and Models 124 4.2.4.1 V2V Measurements in cmWave and mmWave 124 4.2.4.2 mmWave V2V (Sidelink) Channel Modeling 124 4.2.4.3 Multi-Link Shadowing Extensions 132 4.2.5 Summary 134 References 135 5 V2X Radio Interface 137 5.1 Beamforming Techniques for V2X Communication in the mm-Wave Spectrum 138 5.1.1 Beam Refinement for Mobile Multi-User Scenarios 139 5.1.1.1 Algorithm Description 140 5.1.1.2 Illustrative Performance Results 140 5.1.2 Beamformed Multicasting 143 5.1.3 Beam-Based Broadcasting 147 5.2 PHY and MAC Layer Extensions 152 5.2.1 Channel State Information Acquisition and MU-MIMO Receiver Design 152 5.2.1.1 The Importance and Challenges of Channel State Information Acquisition in MU-MIMO Systems 152 5.2.1.2 Interplay Between CSIR Acquisition and MU-MIMO Receiver Design 153 5.2.1.3 Novel Approaches to Near-Optimal MU-MIMO Linear Receiver Design and the Impact of CSIR Errors 156 5.2.1.4 Performance Modeling and Numerical Results in Multi-Antenna Cellular Vehicle Scenarios 157 5.2.2 Reference Signal Design 159 5.2.2.1 Challenges to CSI Acquisition in V2V Sidelink Communication 159 5.2.2.2 Reference Signal Design for V2V Sidelink 160 5.2.2.3 Performance Evaluation 163 5.2.3 Synchronization 164 5.2.4 Scheduling and Power Control 168 5.3 Technology Features Enabled by Vehicular Sidelink 172 5.3.1 UE Cooperation for Enhancing Reliability 173 5.3.1.1 Communication Scenario 173 5.3.1.2 Reliability Analysis – Channels with Equal Power 174 5.3.1.3 Evaluation 176 5.3.1.4 System Design Aspects 178 5.3.2 Full Duplex 181 5.3.2.1 Advantages of Full-Duplex Radio for C-V2X 182 5.4 Summary 184 References 185 6 Network Enhancements 191 6.1 Network Slicing 192 6.1.1 Network Slicing and 3GPP 192 6.1.2 Network Slicing and V2X 194 6.2 Role of SDN and NFV in V2X 196 6.3 Cloudified Architecture 199 6.4 Local End-to-End Path 200 6.5 Multi-Operator Support 202 6.6 Summary 205 References 205 7 Enhancements to Support V2X Application Adaptations 207 7.1 Background 208 7.2 Enhanced Application-Network Interaction for Handling V2X Use Cases 210 7.2.1 C-V2X Connectivity Negotiation 210 7.2.2 Use-Case-Aware Multi-RAT Multi-Link Connectivity 212 7.2.3 Location-Aware Scheduling 214 7.3 Redundant Scheduler for Sidelink and Uu 215 7.3.1 Application or Facilities Layer 216 7.3.2 Transport Level 219 7.3.3 RRC Level 220 7.4 Summary 221 References 221 8 Radio-Based Positioning and Video-Based Positioning 223 8.1 Radio-Based Positioning 225 8.1.1 Use Cases and Requirements 225 8.1.2 Radio-Based Positioning in New Radio Release 16 226 8.1.3 Radio-Based Positioning Beyond Release 16 228 8.1.3.1 The mmWave Channel 228 8.1.3.2 Signal Design 229 8.1.3.3 The Measurement Process 230 8.1.3.4 Localization, Mapping, and Tracking 231 8.1.4 Technology Component Complementation 233 8.1.5 Limitations of Radio-Based Positioning 235 8.1.6 Summary 236 8.2 Video-Based Positioning 237 8.2.1 Vehicle Positioning System Setup 237 8.2.2 Multi-Camera Calibration 239 8.2.3 Vehicle Detection 240 8.2.4 Vehicle Tracking 241 8.2.5 Vehicle Localization 241 8.2.6 Accuracy Evaluation 242 8.2.7 Summary 245 8.3 Conclusions 246 References 246 9 Security and Privacy 251 9.1 V2N Security 252 9.1.1 Security Challenges 253 9.1.2 Isolation Challenges 254 9.1.2.1 System Isolation (Between ECUs) 254 9.1.2.2 Network Isolation (Between Network Slices) 254 9.1.3 Software-Defined Vehicular Networking Security 255 9.1.3.1 Principles and Architecture 255 9.1.3.2 Security Benefits and Threats 255 9.2 V2V/V2I Security 256 9.2.1 Privacy 257 9.2.2 European Union Security Architecture 258 9.2.3 US Security Architecture 260 9.3 Alternative Approaches 261 9.4 Conclusion 262 References 262 10 Status, Recommendations, and Outlook 265 10.1 Future Prospects of C-V2X and the CAD Ecosystem 265 10.1.1 Future Needs for R&D and Standardization in C-V2X 266 10.1.2 Broader Aspects of CAD and CRU Services 268 10.2 Recommendations to Stakeholders 270 10.2.1 Mobile Network Operators 271 10.2.1.1 Network-Sharing Alternatives 271 10.2.1.2 New Business Models for Connected Vehicle Services 271 10.2.1.3 Roaming and Inter-Operator Cooperation 272 10.2.2 Original Equipment Manufacturers 272 10.2.2.1 Connecting Off-Board Sensors 272 10.2.2.2 Vehicle Processing Platforms Supported by Networks 273 10.2.2.3 Automotive Standardization 274 10.2.3 Regulators 274 10.2.3.1 Deployment, Coverage, and Road Infrastructure 274 10.2.3.2 Simplifying and Harmonizing Regulation 275 10.2.3.3 Data Sharing and Monetization 276 10.2.3.4 Spectrum Aspects 276 10.2.4 Suppliers and Certification 277 10.3 Outlook 278 References 279 Index 281

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Book SynopsisTable of ContentsPreface and Text Organization ix List of Notations xiii About the Companion Website xv 1 Antenna Circuit Model. Antenna Matching. Antenna Bandwidth 1 Section 1 Lumped Circuit Model of an Antenna. Antenna Input Impedance 1 Section 2 Antenna with Transmission Line. Antenna Reflection Coefficient. Antenna Matching. VSWR 18 2 Receiving Antenna: Received Voltage, Power, and Transmission Coefficient 31 Section 1 Analytical Model for the Receiving Antenna 31 Section 2 Model of a Two-Port Network for TX/RX Antennas 44 3 Antenna Radiation 55 Section 1 Maxwell Equations and Boundary Conditions 55 Section 2 Solution for Maxwell’s Equations in Terms of Electric and Magnetic Potentials 63 Section 3 Antenna Radiation 71 Section 4 Antenna Directivity and Gain 84 4 Antenna Balun. Antenna Reflector. Method of Images 101 Section 1 Antenna Balun 101 Section 2 Antenna Reflector 116 5 Dipole Antenna Family: Broadband Antennas that Operate as Dipoles at Low Frequencies 135 Section 1 Broadband Dipoles and Monopoles 135 Section 2 Biconical, Wide Blade, and Vivaldi Antennas 141 6 Loop Antennas 155 Section 1 Loop Antenna vs. Dipole Antenna 155 7 Small Antennas 171 Section 1 Fundamental Limits on Antenna Bandwidth 171 Section 2 Practical Antenna Matching and Tuning for a Predefined (50 Ω) Impedance 185 8 Patch and PIFA Antennas 197 Section 1 Patch Antennas 197 Section 2 Planar Inverted F (PIFA) Antenna. Bandwidth Estimations 219 9 Traveling Wave Antennas 233 Section 1 Long Wire Antenna and Yagi-Uda Antenna 233 Section 2 Helical and Spiral Antennas 241 10 Antenna Designer Including Circularly Polarized Antennas 251 Section 1 Fast Analysis and Design of Individual Antennas 251 Section 2 Meaning of Circular Polarization and Proper Antenna Orientation 259 11 Antenna Arrays 271 Section 1 Array Types. Array Factor. Concept of a Scanning Array 271 Section 2 Linear Arrays 287 Section 3 Planar Arrays 303 Index 317

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Book SynopsisExplore thefoundational and advancedsubjects associated with proportional-integral-derivative controllers fromleading authors in the field InPID Passivity-Based Control of Nonlinear Systems with Applications,expert researchers and authors Drs. Romeo Ortega, Jose GuadalupeRomero,Pablo Borja,andAlejandro Donairedelivera comprehensive and detailed discussion of the most crucial and relevant conceptsin the analysis and design ofproportional-integral-derivative controllersusing passivity techniques. The accomplished authors present a formal treatment of the recentresearch in the area and offer readers practical applications of the developed methods to physical systems, including electrical, mechanical, electromechanical, power electronics, and process control. The book offers the material with minimal mathematical background, making it relevant to a wide audience. Familiarity withthe theoretical tools reported in the control systems literature is not necessaryTable of ContentsAuthor Biographies xv Preface xix Acknowledgments xxiii Acronyms xxv Notation xxix 1 Introduction 1 2 Motivation and Basic Construction of PID Passivity-based Control 5 2.1 L2-Stability and Output Regulation to Zero 6 2.2 Well-Posedness Conditions 9 2.3 PID-PBC and the Dissipation Obstacle 10 2.3.1 Passive systems and the dissipation obstacle 11 2.3.2 Steady-state operation and the dissipation obstacle 12 2.4 PI-PBC with y0 and Control by Interconnection 14 3 Use of Passivity for Analysis and Tuning of PIDs: Two Practical Examples 19 3.1 Tuning of the PI Gains for Control of Induction Motors 21 3.1.1 Problem formulation 23 3.1.2 Change of coordinates 27 3.1.3 Tuning rules and performance intervals 30 3.1.4 Concluding remarks 35 3.2 PI-PBC of a Fuel Cell System 36 3.2.1 Control problem formulation 41 3.2.2 Limitations of current controllers and the role of passivity 46 3.2.3 Model linearization and useful properties 48 3.2.4 Main result 50 3.2.5 An asymptotically stable PI-PBC 54 3.2.6 Simulation results 57 3.2.7 Concluding remarks and future work 58 4 PID-PBC for Nonzero Regulated Output Reference 61 4.1 PI-PBC for Global Tracking 63 4.1.1 PI global tracking problem 63 4.1.2 Construction of a shifted passive output 65 4.1.3 A PI global tracking controller 67 4.2 Conditions for Shifted Passivity of General Nonlinear Systems 68 4.2.1 Shifted passivity definition 69 4.2.2 Main results 70 4.3 Conditions for Shifted Passivity of port-Hamiltonian Systems 73 4.3.1 Problems formulation 74 4.3.2 Shifted passivity 75 4.3.3 Shifted passifiability via output-feedback 77 4.3.4 Stability of the forced equilibria 78 4.3.5 Application to quadratic pH systems 79 4.4 PI-PBC of Power Converters 81 4.4.1 Model of the power converters 81 4.4.2 Construction of a shifted passive output 82 4.4.3 PI stabilization 85 4.4.4 Application to a quadratic boost converter 86 4.5 PI-PBC of HVDC Power Systems 89 4.5.1 Background 89 4.5.2 Port-Hamiltonian model of the system 91 4.5.3 Main result 93 4.5.4 Relation of PI-PBC with Akagi’s PQ method 95 4.6 PI-PBC of Wind Energy Systems 96 4.6.1 Background 96 4.6.2 System model 98 4.6.3 Control problem formulation 102 4.6.4 Proposed PI-PBC 104 4.7 Shifted Passivity of PI-Controlled Permanent Magnet Synchronous Motors 107 4.7.1 Background 107 4.7.2 Motor models 108 4.7.3 Problem formulation 111 4.7.4 Main result 113 4.7.5 Conclusions and future research 114 5 Parameterization of All Passive Outputs for port-Hamiltonian Systems 115 5.1 Parameterization of all Passive Outputs 116 5.2 Some Particular Cases 118 5.3 Two Additional Remarks 120 5.4 Examples 121 5.4.1 A level control system 121 5.4.2 A microelectromechanical optical switch 123 6 Lyapunov Stabilization of port-Hamiltonian Systems 125 6.1 Generation of Lyapunov Functions 127 6.1.1 Basic PDE 128 6.1.2 Lyapunov stability analysis 129 6.2 Explicit Solution of the PDE 131 6.2.1 The power shaping output 132 6.2.2 A more general solution 133 6.2.3 On the use of multipliers 135 6.3 Derivative Action on Relative Degree Zero Outputs 137 6.3.1 Preservation of the port-Hamiltonian Structure of I-PBC 138 6.3.2 Projection of the new passive output 140 6.3.3 Lyapunov stabilization with the new PID-PBC 141 6.4 Examples 142 6.4.1 A microelectromechanical optical switch (continued) 143 6.4.2 Boost converter 144 6.4.3 2-dimensional controllable LTI systems 146 6.4.4 Control by Interconnection vs PI-PBC 148 6.4.5 The use of the derivative action 150 7 Underactuated Mechanical Systems 153 7.1 Historical Review and Chapter Contents 153 7.1.1 Potential energy shaping of fully actuated systems 154 7.1.2 Total energy shaping of underactuated systems 156 7.1.3 Two formulations of PID-PBC 157 7.2 Shaping the Energy with a PID 158 7.3 PID-PBC of port-Hamiltonian Systems 161 7.3.1 Assumptions on the system 161 7.3.2 A suitable change of coordinates 163 7.3.3 Generating new passive outputs 165 7.3.4 Projection of the total storage function 167 7.3.5 Main stability result 169 7.4 PID-PBC of Euler-Lagrange Systems 172 7.4.1 Passive outputs for Euler-Lagrange systems 173 7.4.2 Passive outputs for Euler-Lagrange systems in Spong’s normal form 175 7.5 Extensions 176 7.5.1 Tracking constant speed trajectories 176 7.5.2 Removing the cancellation of Va(qa) 178 7.5.3 Enlarging the class of integral actions 179 7.6 Examples 180 7.6.1 Tracking for inverted pendulum on a cart 180 7.6.2 Cart-pendulum on an inclined plane 182 7.7 PID-PBC of Constrained Euler-Lagrange Systems 190 7.7.1 System model and problem formulation 191 7.7.2 Reduced purely differential model 195 7.7.3 Design of the PID-PBC 196 7.7.4 Main stability result 199 7.7.5 Simulation Results 200 7.7.6 Experimental Results 202 8 Disturbance Rejection in port-Hamiltonian Systems 207 8.1 Some Remarks On Notation and Assignable Equilibria 209 8.1.1 Notational simplifications 209 8.1.2 Assignable equilibria for constant d 210 8.2 Integral Action on the Passive Output 211 8.3 Solution Using Coordinate Changes 214 8.3.1 A feedback equivalence problem 214 8.3.2 Local solutions of the feedback equivalent problem 217 8.3.3 Stability of the closed–loop 219 8.4 Solution Using Nonseparable Energy Functions 221 8.4.1 Matched and unmatched disturbances 222 8.4.2 Robust matched disturbance rejection 225 8.5 Robust Integral Action for Fully Actuated Mechanical Systems 230 8.6 Robust Integral Action for Underactuated Mechanical Systems 237 8.6.1 Standard interconnection and damping assignment PBC 239 8.6.2 Main result 241 8.7 A New Robust Integral Action for Underactuated Mechanical Systems 244 8.7.1 System model 244 8.7.2 Coordinate transformation 245 8.7.3 Verification of requisites 246 8.7.4 Robust integral action controller 248 8.8 Examples 248 8.8.1 Mechanical systems with constant inertia matrix 249 8.8.2 Prismatic robot 250 8.8.3 The Acrobot system 255 8.8.4 Disk on disk system 260 8.8.5 Damped vertical take-off and landing aircraft 265 A Passivity and Stability Theory for State-Space Systems 269 A.1 Characterization of Passive Systems 269 A.2 Passivity Theorem 271 A.3 Lyapunov Stability of Passive Systems 273 B Two Stability Results and Assignable Equilibria 275 B.1 Two Stability Results 275 B.2 Assignable Equilibria 276 C Some Differential Geometric Results 279 C.1 Invariant Manifolds 279 C.2 Gradient Vector Fields 280 C.3 A Technical Lemma 281 D Port-Hamiltonian Systems 283 D.1 Definition of port-Hamiltonian Systems and Passivity Property 283 D.2 Physical Examples 284 D.3 Euler-Lagrange Models 286 D.4 Port-Hamiltonian Representation of GAS Systems 288 Index 309

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Book SynopsisTable of ContentsAbout the Authors xix Preface xxi Acknowledgments xxv List of Acronyms xxvii 1 Field Programmable Gate Arrays 1 1.1 Overview 1 1.1.1 FPGA Hardware Architecture 2 1.1.2 Configurable Logic Block 3 1.1.3 Block RAM 4 1.1.4 Digital Signal Processing Slice 4 1.2 Multiprocessing System-on-Chip Architecture 6 1.3 Communication 7 1.4 HIL Emulation 9 1.4.1 Vivado® High-Level Synthesis Tool 9 1.4.2 Vivado® Top-Level Design 11 1.4.3 Number Representation and Operations 13 1.4.4 FPGA Design Schemes 14 1.4.4.1 Pipeline Design Architecture 14 1.4.4.2 Parallel Design Architecture 14 1.4.5 FPGA Experiment 15 1.5 Summary 16 2 Hardware Emulation Building Blocks for Power System Components 17 2.1 Overview 17 2.2 Concept of HEBB 18 2.3 Numerical Integration 18 2.4 Linear Lumped Passive Elements 20 2.4.1 Model Formulation 20 2.4.1.1 Resistance R 20 2.4.1.2 Inductance L 20 2.4.1.3 Capacitance C 22 2.4.1.4 RL Branch 23 2.4.1.5 LC Branch 23 2.4.1.6 RLCG Branch 24 2.4.2 Hardware Emulation of Linear Lumped Passive Elements 26 2.5 Sources 27 2.5.1 Hardware Emulation of Sources 28 2.6 Switches 30 2.6.1 Hardware Emulation of Switches 30 2.7 Transmission Lines 32 2.7.1 Traveling Waves 32 2.7.2 Traveling Wave Model 35 2.7.2.1 Modal Transformation 36 2.7.3 Hardware Emulation of the TWM 39 2.7.3.1 Transformation Unit 39 2.7.3.2 Update Unit 39 2.7.4 Frequency Dependent Line Model 41 2.7.5 Hardware Emulation of FDLM 46 2.7.5.1 Convolution Unit 46 2.7.5.2 Update Unit 47 2.7.6 Universal Line Model 48 2.7.6.1 Frequency-Domain Formulation 48 2.7.6.2 Time-Domain Formulation 49 2.7.7 Hardware Emulation of the ULM 51 2.7.7.1 Update x Unit 52 2.7.7.2 Convolution Unit 52 2.7.7.3 Interpolation Unit 54 2.8 Network Solver 54 2.8.1 Hardware Emulation of Network Solver 55 2.8.2 Paralleled EMT Solution Algorithm 55 2.8.3 Main Control Module 58 2.8.4 Real-Time Emulation Case Study 59 2.9 Nonlinear Elements: Iterative Real-Time EMT Solver 63 2.9.1 Compensation Method 64 2.9.2 Newton–Raphson Method 65 2.9.3 Hardware Emulation of Nonlinear Solver 67 2.9.3.1 Nonlinear Function Evaluation 68 2.9.3.2 Parallel Calculation of J and F(ikm) 68 2.9.3.3 Parallel Gauss–Jordan Elimination 71 2.9.3.4 Computing vc 71 2.9.4 Case Studies 71 2.10 Summary 77 3 Power Transformers 79 3.1 Overview 79 3.2 Nonlinear Admittance-Based Real-Time Transformer Model 80 3.2.1 Linear Model Formulation 80 3.2.2 Linear Module Hardware Design 82 3.2.3 Inode Unit Module 84 3.2.4 Nonlinear Model Solution 85 3.2.4.1 Preisach Hysteresis Model 88 3.2.4.2 Nonlinear Module Hardware Design 89 3.2.5 Frequency-Dependent Eddy Current Model 90 3.2.6 Hardware Emulation of Power Transformer 91 3.2.7 Real-Time Emulation Case Studies 94 3.2.7.1 Case I 94 3.2.7.2 Case II 99 3.3 Nonlinear Magnetic Equivalent Circuit Based Real-time Multi-Winding Transformer Model 100 3.3.1 Topological ST EMT Model 102 3.3.1.1 ST Operating Principle 102 3.3.1.2 Tap-selection Algorithm 102 3.3.1.3 High-Fidelity Nonlinear MEC-Based ST Model 102 3.3.1.4 Iron Core Hysteresis and Eddy Currents 107 3.3.2 High-Fidelity Nonlinear MEC-Based ST Hardware Emulation 109 3.3.2.1 Network Transient Emulation with Embedded ST 109 3.3.3 Real-Time Emulation Case Studies 112 3.3.3.1 Finite Element Modeling and Validation 112 3.3.3.2 Case Studies 112 3.4 Real-Time Finite-Element Model of Power Transformer 123 3.4.1 Magnetodynamic Problem Formulation 123 3.4.1.1 Refined TLM Solution 126 3.4.1.2 Field-Circuit Coupling 130 3.4.2 Hardware Emulation of Finite Element Model 132 3.4.3 Case Studies 136 3.4.3.1 Results and Validation 137 3.4.3.2 Speed-up and Scalability 140 3.5 Summary 141 4 Rotating Machines 143 4.1 Overview 143 4.2 Lumped Universal Machine (UM) Model 144 4.2.1 UM Model Formulation 144 4.2.2 Interfacing UM Model with Network 146 4.2.3 UM HEBB 148 4.2.3.1 Speed & Angle Unit 149 4.2.3.2 FrmTran Unit 150 4.2.3.3 Compidq0 Unit 151 4.2.3.4 Flux & Torque Unit 151 4.2.3.5 Update & CompVc Unit 151 4.2.4 Real-Time Emulation Case Study 152 4.2.5 Overall Power System HEBB for Real-Time EMT Emulation 154 4.3 General Framework for State-Space Electrical Machine Emulation 158 4.3.1 FPGA Design Approaches for Electrical Machine Emulation 159 4.3.2 State-Space Representation of Machine Models 160 4.3.3 System Configuration on FPGA 161 4.3.3.1 Number Representation 161 4.3.3.2 Floating-Point Implementation by VHDL 162 4.3.3.3 Fixed-Point Implementation by Schematic 167 4.3.4 Evaluation of Designed Architectures 170 4.3.4.1 Real-Time Emulation Accuracy Assessment 170 4.3.4.2 Off-line Validation 171 4.3.4.3 Hardware Resource Utilization 172 4.3.5 Real-Time Emulation Case Studies 174 4.3.5.1 Case I: Induction Motor Transients 174 4.3.5.2 Case II: Synchronous Generator Transients 174 4.3.5.3 Case III: Line Start-Permanent Magnet Synchronous Motor Transients 176 4.3.5.4 Case IV: DC Motor Transients 177 4.4 Nonlinear Magnetic Equivalent Circuit Based Induction Machine Model 178 4.4.1 Magnetic Circuit 179 4.4.2 Interfacing of Magnetic and Electric Circuits 181 4.4.3 Electric Circuit 182 4.4.4 Nonlinear Solution of Detailed MEC 182 4.4.5 Hardware Emulation of Nonlinear MEC 183 4.4.5.1 Parallel Gauss–Jordan Elimination Unit 185 4.4.5.2 Parallel Computational Unit for Residual Vector 187 4.4.5.3 Nonlinear Evaluation Unit 187 4.4.6 Evaluation of Real-Time Emulation of Induction Machine 187 4.5 Summary 190 5 Protective Relays 193 5.1 Overview 193 5.2 Hardware Emulation of Multifunction Protection System 195 5.2.1 Signal Processing HEBB 196 5.2.1.1 CORDIC HEBB 196 5.2.1.2 Symmetrical Components HEBB 198 5.2.1.3 DFT HEBB 198 5.2.1.4 Zero-Crossing Detection HEBB 199 5.2.2 Multifunction Protective System HEBB 203 5.2.2.1 Fault Detection HEBB 203 5.2.2.2 Directional Overcurrent Protection HEBB 205 5.2.2.3 Over/Under Voltage Protection HEBB 205 5.2.2.4 Distance Protection HEBB 205 5.2.2.5 Under/Over Frequency Protection HEBB 209 5.3 Test Setup and Real-Time Results 209 5.3.1 Case I 210 5.3.2 Case II 213 5.4 Summary 214 6 Adaptive Time-Stepping Based Real-Time EMT Emulation 217 6.1 Overview 217 6.2 Nonlinear Solution and Adaptive Time-Stepping Schemes 219 6.2.1 Nonlinear Element Solution Methods 219 6.2.1.1 Newton–Raphson Method 219 6.2.1.2 Piecewise Linearization (PWL) Method 219 6.2.1.3 Piecewise N-R Method 220 6.2.2 Adaptive Time-Stepping Schemes 220 6.2.2.1 Local Truncation Error Method 220 6.2.2.2 Iteration Count Method 221 6.2.2.3 DVDT or DIDT Method 221 6.2.3 Combinations of Adaptive Time-Stepping Schemes 222 6.2.3.1 Measurements and Restrictions for Real-Time Emulation 222 6.2.4 Case Studies 223 6.2.4.1 Diode Full-Bridge Circuit 224 6.2.4.2 Power Transmission System 225 6.2.4.3 FPGA Implementation 229 6.2.4.4 Real-Time Emulation Results 234 6.3 Adaptive Time-Stepping Universal Line Model and Universal Machine Model for Real-Time Hardware Emulation 236 6.3.1 Subsystem-Based Adaptive Time-Stepping Scheme 237 6.3.2 Adaptive Time-Stepping ULM and UM Models 238 6.3.2.1 ULM Computation 238 6.3.2.2 Universal Machine Model Computation 242 6.3.3 Real-Time Emulation Case Study 243 6.3.3.1 Hardware Implementation 243 6.3.3.2 Latency and Hardware Resource Utilization 246 6.3.4 Results and Validation 247 6.3.4.1 Validation of the ULM Model 247 6.3.4.2 Real-Time Emulation Results 248 6.4 Summary 252 7 Power Electronic Switches 253 7.1 Overview 253 7.2 IGBT/Diode Nonlinear Behavioral Model 255 7.2.1 Power Diode 256 7.2.1.1 Mathematical Model 256 7.2.1.2 Hardware Module Architecture 257 7.2.2 IGBT 259 7.2.2.1 Model Formulation 259 7.2.2.2 Hardware Module Architecture 263 7.2.2.3 Multiple Parallel Devices 265 7.2.3 Electro-Thermal Network 267 7.2.4 Hardware Emulation Results 268 7.3 Physics-Based Nonlinear IGBT/Diode Model 270 7.3.1 Physics-Based Nonlinear p–i–n Diode Model 271 7.3.1.1 Model Formulation 271 7.3.1.2 Model Discretization and Linearization 272 7.3.1.3 Hardware Emulation on FPGA 274 7.3.2 Physics-Based Nonlinear IGBT Model 276 7.3.2.1 Model Formulation 276 7.3.2.2 Model Discretization and Linearization 279 7.3.2.3 Hardware Emulation on FPGA 281 7.3.3 Hardware Emulation Results 285 7.3.3.1 Test circuit 285 7.3.3.2 Results and comparison 286 7.4 IGBT/Diode Curve-Fitting Model 292 7.4.1 Linear Static Curve-fitting Model 293 7.4.1.1 Static Characteristics 293 7.4.1.2 Switching Transients 293 7.4.2 Nonlinear Dynamic Curve-fitting Model 296 7.4.3 Hardware Emulation Results 298 7.5 Summary 300 8 AC–DC Converters 301 8.1 Overview 301 8.2 Detailed Model 303 8.2.1 Detailed Equivalent Circuit Model 304 8.3 Equivalenced Device-Level Model 305 8.3.1 Power Loss Calculation 307 8.3.2 Thermal Network Calculation 309 8.3.3 Hardware Emulation of SM Model on FPGA 311 8.3.4 MMC System Hardware Emulation 314 8.3.5 Real-Time Emulation Results 316 8.3.5.1 Test Circuit and Hardware Resource Utilization 316 8.3.5.2 Results and Comparison for Single-Phase Five-Level MMC 318 8.3.5.3 Results for Three-Phase Nine-Level MMC 324 8.4 Virtual-Line-Partitioned Device-Level Models 324 8.4.1 TLM-Link Partitioning 326 8.4.2 Hardware Design on FPGA 328 8.4.2.1 Hardware Platform 329 8.4.2.2 Controller Emulation 329 8.4.2.3 MMC Emulation on FPGA 330 8.4.3 Real-Time Emulation Results 335 8.4.3.1 MMC 335 8.4.3.2 Induction Machine Driven by Five-Level MMC 342 8.5 MMC Partitioned by Coupled Voltage–Current Sources 344 8.5.1 V–I Coupling 344 8.5.2 Hardware Emulation Case of NBM-Based MMC 346 8.5.2.1 Power Converter HIL Emulation 346 8.5.2.2 HIL Emulation Results and Validation 347 8.5.2.3 Islanded MMC Performance 348 8.5.2.4 MMC–MVDC Performance 355 8.6 Clamped Double Submodule MMC 355 8.6.1 Operation Principles of CDSM 357 8.6.2 Device-Level Modeling Scheme 359 8.6.2.1 Temperature-Dependent Electrical Interface Parameter Calculation 359 8.6.2.2 Device-Level Linearized Transient Waveform Calculation 361 8.6.3 SM-Level Modeling Scheme 362 8.6.4 Converter-Level Modeling Scheme 362 8.6.5 Case Study and Hardware Implementation 363 8.6.5.1 Design Partition 365 8.6.5.2 Latency and Resource Consumption 367 8.6.6 Real-Time Emulation Results and Analysis 368 8.6.6.1 Steady-State Results 368 8.6.6.2 DC Power Flow Control 368 8.6.6.3 DC Fault Transient Results 371 8.7 Summary 374 9 DC-DC Converters 377 9.1 Overview 377 9.2 Buck–Boost Converter 379 9.2.1 System-Level Modeling 379 9.2.2 Hardware Implementation 380 9.3 Solid-State Transformer Modeling 381 9.3.1 MMC Arm Models 382 9.3.1.1 TLM-Stub Model (TLM-S) 382 9.3.1.2 Nonlinear Switch-Based Model (NSM) 383 9.3.1.3 Hybrid Arm Model 384 9.3.2 Three-Phase Saturable Transformer Model 385 9.3.3 SST EMT Model 385 9.3.4 SST HIL Emulation 386 9.3.5 SST Real-Time HIL Emulation Results 390 9.3.5.1 Device-Level Behavior 390 9.3.5.2 Converter Performance 391 9.3.5.3 System Tests 392 9.4 Summary 394 10 DC Circuit Breakers 397 10.1 Overview 397 10.2 HHB in MTDC System 399 10.2.1 MTDC Test System Schematic 399 10.2.2 DC Line Protection 401 10.2.2.1 Voltage Derivative Protection 401 10.2.2.2 Over Current Protection 401 10.3 Proactive Hybrid HVDC Breaker 402 10.3.1 HHB EMT Model 403 10.3.2 Varistor Model 404 10.3.3 General HHB Unit Model 406 10.3.4 Two-Node IGBT Models 407 10.3.5 IGBT Low-Order Nonlinear Behavioral Model 409 10.3.5.1 IGBT Fourth-Order Behavioral Model 409 10.3.5.2 Parameters Extraction 409 10.3.5.3 Sensitivity Analysis 410 10.3.5.4 Model Parallelization 411 10.3.6 Electro-Thermal Network 412 10.3.7 HHB Hardware Implementation on FPGA 412 10.3.8 HHB HIL Emulation Results 416 10.3.8.1 Device-Level Performance 416 10.3.8.2 System-Level Performance 424 10.4 Ultrafast Mechatronic Circuit Breaker 426 10.4.1 Nonlinear Device-Level Thyristor Model 426 10.4.1.1 Basic Device Characteristics 426 10.4.1.2 Scalable Cascaded Thyristor Model 428 10.4.2 UFMCB Modeling 431 10.4.3 Relaxed Scalar Newton–Raphson (RSNR) 433 10.4.4 UFMCB Hardware Design 435 10.4.5 UFMCB Real-Time Tests and Validation 438 10.4.5.1 Four-Terminal DC Grid Test Case 438 10.4.5.2 UFMCB Design Evaluation by HIL System 438 10.4.5.3 UFMCB in HVDC Grid 442 10.5 Summary 444 11 Large-Scale AC and DC Networks 447 11.1 Overview 447 11.2 Spatial Decomposition and Parallelism 449 11.2.1 Functional Decomposition for Large-Scale Real-Time Emulation 449 11.2.2 Hardware Module Parallelism 451 11.3 Multi-FPGA Hardware Design for Real-Time EMT Emulation 453 11.3.1 Case I: 3-FPGA Hardware Design 454 11.3.2 Case II: 10-FPGA Hardware Design 457 11.3.3 Performance and Scalability of the Real-Time EMT Emulator 460 11.4 CIGRÉ DC Grid Hybrid Modeling Methodology 465 11.4.1 Network Topology 467 11.4.2 Control Scheme 467 11.4.3 Hybrid Modeling Methodology 468 11.4.3.1 Device-Level Electrothermal Model 469 11.4.3.2 Equivalent Circuit Model 469 11.4.3.3 Average Value Model 471 11.4.3.4 Transmission Line Model 471 11.4.4 Real-Time MPSoC-FPGA Based DC Grid Emulator 471 11.4.4.1 System Decomposition 471 11.4.4.2 Hardware Resource Allocation and Task Partitioning 472 11.4.4.3 Design and Implementation 474 11.4.5 Real-Time Emulation Results and Validation 475 11.4.5.1 Steady-State Operation 475 11.4.5.2 Power Flow Command Change 477 11.4.5.3 DC Fault 477 11.5 Real-Time Co-Emulation Framework for Cyber-Physical Systems 479 11.5.1 Communication Network Simulation and Co-Simulation 481 11.5.2 Real-Time Co-Emulation Framework 484 11.5.2.1 RTCE Hardware Architecture 484 11.5.3 Hardware Implementation of RTCE 487 11.5.3.1 Multi-Board EMT Emulation 488 11.5.3.2 Communication Protocol and Implementation 489 11.5.4 Real-Time Emulation Results and Verification 491 11.5.4.1 Processing Delay and Hardware Resource Cost 491 11.5.4.2 Case Study 1: Over-Current Fault 492 11.5.4.3 Case Study 2: Communication Link Failure 493 11.6 Faster-Than-Real-Time Hybrid Dynamic-EMT Emulation of AC–DC Grids 495 11.6.1 Flexible Time-Stepping Algorithm for Dynamic Emulation 496 11.6.1.1 Transient Stability Emulation Methodology 496 11.6.1.2 Local Equipment Based Flexible Time-stepping 497 11.6.2 AC–DC Grid Component Modeling 498 11.6.2.1 AC–DC Grid Interface 498 11.6.2.2 AC Grid Modeling 499 11.6.2.3 DC Grid Modeling 501 11.6.3 FTRT Emulation on FPGAs 503 11.6.4 FTRT Emulation Results and Validation 505 11.6.4.1 Three-Phase-to-Ground Fault 506 11.6.4.2 Generator Outage and Sudden Load Change 507 11.7 Summary 510 Bibliography 513 Appendix A Parameters for Case Studies 531 A.1 Chapter 2 531 A.1.1 Case in Section 2.7 531 A.1.2 Cases in Section 2.8 531 A.2 Chapter 3 531 A.2.1 Cases in Section 3.2 531 A.2.1.1 Cases Study I 531 A.2.1.2 Cases Study II 532 A.2.2 Cases in Section 3.3 532 A.2.2.1 Transformer 532 A.2.2.2 System 532 A.2.3 Cases in Section 3.4 532 A.3 Chapter 4 533 A.3.1 UM Case in Section 4.2 533 A.3.2 Cases in Section 4.3 534 A.3.2.1 State-Space Matrices of Rotating Machines 534 A.3.2.2 Parameters of Rotating Machines 538 A.3.3 MEC Case in Section 4.4 538 A.4 Chapter 5 538 A.5 Chapter 6 539 A.5.1 Cases in Section 6.2 539 A.5.2 Cases in Section 6.3 540 A.6 Chapter 7 540 A.7 Chapter 8 541 A.7.1 Equivalenced Device-Level Model in Section 8.3 541 A.7.2 MMC-IM Case in Section 8.4 541 A.7.3 MVDC Case in Section 8.5 541 A.7.4 MTDC Case in Section 8.6 541 A.8 Chapter 9 541 A.9 Chapter 10 542 A.9.1 HHB Case 542 A.9.2 UFMCB Case 542 A.10 Chapter 11 543 A.10.1 CIGRÉ B4 DC Grid Test System 543 Index 545

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Book SynopsisBackscattering and RF Sensing for Future Wireless Communication Discover what lies ahead in wireless communication networks with this insightful and forward-thinking book written by experts in the fieldBackscattering and RF Sensing for Future Wireless Communication delivers a concise and insightful picture of emerging and future trends in increasing the efficiency and performance of wireless communication networks. The book shows how the immense challenge of frequency saturation could be met via the deployment of intelligent planar electromagnetic structures. It provides an in-depth coverage of the fundamental physics behind these structures and assesses the enhancement of the performance of a communication network in challenging environments, like densely populated urban centers. The distinguished editors have included resources from a variety of leading voices in the field who discuss topics such as the engineering of metasurfaces at a large scale, the electromagnetic analysis of pTable of Contents 1. Intelligent Reflective Surfaces – State of the art Jalil ur Rehman Kazim, Hasan T. Abbas, Muhammad A. Imran, Qammer H. Abbasi 2. Signal Modulation Schemes in Backscatter Communications Yuan Ding, George Goussetis, Ricardo Correia, Nuno Borges Carvalho, Romwald Lihakanga, and Chaoyun Song 3. Electromagnetic Waves Scattering Characteristics of Metasurfaces Muhammad Ali Babar Abbasi, Dmitry E. Zelenchuk, Abdul Quddious 4. Metasurfaces Based on Huygen’s Wave Front Manipulation: A review Abubakar Sharif, Jun Ouyang, Ayman Abdulhadi Althuwayb, Kamran Arshad, Muhammad A. Imran, Qammer H. Abbasi 5. Metasurface: An Insight into Its Applications Fahad Ahmed and Nosherwan Shoaib 6. The Role of Smart Metasurfaces in Smart Grid Energy Management I. Safak Bayram, Muhammad Ismail, and Raka Jovanovic 7. Passive UHF RFID Tag Antennas Based Sensing for Internet of Things Paradigm Abubakar Sharif, Jun Ouyang, Kamran Arshad, Muhammad A. Imran, Qammer H. Abbasi 8. RF Sensing for Healthcare Applications Syed Aziz Shah, Hasan Abbas, Muhammad A. Imran and Qammer H. Abbasi 9. Electromagnetic Wave Manipulation with Metamaterials and Metasurfaces for Future Communication Technologies Muhammad Qasim Mehmood, Junsuk Rho, and Muhammad Zubair 10. Conclusion Qammer H. Abbasi, Hasan T. Abbas, Akram Alomainy, and Muhammad A. Imran

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Book SynopsisIndustry 4.0 Vision for the Supply of Energy and Materials Explore the impact of Industry 4.0 technologies on the supply chain with this authoritative text written by a leader in his field In Industry 4.0 Vision for the Supply of Energy and Materials, distinguished researcher and editor, Dr. Mahdi Sharifzadeh, delivers thematic, analytic, and applied discussions of the Industry 4.0 vision for supply chain design and operation. The book compiles all current aspects and emerging notions of Industry 4.0 into clusters of enablers and analytics of Supply Chain 4.0. Their multifaceted and highly interconnected nature is discussed at length, as are their diverse range of applications. You will discover uses of these new technologies ranging from the supply of conventional energy networks to renewables, pharmaceuticals, and additive manufacturing. You will also learn about their implications for economic prosperity and environmental sustainability. Table of ContentsPreface vii Part I Industry 4.0 Drivers 1 1 Connectivity through Wireless Communications and Sensors 3 2 Blockchain and Smart Contracts 59 3 Robotics: A Key Driver of Industry 4.0 73 4 Cloud Computing and Its Impact on Industry 4.0: An Overview 99 5 Applications of Artificial Intelligence and Big Data in Industry 4.0 Technologies 121 Part II Industry 4.0 Technologies 159 6 Multi-Vector Internet of Energy (IoE): A Key Enabler for the Integration of Conventional and Renewable Power Generation 161 7 The Economic Implication of Integrating Investment Planning and Generation Scheduling: The Application of Big Data Analytics and Machine Learning 189 8 A Systematic Method for Wireless Sensor Placement: A Fault-Tolerant Communi cation Solution for Monitoring Water Distribution Networks 221 9 An Overview of the Evolution of Oil and Gas 4.0 241 10 Electrification of Transportation: Transition Toward Energy Sustainability 269 11 Computer-Aided Molecular Design: Accelerating the Commercialization Cycle 297 12 Pharmaceutical Industry: Challenges and Opportunities for Establishing Pharma 4.0 313 13 Additive Manufacturing: A Game-Changing Paradigm in Manufacturing and Supply Chains 339 Glossary 359 Index 371

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Book SynopsisTable of ContentsBiographies xv Contributors List xvii Foreword xxi Preface xxiii Section 1 Sociotechnical System Types 1 1 Introduction to the Human Systems Engineering Framework 3Holly A. H. Handley 1.1 Introduction 3 1.2 Human-Centered Disciplines 3 1.3 Human Systems Engineering 4 1.4 Development of the HSE Framework 5 1.5 HSE Applications 7 1.6 Conclusion 9 References 9 2 Human Interface Considerations for Situational Awareness 11Christian G. W. Schnedler and Michael Joy 2.1 Introduction 11 2.2 Situational Awareness: A Global Challenge 12 2.3 Putting Situational Awareness in Context: First Responders 13 2.4 Deep Dive on Human Interface Considerations 14 2.5 Putting Human Interface Considerations in Context: Safe Cities 15 2.6 Human Interface Considerations for Privacy-Aware SA 16 Reference 17 3 Utilizing Artificial Intelligence to Make Systems Engineering More Human 19Philip S. Barry and Steve Doskey 3.1 Introduction 19 3.2 Changing Business Needs Drive Changes in Systems Engineering 20 3.3 Epoch 4: Delivering Capabilities in the Sociotechnical Ecosystem 21 3.3.1 A Conceptual Architecture for Epoch 4 22 3.3.2 Temporal Sociotechnical Measures 22 3.3.3 Systems Engineering Frameworks 23 3.3.3.1 Sociotechnical Network Models 23 3.3.3.2 Digital Twins 23 3.4 The Artificial Intelligence Opportunity for Building Sociotechnical Systems 24 3.5 Using AI to Track and Interpret Temporal Sociotechnical Measures 25 3.6 AI in Systems Engineering Frameworks 25 3.7 AI in Sociotechnical Network Models 26 3.8 AI-Based Digital Twins 27 3.9 Discussion 27 3.10 Case Study 30 3.11 Systems Engineering Sociotechnical Modeling Approach 31 3.11.1 Modeling the Project 33 3.12 Results 36 3.13 Summary 38 References 39 4 Life Learning of Smart Autonomous Systems for Meaningful Human-Autonomy Teaming 43Kate J. Yaxley, Keith F. Joiner, Jean Bogais, and Hussein A. Abbass 4.1 Introduction 43 4.2 Trust in Successful Teaming 45 4.3 Meaningful Human-Autonomy Teaming 46 4.4 Systematic Taxonomy for Iterative Through-Life Learning of SAS 47 4.5 Ensuring Successful SAS 51 4.6 Developing Case Study: Airborne Shepherding SAS 53 4.7 Conclusion 57 Acknowledgment 58 References 58 Section 2 Domain Deep Dives 63 5 Modeling the Evolution of Organizational Systems for the Digital Transformation of Heavy Rail 65Grace A. L. Kennedy, William R. Scott, Farid Shirvani, and A. Peter Campbell 5.1 Introduction 65 5.2 Organizational System Evolution 66 5.2.1 Characteristics of Organizational Systems 66 5.2.2 The Organization in Flux 67 5.2.3 Introducing New Technologies 68 5.3 Model-Based Systems Engineering 70 5.4 Modeling Approach for the Development of OCMM 71 5.4.1 Technology Specification 72 5.4.2 Capture System Change 73 5.4.3 Capture Organizational Changes 73 5.4.4 Manage Organization Change 73 5.4.5 Analyze Emergent System 73 5.5 Implementation 74 5.5.1 User Portals 75 5.5.2 OCMM Metamodel 75 5.6 Case Study: Digital Transformation in the Rail Industry 78 5.6.1 Technology Specification 79 5.6.2 Capture System Change 79 5.6.3 Capture Organization Changes 80 5.6.4 Organization Change Management 84 5.6.5 Analyze Emergent System 85 5.6.5.1 Situation Awareness 85 5.6.5.2 Workload Analysis 90 5.7 OCMM Reception 91 5.8 Summary and Conclusions 94 References 94 6 Human Systems Integration in the Space Exploration Systems Engineering Life Cycle 97George Salazar and Maria Natalia Russi-Vigoya 6.1 Introduction 97 6.2 Spacecraft History 98 6.2.1 Mercury/Gemini/Apollo 98 6.2.2 Space Shuttle 100 6.2.3 International Space Station 101 6.2.4 Orion Spacecraft 101 6.3 Human Systems Integration in the NASA Systems Engineering Process 103 6.3.1 NASA Systems Engineering Process and HSI 103 6.4 Mission Challenges 108 6.4.1 Innovation and Future Vehicle Designs Challenge 108 6.4.2 Operations Challenges 109 6.4.3 Maintainability and Supportability Challenges 110 6.4.4 Habitability and Environment Challenges 110 6.4.5 Safety Challenges 110 6.4.6 Training Challenges 111 6.5 Conclusions 111 References 112 7 Aerospace Human Systems Integration: Evolution over the Last 40 Years 113Guy André Boy 7.1 Introduction 113 7.2 Evolution of Aviation: A Human Systems Integration Perspective 114 7.3 Evolution with Respect to Models, Human Roles, and Disciplines 116 7.3.1 From Single-Agent Interaction to Multi-agent Integration 116 7.3.2 Systems Management and Authority Sharing 117 7.3.3 Human-Centered Disciplines Involved 118 7.3.4 From Automation Issues to Tangibility Issues 119 7.4 From Rigid Automation to Flexible Autonomy 120 7.5 How Software Took the Lead on Hardware 122 7.6 Toward a Human-Centered Systemic Framework 123 7.6.1 System of Systems, Physical and Cognitive Structures and Functions 123 7.6.2 Emergent Behaviors and Properties 125 7.6.3 System of Systems Properties 126 7.7 Conclusion and Perspectives 126 References 127 Section 3 Focus on Training and Skill Sets 129 8 Building a Socio-cognitive Evaluation Framework to Develop Enhanced Aviation Training Concepts for Gen Y and Gen Z Pilot Trainees 131Alliya Anderson, Samuel F. Feng, Fabrizio Interlandi, Michael Melkonian, Vladimir Parezanović, M. Lynn Woolsey, Claudine Habak, and Nelson King 8.1 Introduction 131 8.1.1 Gamification Coupled with Cognitive Neuroscience and Data Analysis 132 8.1.2 Generational Differences in Learning 133 8.2 Virtual Technologies in Aviation 134 8.2.1 Potential Approaches for Incorporating Virtual Technologies 135 8.3 Human Systems Engineering Challenges 136 8.4 Potential Applications Beyond Aviation Training 137 8.5 Looking Forward 137 Acknowledgement 137 References 138 9 Improving Enterprise Resilience by Evaluating Training System Architecture: Method Selection for Australian Defense 143Victoria Jnitova, Mahmoud Efatmaneshnik, Keith F. Joiner, and Elizabeth Chang 9.1 Introduction 143 9.2 Defense Training System 144 9.2.1 DTS Conceptualization 144 9.2.2 DTS as an Extended Enterprise Systems 144 9.2.3 Example: Navy Training System 145 9.2.3.1 Navy Training System as a Part of DTS 145 9.2.3.2 Navy Training System as a Part of DoD 145 9.3 Concept of Resilience in the Academic Literature 147 9.3.1 Definition of Resilience: A Multidisciplinary and Historical View 147 9.3.2 Definition of Resilience: Key Aspects 147 9.3.2.1 What? (Resilience Is and Is Not) 147 9.3.2.2 Why? (Resilience Triggers) 159 9.3.2.3 How? (Resilience Mechanisms and Measures) 160 9.4 DTS Case Study Methodology 169 9.4.1 DTS Resilience Measurement Methodology 169 9.4.2 DTS Architecture 169 9.4.3 DTS Resilience Survey 172 9.4.3.1 DTS Resilience Survey Design 172 9.4.3.2 DTS Resilience Survey Conduct 172 9.5 Research Findings and Future Directions 176 References 177 10 Integrating New Technology into the Complex System of Air Combat Training 185Sarah M. Sherwood, Kelly J. Neville, Angus L. M. T. McLean, III, Melissa M. Walwanis, and Amy E. Bolton 10.1 Introduction 185 10.2 Method 187 10.2.1 Data Collection 187 10.2.2 Data Analysis 188 10.3 Results and Discussion 190 10.3.1 Unseen Aircraft Within Visual Range 191 10.3.2 Unexpected Virtual and Constructive Aircraft Behavior 193 10.3.3 Complacency and Increased Risk Taking 194 10.3.4 Human–Machine Interaction 195 10.3.5 Exercise Management 196 10.3.6 Big Picture Awareness 197 10.3.7 Negative Transfer of Training to the Operational Environment 198 10.4 Conclusion 199 Acknowledgments 202 References 202 Section 4 Considering Human Characteristics 205 11 Engineering a Trustworthy Private Blockchain for Operational Risk Management: A Rapid Human Data Engineering Approach Based on Human Systems Engineering 207Marius Becherer, Michael Zipperle, Stuart Green, Florian Gottwalt, Thien Bui-Nguyen, and Elizabeth Chang 11.1 Introduction 207 11.2 Human Systems Engineering and Human Data Engineering 207 11.3 Human-Centered System Design 208 11.4 Practical Issues Leading to Large Complex Blockchain System Development 208 11.4.1 Human-Centered Operational Risk Management 208 11.4.2 Issues Leading to Risk Management Innovation Through Blockchain 209 11.4.3 Issues in Engineering Trustworthy Private Blockchain 209 11.5 Framework for Rapid Human Systems–Human Data Engineering 210 11.6 Human Systems Engineering for Trustworthy Blockchain 210 11.6.1 Engineering Trustworthy Blockchain 210 11.6.2 Issues and Challenges in Trustworthy Private Blockchain 212 11.6.3 Concepts Used in Trustworthy Private Blockchain 213 11.6.4 Prototype Scenario for Trusted Blockchain Network 214 11.6.5 Systems Engineering of the Chain of Trust 214 11.6.6 Design Public Key Infrastructure (PKI) for Trust 215 11.6.6.1 Design of Certificate Authority (CA) 215 11.6.6.2 Design the Trusted Gateways 216 11.6.6.3 Involving Trusted Peers and Orderers 217 11.6.6.4 Facilitate Trust Through Channels 217 11.7 From Human System Interaction to Human Data Interaction 219 11.8 Future Work for Trust in Human Systems Engineering 219 11.8.1 Software Engineering of Trust for Large Engineered Complex Systems 219 11.8.2 Human-Centered AI for the Future Engineering of Intelligent Systems 220 11.8.3 Trust in the Private Blockchain for Big Complex Data Systems in the Future 220 11.9 Conclusion 221 Acknowledgment 222 References 222 12 Light’s Properties and Power in Facilitating Organizational Change 225Pravir Malik 12.1 Introduction 225 12.2 Implicit Properties and a Mathematical Model of Light 226 12.3 Materialization of Light 230 12.3.1 The Electromagnetic Spectrum 231 12.3.2 Quantum Particles 232 12.3.3 The Periodic Table and Atoms 233 12.3.4 A Living Cell 235 12.3.5 Fundamental Capacities of Self 237 12.4 Leveraging Light to Bring About Organizational Change 239 12.5 Summary and Conclusion 243 References 243 Section 5 From the Field 245 13 Observations of Real-Time Control Room Simulation 247Hugh David with an editor introduction by Holly A. H. Handley 13.1 Introduction 247 13.1.1 What Is a “Real-Time Control Room Simulator”? 247 13.1.2 What Is It Used For? 247 13.1.3 What Does It Look Like? 248 13.1.4 How Will They Develop? 249 13.2 Future General-Purpose Simulators 249 13.2.1 Future On-Site Simulators 250 13.3 Operators 251 13.4 Data 252 13.5 Measurement 252 13.5.1 Objective Measures 253 13.5.1.1 Recommended 253 13.5.1.2 Not Recommended 253 13.5.2 Subjective Measures 254 13.5.2.1 Recommended 255 13.5.2.2 Not Recommended 255 13.6 Conclusion 257 Disclaimer 257 References 257 14 A Research Agenda for Human Systems Engineering 259Andreas Tolk 14.1 The State of Human Systems Engineering 259 14.2 Recommendations from the Chapter Contributions 260 14.2.1 Data and Visualization Challenges 260 14.2.2 Next-Generation Computing 261 14.2.3 Advanced Methods and Tools 262 14.2.4 Increased Integration of Social Components into System Artifacts 263 14.3 Uniting the Human Systems Engineering Stakeholders 263 14.3.1 Transdisciplinary Approach 264 14.3.2 Common Formalisms 265 14.3.3 Common Metrics 266 14.4 Summary 266 Disclaimer 267 References 267 Index 271

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Book SynopsisDiscover the latest advances in spintronic materials, devices, and applications In Spintronics: Materials, Devices and Applications, a team of distinguished researchers delivers a holistic introduction to spintronic effects within cutting-edge materials and applications. Containing the perfect balance of academic research and practical application, the book discusses the potentialand the key limitations and challengesof spintronic devices. The latest title in the Wiley Series in Materials for Electronic and Optoelectronic Applications, Spintronics: Materials, Devices and Applications explores giant magneto-resistance (GMR) and tunneling magnetic resistance (TMR) materials, spin-transfer torque and spin-orbit torque materials, spin oscillators, and spin materials for use in artificial neural networks. Applications in multi-ferroelectric and antiferromagnetic materials are presented as well. This book also includes: A thorough introduction to recent research developments in the fields of spintronic materials, devices, and applicationsComprehensive explorations of skymions, magnetic semiconductors, and antiferromagnetic materialsPractical discussions of spin-transfer torque materials and devices for magnetic random-access memoryIn-depth examinations of giant magneto-resistance materials and devices for magnetic sensors Perfect for advanced students and researchers in materials science, physics, electronics, and computer science, Spintronics: Materials, Devices and Applications will also earn a place in the libraries of professionals working in the manufacture of optics, photonics, and nanometrology equipment.Table of ContentsList of Contributors xi Series Preface xiii Preface xv 1 Introduction 1Kaiyou Wang 2 Giant Magnetoresistance (GMR) Materials and Devices for Biomedical and Industrial Applications 3Kai Wu, Diqing Su, Renata Saha, and Jian-Ping Wang 2.1 Introduction 3 2.2 Giant Magnetoresistance (GMR) Effect 4 2.3 Different Types of GMR Sensors 7 2.3.1 Rigid GMR Sensors 7 2.3.1.1 Long-strip GMR Sensors 7 2.3.1.2 Large-area GMR Sensors 8 2.3.2 Flexible GMR Sensors 9 2.3.3 Printable GMR Sensors 11 2.3.4 Granular GMR Sensors (Thin Film- and Solution-based) 11 2.4 GMR Sensors: Surface Modification and Auxiliary Tools 12 2.4.1 GMR Sensor Surface Modification for Biomedical Applications 12 2.4.2 Integration of a Magnetic Flux Concentrator (MFC) 14 2.4.2.1 Superconducting MFC 14 2.4.2.2 Soft-ferromagnetic Material-based MFC 14 2.4.3 Integration of Microfluidic Channels 16 2.5 GMR-based Biomedical Applications 16 2.5.1 GMR-based Immunoassays 16 2.5.1.1 Wash-free and Non-wash-free Immunoassays 17 2.5.1.2 Different Immunoassay Methods 17 2.5.1.3 GMR for Disease Diagnosis 19 2.5.1.4 GMR-based Point-of-Care (POC) Devices 24 2.5.2 GMR-based Genotyping 25 2.5.3 GMR-based Bio-magnetic Field Recording 28 2.5.4 GMR-based Food and Drug Safety Supervision 32 2.6 GMR-based Industrial Applications 34 2.6.1 GMR for Position Sensing 34 2.6.2 GMR for Current Sensing 35 2.6.3 GMR for Material Defect Inspection 37 2.7 Conclusions and Outlook 39 References 40 3 Tunneling Magnetoresistance (TMR) Materials and Devices for Magnetic Sensors 51Zitong Zhou, Kun Zhang, and Qunwen Leng 3.1 Principle of Tunneling Magnetoresistance Effect 52 3.1.1 Tunneling Process 52 3.1.2 Spin-dependent Tunneling Process 53 3.1.3 The Julliére Model 54 3.1.4 Typical Structure of the Magnetic Sensing Unit 56 3.2 Material and Process 56 3.2.1 TMR Barrier Materials 56 3.2.2 Ferromagnetic Layers in TMR 59 3.2.3 TMR Film Stack 61 3.2.4 Perpendicular Magnetic Anisotropy (PMA) in TMR 65 3.2.5 Material Fabrication and Pattern Process 65 3.2.5.1 Magnetron Sputtering 66 3.2.5.2 Ion Beam Deposition (IBD) 67 3.2.5.3 Evaporation 67 3.2.5.4 Chemical Vapor Deposition (CVD) 67 3.2.5.5 Photolithography 69 3.2.5.6 Etching 69 3.3 The Noise of TMR Sensors 70 3.3.1 The Source of Noise from TMR Sensors 70 3.3.2 Methods to Suppress the Noise 72 3.3.2.1 Increase the Number of MTJs in TMR Device 72 3.3.2.2 Optimize Free Layer Volume 73 3.3.2.3 Flux Concentrator 73 3.3.2.4 Applying a Bias Magnetic Field 74 3.4 TMR Sensors and Applications 75 3.4.1 TMR Read Heads 75 3.4.2 The TMR Angle Sensors 76 3.4.3 Geomagnetic Measurement 79 3.4.4 Spin-MEMS Combined Application 80 3.4.5 Nondestructive Testing (NDT) 82 3.4.6 Ultra-low Magnetic Field Detection: Biosensor 83 3.5 Conclusion 85 References 86 4 Spin-Transfer Torque Materials and Devices for Magnetic Random-Access Memory (STT-MRAM) 93Yan Cui and Jun Luo 4.1 The Background and Mechanism of STT-MRAM 93 4.1.1 The Background of STT-MRAM 93 4.1.2 The Mechanism of STT-MRAM 93 4.1.2.1 LLGS Equation 93 4.1.2.2 The Write Mechanism of STT-MRAM 94 4.1.2.3 The Magnetism of STT-MTJ 97 4.1.2.4 The Switching Properties of STT-MTJ 99 4.2 The Integrated Process of STT-MRAM 102 4.2.1 CMP Technology 102 4.2.2 Magnetic Film Deposition Technology 103 4.2.3 Photolithography Technology 103 4.2.4 Etching Technology 103 4.2.5 Dielectric Isolation Technology 104 4.2.6 Contact Technology 104 4.2.7 Passivation Deposition 104 4.3 Testing of the STT-MTJ Device 105 4.4 The Development Status of STT-MRAM 105 References 107 5 Spin-Orbit Torque (SOT) Materials and Devices 113Yucai Li, Kevin William Edmonds, and Kaiyou Wang 5.1 Spin-Orbit Coupling in Materials 113 5.2 Manipulation of Magnetic Materials by SOT 116 5.2.1 The Mechanism of SOT in Ferromagnets 116 5.2.2 Measurement Techniques of SOT 117 5.2.3 Field-Free SOT Magnetization Switching in Ferromagnets 119 5.2.4 Domain Wall and Skyrmion Motion Driven by SOT 121 5.2.5 Manipulation of Antiferromagnets by SOT 122 5.3 SOT Materials 123 5.3.1 Traditional Materials 123 5.3.2 Interfacial Engineering 124 5.3.3 Oxide Heterostructures 125 5.3.4 The van der Waals Materials and Topological Materials 125 5.4 Devices and Application 128 5.4.1 SOT-MTJ and SOT-MRAM 128 5.4.2 In-memory Computing 129 5.4.3 SOT Artificial Intelligence Device 130 5.4.4 Internet of Things 131 5.5 Conclusion 131 References 132 6 Spin Oscillators 139Huayao Tu and Zhongming Zeng 6.1 Introduction 139 6.2 Fundamental Physics 140 6.2.1 Spin Transfer Torque and Magnetization Dynamics 140 6.2.2 Spin Hall Effect (SHE) and Spin-Orbit Torque (SOT) 141 6.2.3 Operation Principle of SO 142 6.3 Device Classification 143 6.3.1 Geometries 143 6.3.2 Magnetic Equilibrium States 145 6.3.3 Material Structures 145 6.3.3.1 Spin Valves 145 6.3.3.2 Magnetic Tunnel Junctions 146 6.3.3.3 Bilayer 146 6.3.3.4 Single Layer 147 6.4 Emerging Spin-torque Oscillators Based on Magnetic Solitons 148 6.4.1 Vortex 148 6.4.2 Skyrmion 149 6.5 Functional Properties 150 6.5.1 Frequency 150 6.5.1.1 Modulation Properties 152 6.5.2 Output Power 152 6.5.3 Linewidth 155 6.5.4 Phase-locking and Synchronization 157 6.6 Applications 159 6.6.1 Microwave Source 159 6.6.2 Spin Wave Emitter 160 6.6.3 Microwave Detector and Energy Harvester 160 6.6.4 Magnetic Field Detector 163 6.6.5 Neuromorphic Computing 164 6.7 Summary and Outlook 166 References 167 7 Magnetic Tunnel Junctions for Artificial Neural Network 179Meiyin Yang, Tengzhi Yang, and Jun Luo 7.1 Introduction of Neural Computing 179 7.2 Hardware Requirements for an Artificial Intelligence Neural Network 182 7.3 Introduction to Magnetic Tunnel Junction Devices 183 7.4 Magnetic Tunnel Junction for Neuron Hardware 185 7.4.1 Introduction of STT-MTJ and SOT-MTJ 185 7.4.2 Different MTJ-Based Neuron Hardware 186 7.4.2.1 Step Function 187 7.4.2.2 Nonlinear Activation Function 188 7.4.2.3 Spike or Probability Based Neuron 189 7.5 Magnetic Tunnel Junctions for Synaptic Devices 192 7.6 Learning Methods Suitable for MTJs 194 7.7 Summary and Outlook 195 References 195 8 Three-Dimensional Magnetic Structures of B20 Chiral Magnets 203Kejing Ran, Dongsheng Song, Weiwei Wang, Haifeng Du, and Shilei Zhang 8.1 Theoretical Development 203 8.2 Observation Technique 206 8.2.1 Electron Holography 206 8.2.1.1 Historical Survey 206 8.2.1.2 Experimental Setup 207 8.2.2 Resonant Elastic X-ray Scattering 209 8.2.2.1 Historical Survey 209 8.2.2.2 Theoretical Treatment 210 8.2.2.3 Experimental Setup 212 8.3 Experimental Results 214 8.3.1 Magnetic Bobbers 214 8.3.2 Surface Twists 216 References 217 9 Multiferroelectric Materials 221Xiaobin Guo and Li Xi 9.1 Electric Field-driven Magnetization Switching 222 9.2 Electric Field-driven Exchange Bias Reversal and Antiferromagnetic Domain Wall Motion 229 9.3 Electric Field-driven Antiferromagnetic Vector Switching 237 Acknowledgements 239 References 240 10 Robust Manipulation of Magnetic Properties in (Ga,Mn)As 243Hailong Wang and Jianhua Zhao 10.1 Background and Introduction 243 10.2 Electric Field Effects on the Magnetic Properties of (Ga,Mn)As 245 10.3 Manipulation of the Magnetism in (Ga,Mn)As by Light and Strain 256 10.4 Giant Modulation of Magnetism via Organic Molecules 257 10.5 Conclusion and Outlook 260 Acknowledgements 262 References 262 11 Antiferromagnetic Materials and Their Manipulations 271Xionghua Liu and Kaiyou Wang 11.1 Introduction 271 11.2 Antiferromagnetic Materials 272 11.2.1 Metallic Antiferromagnets 272 11.2.2 Insulating Antiferromagnets 273 11.2.3 Semiconducting and Semimetallic Antiferromagnets 274 11.3 Manipulations of Antiferromagnetic States 275 11.3.1 Magnetic Control of Antiferromagnets 275 11.3.2 Strain Control of Antiferromagnets 277 11.3.3 Optical Control of Antiferromagnets 279 11.3.4 Electrical Control of Antiferromagnets 281 11.4 Topological Antiferromagnetic Spintronics 283 11.5 Summaries and Prospects 286 References 286 12 Prospects 295Meiyin Yang and Kaiyou Wang Index 299

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Book SynopsisTable of ContentsPreface xi Acknowledgments xix About the Authors xxi 1 Introduction: Multi-agent Coordination by Reinforcement Learning and Evolutionary Algorithms 1 1.1 Introduction 2 1.2 Single Agent Planning 4 1.2.1 Terminologies Used in Single Agent Planning 4 1.2.2 Single Agent Search-Based Planning Algorithms 10 1.2.2.1 Dijkstra’s Algorithm 10 1.2.2.2 A∗ (A-star) Algorithm 11 1.2.2.3 D∗ (D-star) Algorithm 15 1.2.2.4 Planning by STRIPS-Like Language 15 1.2.3 Single Agent RL 17 1.2.3.1 Multiarmed Bandit Problem 17 1.2.3.2 DP and Bellman Equation 20 1.2.3.3 Correlation Between RL and DP 21 1.2.3.4 Single Agent Q-Learning 21 1.2.3.5 Single Agent Planning Using Q-Learning 24 1.3 Multi-agent Planning and Coordination 25 1.3.1 Terminologies Related to Multi-agent Coordination 25 1.3.2 Classification of MAS 26 1.3.3 Game Theory for Multi-agent Coordination 28 1.3.3.1 Nash Equilibrium 31 1.3.3.2 Correlated Equilibrium 36 1.3.3.3 Static Game Examples 38 1.3.4 Correlation Among RL, DP, and GT 40 1.3.5 Classification of MARL 40 1.3.5.1 Cooperative MARL 42 1.3.5.2 Competitive MARL 56 1.3.5.3 Mixed MARL 59 1.3.6 Coordination and Planning by MAQL 84 1.3.7 Performance Analysis of MAQL and MAQL-Based Coordination 85 1.4 Coordination by Optimization Algorithm 87 1.4.1 PSO Algorithm 88 1.4.2 Firefly Algorithm 91 1.4.2.1 Initialization 92 1.4.2.2 Attraction to Brighter Fireflies 92 1.4.2.3 Movement of Fireflies 93 1.4.3 Imperialist Competitive Algorithm 93 1.4.3.1 Initialization 94 1.4.3.2 Selection of Imperialists and Colonies 95 1.4.3.3 Formation of Empires 95 1.4.3.4 Assimilation of Colonies 96 1.4.3.5 Revolution 96 1.4.3.6 Imperialistic Competition 97 1.4.4 Differential Evolution Algorithm 98 1.4.4.1 Initialization 99 1.4.4.2 Mutation 99 1.4.4.3 Recombination 99 1.4.4.4 Selection 99 1.4.5 Off-line Optimization 99 1.4.6 Performance Analysis of Optimization Algorithms 99 1.4.6.1 Friedman Test 100 1.4.6.2 Iman–Davenport Test 100 1.5 Summary 101 References 101 2 Improve Convergence Speed of Multi-Agent Q-Learning for Cooperative Task Planning 111 2.1 Introduction 112 2.2 Literature Review 116 2.3 Preliminaries 118 2.3.1 Single Agent Q-learning 119 2.3.2 Multi-agent Q-learning 119 2.4 Proposed MAQL 123 2.4.1 Two Useful Properties 124 2.5 Proposed FCMQL Algorithms and Their Convergence Analysis 128 2.5.1 Proposed FCMQL Algorithms 129 2.5.2 Convergence Analysis of the Proposed FCMQL Algorithms 130 2.6 FCMQL-Based Cooperative Multi-agent Planning 131 2.7 Experiments and Results 134 2.8 Conclusions 141 2.9 Summary 143 2.A More Details on Experimental Results 144 2.A.1 Additional Details of Experiment 2.1 144 2.A.2 Additional Details of Experiment 2.2 159 2.A.3 Additional Details of Experiment 2.4 161 References 162 3 Consensus Q-Learning for Multi-agent Cooperative Planning 167 3.1 Introduction 167 3.2 Preliminaries 169 3.2.1 Single Agent Q-Learning 169 3.2.2 Equilibrium-Based Multi-agent Q-Learning 170 3.3 Consensus 171 3.4 Proposed CoQL and Planning 173 3.4.1 Consensus Q-Learning 173 3.4.2 Consensus-Based Multi-robot Planning 175 3.5 Experiments and Results 176 3.5.1 Experimental Setup 176 3.5.2 Experiments for CoQL 177 3.5.3 Experiments for Consensus-Based Planning 177 3.6 Conclusions 179 3.7 Summary 180 References 180 4 An Efficient Computing of Correlated Equilibrium for Cooperative Q-Learning-Based Multi-Robot Planning 183 4.1 Introduction 183 4.2 Single-Agent Q-Learning and Equilibrium-Based MAQL 186 4.2.1 Single Agent Q-Learning 187 4.2.2 Equilibrium-Based MAQL 187 4.3 Proposed Cooperative MAQL and Planning 188 4.3.1 Proposed Schemes with Their Applicability 189 4.3.2 Immediate Rewards in Scheme-I and -II 190 4.3.3 Scheme-I-Induced MAQL 190 4.3.4 Scheme-II-Induced MAQL 193 4.3.5 Algorithms for Scheme-I and II 200 4.3.6 Constraint ΩQL-I/ΩQL-II(CΩQL-I/CΩQL-II) 201 4.3.7 Convergence 201 4.3.8 Multi-agent Planning 207 4.4 Complexity Analysis 209 4.4.1 Complexity of CQL 210 4.4.1.1 Space Complexity 210 4.4.1.2 Time Complexity 210 4.4.2 Complexity of the Proposed Algorithms 210 4.4.2.1 Space Complexity 211 4.4.2.2 Time Complexity 211 4.4.3 Complexity Comparison 213 4.4.3.1 Space Complexity 213 4.4.3.2 Time Complexity 214 4.5 Simulation and Experimental Results 215 4.5.1 Experimental Platform 215 4.5.1.1 Simulation 215 4.5.1.2 Hardware 216 4.5.2 Experimental Approach 217 4.5.2.1 Learning Phase 217 4.5.2.2 Planning Phase 217 4.5.3 Experimental Results 218 4.6 Conclusion 226 4.7 Summary 226 4.A Supporting Algorithm and Mathematical Analysis 227 References 228 5 A Modified Imperialist Competitive Algorithm for Multi-Robot Stick-Carrying Application 233 5.1 Introduction 234 5.2 Problem Formulation for Multi-Robot Stick-Carrying 239 5.3 Proposed Hybrid Algorithm 242 5.3.1 An Overview of ICA 242 5.3.1.1 Initialization 242 5.3.1.2 Selection of Imperialists and Colonies 243 5.3.1.3 Formation of Empires 243 5.3.1.4 Assimilation of Colonies 244 5.3.1.5 Revolution 244 5.3.1.6 Imperialistic Competition 245 5.4 An Overview of FA 247 5.4.1 Initialization 247 5.4.2 Attraction to Brighter Fireflies 247 5.4.3 Movement of Fireflies 248 5.5 Proposed ICFA 248 5.5.1 Assimilation of Colonies 251 5.5.1.1 Attraction to Powerful Colonies 251 5.5.1.2 Modification of Empire Behavior 251 5.5.1.3 Union of Empires 252 5.6 Simulation Results 254 5.6.1 Comparative Framework 254 5.6.2 Parameter Settings 254 5.6.3 Analysis on Explorative Power of ICFA 254 5.6.4 Comparison of Quality of the Final Solution 255 5.6.5 Performance Analysis 258 5.7 Computer Simulation and Experiment 265 5.7.1 Average Total Path Deviation (ATPD) 265 5.7.2 Average Uncovered Target Distance (AUTD) 265 5.7.3 Experimental Setup in Simulation Environment 265 5.7.4 Experimental Results in Simulation Environment 266 5.7.5 Experimental Setup with Khepera Robots 268 5.7.6 Experimental Results with Khepera Robots 269 5.8 Conclusion 270 5.9 Summary 272 5.A Additional Comparison of ICFA 272 References 275 6 Conclusions and Future Directions 281 6.1 Conclusions 281 6.2 Future Directions 283 Index 285

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Book SynopsisTable of ContentsAbout the Author xi Preface xiii Acknowledgments xv 1 Introduction 1 1.1 Scope 1 1.2 Background 2 2 Pole Structures 7 2.1 General 7 2.2 Wood Pole Strength 8 2.3 Loads 13 2.4 Embedment Depth 15 2.5 Guying 17 2.6 Column Buckling 19 2.7 Grounding and Bonding 22 3 Pole Installation and Maintenance 23 3.1 Pole Placement 23 3.2 Guys and Anchors 24 3.3 Pole Maintenance 26 4 Wires, Conductors, and Cables 31 4.1 Categories 31 4.2 Messenger Wire/Strand 31 4.3 Electric Supply (Power) Cables 33 4.4 Communications Cables 35 4.5 Wireless Attachments 38 5 Cable Installation 39 5.1 Conductor and Cable Placement 39 5.2 Lashing Operation 40 5.3 Overlashing 41 6 NESC® Requirements (Strength and Loading) 45 6.1 National Electrical Safety Code (NESC) 45 6.2 Loading Requirements 46 6.3 Strength Requirements 49 6.4 Wire Tensions 52 6.5 Guyed Poles 53 6.6 Extreme Wind Loads (“60 ft Limit”) 54 6.7 Allowable Deterioration 56 6.8 Overlashed Cables 57 6.9 Software Tools and Pole Loading Analysis 60 7 NESC® Requirements (Clearances) 63 7.1 Clearances 63 7.2 Clearance Zones 63 7.3 Clearances Above Surfaces and Buildings 66 7.4 Clearances Between Wires 67 7.5 Overlashed Cables 67 8 Principles of Wire Sag 71 8.1 Catenary 71 8.2 Initial and Final Sag 72 8.3 Sag–Tension Relationship 72 8.4 Determining Change in Sag (and Tension) 73 8.5 Ruling Span 76 8.6 Point Load 77 9 General Order 95 (California) 81 9.1 General Order 95 (GO 95) 81 9.2 Loading Requirements 81 9.3 Strength Requirements 83 9.4 Clearances 84 10 Examples 85 10.1 Purpose 85 10.2 Tangent Line 85 10.3 Line Angle 88 10.4 Line Angle – Buckling Consideration 90 10.5 Additional Attachment 94 10.6 Summary 96 Appendix A Properties of Messenger Strands 99 Appendix B Wireless Attachments 101 Appendix C Extreme Wind and Extreme Ice Loadings 103 Appendix D Solution of Cubic Equation 107 Appendix E Point Load 109 E.1 Parabolic Model 109 E.2 Intersecting Straight Lines Model 111 Glossary 115 References 121 Index 123

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Book SynopsisExplore an insightful summary of the major self-containedaiding technologies for pedestrian navigation from established and emerging leaders in the field Pedestrian Inertial Navigation with Self-Contained Aidingdeliversa comprehensive and broad treatment ofself-contained aiding techniques in pedestrian inertial navigation. The book combines an introduction to the general concept of navigationand major navigation and aiding techniques with more specific discussions of topics central to the field, as well as an exploration of the future of the future of the field: Ultimate Navigation Chip (uNavChip) technology. The most commonly used implementation of pedestrian inertial navigation, strapdown inertial navigation, is discussed at length, as are the mechanization, implementation, error analysis, and adaptivity of zero-velocity update aided inertial navigation algorithms.The book demonstrates the implementation of ultrasonic sensors, ultra-wide band (UWB) sensTable of ContentsAuthor Biographies xi List of Figures xiii List of Tables xix 1 Introduction 1 1.1 Navigation 1 1.2 Inertial Navigation 2 1.3 Pedestrian Inertial Navigation 5 1.3.1 Approaches 6 1.3.2 IMU Mounting Positions 7 1.3.3 Summary 8 1.4 Aiding Techniques for Inertial Navigation 9 1.4.1 Non-self-contained Aiding Techniques 9 1.4.1.1 Aiding Techniques Based on Natural Signals 9 1.4.1.2 Aiding Techniques Based on Artificial Signals 10 1.4.2 Self-contained Aiding Techniques 11 1.5 Outline of the Book 13 References 13 2 Inertial Sensors and Inertial Measurement Units 17 2.1 Accelerometers 17 2.1.1 Static Accelerometers 17 2.1.2 Resonant Accelerometers 19 2.2 Gyroscopes 21 2.2.1 Mechanical Gyroscopes 21 2.2.2 Optical Gyroscopes 22 2.2.2.1 Ring Laser Gyroscopes 22 2.2.2.2 Fiber Optic Gyroscopes 23 2.2.3 Nuclear Magnetic Resonance Gyroscopes 24 2.2.4 MEMS Vibratory Gyroscopes 24 2.2.4.1 Principle of Operation 25 2.2.4.2 Mode of Operation 25 2.2.4.3 Error Analysis 27 2.3 Inertial Measurement Units 28 2.3.1 Multi-sensor Assembly Approach 28 2.3.2 Single-Chip Approach 29 2.3.3 Device Folding Approach 30 2.3.4 Chip-Stacking Approach 31 2.4 Conclusions 32 References 32 3 Strapdown Inertial Navigation Mechanism 37 3.1 Reference Frame 37 3.2 Navigation Mechanism in the Inertial Frame 38 3.3 Navigation Mechanism in the Navigation Frame 40 3.4 Initialization 41 3.4.1 Tilt Sensing 42 3.4.2 Gyrocompassing 43 3.4.3 Magnetic Heading Estimation 44 3.5 Conclusions 45 References 45 4 Navigation Error Analysis in Strapdown Inertial Navigation 47 4.1 Error Source Analysis 47 4.1.1 Inertial Sensor Errors 48 4.1.2 Assembly Errors 51 4.1.3 Definition of IMU Grades 53 4.1.3.1 Consumer Grade 54 4.1.3.2 Industrial Grade 54 4.1.3.3 Tactical Grade 55 4.1.3.4 Navigation Grade 55 4.2 IMU Error Reduction 55 4.2.1 Six-Position Calibration 55 4.2.2 Multi-position Calibration 57 4.3 Error Accumulation Analysis 57 4.3.1 Error Propagation in Two-Dimensional Navigation 58 4.3.2 Error Propagation in Navigation Frame 61 4.4 Conclusions 62 References 63 5 Zero-Velocity Update Aided Pedestrian Inertial Navigation 65 5.1 Zero-Velocity Update Overview 65 5.2 Zero-Velocity Update Algorithm 68 5.2.1 Extended Kalman Filter 68 5.2.2 EKF in Pedestrian Inertial Navigation 70 5.2.3 Zero-Velocity Update Implementation 70 5.3 Parameter Selection 73 5.4 Conclusions 76 References 76 6 Navigation Error Analysis in the ZUPT-Aided Pedestrian Inertial Navigation 79 6.1 Human Gait Biomechanical Model 79 6.1.1 Foot Motion in Torso Frame 80 6.1.2 Foot Motion in Navigation Frame 80 6.1.3 Parameterization of Trajectory 81 6.2 Navigation Error Analysis 83 6.2.1 Starting Point 83 6.2.2 Covariance Increase During Swing Phase 84 6.2.3 Covariance Decrease During the Stance Phase 87 6.2.4 Covariance Level Estimation 88 6.2.5 Observations 92 6.3 Verification of Analysis 93 6.3.1 Numerical Verification 93 6.3.1.1 Effect of ARW 93 6.3.1.2 Effect of VRW 95 6.3.1.3 Effect of RRW 95 6.3.2 Experimental Verification 96 6.4 Limitations of the ZUPT Aiding Technique 99 6.5 Conclusions 100 References 101 7 Navigation Error Reduction in the ZUPT-Aided Pedestrian Inertial Navigation 103 7.1 IMU-Mounting Position Selection 104 7.1.1 Data Collection 105 7.1.2 Data Averaging 105 7.1.3 Data Processing Summary 107 7.1.4 Experimental Verification 109 7.2 Residual Velocity Calibration 110 7.3 Gyroscope G-Sensitivity Calibration 115 7.4 Navigation Error Compensation Results 117 7.5 Conclusions 119 References 119 8 Adaptive ZUPT-Aided Pedestrian Inertial Navigation 121 8.1 Floor Type Detection 121 8.1.1 Algorithm Overview 122 8.1.2 Algorithm Implementation 123 8.1.2.1 Data Partition 123 8.1.2.2 Principal Component Analysis 124 8.1.2.3 Artificial Neural Network 125 8.1.2.4 Multiple Model EKF 127 8.1.3 Navigation Result 129 8.1.4 Summary 130 8.2 Adaptive Stance Phase Detection 130 8.2.1 Zero-Velocity Detector 131 8.2.2 Adaptive Threshold Determination 131 8.2.3 Experimental Verification 135 8.2.4 Summary 136 8.3 Conclusions 138 References 139 9 Sensor Fusion Approaches 141 9.1 Magnetometry 141 9.2 Altimetry 142 9.3 Computer Vision 143 9.4 Multiple-IMU Approach 145 9.5 Ranging Techniques 146 9.5.1 Introduction to Ranging Techniques 147 9.5.1.1 Time of Arrival 147 9.5.1.2 Received Signal Strength 147 9.5.1.3 Angle of Arrival 148 9.5.2 Ultrasonic Ranging 149 9.5.2.1 Foot-to-Foot Ranging 150 9.5.2.2 Directional Ranging 150 9.5.3 Ultrawide Band Ranging 153 9.6 Conclusions 154 References 154 10 Perspective on Pedestrian Inertial Navigation Systems 159 10.1 Hardware Development 159 10.2 Software Development 161 10.3 Conclusions 161 References 162 Index 163

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Book Synopsis

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Book SynopsisThis new edition of the definitive arc flash reference guide, fully updated to align with the IEEE''s updated hazard calculations An arc flash, an electrical breakdown of the resistance of air resulting in an electric arc, can cause substantial damage, fire, injury, or loss of life. Professionals involved in the design, operation, or maintenance of electric power systems require thorough and up-to-date knowledge of arc flash safety and prevention methods. Arc Flash Hazard Analysis and Mitigation is the most comprehensive reference guide available on all aspects of arc flash hazard calculations, protective current technologies, and worker safety in electrical environments. Detailed chapters cover protective relaying, unit protection systems, arc-resistant equipment, arc flash analyses in DC systems, and many more critical topics. Now in its second edition, this industry-standard resource contains fully revised material throughout, including a new chapter oTable of ContentsForeword xix Preface to Second Edition xxi Preface to First Edition xxiii Acknowledgement xxv About the Author xxvii 1 Arc Flash Hazards and Their Analyses 1 1.1 Electrical Arcs 2 1.1.1 Arc as a Heat Source 3 1.1.2 Arcing Phenomena in a Cubicle 3 1.2 Arc Flash Hazard and Personal Safety 4 1.3 Time Motion Studies 5 1.4 Arc Flash Hazards 5 1.5 Arc Blast 6 1.6 Electrical Shock Hazard 9 1.6.1 Resistance of Human Body 11 1.7 Fire Hazard 13 1.8 Arc Flash Hazard Analysis 15 1.8.1 Ralph Lee’s and NFPA Equations 17 1.8.2 IEEE 1584 Guide Equations 17 1.9 Personal Protective Equipment 21 1.10 Hazard Boundaries 23 1.10.1 Working Distance 24 1.10.2 Arc Flash Labels 24 1.11 Maximum Duration of an Arc Flash Event and Arc Flash Boundary 25 1.11.1 Arc Flash Hazard with Equipment Doors Closed 25 1.12 Reasons for Internal Arcing Faults 27 1.13 Arc Flash Hazard Calculation Steps 28 1.13.1 NFPA Table 130.7(C)(15)(a) 29 1.14 Examples of Calculations 30 1.15 Reducing Arc Flash Hazard 33 1.15.1 Reduction 34 1.15.2 Arc Flash Labels 37 Review Questions 38 References 38 2 Safety and Prevention Through Design: A New Frontier 41 2.1 Electrical Standards and Codes 42 2.2 Prevention through Design 44 2.3 Limitations of Existing Codes, Regulations, and Standards 45 2.4 Electrical Hazards 46 2.5 Changing the Safety Culture 49 2.6 Risk Analysis for Critical Operation Power Systems 49 2.6.1 Existing Systems 50 2.6.2 New Facilities 50 2.7 Reliability Analysis 51 2.7.1 Data for Reliability Evaluations 52 2.7.2 Methods of Evaluation 53 2.7.3 Reliability and Safety 53 2.8 Maintenance and Operation 54 2.8.1 Maintenance Strategies 55 2.8.2 Reliability-Centered Maintenance (RCM) 56 2.9 Safety Integrity Level and Safety Instrumented System 56 2.10 Electrical Safety in the Workplaces 58 2.10.1 Risk Assessment 58 2.10.2 Responsibility 58 2.10.3 Risk Parameters 58 2.11 Risk Reduction 61 2.12 Risk Evaluation 62 2.13 Risk Reduction Verification 63 2.14 Risk Control 63 Review Questions 64 References 64 3 Calculations According To IEEE Guide 1584, 2018 68 3.1 Model for Incident Energy Calculations 68 3.2 Electrode Configuration 69 3.3 Impact of System Grounding 69 3.4 Intermediate Average Arcing Current 70 3.5 Arcing Current Variation Factor 71 3.6 Calculation of Intermediate Incident Energy 73 3.7 Intermediate Arc Flash Boundary (AFB) 75 3.8 Enclosure Size Correction Factor 77 3.8.1 Shallow and Typical Enclosures 77 3.9 Determine Equivalent Height and Width 77 3.10 Determine Enclosure Size Correction Factor 77 3.11 Determination of Iarc, E, and AFB (600 V < Voc ≤ 15,000 V) 78 3.11.1 Arcing Current 78 3.11.2 Incident Energy (E) 78 3.11.3 Arc Flash Boundary (AFB) 79 3.12 Determination of Iarc, E, and AFB (Voc ≤ 600 V) 80 3.12.1 Arcing Current 80 3.12.2 Incident Energy 80 3.12.3 Arc Flash Boundary (AFB) 80 3.13 A Flow Chart for the Calculations 80 3.14 Examples of Calculations 81 References 82 4 Arc Flash Hazard and System Grounding 84 4.1 System and Equipment Grounding 84 4.1.1 Solidly Grounded Systems 85 4.2 Low Resistance Grounding 89 4.3 High Resistance Grounded Systems 89 4.3.1 Fault Detection, Alarms, and Isolation 92 4.4 Ungrounded Systems 96 4.5 Reactance Grounding 97 4.6 Resonant Grounding 97 4.7 Corner of Delta-Grounded Systems 97 4.8 Surge Arresters 98 4.9 Artificially Derived Neutrals 99 4.10 Multiple Grounded Systems 102 4.10.1 Comparison of Grounding Systems 102 4.11 Arc Flash Hazard in Solidly Grounded Systems 102 4.12 Protection and Coordination in Solidly Grounded Systems 107 4.12.1 Self-Extinguishing Ground Faults 110 4.12.2 Improving Coordination in Solidly Grounded Low Voltage Systems 113 4.13 Ground Fault Coordination in Low Resistance Grounded Medium Voltage Systems 116 4.13.1 Remote Tripping 119 4.13.2 Ground Fault Protection of Industrial Bus-Connected Generators 119 4.13.3 Directional Ground Fault Relays 124 4.14 Monitoring of Grounding Resistors 125 4.15 Selection of Grounding Systems 126 Review Questions 127 References 128 5 Short-Circuit Calculations According To ANSI/IEEE Standards For Arc Flash Analysis 130 5.1 Types of Calculations 131 5.1.1 Assumptions: Short-Circuit Calculations 131 5.1.2 Short-Circuit Currents for Arc Flash Calculations 132 5.2 Rating Structure of HV Circuit Breakers 132 5.3 Low-Voltage Motors 135 5.4 Rotating Machine Model 136 5.5 Calculation Methods 136 5.5.1 Simplified Method X/R ≤ 17 136 5.5.2 Simplified Method X/R > 17 137 5.5.3 E/Z Method for AC and DC Decrement Adjustments 137 5.6 Network Reduction 140 5.7 Calculation Procedure 140 5.7.1 Analytical Calculation Procedure 141 5.8 Capacitor and Static Converter Contributions to Short-Circuit Currents 143 5.9 Typical Computer-Based Calculation Results 143 5.9.1 First-Cycle or Momentary Duty Calculations 143 5.9.2 Interrupting Duty Calculations 146 5.9.3 Low Voltage Circuit Breaker Duty Calculations 146 5.10 Examples of Calculations 146 5.10.1 Calculation of Short-Circuit Duties 152 5.10.2 K-Rated 15 kV Circuit Breakers 152 5.10.3 4.16-kV Circuit Breakers and Motor Starters 157 5.10.4 Transformer Primary Switches and Fused Switches 157 5.10.5 Low Voltage Circuit Breakers 161 5.11 Thirty-Cycle Short-Circuit Currents 161 5.12 Unsymmetrical Short-Circuit Currents 162 5.12.1 Single Line-to-Ground Fault 163 5.12.2 Double Line-to-Ground Fault 165 5.12.3 Line-to-Line Fault 168 5.13 Computer Methods 171 5.13.1 Line-to-Ground Fault 172 5.13.2 Line-to-Line Fault 173 5.13.3 Double Line-to-Ground Fault 173 5.14 Short-Circuit Currents for Arc Flash Calculations 175 Review Questions 176 References 176 6 Accounting For Decaying Short-Circuit Currents In Arc Flash Calculations 178 6.1 Short Circuit of a Passive Element 178 6.2 Systems with No AC Decay 181 6.3 Reactances of a Synchronous Machine 182 6.3.1 Leakage Reactance 182 6.3.2 Subtransient Reactance 183 6.3.3 Transient Reactance 183 6.3.4 Synchronous Reactance 183 6.3.5 Quadrature-Axis Reactances 183 6.3.6 Negative Sequence Reactance 184 6.3.7 Zero Sequence Reactance 184 6.4 Saturation of Reactances 184 6.5 Time Constants of Synchronous Machines 184 6.5.1 Open-Circuit Time Constant 184 6.5.2 Subtransient Short-Circuit Time Constant 184 6.5.3 Transient Short-Circuit Time Constant 185 6.5.4 Armature Time Constant 185 6.6 Synchronous Machine Behavior on Terminal Short Circuit 185 6.6.1 Equivalent Circuits during Fault 186 6.6.2 Fault Decrement Curve 190 6.7 Short Circuit of Synchronous Motors and Condensers 194 6.8 Short Circuit of Induction Motors 194 6.9 A New Algorithm for Arc Flash Calculations with Decaying Short-Circuit Currents 197 6.9.1 Available Computer-Based Calculations 198 6.9.2 Accumulation of Energy from Multiple Sources 198 6.9.3 Comparative Calculations 200 6.10 Crowbar Methods 203 Review Questions 204 References 205 7 Protective Relaying 206 7.1 Protection and Coordination from Arc Flash Considerations 206 7.2 Classification of Relay Types 210 7.3 Design Criteria of Protective Systems 210 7.3.1 Selectivity 211 7.3.2 Speed 211 7.3.3 Reliability 211 7.3.4 Backup Protection 212 7.4 Overcurrent Protection 212 7.4.1 Overcurrent Relays 213 7.4.2 Multifunction Overcurrent Relays 215 7.4.3 IEC Curves 217 7.5 Low Voltage Circuit Breakers 219 7.5.1 Molded Case Circuit Breakers (MCCBs) 219 7.5.2 Current-Limiting MCCBs 225 7.5.3 Insulated Case Circuit Breakers (ICCBs) 227 7.5.4 Low Voltage Power Circuit Breakers (LVPCBs) 228 7.5.5 Short-Time Bands of LVPCBs Trip Programmers 230 7.6 Short-Circuit Ratings of Low Voltage Circuit Breakers 231 7.6.1 Single-Pole Interrupting Capability 235 7.6.2 Short-Time Ratings 235 7.7 Series-Connected Ratings 236 7.8 Fuses 237 7.8.1 Current-Limiting Fuses 238 7.8.2 Low Voltage Fuses 240 7.8.3 High Voltage Fuses 240 7.8.4 Electronic Fuses 241 7.8.5 Interrupting Ratings 242 7.9 Application of Fuses for Arc Flash Reduction 243 7.9.1 Low Voltage Motor Starters 243 7.9.2 Medium Voltage Motor Starters 243 7.9.3 Low Voltage Switchgear 244 7.10 Conductor Protection 247 7.10.1 Load Current Carrying Capabilities of Conductors 248 7.10.2 Conductor Terminations 249 7.10.3 Considerations of Voltage Drops 249 7.10.4 Short-Circuit Considerations 249 7.10.5 Overcurrent Protection of Conductors 251 7.11 Motor Protection 252 7.11.1 Coordination with Motor Thermal Damage Curve 253 7.12 Generator 51-V Protection 261 7.12.1 Arc Flash Considerations 262 Review Questions 265 References 265 8 Unit Protection Systems 267 8.1 Overlapping the Zones of Protection 269 8.2 Importance of Differential Systems for Arc Flash Reduction 271 8.3 Bus Differential Schemes 272 8.3.1 Overcurrent Differential Protection 272 8.3.2 Partial Differential Schemes 273 8.3.3 Percent Differential Relays 273 8.4 High Impedance Differential Relays 274 8.4.1 Sensitivity for Internal Faults 277 8.4.2 High Impedance Microprocessor-Based Multifunction Relays 278 8.5 Low Impedance Current Differential Relays 278 8.5.1 CT Saturation 282 8.5.2 Comparison with High Impedance Relays 282 8.6 Electromechanical Transformer Differential Relays 283 8.6.1 Harmonic Restraint 285 8.7 Microprocessor-Based Transformer Differential Relays 286 8.7.1 CT Connections and Phase Angle Compensation 287 8.7.2 Dynamic CT Ratio Corrections 290 8.7.3 Security under Transformer Magnetizing Currents 293 8.8 Pilot Wire Protection 294 8.9 Modern Line Current Differential Protection 296 8.9.1 The Alpha Plane 297 8.9.2 Enhanced Current Differential Characteristics 299 8.10 Examples of Arc Flash Reduction with Differential Relays 300 Review Questions 303 References 303 9 Arc Fault Detection Relays 305 9.1 Principle of Operation 306 9.2 Light Intensity 306 9.3 Light Sensor Types 307 9.4 Other Hardware 312 9.5 Selective Tripping 313 9.6 Supervision with Current Elements 315 9.7 Applications 315 9.7.1 Medium Voltage Systems 315 9.7.2 Low Voltage Circuit Breakers 317 9.7.3 Self-Testing of Sensors 317 9.8 Examples of Calculation 317 9.9 Arc Vault™ Protection for Low Voltage Systems 317 9.9.1 Detection System 321 Review Questions 323 References 323 10 Overcurrent Coordination 325 10.1 Standards and Requirements 326 10.2 Data for the Coordination Study 326 10.3 Computer-Based Coordination 328 10.4 Initial Analysis 328 10.5 Coordinating Time Interval 329 10.5.1 Relay Overtravel 329 10.6 Fundamental Considerations for Coordination 329 10.6.1 Settings on Bends of Time–Current Coordination Curves 331 10.7 Coordination on Instantaneous Basis 331 10.7.1 Selectivity between Two Series-Connected Current-Limiting Fuses 333 10.7.2 Selectivity of a Current-Limiting Fuse Downstream of Noncurrent-Limiting Circuit Breaker 333 10.7.3 Selectivity of Current-Limiting Devices in Series 337 10.8 NEC Requirements of Selectivity 340 10.8.1 Fully Selective Systems 342 10.8.2 Selection of Equipment Ratings and Trip Devices 343 10.9 The Art of Compromise 346 Review Questions 356 References 357 11 Transformer Protection 358 11.1 NEC Requirements 358 11.2 Arc Flash Considerations 360 11.3 System Configurations of Transformer Connections 361 11.3.1 Auto-Transfer of Bus Loads 366 11.4 Through Fault Current Withstand Capability 366 11.4.1 Category I 367 11.4.2 Category II 367 11.4.3 Category III and IV 367 11.4.4 Observation on Faults during Life Expectancy of a Transformer 369 11.4.5 Dry-Type Transformers 370 11.5 Constructing the through Fault Curve Analytically 374 11.5.1 Protection with Respect to Through Fault Curves 374 11.6 Transformer Primary Fuse Protection 375 11.6.1 Variations in the Fuse Characteristics 375 11.6.2 Single Phasing and Ferroresonance 377 11.6.3 Other Considerations of Fuse Protection 377 11.7 Overcurrent Relays for Transformer Primary Protection 377 11.8 Listing Requirements 379 11.9 Effect of Transformer Winding Connections 383 11.10 Requirements of Ground Fault Protection 385 11.11 Through Fault Protection 385 11.11.1 Primary Fuse Protection 385 11.11.2 Primary Relay Protection 387 11.12 Overall Transformer Protection 387 11.13 A Practical Study for Arc Flash Reduction 388 11.13.1 System Configuration 388 11.13.2 Coordination Study and Observations 388 11.13.3 Arc Flash Calculations: High Hazard Risk Category (HRC) Levels 393 11.13.4 Reducing HRC Levels with Main Secondary Circuit Breakers 395 11.13.5 Maintenance Mode Switches on Low Voltage Trip Programmers 395 11.13.6 Addition of Secondary Relay 401 Review Questions 404 References 405 12 Current Transformers 406 12.1 Accuracy Classification of CTs 407 12.1.1 Metering Accuracies 407 12.1.2 Relaying Accuracies 407 12.1.3 Relaying Accuracy Classification X 408 12.1.4 Accuracy Classification T 409 12.2 Constructional Features of CTs 409 12.3 Secondary Terminal Voltage Rating 411 12.3.1 Saturation Voltage 412 12.3.2 Saturation Factor 412 12.4 CT Ratio and Phase Angle Errors 412 12.5 Interrelation of CT Ratio and C Class Accuracy 415 12.6 Polarity of Instrument Transformers 417 12.7 Application Considerations 418 12.7.1 Select CT Ratio 418 12.7.2 Make a Single-Line Diagram of the CT Connections 420 12.7.3 CT Burden 420 12.7.4 Short-Circuit Currents and Asymmetry 420 12.7.5 Calculate Steady-State Performance 420 12.7.6 Calculate Steady-State Errors 421 12.8 Series and Parallel Connections of CTs 425 12.9 Transient Performance of the CTs 425 12.9.1 CT Saturation Calculations 426 12.9.2 Effect of Remanence 427 12.10 Practicality of Application 428 12.11 CTs for Low Resistance-Grounded Medium Voltage Systems 430 12.12 Future Directions 430 Review Questions 433 References 433 13 Arc-Resistant Equipment 435 13.1 Calculations of Arc Flash Hazard in Arc-Resistant Equipment 436 13.1.1 Probability of Arcing Fault 436 13.2 Qualifications in IEEE Guide 437 13.3 Accessibility Types 438 13.3.1 Type 1 438 13.3.2 Type 2 438 13.3.3 Suffix B 438 13.3.4 Suffix C 438 13.3.5 Suffix D 439 13.4 IEC Accessibility Types 439 13.5 Arc-Resistant Ratings 440 13.5.1 Duration Ratings 440 13.5.2 Device-Limited Ratings 441 13.5.3 Effect of Cable Connections 444 13.6 Testing According to IEEE Guide 444 13.6.1 Criterion 1 444 13.6.2 Criterion 2 445 13.6.3 Criterion 3 445 13.6.4 Criterion 4 445 13.6.5 Criterion 5 445 13.6.6 Maintenance 446 13.7 Pressure Relief 446 13.8 Venting and Plenums 448 13.8.1 Venting into Surrounding Area 448 13.8.2 Plenums 450 13.9 Cable Entries 450 Review Questions 452 References 452 14 Recent Trends and Innovations 454 14.1 Statistical Data of Arc Flash Hazards 454 14.2 Zone-Selective Interlocking 456 14.2.1 Low Voltage ZSI Systems 456 14.2.2 Zone Interlocking in Medium Voltage Systems 463 14.3 Microprocessor-Based Low Voltage Switchgear 466 14.3.1 Microprocessor-Based Switchgear Concept 466 14.3.2 Accounting for Motor Contributions 467 14.3.3 Faults on the Source Side 469 14.3.4 Arc Flash Hazard Reduction 470 14.4 Low Voltage Motor Control Centers 470 14.4.1 Desirable MCC Design Features 471 14.4.2 Recent Design Improvements 471 14.4.3 Higher Short-Circuit Withstand MCCs 478 14.5 Maintenance Mode Switch 478 14.6 Infrared Windows and Sight Glasses 480 14.7 Fault Current Limiters 483 14.8 Partial Discharge Measurements 487 14.8.1 Online versus Offline Measurements 488 14.8.2 Test Methods 489 14.8.3 Current Signature Analysis: Rotating Machines 491 14.8.4 Dissipation Factor Tip-Up 491 Review Questions 493 References 494 15 Arc Flash Hazard Calculations In Dc Systems 496 15.1 Calculations of the Short-Circuit Currents in DC Systems 497 15.2 Sources of DC Short-Circuit Currents 497 15.3 IEC Calculation Procedures 498 15.4 Short Circuit of a Lead Acid Battery 501 15.5 Short Circuit of DC Motors and Generators 505 15.6 Short-Circuit Current of a Rectifier 510 15.7 Short Circuit of a Charged Capacitor 515 15.8 Total Short-Circuit Current 516 15.9 DC Circuit Breakers and Fuses 517 15.9.1 DC Circuit Breakers 517 15.9.2 DC Rated Fuses 520 15.10 Arcing in DC Systems 520 15.11 Equations for Calculation of Incident Energy in DC Systems 525 15.12 Protection of the Semiconductor Devices 527 15.12.1 Controlled Converters 529 Review Questions 530 References 531 16 Application of Ethernet and IEC 61850 Communications 533 16.1 IEC 61850 Protocol 534 16.2 Modern IEDs 535 16.3 Substation Architecture 536 16.4 IEC 61850 Communication Structure 537 16.5 Logical Nodes 539 16.6 Ethernet Connection 539 16.7 Networking Media 543 16.7.1 Copper Twisted Shielded and Unshielded 543 16.7.2 Fiber Optic Cable 544 16.8 Network Topologies 545 16.8.1 Prioritizing GOOSE Messages 547 16.8.2 Technoeconomical Justifications 547 16.9 Application to Arc Flash Relaying and Communications 549 Review Questions 549 References 549 Appendix A Statistics and Probability Applied to Electrical Engineering 551 A.1 Mean Mode and Median 551 A.2 Mean and Standard Deviation 552 A.3 Skewness and Kurtosis 553 A.4 Normal or Gaussian Distribution 554 A.5 Curve Fitting: Least Square Line 556 References 559 Appendix B Tables for Quick Estimation of Incident Energy and PPE in Electrical Systems 560 Index 588

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Book SynopsisHigh-Density and De-Densified Smart Campus Communications Design, deliver, and implement high-density communications solutions High-density campus communications are critical in the operation of densely populated airports, stadiums, convention centers, shopping malls, classrooms, hospitals, dense smart cities, and more. They also drive Smart City and Smart Building use cases as High-Density Communications (HDC) become recognized as an essential fourth utility. However, the unique requirements and designs demanded by HDC make implementation challenging. In High-Density and De-Densified Smart Campus Communications: Technologies, Integration, Implementation and Applications, a team of experienced technology strategists delivers a one-of-a-kind treatment of the requirements, technologies, designs, solutions, and trends associated with HDC. From the functional requirements for HDC and emerging data/Wi-Fi 6/internet access/5G cellular/OTT video, and IoT automaTable of ContentsPreface xi About the Authors xiii Acknowledgments xv 1 Background and Functional Requirements for High‐Density Communications 1 1.1 Background 1 1.2 Requirements for High‐Density Communications 4 1.2.1 Pre‐pandemic/Long‐term Requirements for Airports 5 1.2.2 Pre‐pandemic/Long‐term Requirements for Stadiums 7 1.2.3 Pre‐pandemic/Long‐term Requirements for Convention Centers 7 1.2.4 Pre‐pandemic/Long‐term Requirements for Open Air Gatherings and Amusement Parks 10 1.2.5 Pre‐pandemic/Long‐term Requirements for Classrooms 11 1.2.6 Pre‐pandemic/Long‐term Requirements for Train and Subway Stations 12 1.2.7 Pre‐pandemic/Long‐term Requirements for Dense Office Environments 12 1.2.8 Ongoing Requirements for Dense Smart Warehouses and Distribution Centers 14 1.2.9 Pre‐pandemic/Long‐term Requirements for Dense Smart Cities 14 1.3 Pandemic‐Driven Social Distancing 16 1.3.1 Best Practices 16 1.3.2 Heuristic Density for the Pandemic Era 20 1.4 The Concept of a Wireless SuperNetwork 20 References 22 2 Traditional WLAN Technologies 26 2.1 Overview 26 2.2 WLAN Standards 28 2.3 WLAN Basic Concepts 29 2.3.1 PHY Layer Operation 32 2.3.2 MAC Layer Operation 36 2.4 Hardware Elements 40 2.5 KEY IEEE 802.11ac Mechanisms 42 2.5.1 Downlink Multi‐User MIMO (DL‐MU‐MIMO) 42 2.5.2 Beamforming 45 2.5.3 Dynamic Frequency Selection 45 2.5.4 Space–Time Block Coding 46 2.5.5 Product Waves 48 2.6 Brief Preview of IEEE 802.11ax 48 References 49 3 Traditional DAS Technologies 51 3.1 Overview 51 3.2 Frequency Bands of Cellular Operation 56 3.2.1 Traditional RF Spectrum 56 3.2.2 Citizens Broadband Radio Service (CBRS) 60 3.2.3 Freed‐up Satellite C‐Band 62 3.2.4 5G Bands 64 3.2.5 Motivations for Additional Spectrum 65 3.2.6 Private LTE/Private CBRS 66 3.2.7 5G Network Slicing 68 3.2.8 Supportive Technologies 68 3.3 Distributed Antenna Systems (DASs) 70 3.3.1 Technology Scope 70 3.3.2 More Detailed Exemplary Arrangement 76 3.3.3 Traffic‐aware DAS 81 3.3.4 BBU and DAS/RRU Connectivity 82 3.3.5 Ethernet/IP Transport Connectivity of DAS 84 References 84 4 Traditional Sensor Networks/IoT Services 87 4.1 Overview and Environment 87 4.2 Architectural Concepts 93 4.3 Wireless Technologies for the IoT 96 4.3.1 Pre‐5G Wireless Technologies for the IoT 100 4.3.2 NB‐IoT 104 4.3.3 Lte‐m 105 4.3.4 5G Technologies for the IoT 106 4.3.5 WAN‐Oriented IoT Connectivity Migration Strategies 108 4.4 Examples of Seven‐Layer IoT Protocol Stacks 109 4.4.1 UPnP 109 4.4.2 ZigBee 115 4.4.3 Bluetooth 116 4.5 Gateway‐Based IoT Operation 117 4.6 Edge Computing in the IoT Ecosystem 118 4.7 Session Establishment Example 121 4.8 IoT Security 121 4.8.1 Challenges 121 4.8.2 Applicable Security Mechanisms 125 4.8.3 Hardware Considerations 127 4.8.4 Other Approaches: Blockchains 132 References 132 5 Evolved Campus Connectivity 139 5.1 Advanced Solutions 140 5.1.1 802.11ax Basics 143 5.1.2 Key 802.11ax Processes 154 5.1.3 Summary 156 5.2 Voice Over Wi‐Fi (VoWi‐Fi) 158 5.3 5G Technologies 163 5.3.1 Emerging Services 164 5.3.2 New Access and Core Elements 165 5.3.3 New 5GC Architecture 168 5.3.4 Frequency Spectrum and Propagation Challenges 169 5.3.5 Resource Management 170 5.3.6 Requirements for Small Cells 175 5.3.7 Comparison to Wi‐Fi 6 178 5.4 IoT 178 5.5 5G DAS Solutions 179 5.6 Integrated Solutions 179 References 181 6 De‐densification of Spaces and Work Environments 184 6.1 Overview 184 6.2 Basic Approaches 189 6.3 RTLS Methodologies and Technologies 194 6.3.1 RFID Systems 202 6.3.2 Wi‐Fi‐based Positioning System (WPS) 205 6.3.3 Bluetooth 206 6.3.4 Uwb 207 6.3.5 Automatic Vehicle Location (AVL) 207 6.4 Standards 207 6.5 Applications 209 References 212 7 UWB‐Based De‐densification of Spaces and Work Environments 222 7.1 Review of UWB Technology 223 7.2 Carriage of Information in UWB 226 7.2.1 Pulse Communication 226 7.2.2 UWB Modulation 228 7.3 UWB Standards 232 7.4 IoT Applications for UWB 237 7.5 UWB Applications for Smart Cities and for Real‐Time Locating Systems 239 7.5.1 Applications for Smart Cities 239 7.5.2 UWB Applications to Real‐Time Location Systems 240 7.6 OSD/ODCMA Applications 248 References 253 8 RTLSs and Distance Tracking Using Wi‐Fi, Bluetooth, and Cellular Technologies 258 8.1 Overview 258 8.2 RF Fingerprinting Methods 260 8.3 Wi‐Fi RTLS Approaches 261 8.3.1 Common Approach 261 8.3.2 Design Considerations 266 8.3.3 Drawbacks and Limitations 267 8.3.4 Potential Enhancements 267 8.3.5 Illustrative Examples 269 8.4 Ble 271 8.4.1 Bluetooth and BLE Background 271 8.4.2 RTLS Applications 273 8.4.3 BLE‐Based Contact Tracing 278 8.4.4 Illustrative Examples 280 8.5 Cellular Approaches 283 8.6 Summary 286 References 288 9 Case Study of an Implementation and Rollout of a High‐Density High‐Impact Network 291 9.1 Thurgood Marshall BWI Airport Design Requirements 292 9.1.1 Broad Motivation 293 9.1.2 Status Quo Challenges 294 9.1.3 RFP Requirements 295 9.2 Overview of the Final Design 298 9.2.1 DAS Solutions 300 9.2.2 Broadband, BLE, IoT 305 10 The Age of Wi‐Fi and Rise of the Wireless SuperNetwork (WiSNET) TM 312 10.1 What Preceded the WiSNET 312 10.2 What Comes Next 313 10.3 The Super‐Integration Concept of a Wireless SuperNetwork (WiSNET) 314 10.4 The Multidimensionality of a SuperNetwork (WiSNET) 317 10.5 The Genesis of the WiSNET Concept Defined in this Text 317 10.6 The Definition and Characterization of a WiSNET 320 10.6.1 Architectural Aspects of a WiSNET 321 10.6.2 Technology Aspects of a WiSNET 325 10.6.3 Management Aspects of a WiSNET 328 10.7 Economic Advantages of a WiSNET System 331 10.8 5G Slice Capabilities 332 10.8.1 Motivations and Approaches for 5G Network Slicing 332 10.8.2 Implementation 335 10.8.3 Wi-Fi Slicing 335 10.9 Conclusion 335 References 336 Index 337

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Book SynopsisElectronics in Advanced Research Industries A one-of-a-kind examination of the latest developments in machine control In Electronics in Advanced Research Industries: Industry 4.0 to Industry 5.0 Advances, accomplished electronics researcher and engineer Alessandro Massaro delivers a comprehensive exploration of the latest ways in which people have achieved machine control, including automated vision technologies, advanced electronic and micro-nano sensors, advanced robotics, and more. The book is composed of nine chapters, each containing examples and diagrams designed to assist the reader in applying the concepts discussed within to common issues and problems in the real-world. Combining electronics and mechatronics to show how they can each be implemented in production line systems, the book presents insightful new ways to use artificial intelligence in production line machines. The author explains how facilities can upgrade their systems to an Industry Table of ContentsPreface xiii About the Author xv 1 State of the Art and Technology Innovation 1 1.1 State of the Art of Flexible Technologies in Industry 2 1.1.1 Sensors and Actuators Layer: I/O Layer 3 1.1.2 Agent/Firmware Layer: User Interface Layer 9 1.1.3 Gateway and Enterprise Service Bus Layer 9 1.1.4 IoT Middleware 10 1.1.5 Processing Layer 11 1.1.6 Application Layer 11 1.1.7 File Transfer Protocols 11 1.2 State of the Art of Scientific Approaches Oriented on Process Control and Automatisms 14 1.2.1 Architectures Integrating AI 14 1.2.2 AI Supervised and Unsupersived Algorithms 15 1.2.3 AI Image Processing 18 1.2.4 Production Process Mapping 20 1.2.5 Technologies of Industry 4.0 and Industry 5.0: Interconnection and Main Limits 21 1.2.6 Infrared Thermography in Monitoring Process 26 1.2.7 Key Parameters in Supply Chain and AI Improving Manufacturing Processes 27 1.3 Intelligent Automatic Systems in Industries 30 1.4 Technological Approaches to Transform the Production in Auto-Adaptive Control and Actuation Systems 31 1.5 Basic Concepts of Artificial Intelligence 33 1.6 Knowledge Upgrading in Industries 41 References 45 2 Information Technology Infrastructures Supporting Industry 5.0 Facilities 51 2.1 Production Process Simulation and Object Design Approaches 52 2.1.1 Object Design of a Data Mining Algorithm: Block Functions and Parameter Setting 55 2.1.2 Example 1: BPM Modeling of Wheat Storage Process for Pasta Production 59 2.1.3 Example 2: Block Diagram Design of a Servo Valve Control and Actuation System 61 2.1.4 Example 3: Block Diagram of a Liquid Production System 61 2.1.5 Example 4: UML Design of a Programmable Logic Controller System 62 2.1.6 Example 5: Electronic Logic Timing Diagram 64 2.1.7 Example 6: AR System in Kitchen Production Process 64 2.1.8 Example 7: Intelligent Canned Food Production Line 70 2.2 Electronic Logic Design Oriented on Information Infrastructure of Industry 5.0 71 2.3 Predictive Maintenance: Artificial Intelligence Failure Predictions and Information Infrastructure Layout in the Temperature Monitoring Process 74 2.4 Defect Estimation and Prediction by Artificial Neural Network 77 2.4.1 Other Methodologies to Map and Read Production Failures and Defects 79 2.5 Defect Clustering and Classification: Combined Use of the K-Means Algorithm with Infrared Thermography for Predictive Maintenance 82 2.6 Facilities of a Prototype Network Implementing Advanced Technology: Example of an Advanced Platform Suitable for Industry 5.0 Integrating Predictive Maintenance 84 2.7 Predictive Maintenance Approaches 86 2.7.1 Preventive Maintenance and Predictive Maintenance Operations in the Railway Industry 90 2.8 Examples of Advanced Infrastructures Implementing AI 93 2.9 Examples of Telemedicine Platforms Integrating Advanced Facilities 94 2.9.1 Advanced Telecardiology Platform 94 2.9.2 Advanced Teleoncology Platform 96 2.9.3 Multipurpose E-Health Platform 97 References 99 3 Human–Machine Interfaces 103 3.1 Mechatronic Machine Interface Architectures Integrating Sensor Systems 104 3.1.1 Multiple Mechatronic Boards Managing Different Production Stages 104 3.1.2 Mechatronic Boards Managing Component Processing 104 3.2 Machine-to-Machine Interfaces: New Concepts of Industry 5.0 106 3.3 Production Line Command and Actuation Interfaces in Upgraded Systems 111 3.3.1 PLC, PAC, Industrial PC, and Improvements 111 3.3.2 SCADA Systems for Centralization of Data Production 115 3.4 McCulloch–Pitts Neurons and Logic Port for Automatic Decision-Making Setting Thresholds 123 3.5 Programmable Logic Controller I/O Ports Interfacing with AI Engine 132 3.6 Human–Machine Interface for Data Transfer and AI Data Processing 134 3.7 Example of Interface Configuration of Temperature Control 135 3.8 AI Interfaces Oriented on Cybersecurity Attack Detection 136 3.9 AI Interfaces Oriented on Database Security 139 3.10 Cybersecurity Platform and AI Control Interface 148 References 151 4 Internet of Things Solutions in Industry 155 4.1 Cloud Computing IoT 156 4.1.1 IoT Agent 156 4.1.2 IoT Gateway in Smart Environments 158 4.1.3 Basic Elements of a Smart Industry Environment Controlling Production 160 4.1.3.1 Feedback Control: Basic Concepts 167 4.1.4 Augmented Reality Hardware and Cloud Computing Processing 169 4.1.5 Real-Time Control and Actuation 171 4.1.6 Localization Technologies in an Industrial Environment 175 4.1.7 GPU Processing Units 176 4.1.7.1 Performance of GPUs by Processing Binary Matrices 176 4.2 IoT and External Artificial Intelligence Engines 180 4.2.1 Artificial Engines and Server Location: Artificial Intelligence and Adaptive Production 180 4.2.2 IoT Security Systems in the Working Environment and Implementation Aspects 182 4.2.3 Example of Energy Power Control and Actuation: Energy Routing and Priority Load Management for Energy Efficiency 182 4.2.4 Online Configurators: Cloud DSS 186 4.3 Blockchain and IoT Data Storage Systems 194 4.3.1 Blockchain Implementation Rules 194 4.3.2 Blockchain and IoT Production Traceability 197 4.4 Mechatronic Machine Interface Architectures Integrating Sensor Systems 199 4.5 Multiple Mechatronic Boards Managing Different Production Stages 200 References 202 5 Advanced Robotics 203 5.1 Collaborative Robotics in Industry and Protocols 204 5.1.1 Data Protocols 206 5.1.2 Basic Concepts of Robotic Arms and Control Improvement 206 5.1.3 Collaborative Exoskeleton Communication System Protocols 212 5.1.4 Advanced Robotics and Intelligent Automation in Manufacturing: Logic Conditions and PLC Programming 213 5.2 Artificial Intelligence in Advanced Robotics and Auto-Adaptive Movement 218 5.2.1 General Technological Aspects about Auto-Adaptive Motion in Advanced Robotics 218 5.2.1.1 Main Aspects of Electrostatic Actuators 219 5.2.1.2 Microelectromechanical System Electrostatic Actuators 220 5.2.1.3 Piezoelectric Actuators 221 5.2.1.4 DC Motor Actuation 222 5.2.1.5 Intelligent Control Integrating AI: Speed Regulation 227 5.2.2 Improvement of Collaborative Exoskeletons by Auto-Adaptive Solutions Implementing Artificial Intelligence 231 5.3 Human–Robot Self-Learning Collaboration in Industrial Applications and Electronic Aspects 232 5.3.1 DC–DC Converter 232 5.3.2 Voltage Source Inverter 233 5.3.3 Current-Source Inverter 237 5.3.4 DC Voltage Source 238 5.3.5 Capacitor and Reactor Effects on Signal Control 238 5.3.6 Human-Robot System and Learning Approaches 239 5.3.6.1 Example of PID Implementation of Self-Adapting Gains 243 5.3.7 Unsupervised Learning Approaches 244 5.3.8 Soft Robotics for Intelligent Collaborative Robotics 245 5.4 Robotics in Additive Manufacturing 246 5.4.1 Additive Manufacturing in Industrial Production and Spray Technique 246 5.4.2 Artificial Intelligence Applications in Additive Manufacturing 247 5.4.3 Advanced Electronic for Design-to-Product Transformation: Laser Texturing Manufacturing and Artificial Intelligence 248 References 249 6 Advanced Optoelectronic and Micro-/Nanosensors 253 6.1 Nanotechnology Laboratories in Industries 254 6.1.1 Facilities for Micro-/Nanosensor Fabrication and Characterization 254 6.2 Micro- and Nanosensors as Preliminary Prototypes for Industry Research 260 6.2.1 Nanocomposite Optoelectronic Sensors and Optoelectronic Circuits for Pressure Sensors 260 6.2.1.1 Optical Fiber Nanocomposite Tip 260 6.2.2 Plasmonic Probes 266 6.2.3 Nanocomposite Pressure Sensor 273 6.2.4 Nanocomposite Sensor for Liquid Detection Systems and Fluid Loss Systems 277 6.2.4.1 Nanocomposite Sensor for Liquid Detection Systems Based on a Pillar-Type Layout 278 6.2.4.2 Micro- and Nanosensors in the Monitoring of Production Processes: Leakage Monitoring 285 6.2.5 Examples of Digital MEMS/NEMS Sensors: Technological Aspects and Applications 286 6.2.5.1 Thin Film MEMS 286 6.2.5.2 Nanoprobes for Medical Imaging 288 6.2.5.3 Diamond Thin Film Devices: Sensing Improvements 293 6.3 Multisensor Systems and Big Data Synchronization of Micro-/Nanoprobes 295 References 296 7 Image Vision Advances 301 7.1 Defect Classification by Artificial Intelligence and Data Processor Units 302 7.1.1 Artificial Intelligence Algorithms and Automatism for Defect Classification: Case Study of Tire Production 302 7.1.2 Welding Classification and Nondestructive Testing Suitable for the Quality Check 304 7.1.2.1 Watershed Image Segmentation and Automatic Welding Defect Classification 307 7.1.3 Encoding and Decoding Circuits in Artificial Intelligence Data Processing 309 7.1.4 Electronic Logic Port Implementations: Pixel Matrix Logic Condition 314 7.2 Image Vision Architectures and Electronic Design 314 7.2.1 Infrared Thermography Monitoring Industrial Processes 315 7.2.1.1 Welding Image Vision Processing and Architecture Design: Radiometric Post Processing 315 7.2.2 Electronic and Firmware for Inline Image Monitoring Systems: Hole Precision in Milling Quality Processes 316 7.2.3 Image Vision and Predictive Maintenance by Artificial Intelligence 319 7.2.3.1 Profilometer for Image Vision 319 7.2.3.2 In-Line 3D Image Vision AI System Integrating Profilometer and Image Processing 321 7.2.4 Augmented Reality Systems and Artificial Neural Networks: Image Vision Supporting Production Processes 323 7.2.5 Infrared Thermography Circuit Design and Automated System 324 7.3 Image Segmentation and Image Clustering 327 7.3.1 Electronic and Firmware for In-Line Monitoring Systems: Camera Connection 327 7.3.2 Image Segmentation and Clustering Techniques: Automated In-Line Monitoring Systems 327 7.3.3 Circuit Timing In-Line Monitoring and Data Storage Systems 328 7.3.4 Image Segmentation in Product Quality Monitoring: Snake Contour Approach 329 7.3.5 Advanced Image Clustering: K-Means Applied to Radiometric Images 331 7.4 Image Segmentation for Food Defect Detection 333 7.5 Random Forest Pixel Classification 335 References 339 8 Electronic and Reverse Engineering 341 8.1 Reverse Engineering Systems and Mechanical Precision 342 8.1.1 Reverse Engineering Platform: Tools, Approaches, and Facilities 344 8.2 Working Processing and Adaptation 349 8.2.1 Process Simulations 349 8.2.2 Process Mining Actuation and Digital Aspects Concerning Decision Support Systems Implemented by Data Mining Algorithms 350 8.3 Reverse Engineering and Self-Learning Automatic Working Piece Classification 354 8.4 Tools Supporting RE: AR and Image Processing for Size Measurement 356 8.5 RE in Micrometric Scale: RE Approach for Photonic Crystals 357 8.6 RE for the Production of Pipeline Components 361 8.7 RE in the Precision Manufacturing Process for Thin Film Devices 363 8.7.1 Ring MEMS Manufacturing 363 8.7.2 Thin Film Diamond Antenna 369 8.8 Advanced RE Processes in Industry 5.0 372 8.9 RE in Nanocomposite Production Processes 374 8.10 RE in Electronic Board Production 376 8.10.1 Transfer of the Master to the Copper Plate 378 8.10.2 Chemical Attack of Copper 378 8.10.3 Drilling and Finishing Processes 379 References 379 9 Rapid Prototyping 381 9.1 Rapid Prototyping Tools and Microscale Electronic Systems: Methodological Approaches 382 9.1.1 Photonic Crystal Pillars for Filtering and Optical Resonance 382 9.1.2 Thin Film Microelectromechanical System Prototyping and Photolithography Approach 387 9.1.3 Thin Film GHz Microstructures by the Photolithography Approach 387 9.1.4 Gas Sensing Homemade Experimental Setup for Rapid Prototyping 390 9.2 Examples of Antenna and Detection System Rapid Prototyping 392 9.2.1 GPR Antenna Design for UAV Integration System 392 9.2.2 Example of an Underground Water Leakage Detection System Integrating GPR, UAV, and Infrared Thermal Imaging: System Prototyping 397 9.2.3 Integrated Diamond Patch-Type Antennas and Applications 400 9.3 Principles of Mechanical Piece Rapid Prototyping and Innovative Materials 411 9.3.1 Example of Diamond Material Implementations 413 9.4 Rapid Prototyping and Artificial Intelligence Upgrade 415 9.5 Rapid Prototyping Oriented Toward Patent Development 418 9.5.1 Prototyping of Devices Implementing Nanoparticles 418 9.5.2 Prototyping of an Optoelectronic Device Based on a Nanocomposite Tip 418 9.5.3 DNA Lab-on-Chip 418 9.6 Nanocomposite Artificial Skin Rapid Prototyping Process 437 References 439 10 Scientific Research in Industry 445 10.1 Guidelines to Construct an Advanced Research Unit in Industry in the Electronic and Mechatronic Field 446 10.2 Guidelines to Formulate a Patent 448 10.3 Guideline to Propose Technological Advances for Public Entities and in Industry 5.0 Research Project 449 10.3.1 Setting of a Research Project of Underground Water Leakage 449 10.3.2 Setting a Research Project Involving Technologies for Hydrogeological Risk Monitoring 456 10.3.3 Setting a Research Project in Mechatronics: Production of a Diagnostic Machine by Means of Industry 5.0 Facilities 468 10.4 Innovation Process Projects: Example of a Smart Wine Factory 483 10.5 Guideline for Project Management 485 References 506 Abbreviations and Acronyms 507 Index 515

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Book SynopsisA deep dive into wireless energy transfer technologies for IoT networks In Wireless Energy Transfer: Towards Sustainable Zero-Energy IoT Networks, distinguished researchers Onel L. A. López and Hirley Alves deliver a robust discussion of massive wireless energy transfer and zero-energy, low-cost, Internet of Things networks. Moving beyond the basic theoretical background of the subject, the authors offer a deep analysis of the scenarios and requirements of wireless energy transfer. The book details novel powering schemes recently proposed to face the challenging requirements of the future Internet of Things, as well as a comprehensive review of sustainable IoT wireless networks. Wireless Energy Transfer explains why novel energy efficient solutions will be needed to address the sheer volume of devices currently forecasted to be used in the near future. It explores the challenges technologists and users will face as well as proposed solutionsTable of ContentsPreface ix Acknowledgments xi Acronyms xii Mathematical Notation xvi About the Companion Website xviii 1 Massive IoT 1 1.1 Selected Use-cases and Scenarios 4 1.2 Key Technologies 6 1.3 Requirements and KPIs 10 1.4 Key Enablers 12 1.4.1 Holistic and Globally Scalable Massive IoT 12 1.4.2 Sustainable Connectivity 13 1.5 Final Remarks and Discussions 17 2 Wireless RF Energy Transfer: An Overview 20 2.1 Energy Harvesting 20 2.1.1 EH Sources 20 2.1.2 RF Energy Transfer 22 2.2 RF–EH Performance 24 2.2.1 Analytical Models 24 2.2.2 State-of-the-art on RF EH 26 2.3 RF–EH IoT 30 2.3.1 Architectures of IoT RF EH Networks 30 2.3.2 Green WET 31 2.3.3 WIT-WET Layouts 32 2.3.4 RF EH in IoT Use Cases 32 2.4 Enabling Efficient RF-WET 35 2.4.1 Energy Beamforming 35 2.4.2 CSI-limited Schemes 35 2.4.3 Distributed Antenna System 37 2.4.4 Enhancements in Hardware and Medium 37 2.4.5 New Spectrum Opportunities 39 2.4.6 Resource Scheduling and Optimization 40 2.4.7 Distributed Ledger Technology 41 2.5 Final Remarks 41 3 Ambient RF EH 43 3.1 Motivation and Overview 43 3.1.1 Hybrid of RF–EH and Power Grid 45 3.1.2 Energy Usage Protocols 46 3.1.3 On Efficient Ambient RF–RH Designs 48 3.2 Measurement Campaigns 51 3.2.1 Greater London (2012) 52 3.2.2 Diyarbakir (2014) 52 3.2.3 Flanders (2017-2019) 53 3.2.4 Other Measurements 54 3.3 Energy Arrival Modeling 55 3.3.1 Based on Arbitrary Distributions 56 3.3.2 Based on Stochastic Geometry 56 3.4 A Stochastic Geometry-based Study 57 3.4.1 System Model and Assumptions 57 3.4.2 Energy Coverage Probability 59 3.4.3 Average Harvested Energy 62 3.4.4 Meta-distribution of Harvested Energy 63 3.4.5 Numerical Results 64 3.5 Final Considerations 67 4 Efficient Schemes for WET 68 4.1 EH from Dedicated WET 68 4.2 Energy Beamforming 68 4.2.1 Low-complexity EB Design 71 4.2.2 CSI-limited Energy Beamforming 74 4.2.3 Performance Analysis 76 4.3 CSI-free Multi-antenna Techniques 80 4.3.1 System Model and Assumptions 81 4.3.2 Positioning-agnostic CSI-free WET 82 4.3.3 Positioning-aware CSI-free WET 94 4.4 On the Massive WET Performance 96 4.5 Final Considerations 98 5 Multi-PB Massive WET 99 5.1 On the PBs Deployment 99 5.1.1 Positioning-aware Deployments 99 5.1.2 Positioning-agnostic Deployments 104 5.2 Multi-antenna Energy Beamforming 109 5.2.1 Centralized Energy Beamforming 110 5.2.2 Distributed Energy Beamforming 111 5.2.3 Available RF Energy 111 5.3 Distributed CSI-free WET 113 5.3.1 SA, AA–IS and RPS–EMW 113 5.3.2 AA–SS 114 5.3.3 RAB 117 5.3.4 Positioning-aware CSI-free Schemes 118 5.3.5 Numerical Examples 118 5.4 On the Deployment Costs 120 5.5 Final Remarks 123 6 Wireless-powered Communication Networks 125 6.1 WPCN Models 125 6.2 Reliable Single-user WPCN 127 6.2.1 Harvest-then-transmit (HTT) 127 6.2.2 Allowing Energy Accumulation 130 6.2.3 HTT versus FEIPC 135 6.3 Multi-user Resource Allocation 139 6.3.1 Signal Model 140 6.3.2 Problem Formulation 141 6.3.3 Optimization Framework 142 6.3.4 TDMA versus SDMA 143 6.4 Cognitive MAC 145 6.4.1 Time Sharing and Scheduling 148 6.4.2 MAC Protocol at the Device Side 150 6.4.3 MAC Protocol at the HAP Side 151 6.5 Final Remarks 152 7 Simultaneous Wireless Information and Power Transfer 155 7.1 SWIPT Schemes 155 7.2 Separate EH and ID Receivers 156 7.2.1 Problem Formulation 157 7.2.2 Optimal Solution 158 7.2.3 Performance Results 159 7.3 Co-located EH and ID Receivers 160 7.3.1 Time Switching 162 7.3.2 Power splitting 165 7.3.3 TS versus PS 167 7.4 Enablers for Efficient SWIPT 171 7.4.1 Waveform Optimization 171 7.4.2 Multicarrier SWIPT 174 7.4.3 Cooperative Relaying 175 7.4.4 Interference Exploitation 176 7.4.5 Artificial Intelligence 177 7.5 Final Considerations 177 8 Final Notes 179 8.1 Summary 179 8.2 Future Research Directions 182 A A Brief Overview on Finite Block Length Coding 187 A.1 Finite Block Length Model 187 B Distribution of Transferred RF Energy Under CSI-free WET 191 B.1 Proof of Theorem 4.2 191 B.2 Proof of Theorem 4.4 192 C Clustering Algorithms 198 C.1 Partitioning Methods 198 C.1.1 K-Means 199 C.1.2 K-Medoids 199 C.1.3 K-Modes 199 C.2 Hierarchical Methods 200 C.3 Other Methods 200 C.4 Pre-processing 201 D Required SNR for a Target Decoding Error Probability (Proof of Theorem 6.1) 202 D.1 On the Convergence of Algorithm 3 203 Bibliography 205 Index 226

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Book SynopsisTable of ContentsPreface xiii About the Companion Website xv 1 Frequency Spectra of Digital Signals 1 1.1 EMC Units 1 1.1.1 Logarithm and Decibel Definition 1 1.1.2 Power and Voltage (Current) Gain in dB 1 1.1.3 EMC dB Units 3 1.2 Fourier Series Representation of Periodic Signals 6 1.3 Spectrum of a Clock Signal 7 1.4 Effect of the Rise Time, Signal Amplitude, Fundamental Frequency, and Duty Cycle on the Signal Spectrum 15 1.4.1 Effect of the Rise Time 15 1.4.2 Effect of the Signal Amplitude 15 1.4.3 Effect of the Fundamental Frequency 18 1.4.4 Effect of the Duty Cycle 20 1.5 Laboratory Exercises 22 1.5.1 Spectrum of a Digital Clock Signal 22 1.5.2 Laboratory Equipment and Supplies 22 1.5.3 Measured Spectrum vs. Calculated Spectrum 23 1.5.4 Effect of the Rise Time 27 1.5.5 Effect of the Signal Amplitude 31 1.5.6 Effect of the Fundamental Frequency 33 1.5.7 Effect of the Duty Cycle 37 References 43 2 EM Coupling Mechanisms 45 2.1 Wavelength and Electrical Dimensions 45 2.1.1 Concept of a Wave 45 2.1.2 Uniform Plane EM Wave in Time Domain 46 2.1.3 Uniform Plane EM Wave in Frequency Domain 47 2.2 EMC Interference Problem 50 2.3 Capacitive Coupling 53 2.3.1 Shielding to Reduce Capacitive Coupling 56 2.4 Inductive Coupling 59 2.4.1 Shielding to Reduce Inductive Coupling 61 2.5 Crosstalk Between PCB Traces 66 2.6 Common-Impedance Coupling 70 2.7 Laboratory Exercises 72 2.7.1 Crosstalk Between PCB Traces 72 References 76 3 Non-Ideal Behavior of Passive Components 77 3.1 Resonance in RLC Circuits 77 3.1.1 “Pure” Series Resonance – Non-Ideal Capacitor Model 77 3.1.2 “Pure” Parallel Resonance – Ferrite Bead Model 81 3.1.3 “Hybrid” Series Resonance – Non-Ideal Resistor Model 83 3.1.4 “Hybrid” Parallel Resonance – Non-Ideal Inductor Model 85 3.2 Non-Ideal Behavior of Resistors 87 3.2.1 Circuit Model and Impedance 87 3.2.2 Parasitic Capacitance Estimation – Discrete Components 89 3.2.3 Parasitic Capacitance Estimation – PCB Components 94 3.3 Non-Ideal Behavior of Capacitors 97 3.3.1 Circuit Model and Impedance 97 3.3.2 Parasitic Inductance Estimation – Discrete Components 99 3.3.3 Parasitic Inductance Estimation – PCB Components 101 3.4 Non-Ideal Behavior of Inductors 104 3.4.1 Circuit Model and Impedance 104 3.4.2 Parasitic Capacitance Estimation – Discrete Components 106 3.4.3 Parasitic Capacitance Estimation – PCB Components 108 3.5 Non-Ideal Behavior of a PCB Trace 111 3.5.1 Circuit Model and Impedance 111 3.6 Impact of the PCB Trace Length on Impedance of the Passive Components 114 3.6.1 Impedance of a Resistor – Impact of the PCB Trace 114 3.6.2 Impedance of a Capacitor – Impact of the PCB Trace 114 3.6.3 Impedance of an Inductor – Impact of the PCB Trace 114 3.6.4 Impedance of an Inductor vs. Impedance of the PCB Trace 118 3.7 Laboratory Exercises 118 3.7.1 Non-Ideal Behavior of Capacitors and Inductors, and Impact of the PCB Trace Length on Impedance 118 3.7.2 Laboratory Equipment and Supplies 119 3.7.3 Laboratory Procedure – Non-Ideal Behavior of Capacitors and Inductors 121 3.7.4 Laboratory Procedure – Impact of the PCB Trace Length on Impedance 122 References 122 4 Power Distribution Network 125 4.1 CMOS Inverter Switching 125 4.2 Decoupling Capacitors 125 4.2.1 Decoupling Capacitor Impact – Measurements 130 4.2.2 Decoupling Capacitor Configurations 137 4.3 Decoupling Capacitors and Embedded Capacitance 147 4.3.1 Decoupling Capacitors and Closely vs. Not Closely Spaced Power and Ground Planes 147 4.3.2 Impact of the Number and Values of the Decoupling Capacitors 156 4.4 Laboratory Exercises 168 4.4.1 Decoupling Capacitors 168 4.4.2 Embedded Capacitance and Decoupling Capacitors 172 References 176 5 EMC Filters 177 5.1 Insertion Loss Definition 177 5.2 Basic Filter Configurations 177 5.3 Source and Load Impedance Impact 177 5.4 What Do We Mean by Low or High Impedance? 179 5.5 LC and CL Filters 181 5.5.1 LC Filter 181 5.5.2 CL Filter 186 5.5.3 LC Filter vs. CL Filter 189 5.6 Pi and T Filters 195 5.6.1 Pi Filter 195 5.6.2 T Filter 196 5.6.3 Pi Filter vs. T Filter 197 5.7 LCLC and CLCL Filters 202 5.7.1 LCLC Filter 202 5.7.2 CLCL Filter 205 5.7.3 LCLC Filter vs. CLCL Filter 206 5.8 Laboratory Exercises 212 5.8.1 Input Impedance and Insertion Loss of EMC Filters 212 5.8.2 Laboratory Equipment and Supplies 212 5.8.3 Laboratory Procedure 214 References 217 6 Transmission Lines – Time Domain 219 6.1 Introduction 219 6.1.1 Transmission Line Effects 219 6.1.2 When a Line Is not a Transmission Line 219 6.1.3 Transmission Line Equations 226 6.2 Transient Analysis 229 6.2.1 Reflections at a Resistive Load 229 6.2.2 Reflections at a Resistive Discontinuity 236 6.2.3 Reflections at a Shunt Resistive Discontinuity 239 6.2.4 Reflections with Transmission Lines in Parallel 241 6.2.5 Reflections at a Reactive Load 245 6.2.6 Reflections at a Shunt Reactive Discontinuity 258 6.3 Eye Diagram 266 6.3.1 Fundamental Concepts 266 6.3.2 Impact of Driver, HDMI Cable, and Receiver 271 6.4 Laboratory Exercises 275 6.4.1 Transmission Line Reflections 275 6.4.2 Laboratory Equipment and Supplies 275 6.4.3 Reflections at a Resistive Load 278 6.4.4 Bounce Diagram 281 6.4.5 Reflections at a Resistive Discontinuity 282 References 285 7 Transmission Lines – Frequency Domain 287 7.1 Frequency-Domain Solution 287 7.1.1 The Complete Circuit Model – Voltage, Current, and Input Impedance along the Transmission Line 290 7.1.2 Frequency-Domain Solution – Example 307 7.2 Smith Chart and Input Impedance to the Transmission Line 316 7.2.1 Smith Chart Fundamentals 316 7.2.2 Input Impedance to the Transmission Line 326 7.3 Standing Waves and VSWR 332 7.4 Laboratory Exercises 336 7.4.1 Input Impedance to Transmission Line – Smith Chart 336 7.4.2 Laboratory Procedure – Smith Chart 336 References 337 8 Antennas and Radiation 339 8.1 Bridge Between the Transmission Line Theory and Antennas 339 8.2 Electric (Hertzian) Dipole Antenna 340 8.2.1 Wave Impedance and Far-Field Criterion 343 8.2.2 Wave Impedance in the Near Field 344 8.3 Magnetic Dipole Antenna 345 8.3.1 Wave Impedance and Far-Field Criterion 346 8.3.2 Wave Impedance in the Near Field 347 8.4 Half-Wave Dipole and Quarter-Wave Monopole Antennas 348 8.4.1 Half-Wave Dipole Antenna 348 8.4.2 Quarter-Wave Monopole Antenna 351 8.5 Balanced–Unbalanced Antenna Structures and Baluns 351 8.5.1 Balanced and Unbalanced Half-Wave Dipole Antenna 352 8.5.2 Sleeve (Bazooka) Balun 355 8.5.3 Input Impedance to the Transmission Line 357 8.5.4 Quarter-Wavelength Sleeve Balun 358 8.6 Sleeve Dipole Antenna Design and Build 360 8.6.1 Symmetrically Driven Half-Wave Dipole Antenna 360 8.6.2 Asymmetrically Driven Dipole Antenna and a Sleeve Dipole 361 8.6.3 Sleeve Dipole Antenna Design 362 8.6.4 Sleeve Dipole Antenna Design Through Simulation 362 8.6.5 Construction and Tuning of a Sleeve Dipole 364 8.7 Antennas Arrays 368 8.8 Log-Periodic Antenna 368 8.9 Biconical Antenna 372 8.10 Antenna Impedance and VSWR 373 8.11 Laboratory Exercises 375 8.11.1 Log-Periodic and Bicon Antenna Impedance and VSWR Measurements 376 8.11.2 Loop Antenna Construction 377 References 381 9 Differential- and Common-Mode Currents and Radiation 383 9.1 Differential- and Common-Mode Currents 383 9.1.1 Common-Mode Current Creation 385 9.2 Common-Mode Choke 387 9.3 Differential-Mode and Common-Mode Radiation 391 9.3.1 Differential-Mode Radiation 395 9.3.2 Common-Mode Radiation 397 9.4 Laboratory Exercises 399 9.4.1 Differential-Mode and Common-Mode Current Measurement 399 9.4.2 Laboratory Equipment and Supplies 399 9.4.3 Laboratory Procedure – Differential-Mode and Common-Mode Current Measurements 399 References 406 10 Return-Current Path, Flow, and Distribution 407 10.1 Return-Current Path 407 10.2 Return-Current Flow 412 10.3 Return-Current Distribution 415 10.3.1 Microstrip Line PCB 415 10.3.2 Stripline PCB 422 10.4 Laboratory Exercises 430 10.4.1 Path of the Return Current 430 References 438 11 Shielding to Prevent Radiation 439 11.1 Uniform Plane Wave 439 11.1.1 Skin Depth 442 11.1.2 Current Density in Conductors 443 11.1.3 Reflection and Transmission at a Normal Boundary 444 11.2 Far-Field Shielding 447 11.2.1 Shielding Effectiveness – Exact Solution 450 11.2.2 Shielding Effectiveness – Approximate Solution – Version 1 454 11.2.3 Shielding Effectiveness – Approximate Solution – Version 2 456 11.2.4 Shielding Effectiveness – Simulations 458 11.3 Near-Field Shielding 463 11.3.1 Electric Field Sources 463 11.3.2 Magnetic Field Sources 465 11.3.3 Shielding Effectiveness – Simulations 466 11.3.4 Shielding Effectiveness – Measurements 470 11.4 Laboratory Exercises 477 11.4.1 Shielding Effectiveness – Simulations 477 11.4.2 Shielding Effectiveness – Measurements 477 References 481 12 SMPS Design for EMC 483 12.1 Basics of SMPS Operation 483 12.1.1 Basic SMPS Topology 483 12.1.2 Basic SMPS Design 486 12.2 DC/DC Converter Design with EMC Considerations 491 12.2.1 Switching Frequency 491 12.2.2 Output Inductor 493 12.2.3 Output Capacitor 494 12.2.4 Catch Diode 495 12.2.5 Input Capacitor 495 12.2.6 Bootstrap Capacitor 496 12.2.7 Undervoltage Lockout 496 12.2.8 Feedback Pin 496 12.2.9 Compensation Network 497 12.2.10 Complete Regulator Circuitry 498 12.2.11 EMC Considerations 498 12.3 Laboratory Exercises 500 12.3.1 SMPS Design and Build 500 12.3.2 Laboratory Equipment and Supplies 500 12.3.3 Laboratory Procedure 501 References 502 A Evaluation of EMC Emissions and Ground Techniques on 1- and 2-Layer PCBs with Power Converters 503 A. 1 Top-Level Description of the Design Problem 503 A.. 1 Functional Block Details 503 A.1. 2 One-Layer Board Topologies 506 A.1. 3 Two-Layer Board Topologies 507 A. 2 DC/DC Converter – Baseline EMC Emissions Evaluation 509 A.2. 1 CISPR 25 Radiated Emissions Test Results 510 A.. 2 CISPR 25 Conducted Emissions (Voltage Method) Test Results 512 A.2. 3 CISPR 25 Conducted Emissions (Current Method) Test Results 515 A. 3 DC/DC Converter – EMC Countermeasures – Radiated Emissions Results 515 A.3. 1 EMC-A and EMC-E Input and Output Capacitor Impact 515 A.3. 2 EMC-A Input Inductor Impact 518 A.. 3 EMC-C Switching Inductor Impact 519 A.3. 4 EMC-B and EMC-D Snubber Impact 521 A.3. 5 EMC-A, EMC-E – Conducted Emissions Countermeasures Impact 523 A.3. 6 Impact of the Shield Frame 524 A. 4 DC/DC Converter – EMC Countermeasures – Conducted Emissions Results – Voltage Method 528 A.4. 1 EMC-A and EMC-E Input and Output Capacitor Impact 528 A.4. 2 EMC-A Input Inductor Impact 529 A.4. 3 EMC-A Additional Input Capacitors Impact 530 A.. 4 EMC-A Input Inductor Impact 531 A.4. 5 EMC-C Switching Inductor Impact 532 A.4. 6 EMC-B and EMC-D Snubber Impact 533 A. 5 DC/DC Converter – EMC Countermeasures – Conducted Emissions Results – Current Method 535 A.5. 1 EMC-A, EMC-C, and EMC-E Input and Output Capacitor and Inductor Impact 535 A.5. 2 EMC-B and EMC-D Snubber Impact 536 A. 6 PCB Layout Considerations 537 A.6. 1 Introduction 537 A.6. 2 Visualizing Complete Forward and Return Paths 538 A.6. 3 Return-Plane Split in AC–DC Converter 543 A. 7 AC/DC Converter Design with EMC Considerations 544 A.7. 1 AC/DC Converter Schematics and Design Requirements 544 A.7. 2 EMC Considerations 546 A. 8 AC/DC Converter – Baseline EMC Emissions Evaluation 548 A.8. 1 Radiated Emissions Test Results 548 A.8. 2 Conducted Emissions Test Results 551 A. 9 AC/DC Converter – EMC Countermeasures – Conducted and Radiated Emissions Results 552 A.9. 1 Conducted Emissions Test Results 553 A.9. 2 Radiated Emissions Test Results 555 A. 10 Complete System – Conducted and Radiated Emissions Results 557 A.0. 1 Complete System and Board Topologies 557 A.10. 2 Conducted Emissions Results 558 A.10. 3 Radiated Emissions Results 562 A.10. 4 Conclusions 564 References 565 Index 567

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Book SynopsisExplore the diverse electrical engineering application of polymer composite materials with this in-depth collection edited by leaders in the field Polymer Composites for Electrical Engineering delivers a comprehensive exploration of the fundamental principles, state-of-the-art research, and future challenges of polymer composites. Written from the perspective of electrical engineering applications, like electrical and thermal energy storage, high temperature applications, fire retardance, power cables, electric stress control, and others, the book covers all major application branches of these widely used materials. Rather than focus on polymer composite materials themselves, the distinguished editors have chosen to collect contributions from industry leaders in the area of real and practical electrical engineering applications of polymer composites. The book?s relevance will only increase as advanced polymer composites receive more attention and interest in the area of advanced Table of ContentsList of Contributors xv Preface xix 1 Polymer Composites for Electrical Energy Storage 1 Yao Zhou 1.1 Introduction 1 1.2 General Considerations 1 1.3 Effect of Nanofiller Dimension 3 1.4 Orientation of Nanofillers 7 1.5 Surface Modification of Nanofillers 11 1.6 Polymer Composites with Multiple Nanofillers 13 1.7 Multilayer-structured Polymer Composites 16 1.8 Conclusion 19 References 21 2 Polymer Composites for Thermal Energy Storage 29 Jie Yang, Chang-Ping Feng, Lu Bai, Rui-Ying Bao, Ming-Bo Yang, and Wei Yang 2.1 Introduction 29 2.2 Shape-stabilized Polymeric Phase Change Composites 32 2.2.1 Micro/Nanoencapsulated Method 33 2.2.2 Physical Blending 35 2.2.3 Porous Supporting Scaffolds 36 2.2.4 Solid–Solid Composite PCMs 37 2.3 Thermally Conductive Polymeric Phase Change Composites 39 2.3.1 Metals 40 2.3.2 Carbon Materials 41 2.3.3 Ceramics 41 2.4 Energy Conversion and Storage Based on Polymeric Phase Change Composites 42 2.4.1 Electro-to-Heat Conversion 42 2.4.2 Light-to-Heat Conversion 45 2.4.3 Magnetism-to-Heat Conversion 47 2.4.4 Heat-to-Electricity Conversion 48 2.5 Emerging Applications of Polymeric Phase Change Composites 48 2.5.1 Thermal Management of Electronics 49 2.5.2 Smart Textiles 50 2.5.3 Shape Memory Devices 51 2.6 Conclusions and Outlook 51 Acknowledgments 52 References 52 3 Polymer Composites for High-Temperature Applications 63 Sen Niu, Lixue Zhu, Qiannan Cai, and Yunhe Zhang 3.1 Application of Polymer Composite Materials in High-Temperature Electrical Insulation 63 3.1.1 High-Temperature-Resistant Electrical Insulating Resin Matrix 63 3.1.1.1 Silicone Resins 64 3.1.1.2 Polyimide 64 3.1.1.3 Polyether Ether Ketone 65 3.1.1.4 Polybenzimidazole 65 3.1.1.5 Polyphenylquinoxaline 65 3.1.1.6 Benzoxazine 66 3.1.2 Modification of Resin Matrix with Reinforcements 66 3.1.2.1 Mica 66 3.1.2.2 Glass Fiber 66 3.1.2.3 Inorganic Nanoparticles 67 3.1.3 Modifications in the Thermal Conductivity of Resin Matrix 67 3.1.3.1 Mechanism of Thermal Conductivity 68 3.1.3.2 Intrinsic High Thermal Conductivity Insulating Material 68 3.1.3.3 Filled High Thermal Conductivity Insulating Material 69 3.2 High-Temperature Applications for Electrical Energy Storage 70 3.2.1 General Considerations for High-Temperature Dielectrics 70 3.2.2 High-Temperature-Resistant Polymer Matrix 71 3.2.3 Polymer Composites for High-Temperature Energy Storage Applications 71 3.2.4 Surface Modification of Nanocomposite for High-Temperature Applications 72 3.2.5 Sandwich Structure of Nanoparticles for High-Temperature Applications 75 3.3 Application of High-Temperature Polymer in Electronic Packaging 77 3.3.1 Synthesis of Low Dielectric Constant Polymer Materials Through Molecular Structure Design 80 3.3.1.1 Fluorine-Containing Low Dielectric Constant Polymer 80 3.3.1.2 Low Dielectric Constant Polymer Material Containing Nonpolar Rigid Bulk Group 81 3.3.2 High-Temperature-Resistant Low Dielectric Constant Polymer Composite Material 82 3.3.2.1 Low Dielectric Constant Polyoxometalates/Polymer Composite 83 3.3.2.2 Low Dielectric Constant POSS/Polymer Composite 85 3.4 Application of Polymer Composite Materials in the Field of High-Temperature Wave-Transmitting and Wave-Absorbing Electrical Fields 86 3.4.1 Wave-Transmitting Materials 88 3.4.1.1 The High-Temperature Resin Matrix 88 3.4.1.2 Reinforced Materials 89 3.4.2 Absorbing Material 89 3.4.2.1 The High-Temperature Resin Matrix 90 3.4.2.2 Inorganic Filler 90 3.5 Summary 91 References 92 4 Fire-Retardant Polymer Composites for Electrical Engineering 99 Zhi Li, En Tang, and Xue-Meng Cao 4.1 Introduction 99 4.2 Fire-Retardant Cables and Wires 100 4.2.1 Fundamental Overview 100 4.2.2 Understanding of Fire-Retardant Cables and Wires 101 4.2.2.1 Polyethylene Composites 101 4.2.2.2 Ethylene-Vinyl Acetate (EVA) Copolymer 103 4.2.2.3 Polyvinyl Chloride Composites 105 4.2.2.4 Other Polymers 108 4.3 Fire-Retardant Polymer Composites for Electrical Equipment 109 4.3.1 Fundamental Overview 109 4.3.2 Understanding of Fire-Retardant Polymer Composites for Electrical Equipment 110 4.3.2.1 HIPS and ABS Composites 110 4.3.2.2 PC/ABS Composites 112 4.3.2.3 PC Composites 115 4.3.2.4 PBT Composites 116 4.4 Fire-Retardant Fiber Reinforced Polymer Composites 117 4.4.1 Fundamental Overview 117 4.4.2 Understanding of Fire-Retardant Fiber Reinforced Polymer Composites 118 4.4.2.1 Reinforced PBT and PET Composites 118 4.5 Conclusion and Outlook 118 References 119 5 Polymer Composites for Power Cable Insulation 123 Yoitsu Sekiguchi 5.1 Introduction 123 5.2 Trend in Nanocomposite Materials for Cable Insulation 125 5.2.1 Overview 125 5.2.2 Polymer Materials as Matrix Resin 125 5.2.3 Fillers 128 5.2.4 Nanocomposites 130 5.2.4.1 XLPE Nanocomposites 131 5.2.4.2 PP Nanocomposites 131 5.2.4.3 Nanocomposite with Cluster/Cage Molecule 131 5.2.4.4 Copolymer and Polymer Blend 131 5.3 Factors Influencing Properties 138 5.4 Issues in Nanocomposite Insulation Materials Research 139 5.5 Understanding Dielectric and Insulation Phenomena 140 5.5.1 Electromagnetic Understanding 140 5.5.2 Understanding Space Charge Behavior by Q(t) Method 141 References 146 6 Semi-conductive Polymer Composites for Power Cables 153 Zhonglei Li, Boxue Du, Yutong Zhao, and Tao Han 6.1 Introduction 153 6.1.1 Function of Semi-conductive Composites 153 6.1.2 Development of Semi-conductive Composites 154 6.2 Conductive Mechanism of Semi-conductive Polymer Composites 155 6.2.1 Percolation Theory 157 6.2.2 Tunneling Conduction Theory 157 6.2.3 Mechanism of Positive Temperature Coefficient 158 6.3 Effect of Polymer Matrix on Semi-conductivity 159 6.3.1 Thermoset Polymer Matrix 159 6.3.2 Thermoplastic Polymer Matrix 162 6.3.3 Blended Polymer Matrix 163 6.4 Effect of Conductive Fillers on Semi-conductivity 165 6.4.1 Carbon Black 165 6.4.2 Carbonaceous Fillers with One- and Two-Dimensions 166 6.4.3 Secondary Filler for Carbon Black Filled Composites 167 6.5 Effect of Semi-conductive Composites on Space Charge Injection 169 6.6 Conclusions 172 References 173 7 Polymer Composites for Electric Stress Control 179 Muneaki Kurimoto 7.1 Introduction 179 7.2 Functionally Graded Solid Insulators and Their Effect on Reducing Electric Field Stress 179 7.3 Practical Application of ε-FGMs to GIS Spacer 181 7.4 Application to Power Apparatus 182 References 188 8 Composite Materials Used in Outdoor Insulation 191 Wang Xilin, Jia Zhidong, and Wang Liming 8.1 Introduction 191 8.2 Overview of SIR Materials 192 8.2.1 RTV Coatings 193 8.2.2 Composite Insulators 195 8.2.3 Liquid Silicone Rubber (LSR) 196 8.2.4 Aging Mechanism and Condition Assessment of SIR Materials 197 8.3 New External Insulation Materials 198 8.3.1 Anti-icing Semiconductor Materials 199 8.3.2 Hydrophobic CEP 201 8.4 Summary 202 References 203 9 Polymer Composites for Embedded Capacitors 207 Shuhui Yu, Suibin Luo, Riming Wang, and Rong Sun 9.1 Introduction 207 9.1.1 Development of Embedded Technology 207 9.1.2 Dielectric Materials for Commercial Embedded Capacitors 210 9.2 Researches on the Polymer-Based Dielectric Nanocomposites 213 9.2.1 Filler Particles 213 9.2.2 Epoxy Matrix 216 9.2.2.1 Modification to Improve Dielectric Properties 219 9.2.2.2 Modification to Improve Mechanical Properties 221 9.3 Fabrication Process of Embedded Capacitors 224 9.4 Reliability Tested of Embedded Capacitor Materials 229 9.5 Conclusions and Perspectives 230 References 230 10 Polymer Composites for Generators and Motors 235 Hirotaka Muto, Takahiro Umemoto, and Takahiro Mabuchi 10.1 Introduction 235 10.2 Polymer Composite in High-Voltage Rotating Machines 236 10.3 Ground Wall Insulation 237 10.3.1 Mica/Epoxy Insulation 237 10.3.2 Electrical Defect in the Insulation of Rotating Machines and Degradation Mechanism 238 10.3.3 Insulation Design and V-t Curve 239 10.4 Polymer Nanocomposite for Rotating Machine 240 10.4.1 Partial Discharge Resistance and a Treeing Lifetime of Nanocomposite as Material Property 241 10.4.1.1 PD Resistance 241 10.4.1.2 Electrical Treeing Lifetime 242 10.4.2 Breakdown Lifetime Properties of Realistic Insulation Defect in Rotating Machine 244 10.4.2.1 Voltage Endurance Test of Void Defect 245 10.4.2.2 Voltage Endurance Test in Mica/Epoxy Nanocomposite-Layered Structure 247 10.4.2.3 V-t Curves in Coil Bar Model with Mica/Epoxy Nanocomposite Insulation 248 10.5 Stress-Grading System of Rotating Machines 252 10.5.1 Silicon Carbide Particle-Loaded Nonlinear-Resistive Materials 252 10.5.2 End-turn Stress-Grading System of High-Voltage Rotating Machines 253 References 255 11 Polymer Composite Conductors and Lightning Damage 259 Xueling Yao 11.1 Lightning Environment and Lightning Damage Threat to Composite-Based Aircraft 259 11.1.1 The Lightning Environment 259 11.1.1.1 Formation of Lightning 259 11.1.2 Lightning Test Environment of Aircrafts 261 11.1.2.1 Zone 1 262 11.1.2.2 Zone 2 263 11.1.2.3 Zone 3 263 11.1.2.4 Current Component A – First Return Strike 264 11.1.2.5 Current Component Ah – Transition Zone First Return Strike 264 11.1.2.6 Current Component B – Intermediate Current 264 11.1.2.7 Current Component C – Continuing Current 264 11.1.2.8 Component C* – Modified Component C 264 11.1.2.9 Current Component D – Subsequent Strike Current 266 11.1.3 Waveform Combination in Different Lightning Zones for Lightning Direct Effect Testing 269 11.1.4 Application of CFRP Composites in Aircraft 269 11.2 The Dynamic Conductive Characteristics of CFRP 271 11.2.1 A Review of the Research on the Conductivity of CFRP 271 11.2.2 The Testing Methods 272 11.2.2.1 Specimens 272 11.2.2.2 The Test Fixture 273 11.2.2.3 Lightning Impulse Generator and Lightning Waveforms 274 11.2.3 The Experimental Results of the Dynamic Impedance of CFRP 275 11.2.3.1 The Nondestructive Lightning Current Test 275 11.2.3.2 The Applied Lightning Current Impulse and the Response Voltage Impulse 278 11.2.3.3 Equivalent Conductivity of CFRP Laminates Under Different Lightning Impulses 280 11.2.3.4 Equivalent Conductivity of CFRP Laminates with Different Laminated Structures 282 11.2.4 The Discussion of the Dynamic Conductive Characteristics of CFRP 282 11.2.4.1 The Conduction Path of the CFRP Laminate Under a Lightning Current Impulse 282 11.2.4.2 Dynamic Conductance of CFRP Laminate 284 11.2.4.3 The Inductive Properties of CFRP Laminates 286 11.2.4.4 Equivalent Conductivity of CFRP Laminates Subjected to Lightning Current Impulses with Higher Intensity 288 11.3 The Lightning Strike-Induced Damage of CFRP Strike 289 11.3.1 Introduction of the Lightning Damage of CFRP 289 11.3.2 Single Lightning Strike-Induced Damage 290 11.3.2.1 Experimental Setup for Single Lightning Strike Test 290 11.3.2.2 Experimental Results of Single Lightning Strike-Induced Damage 292 11.3.2.3 Evaluation for Single Lightning Strike-Induced Damage 297 11.3.3 Multiple Lightning Strikes-Induced Damage 300 11.3.3.1 Experimental Method for Multiple Consecutive Lightning Strike Tests 300 11.3.3.2 Experimental Results of Multiple Lightning Damage 303 11.3.3.3 Multiple Lightning Damage Areas and Depths of CFRP Laminates 308 11.3.3.4 Analysis for Multiple Lightning Damage of CFRP Laminates 309 11.3.3.5 Evaluation for Multiple Lightning Damage of CFRP Laminates 313 11.4 The Simulation of Lightning Strike-Induced Damage of CFRP 319 11.4.1 Overview of Lightning Damage Simulation Researches 319 11.4.2 Establishment of the Coupled Thermal-Electrical Model 321 11.4.2.1 Finite Element Model 321 11.4.2.2 Simulated Lightning Component A 322 11.4.2.3 Pyrolysis Degree Calculation 322 11.4.2.4 Dynamic Conductive Properties 322 11.4.2.5 Pyrolysis-Dependent Material Parameters 323 11.4.3 Simulation Physical Fields of Lightning Current on CFRP Laminates 323 11.4.3.1 Temperature and Pyrolysis Fields 323 11.4.3.2 Mechanical Analysis 325 11.4.4 Simulated Lightning Damage Results 325 11.4.4.1 Numerical Criterion for Lightning Damage 325 11.4.4.2 In-Plane Lightning Damage Evaluation 327 11.4.4.3 In-Depth Lightning Damage Evaluation 331 References 331 12 Polymer Composites for Switchgears 339 Takahiro Imai 12.1 Introduction 339 12.2 History of Switchgear 340 12.3 Typical Insulators in Switchgears 342 12.3.1 Epoxy-based Composite Insulators 342 12.3.2 Insulator-Manufacturing Process 343 12.3.2.1 Vacuum Casting Method 344 12.3.2.2 Automatic Pressure Gelation Method 344 12.3.2.3 Vacuum Pressure Impregnation Method 345 12.4 Materials for Epoxy-based Composites 345 12.4.1 Epoxy Resins 345 12.4.2 Hardeners 346 12.4.3 Inorganic Fillers and Fibers 347 12.4.4 Silane Coupling Agents 348 12.4.5 Fabrication of Epoxy-based Composites 349 12.5 Properties of Epoxy-based Composites 351 12.5.1 Necessary Properties of Epoxy-based Composites for Switchgears 351 12.5.2 Resistance to Thermal Stresses 352 12.5.2.1 Glass Transition Temperature 352 12.5.2.2 Coefficient of Thermal Expansion (CTE) 354 12.5.3 Resistances to Electrical Stresses 356 12.5.3.1 Short-term Insulation Breakdown 356 12.5.3.2 Long-term Insulation Breakdown (V-t Characteristics) 357 12.5.3.3 Relative Permittivity and Resistivity 359 12.5.4 Resistances to Ambient Stresses 360 12.5.4.1 Resistance to SF6 Decomposition Gas 360 12.5.4.2 Water Absorption 361 12.5.5 Resistances to Mechanical Stresses 362 12.5.5.1 Flexural and Tensile Strength 362 12.5.5.2 Creep 363 12.5.6 International Standards for Evaluation of Composites 363 12.6 Advances of Epoxy-based Composites for Switchgear 365 12.6.1 Nanocomposites 365 12.6.2 High Thermal Conductive Composites 366 12.6.3 Biomass Material-Based Composites 367 12.6.4 Functionally Graded Materials 368 12.6.5 Estimate of Remaining Life of Composites 370 12.7 Conclusion 372 References 373 13 Glass Fiber-Reinforced Polymer Composites for Power Equipment 377 Yu Chen 13.1 Overview 377 13.2 Glass Fiber-Reinforced Polymer Composites 378 13.2.1 Fibers 378 13.2.1.1 Chemical Description 378 13.2.1.2 Classification of Glass Fibers 380 13.2.1.3 Properties of Glass Fiber 380 13.2.1.4 Glass Fabrics 380 13.2.1.5 Advantages and Disadvantages 381 13.2.1.6 Common Manufacturing Methods 383 13.2.1.7 Applications of Glass Fiber in Various Industries 384 13.2.2 Polymers 386 13.2.2.1 Epoxy 386 13.2.2.2 Polyester (Thermosetting) 386 13.2.2.3 Phenolic 387 13.2.3 Manufacturing Methods 388 13.2.4 Specifications of Several Kinds of GFRP Materials 393 13.2.4.1 Rigid Laminated Sheets 393 13.2.4.2 Industrial Rigid Round Laminated Rolled Tubes 394 13.2.4.3 Insulated Pipe 394 13.2.4.4 Insulated Pull Rod 394 13.3 Application of Glass Fiber-Reinforced Polymer Composites 396 13.3.1 Laminated Sheets 396 13.3.2 Composite Long Rod Insulators 398 13.3.3 UHV-Insulated Pull Rod for GIS 400 13.3.4 Composite Pole 403 13.3.5 Aluminum Conductor Composite Core in an Overhead Conductor 404 13.3.6 Composite Station Post Insulators 405 13.3.7 Composite Hollow Insulators 407 13.3.8 Composite Crossarms 407 Bibliography 414 Index 419

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Book Synopsis

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Book SynopsisTable of ContentsPreface vii 1. Introduction 1 1.1 Introduction 2 1.2 A History of Control Systems 4 1.3 System Configurations 6 1.4 Analysis and Design Objectives 9 Case Study 11 1.5 The Design Process 14 1.6 Computer-Aided Design 19 1.7 The Control Systems Engineer 20 Summary 21 Review Questions 22 Cyber Exploration Laboratory 22 Bibliography 23 2. Modeling In The Frequency Domain 25 2.1 Introduction 26 2.2 Laplace Transform Review 27 2.3 The Transfer Function 36 2.4 Electrical Network Transfer Functions 39 2.5 Translational Mechanical System Transfer Functions 53 2.6 Rotational Mechanical System Transfer Functions 61 2.7 Transfer Functions for Systems with Gears 65 2.8 Electromechanical System Transfer Functions 69 2.9 Electric Circuit Analogs 75 2.10 Nonlinearities 78 2.11 Linearization 79 Case Studies 84 Summary 87 Review Questions 87 Cyber Exploration Laboratory 88 Hardware Interface Laboratory 91 Bibliography 93 3. Modeling In The Time Domain 95 3.1 Introduction 96 3.2 Some Observations 96 3.3 The General State-Space Representation 100 3.4 Applying the State-Space Representation 102 3.5 Converting a Transfer Function to State Space 110 3.6 Converting from State Space to a Transfer Function 116 3.7 Linearization 118 Case Studies 121 Summary 125 Review Questions 126 Cyber Exploration Laboratory 126 Bibliography 128 4. Time Response 130 4.1 Introduction 131 4.2 Poles, Zeros, and System Response 131 4.3 First-Order Systems 135 4.4 Second-Order Systems: Introduction 137 4.5 The General Second-Order System 142 4.6 Underdamped Second-Order Systems 146 4.7 System Response with Additional Poles 155 4.8 System Response with Zeros 159 4.9 Effects of Nonlinearities upon Time Response 165 4.10 Laplace Transform Solution of State Equations 167 4.11 Time Domain Solution of State Equations 171 Case Studies 175 Summary 181 Review Questions 182 Cyber Exploration Laboratory 183 Hardware Interface Laboratory 186 Bibliography 192 5. Reduction of Multiple Subsystems 194 5.1 Introduction 195 5.2 Block Diagrams 195 5.3 Analysis and Design of Feedback Systems 204 5.4 Signal-Flow Graphs 207 5.5 Mason’s Rule 210 5.6 Signal-Flow Graphs of State Equations 213 5.7 Alternative Representations in State Space 215 5.8 Similarity Transformations 224 Case Studies 231 Summary 237 Review Questions 237 Cyber Exploration Laboratory 238 Bibliography 240 6. Stability 242 6.1 Introduction 243 6.2 Routh-Hurwitz Criterion 246 6.3 Routh-Hurwitz Criterion: Special Cases 248 6.4 Routh-Hurwitz Criterion: Additional Examples 254 6.5 Stability in State Space 261 Case Studies 264 Summary 266 Review Questions 266 Cyber Exploration Laboratory 267 Bibliography 268 7. Steady-State Errors 270 7.1 Introduction 271 7.2 Steady-State Error for Unity Feedback Systems 274 7.3 Static Error Constants and System Type 280 7.4 Steady-State Error Specifications 283 7.5 Steady-State Error for Disturbances 286 7.6 Steady-State Error for Nonunity-Feedback Systems 288 7.7 Sensitivity 291 7.8 Steady-State Error for Systems in 0State Space 294 Case Studies 297 Summary 300 Review Questions 301 Cyber Exploration Laboratory 302 Bibliography 303 8. Root Locus Techniques 305 8.1 Introduction 306 8.2 Defining the Root Locus 310 8.3 Properties of the Root Locus 312 8.4 Sketching the Root Locus 314 8.5 Refining the Sketch 319 8.6 An Example 328 8.7 Transient Response Design via Gain Adjustment 331 8.8 Generalized Root Locus 335 8.9 Root Locus for Positive-Feedback Systems 337 8.10 Pole Sensitivity 339 Case Studies 341 Summary 346 Review Questions 347 Cyber Exploration Laboratory 347 Hardware Interface Laboratory 349 Bibliography 356 9. Design Via Root Locus 358 9.1 Introduction 359 9.2 Improving Steady-State Error via Cascade Compensation 362 9.3 Improving Transient Response via Cascade Compensation 371 9.4 Improving Steady-State Error and Transient Response 383 9.5 Feedback Compensation 396 9.6 Physical Realization of Compensation 404 Case Studies 409 Summary 413 Review Questions 414 Cyber Exploration Laboratory 415 Hardware Interface Laboratory 417 Bibliography 419 10. Frequency Response Techniques 421 10.1 Introduction 422 10.2 Asymptotic Approximations: Bode Plots 427 10.3 Introduction to the Nyquist Criterion 446 10.4 Sketching the Nyquist Diagram 451 10.5 Stability via the Nyquist Diagram 456 10.6 Gain Margin and Phase Margin via the Nyquist Diagram 460 10.7 Stability, Gain Margin, and Phase Margin via Bode Plots 462 10.8 Relation Between Closed-Loop Transient and Closed-Loop Frequency Responses 466 10.9 Relation Between Closed- and Open-Loop Frequency Responses 469 10.10 Relation Between Closed-Loop Transient and Open-Loop Frequency Responses 474 10.11 Steady-State Error Characteristics from Frequency Response 478 10.12 Systems with Time Delay 482 10.13 Obtaining Transfer Functions Experimentally 487 Case Study 491 Summary 492 Review Questions 493 Cyber Exploration Laboratory 494 Bibliography 496 11. Design Via Frequency Response 498 11.1 Introduction 499 11.2 Transient Response via Gain Adjustment 500 11.3 Lag Compensation 503 11.4 Lead Compensation 508 11.5 Lag-Lead Compensation 514 Case Studies 523 Summary 525 Review Questions 525 Cyber Exploration Laboratory 526 Bibliography 527 12. Design Via State Space 528 12.1 Introduction 529 12.2 Controller Design 530 12.3 Controllability 537 12.4 Alternative Approaches to Controller Design 540 12.5 Observer Design 546 12.6 Observability 553 12.7 Alternative Approaches to Observer Design 556 12.8 Steady-State Error Design via Integral Control 563 Case Study 567 Summary 572 Review Questions 573 Cyber Exploration Laboratory 574 Bibliography 575 13. Digital Control Systems 577 13.1 Introduction 578 13.2 Modeling the Digital Computer 581 13.3 The z-Transform 584 13.4 Transfer Functions 589 13.5 Block Diagram Reduction 593 13.6 Stability 596 13.7 Steady-State Errors 603 13.8 Transient Response on the z-Plane 607 13.9 Gain Design on the z-Plane 609 13.10 Cascade Compensation via the s-Plane 612 13.11 Implementing the Digital Compensator 616 Case Studies 619 Summary 623 Review Questions 624 Cyber Exploration Laboratory 625 Bibliography 627 Problems P-1 Appendix A1 List of Symbols A-1 Appendix A2 Antenna Azimuth Position Control System A-5 Appendix A3 Unmanned Free-Swimming Submersible Vehicle A-7 Appendix A4 Key Equations A-8 Glossary G-1 Answers To Selected Problems ANS-1 Index I-1 Appendix B Matlab Tutorial (Available in e-text for students) Appendix C Simulink Tutorial (Available in e-text for students) Appendix D LabVIEW Tutorial (Available in e-text for students) Appendix E MATLAB’s GUI Tools Tutorial (Available in e-text for students) Appendix F MATLAB’s Symbolic Math Toolbox Tutorial (Available in e-text for students) Appendix G Matrices, Determinants, and Systems of Equations (Available in e-text for students) Appendix H Control System Computational Aids (Available in e-text for students) Appendix I Derivation of a Schematic for a DC Motor (Available in e-text for students) Appendix J Derivation of the Time Domain Solution of State Equations (Available in e-text for students) Appendix K Solution of State Equations for t0 ≠ 0 (Available in e-text for students) Appendix L Derivation of Similarity Transformations (Available in e-text for students) Appendix M Root Locus Rules: Derivations (Available in e-text for students)

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Book SynopsisTable of ContentsAbout the Author xv Preface xvii 1 A Brief History of Reference Frame Theory 1 References 3 2 Tesla’s Rotating Magnetic Field 5 2.1 Introduction 5 2.2 Rotating Magnetic Field for Symmetrical Two-Phase Stator Windings 5 2.3 Rotating Magnetic Field for Symmetrical Three-Phase Stator Windings 11 2.4 Rotating Magnetic Field for Symmetrical Two-Phase Rotor Windings 13 2.5 Rotating Magnetic Field for Symmetrical Three-Phase Rotor Windings 15 2.6 Closing Comments 17 References 17 3 Tesla’s Rotating Magnetic Field and Reference Frame Theory 19 3.1 Introduction 19 3.2 Transformation of Two-Phase Symmetrical Stator Variables to the Arbitrary Reference Frame 20 3.3 Transformation of Two-Phase Symmetrical Rotor Variables to the Arbitrary Reference Frame 24 3.4 Transformation of Three-Phase Stator and Rotor Variables to the Arbitrary Reference Frame 26 3.5 Balanced Steady-State Stator Variables Viewed from Any Reference Frame 31 3.6 Closing Comments 35 References 35 4 Equivalent Circuits for the Symmetrical Machine 37 4.1 Introduction 37 4.2 Flux-Linkage Equations for a Magnetically Linear Two-Phase Symmetrical Machine 37 4.3 Flux-Linkage Equations in the Arbitrary Reference Frame 39 4.4 Torque Expression in Arbitrary Reference Frame 41 4.5 Instantaneous and Steady-State Phasors 42 4.6 Flux-Linkage Equations for a Magnetically Linear Three-Phase Symmetrical Machine and Equivalent Circuit 45 4.7 Closing Comments 49 References 50 5 Synchronous Machines 51 5.1 Introduction 51 5.2 Synchronous Machine 51 5.3 Equivalent Circuit For Three-Phase Synchronous Generator 53 5.4 Closing Comment 57 Reference 57 6 Brushless dc Drive with Field Orientation 59 6.1 Introduction 59 6.2 The Permanent-Magnet ac Machine 59 6.3 Instantaneous and Steady-State Phasors 62 6.4 Field Orientation of a Brushless dc Drive 65 6.5 Torque Control of a Brushless dc Drive 75 6.6 Closing Comments 78 References 79 7 Field Orientation of Induction Machine Drives 81 7.1 Introduction 81 7.2 Field Orientation of a Symmetrical Machine 81 7.3 Torque Control of Field-Orientated Symmetrical Machine 86 7.4 Closing Comments 89 References 89 8 Additional Applications of Reference Frame Theory 91 8.1 Introduction 91 8.2 Neglecting Stator Transients 91 8.3 Symmetrical Components Derived by Reference Frame Theory 93 8.4 Multiple Reference Frames 97 8.5 Closing Comments 97 References 97 Index 99

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Book SynopsisTable of ContentsList of Contributors xiii Preface xvii 1 Biofuels: Classification, Conversion Technologies, Optimization Techniques and Applications 1 Sakthivel R, Abbhijith H, Harshini G V, Musunuri Shanmukha Vardhan and Krushna Prasad Shadangi 1.1 Introduction 2 1.2 Classification of Biofuels 5 1.2.1 First-Generation Biofuels 5 1.2.2 Second-Generation Biofuels 7 1.2.3 Third-Generation Algal Biofuels 9 1.3 Commonly Used Conversion Technologies 10 1.3.1 Gasification 10 1.3.1.1 Factors Influencing Gasification 12 1.3.2 Pyrolysis 13 1.3.2.1 Production of Bio-Oil from Pyrolysis 13 1.3.3 Hydrothermal Processes 15 1.3.3.1 Hydrothermal Carbonization 16 1.3.3.2 Hydrothermal Liquefaction 16 1.3.3.3 Hydrothermal Gasification 16 1.3.4 Transesterification 17 1.4 Commonly Used Optimization Techniques 19 1.4.1 Response Surface Methodology 19 1.4.2 Genetic Algorithm 22 1.5 Application of Biofuels in Transportation Sector 24 1.5.1 Automobile Sector 24 1.5.2 Aviation Sector 25 Conclusion 27 References 27 2 Technical Challenges and Prospects of Renewable Fuel Generation and Utilization at a Global Scale 31 Rajesh K. Srivastava 2.1 Introduction 32 2.2 Biofuel Synthesis 33 2.2.1 Biomass Energy 34 2.2.2 Biofuels 36 2.2.3 Biodiesel 39 2.3 Challenges for Bioenergy Generation 44 2.3.1 Operation Challenges in Biomass Energy Process 44 2.3.2 Economic Challenges in Biomass Energy Process 48 2.3.3 Social Challenges in Biomass Energy Processes 48 2.3.3.1 Conflicting Decision on Utility of Biomass Resources 48 2.3.3.2 Land Use Issue or Problems on Biomass Cultivation or Utilization 49 2.3.3.3 Environmental Impact of Biomass Resources 49 2.3.4 Policy and Regulatory Challenges for Biomass Energy Utility 49 2.4 Conclusions 50 Abbreviations 50 References 51 3 Engineered Microbial Systems for the Production of Fuels and Industrially Important Chemicals 59 Sushma Chauhan, Balasubramanian Velramar, Sneha Kumari, Anushri Keshri, Shalini Pandey, Shivam Pandey, Tanushree Baldeo Madavi, Vargobi Mukherjee, Meenakshi Jha and Pamidimarri D. V. N. Sudheer 3.1 Introduction 60 3.2 Microbial Systems for Biofuels and Chemicals Production 62 3.2.1 Microbial Systems for Genetic Engineering and Cellular Fabrication 64 3.2.2 Engineering of Microbial Cell Systems for Biofuels Production 65 3.2.2.1 Alcohols 65 3.2.3 Engineering of Microbial Cell Systems for Chemical Synthesis 73 3.2.3.1 Organic Acids 73 3.2.3.2 Fatty Alcohols 76 3.2.3.3 Bioplastic 77 3.3 Conclusions 78 References 87 4 Production of Biomethane and Its Perspective Conversion: An Overview 93 Rajesh K. Srivastava and Prakash Kumar Sarangi 4.1 Introduction 93 4.1.1 Sources of Methane 95 4.1.2 Methane from Human Activity 96 4.1.3 Impact of Methane on Climatic Change and Future 96 4.1.4 Advancements and Challenges 97 References 100 5 Microalgal Biomass Synthesized Biodiesel: A Viable Option to Conventional Fuel Energy in Biorefinery 105 Neha Bothra, P. Maniharika and Rajesh K. Srivastava 5.1 Introduction 106 5.2 Diesel 109 5.2.1 Biodiesel 112 5.3 Production of Biodiesel 113 5.3.1 Origin of Biofuels 113 5.3.2 Biodiesel Production from Algae 114 5.3.3 Intensity of Radiant Light 116 5.3.4 Lipid Content 117 5.3.5 Biomass Culturing Conditions 117 5.3.5.1 Temperature of Cultivation 118 5.3.5.2 pH of Cultivation 119 5.3.5.3 Duration Period of Light of Cultivation 119 5.3.5.4 Carbon Uptake of Cultivation 119 5.3.5.5 Oxygen Generation in Cultivation 119 5.3.5.6 Mixing Rates of Cultivation 120 5.3.5.7 Nutrient Uptake of Cultivation 120 5.4 Harvesting of Microalgae 120 5.4.1 Extraction of Oil 120 5.4.1.1 Varying n-Hexane to Algae Ratio 122 5.4.1.2 Varying the Algal Biomass Size 123 5.4.1.3 Varying Contact Time between n-Hexane and Algae Biomass 123 5.4.2 Transesterification 125 5.5 Conclusion 125 Abbreviations 125 References 126 6 Algae Biofuel Production Techniques: Recent Advancements 131 Trinath Biswal, Krushna Prasad Shadangi and Prakash Kumar Sarangi 6.1 Introduction 131 6.2 Technologies for Conversion if Algal Biofuels 133 6.2.1 Thermochemical Conversion of Microalgae Biomass into Biofuel 133 6.2.1.1 Gasification 133 6.2.1.2 Thermochemical Liquefaction 134 6.2.1.3 Pyrolysis 134 6.2.1.4 Direct Combustion 136 6.2.2 Biochemical Conversion 136 6.2.2.1 Anaerobic Digestion 138 6.2.2.2 Alcoholic Fermentation 139 6.2.2.3 Photobiological Hydrogen Production 139 6.3 Production of Biodiesel from Algal Biomass 140 6.3.1 Transesterification 141 6.4 Genetic Engineering Toward Biofuels Production 142 6.5 Summary 143 References 144 7 Technologies of Microalgae Biomass Cultivation for Bio-Fuel Production: Challenges and Benefits 147 Trinath Biswal, Krushna Prasad Shadangi and Prakash Kumar Sarangi 7.1 Introduction 148 7.2 Challenges Towards Algae Biofuel Technology 149 7.3 Biology Related with Algae 150 7.4 Algae Biofuels 153 7.5 Benefits of Microalgal Biofuels 154 7.6 Technologies for Production of Microalgae Biomass 160 7.6.1 Photoautotrophic Production 161 7.6.1.1 Open Pond Production Systems 161 7.6.1.2 Closed Photobioreactor Systems 163 7.6.1.3 Hybrid Production Systems 165 7.6.2 Heterotrophic Method Production 166 7.6.3 Mixotrophic Production 166 7.6.4 Photoheterotrophic Cultivation 168 7.7 Impact of Microalgae on the Environment 169 7.8 Advantages of Utilizing Microalgae Biomass for Biofuels 171 7.9 Conclusion 172 References 172 8 Agrowaste Lignin as Source of High Calorific Fuel and Fuel Additive 179 Harit Jha and Neha Namdeo 8.1 Agrowaste 179 8.2 Lignin 180 8.2.1 Structure of Lignin 181 8.2.2 Types of Lignin 183 8.2.3 Applications of Lignin 184 8.3 Lignin as Fuel 186 8.3.1 Bioethanol Production 189 8.3.2 Bio-Oil Production 191 8.3.3 Syngas Production 192 8.4 As Fuel Additive 192 8.5 Conclusion 193 References 194 9 Fly Ash Derived Catalyst for Biodiesel Production 203 Trinath Biswal, Krushna Prasad Shadangi and Prakash Kumar Sarangi 9.1 Introduction 204 9.2 Coal Fly Ash: Resources and Utilization 205 9.3 Composition of Coal Fly Ash 209 9.4 Economic Perspective of Biodiesel 212 9.5 Biodiesel from Fly Ash Derived Catalyst 214 9.5.1 Coal Fly Ash-Derived Sodalite as a Heterogeneous Catalyst 214 9.5.1.1 Zeolite Synthesis from Coal Fly Ash 215 9.5.1.2 Production of Biodiesel through Heterogeneous Transesterification 215 9.5.2 CaO/Fly Ash Catalyst for Transesterification of Palm Oil in Production of Biodiesel 216 9.5.2.1 Production of Biodiesel 217 9.5.2.2 Transesterification Reaction 218 9.5.3 Biodiesel Production Catalysed by Sulphated Fly-Ash 218 9.5.4 Composite Catalyst of Palm Mill Fly Ash-Supported Calcium Oxide (Eggshell Powder) 220 9.5.4.1 Preparation of the CaO/PMFA Catalyst 221 9.5.5 Kaliophilite-Fly Ash Based Catalyst for Production of Biodiesel 221 9.5.5.1 Synthesis of Kaliophilite 223 9.5.6 Fly-Ash Derived Zeolites for Production of Biodiesel 223 Conclusion 225 References 226 10 Emerging Biomaterials for Bone Joints Repairing in Knee Joint Arthroplasty: An Overview 233 Shankar Swarup Das 10.1 Introduction 234 10.2 Resources and Selecting Criteria 234 10.3 Reasons for Bone Defects of Tibia Plateau 235 10.4 Classification of Bone Defects of Medial Tibia Plateau 236 10.5 Different Biomaterials for Tibial Plateau Bone Defects 237 10.6 New Biomaterials to Repair Bone Defects in Tibia Plateau 243 10.7 Conclusion 244 References 245 About the Editor 253 Index 255

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Book SynopsisReviews recent advances in catalytic biodiesel synthesis, highlighting various nanocatalysts and nano(bio)catalysts developed for effective biodiesel production Nano- and Biocatalysts for Biodiesel Production delivers an essential reference for academic and industrial researchers in biomass valorization and biofuel industries. The book covers both nanocatalysts and biocatalysts, bridging the gap between homogenous and heterogenous catalysis. Readers will learn about the techno-economical and environmental aspects of biodiesel production using different feedstocks and catalysts. They will also discover how nano(bio)catalysts can be used as effective alternatives to conventional catalysts in biodiesel production due to their unique properties, including reusability, high activation energy and rate of reaction, easy recovery, and recyclability. Readers will benefit from the inclusion of: Introductions to CaO nanocatalysts, zeolite nanocatTable of ContentsPreface xv List of Contributors xix 1 Biodiesel: Different Feedstocks, Conventional Methods, and Factors Affecting its Production 1Hossein Esmaeili and Sajad Tamjidi 1.1 Introduction 1 1.2 Different Feedstocks for Biodiesel Production 3 1.2.1 Vegetable Sources 3 1.2.2 Waste Oils 3 1.2.3 Animal Fats 5 1.2.4 Microalga Oil 6 1.3 Conventional Methods of Biodiesel Production 8 1.3.1 Microemulsion 8 1.3.2 Pyrolysis or Thermal Cracking 8 1.3.3 Transesterification 8 1.4 Catalysts Used in Biodiesel Production 9 1.4.1 Homogeneous Catalysts 9 1.4.1.1 Homogeneous Alkaline Catalysts 9 1.4.1.2 Homogeneous Acidic Catalysts 9 1.4.2 Heterogeneous Catalysts 10 1.4.2.1 Heterogeneous Alkaline Catalysts 10 1.4.2.2 Heterogeneous Acid Catalysts 10 1.4.3 Enzymatic Catalysts 11 1.4.4 Nanocatalysts 12 1.5 Effects of Different Factors on Biodiesel Production Yield 15 1.5.1 Reaction Temperature 15 1.5.2 Alcohol to Oil Molar Ratio 16 1.5.3 Reaction Time 17 1.5.4 Catalyst Dosage 17 1.5.5 pH 17 1.5.6 Mixing Rate 17 1.5.7 Fatty Acids 18 1.5.8 Water Content 18 1.6 Physical Properties of Biodiesel 18 1.7 Conclusions 19 References 20 2 Nano(Bio)Catalysts: An Effective Tool to Utilize Waste Cooking Oil for the Biodiesel Production 31Rushikesh Fopase, Swati Sharma and Lalit M. Pandey 2.1 Introduction 31 2.2 Waste Cooking Oils 33 2.3 Pretreatment of WCOs 33 2.4 Transesterification Process 34 2.4.1 Kinetics of Transesterification 36 2.5 Enzymatic Biocatalysts 37 2.5.1 Lipases 38 2.5.1.1 Extracellular Lipases 38 2.5.1.2 Intracellular Lipases 39 2.6 Enzyme Immobilization Techniques 41 2.7 Physical Methods 42 2.7.1 Adsorption 42 2.7.2 Encapsulation 45 2.7.3 Entrapment 46 2.8 Chemical Methods 47 2.8.1 Covalent Bonding 47 2.8.2 Cross-Linking 49 2.8.3 Summary 50 2.9 Conclusions 50 References 51 3 A Review on the Use of Bio/Nanostructured Heterogeneous Catalysts in Biodiesel Production 59Samuel Santos, Jaime Puna, João Gomes and Jorge Marchetti 3.1 Introduction 59 3.2 Use of Micro- and Nanostructured Heterogeneous Catalysts in Biodiesel Production 62 3.2.1 Microstructured Heterogeneous Catalysts 62 3.2.1.1 Solid Acid Catalysts 62 3.2.1.2 Solid Base Catalysts 63 3.2.2 Nanostructured Heterogeneous Catalysts 65 3.2.2.1 Gas Condensation 65 3.2.2.2 Vacuum Deposition 65 3.2.2.3 Chemical Deposition 66 3.2.2.4 Sol-Gel Method 66 3.2.2.5 Impregnation 67 3.2.2.6 Nanogrinding 68 3.2.2.7 Calcination-Hydration-Dehydration 68 3.3 Enzymatic Catalysis 69 3.3.1 Heterogeneous Biocatalysts (Lipases) and Their Immobilization 69 3.3.1.1 Physical Adsorption 70 3.3.1.2 Entrapment 70 3.3.1.3 Covalent Bonding 71 3.3.1.4 Cross-Linking 72 3.3.2 Nano(Bio)Catalysts: Immobilization of Enzymes on Nanosupports 73 3.3.2.1 Nanoparticles 73 3.3.2.2 Carbon Nanotubes 75 3.3.2.3 Nanofibers 76 3.3.2.4 Nanocomposites 76 3.4 Conclusions 77 References 78 4 Calcium-Based Nanocatalysts in Biodiesel Production 93Priti R. Pandit and Archit Mohapatra 4.1 Introduction 93 4.2 Nanocatalysts 94 4.3 CaO-Based Nanocatalysts for Biodiesel Production 95 4.3.1 Synthesis and Characterization of CaO-Based Nanocatalysts Using Waste Material 99 4.3.2 CaO Nanocatalysts Supported with Metal Oxides for Biodiesel Production 102 4.4 Effects of Different Parameters on Biodiesel Production 105 4.4.1 Reaction Time 105 4.4.2 Temperature 105 4.4.3 Methanol to Oil Molar Ratio 106 4.4.4 Catalyst Load 106 4.5 Reusability and Leaching of Nanocatalysts 106 4.6 Conclusions 107 References 107 5 Titanium Dioxide-Based Nanocatalysts in Biodiesel Production 115Elijah Olawale Ajala, Mary Adejoke Ajala and Harvis Bamidele Saka 5.1 Introduction 115 5.2 Natural Occurrences of Titania 117 5.2.1 Rutile 117 5.2.2 Anatase 118 5.2.3 Rhombic Brookite 118 5.2.4 Kaolin Clays 118 5.2.5 Ilmenites or Manaccanite 120 5.3 Precursors Used for the Synthesis of TiO2 NPs 120 5.3.1 Titanium Tetrachloride 121 5.3.2 Titanium Tetraisopropoxide 121 5.3.3 Titanium Butoxide 122 5.4 Methods for the Synthesis of TiO2 NPs 122 5.4.1 Physical Methods 122 5.4.1.1 Ball Milling 122 5.4.1.2 Laser Ablation/Photoablation 123 5.4.1.3 Sputtering 123 5.4.2 Chemical Methods 123 5.4.2.1 Microemulsion 123 5.4.2.2 Precipitation 124 5.4.2.3 Sol-Gel 124 5.4.2.4 Hydrothermal 125 5.4.2.5 Solvothermal 125 5.4.2.6 Electrochemical/Deposition 125 5.4.2.7 Sonochemical 126 5.4.2.8 Direct Oxidation 126 5.4.3 Biological Methods 126 5.4.3.1 Green Synthesis Using Plant Extracts 126 5.4.3.2 Microbial Synthesis 128 5.4.3.3 Enzyme-Mediated Synthesis 129 5.5 Methods for the Synthesis of TiO2-Based Nanocatalysts 130 5.5.1 Wet Impregnation 130 5.5.2 Dry Impregnation 131 5.6 TiO2-Based Nanocatalysts for Biodiesel Production 131 5.6.1 Sulfated TiO2 Nanocatalysts 131 5.6.2 Alkaline TiO2 Nanocatalysts 133 5.6.3 Co-Transition TiO2 Nanocatalysts 133 5.6.4 Alkali TiO2 Nanocatalysts 134 5.6.5 Bimetallic TiO2 Nanocatalysts 135 5.6.5.1 TiO2-Pd-Ni 135 5.6.5.2 TiO2-Au-Cu 135 5.7 Other TiO2 Nanocomposite Catalysts 135 5.8 Conclusions 136 References 136 6 Zinc-Based Nanocatalysts in Biodiesel Production 143Avinash P. Ingle 6.1 Introduction 143 6.2 Feedstocks Used for Biodiesel Production 144 6.2.1 Vegetable Oils 144 6.2.2 Microbial Oils 145 6.2.3 Animal Fats 145 6.2.4 Waste Oils 145 6.2.5 Biomass 146 6.3 Conventional Methods of Biodiesel Production 146 6.3.1 Pyrolysis 146 6.3.2 Transesterification 146 6.3.2.1 Homogeneous Acid and Base (Alkali)-Catalyzed Transesterification 146 6.3.2.2 Heterogeneous Acid and Base (Alkali)-Catalyzed Transesterification 147 6.3.2.3 Enzymatic Transesterification 147 6.4 Nanotechnology in Biodiesel Production 148 6.5 Zinc-Based Nanocatalysts in Biodiesel Production 148 6.6 Conclusions 151 References 152 7 Carbon-Based Nanocatalysts in Biodiesel Production 157Rahul Bhagat, Harris Panakkal, Indarchand Gupta and Avinash P. Ingle 7.1 Introduction 157 7.2 Feedstocks Used for Biodiesel Production 158 7.2.1 Vegetable Oils 158 7.2.2 Algae 159 7.2.3 Animal Fats 160 7.2.4 Waste Cooking Oils 160 7.3 Conventional Heterogeneous Catalysts 160 7.4 Carbon-Based Heterogeneous Nanocatalysts 164 7.4.1 Carbon Nanotubes 166 7.4.2 Sulfonated Carbon Nanotubes 167 7.4.3 Graphene/Graphene Oxide-Based Nanocatalysts 168 7.4.4 Carbon Nanofibers and Carbon Dots 169 7.4.5 Carbon Nanohorns 170 7.4.6 Other Carbon-Based Nanocatalysts 171 7.5 Conclusions 174 References 174 8 Functionalized Magnetic Nanocatalysts in Biodiesel Production 183Kalyani Rajkumari and Lalthazuala Rokhum 8.1 Introduction 183 8.2 Relevance of Heterogeneous Catalysis in Biodiesel Production 185 8.3 Surface Modification and Functionalization of NPs 186 8.4 Applications of Functionalized Magnetic Nanocatalysts in Biodiesel Production 186 8.4.1 Acid-Functionalized Magnetic Nanocatalysts 186 8.4.2 Base-Functionalized Magnetic Nanocatalysts 189 8.4.3 Magnetic Nanocatalysts Functionalized withWaste Materials 190 8.4.4 Ionic Liquid-Immobilized Magnetic Nanocatalysts 192 8.5 Conclusions 194 References 195 9 Bio-Based Catalysts in Biodiesel Production 201Umer Rashid, Shehu-Ibrahim Akinfalabi, Naeemah A. Ibrahim and Chawalit Ngamcharussrivichai 9.1 Introduction 201 9.2 Biodiesel: A Potential Source of Renewable Energy 204 9.2.1 Progress in Biodiesel Development 204 9.2.2 Development of Biodiesel in Malaysia 205 9.2.3 Biodiesel Feedstocks 206 9.2.3.1 PFAD as a Biodiesel Feedstock 207 9.2.4 Common Methods Used for Biodiesel Reaction 208 9.2.4.1 Esterification 209 9.2.4.2 Transesterification 210 9.3 Homogeneous Catalysis in Biodiesel Production 211 9.4 Heterogeneous Catalysis in Biodiesel Production 213 9.5 Catalyst Supports 215 9.5.1 Alumina 216 9.5.2 Silicate 216 9.5.3 Zirconium Oxide 217 9.5.4 Activated Carbon 217 9.6 Heterogeneous Bio-Based Acid Catalysts 217 9.7 Synthesis of Bio-Based Solid Acid Catalysts 218 9.7.1 Palm Tree Fronds and Spikelets 219 9.7.2 Jatropha curcas 219 9.7.3 Coconut Shells 220 9.7.4 Rice Husks 220 9.7.5 Bamboo 221 9.7.6 Cocoa Pod Husks 221 9.7.7 Hardwoods 222 9.7.8 Peanut Hulls 222 9.7.9 Wood Mixtures 223 9.7.10 Palm Kernel Shells 223 9.8 Magnetic Bio-Based Catalysts for Biodiesel Production 224 9.9 Characterization of Bio-Based Catalysts 228 9.9.1 Field Emission Scanning Electron Microscopy (FESEM) 228 9.9.2 Fourier Transform Infrared (FT-IR) 229 9.9.3 X-Ray Diffraction (XRD) 229 9.9.4 Thermogravimetric Analysis (TGA) 230 9.9.5 Temperature-Programmed Desorption – Ammonia (TPD-NH3) 231 9.9.6 Brunauer–Emmett–Teller (BET) Analysis 231 9.10 Reaction Parameters Affecting Biodiesel Production 232 9.10.1 Reaction Time 232 9.10.2 Catalyst Concentration 232 9.10.3 Methanol to Fat/Oil Molar Ratio 232 9.10.4 Reaction Temperature 233 9.10.5 Mixing Rate 235 9.11 Conclusions 235 References 236 10 Heterogeneous Nanocatalytic Conversion of Waste to Biodiesel 249Nilutpal Bhuyan, Manash J. Borah, Neelam Bora, Dipanka Saikia, Dhanapati Deka and Rupam Kataki 10.1 Introduction 249 10.2 Role of Catalysts in Biodiesel Production 250 10.3 Feedstocks for Biodiesel Production 251 10.3.1 First-Generation Feedstocks or Edible Oils 251 10.3.2 Second-Generation Feedstocks or Non-Edible Oils 252 10.3.3 Third-Generation Feedstocks or Algae 252 10.3.4 Other Feedstocks 253 10.4 Biodiesel Production Process 253 10.4.1 Acid-Catalyzed Transesterification 254 10.4.1.1 Mechanism of Acid-Catalyzed Transesterification 256 10.4.2 Alkali- or Base-Catalyzed Transesterification 256 10.4.2.1 Mechanism of Alkali- or Base-Catalyzed Transesterification 258 10.4.3 Other Types of Transesterification 258 10.5 Variables Affecting Transesterification 259 10.6 Heterogeneous Nanocatalysts for Biodiesel Production 260 10.7 Characterization of Nanoparticles Used for Biodiesel Production 262 10.7.1 X-Ray Diffraction (XRD) 262 10.7.2 Scanning Electron Microscopy (SEM) 262 10.7.3 Energy Dispersive X-Ray Analysis (EDX) 262 10.7.4 Transmission Electron Microscopy (TEM) 264 10.7.5 Atomic Force Microscopy (AFM) 264 10.7.6 Raman Spectroscopy 264 10.7.7 Fourier Transform Infrared Spectroscopy (FT-IR) 264 10.7.8 X-Ray Photoelectron Spectroscopy (XPS) 264 10.7.9 Thermogravimetric Analysis (TGA) 265 10.8 Influence of Nanoparticle Properties on Biodiesel Production 265 10.9 Safety Issues Around the Application of Nanocatalysts in Biodiesel Production 267 10.10 Future Perspectives 267 10.11 Conclusions 268 References 269 11 Application of Rare Earth Cation-Exchanged Nanozeolite as a Support for the Immobilization of Fungal Lipase and their Use in Biodiesel Production 279Guilherme de Paula Guarnieri, Adriano de Vasconcellos, Fábio Rogério de Moraes and José Geraldo Nery 11.1 Introduction 279 11.2 Case Study 282 11.2.1 Origins of Materials and Enzymes 282 11.2.2 Preparation of Na-FAU Nanozeolites 282 11.2.3 Ion-Exchange Experiments 283 11.2.4 Enzyme Immobilization on to Nanozeolitic Supports 283 11.2.5 Physicochemical Characterization of As-Synthesized Nanozeolites and Nanozeolite–Enzyme Complexes 284 11.2.6 Synthesis of FAAEs 286 11.2.7 FAEE Yields Obtained with Nanozeolite Complexes 287 11.2.8 Model of Lipase Immobilization on to Zeolite Supports 287 11.3 Conclusions 290 References 290 12 Lipase-Immobilized Magnetic Nanoparticles: Promising Nanobiocatalysts for Biodiesel Production 295Tooba Touqeer, Muhammad Waseem Mumtaz and Hamid Mukhtar 12.1 Introduction 295 12.2 Transesterification for Biodiesel Production 296 12.2.1 Homogenous Catalysts 296 12.2.2 Heterogeneous Catalysts 297 12.2.3 Enzymatic Catalysts 297 12.3 Advantages of Using Magnetic Nanobiocatalysts 297 12.3.1 High Enzyme Loading and Surface Area to Volume Ratio 298 12.3.2 Low Mass Transfer Restriction and High Brownian Movement 299 12.3.3 Effortless Recovery and Reusability 299 12.3.4 Stability 299 12.4 Synthesis of Nanobiocatalysts 299 12.4.1 Preparation and Functionalization of Nanostructures 299 12.4.2 Immobilizing Enzymes on Nanomaterials 300 12.4.2.1 Adsorption Immobilization 300 12.4.2.2 Covalent Immobilization 301 12.5 Techniques for the Characterization of Nanobiocatalysts 302 12.6 Transesterification Using Magnetic Nanobiocatalysts 303 12.7 Factors Affecting Enzymatic Transesterification 304 12.7.1 Type of Alcohol Used 304 12.7.2 Solvent 305 12.7.3 Reaction Temperature 306 12.7.4 Water Content 306 12.7.5 Alcohol to Oil Molar Ratio 306 12.7.6 Source of Lipase 306 12.8 Conclusions 307 References 307 13 Technoeconomic Analysis of Biodiesel Production Using Different Feedstocks 313Shemelis Nigatu Gebremariam 13.1 Introduction 313 13.2 Biodiesel Production Technologies 315 13.3 Feedstock Types for Biodiesel Production 317 13.4 Technical Performance Evaluation of Biodiesel Production 318 13.4.1 Fuel Properties of Biodiesel 319 13.4.1.1 Flash Point 319 13.4.2 Cold Flow Properties 319 13.4.2.1 Cloud Point 320 13.4.2.2 Pour Point 320 13.4.2.3 Cold Filter Plugging Point (CFPP) 321 13.4.3 Cetane Number 321 13.4.4 Density 322 13.4.5 Viscosity 323 13.4.6 Oxidation Stability 323 13.4.7 Biodiesel Quality Standards 324 13.5 Economic Performance Evaluation of the Biodiesel Production Process 324 13.5.1 Fixed Capital Investment Cost 326 13.5.2 Working Capital (Operating) Cost 329 13.6 Conclusions 330 References 331 Index 339

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Book SynopsisTable of ContentsPreface xvi 1 Introduction 1 1.1 Historical Perspective 1 1.1.1 Need for Fiber-Optic Communications 2 1.1.2 Evolution of Lightwave Systems 4 1.2 Basic Concepts 8 1.2.1 Analog and Digital Signals 8 1.2.2 Channel Multiplexing 11 1.2.3 Modulation Formats 13 1.3 Optical Communication Systems 16 1.4 Lightwave System Components 18 1.4.1 Optical Fibers as a Communication Channel 18 1.4.2 Optical Transmitters 18 1.4.3 Optical Receivers 19 Problems 20 References 21 2 Optical Fibers 24 2.1 Geometrical-Optics Description 24 2.1.1 Step-Index Fibers 25 2.1.2 Graded-Index Fibers 27 2.2 Wave Propagation 29 2.2.1 Maxwell’s Equations 29 2.2.2 Fiber Modes 31 2.2.3 Single-Mode Fibers 34 2.3 Dispersion in Single-Mode Fibers 37 2.3.1 Group-Velocity Dispersion 38 2.3.2 Material Dispersion 39 2.3.3 Waveguide Dispersion 40 2.3.4 Higher-Order Dispersion 41 2.3.5 Polarization-Mode Dispersion 43 2.4 Dispersion-Induced Limitations 44 2.4.1 Basic Propagation Equation 45 2.4.2 Chirped Gaussian Pulses 46 2.4.3 Limitations on the Bit Rate 49 2.5 Fiber Losses 52 2.5.1 Attenuation Coefficient 52 2.5.2 Material Absorption 53 2.5.3 Rayleigh Scattering 54 2.5.4 Waveguide Imperfections 55 2.6 Nonlinear Optical Effects 56 2.6.1 Stimulated Light Scattering 56 2.6.2 Nonlinear Phase Modulation 60 2.6.3 Four-Wave Mixing 63 2.7 Fiber Design and Fabrication 64 2.7.1 Silica Fibers 64 2.7.2 Plastic Optical Fibers 67 2.7.3 Cables and Connectors 69 Problems 70 References 72 3 Optical Transmitters 75 3.1 Semiconductor Laser Physics 75 3.1.1 Spontaneous and Stimulated Emissions 76 3.1.2 Nonradiative Recombination 77 3.1.3 Optical Gain 78 3.1.4 Feedback and Laser Threshold 80 3.1.5 Laser Structures and Modes 81 3.2 Single-Mode Semiconductor Lasers 83 3.2.1 Distributed Feedback Lasers 83 3.2.2 Coupled-Cavity Semiconductor Lasers 85 3.2.3 Tunable Semiconductor Lasers 86 3.2.4 Vertical-Cavity Surface-Emitting Lasers 88 3.3 Semiconductor Laser Characteristics 89 3.3.1 CW Characteristics 89 3.3.2 Modulation Bandwidth 92 3.3.3 Relative Intensity Noise 94 3.3.4 Spectral Linewidth 97 3.4 Modulation Techniques 98 3.4.1 Direct Modulation 99 3.4.2 External Modulation 100 3.5 Light-Emitting Diodes 103 3.5.1 LED Characteristics 104 3.5.2 LED Structures 106 3.6 Transmitter Design 108 3.6.1 Source–Fiber Coupling 108 3.6.2 Driving Circuitry 110 3.6.3 Reliability and Packaging 111 Problems 113 References 115 4 Optical Receivers 119 4.1 Basic Concepts 119 4.1.1 Responsivity and Quantum Efficiency 119 4.1.2 Rise Time and Bandwidth 121 4.2 Common Photodetectors 122 4.2.1 p–n Photodiodes 122 4.2.2 p–i–n Photodiodes 124 4.2.3 Avalanche Photodiodes 127 4.2.4 MSM Photodetectors 133 4.3 Receiver Design 135 4.3.1 The Front End 135 4.3.2 The Linear Channel 137 4.3.3 Data-Recovery Section 138 4.3.4 Integrated Receivers 139 4.4 Receiver Noise 141 4.4.1 Noise Mechanisms 141 4.4.2 SNR of p–i–n Receivers 143 4.4.3 SNR of APD Receivers 144 4.5 Coherent Detection 148 4.5.1 Local Oscillator 148 4.5.2 Homodyne Detection 149 4.5.3 Heterodyne Detection 150 4.5.4 Signal-to-Noise Ratio 150 4.6 Receiver Sensitivity 151 4.6.1 Bit-Error Rate 151 4.6.2 Minimum Received Power 154 4.6.3 Quantum Limit of Photodetection 156 4.7 Sensitivity Degradation 157 4.7.1 Extinction Ratio 157 4.7.2 Intensity Noise 158 4.7.3 Timing Jitter 160 4.8 Receiver Performance 162 Problems 164 References 166 5 Lightwave Systems 170 5.1 System Architectures 170 5.1.1 Point-to-Point Links 170 5.1.2 Distribution Networks 172 5.1.3 Local-Area Networks 173 5.2 Design Guidelines 175 5.2.1 Loss-Limited Lightwave Systems 175 5.2.2 Dispersion-Limited Lightwave Systems 176 5.2.3 Power Budget 177 5.2.4 Rise-Time Budget 179 5.3 Long-Haul Systems 181 5.3.1 Performance-Limiting Factors 181 5.3.2 Terrestrial Lightwave Systems 183 5.3.3 Undersea Lightwave Systems 186 5.4 Sources of Power Penalty 188 5.4.1 Modal Noise 188 5.4.2 Mode-Partition Noise 190 5.4.3 Reflection Feedback and Noise 191 5.4.4 Dispersive Pulse Broadening 194 5.4.5 Frequency Chirping 195 5.4.6 Eye-Closure Penalty 197 5.5 Forward Error Correction 198 5.5.1 Error-Correcting Codes 198 5.5.2 Coding Gain 199 5.6 Computer-Aided Design 200 Problems 202 References 204 6 Multichannel Systems 208 6.1 WDM Systems and Networks 208 6.1.1 High-Capacity Point-to-Point Links 209 6.1.2 Wide-Area and Metro-Area Networks 212 6.1.3 Multiple-Access WDM Networks 215 6.2 WDM Components 216 6.2.1 Optical Filters 217 6.2.2 Multiplexers and Demultiplexers 222 6.2.3 Add–Drop Multiplexers 224 6.2.4 Star Couplers 227 6.2.5 Wavelength Routers 228 6.2.6 WDM Transmitters and Receivers 230 6.3 System Performance Issues 233 6.3.1 Linear Crosstalk 233 6.3.2 Raman-Induced Nonlinear Crosstalk 235 6.3.3 XPM-Induced Nonlinear Crosstalk 237 6.3.4 FWM-Induced Nonlinear Crosstalk 239 6.3.5 Other Design Issues 240 6.4 Time-Division Multiplexing 241 6.4.1 Time-Domain Multiplexing 242 6.4.2 Time-Domain Demultiplexing 243 6.4.3 Performance of OTDM Systems 245 6.5 Subcarrier Multiplexing 246 6.5.1 Analog and Digital SCM Systems 246 6.5.2 Orthogonal Frequency-Division multiplexing 248 6.6 Code-Division Multiplexing 250 6.6.1 Time-Domain Encoding 251 6.6.2 Frequency-Domain Encoding 253 Problems 255 References 257 7 Loss Management 264 7.1 Compensation of Fiber Losses 264 7.1.1 Periodic Amplification Scheme 265 7.1.2 Lumped Versus Distributed Amplification 267 7.1.3 Bidirectional Pumping Scheme 268 7.2 Erbium-Doped Fiber Amplifiers 269 7.2.1 Pumping and Gain Spectrum 269 7.2.2 Two-Level Model 270 7.2.3 Amplifier Noise 273 7.2.4 Multichannel Amplification 275 7.3 Raman Amplifiers 277 7.3.1 Raman Gain and Bandwidth 278 7.3.2 Raman-Induced Signal Gain 279 7.3.3 Multiple-Pump Raman Amplification 281 7.3.4 Noise Figure of Raman Amplifiers 283 7.4 Optical Signal-To-Noise Ratio 285 7.4.1 Lumped Amplification 285 7.4.2 Distributed Amplification 287 7.5 Electrical Signal-To-Noise Ratio 288 7.5.1 ASE-Induced Current Fluctuations 288 7.5.2 Impact of ASE on SNR 290 7.5.3 Noise Buildup in an Amplifier Chain 291 7.6 Receiver Sensitivity and Q Factor 292 7.6.1 Bit-Error Rate 292 7.6.2 Relation between Q Factor and Optical SNR 294 7.7 Role of Dispersive and Nonlinear Effects 295 7.7.1 Noise Growth through Modulation Instability 295 7.7.2 Noise-Induced Signal Degradation 297 7.7.3 Noise-Induced Energy Fluctuations 299 7.7.4 Noise-Induced Timing Jitter 300 7.8 Periodically Amplified Lightwave Systems 300 7.8.1 Numerical Approach 301 7.8.2 Optimum Launched Power 304 Problems 306 References 307 8 Dispersion Management 310 8.1 Dispersion Problem and Its Solution 310 8.2 Dispersion-Compensating Fibers 312 8.2.1 Conditions for Dispersion Compensation 312 8.2.2 Dispersion Maps 313 8.2.3 DCF Designs 315 8.3 Fiber Bragg Gratings 317 8.3.1 Constant-Period Gratings 318 8.3.2 Chirped Fiber Gratings 320 8.3.3 Sampled Gratings 322 8.4 Dispersion-Equalizing Filters 325 8.4.1 Gires–Tournois Filters 325 8.4.2 Mach–Zehnder and Other Filters 327 8.5 Optical Phase Conjugation 329 8.5.1 Principle of Operation 330 8.5.2 Compensation of Self-Phase Modulation 331 8.5.3 Generation of Phase-Conjugated Signal 332 8.6 Advanced Techniques 335 8.6.1 Tunable Dispersion Compensation 335 8.6.2 Higher-Order Dispersion Management 338 8.6.3 PMD Compensation 340 8.7 Electronic Dispersion Compensation 343 8.7.1 Pre-compensation at the Transmitter 343 8.7.2 Post-Compensation at the Receiver 347 Problems 349 References 351 9 Control of Nonlinear Effects 355 9.1 Impact of Fiber Nonlinearity 355 9.1.1 System Design Issues 356 9.1.2 Semianalytic Approach 359 9.1.3 Soliton and Pseudo-linear Regimes 361 9.2 Solitons in Optical Fibers 363 9.2.1 Properties of Optical Solitons 364 9.2.2 Loss-Managed Solitons 367 9.2.3 Dispersion-Managed Solitons 370 9.2.4 Timing Jitter 374 9.3 Pseudo-linear Lightwave Systems 378 9.3.1 Origin of Intrachannel Nonlinear Effects 378 9.3.2 Intrachannel Cross-Phase Modulation 380 9.3.3 Intrachannel Four-Wave Mixing 384 9.4 Management of Nonlinear Effects 387 9.4.1 Optimization of Dispersion Maps 387 9.4.2 Phase-Alternation Technique 390 9.4.3 Polarization Bit Interleaving 392 9.4.4 Optical Phase Conjugation 393 9.4.5 Phase-Sensitive Amplification 395 Problems 396 References 398 10 Coherent Lightwave Systems 402 10.1 Coherent Transmitters 403 10.1.1 Encoding of Optical Signals 403 10.1.2 Amplitude and Phase Modulators 405 10.1.3 Quadrature modulator 406 10.2 Coherent Receivers 408 10.2.1 Synchronous Heterodyne Demodulation 408 10.2.2 Asynchronous Heterodyne Demodulation 410 10.2.3 Optical Delay Demodulation 411 10.2.4 Phase Diversity and Polarization Diversity 413 10.3 Noise and Bit-Error Rate 415 10.3.1 Synchronous Heterodyne Receivers 415 10.3.2 Asynchronous Heterodyne Receivers 418 10.3.3 Receivers with Optical Delay Demodulation 419 10.4 Sources of Performance Degradation 421 10.4.1 Intensity Noise of Lasers 421 10.4.2 Phase Noise of Lasers 422 10.4.3 Effects of Fiber’s Dispersion 424 10.5 Management of Nonlinear Effects 425 10.5.1 Nonlinear Phase Noise 426 10.5.2 Compensation of Nonlinear Phase Noise 429 10.5.3 Nonlinear Interference Noise 432 10.6 Digital Signal Processing 435 10.6.1 Removal of Intermediate Frequency and Phase fluctuations 435 10.6.2 Compensation of GVD and PMD 437 10.6.3 Digital Backward Propagation 440 10.7 Experimental Progress 442 10.7.1 DPSK and DQPSK formats 442 10.7.2 QPSK and QAM formats 445 10.7.3 Coherent Orthogonal FDM 448 10.7.4 Optical Superchannels 450 10.8 Channel Capacity 452 Problems 454 References 455 11 Space-Division Multiplexing 462 11.1 SDM Technique 462 11.2 Modes of Optical Fibers 464 11.2.1 Step-Index Fibers 464 11.2.2 Graded-Index Fibers 467 11.2.3 Multicore Fibers 469 11.3 SDM Components 471 11.3.1 Design of SDM Fibers 471 11.3.2 Spatial Multiplexers and Demultiplexers 474 11.3.3 Multicore/Multimode Fiber Amplifiers 479 11.3.4 Other SDM Components 481 11.4 Modeling of SDM Systems 482 11.4.1 Multimode Coupled Nonlinear Equations 483 11.4.2 Averaged Multimode Nonlinear Equations 486 11.4.3 Nonlinear Effects in MCFs 488 11.4.4 Nonlinear Effects in MMFs 491 11.5 Experimental Progress 494 11.5.1 MCF-Based SDM Systems 494 11.5.2 MMF-Based SDM Systems 496 11.5.3 High-Capacity SDM Systems 498 Problems 499 References 500 12 Advanced Topics 505 12.1 Optical Signal Processing 506 12.1.1 Nonlinear Optical Loop Mirrors 506 12.1.2 Parametric Amplifiers 510 12.1.3 Semiconductor Optical Amplifiers 513 12.1.4 Bistable Optical Devices 516 12.1.5 Optical Flip–Flops 518 12.2 Wavelength Conversion 522 12.2.1 XPM-Based Wavelength Converters 522 12.2.2 FWM-Based Wavelength Converters 525 12.2.3 Semiconductor Waveguides 528 12.2.4 SOA-Based Wavelength Converters 530 12.3 Ultrafast Optical Switching 532 12.3.1 Time-Domain Demultiplexing 532 12.3.2 Packet Switching 536 12.3.3 Format Conversion 538 12.4 Optical Regeneration 540 12.4.1 2R Regenerators 541 12.4.2 3R Regenerators 545 12.4.3 Regeneration of Phase-Encoded Signals 549 12.5 Nonlinear Frequency-Division Multiplexing 552 12.5.1 Nonlinear Fourier Transform 552 12.5.2 Practical Implementation 554 Problems 556 References 557 A System of Units 566 B Acronyms 568 C Formula for Pulse Broadening 572 D Nyquist Pulses 574 References 575 Index 576

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Book SynopsisFlight Simulation Software Explains the many aspects of flight simulator design, including open source tools for developing an engineering flight simulator Flight simulation is an indispensable technology for civil and military aviation and the aerospace industry. Real-time simulation tools span across all aspects of aircraft development, from aerodynamics and flight dynamics to avionics and image generation systems. Knowledge of flight simulation software is vital for aerospace engineering professionals, educators, and students. Flight Simulation Software contains comprehensive and up-to-date coverage of the computer tools required to design and develop a flight simulator. Written by a noted expert with decades of experience developing flight simulators in academia, this highly practical resource enables readers to develop their own simulations with readily available open source software rather than relying on costly commercial simulation packages. The bTable of ContentsPreface xiii Aerospace Series Preface xvii Glossary xix About the Author xxiii About the Companion Website xxv 1 Design of an Engineering Flight Simulator 1 1.1 The Evolution of Flight Simulation 1 1.2 Structure of a Flight Simulator 3 1.3 Real-time Flight Simulation 6 1.3.1 The Concept of Real-time Computing 6 1.3.2 Operating Systems 8 1.3.3 Latency 9 1.4 Distributed Computing 10 1.5 Processes and Threads 15 1.5.1 Multi-tasking 15 1.5.2 Semaphores 16 1.5.3 Asynchronous Input 18 1.5.4 Real-time Scheduling 21 1.6 Software Partitioning 22 1.7 Simulator Data 24 1.8 Input and Output 29 1.8.1 Data Acquisition 29 1.8.2 Digital-to-Analogue Conversion 30 1.8.3 Analogue-to-Digital Conversion 31 1.8.4 Multiplexing 33 1.8.5 Encoders 33 1.8.6 Digital Input/Output 34 1.8.7 Signal Conditioning 35 1.8.8 Embedded Systems 36 1.8.9 USB Interfacing 40 References 42 2 Software Methods in Simulation 45 2.1 The Laplace Transform 45 2.2 Transfer Functions 47 2.3 Discrete-event Systems 54 2.4 Data Fitting 58 2.4.1 Data Sources 58 2.4.2 Least-squares Method 60 2.4.3 Spline Methods 63 2.4.4 Extrapolation 70 2.4.5 Observations on Data Fitting 72 2.5 Numerical Methods 72 2.6 Numerical Stability and Accuracy 80 2.6.1 Numerical Stability 80 2.6.2 Numerical Accuracy 82 2.7 Timing Analysis 84 2.8 Simulation Packages 87 References 92 3 Aircraft Equations of Motion 93 3.1 Atmospheric Model 93 3.1.1 The Atmosphere 93 3.1.2 Wind 96 3.1.3 Turbulence 96 3.1.4 Wind Shear 98 3.2 Axes 99 3.2.1 Body Axes 99 3.2.2 Stability Axes 101 3.2.3 Local Frame 101 3.2.4 Earth-centred Earth-fixed Frame 104 3.2.5 Rotating Earth Frame 104 3.3 Quaternions 105 3.4 Aerodynamics 108 3.4.1 Performance and Handling 109 3.4.2 Coefficient of Lift 110 3.4.3 Coefficient of Drag 112 3.4.4 Coefficient of Side Force 113 3.4.5 Pitching Moment Coefficients 114 3.4.6 Rolling Moment Coefficients 115 3.4.7 Yawing Moment Coefficients 115 3.4.8 Mach Number 116 3.4.9 Observations 117 3.5 Equations of Motion 118 3.5.1 Forces 120 3.5.2 Moments 122 3.5.3 Long-Range Navigation 125 3.6 Propulsion 126 3.6.1 Piston Engines 127 3.6.2 Turbofans 134 3.7 Landing Gear 137 References 144 4 Flight Control Systems 147 4.1 Automatic Flight Control 147 4.2 Development of Flight Control Laws 148 4.2.1 The Case for Offline Development and Testing 148 4.2.2 SimPlot 150 4.2.3 Trimming 152 4.3 PID Control 154 4.4 Automatic Modes 157 4.4.1 Turn Coordinator 157 4.4.2 Yaw Damper 158 4.4.3 Pitch Rate Controller 160 4.4.4 Auto-throttle 163 4.4.5 Vertical Speed Hold 165 4.4.6 Altitude Hold 165 4.4.7 Heading Hold 166 4.4.8 Observations on Automatic Modes 170 4.5 Airbus Control Laws 170 4.5.1 Pitch Normal Law 171 4.5.2 Roll Rate Law 173 4.6 Tracking 174 4.7 Auto-land 177 4.8 Flight Director 180 4.9 Flight Management Systems 181 4.9.1 Flight Control Unit 182 4.9.2 Flight Management Systems Simulation 182 References 187 5 Navigation Systems 189 5.1 The Earth 189 5.1.1 Gravity 189 5.1.2 Magnetic Variation 190 5.2 Sensor Modelling 191 5.3 Navigation Principles 192 5.3.1 Position 192 5.3.2 Airspeed 194 5.3.3 Altitude 195 5.3.4 Heading 195 5.3.5 Distance and Bearing 197 5.4 Navigation Databases 199 5.5 Map Projections and Charts 203 5.6 Navigation Computations 207 5.7 Radio Navigation Aids 212 5.7.1 Automatic Direction Finding (ADF) 212 5.7.2 VHF Omni-directional Range 214 5.7.3 Distance Measuring Equipment 217 5.7.4 Instrument Landing System 218 5.8 Traffic Collision Avoidance Systems 221 5.9 Inertial Navigation Systems 223 5.10 Satellite Navigation 230 References 241 6 Aircraft Displays 243 6.1 OpenGL 243 6.1.1 The Development of OpenGL 243 6.1.2 Legacy OpenGL 244 6.1.3 OpenGL Version 4 245 6.2 glib – A 2D Graphics Library for Flight Simulation 253 6.2.1 GPU Software Interface 254 6.2.2 Dots, Vectors and Triangles 256 6.2.3 Textures 260 6.2.4 Fonts 264 6.2.5 Matrix Transformations 273 6.2.6 Summary of glib Functions 275 6.3 Graphics Libraries 275 6.3.1 GLFW 277 6.3.2 cglm 278 6.3.3 PngLib 278 6.3.4 FreeImage 279 6.3.5 FreeType 281 6.3.6 Compiling, Linking and Loading Shader Programs 281 6.3.7 Svg 281 6.4 Design Considerations 284 6.4.1 Absolute and Relative Rendering 284 6.4.2 Memory Organisation 286 6.5 EFIS Displays 287 6.5.1 Primary Flight Display 288 6.5.2 Navigation Flight Display 292 6.5.3 EICAS Display 294 6.6 Flight Instruments 296 6.7 Soft Panels 303 References 308 7 Image Generation Systems 309 7.1 IG Pipeline 309 7.2 Visual Databases 311 7.2.1 Constructing Visual Databases 311 7.2.2 Visual Database Standards 313 7.2.3 Visual Database Editing Tools 314 7.2.4 Representative Visual Databases 316 7.2.5 Visual Database Organisation 319 7.2.6 Binary-spaced Partition Trees 322 7.3 OpenSceneGraph 323 7.4 X-Plane 11 329 7.4.1 X-Plane 11 API 329 7.4.2 An X-Plane 11 Plug-in 330 7.4.3 OSG versus X-Plane 11 333 7.5 Head-up Displays 335 7.6 Digital Terrain Elevation Data 337 7.7 Visualisation 343 7.8 Observations 343 References 345 8 Sound Generation 347 8.1 Sound Waveforms 347 8.2 Sound Generation Methods 350 8.2.1 WAV Format 351 8.2.2 Fast Fourier Transform 352 8.2.3 FFTW 354 8.2.4 Filters 356 8.3 OpenAL 359 8.3.1 OpenAL Application Programming Interface 359 8.3.2 Loading Sound Files 360 8.3.3 Dynamic Sounds 363 8.4 Tones 364 8.4.1 Outer Marker 365 8.4.2 Middle Marker 367 8.4.3 Morse Code 367 8.4.4 Warnings 368 8.4.5 Background Sounds 369 8.4.6 Turbofan Sounds 369 8.4.7 Real-time Sound Generation 371 8.5 Recordings 372 8.5.1 Airspeed-related Sound 372 8.5.2 Turbofan Engines 373 8.6 Observations 373 References 374 9 The Instructor Station 375 9.1 Requirements 375 9.1.1 User Interfaces 375 9.1.2 Instructor Station Requirements 377 9.2 GUIs 380 9.2.1 User Inputs 380 9.2.2 Colour 381 9.2.3 Prototyping 381 9.2.4 User Actions 384 9.2.5 Software Considerations 384 9.3 Design of the User Interface 387 9.3.1 Classification of Operations 387 9.3.2 Design and Implementation of Menus 390 9.3.3 Widgets 395 9.3.4 Mouse Events 400 9.4 Real-time Operation 402 9.5 Charts and Maps 405 9.6 Flight Data Recording 408 9.6.1 Data Recording 409 9.6.2 Data Display 411 9.7 Scripting 411 9.7.1 A Simple Scripting Language 413 9.7.2 A Stack Machine 414 References 421 10 Validation 423 10.1 Software Verification 423 10.2 Static Validation 424 10.2.1 I/O Systems 425 10.2.2 Control Loading Systems 426 10.2.3 Weather Module 427 10.2.4 Navigation Systems 428 10.2.5 Display Modules 428 10.2.6 Visual Systems 430 10.2.7 The Instructor Station 432 10.2.8 Networking 433 10.3 Aircraft Performance 435 10.3.1 Taxiing 436 10.3.2 Take-off 436 10.3.3 Level Flight 436 10.3.4 Climbing Flight 440 10.3.5 Approach and Touchdown 440 10.3.6 Turning Flight 442 10.3.7 Sideslip 444 10.3.8 Observations 445 10.4 Dynamic Response 445 10.4.1 Longitudinal Dynamics 445 10.4.2 Lateral Dynamics 447 10.4.3 Engine Failure 449 10.4.4 Observations 450 10.5 Octave and MATLAB 451 10.5.1 Longitudinal Model 452 10.5.2 Lateral Model 456 10.6 Simulator Qualification 459 10.6.1 Aeroplane Flight Simulator Evaluation Handbook – Volume I 461 10.6.2 Aeroplane Flight Simulator Evaluation Handbook – Volume II 462 References 463 Appendix 465 A1 System-wide #include files 465 A2 Libraries 466 A3 Boeing 747-100 466 A4 Cessna- 172 468 A5 Supporting Files 469 A6 SimPlot 470 A7 Raspberry Pi 470 A8 Diagnostics 471 A9 MSYS2 471 A10 Miscellaneous 472 Index 473

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Book SynopsisVoltage-Enhanced Processing of Biomass and Biochar A detailed introduction to voltage-enhanced processing of carbonaceous materials While there are many well-established biomass processing techniques that are suitable for a variety of different situations, the utilization of voltage-driven techniques for the processing of biomass and biochar has been shown to have advantages for certain applications. Specifically, the field of thermal plasma gasificationwhere plasma provides the conversion energyis relied upon in certain commercial equipment that is already available on the market. Crucially, however, the field of non-thermal plasma pyrolysis and gasificationchemical reactions are intensified by the presence of the plasma dischargeis still a developing subject with a great scope for innovation in research and development. A timely book considering its potential applications in a greener market, Voltage-Enhanced Processing of Biomass and Biochar helpfully provides a detailed description of voltage-enhanced processing of carbonaceous materials. The book explains aspects of this processing method in thermal and non-thermal plasmas, as well as describing the effects of Joule heating as part of the temperature distribution and conversion rate. In many ways, this book presents a detailed description of different processes and plasma discharges currently available, with the provision of experimental and simulation results gathered over years of research and development. Importantly, it also offers many methods by which we can be environmentally friendly when working with biomass and biochar. Voltage-Enhanced Processing of Biomass and Biochar readers will also find: Simulation results of Joule heating of biomass, biochar, and pyrolytic graphite Descriptions of thermal plasma torches currently available in the marketAccounts of the experimental results of conversion utilizing steam plasmaComparison of results against provided numerical models that predict synthesis gas composition under the presence of thermal plasma discharge Voltage-Enhanced Processing of Biomass and Biochar is a useful reference for researchers and practitioners working on applications of plasma for the conversion of biomass and biochar, as well as graduate students studying mechanical, electrical, and chemical engineering.Table of ContentsContributors xi Preface xiii Acknowledgments xv Acronyms xvii Introduction xix 1 Carbonaceous Material Characterization 1 1.1 Material Characterization 2 1.1.1 Thermophysical properties 3 1.1.2 Moisture Content 3 1.1.3 Ultimate and Proximate analysis 4 1.1.4 Dielectric and electrical properties 4 1.2 Biomass 6 1.3 Biochar 7 1.3.1 Surface area, cation exchange capacity, and pH 9 1.4 Activated carbon 11 1.5 Pyrolytic graphite 11 Bibliography 12 2 Conventional Processing Methods 21 2.1 Biomass Processing 22 2.1.1 Biomass Pyrolysis 23 2.1.2 Biomass Gasification 26 2.2 Biochar production and post processing 28 2.2.1 Biochar Activation 34 Bibliography 44 3 Introduction to Plasmas 49 3.1 Thermal Plasmas 50 3.1.1 Mathematical model 53 3.2 Non-thermal Plasmas 56 3.2.1 DC non-thermal electrical discharges 59 3.2.2 Dielectric barrier discharge 64 3.2.3 Pulsed discharges 65 3.2.4 Gliding arc 66 3.2.5 Microwave-induced discharges 67 3.3 Impedance matching 68 3.4 Discharges in liquids 71 3.4.1 Contact glow discharge electrolysis 72 3.4.2 Plasma electrolysis with AC power 76 3.4.3 Gliding arc in glycerol for hydrogen generation 77 Bibliography 78 4 Voltage-Enhanced Processing of Biomass 85 4.1 Biomass gasification with thermal plasma 86 4.1.1 Plasma parameters 87 4.1.2 Syngas composition 88 4.1.3 Energy balance 92 4.1.4 Temperature decay in plasma/biomass discharge 95 4.2 Dielectric breakdown of biomass 97 4.2.1 Biomass-in-the-loop 98 4.3 Biomass gasification with non-thermal plasma 99 4.3.1 Tar breakdown 100 4.3.2 Circuit configuration 104 4.3.3 Scaling up of the technology 107 Bibliography 107 5 Voltage-Enhanced Processing of Biochar 113 5.1 DC Power Applied to Biochar 114 5.1.1 Joule heating of biochar 114 5.1.2 Joule heating of activated carbon 118 5.1.3 Recent Trends in Mathematical modelling 150 5.2 Physical activation of biochar with non-thermal plasma 159 5.2.1 Plasma-steam activation 160 Bibliography 162 6 Numerical simulations 167 6.1 Background 167 6.2 Modeling approaches 168 6.2.1 Kinetic approach 169 6.2.2 Fluid model approach 172 6.3 Examples of non-thermal plasma modeling 175 6.3.1 Cathode fall of a DC glow discharge 176 6.3.2 RF plasma discharge 179 6.3.3 Plasma chemistry 185 Bibliography 191 7 Control of plasma systems 195 7.1 Control of thermal plasma torches 196 7.1.1 Dynamics 198 7.1.2 Control 201 7.2 Control of nonthermal plasma discharges 207 7.2.1 Plasma diagnostics 208 7.2.2 AI-based control 209 Bibliography 214

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Book SynopsisEnergy Global energy demand has more than doubled since 1970. The use of energy is strongly related to almost every conceivable aspect of development: wealth, health, nutrition, water, infrastructure, education and even life expectancy itself are strongly and significantly related to the consumption of energy per capita. Many development indicators are strongly related to per-capita energy consumption. Fossil fuel is the most conventional source of energy but also increases greenhouse gas emissions. The economic development of many countries has come at the cost of the environment. However, it should not be presumed that a reconciliation of the two is not possible. The nexus concept is the interconnection between the resource energy, water, food, land, and climate. Such interconnections enable us to address trade-offs and seek synergies among them. Energy, water, food, land, and climate are essential resources of our natural environment and support our quality oTable of ContentsPreface or Foreword? 1. Energy crisis and climate change: global concerns and their solutionsSandeepa Singh 2. Advances in Alternative Sources of Energy - Opening new doors for Energy SustainabilityJyoti Tyagi 3. Recent advances in alternative sources of energyMaya Verma,a Ambikab and Pradeep Pratap Singhc* 4. Energy and Development in the 21st Century - A road towards a Sustainable Future: An Indian PerspectiveShikha Menani* and Kiran Yadav 5. Energy Development as a Driver of Economic Growth: Evidence from Developing Nations1Dr Md Rashid Farooqi2Dr Md Akhlaqur Rahman3Dr Md Faiz Ahmad4Supriya 6. Pathways of Energy Transition and its Impact on Economic Growth: A Case Study of BrazilPooja Sharma* 7. Renewable energy: sources, importance and prospects for sustainable futureSHACHI AGRAWAL1 AND RENU SONI*2 8. Clean Energy Sources for A Better and Sustainable Environment of Future GenerationsAPARNA NAUTIYAL1* AND AYYAGARI RAMLAL2 9. Sustainable energy policies of India to address air pollution and climate changePrem Lata Meena1*, Vinay2, Anirudh Sehrawat2 10. A Regime Complex and Technological Innovation in Energy System: A Brazilian ExperiencePooja Sharma* 11. Opportunities in the Living Lights: Special reference to Bioluminescent FungiPramod Kumar Mahish1*, Nagendra Kumar Chandrawanshi2*, Shriram Kunjam3 and S. K. Jadhav2 12. Production of Liquid Biofuels from Lignocellulosic BiomassManoj Kumar Singh1, Sumit Sahni2, Anita Narang3 13. Sustainable Solution for Future Energy Challenges through MicrobesSumit Sahni1*, Manoj Kumar Singh2, Anita Narang3 14. Fungal Microbial Fuel Cells, an opportunity for energy sources: Current Perspective and future challengesSudakshina Tiwari1, Deepali1, Anjali Kosre1, Pramod Kumar Mahish2, S.K. Jadhav1 and Nagendra Kumar Chandrawanshi1* 15. Current Perspective of Sustainable Utilization of Agro-Waste and Biotransformation of Energy in MushroomAnjali Kosre1*, Deepali1 , Pramod Kumar Mahish2 and Nagendra Kumar Chandrawanshi1 Index

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Book SynopsisApplied Smart Health Care Informatics Explores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare Applied Smart Health Care Informatics explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the Internet of Things (IoT), and other smart technologies. Detailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new reTable of ContentsPreface xiii About the Editors xix List of Contributors xxv 1 An Overview of Applied Smart Health Care Informatics in the Context of Computational Intelligence 1Sourav De and Rik Das 1.1 Introduction 1 1.2 Big Data Analytics in Healthcare 2 1.3 AI in Healthcare 3 1.4 Cloud Computing in Healthcare 4 1.5 IoT in Healthcare 4 1.6 Conclusion 5 References 5 2 A Review on Deep Learning Method for Lung Cancer Stage Classification Using PET-CT 9Kaushik Pratim Das, Chandra J, and Dr Nachamai M 2.1 Introduction 9 2.1.1 Scope of the Research 10 2.1.2 TNM Staging 11 2.1.2.1 TNM Descriptors for Staging per IASLC Guidelines 11 2.1.2.2 PET-CT Scan in Lung Cancer Imaging 12 2.2 Related Works 12 2.2.1 Artificial Intelligence in Medical Imaging 14 2.2.2 Classification for Medical Imaging 14 2.2.2.1 Deep Learning 15 2.2.2.2 Image Classification Using Deep-learning Techniques 15 2.3 Methods 15 2.3.1 Transfer Learning 15 2.3.2 AlexNet 16 2.3.3 AlexNet Architecture 16 2.3.4 Experimental Setup 17 2.3.4.1 Image Processing 18 2.3.4.2 Data Augmentation 19 2.3.4.3 Training and Validation 19 2.4 Results and Discussion 19 2.4.1 Primary Tumor (T) 19 2.4.2 Metastasis (M) 21 2.4.3 Lymph Node (N) 21 2.4.4 Classification Accuracy of AlexNet 24 2.4.5 Comparative Analysis 25 2.4.6 Limitations 26 2.5 Conclusion 26 References 27 3 Formal Methods for the Security of Medical Devices 31Srinivas Pinisetty, Nathan Allen, Hammond Pearce, Mark Trew, Manoj Singh Gaur, and Partha Roop 3.1 Introduction 31 3.1.1 Pacemaker Security 33 3.1.2 Overview 34 3.2 Background: Cardiac Pacemakers 34 3.2.1 Pacemakers 35 3.2.1.1 Operation of a DDD Mode Pacemaker 36 3.2.2 The Cardiac System 37 3.2.2.1 Electrograms and Electrocardiograms 38 3.3 State of the Art, Formal Verification Techniques 39 3.3.1 Formal Verification Techniques 40 3.3.1.1 Static Verification Techniques 41 3.3.1.2 Dynamic Verification Techniques 42 3.3.2 Runtime Verification 43 3.3.2.1 A Brief Overview of Some Runtime Verification Frameworks 44 3.3.3 Correcting Execution of a System at Runtime (Runtime Enforcement) 45 3.3.3.1 Runtime Enforcement of Untimed Properties 46 3.3.3.2 Runtime Enforcement Approaches for Timed Properties 46 3.4 Formal Runtime-Based Approaches for Medical Device Security 47 3.4.1 Overview of the Approach 47 3.4.2 Mapping EGM Properties to ECG Properties 48 3.4.3 Security of Pacemakers Using Runtime Verification 49 3.4.3.1 Timed Words, Timed Languages, and Defining Timed Properties 50 3.4.3.2 Runtime Verification Monitor 51 3.4.3.3 Architecture of the Monitoring System 53 3.4.3.4 Implementation of the ECG Processing and RV Monitor Modules 53 3.4.3.5 Summary of Experiments and Results 54 3.4.4 Securing Pacemakers with Runtime Enforcement Hardware 54 3.4.4.1 Preliminaries: Words, Languages, and Defining Properties as DTA 55 3.4.4.2 Runtime Enforcement Monitor 56 3.4.4.3 Verification of the Enforcer Hardware 58 3.4.4.4 How Does the Enforcer Prevent Security Attacks? 58 3.4.4.5 Summary of Experiments and Results 59 3.5 Summary 59 References 60 4 Integrating Two Deep Learning Models to Identify Gene Signatures in Head and Neck Cancer from Multi-Omics Data 67Suparna Saha, Sumanta Ray, and Sanghamitra Bandyopadhyay 4.1 Introduction 67 4.2 Related Work 68 4.3 Materials and Methods 70 4.3.1 A Brief Introduction of the Capsule Network 70 4.3.2 An Introduction to Autoencoders 71 4.4 Results 72 4.4.1 Data Set Details 72 4.4.1.1 Gene Expression Data (Illumina Hiseq) 72 4.4.1.2 Human Methylation 450K 73 4.4.2 Architecture of Autoencoder Model 73 4.4.3 Architecture of the Proposed Capsule Network Model 74 4.4.4 Validation of Two Deep Learning Models 75 4.4.5 Gene Signatures from Primary Capsules 76 4.5 Discussion 77 Acknowledgments 78 References 79 5 A Review of Computational Learning and IoT Applications to High-Throughput Array-Based Sequencing and Medical Imaging Data in Drug Discovery and Other Health Care Systems 83Soham Choudhuri, Saurav Mallik, Bhaswar Ghosh, Tapas Si, Tapas Bhadra, Ujjwal Maulik, and Aimin Li 5.1 Introduction 83 5.2 Biological Terms 84 5.3 Single-Cell Sequencing (scRNA-seq) Data 86 5.3.1 Computational Methods for Interpreting scRNA-seq Data 86 5.3.1.1 Visualizing and Clustering Cells 86 5.3.1.2 Inference and Branching Analysis of Cellular Trajectory 86 5.3.1.3 Identifying Highly Variable Genes 86 5.3.1.4 Identifying Marker and Differentially Expressed Genes 90 5.4 Methods of Multi-Omic Data Integration 90 5.4.1 Unsupervised Data Integration Methods 91 5.4.1.1 Matrix Factorization Methods 91 5.4.1.2 Bayesian Methods 91 5.4.1.3 Network-Based Methods 94 5.4.1.4 Multi-Step Analysis and Multiple Kernel Learning 94 5.4.2 Supervised Data Integration 95 5.4.2.1 Network-Based Methods 95 5.4.2.2 Multiple Kernel Learning 95 5.4.2.3 Multi-Step Analysis 95 5.4.3 Semi-Supervised Data Integration 95 5.4.3.1 GeneticInterPred 97 5.5 AI Drug Discovery 97 5.5.1 AI Primary Drug Screening 97 5.5.1.1 Cell Sorting and Classification with Image Analysis 97 5.5.2 AI Secondary Drug Screening 99 5.5.2.1 Physical Properties Predictions 99 5.5.2.2 Predictions of Bio-Activity 99 5.5.2.3 Prediction of Toxicity 99 5.5.3 AI in Drug Design 99 5.5.3.1 Prediction of Target Protein 3D Structures 99 5.5.3.2 Predicting Drug-Protein Interactions 100 5.5.4 Planning Chemical Synthesis with AI 100 5.5.4.1 Retro-Synthesis Pathway Prediction 100 5.5.4.2 Reaction Yield Predictions and Reaction Mechanism Insights 100 5.6 Medical Imaging Data Analysis 100 5.6.1 Analysis: Radio-Mic Quantification 101 5.6.2 Analysis: Bio-Marker Identification 101 5.7 Applying IoT (Internet of Things) to Biomedical Research 102 5.7.1 IoT and IoMT Applications for Healthcare and Well-Being 102 5.7.1.1 Wireless Medical Devices 102 5.8 Conclusions 102 Acknowledgments 102 References 102 6 Association Rule Mining Based on Ethnic Groups and Classification using Super Learning 111Md Faisal Kabir and Simone A. Ludwig 6.1 Introduction 111 6.2 Background 112 6.3 Motivation and Contribution 114 6.4 Data Analysis 115 6.4.1 Data Description 115 6.4.2 Data Preprocessing 115 6.4.3 Further Preprocessing for Ethnic Group Rule Discovery with Multiple Consequences 115 6.4.3.1 Transaction-Like Database for Association Rule 115 6.4.4 Classification Data Set 116 6.5 Methodology 117 6.5.1 Association Rule Mining 117 6.5.2 Super Learning 118 6.5.2.1 Ensemble or Super Learner Set-Up 118 6.6 Experiments and Results 119 6.6.1 Rules Discovery 120 6.6.1.1 Rules of Breast Cancer Patients Based on Ethnic Groups 120 6.6.1.2 Interpreting Rules 120 6.6.2 Evaluation Criteria of Classification Model 121 6.6.2.1 Super Learner Results 124 6.6.3 Discussion 125 6.7 Conclusion and Future Work 126 References 127 7 Neuro-Rough Hybridization for Recognition of Virus Particles from TEM Images 131Debamita Kumar and Pradipta Maji 7.1 Introduction 131 7.2 Existing Approaches for Virus Particle Classification 132 7.3 Proposed Algorithm 134 7.3.1 Extraction of Local Textural Features 135 7.3.2 Selection of Class-Pair Relevant Features 135 7.3.3 Extraction of Discriminating Features 138 7.3.4 Classification 139 7.4 Experimental Results and Discussion 140 7.4.1 Experimental Setup 140 7.4.2 Methods Compared 140 7.4.3 Database Considered 141 7.4.4 Effectiveness of Proposed Approach 141 7.4.5 Comparative Performance Analysis 143 7.4.5.1 Comparison with Deep Architectures 144 7.4.5.2 Comparison with Existing Approaches 145 7.5 Conclusion 146 References 147 8 Neural Network Optimizers for Brain Tumor Image Detection 151T. Kalaiselvi and S.T. Padmapriya 8.1 Introduction 151 8.2 Related Works 152 8.3 Background 153 8.3.1 Types of Neural Networks 153 8.3.2 Tunable Elements of Neural Networks 154 8.3.2.1 Basic Parameters 154 8.3.2.2 Hyperparameters 154 8.3.2.3 Regularization Techniques 155 8.3.2.4 Neural Network Optimizers 156 8.4 Case Study - Brain Tumor Detection 157 8.4.1 Methodology 157 8.4.2 Data Sets and Metrics 157 8.4.3 Results and Discussion 159 8.5 Conclusion 162 References 162 9 Abnormal Slice Classification from MRI Volumes using the Bilateral Symmetry of Human Head Scans 165N. Kalaichelvi, T. Kalaiselvi, and K. Somasundaram 9.1 Introduction 165 9.1.1 MRIs of the Human Brain 165 9.1.2 Normal and Abnormal Slices 166 9.1.3 Background 167 9.1.3.1 Decision Tree Classifiers 167 9.1.3.2 K-Nearest Neighbours (KNN) Classifiers 168 9.1.3.3 Support Vector Machine (SVM) 168 9.1.3.4 Naive Bayes 169 9.1.3.5 Artificial Neural Network (ANN) 169 9.1.3.6 Back-Propagation Neural Network (BPN) 170 9.1.3.7 Random Forest Classifiers 170 9.2 Literature Review 171 9.3 Methodology 172 9.3.1 Preprocessing 173 9.3.2 Feature Extraction 174 9.3.3 Feature Selection 175 9.3.4 Classification 177 9.3.5 Cross-Validation 177 9.3.6 Training Validation and Testing 178 9.4 Materials and Metrics 179 9.4.1 Confusion Matrix 179 9.5 Results and Discussion 180 9.6 Conclusion 182 References 183 10 Conclusion 187Siddhartha Bhattacharyya References 188 Index 191

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Book SynopsisE-PAPER DISPLAYS An in-depth introduction to a promising technology, curated by one of its pioneering inventors Electronic paper (e-paper) has one of the most promising futures in technology. E-paper's potential is unlimited, as the displays require extremely low power and imitate the aesthetic of ink on the page. This allows e-paper devices to have a wider range of viewing angles than traditional LED products and are capable of being viewed in direct sunlightand without any additional power. As a result, e-paper displays create less eye strain, have a greater flexibility in their use, and have the potential to be used in place of paper for billboard advertising, educational applications, and transport signage, and more. In E-Paper Displays, editor Bo-Ru Yang and his team of experts present a detailed view into the important technologies involved in e-paper displays, with a particlular emphasis on how this technology's unique properties make possible a wTable of ContentsList of Contributors xi Series Editor's Foreword xiii Editor's Preface xv 1 The Rise, and Fall, and Rise of Electronic Paper 1Paul S. Drzaic, Bo-Ru Yang, and Anne Chiang 1.1 Introduction 1 1.2 Why Electronic Paper? 2 1.3 Brightness, Color, and Resolution 2 1.4 Reflectivity and Viewing Angle 4 1.5 Translating Print-on-Paper into Electronic Paper 5 1.6 The Allure of Electronic Paper vs. the Practicality of LCDs 10 1.7 The Evolution of Electrophoretic Display-Based Electronic Paper 11 1.8 Initial Wave of Electrophoretic Display Development 12 1.9 The Revival of EPDs 17 1.10 Developing a Commercial Display 18 1.11 Enhancing Brightness and Contrast 19 1.12 Microencapsulation Breakthrough 20 1.13 Image Retention 21 1.14 Active-Matrix Compatibility 23 1.15 Electronic Book Products, and E Ink Merger 25 1.16 Summary 26 2 Fundamental Mechanisms of Electrophoretic Displays 31Bo-Ru Yang and Kristiaan Neyts 2.1 General View of Electronic Ink Operation 31 2.2 Charging Mechanism with Inverse Micelle Dynamics 33 2.3 Drift and Diffusion of Charged Inverse Micelles 35 2.4 Motion of Charged Inverse Micelles Under External Field Driving 38 2.5 Stern Layer Formation 41 2.6 Charging Mechanism with Particles and Additives 44 2.7 Observations on a Single Particle 44 2.8 Rheological Effects During Driving 47 2.9 Bistability After Removing External Fields 48 2.10 Full Color E-Paper 49 2.11 Conclusion 50 3 Driving Waveforms and Image Processing for Electrophoretic Displays 53Zong Qin and Bo-Ru Yang 3.1 Driving Waveforms of EPDs 53 3.2 Image Processing 61 3.3 Advanced Driving Methods for Future E-Papers 69 4 Fast-Switching Mode with CLEARInk Structure 75Robert J. Fleming 4.1 Introduction 75 4.2 CLEARink Display Optics 78 4.3 CLEARink Reflective Color Displays 85 4.4 Electrophoretic Displays with CLEARink Structure 89 4.5 CLEARink Device Architecture 93 4.6 Manufacturing and Supply Chain 96 4.6.1 Status of Technology and Future Projections 96 Acknowledgments 97 5 Bistable Cholesteric Liquid Crystal Displays -- Review and Writing Tablets 99Clinton Braganza and Mauricio Echeverri 5.1 Introduction 99 5.2 Materials and Optical Properties 99 5.3 Image Creation Using Cholesteric Liquid Crystals 104 5.4 Applications 108 5.5 Writing Tablets 109 5.6 Conclusions 126 6 The Zenithal Bistable Display: A Grating Aligned Bistable Nematic Liquid Crystal Device 131Guy P. Bryan-Brown and J. Cliff Jones 6.1 Introduction 131 6.2 Operating Principles and Geometries 132 6.3 Grating Fabrication and Supply Chain 138 6.4 ZBD LCD Manufacturing Processes 141 6.5 Electrical Addressing 144 6.6 Optical Configurations 145 6.7 Novel Arrangements 149 6.8 Conclusions 150 7 Reflective LCD with Memory in Pixel Structure 153Yoko Fukunaga 7.1 Introduction 153 7.2 Memory in Pixel Technology and Its Super Low Power Operation 154 7.3 Sub-Pixel Pattern to Show Gray Scale 157 7.4 Reflective LCD Optical Design 158 7.5 How to Show a Natural Image 163 7.6 Design Characteristics of Current Market-Available Products and Their Super Low Power Operations 164 7.7 Summary of Power Consumption 167 7.8 Applications 168 7.9 Future Expectations 168 8 Optically Rewritable Liquid Crystal Display 171Wanlong Zhang, Abhishek Srivastava, Vladimir Chigrinov, and Hoi-Sing Kwok 8.1 Introduction 171 8.2 Photoalignment Technology 172 8.3 Flexible Optically Rewritable LCD 186 8.4 Dye-Doped Optically Rewritable LCD 188 8.5 Conclusion 190 9 Electrowetting Displays 197Doeke J. Oostra 9.1 Overviews 197 9.2 Introduction 197 9.3 The Promise of Electrowetting Displays 200 9.4 History of Electrowetting Display Development 204 9.5 Electrowetting Cells 205 9.6 Capabilities for Black and White 206 9.7 Capabilities for Video and Color 209 9.8 Driving 215 9.9 Architectures 216 9.10 Manufacturing 217 9.11 Reliability 220 9.12 Failure Mechanisms 220 9.13 In Conclusion: Electrowetting Displays Have Reached Maturity 221 10 Electrochromic Display 225Norihisa Kobayashi 10.1 Introduction 225 10.2 Structure of Electrochromic Display 226 10.3 EC Materials 228 10.4 Summary 239 11 Phase Change Material Displays 243Ben Broughton and Peiman Hosseini 11.1 Introduction 243 11.2 Phase Change Materials and Devices 243 11.3 Strong Interference in Ultra-Thin Absorbing Films 244 11.4 Potential for High Brightness, Low Power Color Reflective Displays 245 11.5 Solid-State Reflective Displays (SRD®) 248 11.6 SRD Prototype -- Progress and Performance 259 11.7 Other Approaches 263 11.8 Conclusions 265 12 Optical Measurements for E-Paper Displays 271Karlheinz Blankenbach 12.1 Introduction 271 12.2 Fundamentals of Reflection 272 12.3 Reflection Measurements Set-Ups 273 12.4 Display Image Quality Parameters 276 12.5 Temporal Parameters 281 12.6 Further Topics 283 12.7 Summary 283 Glossary incl. Abbreviations 284 References 284 Index 287

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Book SynopsisTable of ContentsForeword xv Preface xvii About the Companion Website xxi 1 Introduction 1 2 An Example: The Scalable Observation and Rescue System 5 3 Better Products – The Value of Systems Architecting 9 3.1 The Share of Systems Architecting in Making Better Products 9 3.2 Benefits that can be Achieved 10 3.2.1 Benefit for the Customer 10 3.2.2 Benefit for the Organization 12 3.3 Benefits that can be Communicated Inside the Organization 14 3.4 Beneficial Elements of Systems Architecting 15 3.5 Benefits of Model-Based Systems Architecting 16 4 Systems, Systems of Systems, and Cyber-Physical Systems 17 4.1 Definition of “System” 17 4.1.1 System Elements 19 4.1.2 System Context 20 4.1.3 System Characteristics 21 4.1.4 Purpose 22 4.1.5 System Evolution 23 4.2 Definition of “System of Systems” 23 4.3 Definition of “Cyber-Physical System” 26 4.4 Composition of a “Cyber-Physical System of Systems” 27 5 Definition of System Architecture 31 5.1 What Is Architecture? – Discussion of Some Existing Definitions 31 5.2 Relations Between Concepts of “System,” “Architecture,” and “Architecture Description” 33 5.3 Definition of “Architecture” 35 5.3.1 Interactions 36 5.3.2 Principles 37 5.3.3 Architecture Decisions 37 5.4 Functional and Physical Architecture 37 5.5 Taxonomy of Physical Architectures 39 5.5.1 Logical Architecture 40 5.5.2 Product Architecture 41 5.5.3 Base Architecture 41 5.6 Architecture Landscape for Systems 41 5.6.1 System Architecture 42 5.6.2 System Design 43 5.6.3 Discipline-Specific Architecture and Design 44 6 Model-Based Systems Architecting 45 7 Model Governance 51 7.1 Overview 51 7.2 Model Governance in Practice 52 8 Architecture Description 57 8.1 Architecture Descriptions for Stakeholders 58 8.2 Definition of “Architecture Description” 60 8.2.1 Architecture Viewpoints 62 8.2.2 Architecture Views 65 8.2.3 Architecture Decisions 67 8.2.4 Architecture Rationales 69 8.3 How to Get Architecture Descriptions? 69 8.3.1 Model-Based Vision 69 8.3.2 Forms and Templates 71 9 Architecture Patterns and Principles 75 9.1 The SYSMOD Zigzag Pattern 76 9.2 The Base Architecture 82 9.3 Cohesion and Coupling 85 9.4 Separation of Definition, Usage, and Run-Time 87 9.5 Separate Stable from Unstable Parts 89 9.6 The Ideal System 89 9.7 View and Model 90 9.8 Diagram Layout 92 9.9 System Model Structure 93 9.10 System Architecture Principles 95 9.11 Heuristics 95 9.11.1 Heuristics as a Tool for the System Architect 95 9.11.2 Simplify, Simplify, Simplify: Strength and Pitfall 97 10 Model-Based Requirements Engineering and Use Case Analysis 99 10.1 Requirement and Use Case Definitions 99 10.2 Model-Based Requirements and Use Case Analysis from the MBSA Viewpoint 102 10.2.1 Identify and Define Requirements 103 10.2.2 Specify the System Context 104 10.2.3 Identify Use Cases 105 10.2.4 Describe Use Case Flows 109 10.2.5 Model the Domain Knowledge 110 10.3 The SAMS Method 112 10.3.1 SAMS Method Definitions 113 10.3.2 SAMS Method 114 10.4 Use Cases 2.0 117 11 Perspectives, Viewpoints and Views in System Architecture 119 11.1 Introduction 119 11.2 The Functional Perspective 121 11.2.1 SysML Modeling of Functional Blocks 123 11.2.2 Architecture Views for the System Architect 124 11.2.3 Different Architecture Views for the Stakeholders of Different Functions 124 11.3 The Physical Perspective 125 11.3.1 Logical Architecture Example 126 11.3.2 Product Architecture Example 127 11.4 The Behavioral Perspective 130 11.5 The Layered Perspective 130 11.5.1 The Layered Approach 130 11.5.2 The Layered Perspective in Systems Architecting 132 11.5.3 Relation to the Domain Knowledge Model 134 11.5.4 Architecting the Layers 136 11.5.5 SysML Modeling of Layers 136 11.6 System Deployment Perspective 142 11.7 Other Perspectives 144 11.8 Relation to the System Context 146 11.8.1 Validity of the System Boundary 146 11.8.2 Using the System Context as a Part of the Stakeholder-Specific Views 146 11.8.3 Special System Context View for Verification 147 11.9 Mapping Different System Elements Across Different Levels 148 11.9.1 Functional-to-Physical Perspective Mapping 149 11.9.2 Mapping More Perspectives 153 11.9.3 Mapping Different Levels 153 11.10 Traceability 155 11.11 Perspectives and Architecture Views in Model-based Systems Architecting 155 11.11.1 Creating Different Architecture Views in a Model-Based Approach 155 11.11.2 Using SysML for Working with Different Perspectives and Architecture Views 157 11.11.3 The Importance of Architecture Viewpoints in Model-Based Systems Architecting 159 12 Typical Architecture Stakeholders 161 12.1 Overview 161 12.2 Requirements Engineering 162 12.3 Verification 163 12.4 Configuration Management 166 12.5 Engineering and Information Technology Disciplines 167 12.6 Project and Product Management 171 12.7 Risk Managers 174 12.8 Development Roadmap Planners 174 12.9 Production and Distribution 177 12.10 Suppliers 178 12.11 Marketing and Brand Management 178 12.12 Management 180 13 Roles 185 13.1 Roles 185 13.2 The System Architect Role 186 13.2.1 Objective 186 13.2.2 Responsibilities 186 13.2.3 Tasks 187 13.2.4 Competences 188 13.2.5 Required Skills of a System Architect 188 13.2.6 Required Skills for Model-Based Systems Architecting 190 13.3 System Architecture Teams 190 13.4 System Architecture Stakeholders 192 13.5 Recruiting System Architecture People 192 13.6 Talent Development for System Architects 194 14 Processes 199 14.1 Systems Architecting Processes 199 14.1.1 Overview 199 14.1.2 Example of Generic Process Steps 201 14.1.3 Example of Concrete Process Steps 202 14.1.4 Validation, Review, and Approval in a Model-Based Environment 203 14.2 Design Definition Process 207 14.3 Change and Configuration Management Processes 207 14.4 Other Processes Involving the System Architect 207 15 Tools for the Architect 209 16 Agile Approaches 213 16.1 The History of Iterative–Incremental Approaches 214 16.1.1 Project Mercury (NASA, 1958) 214 16.1.2 The New New Product Development Game (1986) 215 16.1.3 Boehm’s Spiral Model (1988) 216 16.1.4 Lean (1945 Onwards) 217 16.1.5 Dynamic Systems Development Method (DSDM, 1994) 219 16.1.6 Scrum (1995) 220 16.2 The Manifesto for Agile Software Development (2001) 221 16.3 Agile Principles in Systems Engineering 223 16.3.1 Facilitate Face-to-Face Communication 223 16.3.2 Create a State of Confidence 224 16.3.3 Build Transdisciplinary and Self-Organized Teams 225 16.3.4 Create a Learning Organization 225 16.3.5 Design, but No Big Design (Up-Front) 226 16.3.6 Reduce Dependencies 227 16.3.7 Foster a Positive Error Culture 228 16.4 Scaling Agile 228 16.5 System Architects in an Agile Environment 230 17 The FAS Method 233 17.1 Motivation 234 17.2 Functional Architectures for Systems 236 17.3 How the FAS Method Works 239 17.4 FAS Heuristics 242 17.5 FAS with SysML 244 17.5.1 Identifying Functional Groups 244 17.5.2 Modeling the Function Structure 246 17.5.3 Modeling the Functional Architecture 249 17.6 SysML Modeling Tool Support 250 17.6.1 Create Initial Functional Groups 251 17.6.2 Changing and Adding Functional Groups 254 17.6.3 Creating Functional Blocks and their Interfaces 254 17.7 Mapping of a Functional Architecture to a Physical Architecture 254 17.8 Experiences with the FAS Method 256 17.9 FAS Workshops 258 17.10 Quality Requirements and the Functional Architecture 259 17.11 Functional Architectures and the Zigzag Pattern 262 17.12 CPS-FAS for Cyber-physical Systems 263 18 Product Lines and Variants 269 18.1 Definitions Variant Modeling 270 18.2 Variant Modeling with SysML 271 18.3 Other Variant Modeling Techniques 276 19 Architecture Frameworks 279 19.1 Enterprise Architectures 280 19.2 Characteristics of System of Systems (SoS) 282 19.2.1 Emergence 283 19.3 An Overview of Architecture Frameworks 285 19.3.1 Zachman FrameworkTM 285 19.3.2 The TOGAF® Standard 286 19.3.3 Federal Enterprise Architecture Framework (FEAF) 288 19.3.4 Department of Defense Architecture Framework (DoDAF) 289 19.3.5 Ministry of Defense Architecture Framework (MODAF) 290 19.3.6 NATO Architecture Framework (NAF) 291 19.3.7 TRAK 292 19.3.8 European Space Agency Architectural Framework (ESA-AF) 293 19.3.9 OMG Unified Architecture Framework® (UAF®) 295 19.4 System Architecture Framework (SAF) 296 Together with Michael Leute 296 19.4.1 SAF and Enterprise Frameworks 296 19.4.2 SAF Ontology 298 19.5 What to Do When We Come in Touch With Architecture Frameworks 298 20 Cross-cutting Concerns 301 20.1 The Game-Winning Nonfunctional Aspects 301 20.2 Human System Interaction and Human Factors Engineering 303 20.3 Risk Management 304 20.4 Trade Studies 305 20.5 Budgets 306 21 Architecture Assessment 307 22 Making It Work in the Organization 313 22.1 Overview 313 22.2 Organizational Structure for Systems Architecting 314 22.3 Recipes from the Authors’ Experience 318 22.3.1 Be Humble 319 22.3.2 Appraise the Stakeholders 319 22.3.3 Care About Organizational Interfaces 319 22.3.4 Show that it Was Always There 321 22.3.5 Lead by Good Example 321 22.3.6 Collect Success Stories and Share them When Appropriate 322 22.3.7 Acknowledge that Infections Beat Dictated Rollout 323 22.3.8 Assign the System Architect Role to Yourself 324 22.3.9 Be a Leader 324 23 Soft Skills 327 23.1 It’s All About Communication 328 23.1.1 Losses in Communication 329 23.1.2 The Anatomy of a Message 330 23.1.3 Factors Influencing Communication 333 23.1.3.1 The Language 333 23.1.3.2 The Media Used 333 23.1.3.3 Spatial Distance 333 23.1.3.4 Various Connotations of Words 335 23.1.4 The Usage of Communication Aids and Tools 335 23.2 Personality Types 338 23.2.1 Psychological Types by C. G. Jung 338 23.2.2 The 4MAT System by Bernice McCarthy 340 23.3 Team Dynamics 341 23.4 Diversity and Psychological Safety 342 23.4.1 Project Aristotle (Google) 342 23.4.2 Elements of Psychological Safety 343 23.5 Intercultural Collaboration Skills 344 24 Outlook: The World After Artificial Intelligence 347 Appendix A OMG Systems Modeling Language 349 A.1 Architecture of the Language 350 A.2 Diagram and Model 352 A.3 Structure Diagrams 353 A.3.1 Block Definition Diagram 354 A.3.2 Internal Block Diagram 357 A.3.3 Parametric Diagram 361 A.3.4 Package Diagram 362 A.4 Behavior Diagrams 363 A.4.1 Use Case Diagram 364 A.4.2 Activity Diagram 366 A.4.3 State Machine Diagram 369 A.4.4 Sequence Diagram 371 A.5 Requirements Diagram 372 A.6 Extension of SysML with Profiles 374 A.7 Next-Generation Modeling Language SysML v2 376 Appendix B The V-Model 381 B.1 A Brief History of the V-Model or the Systems Engineering Vee 381 B.2 A Handy Illustration but No Comprehensive Process Description 383 B.3 Critical Considerations 385 B.3.1 The V-Model as Process Description 386 B.3.2 The V-Model Does Not Impose a Waterfall Process 386 B.3.3 The V-Model Accommodates Iterations 387 B.3.4 The V-Model Permits Incremental Development 387 B.3.5 The V-Model and Concurrent Engineering 388 B.3.6 The V-Model Accommodates Change 388 B.3.7 The V-Model Permits Early Verification Planning 388 B.3.8 The V-Model Shows Where to Prevent Dissatisfaction 388 B.4 Reading Instruction for a Modern Systems Engineering Vee 389 B.4.1 The Vertical Dimension 389 B.4.2 The Horizontal Dimension 389 B.4.3 The Left Side 389 B.4.4 The Right Side 390 B.4.5 The Levels 390 B.4.6 Life Cycle Processes 390 B.4.7 The Third Dimension 390 Appendix C Glossary 391 C.1 Heritage of the Term “Glossary” 391 C.2 Terms with Specific Meaning 393 References 399 Index 417

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Book SynopsisExplore the theoretical foundations and real-world power system applications of convex programming In Mathematical Programming for Power System Operation with Applications in Python, Professor Alejandro Garces delivers a comprehensive overview of power system operations models with a focus on convex optimization models and their implementation in Python. Divided into two parts, the book begins with a theoretical analysis of convex optimization models before moving on to related applications in power systems operations. The author eschews concepts of topology and functional analysis found in more mathematically oriented books in favor of a more natural approach. Using this perspective, he presents recent applications of convex optimization in power system operations problems. Mathematical Programming for Power System Operation with Applications in Python uses Python and CVXPY as tools to solve power system optimization problems and includes mTable of ContentsAcknowledgment ix Introduction xi 1 Power systems operation 1 1.1 Mathematical programming for power systems operation 1 1.2 Continuous models 3 1.2.1 Economic and environmental dispatch 3 1.2.2 Hydrothermal dispatch 3 1.2.3 Effect of the grid constraints 5 1.2.4 Optimal power flow 5 1.2.5 Hosting capacity 7 1.2.6 Demand-side management 7 1.2.7 Energy storage management 9 1.2.8 State estimation and grid identification 9 1.3 Binary problems in power systems operation 11 1.3.1 Unit commitment 12 1.3.2 Optimal placement of distributed generation and capacitors 12 1.3.3 Primary feeder reconfiguration and topology identification 13 1.3.4 Phase balancing 13 1.4 Real-time implementation 14 1.5 Using Python 15 Part I Mathematical programming 17 2 A brief introduction to mathematical optimization 19 2.1 About sets and functions 19 2.2 Norms 22 2.3 Global and local optimum 24 2.4 Maximum and minimum values of continuous functions 25 2.5 The gradient method 26 2.6 Lagrange multipliers 32 2.7 The Newton’s method 33 2.8 Further readings 35 2.9 Exercises 35 3 Convex optimization 39 3.1 Convex sets 39 3.2 Convex functions 45 3.3 Convex optimization problems 47 3.4 Global optimum and uniqueness of the solution 50 3.5 Duality 52 3.6 Further readings 56 3.7 Exercises 58 4 Convex Programming in Python 61 4.1 Python for convex optimization 61 4.2 Linear programming 62 4.3 Quadratic forms 67 4.4 Semidefinite matrices 69 4.5 Solving quadratic programming problems 71 4.6 Complex variables 74 4.7 What is inside the box? 75 4.8 Mixed-integer programming problems 76 4.9 Transforming MINLP into MILP 79 4.10 Further readings 80 4.11 Exercises 81 5 Conic optimization 85 5.1 Convex cones 85 5.2 Second-order cone optimization 85 5.2.1 Duality in SOC problems 90 5.3 Semidefinite programming 92 5.3.1 Trace, determinant, and the Shur complement 92 5.3.2 Cone of semidefinite matrices 95 5.3.3 Duality in SDP 97 5.4 Semidefinite approximations 98 5.5 Polynomial optimization 102 5.6 Further readings 105 5.7 Exercises 106 6 Robust optimization 109 6.1 Stochastic vs robust optimization 109 6.1.1 Stochastic approach 110 6.1.2 Robust approach 110 6.2 Polyhedral uncertainty 111 6.3 Linear problems with norm uncertainty 113 6.4 Defining the uncertainty set 115 6.5 Further readings 121 6.6 Exercises 121 Part II Power systems operation 125 7 Economic dispatch of thermal units 127 7.1 Economic dispatch 127 7.2 Environmental dispatch 133 7.3 Effect of the grid 136 7.4 Loss equation 140 7.5 Further readings 143 7.6 Exercises 143 8 Unit commitment 145 8.1 Problem definition 145 8.2 Basic unit commitment model 146 8.3 Additional constraints 150 8.4 Effect of the grid 151 8.5 Further readings 153 8.6 Exercises 153 9 Hydrothermal scheduling 155 9.1 Short-term hydrothermal coordination 155 9.2 Basic hydrothermal coordination 156 9.3 Non-linear models 159 9.4 Hydraulic chains 162 9.5 Pumped hydroelectric storage 165 9.6 Further readings 168 9.7 Exercises 169 10 Optimal power flow 171 10.1 OPF in power distribution grids 171 10.1.1 A brief review of power flow analysis 173 10.2 Complex linearization 177 10.2.1 Sequential linearization 181 10.2.2 Exponential models of the load 182 10.3 Second-order cone approximation 184 10.4 Semidefinite approximation 188 10.5 Further readings 190 10.6 Exercises 190 11 Active distribution networks 195 11.1 Modern distribution networks 195 11.2 Primary feeder reconfiguration 196 11.3 Optimal placement of capacitors 200 11.4 Optimal placement of distributed generation 203 11.5 Hosting capacity of solar energy 205 11.6 Harmonics and reactive power compensation 208 11.7 Further readings 212 11.8 Exercises 212 12 State estimation and grid identification 215 12.1 Measurement units 215 12.2 State estimation 216 12.3 Topology identification 221 12.4 Ybus estimation 224 12.5 Load model estimation 228 12.6 Further readings 231 12.7 Exercises 232 13 Demand-side management 235 13.1 Shifting loads 235 13.2 Phase balancing 240 13.3 Energy storage management 246 13.4 Further readings 249 13.5 Exercises 249 A The nodal admittance matrix 253 B Complex linearization 257 C Some Python examples 263 C.1 Basic Python 263 C.2 NumPy 266 C.3 MatplotLib 268 C.4 Pandas 268 Bibliography 271 Index 281

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Book SynopsisPOWER QUALITY MEASUREMENT AND ANALYSIS USING HIGHER-ORDER STATISTICS Help protect your network with this important reference work on cyber security Power quality (PQ) in electrotechnical systems refers to a set of characteristics related to the movement of energy and the delivery of voltage to consumers in the highest standard. As electricity networks change and adapt to new technologies and concepts of energy within a future Smart Grid, it has become clear that standardized methods by which stability and accuracy of electrical service along a network are currently measured are no longer enough to solve inherent issues in service and ensure established requirements are met. Power Quality Measurement and Analysis using Higher-Order Statistics reflects the latest information related to PQ (Power Quality) analysis solutions, particularly that related to the implementation of new quality indices in the domain of higher-order statistics (HOS). Table of ContentsPOWER QUALITY MEASUREMENT AND ANALYSIS USING HIGHER-ORDER STATISTICS 1 Understanding HOS contribution on the Smart(er) Grid 1 POWER QUALITY MEASUREMENT AND ANALYSIS USING HIGHER-ORDER STATISTICS 3 Understanding HOS contribution on the Smart(er) Grid 3 LOGO 3 Contents 11 Contributors 14 Foreword 17 Acronyms 21 Acknowledgments 24 Chapter 1. Power quality monitoring and higher-order statistics. State of the Art 26 1.1 Introduction 27 1.2 Background on power quality 27 1.3 PQ Practices at the Industrial Level 33 1.4 A new PQ monitoring Framework 33 1.4.1 The Smart Grid 35 1.4.2 The Smart Grid and the Power Quality 35 1.4.3 Performance Indicators 36 1.4.4 Existing measurement and instrumentation solutions 37 1.4.5 New approach in Measurement and Instrumentation solutions in the SG 38 1.4.6 Economic Issues for PQ 39 1.4.7 Power Quality and Big Data 39 1.4.8 Signal Processing for PQ 40 1.4.9 HOS for PQ analysis 43 Chapter 2. HOS Measurements in the time domain 47 HOS Measurements in the time domain 48 2.1 Introduction 48 2.2 Background on power quality 48 2.3 Traditional theories of electrical time domain 49 2.4 HOS contribution in the PQ field 51 2.4.1 HOS indices definitions 51 2.4.2 HOS performance in signal processing 52 2.4.3 HOS versus electrical time domain indices 53 2.5 Regulations 55 2.6 The Sliding Window Method for HOS feature extraction 56 2.6.1 Amplitude Changes 57 2.6.2 Phase Angle Jumps 58 2.6.3 Fundamental Frequency 60 2.6.4 Waveform shape deviation 62 2.7 PQ index based on HOS 64 2.8 Representations used by the time-domain 67 Chapter 3. Event Detection Strategies based on HOS feature extraction 72 3.1 Introduction 73 3.2 Detection methods based in HOS 73 3.3 Experiment description 73 3.3.1 Computational Strategy 73 3.3.2 HOS for Sag Detection under Symmetrical and Sinusoidal Conditions 74 3.3.2 HOS for Sag Detection including Phase-Angle Jump based on Non-Symmetrical & Non-Sinusoidal conditions 75 3.3.2.1 HOS range for Transient detection including Phase-Angle Jump based on Non-Symmetrical & Non- Sinusoidal conditions 87 3.3 Flow Diagram of HOS monitoring strategy focus on detecting short duration events: detecting amplitude, symmetry, and sinusoidal states 87 3.4 Continuous event’s characterization fundamental frequency 90 3.4.1 Frequency deviation regions in the HOS planes 92 3.4.2 Frequency deviation regions in the HOS planes 94 3.5 Detection of Harmonics with HOS in the time domain 95 3.6 Conclusions 97 Chapter 4. Measurements in the Frequency domain 100 4.1 Introduction 101 4.2 Frequency-domain 101 4.3 HOS in Frequency-domain 102 4.3.1 Spectral Kurtosis in Power Quality 103 4.4 Harmonic distortion 103 4.4.1 Types of Harmonic distortion 104 4.4.2 Sources of Harmonic distortion 105 4.4.3 Impact of harmonic distortion over power system 105 4.5 Traditional theories of electrical frequency-domain indicators 105 4.5.1 Harmonic measure 105 4.5.2 DFT derived measures 107 4.6 HOS contribution in PQ in the frequency-domain 107 4.6.1 Spectral Kurtosis 108 4.6.2 Spectral Kurtosis basic usage 115 4.6.3 Spectral Kurtosis and Power quality 118 Chapter 5 Measurement Campaigns and Virtual Instruments 124 5.1 Introduction 125 5.2 Virtual Instrument 126 5.2.1 Measurement Analysis Framework 126 5.2.2 Experimental Strategy for PQM through a Virtual Instrument 128 5.2.3 Configuration of the Virtual Instrument 128 5.2.4 Results 131 5.3 PQ continuous monitoring based on HOS for consumers characterization, public networks and household 132 5.3.1 Measurement and Analysis Framework 132 5.3.2 Evolution of the individual statistics histograms during several weeks 133 References 149 Annex A. Voltage Waveform 1 Theoretical power system waveform 1 Annex. B. Time-domain cumulants 1 Annex. C. HOS Range for Sag Detection, one cycle 3 Annex. D. HOS Range for Sag Detection, 10 cycles 7

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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

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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

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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

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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

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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

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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

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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

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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

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Book Synopsis

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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

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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

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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

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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

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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

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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

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