Electronics and communications engineering Books

Book Synopsis>IoT-Enabled Smart Healthcare Systems, Services and Applications Explore the latest healthcare applications of cutting-edge technologies In IoT-Enabled Smart Healthcare Systems, Services and Applications, an accomplished team of researchers delivers an insightful and comprehensive exploration of the roles played by cutting-edge technologies in modern healthcare delivery. The distinguished editors have included resources from a diverse array of learned experts in the field that combine to create a broad examination of a rapidly developing field. With a particular focus on Internet of Things (IoT) technologies, readers will discover how new technologies are impacting healthcare applications from remote monitoring systems to entire healthcare delivery methodologies. After an introduction to the role of emerging technologies in smart health care, this volume includes treatments of ICN-Fog computing, edge computing, security and privacy, IoT architecture, vehiTable of ContentsPreface ix Acknowledgments xi About the Editors xiii List of Contributors xvii 1 The Role of Emerging Technologies in Smart Healthcare 1Masooma Zehra Syeda, Dur-e-hassan Syeda, and Himanshi Babbar 1.1 Introduction 1 1.2 Emerging Technologies in Smart Healthcare 3 1.3 Realization of SHC through Emerging Technologies (Applications) 9 1.4 Conclusion 15 Author Biography 15 References 15 2 ICN-Fog Computing for IoT-Based Healthcare: Architecture and Challenges 19Divya Gupta, Shalli Rani, and Syed Hassan Ahmed Shah 2.1 Introduction 19 2.2 ICN in IoT 21 2.3 IoT in Healthcare 24 2.4 Role of Fog Computing in IoT 26 2.5 Fog Computing in Healthcare: A Classification Approach 28 2.6 ICN-Fog Leveraging Healthcare Architecture 30 2.7 Challenges in the Healthcare System 33 2.8 Conclusion 35 References 35 3 Internet of Things (IoT) Enabled Software Defined Networking (SDN) for Load Balancing, Edge, Cloud Computing in Healthcare 39Himanshi Babbar, Shalli Rani, and Neeraj Kumar 3.1 Overview of Software-Defined Networking 39 3.2 Overview of Healthcare 40 3.3 Technologies Used in Software-Defined Networking (SDN) and HealthCare 42 3.4 Use Cases of Software-Defined Networking in Healthcare 52 3.5 Research Directions 55 3.6 Conclusion 55 Key Points 56 Author Biography 57 References 59 4 Security and Privacy Issues in Smart Healthcare System Using Internet of Things 63R. Nidhya, Manish Kumar, R. Maheswar, and D. Pavithra 4.1 Introduction 63 4.2 Overview of Internet of Things in Smart Healthcare Systems 64 4.3 Policies and Legislation Related to Smart Healthcare 66 4.4 Security and Privacy Issues in Smart Healthcare Based on Internet of Things 69 4.5 Issues in Location Privacy 77 4.6 Issues in Privacy of Stored Data and Threats Identity 77 4.7 Conclusion 77 Author Biography 78 References 79 5 An Overview of Architecture and Applications of IoT-Based Health Care Systems 87M. Saravanan, J. Ajayan, R. Maheswar, and E. Parthasarathy 5.1 Introduction 87 5.2 Overview of the Healthcare System 88 5.3 Working Prototype of Healthcare System Using BAN 89 5.4 On-Body Sensors and Sensor Embodiment 93 5.5 Vital Signs Monitoring 97 5.6 Biosensors in M-Health 104 5.7 IoT-Based Rehabilitation System 109 5.8 Future Work 115 5.9 Conclusion 115 Authors’ Biography 115 References 117 6 A Review of e-Healthcare System of India and Thailand 123Shanu Bhardwaj, S.N. Panda, Priyanka Datta, Rajesh Kumar Kaushal, and Naveen Kumar 6.1 Introduction 123 6.2 Literature Review 124 6.3 Problem Statement 134 6.4 Methodology 134 6.5 Discussion 134 6.6 Conclusion 137 References 138 7 WSN-and IoT-Based Smart Surveillance Systems for Patients with Closed-Loop Alarm 143Amit Sundas, Sumit Badotra, Shalli Rani, and Chhabildas Madhukar Gajare 7.1 Introduction 143 7.2 Literature Review 152 7.3 Proposed Work : WSN and IoT-Based Smart Surveillance System for Patients with Closed-Loop Alarm 155 7.4 Implementation and Evaluation of the WSN and IoT-Based Smart Surveillance System for Patients with Closed-Loop Alarm 160 7.5 Conclusion and Future Work 173 References 174 8 An IoMT-Based Smart Remote Monitoring System for Healthcare 177Chetna Kaushal, Md Khairul Islam, Anshu Singla, and Md Al Amin 8.1 Introduction 177 8.2 Literature Review 181 8.3 Methodology 183 8.4 Use Case of Real-Time Remote Monitoring System 191 8.5 Conclusion 193 References 194 9 A Multi-Domain Perspective of Future Directions for VANETs for Emergency Message Dissemination 199Ravneet Kaur, Ramkumar Ketti Ramachandran, Robin Doss, and Lei Pan 9.1 Introduction 199 9.2 Future Directions of Multi-Domain VANETs Emergency Message Dissemination 200 9.3 Role of VANETs in Healthcare Systems 203 9.4 Techniques Used for Fast Delivery of Emergency Message in VANETs to Help the Healthcare System 205 9.5 Current Facilities and Limitations for Implementing the Real-Time Environment 210 9.6 Discussion on Upcoming Trends and Possibilities with Future Readings 211 9.7 Conclusion 214 Notes 214 Author Biography 214 References 216 Index 219

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Book SynopsisHANDBOOK of BIOMASS VALORIZATION for INDUSTRIAL APPLICATIONS The handbook provides a comprehensive view of cutting-edge research on biomass valorization, from advanced fabrication methodologies through useful derived materials, to current and potential application sectors. Industrial sectors, such as food, textiles, petrochemicals and pharmaceuticals, generate massive amounts of waste each year, the disposal of which has become a major issue worldwide. As a result, implementing a circular economy that employs sustainable practices in waste management is critical for any industry. Moreover, fossil fuels, which are the primary sources of fuel in the transportation sector, are also being rapidly depleted at an alarming rate. Therefore, to combat these global issues without increasing our carbon footprint, we must look for renewable resources to produce chemicals and biomaterials. In that context, agricultural waste materials are gaining popularity as cost-effective and abundantly availabl

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Book SynopsisDesign for Embedded Image Processing on FPGAs Bridge the gap between software and hardware with this foundational design reference Field-programmable gate arrays (FPGAs) are integrated circuits designed so that configuration can take place. Circuits of this kind play an integral role in processing images, with FPGAs increasingly embedded in digital cameras and other devices that produce visual data outputs for subsequent realization and compression. These uses of FPGAs require specific design processes designed to mediate smoothly between hardware and processing algorithm. Design for Embedded Image Processing on FPGAs provides a comprehensive overview of these processes and their applications in embedded image processing. Beginning with an overview of image processing and its core principles, this book discusses specific design and computation techniques, with a smooth progression from the foundations of the field to its advanced principles. Readers of the second edition of Design for Embedded Image Processing on FPGAs will also find: Detailed discussion of image processing techniques including point operations, histogram operations, linear transformations, and moreNew chapters covering Deep Learning algorithms and Image and Video CodingExample applications throughout to ground principles and demonstrate techniques Design for Embedded Image Processing on FPGAs is ideal for engineers and academics working in the field of Image Processing, as well as graduate students studying Embedded Systems Engineering, Image Processing, Digital Design, and related fields.Table of ContentsPreface xiii Acknowledgments xix About the Companion Website xxi 1 Image Processing 1 1.1 Basic Definitions 1 1.2 Image Formation 3 1.2.1 Optics 3 1.2.2 Colour 5 1.3 Image Processing Operations 6 1.4 Real-time Image Processing 8 1.5 Embedded Image Processing 9 1.6 Computer Architecture 10 1.7 Parallelism 11 1.7.1 Temporal or Task Parallelism 12 1.7.2 Spatial or Data Parallelism 13 1.7.3 Logical Parallelism 14 1.7.4 Stream Processing 14 1.8 Summary 15 References 16 2 Field-programmable Gate Arrays 19 2.1 Hardware Architecture of FPGAs 19 2.1.1 Logic 21 2.1.2 DSP Blocks 22 2.1.3 Memory 23 2.1.4 Embedded CPU 23 2.1.5 Interconnect 24 2.1.6 Input and Output 24 2.1.7 Clocking 26 2.1.8 Configuration 26 2.1.9 FPGAs vs. ASICs 27 2.2 Programming FPGAs 28 2.2.1 Register Transfer Level 30 2.2.2 Hardware Description Languages 32 2.2.3 High-level Synthesis 33 2.3 FPGAs and Image Processing 38 2.3.1 Choosing an FPGA or Development Board 39 2.4 Summary 40 References 41 3 Design Process 45 3.1 Problem Specification 45 3.2 Algorithm Development 47 3.2.1 Algorithm Development Process 47 3.2.2 Algorithm Structure 48 3.2.3 FPGA Development Issues 51 3.3 Architecture Selection 51 3.3.1 System Architecture 52 3.3.2 Partitioning Between Hardware and Software 53 3.3.3 Computational Architecture 55 3.4 System Implementation 60 3.4.1 Mapping to FPGA Resources 60 3.4.2 Algorithm Mapping Issues 62 3.5 Testing and Debugging 63 3.5.1 Design 63 3.5.2 Implementation 64 3.5.3 Common Implementation Bugs 64 3.5.4 Timing 66 3.5.5 System Debugging 68 3.5.6 Algorithm Tuning 70 3.5.7 In-field Diagnosis 71 3.6 Summary 72 References 73 4 Design Constraints 77 4.1 Timing Constraints 77 4.1.1 Low-level Pipelining 78 4.1.2 Process Synchronisation 80 4.1.3 Synchronising Between Clock Domains 82 4.1.4 I/O Constraints 83 4.2 Memory Bandwidth Constraints 84 4.2.1 Memory Architectures 84 4.2.2 Caching 86 4.2.3 Row Buffering 87 4.3 Resource Constraints 88 4.3.1 Bit-serial Computation 89 4.3.2 Resource Multiplexing 89 4.3.3 Arbitration 92 4.3.4 Resource Controllers 94 4.3.5 Reconfigurability 95 4.4 Power Constraints 97 4.5 Performance Metrics 98 4.5.1 Speed 99 4.5.2 Resources 99 4.5.3 Power 99 4.5.4 Cost 100 4.5.5 Application Metrics 100 4.6 Summary 101 References 102 5 Computational Techniques 105 5.1 Number Systems 105 5.1.1 Binary Integers 105 5.1.2 Residue Systems 106 5.1.3 Redundant Representations 107 5.1.4 Fixed-point Numbers 107 5.1.5 Floating-point Numbers 108 5.1.6 Logarithmic Number System 110 5.1.7 Posit Numbers 110 5.2 Elementary Functions 111 5.2.1 Square Root 111 5.2.2 Trigonometric Functions 112 5.2.3 Linear CORDIC 116 5.2.4 Hyperbolic Functions 117 5.2.5 Logarithms and Exponentials 118 5.2.6 Lookup Tables 118 5.2.7 Polynomial Approximations 122 5.2.8 Iterative Techniques 123 5.3 Other Computation Techniques 124 5.3.1 Incremental Update 124 5.3.2 Separability 124 5.4 Memory Structures 124 5.4.1 FIFO Buffer 124 5.4.2 Zigzag Buffers 126 5.4.3 Stacks 126 5.4.4 Linked Lists 127 5.4.5 Trees 128 5.4.6 Graphs 129 5.4.7 Hash Tables 129 5.5 Summary 130 References 131 6 Interfacing 135 6.1 Camera Input 135 6.1.1 Analogue Video 136 6.1.2 Direct Digital Interface 137 6.1.3 MIPI Camera Serial Interface 138 6.1.4 Camera Link 139 6.1.5 USB Cameras 139 6.1.6 GigE Vision 139 6.1.7 Camera Processing Pipeline 140 6.2 Display Output 143 6.2.1 Display Driver 143 6.2.2 Display Content 146 6.3 Serial Communication 147 6.3.1 Rs- 232 147 6.3.2 I 2 c 148 6.3.3 Serial Peripheral Interface (SPI) 149 6.3.4 Universal Serial Bus (USB) 150 6.3.5 Ethernet 150 6.3.6 PCI Express 151 6.4 Off-chip Memory 151 6.4.1 Static RAM 152 6.4.2 Dynamic RAM 152 6.4.3 Flash Memory 155 6.5 Processors 155 6.5.1 AXI Interface 155 6.5.2 Avalon Bus 156 6.5.3 Operating Systems 157 6.5.4 Implications for System Design 157 6.6 Summary 157 References 158 7 Point Operations 161 7.1 Point Operations on a Single Image 161 7.1.1 Contrast and Brightness Adjustment 161 7.1.2 Global Thresholding and Contouring 164 7.1.3 Lookup Table Implementation 166 7.2 Point Operations on Multiple Images 167 7.2.1 Image Averaging 168 7.2.2 Image Subtraction 170 7.2.3 Background Modelling 172 7.2.4 Intensity Scaling 175 7.2.5 Masking 175 7.2.6 High Dynamic Range (HDR) Imaging 177 7.3 Colour 179 7.3.1 False Colour 179 7.3.2 Colour Space Conversion 180 7.3.3 Colour Thresholding 192 7.3.4 Colour Enhancement 193 7.3.5 White Balance 194 7.4 Multi-spectral and Hyperspectral Imaging 197 7.4.1 Hyperspectral Image Acquisition 197 7.4.2 Processing Steps 198 7.5 Summary 199 References 199 8 Histogram Operations 203 8.1 Greyscale Histogram 203 8.1.1 Building the Histogram 203 8.1.2 Data Gathering 205 8.1.3 Histogram Equalisation 209 8.1.4 Automatic Exposure 214 8.1.5 Threshold Selection 215 8.1.6 Histogram Similarity 220 8.2 Multidimensional Histograms 220 8.2.1 Triangular Arrays 221 8.2.2 Multidimensional Statistics 222 8.2.3 Colour Segmentation 225 8.2.4 Colour Indexing 228 8.2.5 Texture Analysis 229 8.3 Summary 231 References 231 9 Local Filters 235 9.1 Window Caching 235 9.1.1 Border Handling 237 9.1.2 Filter Latency 239 9.2 Linear Filters 239 9.2.1 Filter Techniques 240 9.2.2 Noise Smoothing 243 9.2.3 Edge Detection 246 9.2.4 Edge Enhancement 248 9.3 Nonlinear Filters 249 9.3.1 Gradient Magnitude 249 9.3.2 Edge Orientation 250 9.3.3 Peak Detection and Non-maximal Suppression 251 9.3.4 Zero-crossing Detection 252 9.3.5 Bilateral Filter 252 9.3.6 Adaptive Thresholding 253 9.3.7 High Dynamic Range Tone Mapping 255 9.4 Rank Filters 256 9.4.1 Sorting Networks 258 9.5 Adaptive Histogram Equalisation 262 9.6 Morphological Filters 262 9.6.1 Binary Morphology 262 9.6.2 Greyscale Morphology 266 9.7 Colour Filtering 268 9.7.1 Colour Morphology and Vector Median 269 9.7.2 Edge Enhancement 269 9.7.3 Bayer Pattern Demosaicing 271 9.7.4 White Balancing 272 9.8 Summary 273 References 274 10 Geometric Transformations 281 10.1 Reverse Mapping 282 10.1.1 Anti-alias Filtering 283 10.1.2 Interpolation 284 10.2 Forward Mapping 291 10.2.1 Separable Mapping 292 10.2.2 Hybrid Approach 296 10.3 Common Mappings 297 10.3.1 Affine Transformation 297 10.3.2 Perspective Mapping 297 10.3.3 Polynomial Mapping 298 10.3.4 Lens Distortion 299 10.3.5 Non-parametric Mappings 302 10.4 Image Registration 302 10.4.1 Feature-based Methods 303 10.4.2 Area-based Methods 307 10.4.3 Applications 314 10.5 Summary 315 References 315 11 Linear Transforms 321 11.1 Discrete Fourier Transform 322 11.1.1 Fast Fourier Transform (FFT) 323 11.1.2 Goertzel’s Algorithm 331 11.1.3 Applications 332 11.2 Discrete Cosine Transform (DCT) 336 11.3 Wavelet Transform 338 11.3.1 Filter Implementations 340 11.3.2 Applications 344 11.4 Summary 345 References 345 12 Image and Video Coding 349 12.1 Compression Techniques 350 12.1.1 Colour Conversion 350 12.1.2 Prediction and Transformation 350 12.1.3 Motion Estimation and Compensation 351 12.1.4 Quantisation 352 12.1.5 Run-length Coding 353 12.1.6 Entropy Coding 354 12.2 DCT-based Codecs 357 12.2.1 DCT Block Processing 357 12.2.2 Jpeg 357 12.2.3 Video Codecs 358 12.3 Wavelet-based Codecs 359 12.4 Lossless Compression 360 12.5 Perceptual Coding 361 12.6 Coding Hyperspectral Images 362 12.7 Summary 362 References 363 13 Blob Detection and Labelling 367 13.1 Bounding Box 367 13.2 Run-length Coding 369 13.3 Chain Coding 369 13.3.1 Sequential Implementation 370 13.3.2 Single-pass Stream Processing Algorithms 370 13.3.3 Feature Extraction 372 13.4 Connected Component Labelling (CCL) 374 13.4.1 Random Access Algorithms 374 13.4.2 Multiple Pass Algorithms 374 13.4.3 Two-pass Algorithms 375 13.4.4 Parallel Algorithms 377 13.4.5 Hysteresis Thresholding 377 13.5 Connected Component Analysis (CCA) 377 13.5.1 Basic Single-pass Algorithm 378 13.5.2 Reducing Memory Requirements 379 13.5.3 Eliminating End-of-row Overheads 379 13.5.4 Parallel Algorithms 380 13.5.5 Further Considerations and Optimisations 381 13.6 Distance Transform 381 13.6.1 Morphological Approaches 381 13.6.2 Chamfer Distance 382 13.6.3 Euclidean Distance 384 13.6.4 Applications 386 13.6.5 Geodesic Distance Transform 386 13.7 Watershed Transform 387 13.7.1 Flow Algorithms 388 13.7.2 Immersion Algorithms 389 13.8 Hough Transform 391 13.8.1 Line Hough Transform 391 13.8.2 Circle Hough Transform 394 13.8.3 Generalised Hough Transform 395 13.9 Summary 396 References 396 14 Machine Learning 403 14.1 Training 403 14.1.1 Loss and Cost Functions 404 14.1.2 Model Optimisation 405 14.1.3 Considerations 406 14.2 Regression 409 14.2.1 Linear Regression 409 14.2.2 Nonlinear Regression 409 14.2.3 Neural Networks 409 14.3 Classification 411 14.3.1 Decision Trees 411 14.3.2 Random Forests 412 14.3.3 Bayesian Classification 412 14.3.4 Quadratic Discriminant Analysis 414 14.3.5 Linear Discriminant Analysis 414 14.3.6 Support Vector Machines 415 14.3.7 Neural Networks 416 14.3.8 Clustering 417 14.4 Deep Learning 418 14.4.1 Building Blocks 419 14.4.2 Architectures and Applications 421 14.4.3 Training 427 14.4.4 Implementation Issues 428 14.5 Summary 433 References 433 15 Example Applications 441 15.1 Coloured Region Tracking 441 15.2 Foveal Sensor 443 15.2.1 Foveal Mapping 444 15.2.2 Using the Sensor 447 15.3 Real-time Produce Grading 448 15.3.1 Software Algorithm 448 15.3.2 Hardware Implementation 450 15.4 Stereo Imaging 453 15.4.1 Rectification 454 15.4.2 Calculating the Depth 456 15.4.3 Stereo Matching Design 457 15.5 Face Detection 459 15.5.1 Design 460 15.6 Summary 461 References 461 Index 465

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Book SynopsisComprehensive resource presenting the fundamentals and state of the art concepts, design examples, relevant components, and technology Slow-wave Microwave and mm-wave Passive Circuits presents the fundamentals and state of the art concepts, design examples, relevant components, and technology of the subject, plus examples of circuit layout optimization using slow-wave circuits. Recent advances in aspects of the slow-wave concept are covered, with potential applications including automotive radars, medical and security applications, and 5G and future 6G for very high-speed communications. The text considers a variety of slow-wave structures and associated concepts which are useful for circuit design, each structure electrically modeled with clear illustration. The highly qualified authors show that the use of the slow-wave concept can, in some cases, improve the performance of passive circuits. The techniques proposed make it possible to reduce the size and/or the performance of the circuits, with a beneficial cost-saving effect on semiconductor materials. Concepts are applied to several technologies, namely CMOS, PCB (Printed Circuit Board) and nanowires. Sample topics covered include: Concepts of energy storage with examples of slow-wave CPW (S-CPW), slow-wave SIW (SW-SIW), and slow-wave microstrip (S-MS),Transmission line topology and application in integrated technologies (CMOS), including possibilities offered by the BEOL (Back-End-Of-Line),Effect of the geometrical dimensions on the transmission line parameters (Zc, a, ereff, and Q) and comparisons between conventional CPW and CPS, and slow-wave CPW and CPS, Performance of slow-wave coupled lines and comparison with conventional microstrip coupled lines. Slow-wave Microwave and mm-wave Passive Circuits is a highly useful resource for graduate students (best complemented with a basic book on microwaves), engineers, and researchers. The text is also valuable for physicists wishing to implement comparable techniques in optics or mechanics.

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Book SynopsisTHE WILEY 5G REF Explore cutting-edge subjects in 5G privacy and security In The Wiley 5G REF: Security, a team of distinguished researchers delivers an insightful collection of articles selected from the online-only The Wiley 5G Reference. The editors introduce the security landscape of 5G, including the significant security and privacy risks associated with 5G networks. They also discuss different security solutions for various segments of the 5G network, like the radio, edge, access, and core networks. The book explores the security threats associated with key network softwarization technologies, like SDN, NFV, NS, and MEC, as well as those that come with new 5G and IoT services. There is also a detailed discussion on the privacy of 5G networks. The included articles are written by leading international experts in security and privacy for telecommunication networks. They offer learning opportunities for everyone from graduate-level students toTable of ContentsForeword List of Contributors 1. 5G Mobile Networks Security Landscape and Major Risks 2. SDMN Security 3. 5G Security – Complex Challenges 4. Physical-Layer Security for 5G and Beyond 5. Security for Handover and D2D Communication in 5G HetNets 6. Authentication and Access Control for 5G 7. 5G-Core Network Security 8. MEC and Cloud Security 9. Security in Network Slicing 10. VNF Placement and Sharing in NFV-Based Cellular Networks 11. Security Monitoring and Management in 5G 12. Security for Vertical Industries 13. Introduction to IoT Security 14. Privacy in the 5G World: The GDPR in a Datafied Society 15. Structural Safety Assessment of 5G Network Infrastructures Index

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Book SynopsisPower Flow Control Solutions for a Modern Grid using SMART Power Flow Controllers Provides students and practicing engineers with the foundation required to perform studies of power system networks and mitigate unique power flow problems Power Flow Control Solutions for a Modern Grid using SMART Power Flow Controllers is a clear and accessible introduction to power flow control in complex transmission systems. Starting with basic electrical engineering concepts and theory, the authors provide step-by-step explanations of the modeling techniques of various power flow controllers (PFCs), such as the voltage regulating transformer (VRT), the phase angle regulator (PAR), and the unified power flow controller (UPFC). The textbook covers the most up-to-date advancements in the Sen transformer (ST), including various forms of two-core designs and hybrid architectures for a wide variety of applications. Beginning with an overview of the origin and development of modern power flow controllers, Table of ContentsAuthors’ Biographies xiii Foreword xv Nomenclature xix Preface xxv Acknowledgments xxix About the Companion Website xxxi 1 Smart Controllers 1 1.1 Why is a Power Flow Controller Needed? 1 1.2 Traditional Power Flow Control Concepts 5 1.3 Modern Power Flow Control Concepts 14 1.4 Cost of a Solution 22 1.4.1 Defining a Cost-Effective Solution 22 1.4.2 Payback Time 24 1.4.3 Economic Analysis 24 1.5 Independent Active and Reactive PFCs 26 1.6 SMART Power Flow Controller (SPFC) 39 1.6.1 Example of an SPFC 40 1.6.2 Justification 41 1.6.3 Additional Information 41 1.7 Discussion 42 2 Power Flow Control Concepts 45 2.1 Power Flow Equations for a Natural or Uncompensated Line 60 2.2 Power Flow Equations for a Compensated Line 63 2.2.1 Shunt-Compensating Voltage 67 2.2.1.1 Power Flow at the Modified Sending End with a Shunt-Compensating Voltage 70 2.2.1.2 Power Flow at the Receiving End with a Shunt-Compensating Voltage 73 2.2.1.3 Exchanged Power by a Shunt-Compensating Voltage 79 2.2.1.4 Representation of a Shunt-Compensating Voltage as a Shunt-Compensating Impedance 79 2.2.2 Series-Compensating Voltage as an Impedance Regulator, Voltage Regulator, and Phase Angle Regulator (Asymmetric) 80 2.2.2.1 Power Flow at the Sending End with a Series-Compensating Voltage 92 2.2.2.2 Power Flow at the Receiving End with a Series-Compensating Voltage 95 2.2.2.3 Power Flow at the Modified Sending End with a Series-Compensating Voltage 100 2.2.2.4 Exchanged Power by a Series-Compensating Voltage 109 2.2.2.5 Additional Series-Compensating Voltages 126 2.2.2.5.1 Phase Angle Regulator (Symmetric) 126 2.2.2.5.2 Reactance Regulator 129 2.2.2.5.2.1 Reactance Control Method 137 2.2.2.5.2.2 Voltage Control Method 139 2.2.2.6 Representation of a Series-Compensating Voltage as a Series-Compensating Impedance 145 2.2.2.6.1 Equivalent Impedance of a Voltage Regulator (VR) 152 2.2.2.6.2 Equivalent Impedance of a Phase Angle Regulator (Asymmetric) 154 2.2.2.6.3 Equivalent Impedance of a Phase Angle Regulator (Symmetric) 157 2.2.2.6.4 Equivalent Impedance of a Reactance Regulator 160 2.2.3 Comparison Between Series- and Shunt-Compensating Voltages 165 2.3 Implementation of Power Flow Control Concepts 168 2.3.1 Voltage Regulation 168 2.3.1.1 Direct Method 168 2.3.1.2 Indirect Method 170 2.3.2 Phase Angle Regulation 173 2.3.2.1 Single-core Phase Angle Regulator 173 2.3.2.2 Dual-core Phase Angle Regulator 176 2.3.3 Series Reactance Regulation 178 2.3.3.1 Direct Method 178 2.3.3.2 Indirect Method 178 2.3.4 Impedance Regulation 179 2.3.4.1 Unified Power Flow Controller (UPFC) 181 2.3.4.2 Sen Transformer (ST) 183 2.4 Interline Power Flow Concept 185 2.4.1 Back-to-Back SSSC 186 2.4.2 Multiline Sen Transformer (MST) 188 2.4.3 Back-to-Back STATCOM 192 2.4.4 Generalized Power Flow Controller 194 2.5 Figure of Merits Among Various PFCs 196 2.5.1 VR 196 2.5.2 PAR (sym) 196 2.5.3 PAR (asym) 198 2.5.4 RR 202 2.5.5 IR 204 2.5.6 RPI, LI, and APR of a PFC 206 2.6 Comparison Between Shunt-Compensating Reactance and Series-Compensating Reactance 228 2.6.1 Shunt-Compensating Reactance 230 2.6.1.1 Restoration of Voltage at the Midpoint of the Line 230 2.6.1.2 Restoration of Voltage at the One-Third and Two-Third Points of the Line 232 2.6.1.3 Restoration of Voltage at the One-Fourth, Half, and Three-Fourth Points of the Line 233 2.6.1.4 Restoration of Voltage at n Points of the Line 235 2.6.2 Series-Compensating Reactance 239 2.7 Calculation of RPI, LI, and APR for a PAR (sym), a PAR (asym), a RR, and an IR in a Lossy Line 242 2.7.1 PAR (sym) 245 2.7.2 PAR (asym) 246 2.7.3 RR 248 2.7.4 IR 249 2.8 Sen Index of a PFC 253 3 Modeling Principles 255 3.1 The Modeling in EMTP 255 3.1.1 A Single-Generator/Single-Line Model 259 3.1.2 A Two-Generator/Single-Line Model 264 3.2 Vector Phase-Locked Loop (VPLL) 277 3.3 Transmission Line Steady-State Resistance Calculator 280 3.4 Simulation of an Independent PFC, Integrated in a Two-Generator/Single-Line Power System Network 281 4 Transformer-Based Power Flow Controllers 297 4.1 Voltage-Regulating Transformer (VRT) 297 4.1.1 Voltage Regulating Transformer (Shunt-Series Configuration) 298 4.1.2 Two-Winding Transformer 315 4.2 Phase Angle Regulator (PAR) 322 4.2.1 PAR (Asymmetric) 322 4.2.2 PAR (Symmetric) 332 5 Mechanically-Switched Voltage Regulators and Power Flow Controllers 341 5.1 Shunt Compensation 341 5.1.1 Mechanically-Switched Capacitor (MSC) 341 5.1.2 Mechanically-Switched Reactor (MSR) 353 5.2 Series Compensation 354 5.2.1 Mechanically-Switched Reactor (MSR) 354 5.2.2 Mechanically-Switched Capacitor (MSC) with a Reactor 363 5.2.3 Series Reactance Emulator 369 6 Sen Transformer 375 6.1 Existing Solutions 377 6.1.1 Voltage Regulation 383 6.1.2 Phase Angle Regulation 385 6.2 Desired Solution 386 6.2.1 ST as a New Voltage Regulator 389 6.2.2 ST as an Independent PFC 392 6.2.3 Control of ST 394 6.2.3.1 Impedance Emulation 395 6.2.3.2 Resistance Emulation 396 6.2.3.3 Reactance Emulation 396 6.2.3.4 Closed-Loop Power Flow Control 397 6.2.3.5 Open-Loop Power Flow Control 398 6.2.4 Simulation of ST Integrated in a Two-Generator/One-Line Power System Network 425 6.2.5 Simulation of ST Integrated in a Three-Generator/Four-Line Power System Network 439 6.2.6 Testing of ST 453 6.2.7 Limited-Angle Operation of ST 485 6.2.8 ST Using LTCs with Lower Current Rating 498 6.2.9 ST with a Two-Core Design 501 6.3 Comparison Among the VRT, PAR, UPFC, and ST 510 6.3.1 Power Flow Enhancement 510 6.3.2 Speed of Operation 511 6.3.3 Losses 512 6.3.4 Switch Rating 512 6.3.5 Magnetic Circuit Design 513 6.3.6 Optimization of Transformer Rating 513 6.3.7 Harmonic Injection into the Power System Network 515 6.3.8 Operation During Line Faults 515 6.4 Multiline Sen Transformer 516 6.4.1 Basic Differences Between the MST and BTB-SSSC 519 6.5 Flexible Operation of the ST 520 6.6 ST with a Shunt-Compensating Voltage 522 6.7 Limited Angle Operation of the ST with Shunt-Compensating Voltages 526 6.8 MST with Shunt-Compensating Voltages 531 6.9 Generalized Sen Transformer 532 6.10 Summary 533 Appendix A Miscellaneous 535 A.1 Three-Phase Balanced Voltage, Current, and Power 535 A.2 Symmetrical Components 538 A.3 Separation of Positive-, Negative-, and Zero-Sequence Components in a Multiple Frequency Composite Variable 544 A.4 Three-Phase Unbalanced Voltage, Current, and Power 547 A.5 d-q Transformation (3-Phase System, Transformed into d-q axes; d-axis Is the Active Component and q-axis Is the Reactive Component) 551 A.5.1 Conversion of a Variable Containing Positive-, Negative-, and Zero-Sequence Components into d-q Frame 556 A.5.2 Calculation of Instantaneous Power into d-q Frame 560 A.5.3 Calculation of Instantaneous Power into d-q frame for a Three-Phase, Three-Wire System 560 A.6 Fourier Analysis 566 A.7 Adams-Bashforth Numerical Integration Formula 569 Appendix B Power Flow Equations in a Lossy Line 571 B.1 Power Flow Equations for a Natural or Uncompensated Line 575 B.2 Power Flow Equations for a Compensated Line 582 B.2.1 Shunt-Compensating Voltage 583 B.2.1.1 Power Flow at the Modified Sending End with a Shunt-Compensating Voltage 584 B.2.1.2 Power Flow at the Receiving End with a Shunt-Compensating Voltage 587 B.2.1.3 Exchanged Power by a Shunt-Compensating Voltage 590 B.2.1.4 Representation of a Shunt-Compensating Voltage as a Shunt-Compensating Impedance 590 B.2.2 Series-Compensating Voltage as an Impedance Regulator, Voltage Regulator, and Phase Angle Regulator (Asymmetric) 591 B.2.2.1 Power Flow at the Sending End with a Series-Compensating Voltage 596 B.2.2.2 Power Flow at the Receiving End with a Series-Compensating Voltage 600 B.2.2.3 Power Flow at the Modified Sending End with a Series-Compensating Voltage 606 B.2.2.4 Exchanged Power by a Series-Compensating Voltage 615 B.2.2.5 Additional Series-Compensating Voltages 624 B.2.2.5.1 Phase Angle Regulator (Symmetric) 624 B.2.2.5 2 Reactance Regulator 628 B.2.2.6 Representation of a Series-Compensating Voltage as a Series-Compensating Impedance 631 B.2.2.6.1 Equivalent Impedance of a Voltage Regulator (VR) 635 B.2.2.6.2 Equivalent Impedance of a Phase Angle Regulator (Asymmetric) 636 B.2.2.6.3 Equivalent Impedance of a Phase Angle Regulator (Symmetric) 638 B.2.2.6.4 Equivalent Impedance of a Reactance Regulator 640 B.2.2.7 RPI, LI, and APR of a PFC 640 B.3 Descriptions of the Examples in Chapter 2 644 Appendix C Modeling of the Sen Transformer in PSS®E 647 C.1 Sen Transformer 647 C.2 Modeling with Two Transformers in Series 648 C.3 Relating the Sen Transformer with the PSSE ® E Model 649 C.4 Chilean Case Study 650 C.5 Limitations – PSS®E Two-Transformer Model 654 C.6 Conclusion 655 References 657 Index 669

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Book SynopsisA practical introduction to microwave plasma for processing applications at a variety of pressures In Microwave Plasma Sources and Methods in Processing Technology, the authors deliver a comprehensive introduction to microwaves and microwave-generated plasmas. Ideal for anyone interested in non-thermal gas discharge plasmas and their applications, the book includes detailed descriptions, explanations, and practical guidance for the study and use of microwave power, microwave components, plasma, and plasma generation. This reference includes over 130 full-color diagrams to illustrate the concepts discussed within. The distinguished authors discuss the plasmas generated at different levels of power, as well as their applications at reduced, atmospheric and higher pressures. They also describe plasmas inside liquids and plasma interactions with combustion flames. Microwave Plasma Sources and Methods in Processing Technology concludes with an inTable of ContentsForeword from the Authors ix 1 Basic Principles and Components in the Microwave Techniques and Power Systems 1 1.1 History in Brief – From Alternating Current to Electromagnetic Waves and to Microwaves 1 1.2 Microwave Generators 3 1.3 Waveguides and Electromagnetic Modes in Wave Propagation 5 1.3.1 The Cut-off Frequency and the Wavelength in Waveguides 7 1.3.2 Waveguides Filled by Dielectrics 9 1.3.3 Wave Impedance and Standing Waves in Waveguides 10 1.3.4 Coaxial Transmission Lines 12 1.3.5 Microwave Resonators 14 1.4 Waveguide Power Lines 14 1.4.1 Magnetron Tube Microwave Generator 16 1.4.2 Microwave Insulators 16 1.4.3 Impedance Tuners 17 1.4.4 Directional Couplers 19 1.4.5 Passive Waveguide Components – Bends, Flanges, Vacuum Windows 20 1.4.6 Tapered Waveguides and Waveguide Transformers 22 1.4.7 Power Loads and Load Tuners 23 1.4.8 Waveguide Phase Shifters 25 1.4.9 Waveguide Shorting Plungers 25 1.4.10 Coupling from Rectangular to Circular Waveguide: Resonant Cavities for Generation of Plasma 26 1.5 Microwave Oven – A Most Common Microwave Power Device 28 References 33 2 Gas Discharge Plasmas 37 2.1 Basic Understanding of the Gas Discharge Plasmas 37 2.2 Generation of the Plasma, Townsend Coefficients, Paschen Curve 40 2.3 Generation of the Plasma by AC Power, Plasma Frequency, Cut-off Density 43 2.4 Space-charge Sheaths at Different Frequencies of the Incident Power 50 2.5 Classification of Gas Discharge Plasmas, Effects of Gas Pressure, Microwave Generation of Plasmas 55 2.5.1 Classification of Gas Discharge Plasmas 55 2.5.2 Effects of the Gas Pressure on Particle Collisions in the Plasma 58 2.5.3 Microwave Generation of Plasmas 61 References 64 3 Interactions of Plasmas with Solids and Gases 67 3.1 Plasma Processing, PVD, and PE CVD 67 3.2 Sputtering, Evaporation, Dry Etching, Cleaning, and Oxidation of Surfaces 72 3.3 Particle Transport in Plasma Processing and Effects of Gas Pressure 75 3.3.1 Movements of Neutral Particles 76 3.3.2 Movements of Charged Particles 77 3.3 Effect of the Gas Pressure on the Plasma Processing 79 3.4 Afterglow and Decaying Plasma Processing 81 References 83 4 Microwave Plasma Systems for Plasma Processing at Reduced Pressures 85 4.1 Waveguide-Generated Isotropic and Magnetoactive Microwave Plasmas 85 4.1.1 Waveguide-Generated Isotropic Microwave Oxygen Plasma for Silicon Oxidation 87 4.1.2 ECR and Higher Induction Magnetized Plasma Systems for Silicon Oxidation 93 4.2 PE CVD of Silicon Nitride Films in the Far Afterglow 105 4.3 Microwave Plasma Jets for PE CVD of Films 111 4.3.1 Deposition of Carbon Nitride Films 115 4.3.2 Surfajet Plasma Parameters and an Arrangement for Expanding the Plasma Diameter 119 4.4 Hybrid Microwave Plasma System with Magnetized Hollow Cathode 122 References 129 5 Microwave Plasma Systems at Atmospheric and Higher Pressures 135 5.1 Features of the Atmospheric Plasma and Cold Atmospheric Plasma (CAP) Sources 136 5.2 Atmospheric Microwave Plasma Sources Assisted by Hollow Cathodes 140 5.2.1 Applications of the H-HEAD Plasma Source in Surface Treatments 144 5.3 Microwave Treatment of Diesel Exhaust 151 5.4 Microwave Plasma in Liquids 154 5.5 Microwave Plasma Interactions with Flames 157 5.6 Microwave Plasmas at Very High Pressures 161 References 162 6 New Applications and Trends in the Microwave Plasmas 169 References 176 7 Appendices 181 7.1 List of Symbols and Abbreviations 181 7.2 Constants and Numbers 188 Index 189

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Book SynopsisFUNDAMENTALS AND APPLICATIONS OF COLOUR ENGINEERING EXPERT OVERVIEW OF THE WORLD OF COLOUR ENGINEERING IN THE 21ST CENTURY, WITH NEW, UPDATED TECHNOLOGIES AND A MATLAB TOOLBOX Fundamentals and Applications of Colour Engineering provides important coverage on topics that hold the power to extend our knowledge of colour reproduction, such as colour measurement and appearance and the methods used, with additional discussion of the technologies responsible for reproducing colour across a wide range of devices, together with the colour management systems that are used to connect devices and exchange information. Composed of 20 chapters, the Editor and his team of expert contributors consider the new ICC.2 architecture, an approach that introduces an evolutionary step in colour engineering, ensuring wider possibilities for technology. The text also considers the emerging applications for advanced colour management, such as processing spectral data, handling HDR images, and the capture and rTable of ContentsSeries Editor's Foreword xvii Preface xix Introductory Notes xxi 1 Instruments and Methods for the Colour Measurements Required in Colour Engineering 1Danny Rich 1.1 Introduction 1 1.2 Visual Colorimetry 3 1.3 Analogue Simulation of Visual Colorimetry 7 1.4 Digital Simulation of Visual Colorimetry 12 1.5 Selecting and Using Colorimeters and Spectrocolorimeters 15 1.6 Geometric Requirements for Colour Measurements 18 1.7 Conclusions and Expectations 22 2 Colorimetry and Colour Difference 27Phil Green 2.1 Introduction 27 2.2 Colorimetry 27 2.3 Normalization 28 2.4 Colour Matching Functions 29 2.5 Illuminants 29 2.6 Data for Observers and Illuminants 30 2.7 Range and Interval 30 2.8 Calculation of Chromaticity 31 2.9 Calculation of CIE 1976 Uniform Colour Spaces 31 2.10 Inversion of CIELAB Equations 34 2.11 Colour Difference 34 2.12 Problems with Using UCS Colour Difference 35 2.13 Uniformity of the Components of Colour Difference 35 2.14 Viewing Conditions 36 2.15 Surface Characteristics 37 2.16 Acceptability of Colour Differences 37 2.17 Overcoming the Limitations of UCS Colour Difference with Advanced Colour Difference Metrics 37 2.18 CIE94 37 2.19 CIEDE2000 39 2.20 Progress on Colour Difference Metrics since CIEDE2000 41 2.21 3D Colour Difference 41 2.22 Colour Difference in High Luminance Conditions 41 2.23 Colour Difference Formulas Based on Colour Appearance Models 41 2.24 Limitations in the Use of Advanced Colour Difference Metrics in Colour Imaging 42 2.25 Basis Conditions 42 2.26 Colour Difference in Complex Images 43 2.27 Acceptability and Perceptibility 44 2.28 Large vs Small Differences 44 2.29 Deriving Colour Difference Tolerances 44 2.30 Sample Preparation 45 2.31 Psychophysical Experiments 45 2.32 Colour Difference Judgements by Observers with a Colour Vision Deficiency 46 2.33 Calculating Colour Tolerances from Experimental Data 46 2.34 Calculation of Discrimination Ellipsoids and Tolerance Distributions 46 2.34.1 Calculation of Parametric Constants in Weightings Functions 47 2.35 Calculation of Acceptability Thresholds 48 2.36 Evaluating Colour Difference Metrics 48 2.37 Conclusion 48 3 Fundamentals of Device Characterization 53Phil Green 3.1 Introduction 53 3.2 Characterization Methods 54 3.3 Numerical Models 57 3.4 Look-Up Tables with Interpolation 63 3.5 Evaluating Accuracy -- Training and Test Data 67 4 Characterization of Input Devices 71Phil Green 4.1 Input Channels 71 4.2 Characterization Goals 72 4.3 Transform Encoding 73 4.4 Dynamic Range 73 4.5 Input Characterization Methods 74 4.5.1 Scanners 74 4.6 Targets 74 4.7 Modelling 74 4.7.1 Digital Cameras 75 4.8 Target-Based Characterization 75 4.9 Targets 75 4.10 Modelling 76 5 Color Processing for Digital Cameras 81Michael S. Brown 5.1 Introduction 81 5.2 Basics of a Camera Sensor 82 5.3 The Camera Pipeline 83 5.4 Multi-Frame Processing 93 5.5 Towards the Neural ISP 94 5.6 Concluding Remarks 95 6 Display Calibration 99Catherine Meininger, Tom Lianza, and Grace Annese 6.1 Introduction 99 6.2 From CRT to Contemporary Display Technologies 99 6.3 The Display Never Sleeps... Merging Television and Computer Display Standards 102 6.4 The Evolution of Display Calibration Capabilities 103 6.5 Measurement Set Requirements 111 6.6 Calibration Validation Methodologies 113 6.7 Low Blue Light Developments 114 6.8 Conclusions 117 7 Characterizing Hard Copy Printers 119Phil Green 7.1 Introduction 119 7.2 Properties of Hard Copy Printers 120 7.3 Substrates and Inks 120 7.4 Colour Gamut 120 7.5 Halftoning 121 7.6 Mechanical Printing Systems 122 7.7 Printing Conditions 122 7.8 Digital Systems 122 7.9 RGB Printers 122 7.10 Test Charts 123 7.11 Printer Models 124 7.12 Block Dye Model 125 7.13 Physical Models 126 7.14 Numerical Models and Look-up Tables 134 7.15 Inverting the Model 137 7.16 Multi-Colour and Spot Colour Characterization 137 7.17 Spectral Characterization 137 7.18 White Ink 138 7.19 Reducing the Frequency of Characterization 138 7.20 Conclusions 138 8 Colour Encodings 143Phil Green 8.1 Introduction 143 8.2 Colour Encoding Components 143 8.3 Colour Spaces 144 8.4 Device and Colour Space Encodings 144 8.5 Colorimetric Interpretation 144 8.6 Image State 145 8.7 Standard 3-Component Colour Space Encodings 146 8.8 Colour Gamut 146 8.8.1 Extended Colour Gamut 147 8.9 Precision and Range 147 8.9.1 High Dynamic Range 148 8.9.2 Negative Values 149 8.10 Luminance/Chrominance Encodings 149 8.11 Conversion to Colorimetry 150 8.12 Implementation Issues 150 8.13 File Formats 152 9 Colour Gamut Communication 155Kiran Deshpande 9.1 Introduction 155 9.2 How to Describe Colour Gamuts 157 9.3 How to Obtain a Colour Gamut of a Printing System 162 9.4 How to Obtain a Colour Gamut of a Display 163 9.5 How to Calculate Gamut Volume 163 9.6 How to Analyse Colour Gamuts 164 9.7 How to Visualize Colour Gamuts 167 9.8 How to Communicate Colour Gamuts 171 9.9 Summary 173 10 The ICC Colour Management Architecture 177Phil Green 10.1 Origins of the ICC 177 10.2 Fundamentals of the ICC Architecture: The PCS, the ICC Profile, Transforms and the CMM 178 10.3 Other CMM Operations 185 10.4 Workflows 187 10.5 Current Status of ICC.1 188 10.6 ICC.2 189 11 iccMAX Color Management -- Philosophy, Overview, and Basics 193Max Derhak 11.1 Background and Philosophy Leading to iccMAX 193 11.2 Overview 194 11.3 Creating Transforms 207 11.4 Specification Subsets via ICSs 209 11.5 Domain Specific Examples 210 11.6 Getting Started with iccMAX (Where Color Engineering Comes to Play) 212 11.7 Conclusion 213 12 Sensor Adjustment 215Phil Green 12.1 Introduction 215 12.2 Aims of Sensor Adjustment 215 12.3 Luminance Adjustment 216 12.4 Chromatic Adaptation 218 12.5 Material-Equivalent Adjustment 220 12.6 Local Adaptation 221 12.7 Incomplete Adaptation 222 13 Evaluating Colour Transforms 227Phil Green 13.1 Introduction 227 13.2 Accuracy 227 13.3 Cost 232 13.4 Subjective Preference 233 14 Appearance Beyond Colour: Gloss and Translucency Perception 239Davit Gigilashvili and Jean-Baptiste Thomas 14.1 Introduction 239 14.2 Gloss Perception 240 14.3 Translucency Perception 244 14.4 Interaction among Appearance Attributes 248 14.5 Impact on Colour Technologies 250 14.6 Conclusion 252 15 Colour Management of Material Appearance 259Tanzima Habib 15.1 Introduction 259 15.2 Material Appearance Modelling 260 15.3 Appearance Support in Colour Management 263 15.4 A Colour Management Workflow for Material Appearance 264 15.5 Conclusion 269 16 Color on the Web 271Chris Lilley 16.1 Early History 271 16.2 Color on the Legacy Web 272 16.3 Wide Color Gamut (WCG) Comes to the Web 277 16.4 Color on the Wide Gamut Web 281 16.5 HDR Comes to the Web 286 17 High Dynamic Range Imaging 293Mekides Assefa Abebe 17.1 Introduction and Background 293 17.2 High Dynamic Range Imaging 296 17.3 Conclusion 308 18 HDR and Wide Color Gamut Display Technologies and Considerations 311Timo Kunkel and Ajit Ninan 18.1 Introduction 311 18.2 Early HDR Display Systems 312 18.3 Transmissive Displays 313 18.4 Emissive Displays 317 18.5 Projection Systems 319 18.6 Reflective Displays 320 18.7 Achieving Wide Color Gamuts 321 18.8 Spatial Display Properties 326 18.9 Temporal Display Properties 327 18.10 Signaling 328 18.11 Characterization and Calibration 330 18.12 Ambient Effects 330 18.13 Conclusion 332 19 Colour in AR and VR 335Michael J. Murdoch 19.1 Introduction 335 19.2 Colour Synthesis in AR and VR Displays 337 19.3 Colour Appearance in AR and VR 342 19.4 Colour Imaging and Graphics in AR and VR 350 19.5 Conclusion 351 20 Colour Engineering Toolbox and Other Open Source Tools 355Phil Green 20.1 Colour Engineering Toolbox 2.0 355 20.2 Polar Calculations 357 20.3 Media-Relative and PCS Scaling 357 20.4 DemoIccMax 360 20.5 Color.js 360 20.6 Little CMS 360 20.7 Argyll 361 20.8 Colour 361 References 361 Index 363

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Book SynopsisMachine Learning for Business Analytics Machine learningalso known as data mining or data analyticsis a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: ATable of ContentsForeword by Ravi Bapna xxi Preface to the RapidMiner Edition xxiii Acknowledgments xxvii PART I PRELIMINARIES CHAPTER 1 Introduction 3 1.1 What Is Business Analytics? 3 1.2 What Is Machine Learning? 5 1.3 Machine Learning, AI, and Related Terms 5 1.4 Big Data 7 1.5 Data Science 8 1.6 Why Are There So Many Different Methods? 9 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 12 1.9 Using RapidMiner Studio 14 CHAPTER 2 Overview of the Machine Learning Process 19 2.1 Introduction 19 2.2 Core Ideas in Machine Learning 20 2.3 The Steps in a Machine Learning Project 23 2.4 Preliminary Steps 25 2.5 Predictive Power and Overfitting 32 2.6 Building a Predictive Model with RapidMiner 37 2.7 Using RapidMiner for Machine Learning 45 2.8 Automating Machine Learning Solutions 47 2.9 Ethical Practice in Machine Learning 52 PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 63 3.1 Introduction 63 3.2 Data Examples 65 3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 66 3.4 Multidimensional Visualization 75 3.5 Specialized Visualizations 87 3.6 Summary: Major Visualizations and Operations, by Machine Learning Goal 92 CHAPTER 4 Dimension Reduction 97 4.1 Introduction 97 4.2 Curse of Dimensionality 98 4.3 Practical Considerations 98 4.4 Data Summaries 100 4.5 Correlation Analysis 103 4.6 Reducing the Number of Categories in Categorical Attributes 105 4.7 Converting a Categorical Attribute to a Numerical Attribute 107 4.8 Principal Component Analysis 107 4.9 Dimension Reduction Using Regression Models 117 4.10 Dimension Reduction Using Classification and Regression Trees 119 PART III PERFORMANCE EVALUATION CHAPTER 5 Evaluating Predictive Performance 125 5.1 Introduction 125 5.2 Evaluating Predictive Performance 126 5.3 Judging Classifier Performance 131 5.4 Judging Ranking Performance 146 5.5 Oversampling 151 PART IV PREDICTION AND CLASSIFICATION METHODS CHAPTER 6 Multiple Linear Regression 163 6.1 Introduction 163 6.2 Explanatory vs. Predictive Modeling 164 6.3 Estimating the Regression Equation and Prediction 166 6.4 Variable Selection in Linear Regression 171 CHAPTER 7 k-Nearest Neighbors (k-NN) 189 7.1 The k-NN Classifier (Categorical Label) 189 7.2 k-NN for a Numerical Label 200 7.3 Advantages and Shortcomings of k-NN Algorithms 202 CHAPTER 8 The Naive Bayes Classifier 209 8.1 Introduction 209 8.2 Applying the Full (Exact) Bayesian Classifier 211 8.3 Solution: Naive Bayes 213 8.4 Advantages and Shortcomings of the Naive Bayes Classifier 223 CHAPTER 9 Classification and Regression Trees 229 9.1 Introduction 229 9.2 Classification Trees 232 9.3 Evaluating the Performance of a Classification Tree 240 9.4 Avoiding Overfitting 245 9.5 Classification Rules from Trees 255 9.6 Classification Trees for More Than Two Classes 256 9.7 Regression Trees 256 9.8 Improving Prediction: Random Forests and Boosted Trees 259 9.9 Advantages and Weaknesses of a Tree 261 CHAPTER 10 Logistic Regression 269 10.1 Introduction 269 10.2 The Logistic Regression Model 271 10.3 Example: Acceptance of Personal Loan 272 10.4 Logistic Regression for Multi-class Classification 283 10.5 Example of Complete Analysis: Predicting Delayed Flights 286 CHAPTER 11 Neural Networks 305 11.1 Introduction 306 11.2 Concept and Structure of a Neural Network 306 11.3 Fitting a Network to Data 307 11.4 Required User Input 321 11.5 Exploring the Relationship Between Predictors and Target Attribute 322 11.6 Deep Learning 323 11.7 Advantages and Weaknesses of Neural Networks 334 CHAPTER 12 Discriminant Analysis 337 12.1 Introduction 337 12.2 Distance of a Record from a Class 340 12.3 Fisher’s Linear Classification Functions 341 12.4 Classification Performance of Discriminant Analysis 346 12.5 Prior Probabilities 348 12.6 Unequal Misclassification Costs 348 12.7 Classifying More Than Two Classes 349 12.8 Advantages and Weaknesses 351 CHAPTER 13 Generating, Comparing, and Combining Multiple Models 359 13.1 Automated Machine Learning (AutoML) 359 13.2 Explaining Model Predictions 367 13.3 Ensembles 373 13.4 Summary 381 PART V INTERVENTION AND USER FEEDBACK CHAPTER 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 387 14.1 A/B Testing 387 14.2 Uplift (Persuasion) Modeling 393 14.3 Reinforcement Learning 400 14.4 Summary 405 PART VI MINING RELATIONSHIPS AMONG RECORDS CHAPTER 15 Association Rules and Collaborative Filtering 409 15.1 Association Rules 409 15.2 Collaborative Filtering 424 15.3 Summary 438 CHAPTER 16 Cluster Analysis 445 16.1 Introduction 445 16.2 Measuring Distance Between Two Records 449 16.3 Measuring Distance Between Two Clusters 455 16.4 Hierarchical (Agglomerative) Clustering 457 16.5 Non-Hierarchical Clustering: The k-Means Algorithm 466 PART VII FORECASTING TIME SERIES CHAPTER 17 Handling Time Series 479 17.1 Introduction 480 17.2 Descriptive vs. Predictive Modeling 481 17.3 Popular Forecasting Methods in Business 481 17.4 Time Series Components 482 17.5 Data Partitioning and Performance Evaluation 486 CHAPTER 18 Regression-Based Forecasting 497 18.1 A Model with Trend 498 18.2 A Model with Seasonality 504 18.3 A Model with Trend and Seasonality 508 18.4 Autocorrelation and ARIMA Models 509 CHAPTER 19 Smoothing and Deep Learning Methods for Forecasting 533 19.1 Smoothing Methods: Introduction 534 19.2 Moving Average 534 19.3 Simple Exponential Smoothing 541 19.4 Advanced Exponential Smoothing 545 19.5 Deep Learning for Forecasting 549 PART VIII DATA ANALYTICS CHAPTER 20 Social Network Analytics 563 20.1 Introduction 563 20.2 Directed vs. Undirected Networks 564 20.3 Visualizing and Analyzing Networks 567 20.4 Social Data Metrics and Taxonomy 571 20.5 Using Network Metrics in Prediction and Classification 577 20.6 Collecting Social Network Data with RapidMiner 584 20.7 Advantages and Disadvantages 584 CHAPTER 21 Text Mining 589 21.1 Introduction 589 21.2 The Tabular Representation of Text: Term–Document Matrix and “Bag-of-Words’’ 590 21.3 Bag-of-Words vs. Meaning Extraction at Document Level 592 21.4 Preprocessing the Text 593 21.5 Implementing Machine Learning Methods 602 21.6 Example: Online Discussions on Autos and Electronics 602 21.7 Example: Sentiment Analysis of Movie Reviews 607 21.8 Summary 614 CHAPTER 22 Responsible Data Science 617 22.1 Introduction 617 22.2 Unintentional Harm 618 22.3 Legal Considerations 620 22.4 Principles of Responsible Data Science 621 22.5 A Responsible Data Science Framework 624 22.6 Documentation Tools 628 22.7 Example: Applying the RDS Framework to the COMPAS Example 631 22.8 Summary 641 PART IX CASES CHAPTER 23 Cases 647 23.1 Charles Book Club 647 23.2 German Credit 653 23.3 Tayko Software Cataloger 658 23.4 Political Persuasion 662 23.5 Taxi Cancellations 665 23.6 Segmenting Consumers of Bath Soap 667 23.7 Direct-Mail Fundraising 670 23.8 Catalog Cross-Selling 672 23.9 Time Series Case: Forecasting Public Transportation Demand 673 23.10 Loan Approval 675 Index 685

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Book SynopsisBIOFUEL EXTRACTION TECHNIQUES The energy industry and new energy sources and innovations are rapidly changing and evolving. This new volume addresses the current state-of-the-art concepts and technologies associated with biofuel extraction technologies. Biofuels are a viable alternative to petroleum-based fuel because they are produced from organic materials such as plants and their wastes, agricultural crops, and by-products. The development of cutting-edge technology has increased the need for energy significantly, which has resulted in an overreliance on fossil fuels. Renewable fuels are an important subject of research because of their biodegradability, eco-friendliness, decrease in greenhouse gas (GHG) emissions, and favorable socioeconomic consequences to counteract imitations of fossil fuels. Different extraction techniques are used for the production of biofuel from renewable feedstocks. A good example is biodiesel, a promising biofuel which is produced bTable of ContentsPreface xix 1 Plant Seed Oils and Their Potential for Biofuel Production in India 1L. C. Meher and S. N. Naik 2 Processing of Feedstock in Context of Biodiesel Production 25Durgawati and Rama Chandra Pradhan 3 Extraction Techniques for Biodiesel Production 51Soumya Parida and Subhalaxmi Pradhan 4 Role of Additives on Anaerobic Digestion, Biomethane Generation, and Stabilization of Process Parameters 101Adya Isha, Bhaskar Jha, Tinku Casper D'Silva, Subodh Kumar, Sameer Ahmed Khan, Dushyant Kumar, Ram Chandra and Virendra Kumar Vijay 5 An Overview on Established and Emerging Biogas Upgradation Systems for Improving Biomethane Quality 125Tinku Casper D'Silva, Adya Isha, Subodh Kumar, Sameer Ahmad Khan, Dushyant Kumar, Ram Chandra and Virendra Kumar Vijay 6 Renewable Feedstocks for Biofuels 151Monika Chauhan, Vanshika, Ajay Kumar, Diwakar Chauhan and Arvind Kumar Jain 7 Extraction Techniques of Gas-to-Liquids (GtL) Fuels 177Sonali Kesarwani, Divya Bajpai Tripathy and Pooja Bhadana 8 Second Generation Biofuels and Extraction Techniques 207Prashant Kumar, Praveen Kumar Sharma, Shreya Tripathi, Deepak Kumar, Ashween Deepak Nannaware, Shivani Chaturvedi and Prasant Kumar Rout 9 Bio-Alcohol: Production, Purification, and Analysis Using Analytical Techniques 257Smrita Singh, Susanta Roy, Lalit Prasad and Ashutosh Singh Chauhan 10 Studies on Extraction Techniques of Bio-Hydrogen 291C. S. Madankar, Priti Borde and P. D. Meshram 11 Valorization of By-Products Produced During the Extraction and Purification of Biofuels 307Subodh Kumar, Tinku Casper D'Silva, Dushyant Kumar, Adya Isha, Sameer Ahmad Khan, Ram Chandra, Anushree Malik and Virendra Kumar Vijay 12 Valorization of Byproducts Produced During Extraction and Purification of Biodiesel: A Promising Biofuel 333Gunjan, Radhika Singh and Subhalaxmi Pradhan 13 Biofuel Applications: Quality Control and Assurance, Techno-Economics and Environmental Sustainability 367Sameer Ahmad Khan, Dushyant Kumar, Subodh Kumar, Adya Isha, Tinku Casper D'Silva, Ram Chandra and Virendra Kumar Vijay 14 Role of CO2 Triggered Switchable Polarity Solvents and Supercritical Solvents During Biofuel Extraction 421Anupama Sharma, Pinki Chakraborty, Karthikay Sankhyadhar, Sandeep Kumar and Monisha Singh 15 Efficiency of Catalysts During Biofuel Extraction 441Gajanan Sahu, Sudipta Datta, Sujan Saha, Prakash D. Chavan, Deshal Yadav and Vishal Chauhan 16 Microorganisms as Effective CO2 Assimilator for Biofuel Production 495Chandreyee Saha and Subhalaxmi Pradhan 17 Global Aspects of Biofuel Extraction 523Shilpi Bhatnagar and Shilpi Khurana 18 New Advancements of Biofuel Extractions and Future Trends 543Rita Sharma, Kuldip Dwivedi, Bhavna Sharma and Shashank Sharma References 556 About the Editors 559 Index 561

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Book SynopsisPROTON EXCHANGE MEMBRANE FUEL CELLS Edited by one of the most well-respected and prolific engineers in the world and his team, this book provides a comprehensive overview of hydrogen production, conversion, and storage, offering the scientific literature a comprehensive coverage of this important fuel. Proton exchange membrane fuel cells (PEMFCs) are among the most anticipated stationary clean energy devices in renewable and alternative energy. Despite the appreciable improvement in their cost and durability, which are the two major commercialization barriers, their availability has not matched demand. This is mainly due to the use of expensive metal-catalyst, less durable membranes, and poor insight into the ongoing phenomena inside proton exchange membrane fuel cells. Efforts are being made to optimize the use of precious metals as catalyst layers or find alternatives that can be durable for more than 5000 hours. Computational models are also being developed and studied to get an inTable of ContentsPreface xiii 1 Stationary and Portable Applications of Proton Exchange Membrane Fuel Cells 1 Shahram Mehdipour-Ataei and Maryam Mohammadi 1.1 Introduction 1 1.2 Proton Exchange Membrane Fuel Cells 3 1.2.1 Stationary Applications 3 1.2.2 Portable Applications 5 1.2.3 Hydrogen PEMFCs 6 1.2.4 Alcohol PEMFCs 6 1.2.4.1 Direct Methanol Fuel Cell 6 1.2.4.2 Direct Dimethyl Ether Fuel Cell 7 1.2.5 Microbial Fuel Cells 8 1.2.5.1 Electricity Generation 8 1.2.5.2 Microbial Desalination Cells 9 1.2.5.3 Removal of Metals From Industrial Waste 9 1.2.5.4 Wastewater Treatment 9 1.2.5.5 Microbial Solar Cells and Fuel Cells 10 1.2.5.6 Biosensors 11 1.2.5.7 Biohydrogen Production 11 1.2.6 Micro Fuel Cells 11 1.3 Conclusion and Future Perspective 12 References 13 2 Graphene-Based Membranes for Proton Exchange Membrane Fuel Cells 17 Beenish Saba 2.1 Introduction 18 2.2 Membranes 19 2.3 Graphene: A Proton Exchange Membrane 19 2.4 Synthesis of GO Composite Membranes 20 2.5 Graphene Oxide in Fuel Cells 21 2.5.1 Electrochemical Fuel Cells 22 2.5.1.1 Hydrogen Oxide Polymer Electrolyte Membrane Fuel Cells 22 2.5.1.2 Direct Methanol Fuel Cells 23 2.5.2 Bioelectrochemical Fuel Cells 24 2.6 Characterization Techniques of GO Composite Membranes 25 2.7 Conclusion 26 References 27 3 Graphene Nanocomposites as Promising Membranes for Proton Exchange Membrane Fuel Cells 33 Ranjit Debnath and Mitali Saha 3.1 Introduction 34 3.2 Recent Kinds of Fuel Cells 35 3.2.1 Proton Exchange Membrane Fuel Cells 36 3.3 Conclusion 45 Acknowledgements 45 References 45 4 Carbon Nanotube–Based Membranes for Proton Exchange Membrane Fuel Cells 51 Umesh Fegade and K. E. Suryawanshi 4.1 Introduction 52 4.2 Overview of Carbon Nanotube–Based Membranes PEM Cells 54 References 64 5 Nanocomposite Membranes for Proton Exchange Membrane Fuel Cells 73 P. Satishkumar, Arun M. Isloor and Ramin Farnood 5.1 Introduction 74 5.2 Nanocomposite Membranes for PEMFC 77 5.3 Evaluation Methods of Proton Exchange Membrane Properties 80 5.3.1 Proton Conductivity Measurement 80 5.3.2 Water Uptake Measurement 81 5.3.3 Oxidative Stability Measurement 81 5.3.4 Thermal and Mechanical Properties Measurement 81 5.4 Nafion-Based Membrane 82 5.5 Poly(Benzimidazole)–Based Membrane 86 5.6 Sulfonated Poly(Ether Ether Ketone)–Based Membranes 91 5.7 Poly(Vinyl Alcohol)–Based Membranes 95 5.8 Sulfonated Polysulfone–Based Membranes 98 5.9 Chitosan-Based Membranes 100 5.10 Conclusions 103 References 103 6 Organic-Inorganic Composite Membranes for Proton Exchange Membrane Fuel Cells 111 Guocai Tian 6.1 Introduction 111 6.2 Proton Exchange Membrane Fuel Cell 112 6.3 Proton Exchange Membrane 116 6.3.1 Perfluorosulfonic Acid PEM 117 6.3.2 Partial Fluorine-Containing PEM 117 6.3.3 Non-Fluorine PEM 118 6.3.4 Modification of Proton Exchange Membrane 118 6.4 Research Progress of Organic-Inorganic Composite PEM 120 6.4.1 Inorganic Oxide/Polymer Composite PEM 120 6.4.2 Two-Dimensional Inorganic Material/Polymer Composite PEM 122 6.4.3 Carbon Nanotube/Polymer Composite PEM 124 6.4.4 Inorganic Acid–Doped Composite Film 125 6.4.5 Heteropoly Acid–Doped Composite PEM 126 6.4.6 Zirconium Phosphate–Doped Composite PEM 127 6.4.7 Polyvinyl Alcohol/Inorganic Composite Membrane 127 6.5 Conclusion and Prospection 128 Acknowledgments 130 Conflict of Interest 130 References 130 7 Thermoset-Based Composite Bipolar Plates in Proton Exchange Membrane Fuel Cell: Recent Developments and Challenges 137 Salah M.S. Al-Mufti and S.J.A. Rizvi 7.1 Introduction 138 7.2 Theories of Electrical Conductivity in Polymer Composites 142 7.2.1 Percolation Theory 145 7.2.2 General Effective Media Model 146 7.2.3 McLachlan Model 147 7.2.4 Mamunya Model 148 7.2.5 Taherian Model 149 7.3 Matrix and Fillers 151 7.3.1 Thermoset Resins 151 7.3.1.1 Epoxy 152 7.3.1.2 Unsaturated Polyester Resin 152 7.3.1.3 Vinyl Ester Resins 152 7.3.1.4 Phenolic Resins 153 7.3.1.5 Polybenzoxazine Resins 153 7.3.2 Fillers 153 7.3.2.1 Graphite 156 7.3.2.2 Graphene 157 7.3.2.3 Expanded Graphite 158 7.3.2.4 Carbon Black 158 7.3.2.5 Carbon Nanotube 159 7.3.2.6 Carbon Fiber 160 7.4 The Manufacturing Process of Thermoset-Based Composite BPs 162 7.4.1 Compression Molding 162 7.4.2 The Selective Laser Sintering Process 163 7.4.3 Wet and Dry Method 164 7.4.4 Resin Vacuum Impregnation Method 164 7.5 Effect of Processing Parameters on the Properties Thermoset-Based Composite BPs 166 7.5.1 Compression Molding Parameters 166 7.5.1.1 Pressure 166 7.5.1.2 Temperature 168 7.5.1.3 Time 169 7.5.2 The Mixing Time Effect on the Properties of Composite Bipolar Plates 170 7.6 Effect of Polymer Type, Filler Type, and Composition on Properties of Thermoset Composite BPs 170 7.6.1 Electrical Properties 171 7.6.2 Mechanical Properties 173 7.6.3 Thermal Properties 174 7.7 Testing and Characterization of Polymer Composite-Based BPs 176 7.7.1 Electrical Analysis 176 7.7.1.1 In-Plane Electrical Conductivity 176 7.7.1.2 Through-Plane Electrical Conductivity 189 7.7.2 Thermal Analysis 190 7.7.2.1 Thermal Gravimetric Analysis 190 7.7.2.2 Differential Scanning Calorimetry 190 7.7.2.3 Thermal Conductivity 191 7.7.3 Mechanical Analysis 192 7.7.3.1 Flexural Strength 192 7.7.3.2 Tensile Strength 192 7.7.3.3 Compressive Strength 193 7.8 Conclusions 193 Abbreviations 194 References 195 8 Metal-Organic Framework Membranes for Proton Exchange Membrane Fuel Cells 213 Yashmeen, Gitanjali Jindal and Navneet Kaur 8.1 Introduction 213 8.2 Aluminium Containing MOFs for PEMFCs 216 8.3 Chromium Containing MOFs for PEMFCs 217 8.4 Copper Containing MOFs for PEMFCs 224 8.5 Cobalt Containing MOFs for PEMFCs 225 8.6 Iron Containing MOFs for PEMFCs 227 8.7 Nickel Containing MOFs for PEMFCs 230 8.8 Platinum Containing MOFs for PEMFCs 230 8.9 Zinc Containing MOFs for PEMFCs 232 8.10 Zirconium Containing MOFs for PEMFCs 234 8.11 Conclusions and Future Prospects 239 References 240 9 Fluorinated Membrane Materials for Proton Exchange Membrane Fuel Cells 245 Pavitra Rajendran, Valmiki Aruna, Gangadhara Angajala and Pulikanti Guruprasad Reddy Abbreviations 246 9.1 Introduction 247 9.2 Fluorinated Polymeric Materials for PEMFCs 250 9.3 Poly(Bibenzimidazole)/Silica Hybrid Membrane 250 9.4 Poly(Bibenzimidazole) Copolymers Containing Fluorine-Siloxane Membrane 252 9.5 Sulfonated Fluorinated Poly(Arylene Ethers) 253 9.6 Fluorinated Sulfonated Polytriazoles 255 9.7 Fluorinated Polybenzoxazole (6F-PBO) 257 9.8 Poly(Bibenzimidazole) With Poly(Vinylidene Fluoride-Co-Hexafluoro Propylene) 258 9.9 Fluorinated Poly(Arylene Ether Ketones) 259 9.10 Fluorinated Sulfonated Poly(Arylene Ether Sulfone) (6fbpaqsh-xx) 260 9.11 Fluorinated Poly(Aryl Ether Sulfone) Membranes Cross-Linked Sulfonated Oligomer (c-SPFAES) 261 9.12 Sulfonated Poly(Arylene Biphenylether Sulfone)- Poly(Arylene Ether) (SPABES-PAE) 261 9.13 Conclusion 266 Conflicts of Interest 266 Acknowledgements 267 References 267 10 Membrane Materials in Proton Exchange Membrane Fuel Cells (PEMFCs) 271 Foad Monemian and Ali Kargari 10.1 Introduction 271 10.2 Fuel Cell: Definition and Classification 272 10.3 Historical Background of Fuel Cell 273 10.4 Fuel Cell Applications 274 10.4.1 Transportation 275 10.4.2 Stationary Power 275 10.4.3 Portable Applications 275 10.5 Comparison between Fuel Cells and Other Methods 278 10.6 PEMFCs: Description and Characterization 280 10.6.1 Ion Exchange Capacity–Conductivity 281 10.6.2 Durability 281 10.6.3 Water Management 282 10.6.4 Cost 282 10.7 Membrane Materials for PEMFC 282 10.7.1 Statistical Copolymer PEMs 283 10.7.2 Block and Graft Copolymers 286 10.7.3 Polymer Blending and Other PEM Compounds 289 10.8 Conclusions 296 References 296 11 Nafion-Based Membranes for Proton Exchange Membrane Fuel Cells 299 Santiago Pablo Fernandez Bordín, Janet de los Angeles Chinellato Díaz and Marcelo Ricardo Romero 11.1 Introduction: Background 300 11.2 Physical Properties 302 11.3 Nafion Structure 304 11.4 Water Uptake 307 11.5 Protonic Conductivity 310 11.6 Water Transport 316 11.7 Gas Permeation 319 11.8 Final Comments 324 Acknowledgements 324 References 325 12 Solid Polymer Electrolytes for Proton Exchange Membrane Fuel Cells 331 Nitin Srivastava and Rajendra Kumar Singh 12.1 Introduction 331 12.2 Type of Fuel Cells 334 12.2.1 Alkaline Fuel Cells 334 12.2.2 Polymer Electrolyte Fuel Cells 335 12.2.3 Phosphoric Acid Fuel Cells 337 12.2.4 Molten Carbonate Fuel Cells 338 12.2.5 Solid Oxide Fuel Cells 338 12.3 Basic Properties of PEMFC 339 12.4 Classification of Solid Polymer Electrolyte Membranes for PEMFC 341 12.4.1 Perfluorosulfonic Membrane 341 12.4.2 Partially Fluorinated Polymers 343 12.4.3 Non-Fluorinated Hydrocarbon Membrane 344 12.4.4 Nonfluorinated Acid Membranes With Aromatic Backbone 344 12.4.5 Acid Base Blend 344 12.5 Applications 345 12.5.1 Application in Transportation 346 12.6 Conclusions 347 References 347 13 Computational Fluid Dynamics Simulation of Transport Phenomena in Proton Exchange Membrane Fuel Cells 353 Maryam Mirzaie and Mohamadreza Esmaeilpour 13.1 Introduction 354 13.2 PEMFC Simulation and Mathematical Modeling 356 13.2.1 Governing Equations 359 13.2.1.1 Continuity Equation 359 13.2.1.2 Momentum Equation 360 13.2.1.3 Mass Transfer Equation 360 13.2.1.4 Energy Transfer Equation 362 13.2.1.5 Equation of Charge Conservation 362 13.2.1.6 Formation and Transfer of Liquid Water 362 13.3 The Solution Procedures 363 13.3.1 CFD Simulations 363 13.3.2 OpenFOAM 374 13.3.3 Lattice Boltzmann 381 13.4 Conclusions 389 References 390 Index 395

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Book SynopsisDiscover theanalyticalfoundations of electric machine, power electronics, electric drives, and electric power systems InIntroduction to the Analysis of Electromechanical Systems, an accomplished team of engineers deliversan accessible and robust analysis of fundamental topics in electrical systems and electrical machine modelingoriented to their control with power converters. The book begins with an introduction to the electromagnetic variables in rotatory and stationary reference frames before moving ontodescriptions ofelectric machines. The authors discuss direct current, round-rotor permanent-magnet alternating current, and induction machines, as well as brushless direct currentand induction motor drives. Synchronous generators and various other aspects of electric power system engineering are coveredas well, showing readers how to describe the behavior of electromagnetic variables and how to approach their control with modern power converters. Introduction to the Analysis of ElectrTable of ContentsPreface ix About the Authors xi 1 Basic System Analysis 1 1.1 Introduction 1 1.2 Phasor Analysis and Power Calculations 1 1.2.1 Power and Reactive Power 6 1.3 Elementary Magnetic Circuits 8 1.3.1 Field Energy and Coenergy 12 1.4 Stationary Coupled Circuits – The Transformer 16 1.4.1 Magnetically Linear Transformer 17 1.4.2 Field Energy 20 1.5 Two- and Three-phase Systems 23 1.5.1 Two-phase Systems 23 1.5.2 Three-phase Systems 25 1.6 Problems 29 2 Fundamentals of Electric Machine Analysis 31 2.1 Introduction 31 2.2 Coupled Circuits in Relative Motion 32 2.2.1 Field Energy 35 2.3 Electromagnetic Force and Torque 36 2.4 Winding Configurations 42 2.4.1 Concentrated Winding 42 2.4.2 Distributed Windings 46 2.5 Rotating Air-gap mmf – Tesla’s Rotating Magnetic Field 49 2.5.1 Two-pole Two-phase Stator 50 2.5.2 Three-phase Stator 55 2.6 Change of Variables 57 2.6.1 Two-phase Transformation 57 2.6.2 Three-phase Transformation 59 2.7 Stator Voltage Equations in Arbitrary Reference Frame 61 2.8 Instantaneous and Steady-state Phasors 63 2.9 P-pole Machines 65 2.10 Problems 70 References 72 3 Electric Machines 73 3.1 Introduction 73 3.2 Direct-current Machine 73 3.2.1 Commutation 74 3.2.2 Voltage and Torque Equations 77 3.2.3 Permanent-magnet dc Machine 79 3.3 Permanent-magnet ac Machine 82 3.3.1 Two-phase Permanent-magnet ac Machine 82 3.3.2 Reference Frame Analysis of a Permanent-magnet ac Machine 86 3.3.3 Three-phase Permanent-magnet ac Machine 90 3.3.4 Steady-state Analysis 90 3.4 Symmetrical Induction Machines 95 3.4.1 Two-phase Induction Machine 96 3.4.2 Symmetrical Rotor Windings 98 3.4.3 Substitute Variables for Symmetrical Rotating Circuits 101 3.4.4 Torque 105 3.4.5 Phasors and Steady-state Equivalent Circuit 108 3.5 Problems 115 References 117 4 Power Electronics 119 4.1 Introduction 119 4.2 Switching-circuit Fundamentals 120 4.2.1 Power Conversion Principles 120 4.2.2 Switches and Switching Functions 121 4.2.3 Energy Storage Elements 125 4.3 dc–dc Conversion 127 4.3.1 Buck Converter 127 4.3.2 Boost Converter 137 4.3.3 Advanced Circuit Topologies 141 4.4 ac–dc Conversion 141 4.4.1 Half-wave Rectifier 141 4.4.2 Full-wave Rectifier 148 4.5 dc–ac Conversion 156 4.5.1 Single-phase Inverter 156 4.6 Problems 160 References 163 5 Electric Drives 165 5.1 Introduction 165 5.2 dc Drive 165 5.2.1 Average-value Time-domain Block Diagram 168 5.2.2 Torque Control 170 5.3 Brushless dc Drive 172 5.3.1 Operation of Brushless Dc Drive with Φ V = 0 175 5.3.2 Torque Control 177 5.4 Induction Motor Drive 182 5.4.1 Torque Control 187 5.5 Problems 191 References 191 6 Power Systems 193 6.1 Introduction 193 6.2 Three-phase Transformer Connections 193 6.2.1 Wye–Wye Connection 194 6.2.2 Delta–Delta Connection 196 6.2.3 Wye–Delta or Delta–Wye Connection 197 6.2.4 Ideal Transformers 198 6.3 Synchronous Generator 200 6.3.1 Damper Windings 204 6.3.2 Torque 205 6.3.3 Steady-state Operation and Rotor Angle 206 6.4 Reactive Power and Power-Factor Correction 212 6.5 Per Unit System 218 6.6 Discussion of Transient Stability 221 6.6.1 Three-phase Fault 222 6.7 Problems 226 References 227 Appendix A Abbreviations, Constants, Conversions, and Identities 229 Index 233

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Book SynopsisEssentials of Electrical and Computer Engineeringis for an introductory course or course sequence for nonmajors, focused on the essentials of electrical and computer engineering that are required for all engineering students, and to pass the electrical engineering portion of the Fundamentals of Engineering (FE) exam. The text gently yet thoroughly introduces students to the full spectrum of fundamental topics, and the modular presentation gives instructors great flexibility. Special chapters and sections not typically found in nonmajors books: The Electric Power System explains how the components of the Grid work together to produce and deliver electric power. (Ch 8)Load line analysis is integrated with small-signal analysis, providing wide application for enhancing students' understanding of transistor and circuit operation and the options for analysis. (Ch 9)Instrumentation looks at how electrical measurements support the analysis and development of engineering systems. (Ch 13) Modern electronic devices and applications are presented in way useful for all majors, at a level presuming no prior knowledge. Technologies such as MEMS (Microelectromechanical Systems) are included to illustrate how modern technologies are interdisciplinary. This text may also be useful for self-study readers learning the fundamentals of electrical and computer engineering.

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Book SynopsisNUCLEAR ELECTRONICS WITH QUANTUM CRYOGENIC DETECTORS An ideal, comprehensive reference on quantum cryogenic detector instrumentation for the semiconductor and nuclear electronics industries Quantum nuclear electronics is an important scientific and technological field that overviews the development of the most advanced analytical instrumentation. This instrumentation covers a broad range of applications such as astrophysics, fundamental nuclear research facilities, chemical nano-spectroscopy laboratories, remote sensing, security systems, forensic investigations, and more. In the years since the first edition of this popular resource, the discipline has developed from demonstrating the unprecedented energy resolving power of individual devices to building large frame cameras with hundreds of thousands of pixel arrays capable of measuring and processing massive information flow. Building upon its first edition, the second edition of Nuclear Electronics with Quantum Cryogenic Detectors Table of ContentsPREFACE Chapter 1. Interaction of nuclear radiation with detector absorbers Introduction. 1.1. Intrinsic quantum efficiency of radiation detectors. 1.2. Detection of charged particles. 1.2.1. Light charged particles. 1.2.2. Continuous “braking” radiation (bremsstrahlung). 1.2.3. Backscattering of charged particles. 1.2.4. Heavy charged particles. 1.3. Primary interactions of X- and γ-ray photons with solid-state absorbers. 1.3.1. The photoelectric effect. 1.3.2. The Compton scattering. 1.3.3. The pair production. 1.3.4. Attenuation of photon radiation in solid-state detector absorbers 1.4. Detection of neutrons with solid-state radiation sensors. 1.5. Heat generation in athermal absorbers. Chapter 2. Radiation detectors with superconducting absorbers Introduction. 2.1. Selected topics of the superconductivity theory 2.1.1. The electron-phonon interaction and Cooper pairing mechanisms 2.1.2. The behaviour of superconductors in the magnetic field. 2.1.3. The tunnel Josephson junction. 2.1.4. The superconducting transmission line: the kinetic inductance. 2.2. Superconducting absorbers: the down-conversion of particle energy, intrinsic energy resolution. 2.2.1. The energy down-conversion process in superconducting absorbers. 2.2.2. The intrinsic energy resolution of quasi-particle detectors with superconducting absorbers. 2.3. Transport in the non-equilibrium superconductors. Incomplete charge collection mechanisms 2.3.1. The recombination time of quasi-particles in superconducting absorbers 2.3.2. The Rothwarf-Taylor phenomenological framework 2.3.3. The diffusion of quasi-particles in thin-film superconducting absorbers. Incomplete charge collection 2.3.4. Noise Equivalent Power (NEP) of superconducting absorbers 2.4. Quasi-particle radiation detectors with Superconducting Tunnel Junction (STJ) readout 2.4.1. The bandgap engineering and fabrication of STJ detectors. 2.4.2. The Giaever I-V curve of the STJ. 2.4.3. The tunneling mechanisms in STJs. 2.4.4. Pile-up and count rate capability of the STJ detectors. 2.5. Quasi-particle radiation detectors with microwave kinetic inductance sensors (MKID) 2.5.1. The operating principle of microwave kinetic inductance sensors. 2.5.2. The DROID X-ray detector with microwave kinetic inductance sensor readout. 2.6. STJ detectors frequency domain multiplexing with microwave SQUIDs Chapter 3. Radiation detectors with normal metal absorbers Introduction 3.1. Spectrometers based on Transition Edge Sensor (TES) microcalorimeters. 3.1.1. Fundamentals of TES design. 3.1.2. The electro-thermal feedback in TES microcalorimeters. 3.2. TES Microcalorimeters with Microwave SQUID (MSQUID) readout. Imaging cameras 3.3. Hot electron microcalorimeter with the NIS tunnel junction thermometer Chapter 4. Radiation detectors with semiconductor absorbers Introduction 4.1. Semiconductor transport. 4.1.1. Valence bond and energy band models. 4.1.2. Carrier scattering mechanisms and mobility in the semiconductor bulk materials. 4.1.3. Carrier generation and recombination (G-R) processes. 4.1.4. Effects of the G-R transport on the performance of radiation detectors. 4.1.5. Tunneling-assisted transport in semiconductor materials. 4.1.6. Tunneling transport across the thin dielectric barrier. 4.1.7. The semiconductor-vacuum interface. Surface transport 4.2. Macroscopic modelling of semiconductor devices 4.2.1. Microscopic models based on the Schroedinger Equation 4.2.2. The semi-classical transport models 4.2.3. The initial and boundary conditions in device modeling. The Ramo-Shockley theorem 4.3. Front windows in semiconductor radiation detectors 4.3.1. Entrance window based on the Schottky barrier junction 4.3.2. Front window based Metal-Insulator-Semiconductor (MIS) junction 4.3.3. The pn junction based front window in radiation detectors 4.4. Fabrication of semiconductor drift detectors (SDD) 4.4.1. The epitaxially grown ultra-shallow p+n junction entrance windows 4.4.2. The pureB technology for ultra-shallow entrance windows 4.5. Semiconductor drift detectors 4.5.1. Semiconductor detectors: operation principle and performance specifications 4.5.2. The intrinsic energy resolution of semiconductor detectors 4.5.3. Time response of SDDs 4.6. The quantum calorimetric electron-hole detector with semiconductor absorber 4.6.1. The phonon system dynamics in semiconductor materials 4.6.2. The design and performance of the quantum electron-hole detectors Chapter 5. Front End Readout Electronic Circuits for Quantum Cryogenic Detectors. Introduction 5.1 JFET transconductance preamplifiers 5.1.1. Principles of JFET transconductance amplifiers 5.1.2. Settling time of preamplifiers 5.2Dynamic and noise properties of JFET amplifiers 5.2.1. Static and dynamic parameters of JFETs 5.2.2. Noise characteristics of JFETs 5.2.3. PentaFET. High precision reset mechanism 5.2.4. The JFET cascode stage 5.2.5. The source follow-based charge-sensitive preamplifier 5.2.6. The differential stage based on matched JFETs 5.3 High Electron Mobility Transistor (HEMT) low noise amplifiers 5.4. The dc SQUID current amplifiers 5.4.1. The dcSQUID as a superconducting parametric amplifier 5.4.2. The dcSQUID with an intermediary input transformer 5.4.3. The coupled energy resolution of a double transformer dcSQUID 5.4.4. The dcSQUID readout electronics 5.4.5. The dcSQUID with the digital Bode FLL controller 5.4.6. The dcSQUID amplifier in the small-signal limit (noise) 5.4.7. The dcSQUID current amplifier in the large signal limit (dynamics) 5.4.8. The dcSQUID current amplifier in the large signal limit (noise) 5.5dc SQUID current amplifier at ultra low temperature 5.5.1. A double-stage amplifier with a single front ULT dcSQUID 5.5.2. A double stage amplifier with the front ULT SQUID array 5.6 Microwave SQUID parametric amplifier 5.6.1. Operation principle of microwave SQUIDs with external pumping (MSQUIDs) 5.6.2. The non-linearities in the MSQUID readout 5.6.3. The flux-ramp modulation methodology 5.6.4. Performance of MSQUID current amplifier 5.7 Design methodologies of analogue circuitries 5.7.1. The Laplace transform. Transfer functions of electronic networks 5.7.2. Design of analog pulse-shaping filter cells 5.7.3. Design of low-pass filters 5.7.4. Graphical methods of analysis and synthesis in the frequency domain 5.7.5. The describing function of non-linear elements in the frequency domain 5.7.6. Systems with synchronous multipliers Chapter 6. The Energy Resolution of Radiation Spectrometers. Introduction 6.1 Signal-to-noise ratio, equivalent noise charge of radiation spectrometers. General definitions 6.2Energy resolution of quasi-particle detectors (STJs, SDDs) 6.2.1. The tunnel junction coupled to a JFET transconductance amplifier 6.2.2. Energy resolution of STJ sensors read out with SQUID current preamps 6.3Optimal filtration in radiation spectrometers 6.4Energy resolution of TES microcalorimeters 6.5Matrix readout multiplexing of STJ detectors 6.5.1. Matrix readout of STJ sensors with JFET transconductance amplifiers 6.5.2. Matrix readout with SQUID current amplifiers 6.6Time division multiplexing (TDM) 6.7 Frequency division multiplexing (FDM) with microwave SQUIDs (μMUX) 6.8Code division multiplexing (CDM). Spread spectrum modulation (SSM) Chapter 7. Signal processing in radiation spectrometers Introduction 7.1. Signal conditioning units 7.1.1. Overview digital pulse processing architectures 7.1.2. AC coupled digital spectrometers 7.1.3. Digital pulse processing with moving window deconvolution 7.1.4. DC-coupled digital pulse processors 7.1.5. DC-coupled digital pulse processors with a sliding window signal conditioner 7.2.Analogue-to-digital conversion 7.2.1. Analog-to-digital converters. Basic information 7.2.2. The quantisation noise model of ADC 7.2.3. Nonlinearities of ADC 7.2.4. Aperture time of ADCs 7.2.5. Aperture uncertainty of ADCs 7.2.6. Reduction of the differential nonlinearity with the sliding scale method 7.3.Digital filtration 7.3.1. Z-transform methodology 7.3.2. Design of digital filters with z-transform 7.3.3. The stability of digital filters 7.3.4. Trapezoidal pulse shaping digital filter 7.3.5. Moving average pulse processing 7.4.Throughput of digital spectrometers 7.4.1. Pulse recognition channel. Pile up detection 7.4.2. Timing resolution of digital spectrometers 7.4.3. The pile up decoding in digital pulse processors 7.4.4. Digital rise (fall) time discriminators 7.5.Selected topics on the hardware design 7.5.1. Noise reduction in systems with switching power supplies 7.5.2. PCB layout 7.5.3. Layout, decoupling, and grounding of ADCs 7.5.4. Grounding aspects of the system design Chapter 8. Ultra-Low Temperature (ULT) cryogenic arrangement. Introduction 8.1. Cooling technologies for sub-1K temperature 8.1.1. The 3He refrigerator 8.1.2. The adiabatic demagnetisation refrigerator (ADR) 8.1.3. Temperature control in ADRs 8.2.Magnetic shielding at ultra low temperature 8.2.1. The µ-metal shield 8.2.2. The superconducting shielding 8.2.3. Solenoid inside a cylindrical superconducting shield 8.3.Thermal load on ULT stages 8.3.1.Thermal conduction through solids 8.3.2. Thermal conduction through the gas 8.3.3. Thermal radiation 8.4.Cryogenic packaging for the Focal Plane Array (FPA) unit 8.4.1. Design of the FPA unit implementing the TDM technique 8.4.2. The collimation of the FPA unit 8.4.3. Solid angle of the nuclear radiation spectrometer 8.4.4. Focusing poly-capillary optics 8.4.5. Wiring at mK temperatures 8.5.Cryogenic design for detectors with micro-wave frequency division multiplexing 8.6.The collection efficiency of radiation spectrometers Chapter 9. Applications of radiation spectrometers based on quantum cryogenic detectors Introduction 9.1.Nano-analytical chemistry with the SEM electron probe 9.1.1. The SEM-based energy dispersive spectroscopy (EDS) 9.1.2. The dual array TES-based EDS 9.1.3. Complementary techniques in the electron probe nano-analysis: the Auger spectroscopy 9.1.4. Complementary techniques in the electron probe nano-analysis: the wavelength dispersive spectrometers 9.2.Energy dispersive MALDI-TOF mass spectrometry for biochemical analysis Index

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Book SynopsisSOCIAL NETWORK ANALYSIS As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose. Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion. The book features: Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network. An understanding of network analysis and motivations to model phenomena as networks. Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted. Exemplifies information cascades that spread through an undeTable of ContentsPreface xi 1 Overview of Social Network Analysis and Different Graph File Formats 1Abhishek B. and Sumit Hirve 1.1 Introduction—Social Network Analysis 2 1.2 Important Tools for the Collection and Analysis of Online Network Data 3 1.3 More on the Python Libraries and Associated Packages 9 1.4 Execution of SNA in Terms of Real-Time Application: Implementation in Python 13 1.5 Clarity Toward the Indices Employed in the Social Network Analysis 14 1.5.1 Centrality 14 1.5.2 Transitivity and Reciprocity 15 1.5.3 Balance and Status 15 1.6 Conclusion 15 References 15 2 Introduction To Python for Social Network Analysis 19Agathiya Raja, Gavaskar Kanagaraj and Mohammad Gouse Galety 2.1 Introduction 20 2.2 SNA and Graph Representation 21 2.2.1 The Common Representation of Graphs 21 2.2.2 Important Terms to Remember in Graph Representation 23 2.3 Tools To Analyze Network 24 2.3.1 MS Excel 24 2.3.2 Ucinet 26 2.4 Importance of Analysis 26 2.5 Scope of Python in SNA 26 2.5.1 Comparison of Python With Traditional Tools 27 2.6 Installation 27 2.6.1 Good Practices 28 2.7 Use Case 29 2.7.1 Facebook Case Study 30 2.8 Real-Time Product From SNA 32 2.8.1 Nevaal Maps 33 References 34 3 Handling Real-World Network Data Sets 37Arman Abouali Galehdari, Behnaz Moradi and Mohammad Gouse Galety 3.1 Introduction 37 3.2 Aspects of the Network 38 3.3 Graph 41 3.3.1 Node, Edges, and Neighbors 41 3.3.2 Small-World Phenomenon 42 3.4 Scale-Free Network 43 3.5 Network Data Sets 46 3.6 Conclusion 49 References 49 4 Cascading Behavior in Networks 51Vasanthakumar G. U. 4.1 Introduction 51 4.1.1 Types of Data Generated in OSNs 52 4.1.2 Unstructured Data 52 4.1.3 Tools for Structuring the Data 53 4.2 User Behavior 53 4.2.1 Profiling 54 4.2.2 Pattern of User Behavior 54 4.2.3 Geo-Tagging 55 4.3 Cascaded Behavior 56 4.3.1 Cross Network Behavior 56 4.3.2 Pattern Analysis 58 4.3.3 Models for Cascading Pattern 59 References 60 5 Social Network Structure and Data Analysis in Healthcare 63Sailee Bhambere 5.1 Introduction 64 5.2 Prognostic Analytics—Healthcare 64 5.3 Role of Social Media for Healthcare Applications 65 5.4 Social Media in Advanced Healthcare Support 67 5.5 Social Media Analytics 67 5.5.1 Phases Involved in Social Media Analytics 68 5.5.2 Metrics of Social Media Analytics 69 5.5.3 Evolution of NIHR 70 5.6 Conventional Strategies in Data Mining Techniques 71 5.6.1 Graph Theoretic 72 5.6.2 Opinion Evaluation in Social Network 74 5.6.3 Sentimental Analysis 75 5.7 Research Gaps in the Current Scenario 75 5.8 Conclusion and Challenges 77 References 78 6 Pragmatic Analysis of Social Web Components on Semantic Web Mining 83Sasmita Pani, Bibhuprasad Sahu, Jibitesh Mishra, Sachi Nandan Mohanty and Amrutanshu Panigrahi 6.1 Introduction 84 6.2 Background 87 6.2.1 Web 87 6.2.2 Agriculture Information Systems 88 6.2.3 Ontology in Web or Mobile Web 90 6.3 Proposed Model 90 6.3.1 Developing Domain Ontology 91 6.3.2 Building the Agriculture Ontology with OWL-DL 94 6.3.2.1 Building Class Axioms 94 6.3.3 Building Object Property Between the Classes in OWL-DL 95 6.3.3.1 Building Object Property Restriction in OWL-DL 96 6.3.4 Developing Social Ontology 97 6.3.4.1 Building Class Axioms 99 6.3.4.2 Analysis of Social Web Components on Domain Ontology Under Agriculture System 100 6.4 Building Social Ontology Under the Agriculture Domain 100 6.4.1 Building Disjoint Class 100 6.4.2 Building Object Property 103 6.5 Validation 104 6.6 Discussion 104 6.7 Conclusion and Future Work 105 References 106 7 Classification of Normal and Anomalous Activities in a Network by Cascading C4.5 Decision Tree and K-Means Clustering Algorithms 109Gouse Baig Mohammad, S. Shitharth and P. Dileep 7.1 Introduction 110 7.1.1 Cascade Blogosphere Information 111 7.1.2 Viral Marketing Cascades 112 7.1.3 Cascade Network Building 113 7.1.4 Cascading Behavior Empirical Research 113 7.1.5 Cascades and Impact Nodes Detection 114 7.1.6 Topologies of Cascade Networks 114 7.1.7 Proposed Scheme Contributions 117 7.2 Literature Survey 118 7.2.1 Network Failures 122 7.3 Methodology 123 7.3.1 K-Means Clustering for Anomaly Detection 123 7.3.2 C4.5 Decision Trees Anomaly Detection 124 7.4 Implementation 125 7.4.1 Training Phase ZI 125 7.4.2 Testing Phase 126 7.5 Results and Discussion 127 7.5.1 Data Sets 127 7.5.2 Experiment Evaluation 127 7.6 Conclusion 127 References 128 8 Machine Learning Approach To Forecast the Word in Social Media 133R. Vijaya Prakash 8.1 Introduction 133 8.2 Related Works 135 8.3 Methodology 135 8.3.1 TF-IDF Technique 136 8.3.2 Times Series 137 8.4 Results and Discussion 138 8.5 Conclusion 141 References 145 9 Sentiment Analysis-Based Extraction of Real-Time Social Media Information From Twitter Using Natural Language Processing 149Madhuri Thimmapuram, Devasish Pal and Gouse Baig Mohammad 9.1 Introduction 150 9.1.1 Applications for Social Media 153 9.1.2 Social Media Data Challenges 154 9.2 Literature Survey 157 9.2.1 Techniques in Sentiment Analysis 164 9.3 Implementation and Results 166 9.3.1 Online Commerce 166 9.3.2 Feature Extraction 167 9.3.3 Hashtags 167 9.3.4 Punctuations 167 9.4 Conclusion 168 9.5 Future Scope 171 References 171 10 Cascading Behavior: Concept and Models 175Bithika Bishesh 10.1 Introduction 175 10.2 Cascade Networks 177 10.3 Importance of Cascades 178 10.4 Purposes for Studying Cascades 179 10.5 Collective Action 179 10.6 Cascade Capacity 180 10.7 Models of Network Cascades 180 10.7.1 Decision-Based Diffusion Models 181 10.7.2 Probabilistic Model of Cascade 181 10.7.3 Linear Threshold Model 183 10.7.4 Independent Cascade Model 183 10.7.5 SIR Epidemic Model 184 10.8 Centrality 186 10.9 Cascading Failures 189 10.10 Cascading Behavior Example Using Python 189 10.11 Conclusion 192 References 202 11 Exploring Social Networking Data Sets 205Arulkumar N., Joy Paulose, Mohammad Gouse Galety, Manimaran A., S. Saravanan and Saleem Raja A. 11.1 Introduction 206 11.1.1 Network Theory 206 11.1.2 Social Network Analysis 207 11.2 Establishing a Social Network 208 11.2.1 Designing the Symmetric Social Network 208 11.2.2 Creating an Asymmetric Social Network 210 11.2.3 Implementing and Visualizing Weighted Social Networks 212 11.2.4 Developing the Multigraph for Social Networks 213 11.3 Connectivity of Users in Social Networks 214 11.3.1 The Degree to which a Network Exists 214 11.3.2 Coefficient of Clustering 215 11.3.3 The Shortest Routes and Length Between Two Nodes 215 11.3.4 Eccentricity Distribution of a Node in a Social Network 217 11.3.5 Scale-Independent Social Networks 218 11.3.6 Transitivity 218 11.4 Centrality Measures in Social Networks 218 11.4.1 Centrality by Degree 219 11.4.2 Centrality by Eigenvectors 219 11.4.3 Centrality by Betweenness 220 11.4.4 Closeness to All Other Nodes 220 11.5 Case Study of Facebook 221 11.6 Conclusion 226 References 227 Index 229

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Book SynopsisINTRELLIGENT MANUFACTURING MANAGEMENT SYSTEMS The book explores the latest manufacturing techniques in relation to AI and evolutionary algorithms that can monitor and control the manufacturing environment. The concepts that pertain to the application of digital evolutionary technologies in the sphere of industrial engineering and manufacturing are presented in this book. A few chapters demonstrate stepwise discussion, case studies, structured literature review, rigorous experimentation results, and applications. Further chapters address the challenges encountered by industries in integrating these digital technologies into their operational activities, as well as the opportunities for this integration. In addition, the reader will find: Systemic explanations of the unique characteristics of big data, cloud computing, and AI used for decision-making in intelligent production systems; Highlights of the current and highly relevant topics in manufactTable of ContentsPreface xvii Part I: Smart Technologies in Manufacturing 1 1 Smart Manufacturing Systems for Industry 4.0 3 Gaijinliu Gangmei and Polash Pratim Dutta Abbreviations 3 1.1 Introduction 4 1.2 Research Methodology 5 1.3 Pillars of Smart Manufacturing 6 1.3.1 Manufacturing Technology and Processes 6 1.3.2 Materials 7 1.3.3 Data 8 1.3.4 Sustainability 8 1.3.5 Resource Sharing and Networking 9 1.3.6 Predictive Engineering 9 1.3.7 Stakeholders 10 1.3.8 Standardization 10 1.4 Enablers and Their Applications 11 1.4.1 Smart Design 12 1.4.2 Smart Machining 12 1.4.3 Smart Monitoring 13 1.4.4 Smart Control 13 1.4.5 Smart Scheduling 14 1.5 Assessment of Smart Manufacturing Systems 14 1.6 Challenges in Implementation of Smart Manufacturing Systems 15 1.6.1 Technological Issue 16 1.6.2 Methodological Issue 16 1.7 Implications of the Study for Academicians and Practitioners 17 1.8 Conclusion 17 References 18 2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities 23 S. Deepak Kumar, G. Arun Manohar, R. Surya Teja, P. S. V. Ramana Rao, A. Mandal, Ajit Behera and P. Srinivasa Rao Abbreviations 24 2.1 Introduction to Smart Manufacturing 24 2.1.1 Background of SM 24 2.1.2 Traditional Manufacturing versus Smart Manufacturing 25 2.1.3 Concept and Evolution of Industry 4.0 25 2.1.4 Motivations for Research in Smart Manufacturing 28 2.1.5 Objectives and Need of Industry 4.0 29 2.1.6 Research Methodology 30 2.1.7 Principles of I4. 0 30 2.1.8 Benefits/Advantages of Industry 4.0 31 2.2 Technology Pillars of Industry 4.0 31 2.2.1 Automation in Industry 4.0 33 2.2.1.1 Need of Automation 33 2.2.1.2 Components of Automation 33 2.2.1.3 Applications of Automation 34 2.2.2 Robots in Industry 4.0 34 2.2.2.1 Need of Robots 35 2.2.2.2 Advantages of Robots 35 2.2.2.3 Applications of Robots 37 2.2.2.4 Advances Robotics 37 2.2.3 Additive Manufacturing (AM) 38 2.2.3.1 Additive Manufacturing’s Potential Applications 39 2.2.4 Big Data Analytics 40 2.2.5 Cloud Computing 41 2.2.6 Cyber Security 43 2.2.6.1 Cyber-Security Challenges in Industry 4.0 43 2.2.7 Augmented Reality and Virtual Reality 44 2.2.8 Simulation 46 2.2.8.1 Need of Simulation in Smart Manufacturing 46 2.2.8.2 Advantages of Simulation 47 2.2.8.3 Simulation and Digital Twin 47 2.2.9 Digital Twins 47 2.2.9.1 Integration of Horizontal and Vertical Systems 48 2.2.10 IoT and IIoT in Industry 4.0 48 2.2.11 Artificial Intelligence in Industry 4.0 49 2.2.12 Implications of the Study for Academicians and Practitioners 51 2.3 Summary and Conclusions 51 2.3.1 Benefits of Industry 4.0 51 2.3.2 Challenges in Industry 4.0 52 2.3.3 Future Directions 52 Acknowledgement 53 References 53 3 IoT-Based Intelligent Manufacturing System: A Review 59 Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Pritam Chakraborty 3.1 Introduction 60 3.2 Literature Review 60 3.3 Research Procedure 64 3.3.1 The Beginning and Advancement of SM/IM 64 3.3.2 Beginning of SM/IM 64 3.3.3 Defining SM/IM 65 3.3.4 Potential of SM/IM 66 3.3.5 Statistical Analysis of SM/IM 68 3.3.6 Future Endeavour of SM/IM 68 3.3.7 Necessary Components of IoT Framework 69 3.3.8 Proposed System Based on IoT 71 3.3.9 Development of IoT in Industry 4.0 72 3.4 Smart Manufacturing 73 3.4.1 Re-Configurability Manufacturing System 73 3.4.2 RMS Framework Based Upon IoT 75 3.4.3 Machine Control 76 3.4.4 Machine Intelligence 77 3.4.5 Innovation and the IIoT 78 3.4.6 Wireless Technology 78 3.4.7 IP Mobility 78 3.4.8 Network Functionality Virtualization (NFV) 79 3.5 Academia Industry Collaboration 79 3.6 Conclusions 80 References 81 4 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process 85 Kali Charan Rath, Prasenjit Chatterjee, Pankajkumar Munibara Patro, Polaiah Bojja, Amaresh Kumar and Rashmi Prava Das Abbreviations 86 4.1 Introduction and Literature Reviews 86 4.1.1 Motivation Behind the Study 88 4.1.2 Objective of the Chapter 89 4.2 Network in Smart Manufacturing System 89 4.2.1 Challenges for Smart Manufacturing Industries 90 4.2.2 Smart Manufacturing Current Market Scenario 93 4.3 Data Drives in Smart Manufacturing 93 4.3.1 Benefits of Data-Driven Manufacturing 94 4.4 Manufacturing of Product Through 3D Printing Process 97 4.4.1 3D Printing Technology 99 4.4.2 3D Printing Technologies Classification 100 4.4.3 3D Printer Parameters 101 4.4.4 Significance of Honeycomb Structure 102 4.4.5 Acrylonitrile Butadiene Styrene (ABS) Thermoplastic Polymer Used for Honeycomb Structures Model 103 4.4.6 3D Printing Parameters and Their Descriptions 107 4.5 Conclusion 107 References 109 5 Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management 113 Hiranmoy Samanta and Kamal Golui 5.1 Introduction 114 5.2 Objectives 114 5.3 Research Methodology 114 5.4 Literature Review 115 5.5 Components of SIM 116 5.5.1 Supply Chain Management (SCM) 116 5.5.2 Inventory Management System (IMS) 117 5.5.3 Internet of Things (IoT) 120 5.5.4 RFID System 121 5.5.5 Maintenance, Repair, and Operations 123 5.5.6 Deep Reinforcement Learning 125 5.6 Framework 127 5.7 Optimization 130 5.7.1 Inventory Optimization 130 5.8 Results and Discussion 131 5.9 A Mirror to Researchers and Managers 132 5.10 Conclusions 133 5.11 Future Scope 133 References 134 6 Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0 141 Bikash Chandra Behera, Bikash Ranjan Moharana, Matruprasad Rout and Kishore Debnath 6.1 Introduction 142 6.2 Machine Learning 143 6.3 Smart Factory 146 6.4 Intelligent Machining 148 6.5 Machine Learning Processes Used in Machining Process 150 6.6 Performance Improvement of Machine Structure Using Machine Learning 152 6.7 Conclusions 153 References 153 7 Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies 157 Jagadeesha T., Thutupalli Srinivasa Advaith, Choppala Sarath Wesley, Grandhi Sri Sai Charith and Doppalapudi Manohar Abbreviations 158 7.1 Introduction 158 7.2 Literature Review 159 7.3 Methodology 161 7.3.1 Dataset Preparation 161 7.3.2 CWRU Dataset 161 7.3.3 Methodology Flow Chart 161 7.3.4 Data Pre-Processing 162 7.3.5 Models Deployed 163 7.3.6 Training and Testing 163 7.4 Analysis 164 7.4.1 Datasets 164 7.4.2 Feature Extraction 168 7.4.3 Splitting of Data into Samples 168 7.4.4 Algorithms Used 169 7.4.4.1 Multinomial Logistic Regression 169 7.4.4.2 K-Nearest Neighbors 170 7.4.4.3 Decision Tree 172 7.4.4.4 Support Vector Machine (SVM) 173 7.4.4.5 Random Forest 175 7.5 Results and Discussion 177 7.5.1 Importance of Classification Reports 177 7.5.2 Importance of Confusion Matrices 177 7.5.3 Decision Tree 178 7.5.4 Random Forest 180 7.5.5 K-Nearest Neighbors 182 7.5.6 Logistic Regression 185 7.5.7 Support Vector Machine 185 7.5.8 Comparison of the Algorithms 188 7.5.8.1 Accuracies 188 7.5.8.2 Precision and Recall 188 7.6 Conclusions 191 7.7 Scope of Future Work 191 References 192 8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment 195 K. Sujatha, R. Krishnakumar, N.P.G. Bhavani, U. Jayalatsumi, V. Srividhya, C. Kamatchi and R. Vani 8.1 Introduction 196 8.1.1 Color Image Processing 197 8.1.2 Motivation 199 8.1.3 Objectives 199 8.2 Literature Review 200 8.2.1 Gas Turbine Power Plants 200 8.2.2 Artificial Intelligent Methods 201 8.3 Materials and Methods 202 8.3.1 Feature Extraction 202 8.3.2 Classification 203 8.4 Results and Discussion 204 8.4.1 Fisher’s Linear Discriminant Function (FLDA) and Curvelet 204 8.5 Conclusion 219 8.5.1 Future Scope of Work 220 References 221 9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate 223 Sidharth Patro, Trupti Ranjan Mahapatra, Romeo S. Fono Tamo, Allu Vikram Kishore Murty, Soumya Ranjan Parimanik and Debadutta Mishra Abbreviations 224 9.1 Introduction 224 9.2 Numerical Experimentation Program 227 9.3 Discussion of the Results 239 9.4 Conclusion 244 Acknowledgements 245 References 245 Part II: Integration of Digital Technologies to Operations 249 10 Edge Computing-Based Conditional Monitoring 251 Granville Embia, Aezeden Mohamed, Bikash Ranjan Moharana and Kamalakanta Muduli 10.1 Introduction 252 10.1.1 Problem Statement 252 10.2 Literature Review 253 10.3 Edge Computing 257 10.4 Methodology 259 10.5 Discussion 263 10.5.1 Predictive Maintenance 263 10.5.2 Energy Efficiency Management 264 10.5.3 Smart Manufacturing 265 10.5.4 Conditional Monitoring via Edge Computing Locally 266 10.5.5 Lesson Learned 266 10.6 Conclusion 267 References 267 11 Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges 271 Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Atiur Alam 11.1 Introduction 272 11.2 Literature Review 273 11.3 Intelligent Manufacturing System Framework 275 11.3.1 Principles of Developing Industry 4.0 Solutions 277 11.3.2 Quantitative Analysis 279 11.3.2.1 Optimization Characteristics and Requirements of Industry 4.0 279 11.3.3 Optimization Methodologies and Algorithms 281 11.4 Bayesian Networks (BNs) 287 11.4.1 Instance-Based Learning (IBL) 288 11.4.2 The IB1 Algorithm 288 11.4.3 Artificial Neural Networks 289 11.4.4 A Comparison Between Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) 291 11.5 Problems of Implementing Machine Learning in Manufacturing 293 11.6 Conclusions 293 References 294 12 Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company 297 Natarajan Ramanathan, Neeraj Vairagi, Sakti Parida, Sushanta Tripathy, Ashok Kumar Sar, Kumar Mohanty and Alisha Lakra 12.1 Introduction 298 12.2 Literature Review 300 12.2.1 Shortage of Space 301 12.2.2 Non-Moving Materials 301 12.2.3 Lack of Action on Liquidation 302 12.2.4 Defective Material from Both Ends 302 12.2.5 Gap Between the Demand and the Supply 302 12.2.6 Multiple Price Revision 303 12.2.7 More Manual Timing for Loading and Unloading 303 12.2.8 Operational Challenges for Seasonal Products 303 12.2.9 Lack of Automation 303 12.2.10 Manpower Balancing Between Peak and Off 304 12.3 The Proposed ISM Methodology 304 12.3.1 Establishment of the Structural Self-Interaction Matrix (SSIM) 306 12.3.2 Creation of the Reachability Matrix 307 12.3.3 Implementation of the Level Partitions 308 12.3.4 Classification of the Selected Challenges 309 12.3.5 Development of the Final ISM Model 310 12.4 Results and Discussion 311 12.5 Practical Implications 312 12.6 Conclusions 313 References 314 13 The Impact of Organizational Ergonomics on Teaching Rapid Prototyping 319 Yaone Rapitsenyane, Patience Erick, Oanthata Jester Sealetsa and Richie Moalosi Abbreviations 320 13.1 Introduction 320 13.2 Organizational Ergonomics 322 13.2.1 Aim of Organizational Ergonomics 323 13.3 Rapid Prototyping and Teaching Rapid Prototyping 323 13.4 Industry 4.0 Factors Associated with Organizational Ergonomics in a Rapid Prototyping/Manufacturing Facility 325 13.4.1 Technology 326 13.4.2 Communication 327 13.4.3 Teamwork 328 13.4.4 Human Resource 328 13.4.5 Quality Management 329 13.5 Implications of Industry 4.0 on Prototyping and Prototyping Facilities in Design Schools 329 13.6 The Influence of Cooperative Working Ergonomics of Distributed Manufacturing in Teaching and Learning Rapid Prototyping 332 13.7 Health and Safety in Rapid Prototyping Laboratories 333 13.7.1 Common Health Hazards in 3D Printing 333 13.7.2 Chemical Hazards 335 13.7.3 Flammable/Explosion Hazards 336 13.7.4 UV and Laser Radiation Hazard 336 13.7.5 Other Hazards 336 13.7.6 Hazard Controls 337 13.7.7 Engineering Controls 337 13.7.8 Administrative Controls 338 13.7.9 Personal Protective Equipment 338 13.8 Impact of Digital/Rapid Prototyping on Organizational Ergonomics 339 13.9 Implications of the Study for Academicians and Practitioners 340 13.10 Conclusions and Future Work 341 References 343 14 Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer 349 Kiren Piso, Aezeden Mohamed, Bikash Ranjan Moharana, Kamalakanta Muduli and Noorhafiza Muhammad 14.1 Introduction 350 14.2 Literature Review 352 14.3 Research Set Up 354 14.4 Additive Manufacturing Techniques 356 14.4.1 Types of Additive Manufacturing 356 14.4.1.1 Fused Deposition Modelling (FDM) 356 14.4.1.2 Stereolithography (SLA) 356 14.4.1.3 Selective Laser Sintering (SLS) 357 14.4.1.4 Direct Energy Deposition (DED) 357 14.4.1.5 Digital Light Processing (DLP) 358 14.5 Strategies Used by Production Company 358 14.5.1 Maintenance Strategies 358 14.5.1.1 Breakdown Maintenance (BM) 358 14.5.1.2 Preventive Maintenance (PM) 358 14.5.1.3 Periodic Maintenance (Time Based Maintenance – TBM) 359 14.5.1.4 Predictive Maintenance (PM) 359 14.5.1.5 Corrective Maintenance (CM) 359 14.5.1.6 Maintenance Prevention (PM) 359 14.5.2 Inventory Control in Manufacturing 359 14.5.2.1 Inventory Control and Maintenance in Manufacturing 360 14.5.2.2 Warehouse Storages 360 14.5.3 Time Factor in Manufacturing 361 14.5.3.1 Breakdown Time 361 14.5.3.2 Set-Up Time 361 14.5.3.3 Manned Time (Available Time) 361 14.5.3.4 Operating Working Time 361 14.5.3.5 Operating Time 362 14.5.3.6 Production Time 362 14.6 Sustainable Manufacturing 362 14.6.1 Social Aspect of Sustainable Manufacturing 363 14.6.2 Environmental Aspects of Sustainable Manufacturing 364 14.6.3 Economical Aspect of Sustainable Manufacturing 364 14.7 Sustainable Additive Manufacturing 365 14.7.1 Energy 365 14.7.2 Cost 366 14.7.2.1 Downtime Cost 366 14.7.3 Supply Chain 368 14.7.4 Maintenance with Additive Manufacturing 368 14.8 Additive Manufacturing with IFC CMD: A Case Study 369 14.9 Contribution of Additive Manufacturing Towards Sustainability 370 14.10 Limitations of Additive Manufacturing 372 14.11 Conclusions and Recommendations 373 References 373 Index 377

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Book SynopsisTable of ContentsPreface xix Acknowledgements xxiii 1 Self-Powered Sensory Transducers: A Way Toward Green Internet of Things 1Rajeev Ranjan 1.1 Introduction 1 1.2 Need of the Work 3 1.3 Energy Scavenging Schemes in WSAN 4 1.4 Self Powered Systems and Green IoT (G-IoT) 10 1.5 Application Area and Scope of Self-Powered System in G-IoT 11 1.6 Challenges and Future Scope of the Self-Powered G-IoT 22 1.7 Conclusion 27 2 Self-Powered Wireless Sensor Networks in Cyber Physical System 41Srividya P. 2.1 Introduction 42 2.2 Wireless Sensor Networks in CPS 43 2.3 Architecture of WSNs with Energy Harvesting 44 2.4 Energy Harvesting for WSN 44 2.5 Energy Harvesting Due to Mechanical Vibrations 45 2.6 Piezoelectric Generators 46 2.7 Piezoelectric Materials 47 2.8 Types of Piezoelectric Structures 48 2.9 Hybridized Nanogenerators for Energy Harvesting 55 2.10 Conclusion 56 3 The Emergence of Cyber-Physical System in the Context of Self-Powered Soft Robotics 57Darwin S. and Fantin Irudaya Raj E. 3.1 Introduction 58 3.2 Actuators and Its Types 59 3.3 Soft Actuator Electrodes 69 3.4 Sensors 72 3.5 Soft Robotic Structures and Control Methods 74 3.6 Soft Robot Applications 76 3.7 Future Scope 79 3.8 Conclusion 82 4 Dynamic Butterfly Optimization Algorithm-Based Task Scheduling for Minimizing Energy Consumption in Distributed Green Data Centers 91Sengathir Janakiraman and Deva Priya M. 4.1 Introduction 92 4.2 Related Work 94 4.3 Improved Dynamic Butterfly Optimization Algorithm (IDBOA)-Based Task Scheduling (IDBOATS) 99 4.4 Results and Discussion 106 4.5 Conclusion 110 5 Wireless Power Transfer for IoT Applications--A Review 115Sasikala G. and Rajeev Ranjan 5.1 Introduction 116 5.2 Sensors 116 5.3 Actuators 118 5.4 Energy Requirement in Wireless Sensor Networks (WSNs) 119 5.5 Wireless Sensor Network and Green IoT (G-IoT) 121 5.6 Purpose of G-IoT 122 5.7 Motivation 124 5.8 Contribution 124 5.9 Need of the Work 125 5.10 Energy Transferring Schemes in WSAN 126 5.11 Electromagnetic Induction 127 5.12 Inductive Coupling 131 5.13 Resonance Inductive Coupling 132 5.14 Wireless Power Transmission Using Microwaves 133 5.15 Electromagnetic Radiations 135 5.16 Conclusion 135 6 Adaptive Energy Intelligence Using AI/ML Techniques 141Gowthamani R., Sasi Kala Rani K., Manikandan M. and Rohini M. 6.1 Introduction 142 6.2 Evolution of Cyber Physical System 144 6.3 Relationship With Internet of Things 146 6.4 Challenges in Design and Integration of Cyber Physical Systems 147 6.5 Future Challenges and Promises 149 6.6 Machine Learning Models 149 6.7 Estimation of Building Energy Consumption 150 6.8 Development of Artificial Intelligence 150 6.9 Usage of AI/ML in Adaptive Energy Management 151 6.10 Use of Hybrid/Ensemble Machine Learning Algorithm for Better Prediction 152 6.11 Conclusion 155 7 Renewable Energy Smart Grids for Electric Vehicles 159Vishal H. Kanchan, Preethesh B., Hithesh Alen D'Costa, Sohan R. Alva and Rathishchandra Ramachandra Gatti 7.1 Introduction 160 7.2 Integration of Electric Vehicles (EVs) into the Power Grid 161 7.3 EV Charging and Electric Grid Interaction 161 7.4 EVs with V2G System Architecture 163 7.5 EVs and Smart Grid Infrastructure 164 7.6 Renewable Energy Sources Integration With EVs 165 7.7 Application in Transport Sector 167 7.8 Application in Micro-Grid 169 7.9 State-of-the-Art Review 170 7.10 Future Trends 172 8 Recent Advances in Integrating Renewable Energy Micro-Grid Systems With Electric Vehicles 177Hithesh Alen D'Costa, Sohan R. Alva, Vishal H. Kanchan, Preethesh B. and Rathishchandra R. Gatti 8.1 Introduction 178 8.2 Electric Vehicles and Renewable Energy Sources: A General Overview 179 8.3 Microgrid 183 8.4 Interactions Between Cost-Conscious EVs and RESs 186 8.5 Interaction Between Efficiency-Conscious EVs and RESs 188 8.6 Open Problems 190 8.7 Conclusion 191 9 Overview of Fast Charging Technologies of Electric Vehicles 193Sohan R. Alva, Vishal H. Kanchan, Preethesh B., Hithesh Alen D'Costa and Rathishchandra Ramachandra Gatti 9.1 Introduction 194 9.2 Different Levels of Charging Electric Vehicles 194 9.3 State-of-the-Art Fast-Charging Implementation 197 9.4 DC Fast-Charging Structure 199 9.5 Fast Chargers 200 9.6 Today's Situation and Future Needs 201 9.7 Fast-Charging Point Power Requirements 202 9.8 Recent Technologies in Fast Charging, Machine Learning, and Artificial Intelligence 203 9.9 Effect of Fast Charging on EV Powertrain Systems 205 9.10 Grid Impacts Caused by EV Charging 207 9.11 Fast-Charging Technologies on the Self-Powered Automotive Cyber-Physical Systems 208 9.12 Conclusions 209 10 A Survey of VANET Routing Attacks and Defense Mechanisms in Intelligent Transportation System 213Allam Balaram, P. Chandana, Shaik Abdul Nabi and M. SilpaRaj 10.1 Introduction 214 10.2 Attacks in VANET 215 10.3 Impacts of Attacks on VANET Routing 216 10.4 Nonintentional Misbehavior 217 10.5 Intentional Misbehavior 217 10.6 Defence Mechanism of Routing Attacks in VANET Routing 218 10.7 Intrusion Detection Techniques in VANETs 220 10.8 Anonymous Routing in VANETs 221 10.9 Challenges and Future Directions 222 10.10 Conclusion 223 11 ANN-Based Cracking Model for Flexible Pavement in the Urban Roads 227Athiappan K., Kandasamy A., Karthik C. and Rajalakshmi M. 11.1 Introduction 228 11.2 Literature Review 229 11.3 Methodology 230 11.4 Structural Number 234 11.5 Modeling Methodology 235 11.6 Model Validation 238 11.7 Sensitivity Analysis 238 11.8 Conclusions 241 11.9 Limitations 241 11.10 Future Scope of Study 241 12 A Review of Autonomous Vehicles 243Joyston J. D'Costa and Ajith B.S. 12.1 Introduction 244 12.2 History 245 12.3 Degrees in Automation 246 12.4 Benefits and Drawbacks 247 12.5 Working Principle of Autonomous Vehicles 249 12.6 Mechanics Involved 250 12.7 Conclusion 252 13 Meeting Privacy Concerns in Intelligent Transportation Systems 255Sharon D. John 13.1 Introduction 255 13.2 Synopsis of ITS 257 13.3 Future Research Direction 260 13.4 Contributions to this Research 261 13.5 Conclusions 262 14 Feasibility Study of Digital Twin in Automotive Industry--Trends and Challenges 265Preethesh B., Hithesh Alen D'Costa, Sohan R. Alva, Vishal H. Kanchan and Rathishchandra R. Gatti 14.1 Introduction 266 14.2 Industrial Evolution 267 14.3 Influence of IoT on Digital Twin 268 14.4 Digital Twin in CPS Applications 269 14.5 Digital Twin Types 270 14.6 Levels of Digital Twin 271 14.7 Digital Thread 272 14.8 State-of-the-Art Digital Twin Deployment 273 14.9 Benefits of Digital Twin 274 14.10 Digital Twin Life Cycle 275 14.11 Digital Twin in Automotive Industry 276 14.12 Applications of Digital Twinning Technology in the Automotive Industry 277 14.13 Role of Digital Twins in Addressing Current Automotive Challenges 279 14.14 Challenges for Implementing Digital Twin in Automotive Industry 280 14.15 Bridging the Gap 280 15 State-of-the-Art and Future Applications of Farming Robotics 283Badrinath A.R., Abhishek Kamath, Veerishetty Arun Kumar, Nishan Rai and Rathishchandra R. Gatti 15.1 Introduction 283 15.2 Components of Agricultural Robots 285 15.3 Types of Agricultural Robots 288 15.4 Implementation of Robotics in the Agricultural Process 290 15.5 Challenges 294 15.6 Conclusions 295 16 Review on Robot Operating System 297G. Vijeth and Rathishchandra R. Gatti 16.1 Introduction 297 16.2 Nomenclature 301 16.3 ROS Implementation 303 16.4 Conclusion 306 17 An Overview of Collaborative Robots and Their Applications 309Rao S. Krishna and Lawrence J. Fernandes 17.1 Introduction 309 17.2 Art of Study 310 17.3 Implementation of Collaborative Robots 314 17.4 Conclusion 318 18 State-of-the-Art and Future Applications of Powered Exoskeleton 321C.P. Dheeshith, K. Abhijith, A. Shahaas, Rithin B. Nambiar and Rathishchandra R. Gatti 18.1 Introduction 321 18.2 Powered Exoskeleton 323 18.3 State of the Art 324 18.4 Design Parameters to be Considered 325 18.5 Challenges to Tackle 328 18.6 Applications of Powered Exoskeleton 328 18.7 Conclusion 330 19 An Overview of Recent Trends in Consumer Robotics 333Pramod Rao M., Shrihari P.C., Manoj, Shankar Gouda S. and Rathishchandra R. Gatti 19.1 Introduction 333 19.2 Entertainment Robot 334 19.3 Educational Robot 335 19.4 Social Robot 336 19.5 Toy Robot 337 19.6 Conclusion 338 20 Soft Robotics in Waste Management 341S. Rithvik, Vijith Rai, Surya Dornal, Deepak J. and B.C. Pramod 20.1 Introduction 341 20.2 Soft Robotics Insights 342 20.3 Soft Robots in Waste Management 343 20.4 Are Soft Robots the First Step for a Sustainable Future? 346 20.5 Conclusions 347 21 State-of-the-Art Review of Robotics in Crop Agriculture 349A. Shahaas, Rithin, B. Nambiar, C.P. Dheeshith, K. Abhijith and Rathishchandra R. Gatti 21.1 Introduction 349 21.2 Scope 350 21.3 Advantages 351 21.4 Disadvantages 352 21.5 Applications 352 21.6 Automation in Agriculture 354 21.7 Precision Agriculture 356 21.8 Conclusion 357 References 357 Index 359

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Book SynopsisTable of ContentsPreface xv 1 Overview of Digital Twin 1 Manisha Vohra 1.1 A Simplistic Introduction to Digital Twin 1 1.2 Basic Definition and Explanation of What is Digital Twin 5 1.3 The History of Digital Twin 7 1.4 Working 9 1.5 Features 11 1.5.1 Replication of Each and Every Aspect of the Original Device or Product 11 1.5.2 Helps in Product Lifecycle Management 11 1.5.3 Digital Twin can Prevent Downtime 11 1.6 Advantages of Digital Twin 11 1.6.1 Digital Twin is Helpful in Preventing Issues or Errors in the Actual Object, Product or Process 11 1.6.2 Helps in Well Utilization of Resources 12 1.6.3 Keeping Vigilance of the Actual Object, Product or Process Through Digital Twin is Possible 12 1.6.4 Helps in Efficient Handling and Managing of Objects, Device, Equipment, etc. 12 1.6.5 Reduction in Overall Cost of Manufacturing of Objects, Products, etc. 13 1.7 Applications 13 1.8 A Simple Example of Digital Twin Application 13 1.9 Digital Twin Technology and the Metaverse 14 1.10 Challenges 15 1.10.1 Careful Handling of Different Factors Involved in Digital Twin 15 1.10.2 Expertise Required 15 1.10.3 Data Security and Privacy 15 1.11 Conclusion 16 References 16 2 Introduction, History, and Concept of Digital Twin 19 N. Rajamurugu and M. K. Karthik 2.1 Introduction 19 2.2 History of Digital Twin 21 2.3 Concept of Digital Twin 23 2.3.1 DTP 23 2.3.2 DTI 24 2.3.3 DTE 24 2.3.4 Conceptualization 25 2.3.5 Comparison 25 2.3.6 Collaboration 25 2.4 Working Principle 26 2.5 Characteristics of Digital Twin 27 2.5.1 Homogenization 27 2.5.2 Digital Trail 27 2.5.3 Connectivity 27 2.6 Advantages 28 2.6.1 Companies Can Benefit From Digital Twin by Tracking Performance-Related Data 28 2.6.2 Different Sector’s Progress Can Be Accelerated 28 2.6.3 Digital Twins Can Be Used for Various Application 28 2.6.4 Digital Twin Can Help Decide Future Course of Work 28 2.6.5 Manufacturing Work Can Be Monitored 29 2.7 Limitations 29 2.7.1 Data Transmission Could Have Delays and Distortions 29 2.7.2 Digital Twin Implementation Will Need Required Skills and Sound Knowledge About It 29 2.8 Example of Digital Twin Application 29 2.8.1 Digital Twin Application in General Electric (GE) Renewable Energy 29 2.9 Conclusion 30 References 30 3 An Insight to Digital Twin 33 Anant Kumar Patel, Ashish Patel and Kanchan Mona Patel 3.1 Introduction 33 3.2 Understanding Digital Twin 35 3.3 Digital Twin History 36 3.4 Essential Aspects From Working Perspectives of Digital Twin 37 3.5 How Does a Digital Twin Work? 37 3.6 Insights to Digital Twin Technology Concept 38 3.6.1 Parts Twins 38 3.6.2 Product Twins 39 3.6.3 System Twins 39 3.6.4 Process Twins 39 3.7 Types of Digital Twin 39 3.7.1 Digital Twin Prototype (DTP) 40 3.7.2 Digital Twin Instance (DTI) 40 3.7.3 Digital Twin Environment (DTE) 40 3.8 Traits of Digital Twin 40 3.8.1 Look Same as the Original Object 40 3.8.2 Consists Different Details of the Original Object 41 3.8.3 Behaves Same as the Original Object 41 3.8.4 Can Predict and Inform in Advance About Problems That Could Occur 41 3.9 Value of Digital Twin 41 3.10 Advantages of Digital Twin 42 3.11 Real-World Examples of Use of Digital Twin 43 3.12 Conclusion 44 References 45 4 Digital Twin Solution Architecture 47 Suhas D. Joshi 4.1 Introduction 47 4.2 Previous Work 48 4.2.1 How This Work Differs 49 4.3 Use Cases 50 4.4 Architecture Considerations 51 4.5 Understanding the Physical Object 52 4.5.1 Modeling Considerations 55 4.6 Digital Twin and IoT 56 4.7 Digital Twin Solution Architecture 57 4.7.1 Conceptual Digital Twin Solution Architecture 57 4.7.2 Infrastructure Platform and IoT Services 57 4.7.3 Digital Twin Data and Process Model 57 4.7.4 Digital Twin Services 60 4.7.5 Digital Twin Applications 61 4.7.6 Sample Basic Data Flow through Digital Twin 61 4.7.7 Sample Data Flow for Exception Handling 63 4.7.8 Sample Data Flow through Digital Twin Applications 63 4.7.9 Development Considerations 65 4.8 Database Considerations 66 4.9 Messaging 67 4.10 Interfaces 69 4.11 User Experience 70 4.12 Cyber Security 70 4.13 Use Case Coverage 71 4.14 Future Direction and Trends 73 4.15 Conclusion 74 References 74 5 Role of Digital Twin Technology in Medical Sector—Toward Ensuring Safe Healthcare 77 S.N. Kumar, A. Lenin Fred, L.R. Jonisha Miriam, Christina Jane I., H. Ajay Kumar, Parasuraman Padmanabhan and Balazs Gulyas 5.1 Introduction to Digital Twin 78 5.2 Generic Applications of Digital Twin 79 5.3 Digital Twin Applications in Medical Field 83 5.3.1 Biosignal and Physiological Parameters Analysis for Body Area Network 84 5.3.2 Medicinal Drug Delivery 85 5.3.3 Surgical Preplanning 86 5.3.4 COVID 19 Screening and Diagnosis 87 5.4 Ongoing and Future Applications of Digital Twin in Healthcare Sector 89 5.5 Conclusion 89 Acknowledgments 90 References 90 6 Digital Twin as a Revamping Tool for Construction Industry 97 Greeshma A. S. and Philbin M. Philip 6.1 Introduction 97 6.2 Introduction to Digital Twin 99 6.3 Overview of Digital Twin in Construction 100 6.4 The Perks of Digital Twin 101 6.5 The Evolution of Digital Twin 102 6.6 Application of Digital Twin Technology in Construction Industry 103 6.7 Digital Twins Application for Construction Working Personnel Safety 106 6.8 Digital Twin Applications in Smart City Construction 107 6.9 Discussion 107 6.10 Conclusion 108 References 109 7 Digital Twin Applications and Challenges in Healthcare 111 Pavithra S., Pavithra D., Vanithamani R. and Judith Justin 7.1 Introduction 111 7.2 Digital Twin 112 7.3 Applications of Digital Twin 114 7.3.1 Smart Cities 114 7.3.2 Manufacturing Sector 115 7.3.3 Healthcare 115 7.3.4 Aviation 115 7.3.5 The Disney Park 115 7.4 Challenges with Digital Twin 115 7.5 Digital Twin in Healthcare 116 7.5.1 Digital Twin for Hospital Workflow Management 116 7.5.2 Digital Twin for a Healthcare Facility 117 7.5.3 Digital Twin for Different Medical Product Manufacturing 118 7.5.4 Cardiovascular Digital Twin 118 7.5.5 Digital Twin Utilization for Supporting Personalized Treatment 119 7.5.6 Digital Twin for Multiple Sclerosis (MS) 119 7.6 Digital Twin Challenges in Healthcare 119 7.6.1 Need of Training and Knowledge 120 7.6.2 Cost Factor 120 7.6.3 Trust Factor 120 7.7 Conclusion 121 References 122 8 Monitoring Structural Health Using Digital Twin 125 Samaya Pillai, Venkatesh Iyengar and Pankaj Pathak 8.1 Introduction 126 8.1.1 Digital Twin—The Approach and Uses 126 8.2 Structural Health Monitoring Systems (SHMS) 128 8.2.1 Criticality and Need for SHMS Approach 128 8.2.2 Passive and Active SHMS 129 8.3 Sensor Technology, Digital Twin (DT) and Structural Health Monitoring Systems (SHMS) 130 8.4 Conclusion 135 References 136 9 Role and Advantages of Digital Twin in Oil and Gas Industry 141 Prakash J. 9.1 Introduction 141 9.2 Digital Twin 142 9.3 Evolution of Digital Twin Technology 144 9.4 Various Digital Twins that Can Be Built 145 9.4.1 Parts Twins 145 9.4.2 Product Twins or Asset Twins 146 9.4.3 System Twins or Unit Twins 146 9.4.4 Process Twins 146 9.5 Advantage of Digital Twin 146 9.5.1 Paced Prototypin 147 9.5.2 Prediction 147 9.5.3 Enhanced Maintenance 147 9.5.4 Monitoring 147 9.5.5 Safety 147 9.5.6 Reduced Waste 147 9.6 Applications of Digital Twin 148 9.6.1 Aerospace 148 9.6.2 Power-Generation Equipment 148 9.6.3 Structures and Their Systems 148 9.6.4 Manufacturing Operations 149 9.6.5 Healthcare Services 149 9.6.6 Automotive Industry 149 9.6.7 Urban Planning and Construction 149 9.6.8 Smart Cities 149 9.6.9 Industrial Applications 149 9.7 Characteristics of Digital Twin 150 9.7.1 High-Fidelity 150 9.7.2 Lively 150 9.7.3 Multidisciplinary 150 9.7.4 Homogenization 150 9.7.5 Digital Footprint 151 9.8 Digital Twin in Oil and Gas Industry 151 9.9 Role of Digital Twin in the Various Areas of Oil and Gas Industry 152 9.9.1 Planning of Drilling Process 153 9.9.2 Performance Monitoring of Oil Field 153 9.9.3 Data Analytics and Simulation for Oil Field Production 153 9.9.4 Improving Field Personnel and Workforce Safety 153 9.9.5 Predictive Maintenance 153 9.10 The Advantages of Digital Twin in the Oil and Gas Industry 154 9.10.1 Production Efficacy 154 9.10.2 Preemptive Maintenance 154 9.10.3 Scenario Development 154 9.10.4 Different Processes Monitoring 155 9.10.5 Compliance Criteria 155 9.10.6 Cost Savings 155 9.10.7 Workplace Safety 155 9.11 Conclusion 155 References 156 10 Digital Twin in Smart Cities: Application and Benefits 159 Manisha Vohra 10.1 Introduction 159 10.2 Introduction of Digital Twin in Smart Cities 162 10.3 Applications of Digital Twin in Smart Cities 164 10.3.1 Traffic Management 164 10.3.2 Construction 165 10.3.3 Structural Health Monitoring 166 10.3.4 Healthcare 167 10.3.5 Digital Twin for Drainage System 168 10.3.6 Digital Twin for Power Grid 169 10.4 Conclusion 169 References 170 11 Digital Twin in Pharmaceutical Industry 173 Anant Kumar Patel, Ashish Patel and Kanchan Mona Patel 11.1 Introduction 173 11.2 What is Digital Twin? 175 11.2.1 Digital Twin Prototype (DTP) 176 11.2.2 Digital Twin Instance 176 11.2.3 Parts Twins 177 11.2.4 Product Twins 177 11.2.5 System Twins 177 11.2.6 Process Twins 178 11.3 Digital Twin in the Pharmaceutical Industry 178 11.4 Digital Twin Applications in Pharmaceutical Industry 180 11.4.1 Digital Twin of the Pharmaceutical Manufacturing Process 180 11.4.2 Digital Twin for Pharmaceutical Supply Chains 180 11.5 Examples of Use of Digital Twin in Pharmaceutical Industry 181 11.5.1 Digital Twin Simulator for Supporting Scientific Exchange of Views With Expert Physicians 181 11.5.2 Digital Twin for Medical Products 182 11.5.3 Digital Twin for Pharmaceutical Companies 182 11.6 Advantages of Digital Twin in the Pharmaceutical Industry 182 11.6.1 Wastage Can Be Reduced 182 11.6.2 Cost Savings 183 11.6.3 Faster Time to Market 183 11.6.4 Smooth Management 183 11.6.5 Remote Monitoring 184 11.7 Digital Twin in the Pharmaceutical Industry as a Game-Changer 184 11.8 Conclusion 184 References 185 12 Different Applications and Importance of Digital Twin 189 R. Suganya, Seyed M. Buhari and S. Rajaram 12.1 Introduction 189 12.2 History of Digital Twin 191 12.3 Applications of Digital Twin 192 12.3.1 Agriculture 193 12.3.2 Education 193 12.3.3 Healthcare 194 12.3.4 Manufacturing and Industry 195 12.3.5 Automotive Industry 197 12.3.6 Security 198 12.3.7 Smart Cities 199 12.3.8 Weather Forecasting and Meteorology 199 12.4 Importance of Digital Twin 199 12.5 Challenges 200 12.6 Conclusion 200 References 201 13 Digital Twin in Development of Products 205 Pedro Pablo Chambi Condori 13.1 Introduction 206 13.2 Digital Twin 207 13.2.1 Digital Twin Types 210 13.3 Different Aspects of an Organization and Digital Twin in Development of Products in Organizations 210 13.4 Implications of Digital Twin in Development of Products in Organizations 214 13.5 Advantages 214 13.5.1 Digital Twin Helps in Decision Making 214 13.5.2 Avoiding Downtine 215 13.5.3 Maximizing Efficiency 215 13.5.4 Cost Savings 215 13.5.5 Optimum Use of Resources 215 13.6 Conclusion 215 References 216 14 Possibilities with Digital Twin 219 Vismay Shah and Anilkumar Suthar 14.1 Introduction 219 14.2 What is Digital Twin Technology? 220 14.3 Possibilities With Digital Twin in Aviation Sector 224 14.3.1 Aviation Engineering in Combination With Digital Twin 224 14.3.2 Concept of Digital Twin for Aviation Components 225 14.3.3 How Important is Digital Twin in the Aviation Industry? 225 14.4 Possibilities With Digital Twin in Automotive Industry 226 14.4.1 Digital Twin in Automotive Industry 226 14.5 How Can Digital Twin Help in Improving Supply Chain Management? 228 14.6 Discussion 229 14.7 Conclusion 229 References 229 15 Digital Twin: Pros and Cons 233 Prakash J. 15.1 Introduction 233 15.2 Introduction to Digital Twin 234 15.3 Pros of Digital Twin 238 15.3.1 Digital Twin Can Forecast the Problem in Advance Before Its Arrival 238 15.3.2 Digital Twin Can Be Used in Monitoring Work 239 15.3.3 Reduction in Waste 240 15.3.4 Helps Avoid Hazardous Situations at Work 240 15.3.5 Increases Speed of Work Completion 240 15.4 Cons of Digital Twin 240 15.4.1 Deep Knowledge Will Be Needed for Creating and Handling the Digital Twin 241 15.4.2 Issues with Sensors Issue Can Affect the Digital Twin 241 15.4.3 Security 241 15.5 Application Wise Pros of Digital Twin 241 15.5.1 Oil and Gas Sector 242 15.5.2 Industrial Sector 242 15.5.3 Automotive Sector 242 15.5.4 Construction Sector 242 15.6 Conclusion 243 References 243 Index 247

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Book SynopsisHybrid Project Management A how-to guide for leaders of hybrid projects that covers technical and leadership principles across the project delivery spectrum. Hybrid Project Management offers practical guidance for combining waterfall and adaptive (Agile) project management approaches. This helpful guide includes advice on when to use each approach and how various methods can be combined and customized to meet the needs of projects and stakeholders. A sample case study demonstrates how to apply the concepts described throughout the text. An exciting new title from bestselling author Cyndi Snyder Dionisio on a top trending topic in the field, sample topics covered in Hybrid Project Management include: Variables to consider when choosing a development approach Project roles such as sponsors, product owners, project managers, scrum masters, and the project team Launching a hybrid project (vision statements and charters) and structuring the project (development approach, delivery cadence, lTable of ContentsAcknowledgmentsxii Introduction xiii 1 Introducing Project Management 1 The Spectrum of Development Approaches 2 Waterfall 3 Iterative 4 Incremental 6 Agile 8 Hybrid Project Management and Development Approaches 9 Summary 11 Key Terms 11 2 Choosing a Development Approach 12 Product Variables 12 Innovation 13 Scope Stability 13 Requirements Certainty 14 Ease of Change 14 Risk 15 Criticality 15 Safety 16 Regulatory 16 Project Variables 16 Stakeholders 17 Delivery Options 17 Funding Availability 18 Organization Variables 18 Structure 18 Culture 19 Project Team 19 Experience and Commitment 20 Development Approach Evaluation Tool 21 Product Variables 21 Project Variables 22 Organizational Variables 23 Creating a Visual Display of The Variables 24 Summary 25 Key Terms 25 3 Project Roles 26 Project Sponsor 26 Initiating Projects 27 Up- Front Planning 27 Monitoring Progress 28 Supporting the Project Manager 28 Project Manager 29 Leadership Skills 29 Management Skills 30 Product Owner 31 Product Functions 31 People Activities 32 Scrum Master 32 Facilitation 32 Support 33 The Team 33 Generalizing Specialists 34 Hybrid Options 35 Summary 36 Key Terms 36 4 Launching a Hybrid Project 37 Vision Statements 38 Organizations’ Vision Statements 38 Project Vision Statements 39 Project Charter 40 Case Study 42 Background 42 Case Study Vision Statement 42 Case Study Charter 43 Assumptions and Constraints 46 Summary 47 Key Terms 47 5 Hybrid Project Planning and Structure 48 Planning Fundamentals 49 Progressive Elaboration and Rolling Wave Planning 49 Competing Demands 50 The Project Management Plan 51 Subsidiary Plans 51 Tailoring the Project Management Plan for Hybrid Projects 53 Project Life Cycles 54 Key Reviews 57 Project Management Plan for a Hybrid Project 58 Development Approach 58 Life Cycle 60 Subsidiary Plans 62 Key Reviews 63 Roadmap 63 Summary 64 Key Terms 65 6 Defining Scope in Hybrid Projects 66 Planning for Scope with a Scope Management Plan 66 Elaborating Scope with a Scope Statement 69 Narrative Description 69 Deliverables 70 Out of Scope 72 Organizing Scope with a Work Breakdown Structure 72 WBS Levels 72 Work Packages, Planning Packages, and Control Accounts 74 Steps to Create a WBS 76 Getting into the Detail with A WBS Dictionary 76 Working with Requirements 76 Elicitation 78 Prioritization 79 Documenting Requirements 81 Prioritizing Scope with a Backlog 83 Summary 84 Key Terms 84 7 Building a Predictive Schedule 85 Organizing with a Schedule Management Plan 85 Predictive Scheduling 88 Identify Tasks 88 Sequence Tasks 89 Assign Team Members 92 Estimate Durations 97 Summary 98 Key Terms 98 8 Analyzing and Finalizing a Predictive Schedule 100 Analyzing the Schedule 100 Convergence and Divergence 101 Resource Allocation 102 The Critical Path 104 Float 104 Finalizing the Schedule 106 Schedule Compression 106 Schedule Buffer 108 Baselining the Schedule 109 Summary 110 Key Terms 110 9 Adaptive and Hybrid Scheduling 111 Adaptive Scheduling 111 Release Planning 112 Task Boards 114 Hybrid Scheduling 115 Predictive with Releases and Iterations 115 Predictive with Iterations Inserted 116 Adaptive then Predictive 116 Dependencies in Hybrid Schedules 116 Summary 117 Key Terms 118 10 Estimating 119 Estimating Ranges 119 Estimating Methods 120 Analogous Estimating 121 Parametric Estimating 123 Multipoint Estimating 123 Uses and Benefits 124 Affinity Grouping 125 Wideband Delphi 127 Bottom- Up Estimating 128 Basis of Estimates 128 Estimating The Budget 129 Summary 131 Key Terms 132 11 Stakeholder Engagement 133 Identifying your Stakeholders 133 Analyzing Stakeholders 134 Grids and Matrixes 135 Analyzing Stakeholders by Role 137 Direction of Influence 137 Awareness and Support 137 Stakeholder Register 138 Planning for Successful Engagement 139 Planning Project Communication 140 Communication Methods 141 Communication Technology 141 Stakeholder Communication Plan 142 Summary 144 Key Terms 144 12 Maintaining Stakeholder Engagement 145 Engaging Stakeholders 145 Communication Competence 146 When Someone Is Speaking 147 When You Are Speaking 148 When You Are Writing 148 Feedback 149 Communication Blockers 150 Project Meetings 151 Adaptive Meetings 152 Predictive Meetings 156 Summary 157 Key Terms 157 13 Leadership in a Hybrid Environment 158 Emotional Intelligence 159 Self- Awareness 159 Self- Regulation 159 Social Awareness 160 Social Skills 160 Motivation 160 Motivators 161 Motivating Your Team 161 Example of Motivation in the Workplace 162 Agile Leadership Practices 162 Servant Leadership 162 Self- Managing Teams 163 Tailoring for a Hybrid Environment 166 Developing a High- Performing Team 166 Traits of High- Performing Teams 167 Building Relationships 167 Summary 168 Key Terms 168 14 Planning for Risk 169 Introduction to Risk Management 169 Risk Tolerance and Thresholds 171 Risk Management Plan 171 Elements in a Risk Management Plan 172 Sample Risk Management Plan 174 Risk Management Plan 174 Funding 175 Timing 175 Risk Categories 176 Definitions of Probability 176 Definitions of Impact 176 Probability and Impact Matrix 176 Summary 177 Key Terms 177 15 Identifying and Prioritizing Risk 178 Identifying Risks 178 Identification Methods 179 Documenting Risks 181 Analyzing and Prioritizing Risks 183 Filling out the Probability and Impact Matrix 183 Assessing Additional Risk Parameters 184 Simple Quantitative Analysis Methods 186 Expected Monetary Value 186 Decision Trees 187 Summary 188 Key Terms 188 16 Reducing Risk 189 Risk Responses 189 Risk Avoidance 190 Risk Mitigation 190 Risk Transference 190 Risk Escalation 191 Risk Acceptance 191 Implementing Responses 192 Risk- Adjusted Backlog 193 Reserve 195 Contingency Reserve 195 Management Reserve 199 Summary 199 Key Terms 200 17 Leading the Team 201 Establishing a Healthy Environment 201 Psychological Safety 202 Creating a Safe Environment 202 Cultivating Adaptability 203 Fostering Resilience 205 Ways of Thinking 205 Critical Thinking 206 Working with Bias 208 System Thinking 209 Supporting the Team 209 Solving Problems 210 Making Decisions 210 Resolving Conflicts 211 Considerations for Virtual Teams 213 Engagement 213 Structure 214 Virtual Meetings 215 Summary 216 Key Terms 216 18 Maintaining Momentum 217 Working with Change 217 Change Management Plan 218 Change Requests 219 Change Log 220 Requirements Traceability Matrix 221 Managing Change in a Hybrid Environment 221 Change for Predictive Deliverables 222 Change for Adaptive Deliverables 222 Helpful Tools 222 Decision Log 223 Issue Log 223 Impediment Log 224 Summary 224 Key Terms 224 19 Metrics for Predictive Deliverables 225 Predictive Measures 225 Schedule Measures 226 Cost Measures 228 Earned Value Management 231 Planning for Earned Value 231 Determining Earned Value and Actual Cost 236 Calculating Schedule and Cost Variances 237 Calculating Schedule and Cost Indexes 238 Forecasts 239 Estimate to Complete 240 Estimate at Completion 240 Summary 241 Key Terms 242 20 Metrics for Adaptive Deliverables 243 Adaptive Measures 243 Burndown Charts 244 Burnup Charts 246 Estimating Velocity 247 Cumulative Flow Diagrams 248 Creating a Cumulative Flow Diagram 250 Stakeholder Measures 253 Net Promoter Score ® 253 Mood Chart 254 Summary 255 Key Terms 255 21 Reporting for Hybrid Projects 256 Reporting 256 Narrative Reports 257 Visual Reports 260 Dashboards 260 Information Radiators 270 Hybrid Dashboards 270 Tips 272 Benefits 272 Summary 272 Key Terms 272 22 Corrective Actions and Closure 273 Preventive and Corrective Actions 273 Potential Causes and Responses for Performance Issues 274 Updating the Baseline 276 Project Closure 276 Transition 277 Administrative Closure 277 Acknowledgment 277 Evaluating Success 278 Close- Out Reports 278 Summary 280 Key Terms 280 23 Making the Move to a Hybrid Environment 281 Establish Criteria 281 Establish the Right Environment 282 Process First 282 Glossary 284 Index 292

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Book SynopsisAdvances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics. To aid in reader comprehension, each chapteTable of ContentsAbout the Editors xix List of Contributors xx Preface xxvi Section I Introduction to AI-Based Regression and Classification 1 1 Introduction to Neural Networks 3 Isha Garg and Kaushik Roy 1.1 Taxonomy 3 1.1.1 Supervised Versus Unsupervised Learning 3 1.1.2 Regression Versus Classification 4 1.1.3 Training, Validation, and Test Sets 4 1.2 Linear Regression 5 1.2.1 Objective Functions 6 1.2.2 Stochastic Gradient Descent 7 1.3 Logistic Classification 9 1.4 Regularization 11 1.5 Neural Networks 13 1.6 Convolutional Neural Networks 16 1.6.1 Convolutional Layers 17 1.6.2 Pooling Layers 18 1.6.3 Highway Connections 19 1.6.4 Recurrent Layers 19 1.7 Conclusion 20 References 20 2 Overview of Recent Advancements in Deep Learning and Artificial Intelligence 23 Vijaykrishnan Narayanan, Yu Cao, Priyadarshini Panda, Nagadastagiri Reddy Challapalle, Xiaocong Du, Youngeun Kim, Gokul Krishnan, Chonghan Lee, Yuhang Li, Jingbo Sun, Yeshwanth Venkatesha, Zhenyu Wang, and Yi Zheng 2.1 Deep Learning 24 2.1.1 Supervised Learning 26 2.1.1.1 Conventional Approaches 26 2.1.1.2 Deep Learning Approaches 29 2.1.2 Unsupervised Learning 35 2.1.2.1 Algorithm 35 2.1.3 Toolbox 37 2.2 Continual Learning 38 2.2.1 Background and Motivation 38 2.2.2 Definitions 38 2.2.3 Algorithm 38 2.2.3.1 Regularization 39 2.2.3.2 Dynamic Network 40 2.2.3.3 Parameter Isolation 40 2.2.4 Performance Evaluation Metric 41 2.2.5 Toolbox 41 2.3 Knowledge Graph Reasoning 42 2.3.1 Background 42 2.3.2 Definitions 42 2.3.3 Database 43 2.3.4 Applications 43 2.3.5 Toolbox 44 2.4 Transfer Learning 44 2.4.1 Background and Motivation 44 2.4.2 Definitions 44 2.4.3 Algorithm 45 2.4.4 Toolbox 46 2.5 Physics-Inspired Machine Learning Models 46 2.5.1 Background and Motivation 46 2.5.2 Algorithm 46 2.5.3 Applications 49 2.5.4 Toolbox 50 2.6 Distributed Learning 50 2.6.1 Introduction 50 2.6.2 Definitions 51 2.6.3 Methods 51 2.6.4 Toolbox 54 2.7 Robustness 54 2.7.1 Background and Motivation 54 2.7.2 Definitions 55 2.7.3 Methods 55 2.7.3.1 Training with Noisy Data/Labels 55 2.7.3.2 Adversarial Attacks 55 2.7.3.3 Defense Mechanisms 56 2.7.4 Toolbox 56 2.8 Interpretability 56 2.8.1 Background and Motivation 56 2.8.2 Definitions 57 2.8.3 Algorithm 57 2.8.4 ToolBox 58 2.9 Transformers and Attention Mechanisms for Text and Vision Models 58 2.9.1 Background and Motivation 58 2.9.2 Algorithm 59 2.9.3 Application 60 2.9.4 Toolbox 61 2.10 Hardware for Machine Learning Applications 62 2.10.1 Cpu 62 2.10.2 Gpu 63 2.10.3 ASICs 63 2.10.4 Fpga 64 Acknowledgment 64 References 64 Section II Advancing Electromagnetic Inverse Design with Machine Learning 81 3 Breaking the Curse of Dimensionality in Electromagnetics Design Through Optimization Empowered by Machine Learning 83 N. Anselmi, G. Oliveri, L. Poli, A. Polo, P. Rocca, M. Salucci, and A. Massa 3.1 Introduction 83 3.2 The SbD Pillars and Fundamental Concepts 85 3.3 SbD at Work in EMs Design 88 3.3.1 Design of Elementary Radiators 88 3.3.2 Design of Reflectarrays 92 3.3.3 Design of Metamaterial Lenses 93 3.3.4 Other SbD Customizations 96 3.4 Final Remarks and Envisaged Trends 101 Acknowledgments 101 References 102 4 Artificial Neural Networks for Parametric Electromagnetic Modeling and Optimization 105 Feng Feng, Weicong Na, Jing Jin, and Qi-Jun Zhang 4.1 Introduction 105 4.2 ANN Structure and Training for Parametric EM Modeling 106 4.3 Deep Neural Network for Microwave Modeling 107 4.3.1 Structure of the Hybrid DNN 107 4.3.2 Training of the Hybrid DNN 108 4.3.3 Parameter-Extraction Modeling of a Filter Using the Hybrid DNN 108 4.4 Knowledge-Based Parametric Modeling for Microwave Components 111 4.4.1 Unified Knowledge-Based Parametric Model Structure 112 4.4.2 Training with l 1 Optimization of the Unified Knowledge-Based Parametric Model 115 4.4.3 Automated Knowledge-Based Model Generation 117 4.4.4 Knowledge-Based Parametric Modeling of a Two-Section Low-Pass Elliptic Microstrip Filter 117 4.5 Parametric Modeling Using Combined ANN and Transfer Function 121 4.5.1 Neuro-TF Modeling in Rational Form 121 4.5.2 Neuro-TF Modeling in Zero/Pole Form 122 4.5.3 Neuro-TF Modeling in Pole/Residue Form 123 4.5.4 Vector Fitting Technique for Parameter Extraction 123 4.5.5 Two-Phase Training for Neuro-TF Models 123 4.5.6 Neuro-TF Model Based on Sensitivity Analysis 125 4.5.7 A Diplexer Example Using Neuro-TF Model Based on Sensitivity Analysis 126 4.6 Surrogate Optimization of EM Design Based on ANN 129 4.6.1 Surrogate Optimization and Trust Region Update 129 4.6.2 Neural TF Optimization Method Based on Adjoint Sensitivity Analysis 130 4.6.3 Surrogate Model Optimization Based on Feature-Assisted of Neuro-TF 130 4.6.4 EM Optimization of a Microwave Filter Utilizing Feature-Assisted Neuro-TF 131 4.7 Conclusion 133 References 133 5 Advanced Neural Networks for Electromagnetic Modeling and Design 141 Bing-Zhong Wang, Li-Ye Xiao, and Wei Shao 5.1 Introduction 141 5.2 Semi-Supervised Neural Networks for Microwave Passive Component Modeling 141 5.2.1 Semi-Supervised Learning Based on Dynamic Adjustment Kernel Extreme Learning Machine 141 5.2.1.1 Dynamic Adjustment Kernel Extreme Learning Machine 142 5.2.1.2 Semi-Supervised Learning Based on DA-KELM 147 5.2.1.3 Numerical Examples 150 5.2.2 Semi-Supervised Radial Basis Function Neural Network 157 5.2.2.1 Semi-Supervised Radial Basis Function Neural Network 157 5.2.2.2 Sampling Strategy 161 5.2.2.3 SS-RBFNN With Sampling Strategy 162 5.3 Neural Networks for Antenna and Array Modeling 166 5.3.1 Modeling of Multiple Performance Parameters for Antennas 166 5.3.2 Inverse Artificial Neural Network for Multi-objective Antenna Design 175 5.3.2.1 Knowledge-Based Neural Network for Periodic Array Modeling 183 5.4 Autoencoder Neural Network for Wave Propagation in Uncertain Media 188 5.4.1 Two-Dimensional GPR System with the Dispersive and Lossy Soil 188 5.4.2 Surrogate Model for GPR Modeling 190 5.4.3 Modeling Results 191 References 193 Section III Deep Learning for Metasurface Design 197 6 Generative Machine Learning for Photonic Design 199 Dayu Zhu, Zhaocheng Liu, and Wenshan Cai 6.1 Brief Introduction to Generative Models 199 6.1.1 Probabilistic Generative Model 199 6.1.2 Parametrization and Optimization with Generative Models 199 6.1.2.1 Probabilistic Model for Gradient-Based Optimization 200 6.1.2.2 Sampling-Based Optimization 200 6.1.2.3 Generative Design Strategy 201 6.1.2.4 Generative Adversarial Networks in Photonic Design 202 6.1.2.5 Discussion 203 6.2 Generative Model for Inverse Design of Metasurfaces 203 6.2.1 Generative Design Strategy for Metasurfaces 203 6.2.2 Model Validation 204 6.2.3 On-demand Design Results 206 6.3 Gradient-Free Optimization with Generative Model 207 6.3.1 Gradient-Free Optimization Algorithms 207 6.3.2 Evolution Strategy with Generative Parametrization 207 6.3.2.1 Generator from VAE 207 6.3.2.2 Evolution Strategy 208 6.3.2.3 Model Validation 209 6.3.2.4 On-demand Design Results 209 6.3.3 Cooperative Coevolution and Generative Parametrization 210 6.3.3.1 Cooperative Coevolution 210 6.3.3.2 Diatomic Polarizer 211 6.3.3.3 Gradient Metasurface 211 6.4 Design Large-Scale, Weakly Coupled System 213 6.4.1 Weak Coupling Approximation 214 6.4.2 Analog Differentiator 214 6.4.3 Multiplexed Hologram 215 6.5 Auxiliary Methods for Generative Photonic Parametrization 217 6.5.1 Level Set Method 217 6.5.2 Fourier Level Set 218 6.5.3 Implicit Neural Representation 218 6.5.4 Periodic Boundary Conditions 220 6.6 Summary 221 References 221 7 Machine Learning Advances in Computational Electromagnetics 225 Robert Lupoiu and Jonathan A. Fan 7.1 Introduction 225 7.2 Conventional Electromagnetic Simulation Techniques 226 7.2.1 Finite Difference Frequency (FDFD) and Time (FDTD) Domain Solvers 226 7.2.2 The Finite Element Method (FEM) 229 7.2.2.1 Meshing 229 7.2.2.2 Basis Function Expansion 229 7.2.2.3 Residual Formulation 230 7.2.3 Method of Moments (MoM) 230 7.3 Deep Learning Methods for Augmenting Electromagnetic Solvers 231 7.3.1 Time Domain Simulators 231 7.3.1.1 Hardware Acceleration 231 7.3.1.2 Learning Finite Difference Kernels 232 7.3.1.3 Learning Absorbing Boundary Conditions 234 7.3.2 Augmenting Variational CEM Techniques Via Deep Learning 234 7.4 Deep Electromagnetic Surrogate Solvers Trained Purely with Data 235 7.5 Deep Surrogate Solvers Trained with Physical Regularization 240 7.5.1 Physics-Informed Neural Networks (PINNs) 240 7.5.2 Physics-Informed Neural Networks with Hard Constraints (hPINNs) 241 7.5.3 WaveY-Net 243 7.6 Conclusions and Perspectives 249 Acknowledgments 250 References 250 8 Design of Nanofabrication-Robust Metasurfaces Through Deep Learning-Augmented Multiobjective Optimization 253 Ronald P. Jenkins, Sawyer D. Campbell, and Douglas H. Werner 8.1 Introduction 253 8.1.1 Metasurfaces 253 8.1.2 Fabrication State-of-the-Art 253 8.1.3 Fabrication Challenges 254 8.1.3.1 Fabrication Defects 254 8.1.4 Overcoming Fabrication Limitations 255 8.2 Related Work 255 8.2.1 Robustness Topology Optimization 255 8.2.2 Deep Learning in Nanophotonics 256 8.3 DL-Augmented Multiobjective Robustness Optimization 257 8.3.1 Supercells 257 8.3.1.1 Parameterization of Freeform Meta-Atoms 257 8.3.2 Robustness Estimation Method 259 8.3.2.1 Simulating Defects 259 8.3.2.2 Existing Estimation Methods 259 8.3.2.3 Limitations of Existing Methods 259 8.3.2.4 Solver Choice 260 8.3.3 Deep Learning Augmentation 260 8.3.3.1 Challenges 261 8.3.3.2 Method 261 8.3.4 Multiobjective Global Optimization 267 8.3.4.1 Single Objective Cost Functions 267 8.3.4.2 Dominance Relationships 267 8.3.4.3 A Robustness Objective 269 8.3.4.4 Problems with Optimization and DL Models 269 8.3.4.5 Error-Tolerant Cost Functions 269 8.3.5 Robust Supercell Optimization 270 8.3.5.1 Pareto Front Results 270 8.3.5.2 Examples from the Pareto Front 271 8.3.5.3 The Value of Exhaustive Sampling 272 8.3.5.4 Speedup Analysis 273 8.4 Conclusion 275 8.4.1 Future Directions 275 Acknowledgments 276 References 276 9 Machine Learning for Metasurfaces Design and Their Applications 281 Kumar Vijay Mishra, Ahmet M. Elbir, and Amir I. Zaghloul 9.1 Introduction 281 9.1.1 ML/DL for RIS Design 283 9.1.2 ML/DL for RIS Applications 283 9.1.3 Organization 285 9.2 Inverse RIS Design 285 9.2.1 Genetic Algorithm (GA) 286 9.2.2 Particle Swarm Optimization (PSO) 286 9.2.3 Ant Colony Optimization (ACO) 289 9.3 DL-Based Inverse Design and Optimization 289 9.3.1 Artificial Neural Network (ANN) 289 9.3.1.1 Deep Neural Networks (DNN) 290 9.3.2 Convolutional Neural Networks (CNNs) 290 9.3.3 Deep Generative Models (DGMs) 291 9.3.3.1 Generative Adversarial Networks (GANs) 291 9.3.3.2 Conditional Variational Autoencoder (cVAE) 293 9.3.3.3 Global Topology Optimization Networks (GLOnets) 293 9.4 Case Studies 294 9.4.1 MTS Characterization Model 294 9.4.2 Training and Design 296 9.5 Applications 298 9.5.1 DL-Based Signal Detection in RIS 302 9.5.2 DL-Based RIS Channel Estimation 303 9.6 DL-Aided Beamforming for RIS Applications 306 9.6.1 Beamforming at the RIS 306 9.6.2 Secure-Beamforming 308 9.6.3 Energy-Efficient Beamforming 309 9.6.4 Beamforming for Indoor RIS 309 9.7 Challenges and Future Outlook 309 9.7.1 Design 310 9.7.1.1 Hybrid Physics-Based Models 310 9.7.1.2 Other Learning Techniques 310 9.7.1.3 Improved Data Representation 310 9.7.2 Applications 311 9.7.3 Channel Modeling 311 9.7.3.1 Data Collection 311 9.7.3.2 Model Training 311 9.7.3.3 Environment Adaptation and Robustness 312 9.8 Summary 312 Acknowledgments 313 References 313 Section IV Rf, Antenna, Inverse-scattering, and other Em Applications of Deep Learning 319 10 Deep Learning for Metasurfaces and Metasurfaces for Deep Learning 321 Clayton Fowler, Sensong An, Bowen Zheng, and Hualiang Zhang 10.1 Introduction 321 10.2 Forward-Predicting Networks 322 10.2.1 FCNN (Fully Connected Neural Networks) 323 10.2.2 CNN (Convolutional Neural Networks) 324 10.2.2.1 Nearly Free-Form Meta-Atoms 324 10.2.2.2 Mutual Coupling Prediction 327 10.2.3 Sequential Neural Networks and Universal Forward Prediction 330 10.2.3.1 Sequencing Input Data 331 10.2.3.2 Recurrent Neural Networks 332 10.2.3.3 1D Convolutional Neural Networks 332 10.3 Inverse-Design Networks 333 10.3.1 Tandem Network for Inverse Designs 333 10.3.2 Generative Adversarial Nets (GANs) 335 10.4 Neuromorphic Photonics 339 10.5 Summary and Outlook 340 References 341 11 Forward and Inverse Design of Artificial Electromagnetic Materials 345 Jordan M. Malof, Simiao Ren, and Willie J. Padilla 11.1 Introduction 345 11.1.1 Problem Setting 346 11.1.2 Artificial Electromagnetic Materials 347 11.1.2.1 Regime 1: Floquet–Bloch 348 11.1.2.2 Regime 2: Resonant Effective Media 349 11.1.2.3 All-Dielectric Metamaterials 350 11.2 The Design Problem Formulation 351 11.3 Forward Design 352 11.3.1 Search Efficiency 353 11.3.2 Evaluation Time 354 11.3.3 Challenges with the Forward Design of Advanced AEMs 354 11.3.4 Deep Learning the Forward Model 355 11.3.4.1 When Does Deep Learning Make Sense? 355 11.3.4.2 Common Deep Learning Architectures 356 11.3.5 The Forward Design Bottleneck 356 11.4 Inverse Design with Deep Learning 357 11.4.1 Why Inverse Problems Are Often Difficult 359 11.4.2 Deep Inverse Models 360 11.4.2.1 Does the Inverse Model Address Non-uniqueness? 360 11.4.2.2 Multi-solution Versus Single-Solution Models 360 11.4.2.3 Iterative Methods versus Direct Mappings 361 11.4.3 Which Inverse Models Perform Best? 361 11.5 Conclusions and Perspectives 362 11.5.1 Reducing the Need for Training Data 362 11.5.1.1 Transfer Learning 362 11.5.1.2 Active Learning 363 11.5.1.3 Physics-Informed Learning 363 11.5.2 Inverse Modeling for Non-existent Solutions 363 11.5.3 Benchmarking, Replication, and Sharing Resources 364 Acknowledgments 364 References 364 12 Machine Learning-Assisted Optimization and Its Application to Antenna and Array Designs 371 Qi Wu, Haiming Wang, and Wei Hong 12.1 Introduction 371 12.2 Machine Learning-Assisted Optimization Framework 372 12.3 Machine Learning-Assisted Optimization for Antenna and Array Designs 375 12.3.1 Design Space Reduction 375 12.3.2 Variable-Fidelity Evaluation 375 12.3.3 Hybrid Optimization Algorithm 378 12.3.4 Robust Design 379 12.3.5 Antenna Array Synthesis 380 12.4 Conclusion 381 References 381 13 Analysis of Uniform and Non-uniform Antenna Arrays Using Kernel Methods 385 Manel Martínez-Ramón, José Luis Rojo Álvarez, Arjun Gupta, and Christos Christodoulou 13.1 Introduction 385 13.2 Antenna Array Processing 386 13.2.1 Detection of Angle of Arrival 387 13.2.2 Optimum Linear Beamformers 388 13.2.3 Direction of Arrival Detection with Random Arrays 389 13.3 Support Vector Machines in the Complex Plane 390 13.3.1 The Support Vector Criterion for Robust Regression in the Complex Plane 390 13.3.2 The Mercer Theorem and the Nonlinear SVM 393 13.4 Support Vector Antenna Array Processing with Uniform Arrays 394 13.4.1 Kernel Array Processors with Temporal Reference 394 13.4.1.1 Relationship with the Wiener Filter 394 13.4.2 Kernel Array Processor with Spatial Reference 395 13.4.2.1 Eigenanalysis in a Hilbert Space 395 13.4.2.2 Formulation of the Processor 396 13.4.2.3 Relationship with Nonlinear MVDM 397 13.4.3 Examples of Temporal and Spatial Kernel Beamforming 398 13.5 DOA in Random Arrays with Complex Gaussian Processes 400 13.5.1 Snapshot Interpolation from Complex Gaussian Process 400 13.5.2 Examples 402 13.6 Conclusion 403 Acknowledgments 404 References 404 14 Knowledge-Based Globalized Optimization of High-Frequency Structures Using Inverse Surrogates 409 Anna Pietrenko-Dabrowska and Slawomir Koziel 14.1 Introduction 409 14.2 Globalized Optimization by Feature-Based Inverse Surrogates 411 14.2.1 Design Task Formulation 411 14.2.2 Evaluating Design Quality with Response Features 412 14.2.3 Globalized Search by Means of Inverse Regression Surrogates 414 14.2.4 Local Tuning Procedure 418 14.2.5 Global Optimization Algorithm 420 14.3 Results 421 14.3.1 Verification Structures 422 14.3.2 Results 423 14.3.3 Discussion 423 14.4 Conclusion 428 Acknowledgment 428 References 428 15 Deep Learning for High Contrast Inverse Scattering of Electrically Large Structures 435 Qing Liu, Li-Ye Xiao, Rong-Han Hong, and Hao-Jie Hu 15.1 Introduction 435 15.2 General Strategy and Approach 436 15.2.1 Related Works by Others and Corresponding Analyses 436 15.2.2 Motivation 437 15.3 Our Approach for High Contrast Inverse Scattering of Electrically Large Structures 438 15.3.1 The 2-D Inverse Scattering Problem with Electrically Large Structures 438 15.3.1.1 Dual-Module NMM-IEM Machine Learning Model 438 15.3.1.2 Receiver Approximation Machine Learning Method 440 15.3.2 Application for 3-D Inverse Scattering Problem with Electrically Large Structures 441 15.3.2.1 Semi-Join Extreme Learning Machine 441 15.3.2.2 Hybrid Neural Network Electromagnetic Inversion Scheme 445 15.4 Applications of Our Approach 450 15.4.1 Applications for 2-D Inverse Scattering Problem with Electrically Large Structures 450 15.4.1.1 Dual-Module NMM-IEM Machine Learning for Fast Electromagnetic Inversion of Inhomogeneous Scatterers with High Contrasts and Large Electrical Dimensions 450 15.4.1.2 Nonlinear Electromagnetic Inversion of Damaged Experimental Data by a Receiver Approximation Machine Learning Method 454 15.4.2 Applications for 3-D Inverse Scattering Problem with Electrically Large Structures 459 15.4.2.1 Super-Resolution 3-D Microwave Imaging of Objects with High Contrasts by a Semi-Join Extreme Learning Machine 459 15.4.2.2 A Hybrid Neural Network Electromagnetic Inversion Scheme (HNNEMIS) for Super-Resolution 3-Dimensional Microwave Human Brain Imaging 473 15.5 Conclusion and Future work 480 15.5.1 Summary of Our Work 480 15.5.1.1 Limitations and Potential Future Works 481 References 482 16 Radar Target Classification Using Deep Learning 487 Youngwook Kim 16.1 Introduction 487 16.2 Micro-Doppler Signature Classification 488 16.2.1 Human Motion Classification 490 16.2.2 Human Hand Gesture Classification 494 16.2.3 Drone Detection 495 16.3 SAR Image Classification 497 16.3.1 Vehicle Detection 497 16.3.2 Ship Detection 499 16.4 Target Classification in Automotive Radar 500 16.5 Advanced Deep Learning Algorithms for Radar Target Classification 503 16.5.1 Transfer Learning 504 16.5.2 Generative Adversarial Networks 506 16.5.3 Continual Learning 508 16.6 Conclusion 511 References 511 17 Koopman Autoencoders for Reduced-Order Modeling of Kinetic Plasmas 515 Indranil Nayak, Mrinal Kumar, and Fernando L. Teixeira 17.1 Introduction 515 17.2 Kinetic Plasma Models: Overview 516 17.3 EMPIC Algorithm 517 17.3.1 Overview 517 17.3.2 Field Update Stage 519 17.3.3 Field Gather Stage 521 17.3.4 Particle Pusher Stage 521 17.3.5 Current and Charge Scatter Stage 522 17.3.6 Computational Challenges 522 17.4 Koopman Autoencoders Applied to EMPIC Simulations 523 17.4.1 Overview and Motivation 523 17.4.2 Koopman Operator Theory 524 17.4.3 Koopman Autoencoder (KAE) 527 17.4.3.1 Case Study I: Oscillating Electron Beam 529 17.4.3.2 Case Study II: Virtual Cathode Formation 532 17.4.4 Computational Gain 534 17.5 Towards A Physics-Informed Approach 535 17.6 Outlook 536 Acknowledgments 537 References 537 Index 543

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Book SynopsisInterval Methods for Uncertain Power System Analysis In Interval Methods for Uncertain Power System Analysis, accomplished engineer Dr. Alfredo Vaccaro delivers a comprehensive discussion of the mathematical foundations of range analysis and its application to solving traditional power system operation problems in the presence of strong and correlated uncertainties. The book explores highly relevant topics in the area, from interval methods for uncertainty representation and management to a variety of application examples. The author offers readers the latest methodological breakthroughs and roadmaps to implementing the mathematics discussed within, as well as best practices commonly employed across the industry. Interval Methods for Uncertain Power System Analysis includes examinations of linear and non-linear equations, as well as: A thorough introduction to reliable computing, including discussions of interval arithmetic and interval-based operatorTable of ContentsAbout the Author ix Preface xi Acknowledgments xiii Acronyms xv Introduction 1 1 Introduction to Reliable Computing 3 1.1 Elements of Reliable Computing 4 1.2 Interval Analysis 7 1.3 Interval-Based Operators 8 1.4 Interval Extensions of Elementary Functions 9 1.5 Solving Systems of Linear Interval Equations 11 1.6 Finding Zeros of Nonlinear Equations 15 1.7 Solution of Systems of Nonlinear Interval Equations 16 1.8 The Overestimation Problem 20 1.9 Affine Arithmetic 22 1.9.1 Conversion Between AA and IA 25 1.9.2 AA-Based Operators 25 1.9.3 Chebyshev Approximation of Univariate Nonaffine Functions 28 1.9.4 Multiplication of Affine Forms 31 1.9.5 Effects of Recursive Solution Schemes 35 1.10 Integrating AA and IA 35 2 Uncertain Power Flow Analysis 37 2.1 Sources of Uncertainties in Power Flow Analysis 39 2.2 Solving Uncertain Linearized Power Flow Equations 41 2.3 Solving Uncertain Power Flow Equations 46 2.3.1 Optimization-Based Method 48 2.3.2 Domain Contraction Method 52 3 Uncertain Optimal Power Flow Analysis 59 3.1 Range Analysis-Based Solution 61 3.1.1 Optimal Economic Dispatch 63 3.1.2 Reactive Power Dispatch 66 3.2 AA-Based Solution 70 4 Uncertain Markov Chain Analysis 75 4.1 Mathematical Preliminaries 77 4.2 Effects of Data Uncertainties 78 4.3 Matrix Notation 79 4.4 AA-Based Uncertain Analysis 80 4.5 Application Examples 83 4.5.1 Case Study 1: Grid Resilience Analysis 83 4.5.2 Case Study 2: Energy Storage Model 84 4.5.3 Summary 86 5 Small-Signal Stability Analysis of Uncertain Power Systems 87 5.1 Problem Formulation 89 5.2 The Interval Eigenvalue Problem 90 5.3 Applications 92 5.3.1 Case Study 1 92 5.3.2 Case Study 2 93 6 Uncertain Power Components Thermal Analysis 95 6.1 Thermal Rating Assessment of Overhead Lines 96 6.1.1 Sources of Data Uncertainties 98 6.1.2 AA-Based Thermal Rating Assessment 99 6.1.3 Application Examples 100 6.2 Thermal Rating Assessment of Power Cables 104 6.2.1 Thermal Modeling of Power Cables 105 6.2.2 Sources of Data Uncertainties 107 6.2.3 Tolerance Analysis of Cable Thermal Dynamics by IA 108 6.2.4 Application Examples 109 References 112 Index 119

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Book SynopsisImage Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authorssuch as convolutional neural networks, graph convolutional networks, deformable convolution, and model compressionto assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.Table of ContentsPreface About the Authors List of Abbreviations Part One: Principle 1 Introduction to Image Segmentation 2 Principles of Clustering 3 Principles of Mathematical Morphology 4 Principles of Neural Network Part Two: Methods 5 Fast and Robust Image Segmentation Using Clustering 6 Fast Image Segmentation Using Watershed Transform 7 Superpixel-based Fast Image Segmentation Part Three: Application 8 Image Segmentation for Traffic Scene Analysis 9 Image Segmentation for Medical Analysis 10 Image Segmentation for Remote Sensing Analysis 11 Image Segmentation for Material Analysis

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Book SynopsisTable of ContentsAuthor Biographies xiii Preface xv 1 Introduction 1 Preliminary Remarks 1 History 6 References 7 2 Basic Terms 9 Average Values 9 Amplitude Distribution 10 Autocorrelation 12 Cross-Correlation 15 Noise Spectra 18 Autocorrelation Function and Spectral Power Density 19 Band-Limited Noise on the Spectrum Analyzer 20 References 22 3 Noise Sources 23 Thermal Noise 23 Nyquist Formula and Thermal Radiation 24 Validity and Experimental Confirmation of the Nyquist Formula 27 Thermal Noise Under Extreme Conditions 28 Shot Noise 29 Plasma Noise 33 Current Noise of Resistors and Contacts 34 Technical Resistors 34 Resistors Consisting of Semiconductor Material 36 Contact Noise 37 Generation–Recombination Noise 38 LF Noise from Transistors 40 References 42 4 Noise and Linear Networks 45 Narrowband Noise 45 Calculating with Phasors 45 Noise Source with Complex Internal Resistance 51 The Equivalent Noise Bandwidth 52 Network Components at Different Temperatures 54 Noise Generator and Attenuator 58 References 58 5 Nonlinear Networks 59 Mixing 59 Band-Limited RF Noise at Input 59 Amplitude Clipping 62 The Detector as a Nonlinear Network 63 The Noise Spectrum Behind a Quadratic Detector 65 The Noise Spectrum Behind a Linear Detector 69 The Sensitivity Limit 70 Noise with Signal 73 The Phase Sensitive Rectifier 74 Trace Averaging 76 References 78 6 The Noise Factor 79 Amplifier and Noise Power 79 The Noise Factor F 80 Cascaded Amplifiers 83 The Noise measure m 85 Definitions of Gain 85 Source and Load 89 Broadband and Spot Noise Factor 91 Noise Factor of a Passive Network 92 Antenna Temperature 93 The Reference Temperature T 0 = 290 K 98 Noise Factor and Detection Limit 99 References 100 7 Noise of Linear Two-Ports 101 Representation of Two-Ports 101 Noise Modeling Using the Chain Matrix 102 References 108 8 Calculation Methods for Noise Quantities 109 Noise Voltages, Currents, and Spectra 109 Calculating with Current, Voltage, and Noise Waves 112 The Noise Correlation Matrix 115 The Correlation Matrix of Passive Components 117 The Noise of Simple Passive Networks 119 Transformation of Noise Sources in Different Network Representations 128 Correlation Matrix and IEEE Elements 131 FET-Like Network with the Y-Correlation Matrix 134 Noise Sources at Input with ABCD Correlation Matrix 138 References 142 9 Diodes and Bipolar Transistors 143 Semiconductor Diode 143 Bipolar Transistor 145 Small-Signal Equivalent Circuit 147 Hawkins BJT Noise Model 148 Two Approaches for the Collector Noise Current Source 155 BJT Noise Model with Correlation Matrices 157 The Π-Model 157 The T-Model with Correlation Matrices 161 Transformation of the Y-Sources to the Input 165 Modeling of a Microwave Transistor with Correlation Matrices 168 Simplest Π-Model 174 Contour Diagram 177 Transistor in the Circuit 179 Using the Contour Diagram 183 References 185 10 Operational Amplifier 187 Operational Amplifier as Circuit Element 187 Noise Sources of the Operational Amplifier 188 Consideration of 1/f Noise 193 Operational Amplifier as an Active Low-Pass Filter 195 References 198 11 Field Effect Transistors 201 Jfet 201 Mode of Operation of the FET 201 The Channel Noise 204 NoiseSourcesattheGate 205 The Correlation 206 Transformation to the Input 206 Simple Approximations 211 Field Effect Transistors for the Microwave Range (MESFET, HFET) 214 The Pucel Model 215 The Pospieszalski model 218 Discussion of the Results 225 Criteria for Noise Data 225 References 229 12 Theory of Noise Measurement 231 Measurements of Two-Ports 231 The Equivalent Noise Resistance 234 Voltage and Current Source 235 Voltage and Current Source with Correlation 237 3 dB and Y-Method 241 References 243 13 Basics of Measuring Technique 245 Principles of the RF-Receiver 245 The Detection Limit 245 Diode as RF Receiver (Video Detector) 249 RF and Microwave Range Receiver 254 Dicke Radiometer 258 Correlation Radiometer in the Microwave Range 261 Network Analyzer as a Noise Measurement Device 263 References 265 14 Equipment and Measurement Methods 267 Noise Measurement Receiver 267 Spectrum Analyzer 269 The Y-Method 273 Measurements in the Microwave Range 275 Selection Criteria of the Mixer 278 Image Rejection 279 Complete Noise Characterization 282 Analysis of Multi-impedance Measurements 283 Cold Source Method 285 The 7-State Method 287 On-Wafer Measurement of Cold Source 288 On-Wafer with Noise Generator According to the Y-method 293 References 296 15 Noise Generators 299 Vacuum Diode 299 Gas Discharge 300 Semiconductor Diodes 302 Excess Noise Ratio (ENR) 303 Hot–Cold Sources 305 References 307 16 Impedance Tuners 309 Impedance Transformation with Simple Methods 309 Mechanical Components for the Microwave Range 311 Electronic Components 313 Precision Automatic Tuner 315 Attenuation of the Tuner 317 References 318 17 Examples of Measurement Problems 319 Transistor in a Test Fixture 319 The Low Noise Block (LNB) of Satellite Television 322 Verification of a Noise Measurement 325 References 327 18 Measurement and Modeling of Low-Frequency Noise 329 Correlation Radiometer for Low Frequencies (f < 10 MHz) 329 The Low-Frequency Noise of Transistors 333 Measurement Setup for LF Noise 334 Examples of LF Noise Measurements on GaAs-HBT 336 Modeling of LF Noise 337 The Noise of the Microphone 337 References 342 19 Measurement Accuracy and Sources of Error 345 Accuracy of Measured Data 345 Error of Measurements 345 Inaccuracies of the Noise Measurement 346 Uncertainty of the ENR Calibration 349 Noise Source Mismatch 350 T0 = 290 K Is not TOFF 352 Mismatch in the System 353 Linearity of the Receiver 356 References 357 20 Phase Noise 359 Basics 359 Reciprocal Mixing 361 Description of Phase Noise 363 Spectral Power Density of Phase Fluctuations Sφ (f) 364 The Single Sideband Phase Noise L(f) 365 Spectral Power Density of Frequency Fluctuations SΔf (f) 365 Excursus on Frequency and Phase Modulation 366 The Allan Variance σ2Y (τ) 368 Residual FM 370 Multiplication and Division 371 Amplitude Noise 371 Phase Noise and Jitter 372 References 374 21 Physics of the Oscillator 377 Oscillation Condition [1] 377 Simple Model of the Phase Disturbance [2] 378 Phase Slope, Resonator Quality, and Frequency Stability [3] 379 The Formula of Leeson [4] 382 Components of Oscillators 384 Influence of the Varactor Diode 386 Upward Mixing of LF Noise 390 The Influence of Microwave Noise on Phase Noise 393 References 396 22 Phase Noise Measurement 399 Basic Parameters 399 Spectrum Analyzer 399 Phase Detector Method 406 The Sensitivity of the Phase Detector 407 Example Calibration and Measurement 409 Keeping the Quadrature by a PLL 410 Delay Line as Frequency Discriminator 412 The Sensitivity of the Delay-Line Method 414 Configuration and Calibration 418 Resonator as Frequency Discriminator 420 Detection Limit 421 Comparison of Measurement Systems 422 Cross-Correlation Technique 423 Amplitude Noise 425 Problems with On-Wafer Measurement 429 Residual Phase Noise 430 References 432 Appendix 435 Noise Signals and Deterministic Signals 435 Random Signals 436 Characteristic Values 437 The Probability Density Function 438 Example Sine Function 439 Example Sawtooth Voltage 440 Example White Noise 440 Example Sinusoidal Signal with Noise 441 Example Narrowband Noise 441 The Autocorrelation Function 444 Example Sine 444 Example Sawtooth 444 Example Noisy Sine 445 Example White Noise 446 Example Low-Pass Noise 447 Example Bandpass Noise 449 Fourier Series 451 Sine–Cosine Spectrum 452 Amplitude–Phase Spectrum 452 Complex Fourier Series 452 The Fourier Integral 453 Energy and Power Signals 456 Example Transient Time Function 457 The Parseval Equation 459 Example Voltage Pulse 460 Fourier Transform and Power Spectral Density 462 Example Rectangular Pulse 463 Time-Limited Noise Signal 465 Example of a Time-Limited Wave Train 466 The Wiener–Khinchin Theorem 468 Cross Correlation 470 Example of Two Sine Functions 471 Example of Two White Noise Signals 472 Example of Two Bandpass Noise Signals 472 Example White Noise and Bandpass Noise 474 Cross-Correlation After Splitting into Two Branches 474 Power Spectral Density Real and Complex 477 The Cross-Spectral Density 478 Complex Representation of the Cross-Spectral Density 479 Transmission of Noise by Networks 479 References 485 Glossary of Symbols 487 Index 491

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Book SynopsisTropospheric and Ionospheric Effects on Global Navigation Satellite Systems Explore atmospheric effects on radio frequency propagation in the context of Global Navigation Satellite System communication In Tropospheric and Ionospheric Effects on Global Navigation Satellite Systems, a team of distinguished researchers deliver an accessible and authoritative introduction to all scientifically relevant effects caused by the ionosphere and troposphere on GNSS RF signals. The book explores the origin of each type of propagation effect and explains it from a fundamental physical perspective. Each of the major methods used for the measurement, prediction, and mitigation of ionospheric and tropospheric effects on GNSS are discussed in detail. The authors also provide the mechanisms that drive ionization and plasma transport in the ionosphere, propagation phenomena (including scattering, absorption, and scintillations), and the predominant predictive models used to predict ionospheric propagaTable of Contents1. Overview of the Global Positioning System 11 1.1. Introduction 12 1.2. Applications of GNSS 14 1.3. GPS Segments 17 1.3.1. Space Segment 17 1.3.2. Control Segment 21 1.3.3. User Segment 23 1.4. Keplerian Orbits 27 1.5. Satellite Broadcast 33 1.5.1. Carrier Frequencies 33 1.5.2. Digital Modulation 34 1.5.3. Ranging Codes 41 1.5.4. Navigation Message 47 2. Principles of GNSS Positioning 57 2.1. Introduction 58 2.2. Basic GNSS Observables 60 2.2.1. Pseudorange 60 2.2.2. Carrier Phase 62 2.2.3. Doppler Shift 68 2.3. GNSS Error Sources 73 2.3.1. Clock and Ephemeris Errors 74 2.3.2. Relativistic E_ects 76 2.3.3. Carrier Phase Wind-Up 82 2.3.4. Atmospheric E_ects 83 2.3.5. Multipath, Di_raction, and Interference E_ects 83 2.3.6. Hardware-Related Errors 87 2.3.7. Dilution of Precision 89 2.3.8. Additional Error Sources 90 2.4. Point Positioning 91 2.4.1. Positioning Using Pseudorange 92 2.4.2. Accounting for Random Error 97 2.4.3. Dilution of Precision 102 2.5. Data Combinations and Relative Positioning 107 2.5.1. Multi-Frequency Combinations 107 2.5.2. Relative Positioning 113 3. Tropospheric Propagation 121 3.1. Introduction 121 3.2. Tropospheric Group Delay 122 3.3. Tropospheric Refraction 128 3.4. Extinction 132 3.4.1. Beer-Lambert Law 132 3.4.2. Scattering 136 3.4.3. Gaseous Absorption 137 3.4.4. Hydrometeor Attenuation 140 3.5. Tropospheric Scintillations 142 4. Predictive Models of the Troposphere 145 4.1. Introduction 145 4.2. Saastamoinen Model 145 4.3. Hop_eld Model 159 4.4. U.S. Standard Atmosphere 163 4.4.1. Model Assumptions 164 4.4.2. Computational Equations 175 4.4.3. Data Sources and Implementation 178 5. Physics of the Ionosphere 181 5.1. Introduction 182 5.2. Solar-Terrestrial Relations 183 5.2.1. The Sun 183 5.2.2. The Interplanetary Medium 186 5.2.3. Earth's Magnetic Field 188 5.2.4. The Magnetosphere 196 5.2.5. Earth's Atmosphere 200 5.3. Physics of Ionization 203 5.3.1. Neutral Atmosphere 203 5.3.2. Ionization 206 5.3.3. Recombination and Attachment 209 5.3.4. Photochemical Processes in the Ionosphere 210 5.4. Chapman's Theory of Ionospheric Layer Formation 213 5.5. Plasma Transport 222 5.5.1. Di_usion 223 5.5.2. Neutral Winds 226 5.5.3. Electromagnetic Drift 228 5.5.4. Combined E_ects of Neutral Wind and Electromagnetic Drift 231 5.5.5. Continuity Equation 237 6. Experimental Observation of the Ionosphere 239 6.1. Introduction 240 6.2. Ionospheric Measurement Techniques 242 6.2.1. Ionosondes 242 6.2.2. Incoherent Scatter Radar 254 6.2.3. In Situ Measurements 262 6.3. Morphology of the Ionosphere 269 6.4. Variability of the Ionosphere 276 6.4.1. F2 Layer Anomalies 276 6.4.2. Solar Activity 282 6.4.3. Magnetic Variation 286 6.4.4. Ionospheric Irregularities 298 7. Ionospheric Propagation 303 7.1. Introduction 304 7.2. Magnetoionic Propagation 305 7.3. Propagation E_ects of the Background Ionosphere 315 7.3.1. Total Electron Content 317 7.3.2. Ionospheric Refraction 322 7.3.3. Group Delay and Phase Advance 325 7.3.4. Dispersion 334 7.3.5. Faraday Rotation 335 7.3.6. Absorption 338 7.4. Scintillations 341 8. Predictive Models of the Ionosphere 351 8.1. Introduction 352 8.2. Group Delay Models for Single-Frequency GNSS Receivers 353 8.2.1. Klobuchar Model 353 8.2.2. NeQuick 363 8.3. Global Ionospheric Scintillation Model 373 8.3.1. Ray Tracing in the Ionosphere 373 8.3.2. Multiple Phase Screen Method 375 8.4. International Reference Ionosphere 379 8.4.1. Data Sources, Inputs, and Outputs 381 8.4.2. Important Functions 387 8.4.3. Characteristic Heights and Electron Densities 392 8.4.4. Electron Density 400 8.4.5. Electron Temperature 416 8.4.6. Ion Temperature 422 8.4.7. Ion Composition 424 8.4.8. Additional Parameters 427 Appendices 431 A. Review of Electromagnetics Concepts 433 A.1. Electromagnetic Waves 434 A.1.1. Maxwell's Equations and the Wave Equation 434 A.1.2. Plane Wave Solutions 436 A.1.3. Constraints Via Maxwell's Equations 440 A.1.4. Poynting Vector 443 A.2. Phase and Group Velocity 446 A.2.1. Phase Velocity 446 A.2.2. Modulated Signals and Group Velocity 446 A.2.3. Group Index of Refraction 448 A.2.4. Relationship Between Phase and Group Velocities 449 A.3. Polarization 450 A.3.1. Linear Polarization 450 A.3.2. Circular Polarization 452 A.3.3. Elliptical Polarization 455 A.3.4. Jones Vectors and Decomposing Polarizations 457 A.4. Derivation of Rayleigh Scattering 462 B. Electromagnetic Properties of Media 473 B.1. Introduction 474 B.2. Dielectric Polarization 475 B.2.1. Induced Dielectric Polarization 475 B.2.2. Electric Susceptibility 476 B.3. Lossy and Dispersive Media 478 B.3.1. Absorption 478 B.3.2. Dispersion 478 B.3.3. Graphical Analysis 479 B.3.4. Multiple Resonances 482 B.4. Conducting Media 484 B.4.1. Time-Varying Conduction Current 484 B.4.2. Propagation in Conducting Media 485 B.4.3. Combined E_ects of Dispersion and Conduction 488 B.5. Kramers-Kronig Relations 489 B.6. Anisotropic Media 492 B.6.1. Dielectric Tensor Properties 492 B.6.2. Wave Equation in Anisotropic Media 494 B.6.3. Optical Axes 496 B.6.4. Index Ellipsoid 499 B.6.5. Phase and Group Velocity in Anisotropic Media 501 B.6.6. Birefringence and Spatial Walk-o_ in ~k Surfaces 503 B.7. Gyrotropic Media 506 B.7.1. Gyrotropic Susceptibility Tensor 506 B.7.2. Propagation in Gyrotropic Media 509 Bibliography 513

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Book SynopsisA unified and systematic theoretical framework for solving problems related to finite impulse response (FIR) estimate Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches is a comprehensive investigation into batch state estimators and recursive forms. The work begins by introducing the reader to the state estimation approach and provides a brief historical overview. Next, the work discusses the specific properties of finite impulse response (FIR) state estimators. Further chapters give the basics of probability and stochastic processes, discuss the available linear and nonlinear state estimators, deal with optimal FIR filtering, and consider a limited memory batch and recursive algorithms. Other topics covered include solving the q-lag FIR smoothing problem, introducing the receding horizon (RH) FIR state estimation approach, and developing the theory of FIR state estimation under disturbances. The book closes by discussing thTable of Contents1 Introduction 1 1.1 What is System State? 2 1.1.1 Why and How do We Estimate State? 2 1.1.2 What Model to Estimate State? 3 1.1.3 What are Basic State Estimates in Discrete Time? 5 1.2 Properties of State Estimators 6 1.2.1 Structures and Types 6 1.2.2 Optimality 10 1.2.3 Unbiased Optimality (Maximum Likelihood) 11 1.2.4 Suboptimality 14 1.2.5 Unbiasedness 17 1.2.6 Deadbeat 17 1.2.7 Denoising (Noise Power Gain) 17 1.2.8 Stability 18 1.2.9 Robustness 18 1.2.10 Computational Complexity 19 1.2.11 Memory Use 20 1.3 More About FIR State Estimators 20 1.4 Historical Overview and Most Noticeable Works 21 1.5 Summary 26 1.6 Problems 27 2 Probability and Stochastic Processes 31 2.1 Random Variables 31 2.1.1 Moments and Cumulants 33 2.1.2 Product Moments 39 2.1.3 Vector Random Variables 41 2.1.4 Conditional Probability. Bayes’ Rule 42 2.1.5 Transformation of Random Variables 45 2.2 Stochastic Processes 47 2.2.1 Correlation Function 48 2.2.2 Power Spectral Density 51 2.2.3 Gaussian Processes 53 2.2.4 White Gaussian Noise 55 2.2.5 Markov Processes 57 2.3 Stochastic Differential Equation 60 2.3.1 Standard Stochastic Differential Equation 61 2.3.2 Itˆo and Stratonovich Stochastic Calculus 61 2.3.3 Diffusion Process Interpretation 62 2.3.4 Fokker-Planck-Kolmogorov Equation 63 2.3.5 Langevin Equation 64 2.4 Summary 65 2.5 Problems 66 3 State Estimation 71 3.1 Lineal Stochastic Process in State Space 71 3.1.1 Continuous-Time Model 73 3.1.2 Discrete-Time Model 77 3.2 Methods of Linear State Estimation 81 3.2.1 Bayesian Estimator 82 3.2.2 Maximum Likelihood Estimator 85 3.2.3 Least Squares Estimator 86 3.2.4 Unbiased Estimator 87 3.2.5 Kalman Filtering Algorithm 88 3.2.6 Backward Kalman Filter 94 3.2.7 Alternative Forms of Kalman Filter 96 3.2.8 General Kalman Filter 98 3.2.9 Kalman-Bucy Filter 110 3.3 Linear Recursive Smoothing 113 3.3.1 Rauch-Tung-Striebel Algorithm 113 3.3.2 Bryson-Frazier Algorithm 114 3.3.3 Two-Filter (Forward-Backward) Smoothing 115 3.4 Nonlinear Models and Estimators 116 3.4.1 Extended Kalman Filter 117 3.4.2 Unscented Kalman Filter 119 3.4.3 Particle Filtering 122 3.5 Robust State Estimation 126 3.5.1 Robustified Kalman Filter 127 3.5.2 Robust Kalman Filter 128 3.5.3 H8 Filtering 131 3.5.4 Game Theory H8 Filter 132 3.6 Summary 133 3.7 Problems 134 4 Optimal FIR and Limited Memory Filtering 139 4.1 Extended State-Space Model 140 4.2 The a posteriori Optimal FIR Filter 142 4.2.1 Batch Estimate and Error Covariance 143 4.2.2 Recursive Forms 145 4.2.3 System Identification 149 4.3 The a posteriori Optimal Unbiased FIR Filter 149 4.3.1 Batch OUFIR-I Estimate and Error Covariance 150 4.3.2 Recursive Forms for OUFIR-I Filter 151 4.3.3 Batch OUFIR-II Estimate and Error Covariance 153 4.3.4 Recursion Forms for OUFIR-II Filter 154 4.4 Maximum Likelihood FIR Estimator 158 4.4.1 ML-I FIR Filtering Estimate 158 4.4.2 Equivalence of ML-I FIR and OUFIR Filters 159 4.4.3 ML-II FIR Filtering Estimate 162 4.4.4 Properties of ML FIR State Estimators 163 4.5 The a priori FIR Filters 164 4.5.1 The a priori Optimal FIR Filter 164 4.5.2 The a priori Optimal Unbiased FIR Filter 165 4.6 Limited Memory Filtering 165 4.6.1 Batch Limited Memory Filter 166 4.6.2 Iterative LMF Algorithm using Recursions 168 4.7 Continuous-Time Optimal FIR Filter 169 4.7.1 Optimal Impulse Response 169 4.7.2 Differential Equation Form 171 4.8 Extended a posteriori OFIR Filtering 172 4.9 Properties of FIR State Estimators 174 4.10 Summary 179 4.11 Problems 182 5 Optimal FIR Smoothing 187 5.1 Introduction 187 5.2 Smoothing Problem 188 5.3 Forward Filter/Forward Model q-lag OFIR Smoothing 189 5.3.1 Batch Smoothing Estimate 190 5.3.2 Error Covariance 193 5.4 Backward OFIR Filtering 195 5.4.1 Backward State-Space Model 195 5.4.2 Batch Estimate 196 5.4.3 Recursive Estimate and Error Covariance 198 5.5 Backward Filter/Backward Model g-lag OFIR Smoother 202 5.5.1 Batch Smoothing Estimate 203 5.5.2 Error Covariance 204 5.6 Forward Filter/Backward Model q-Lag OFIR Smoother 205 5.6.1 Batch Smoothing Estimate 205 5.6.2 Error Covariance 208 5.7 Backward Filter/Forward Model q-Lag OFIR Smoother 208 5.7.1 Batch Smoothing Estimate 208 5.7.2 Error Covariance 211 5.8 Two-Filter q-lag OFIR Smoother 213 5.9 q-Lag ML FIR Smoothing 214 5.9.1 Batch q-lag ML FIR Estimate 215 5.9.2 Error Covariance 216 5.10 Summary 216 5.11 Problems 217 6 Unbiased FIR State Estimation 221 6.1 Introduction 221 6.2 The a posteriori UFIR Filter 222 6.2.1 Batch Form 222 6.2.2 Iterative Algorithm Using Recursions 224 6.2.3 Recursive Error Covariance 226 6.2.4 Optimal Averaging Horizon 228 6.3 Backward a posteriori UFIR Filter 234 6.3.1 Batch Form 235 6.3.2 Recursions and Iterative Algorithm 236 6.3.3 Recursive Error Covariance 239 6.4 The q-lag UFIR Smoother 240 6.4.1 Batch and Recursive Forms 240 6.4.2 Error Covariance 242 6.4.3 Equivalence of UFIR Smoothers 244 6.5 State Estimation using Polynomial Models 245 6.5.1 Problems Solved with UFIR Structures 246 6.5.2 The p-shift UFIR Filter 247 6.5.3 Filtering of Polynomial Models 250 6.5.4 Discrete Shmaliy Moments 252 6.5.5 Smoothing Filtering and Smoothing 252 6.5.6 Generalized Savitzky-Golay Filter 254 6.5.7 Predictive Filtering and Prediction 255 6.6 UFIR State Estimation under Colored Noise 256 6.6.1 Colored Measurement Noise 256 6.6.2 Colored Process Noise 259 6.7 Extended UFIR Filtering 262 6.7.1 First-Order Extended UFIR Filter 263 6.7.2 Second-Order Extended UFIR Filter 263 6.8 Robustness of UFIR Filter 266 6.8.1 Errors in Noise Covariances and Weighted Matrices 268 6.8.2 Model Errors 271 6.8.3 Temporary Uncertainties 274 6.9 Implementation of Polynomial UFIR Filters 276 6.9.1 Filter Structures in z-Domain 276 6.9.2 Transfer Function in DFT Domain 282 6.10 Summary 287 6.11 Problems 288 7 FIR Prediction and Receding Horizon Filtering 295 7.1 Introduction 295 7.2 Prediction Strategies 296 7.2.1 Kalman Predictor 296 7.3 Extended Predictive State-Space Model 298 7.4 UFIR Predictor 298 7.4.1 Batch UFIR Predictor 299 7.4.2 Iterative Algorithm using Recursions 299 7.4.3 Recursive Error Covariance 303 7.5 Optimal FIR Predictor 304 7.5.1 Batch Estimate and Error Covariance 305 7.5.2 Recursive Forms and Iterative Algorithm 306 7.6 Receding Horizon FIR Filtering 308 7.6.1 MVF-I Filter for Stationary Processes 309 7.6.2 MVF-II Filter for Nonstationary Processes 311 7.7 Maximum Likelihood FIR Predictor 313 7.7.1 ML-I FIR Predictor 314 7.7.2 ML-II FIR Predictor 315 7.8 Extended OFIR Prediction 315 7.9 Summary 317 7.10 Problems 318 8 Robust FIR State Estimation under Disturbances 323 8.1 Extended Models under Disturbances 324 8.2 The a posteriori H2 FIR Filtering 326 8.2.1 H2-OFIR Filter 328 8.2.2 Optimal Unbiased H2 FIR Filter 330 8.2.3 Suboptimal H2 FIR Filtering Algorithms 336 8.3 H2 FIR Prediction 338 8.3.1 H2-OFIR Predictor 339 8.3.2 Bias-constrained H2-OUFIR Predictor 341 8.3.3 Suboptimal H2 FIR Predictive Algorithms 341 8.3.4 Receding Horizon H2-MVF Filter 343 8.4 H8 FIR State Estimation 344 8.4.1 The a posteriori H8 FIR Filter 346 8.4.2 H8 FIR Predictor 350 8.5 H2{H8 FIR Filter and Predictor 354 8.6 Generalized H2 FIR State Estimation 355 8.6.1 Energy-to-Peak Lemma 355 8.6.2 L2-to-L8 FIR Filter and Predictor 359 8.7 L1 FIR State Estimation 362 8.7.1 Peak-to-Peak Lemma 363 8.7.2 L8-to-L8 FIR Filtering and Prediction 365 8.8 Game Theory FIR State Estimation 367 8.8.1 The a posteriori Energy-to-Power FIR Filter 368 8.8.2 Energy-to-Power FIR Predictor 370 8.9 Recursive Computation of Robust FIR Estimates 371 8.9.1 Uncontrolled Processes 372 8.9.2 Controlled Processes 372 8.10 FIR Smoothing under Disturbances 374 8.11 Summary 374 8.12 Problems 376 9 Robust FIR State Estimation for Uncertain Systems 379 9.1 Extended Models for Uncertain Systems 380 9.2 The a posteriori H2 FIR Filtering 386 9.2.1 H2-OFIR Filter 387 9.2.2 Bias-constrained H2-OFIR Filter 392 9.3 H2 FIR Prediction 394 9.3.1 Optimal H2 FIR Predictor 395 9.3.2 Bias-constrained H2-OUFIR Predictor 399 9.4 Suboptimal H2 FIR Structures using LMI 400 9.4.1 Suboptimal H2 FIR Filter 401 9.4.2 Bias-Constrained Suboptimal H2 FIR Filter 402 9.4.3 Suboptimal H2 FIR Predictor 403 9.4.4 Bias-Constrained Suboptimal H2 FIR Predictor 404 9.5 H8 FIR State Estimation for Uncertain Systems 405 9.5.1 The a posteriori H8 FIR Filter 405 9.5.2 H8 FIR Predictor 407 9.6 Hybrid H2{H8 FIR Structures 410 9.7 Generalized H2 FIR Structures for Uncertain Systems 411 9.7.1 The a posteriori L2-to-L8 FIR Filter 412 9.7.2 L2-to-L8 FIR Predictor 414 9.8 Robust L1 FIR Structures for Uncertain Systems 416 9.8.1 The a posteriori L8-to-L8 FIR Filter 417 9.8.2 L8-to-L8 FIR Predictor 417 9.9 Summary 418 9.10 Problems 419 10 Advanced Topics in FIR State Estimation 423 10.1 Distributed Filtering over Networks 423 10.1.1 Consensus in Measurements 424 10.1.2 Consensus in Estimates 429 10.2 Optimal Fusion Filtering under Correlated Noise 433 10.2.1 Error Covariances under Cross Correlation 436 10.3 Hybrid Kalman/UFIR Filter Structures 438 10.3.1 Fusing Estimates with Probabilistic Weights 438 10.3.2 Fusing Kalman and Weighted UFIR Estimates 442 10.4 Estimation under Delayed and Missing Data 444 10.4.1 Deterministic Delays and Missing Data 445 10.4.2 Randomly Delayed and Missing Data 449 10.5 Summary 453 10.6 Problems 454 11 Applications of FIR State Estimators 457 11.1 UFIR Filtering and Prediction of Clock States 458 11.1.1 Clock Model 458 11.1.2 Clock State Estimation over GPS-Based TIE Data 459 11.1.3 Master Clock Error Prediction 460 11.2 Suboptimal Clock Synchronization 463 11.2.1 Clock Digital Synchronization Loop 463 11.3 Localization over WSNs Using Particle/UFIR Filter 468 11.3.1 Sample Impoverishment Issue 470 11.3.2 Hybrid Particle/UFIR Filter 471 11.4 Self-localization over RFID Tag Grids 473 11.4.1 State Space Localization Problem 474 11.4.2 Localization Performance 476 11.5 INS/UWB-Based Quadrotor Localization 478 11.5.1 Quadrotor State Space Model under CMN 479 11.5.2 Localization Performance 481 11.6 Processing of Biosignals 481 11.6.1 ECG Signal Denoising using UFIR Smoothing 482 11.6.2 EMG Envelope Extraction using UFIR Filter 484 11.7 Summary 487 11.8 Problems 488 A Matrix Forms and Relationships 489 A.1 Derivatives 489 A.2 Matrix Identities 489 A.3 Special Matrices 490 A.4 Equations and Inequalities 491 A.5 Linear Matrix Inequalities 493 B Norms 495 B.1 Vector Norms 495 B.2 Matrix Norms 496 B.3 Signal Norms 497 B.4 System Norms 499 References 501

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Book SynopsisBiogas Plants Comprehensive resource highlighting the global significance of biogas and reviewing the current status of biogas production. Biogas Plants presents an overview of biogas production, starting from the substrates (characteristics, pretreatment, and storage), addressing technical and technological aspects of fermentation processes, and covering the environmental and agricultural significance of obtained digestate. Written by a team of experts with extensive theoretical and practical experience in the areas of bio-waste, biogas plants, and reduction of greenhouse gas emissions, Biogas Plants discusses keys topics including: Anaerobic digestion, including discussion of substrates and products Advantages of biogas plants, with emphasis on their future potential for stable and controlled renewable energy Global significance of the biogas sector, including its importance in electro-energy system stabilization, biogasTable of ContentsList of Contributors xvii Series Preface xxi 1 Anaerobic Digestion Process and Biogas Production 1 Liangliang Wei, Weixin Zhao, Likui Feng, Jianju Li, Xinhui Xia, Hang Yu, and Yu Liu 1.1 Introduction 1 1.2 Basic Knowledges of AD Processes and Operations 2 1.2.1 Fundamental Mechanisms and Typical Processes of AD 2 1.2.2 Factors Affecting the AD Process of Biogas Production 4 1.2.2.1 Temperature 4 1.2.2.2 pH 5 1.2.2.3 Organic Loading Rate (OLR) 5 1.2.2.4 Carbon–Nitrogen Ratio 5 1.2.2.5 Inoculum-to-Substrate Ratio (ISR) 6 1.2.2.6 Solids Concentration 6 1.2.2.7 Hydraulic Retention Time (HRT) 6 1.3 Current Challenges of AD Process and Biogas Production 7 1.3.1 Ammonia Inhibition 7 1.3.2 Volatile Fatty Acid Inhibition 10 1.3.3 Psychrophilic Temperature Inhibition 12 1.4 Proposed Strategies for Enhanced Biogas Production 14 1.4.1 Promoting Direct Interspecies Electron Transfer via Conductive Materials Additive 14 1.4.2 Co-digestion of Different Substrates 16 1.4.3 Bioaugmentation 19 1.4.4 Bioelectrochemical System-Assisted AD 20 1.5 Techno-Economic and Environmental Assessment of Anaerobic Digestion for Biogas Production 22 1.5.1 Techno-Economic Analysis 22 1.5.2 Environmental Feasibility and Benefit Assessment 24 References 26 2 Pretreatment of Lignocellulosic Materials to Enhance Biogas Recovery 37 Jonathan T. E. Lee, Nalok Dutta, To-Hung Tsui, Ee Y. Lim, Yanjun Dai, and Yen W. Tong 2.1 Introduction 37 2.1.1 Lignocellulosic Waste Material Production 38 2.1.2 Structural Insight of Lignocellulosic Materials 39 2.1.3 Biogas Production from Lignocellulosic Materials and the Need for Pretreatment 40 2.2 Available Pretreatment Technologies for Lignocellulosic Materials and the Corresponding Biogas Recovery Associated 41 2.2.1 Physical Pretreatment 41 2.2.1.1 Comminution 43 2.2.1.2 Microwave Thermal Pretreatment 43 2.2.1.3 Extrusion 44 2.2.1.4 Ultrasonication 45 2.2.2 Chemical Pretreatment 45 2.2.2.1 Acid Hydrolysis Pretreatment 45 2.2.2.2 Alkali Hydrolysis Pretreatment 47 2.2.2.3 Ionic Liquids Pretreatment 48 2.2.2.4 Deep Eutectic Solvents Pretreatment 48 2.2.2.5 Organosolvents Pretreatment 49 2.2.3 Biological Pretreatment 49 2.2.3.1 Enzymatic Pretreatment 50 2.2.3.2 Whole-cell Microbial Pretreatment 51 2.2.3.3 Fungal Pretreatment 52 2.2.3.4 Ensiling 52 2.2.3.5 Summary of Individual Pretreatment Efficiencies 53 2.2.4 Physiochemical Pretreatment of Lignocellulosic Biomass in the Production of Biogas 54 2.2.4.1 Hybrid State of Art Lignocellulosic Pretreatments 54 2.3 Pertinent Perspectives 58 2.3.1 Integrated Biorefinery While Treating Various Wastes 58 2.3.1.1 Municipal Solid Waste (MSW) 58 2.3.1.2 Forestry Waste 59 2.3.1.3 Crop Straw 59 2.3.2 Biogas Production from Lignocellulosic Waste and Its Economic Viability 59 2.4 Conclusions 60 Acknowledgments 61 References 61 3 Biogas Technology and the Application for Agricultural and Food Waste Treatment 73 Wei Qiao, Simon M. Wandera, Mengmeng Jiang, Yapeng Song, and Renjie Dong 3.1 Development of Biogas Plants 73 3.1.1 Agricultural Waste 74 3.1.1.1 Livestock and Poultry Manure 74 3.1.1.2 Crop Straw 74 3.1.2 Municipal Solid Waste 75 3.1.2.1 Municipal Solid Waste 75 3.1.2.2 Sewage Sludge 75 3.2 Anaerobic Digestion Process 76 3.3 Biogas Production from Livestock and Poultry Manure 77 3.3.1 Successful AD of Cattle and Swine Manure 77 3.3.1.1 Industrial-scale AD of Cattle Manure 77 3.3.1.2 Industrial-scale AD of Swine Manure 77 3.3.2 Successful Anaerobic Digestion of Chicken Manure in a Large Plant 77 3.3.3 Strategies for Mitigating Ammonia Inhibition in Chicken Manure AD 78 3.3.3.1 Supplementation with Trace Elements 78 3.3.3.2 In-situ Ammonia Stripping for Chicken Manure Digesters 79 3.4 Food Waste Anaerobic Digestion 79 3.4.1 Challenges of Food Waste AD and the Solutions 79 3.4.1.1 VFAs Accumulation in Thermophilic AD of Food Waste 79 3.4.1.2 AD Technologies for Food Waste 80 3.4.1.3 Anaerobic Membrane Bioreactor Technology for Food Waste 81 References 81 4 Biogas Production from High-solid Anaerobic Digestion of Food Waste and Its Co-digestion with Other Organic Wastes 85 Le Zhang, To-Hung Tsui, Kai-Chee Loh, Yanjun Dai, Jingxin Zhang, and Yen Wah Tong 4.1 Introduction 85 4.2 Reactor Systems for HSAD 86 4.2.1 High-solid Anaerobic Membrane Bioreactor 86 4.2.2 Two-stage HSAD Reactor System 87 4.2.3 High-solid Plug-flow Bioreactor 88 4.3 Intensification Strategies for HSAD 89 4.3.1 High-solid Anaerobic Co-digestion (HS-AcD) 89 4.3.2 Supplementation of Additives 90 4.3.3 Bioaugmentation Strategies for HSAD 91 4.3.4 Optimization of Process Parameters 91 4.4 Microbial Communities for HSAD 93 4.5 Digestate Management for HSAD 94 4.6 Conclusions and Perspectives 94 Acknowledgments 95 References 95 5 Biomethane – Production and Management 101 Wojciech Czekała, Aleksandra Łukomska, and Martyna Kulińska 5.1 Introduction 101 5.2 Purification and Usage of Biogas 103 5.2.1 Biological Desulfurization Within the Digester 104 5.2.2 Desulfurization by Adsorption on Iron Hydroxide 104 5.2.3 Desulfurization by Adsorption on Activated Carbon 104 5.3 Opportunities for Biogas Upgrading 105 5.3.1 CO2 Separation Through Membranes 105 5.3.2 CO2 Separation by Water Scrubbing 106 5.3.3 Chemical Separation of CO2/Chemical Scrubbing 108 5.3.4 Pressure Separation of CO2 (Pressure Swing Adsorption) 109 5.3.5 Cryogenic CO2 Separation 109 5.4 Possibilities of Using Biomethane 110 5.4.1 Production of bioCNG and bioLNG Fuels 111 5.4.2 Production of Biohydrogen 111 5.5 Profitability of Biomethane Production and Recommended Support Systems 112 5.6 Conclusion 113 References 114 6 The Biogas Use 117 Muhammad U. Khan, Abid Sarwar, Nalok Dutta, and Muhammad Arslan 6.1 Introduction 117 6.2 Biogas Utilization Technologies 118 6.3 Use of Biogas as Trigeneration 119 6.4 Biogas as a Transportation Fuels 120 6.5 Use of Biogas in Reciprocating Engine 121 6.6 Spark Ignition Gas Engine 123 6.7 Use of Biogas in Generator 124 6.8 Use of Biogas in Gas Turbines 125 6.9 Usage of Biogas in Fuel Cell 125 6.10 Hydrogen Production from Biogas 125 6.11 Biogas Cleaning for its Utilization 125 6.11.1 Carbon Dioxide 125 6.11.2 Water 126 6.11.3 Hydrogen Sulfide 126 6.11.4 Oxygen and Nitrogen 126 6.11.5 Ammonia 127 6.11.6 Volatile Organic Compounds 127 6.11.7 Particles 127 6.11.8 Foams and Solid Particles 127 6.12 Different Approaches for H2S Removal 128 6.12.1 Iron Sponge 128 6.12.2 Proprietary Scrubber Systems 129 6.12.3 Ferric Chloride Injection 129 6.12.4 Biological Method 130 6.13 Different Approaches for Moisture Reduction 130 6.13.1 Compression or Condensation 130 6.13.2 Adsorption 130 6.13.3 Absorption 130 6.14 Siloxane Removal 131 6.14.1 Gas Drying 131 6.15 CO2 Separation 132 6.15.1 Cryogenic Technique 132 6.15.2 Water Scrubber 133 6.15.3 Adsorption 133 6.15.4 Membrane Separation 134 6.16 Conclusion 135 References 136 7 Digestate from Agricultural Biogas Plant – Properties and Management 141 Wojciech Czekała 7.1 Introduction 141 7.2 Digestate from Agricultural Biogas Plant – Production, Properties, and Processing 142 7.2.1 Production 142 7.2.2 Properties 142 7.2.3 Processing 144 7.3 Digestate from Agricultural Biogas Plant – Management 145 7.3.1 Raw Digestate Fertilization 145 7.3.2 Liquid Fraction Management 146 7.3.3 Solid Fraction Management 147 7.3.4 Energy Management of the Solid Fraction 149 7.4 Conclusion 150 References 150 8 Environmental Aspects of Biogas Production 155 Yelizaveta Chernysh, Viktoriia Chubur, and Hynek Roubík 8.1 Introduction 155 8.2 Impact of Farms and Livestock Complexes on the Environment 157 8.3 The Environmental Benefits of Biogas Production 158 8.4 Environmental Safety of the Integrated Model of Bioprocesses of Hydrogen Production and Methane Generation in the Stages of Anaerobic Fermentation of Waste 162 8.5 Life Cycle Assessment for Biogas Production 165 8.6 Environmental Issue of Biogas Market in Ukraine – Case Study 167 8.7 Conclusion 172 References 172 9 Hybrid Environmental and Economic Assessment of Biogas Plants in Integrated Organic Waste Management Strategies 179 Amal Elfeky, Kazi Fattah, and Mohamed Abdallah 9.1 Introduction 179 9.2 Methodology 180 9.2.1 Overview 180 9.2.2 Waste Management Scenarios 181 9.2.3 Life Cycle Assessment 182 9.2.3.1 Goal and Scope Definition 182 9.2.3.2 Inventory Analysis 183 9.2.3.3 Impact Assessment 183 9.2.3.4 Interpretation 184 9.2.4 Life Cycle Costing 184 9.2.5 Eco-Efficiency Analysis 185 9.2.6 Case Study: The UAE 185 9.3 Results and Discussion 185 9.3.1 Material and Energy Recovery 186 9.3.2 Life Cycle Assessment 188 9.3.2.1 Overall Impact Assessment 188 9.3.3 Life Cycle Costing 190 9.3.3.1 Cost and Revenue Streams 190 9.3.3.2 Net Present Value 191 9.3.4 Eco-Efficiency Analysis 192 9.4 Conclusion 193 References 193 10 Reduction of the Carbon Footprint in Terms of Agricultural Biogas Plants 195 Agnieszka Wawrzyniak Acronyms 195 10.1 Introduction 196 10.1.1 Manure Management and Biomethane Potential in Poland and EU Countries 196 10.1.2 Substrates Used for Biogas Plants in Poland 196 10.1.3 GHG Emissions from Agriculture and Biogas Plants as Tool for its Reduction 198 10.2 Methodology of CF 201 10.2.1 GHG Fluxes from Agriculture and Tools for its Calculations 202 10.2.2 System Boundaries for Biogas Plant and Data Collection 203 10.3 Life Cycle CO2 Footprints of Various Biogas Projects – Comparison with Literature Results 204 10.4 Conclusions 207 References 207 11 Financial Sustainability and Stakeholder Partnerships of Biogas Plants 211 To-Hung Tsui, Le Zhang, Jonathan T. E. Lee, Yanjun Dai, and Yen Wah Tong 11.1 Introduction 211 11.2 Basic Technological Factors 212 11.3 Economic Evaluation and Failures 214 11.3.1 Investment Risks for Fixed Assets 214 11.3.2 Failures and Intervention 215 11.4 Stakeholders Partnership and Co-governance 216 11.4.1 Government 216 11.4.2 Consultant and Constructor 216 11.4.3 Source of Waste Streams 217 11.4.4 Customers for Energy and Resource 217 11.5 Summary and Outlooks 217 Acknowledgments 218 References 218 12 Measuring the Resilience of Supply Critical Systems: The Case of the Biogas Value Chain 221 Raul Carlsson and Tatiana Nevzorova 12.1 Introduction 221 12.2 Background 222 12.3 Methodology 223 12.4 Measurement Scheme 224 12.4.1 Introduction to the Measurement Concept 224 12.4.2 Measuring Management System Resilience 227 12.4.3 Measuring the Resilience of Physical Resources and Assets 229 12.4.4 Total System Resilience 230 12.4.5 Applying the System Resilience Model to the Biogas Value Chain 231 12.4.5.1 Analysis of Two Supply Chains Without Disruptions 231 12.4.5.2 Disrupting Scenarios with Parametrized Resilience Functions 233 12.4.5.3 Analysis of Two Supply Chains with Disruptions 234 12.5 Conclusion and Recommendations 239 References 240 13 Theory and Practice in Strategic Niche Planning: The Polish Biogas Case 243 Stelios Rozakis, Katerina Troullaki, and Piotr Jurga 13.1 Introduction 243 13.1.1 The Promising Potential of Biogas Transition in Central Eastern European Countries 243 13.1.2 State-of-the-Art Research for Navigating Sustainability Transitions 245 13.1.3 Chapter Organization 246 13.2 Main Conceptual Frameworks for Studying Sustainability Transitions 246 13.2.1 Strategic Niche Management (SNM) 246 13.2.2 Multi-Level Perspective (MLP) 247 13.2.3 Transition Management (TM) 248 13.2.4 Technological Innovation Systems (TIS) 248 13.3 Studying Biogas from a Sustainability Transitions Perspective 249 13.3.1 Landscape, Regime, and Niche Dynamics 249 13.3.2 Policy Coherence for Niche Development 250 13.3.3 Transition Pathways 252 13.3.4 Social Network Analysis 252 13.4 Strategic Niche Planning for Sustainable Transitions 255 13.4.1 Methodological Steps 255 13.4.2 Case Study: Biogas Sector in Poland 259 13.5 Strategic Propositions and Concluding Comments 261 13.5.1 Research and Development 261 13.5.2 Education Activity – Enhance Brokerage 271 13.5.3 Networking-Clusters 271 13.5.4 Resource Mobilization 271 13.5.5 Elaborate Legislation 272 13.5.6 Legitimation 272 13.5.7 Incentives for Market Penetration 272 13.5.8 Demand Pull Actions and Rural Development 273 13.6 Conclusion 273 References 274 14 Social Aspects of Agricultural Biogas Plants 279 Wojciech Czekała 14.1 Introduction 279 14.2 The Benefits of Agricultural Biogas Plants for Society 280 14.2.1 Biogas Plant as a Renewable Energy Production Facility 280 14.2.2 Reducing the Negative Impact of Waste on the Environment 280 14.2.3 Create Markets for Substrates Used in Biogas Production 281 14.2.4 Integration with Agro-Industrial Plants 281 14.2.5 Production and Use of Electricity 282 14.2.6 Production and Use of Heat 282 14.2.7 Possibility of Biomethane Production 283 14.2.8 Local Fuel in Developing Countries 283 14.2.9 Production of Valuable Fertilizer 284 14.2.10 Creating New Jobs for the Local Community 284 14.2.11 Development of Nearby Infrastructure and Companies 285 14.2.12 Tax Revenues to the Budget of Local Government Units 285 14.3 Social Acceptability of Agricultural Biogas Plants 285 14.3.1 Fear of Something New 286 14.3.2 Concerns About Unpleasant Odors 286 14.3.3 Concerns About Contamination of Soils and Groundwater When Using Digestate as Fertilizer 286 14.3.4 Concerns About Declining Property Values Around Biogas Plants 287 14.3.5 Concerns About the Destruction of Access Roads 287 14.4 Conclusion 287 References 288 15 Practices in Biogas Plant Operation: A Case Study from Poland 291 Tomasz Jasiński, Jan Jasiński, and Wojciech Czekała 15.1 Introduction 291 15.2 Legal Aspects Related to Running a Business in the Field of Biogas Production and Waste Management 292 15.2.1 Integrated Permit or Waste Processing Permit 293 15.2.2 Approval of the Plant by Veterinary Services for the Disposal of Waste of Animal Origin 294 15.2.3 Permit to Place Digestate on the Market 295 15.2.4 Permit to Introduce to the Electricity Distribution Network 296 15.3 Biogas Plant Components: A Case Study from Poland 297 15.3.1 Hall for Receiving and Processing Slaughterhouse Waste 297 15.3.2 Substrate Storage Yard 297 15.3.3 Solid Substrate Dispenser 297 15.3.4 Receiving Buffer Tank for Liquid Substrates 298 15.3.5 Solid Substrate Buffer Tank 298 15.3.6 Mixing Buffer Tank 298 15.3.7 Buffer and Mixing Tank 298 15.3.8 Technological Steam Generator 298 15.3.9 Main Pumping Station 299 15.3.10 First-stage Fermentation Tanks 299 15.3.11 Second-stage Fermentation Tank (3900 m3) with Biogas Tank (1800 m3) 300 15.3.12 Condensing Circuit 301 15.3.13 Biogas Refining System 301 15.3.14 Cogeneration Modules 301 15.3.15 Digestate Storage Reservoirs 301 15.3.16 Biogas Torch 302 15.3.17 Biofilter 302 15.4 Functioning of a Biogas Plant Processing Problematic Waste: A Case Study from Poland 302 15.4.1 Searching and Obtaining Substrates 303 15.4.2 Receiving, Storage, and Processing of the Substrate, Feeding of Raw Materials 304 15.4.3 Energy Production and Biogas Management 305 15.4.4 Digestate Management 306 15.4.5 Management of an Agricultural Biogas Plant 307 15.5 Summary 308 References 309 Index 311

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Book SynopsisA PROJECT MANAGER'S BOOK OF TEMPLATES A helpful compendium of ready-made templates for managing every project in alignment with the latest PMBOK Guide, 7th ed. Project Management is a growing discipline that has seen considerable recent development. Project managers are now expected to deploy predictive and adaptive methods, and to draw upon a considerable base of knowledge in developing and formalizing project plans. The Project Management Institute (PMI) publishes the authoritative Project Management Body of Knowledge (PMBOK Guide), which contains the global standard for the Project Management profession. A Project Manager's Book of Templates is a vital companion to the PMBOK Guide, providing a comprehensive set of templates and reports that helps project managers translate the content of the Guide into practical applications. It promises to be an indispensable resource for professionals in this fast-moving field. A Project Manager's Book of Templates readers will also find: Templates covering all types of work, such as starting, planning, project documents, logs and registers, and reports and audits. Templates representing all updated features of the PMBOK Guide, including hybrid, adaptive and iterative practices, including AgileEasy, readable structure that moves project managers through the different types of work that is performed in project A Project Manager's Book of Templates isan essential companion for those preparing for the PMP Certification Exam, as well as practitioners and consultants to a range of global industries.Table of ContentsAcknowledgments vii About the Companion Website viii Introduction ix Audience ix Organization ix 1 Starting the Project 1 1.1 Project Proposal 2 1.2 Business Case 5 1.3 Project Startup Canvas 9 1.4 Project Vision Statement 12 1.5 Project Charter 15 1.6 Project Brief 21 1.7 Project Roadmap 25 2 Project Plans 27 2.1 Scope Management Plan 28 2.2 Requirements Management Plan 32 2.3 Schedule Management Plan 36 2.4 Release Plan 40 2.5 Cost Management Plan 42 2.6 Quality Management Plan 45 2.7 Resource Management Plan 49 2.8 Communication Plan 53 2.9 Risk Management Plan 56 2.10 Procurement Management Plan 62 2.11 Stakeholder Engagement Plan 67 2.12 Change Management Plan 70 2.13 Project Management Plan 74 3 Project Documents 81 3.1 Change Request 82 3.2 Requirements Documentation 86 3.3 Requirements Traceability Matrix 89 3.4 Project Scope Statement 94 3.5 WBS Dictionary 97 3.6 Effort/Duration Estimates 100 3.7 Effort--Duration Estimating Worksheet 103 3.8 Cost Estimates 107 3.9 Cost Estimating Worksheet 109 3.10 Responsibility Assignment Matrix 114 3.11 Team Charter 117 3.12 Probability and Impact Assessment 121 3.13 Risk Data Sheet 127 3.14 Procurement Strategy 130 3.15 Source Selection Criteria 133 3.16 Stakeholder Analysis 136 3.17 User Story 138 3.18 Retrospective 140 4 Logs and Registers 143 4.1 Assumption Log 144 4.2 Backlog 147 4.3 Change Log 149 4.4 Decision Log 152 4.5 Issue Log 154 4.6 Stakeholder Register 157 4.7 Risk Register 160 4.8 Lessons Learned Register 163 5 Reports and Audits 167 5.1 Team Member Progress Report 167 5.2 Project Status Report 173 5.3 Variance Analysis Report 179 5.4 Earned Value Analysis 183 5.5 Risk Report 187 5.6 Contractor Status Report 193 5.7 Contract Closeout Report 197 5.8 Lessons Learned Report 201 5.9 Project Closeout Report 206 5.10 Quality Audit 210 5.11 Risk Audit 213 5.12 Procurement Audit 217 Appendix: Combination Templates 221 Index 231

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Book SynopsisTable of ContentsPreface xv Acknowledgment xxi 1 Reliability Indices of a Computer System with Priority and Server Failure 1 S.C. Malik, R.K. Yadav and N. Nandal 1.1 Introduction 2 1.2 Some Fundamentals 4 1.2.1 Reliability 4 1.2.2 Mean Time to System Failure (MTSF) 4 1.2.3 Steady State Availability 4 1.2.4 Redundancy 5 1.2.5 Semi-Markov Process 5 1.2.6 Regenerative Point Process 6 1.3 Notations and Abbreviations 6 1.4 Assumptions and State Descriptions 8 1.5 Reliability Measures 9 1.5.1 Transition Probabilities 9 1.5.2 Mst 10 1.5.3 Reliability and MTCSF 10 1.5.4 Availability 11 1.5.5 Expected Number of Hardware Repairs 12 1.5.6 Expected Number of Software Upgradations 13 1.5.7 Expected Number of Treatments Given to the Server 14 1.5.8 Busy Period of Server Due to H/w Repair 15 1.5.9 Busy Period of Server Due to Software Upgradation 16 1.6 Profit Analysis 17 1.7 Particular Case 18 1.8 Graphical Presentation of Reliability Indices 19 1.9 Real-Life Application 20 1.10 Conclusion 21 References 21 2 Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm 23 Monika Saini, Nivedita Gupta and Ashish Kumar 2.1 Introduction 23 2.2 System Description, Notations, and Assumptions 25 2.2.1 System Description 25 2.2.2 Notations 27 2.2.3 Assumptions 28 2.3 Mathematical Modeling of the System 28 2.4 Optimization 33 2.4.1 Genetic Algorithm 33 2.5 Results and Discussion 34 2.6 Conclusion 36 References 45 3 Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design 47 Aishwarya Mehta, Jitesh Jangid, Akash Saxena, Shalini Shekhawat and Rajesh Kumar 3.1 Introduction 48 3.2 Problem Formulation of Harmonics 52 3.3 Development of Laplacian Artificial Bee Colony Algorithm 54 3.3.1 Basic Concepts of ABC 54 3.3.2 The Proposed LABC Algorithm 56 3.4 Discussion 58 3.5 Numerical Validation of Proposed Variant 58 3.5.1 Comparative Analysis of LABC with Other Meta-Heuristics 59 3.5.2 Benchmark Test on CEC-17 Functions 70 3.6 Analytical Validation of Proposed Variant 72 3.6.1 Convergence Rate Test 75 3.6.2 Box Plot Analysis 77 3.6.3 Wilcoxon Rank Sum Test 77 3.6.4 Scalability Test 81 3.7 Design Analysis of Harmonic Estimator 81 3.7.1 Assessment of Harmonic Estimator Design Problem 1 81 3.7.2 Assessment of Harmonic Estimator Design Problem 2 87 3.8 Conclusion 92 References 93 4 Applications of Cuckoo Search Algorithm in Reliability Optimization 97 V. Kaviyarasu and V. Suganthi 4.1 Introduction 98 4.2 Cuckoo Search Algorithm 98 4.2.1 Performance of Cuckoo Search Algorithm 98 4.2.2 Levy Flights 99 4.2.3 Software Reliability 99 4.3 Modified Cuckoo Search Algorithm (MCS) 100 4.4 Optimization in Module Design 102 4.5 Optimization at Dynamic Implementation 103 4.6 Comparative Study of Support of Modified Cuckoo Search Algorithm 104 4.7 Results and Discussions 105 4.8 Conclusion 107 References 108 5 Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula 109 Muhammad Salihu Isa, Ibrahim Yusuf, Uba Ahmad Ali and Wu Jinbiao 5.1 Introduction 110 5.2 Assumptions, Notations, and Description of the System 112 5.2.1 Notations 112 5.2.2 Assumptions 114 5.2.3 Description of the System 114 5.3 Reliability Formulation of Models 116 5.3.1 Solution of the Model 117 5.4 Some Particular Cases Based on Analytical Analysis of the Model 120 5.4.1 Availability Analysis 120 5.4.2 Reliability Analysis 121 5.4.3 Mean Time to Failure (MTTF) 122 5.4.4 Cost-Benefit Analysis 124 5.5 Conclusions Through Result Discussion 125 References 126 6 Applications of Artificial Intelligence in Sustainable Energy Development and Utilization 129 Aditya Kolakoti, Prasadarao Bobbili, Satyanarayana Katakam, Satish Geeri and Wasim Ghder Soliman 6.1 Energy and Environment 130 6.2 Sustainable Energy 130 6.3 Artificial Intelligence in Industry 4.0 131 6.4 Introduction to AI and its Working Mechanism 132 6.5 Biodiesel 135 6.6 Transesterification Process 136 6.7 AI in Biodiesel Applications 138 6.8 Conclusion 140 References 140 7 On New Joint Importance Measures for Multistate Reliability Systems 145 Chacko V. M. 7.1 Introduction 145 7.2 New Joint Importance Measures 147 7.2.1 Multistate Differential Joint Reliability Achievement Worth (MDJRAW) 148 7.2.2 Multistate Differential Joint Reliability Reduction Worth (MDJRRW) 150 7.2.3 Multistate Differential Joint Reliability Fussel-Vesely (MDJRFV) Measure 152 7.3 Discussion 153 7.4 Illustrative Example 154 7.5 Conclusion 157 References 157 8 Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data 159 Kapil Kumar and Anita Kumari 8.1 Introduction 159 8.2 Model Description 161 8.3 Classical Estimation 163 8.3.1 Maximum Likelihood Estimation 163 8.3.2 Asymptotic Confidence Interval 164 8.4 Bayesian Estimation 166 8.4.1 Tierney-Kadane’s Approximation 167 8.4.2 Metropolis-Hastings Algorithm 169 8.4.3 HPD Credible Interval 170 8.5 Simulation Study 170 8.6 Real-Life Application 176 8.7 Conclusions 177 References 177 9 Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data 181 Rashmi Bundel, M. S. Panwar and Sanjeev K. Tomer 9.1 Introduction 182 9.2 Fuzzy Lifetime Data 183 9.2.1 Fuzzy Set 183 9.2.2 Fuzzy Numbers and Membership Function 184 9.2.3 Fuzzy Event and its Probability 187 9.3 Modeling with Fuzzy Lifetime Data in Presence of Competing Risks 187 9.4 Maximum Likelihood Estimation with Exponential Lifetimes 189 9.4.1 Bootstrap Confidence Interval 192 9.5 Bayes Estimation 192 9.5.1 Highest Posterior Density Confidence Estimates 194 9.6 Numerical Illustration 195 9.6.1 Simulation Study 196 9.6.2 Reliability Analysis Using Simulated Data 210 9.7 Real Data Study 212 9.8 Conclusion 212 References 215 10 Cost-Benefit Analysis of a Redundant System with Refreshment 217 M.S. Barak and Dhiraj Yadav 10.1 Introduction 218 10.2 Notations 219 10.3 Average Sojourn Times and Probabilities of Transition States 220 10.4 Mean Time to Failure of the System 223 10.5 Steady-State Availability 223 10.6 The Period in Which the Server is Busy With Inspection 224 10.7 Expected Number of Visits for Repair 227 10.8 Expected Number of Refreshments 227 10.9 Particular Case 228 10. 10 Cost-Benefit Examination 230 10.11 Discussion 230 10.12 Conclusion 233 References 233 11 Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making 235 Ram Naresh Saraswat and Sapna Gahlot 11.1 Introduction 235 11.2 New f-Divergence Measure on Fuzzy Sets 237 11.3 New Fuzzy Information Inequalities Using Fuzzy New f-Divergence Measure and Fuzzy Triangular Divergence Measure 239 11.4 Applications for Some Fuzzy f-Divergence Measures 241 11.5 Applications in MCDM 244 11.5.1 Case Study 246 11.6 Conclusion 247 References 248 12 Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System 251 M.S. Barak, Ajay Kumar and Reena Garg 12.1 Introduction 252 12.2 The Assumptions and Notations Used to Solve the System 254 12.3 The Probabilities of States Transitions 256 12.4 Mean Sojourn Time 257 12.5 Mean Time to Failure of the System 257 12.6 Steady-State Availability 258 12.7 Busy Period of the Server Due to Repair of the Failed Unit 259 12.8 Busy Period of the Server Due to Refreshment 259 12.9 Estimated Visits Made by the Server 260 12.10 Particular Cases 261 12.11 Profit Analysis 262 12.12 Discussion 262 12.13 Conclusion 264 12.14 Contribution of Refreshment 265 12.15 Future Scope 265 References 265 13 Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm 269 Monika Saini, Nivedita Gupta and Ashish Kumar 13.1 Introduction 270 13.2 System Description, Notations, and Assumptions 271 13.2.1 System Description 271 13.2.2 Notations 272 13.2.3 Assumptions 273 13.3 Mathematical Modeling of the System 273 13.4 Availability Optimization of Proposed Model 278 13.5 Results and Discussion 280 13.6 Conclusion 285 References 285 14 Investigation of Reliability and Maintainability of Piston Manufacturing Plant 287 Monika Saini, Deepak Sinwar and Ashish Kumar 14.1 Introduction 288 14.2 System Description and Data Collection 290 14.3 Descriptive Analysis 294 14.4 Power Law Process Model 295 14.5 Trend and Serial Correlation Analysis 300 14.6 Reliability and Maintainability Analysis 302 14.7 Conclusion 306 References 307 Index 311

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Book SynopsisMACHINE INTELLIGENCE, BIG DATA ANALYTICS, AND IoT IN IMAGE PROCESSING Discusses both theoretical and practical aspects of how to harness advanced technologies to develop practical applications such as drone-based surveillance, smart transportation, healthcare, farming solutions, and robotics used in automation. The concepts of machine intelligence, big data analytics, and the Internet of Things (IoT) continue to improve our lives through various cutting-edge applications such as disease detection in real-time, crop yield prediction, smart parking, and so forth. The transformative effects of these technologies are life-changing because they play an important role in demystifying smart healthcare, plant pathology, and smart city/village planning, design and development. This book presents a cross-disciplinary perspective on the practical applications of machine intelligence, big data analytics, and IoT by compiling cutting-edge research and insights from researchers, Table of ContentsPreface xv Part I: Demystifying Smart Healthcare 1 1 Deep Learning Techniques Using Transfer Learning for Classification of Alzheimer’s Disease 3 Monika Sethi, Sachin Ahuja and Puneet Bawa 1.1 Introduction 4 1.2 Transfer Learning Techniques 6 1.3 AD Classification Using Conventional Training Methods 9 1.4 AD Classification Using Transfer Learning 12 1.5 Conclusion 16 References 16 2 Medical Image Analysis of Lung Cancer CT Scans Using Deep Learning with Swarm Optimization Techniques 23 Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao 2.1 Introduction 24 2.2 The Major Contributions of the Proposed Model 26 2.3 Related Works 28 2.4 Problem Statement 32 2.5 Proposed Model 33 2.5.1 Swarm Optimization in Lung Cancer Medical Image Analysis 33 2.5.2 Deep Learning with PSO 34 2.5.3 Proposed CNN Architectures 35 2.6 Dataset Description 37 2.7 Results and Discussions 39 2.7.1 Parameters for Performance Evaluation 39 2.8 Conclusion 47 References 48 3 Liver Cancer Classification With Using Gray-Level Co-Occurrence Matrix Using Deep Learning Techniques 51 Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao 3.1 Introduction 52 3.1.1 Liver Roles in Human Body 53 3.1.2 Liver Diseases 53 3.1.3 Types of Liver Tumors 55 3.1.3.1 Benign Tumors 55 3.1.3.2 Malignant Tumors 57 3.1.4 Characteristics of a Medical Imaging Procedure 58 3.1.5 Problems Related to Liver Cancer Classification 60 3.1.6 Purpose of the Systematic Study 61 3.2 Related Works 62 3.3 Proposed Methodology 66 3.3.1 Gaussian Mixture Model 68 3.3.2 Dataset Description 69 3.3.3 Performance Metrics 70 3.3.3.1 Accuracy Measures 70 3.3.3.2 Key Findings 74 3.3.3.3 Key Issues Addressed 75 3.4 Conclusion 77 References 77 4 Transforming the Technologies for Resilient and Digital Future During COVID-19 Pandemic 81 Garima Kohli and Kumar Gourav 4.1 Introduction 82 4.2 Digital Technologies Used 84 4.2.1 Artificial Intelligence 85 4.2.2 Internet of Things 85 4.2.3 Telehealth/Telemedicine 87 4.2.4 Cloud Computing 87 4.2.5 Blockchain 88 4.2.6 5g 89 4.3 Challenges in Transforming Digital Technology 90 4.3.1 Increasing Digitalization 91 4.3.2 Work From Home Culture 91 4.3.3 Workplace Monitoring and Techno Stress 91 4.3.4 Online Fraud 92 4.3.5 Accessing Internet 92 4.3.6 Internet Shutdowns 92 4.3.7 Digital Payments 92 4.3.8 Privacy and Surveillance 93 4.4 Implications for Research 93 4.5 Conclusion 94 References 95 Part II: Plant Pathology 101 5 Plant Pathology Detection Using Deep Learning 103 Sangeeta V., Appala S. Muttipati and Brahmaji Godi 5.1 Introduction 104 5.2 Plant Leaf Disease 105 5.3 Background Knowledge 109 5.4 Architecture of ResNet 512 V 2 111 5.4.1 Working of Residual Network 112 5.5 Methodology 113 5.5.1 Image Resizing 113 5.5.2 Data Augmentation 113 5.5.2.1 Types of Data Augmentation 114 5.5.3 Data Normalization 114 5.5.4 Data Splitting 116 5.6 Result Analysis 116 5.6.1 Data Collection 117 5.6.2 Feature Extractions 117 5.6.3 Plant Leaf Disease Detection 117 5.7 Conclusion 119 References 120 6 Smart Irrigation and Cultivation Recommendation System for Precision Agriculture Driven by IoT 123 N. Marline Joys Kumari, N. Thirupathi Rao and Debnath Bhattacharyya 6.1 Introduction 124 6.1.1 Background of the Problem 127 6.1.1.1 Need of Water Management 127 6.1.1.2 Importance of Precision Agriculture 127 6.1.1.3 Internet of Things 128 6.1.1.4 Application of IoT in Machine Learning and Deep Learning 129 6.2 Related Works 131 6.3 Challenges of IoT in Smart Irrigation 133 6.4 Farmers’ Challenges in the Current Situation 135 6.5 Data Collection in Precision Agriculture 136 6.5.1 Algorithm 136 6.5.1.1 Environmental Consideration on Stage Production of Crop 140 6.5.2 Implementation Measures 141 6.5.2.1 Analysis of Relevant Vectors 141 6.5.2.2 Mean Square Error 141 6.5.2.3 Potential of IoT in Precision Agriculture 141 6.5.3 Architecture of the Proposed Model 143 6.6 Conclusion 147 References 147 7 Machine Learning-Based Hybrid Model for Wheat Yield Prediction 151 Haneet Kour, Vaishali Pandith, Jatinder Manhas and Vinod Sharma 7.1 Introduction 152 7.2 Related Work 153 7.3 Materials and Methods 155 7.3.1 Methodology for the Current Work 155 7.3.1.1 Data Collection for Wheat Crop 155 7.3.1.2 Data Pre-Processing 156 7.3.1.3 Implementation of the Proposed Hybrid Model 157 7.3.2 Techniques Used for Feature Selection 159 7.3.2.1 ReliefF Algorithm 159 7.3.2.2 Genetic Algorithm 161 7.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction 162 7.3.3.1 K-Nearest Neighbor 162 7.3.3.2 Artificial Neural Network 163 7.3.3.3 Logistic Regression 164 7.3.3.4 Naïve Bayes 164 7.3.3.5 Support Vector Machine 165 7.3.3.6 Linear Discriminant Analysis 166 7.4 Experimental Result and Analysis 167 7.5 Conclusion 173 Acknowledgment 173 References 174 8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences 177 Abhishek Bhola, Suraj Srivastava, Ajit Noonia, Bhisham Sharma and Sushil Kumar Narang 8.1 Introduction 178 8.2 Types of Wireless Sensor for Smart Agriculture 179 8.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture 179 8.4 ml and WSN-Based Techniques for Smart Agriculture 185 8.5 Future Scope in Smart Agriculture 188 8.6 Conclusion 190 References 190 Part III: Smart City and Villages 197 9 Impact of Data Pre-Processing in Information Retrieval for Data Analytics 199 Huma Naz, Sachin Ahuja, Rahul Nijhawan and Neelu Jyothi Ahuja 9.1 Introduction 200 9.1.1 Tasks Involved in Data Pre-Processing 200 9.2 Related Work 202 9.3 Experimental Setup and Methodology 205 9.3.1 Methodology 205 9.3.2 Application of Various Data Pre-Processing Tasks on Datasets 206 9.3.3 Applied Techniques 207 9.3.3.1 Decision Tree 207 9.3.3.2 Naive Bayes 207 9.3.3.3 Artificial Neural Network 208 9.3.4 Proposed Work 208 9.3.4.1 PIMA Diabetes Dataset (PID) 208 9.3.5 Cleveland Heart Disease Dataset 211 9.3.6 Framingham Heart Study 215 9.3.7 Diabetic Dataset 217 9.4 Experimental Result and Discussion 220 9.5 Conclusion and Future Work 222 References 222 10 Cloud Computing Security, Risk, and Challenges: A Detailed Analysis of Preventive Measures and Applications 225 Anurag Sinha, N. K. Singh, Ayushman Srivastava, Sagorika Sen and Samarth Sinha 10.1 Introduction 226 10.2 Background 228 10.2.1 History of Cloud Computing 228 10.2.1.1 Software-as-a-Service Model 230 10.2.1.2 Infrastructure-as-a-Service Model 230 10.2.1.3 Platform-as-a-Service Model 232 10.2.2 Types of Cloud Computing 232 10.2.3 Cloud Service Model 232 10.2.4 Characteristics of Cloud Computing 234 10.2.5 Advantages of Cloud Computing 234 10.2.6 Challenges in Cloud Computing 235 10.2.7 Cloud Security 236 10.2.7.1 Foundation Security 236 10.2.7.2 SaaS and PaaS Host Security 237 10.2.7.3 Virtual Server Security 237 10.2.7.4 Foundation Security: The Application Level 238 10.2.7.5 Supplier Data and Its Security 238 10.2.7.6 Need of Security in Cloud 239 10.2.8 Cloud Computing Applications 239 10.3 Literature Review 241 10.4 Cloud Computing Challenges and Its Solution 242 10.4.1 Solution and Practices for Cloud Challenges 246 10.5 Cloud Computing Security Issues and Its Preventive Measures 248 10.5.1 General Security Threats in Cloud 249 10.5.2 Preventive Measures 254 10.6 Cloud Data Protection and Security Using Steganography 258 10.6.1 Types of Steganography 259 10.6.2 Data Steganography in Cloud Environment 260 10.6.3 Pixel Value Differencing Method 261 10.7 Related Study 263 10.8 Conclusion 263 References 264 11 Internet of Drone Things: A New Age Invention 269 Prachi Dahiya 11.1 Introduction 269 11.2 Unmanned Aerial Vehicles 271 11.2.1 UAV Features and Working 274 11.2.2 IoDT Architecture 275 11.3 Application Areas 280 11.3.1 Other Application Areas 284 11.4 IoDT Attacks 285 11.4.1 Counter Measures 291 11.5 Fusion of IoDT With Other Technologies 296 11.6 Recent Advancements in IoDT 299 11.7 Conclusion 302 References 303 12 Computer Vision-Oriented Gesture Recognition System for Real-Time ISL Prediction 305 Mukul Joshi, Gayatri Valluri, Jyoti Rawat and Kriti 12.1 Introduction 305 12.2 Literature Review 307 12.3 System Architecture 309 12.3.1 Model Development Phase 309 12.3.2 Development Environment Phase 311 12.4 Methodology 312 12.4.1 Image Pre-Processing Phase 312 12.4.2 Model Building Phase 313 12.5 Implementation and Results 314 12.5.1 Performance 314 12.5.2 Confusion Matrix 318 12.6 Conclusion and Future Scope 318 References 319 13 Recent Advances in Intelligent Transportation Systems in India: Analysis, Applications, Challenges, and Future Work 323 Elamurugan Balasundaram, Cailassame Nedunchezhian, Mathiazhagan Arumugam and Vinoth Asaikannu 13.1 Introduction 324 13.2 A Primer on ITS 325 13.3 The ITS Stages 326 13.4 Functions of ITS 327 13.5 ITS Advantages 328 13.6 ITS Applications 329 13.7 ITS Across the World 331 13.8 India’s Status of ITS 333 13.9 Suggestions for Improving India’s ITS Position 334 13.10 Conclusion 335 References 335 14 Evolutionary Approaches in Navigation Systems for Road Transportation System 341 Noopur Tyagi, Jaiteg Singh and Saravjeet Singh 14.1 Introduction 342 14.1.1 Navigation System 343 14.1.2 Genetic Algorithm 347 14.1.3 Differential Evolution 348 14.2 Related Studies 349 14.2.1 Related Studies of Evolutionary Algorithms 351 14.3 Navigation Based on Evolutionary Algorithm 352 14.3.1 Operators and Terms Used in Evolutionary Algorithms 353 14.3.2 Operator and Terms Used in Evolutionary Algorithm 357 14.4 Meta-Heuristic Algorithms for Navigation 359 14.4.1 Drawbacks of DE 362 14.5 Conclusion 362 References 363 15 IoT-Based Smart Parking System for Indian Smart Cities 369 E. Fantin Irudaya Raj, M. Appadurai, M. Chithamabara Thanu and E. Francy Irudaya Rani 15.1 Introduction 370 15.2 Indian Smart Cities Mission 371 15.3 Vehicle Parking and Its Requirements in a Smart City Configuration 373 15.4 Technologies Incorporated in a Vehicle Parking System in Smart Cities 375 15.5 Sensors for Vehicle Parking System 383 15.5.1 Active Sensors 384 15.5.2 Passive Sensors 386 15.6 IoT-Based Vehicle Parking System for Indian Smart Cities 387 15.6.1 Guidance to the Customers Through Smart Devices 389 15.6.2 Smart Parking Reservation System 391 15.7 Advantages of IoT-Based Vehicle Parking System 392 15.8 Conclusion 392 References 393 16 Security of Smart Home Solution Based on Secure Piggybacked Key Exchange Mechanism 399 Jatin Arora and Saravjeet Singh 16.1 Introduction 400 16.2 IoT Challenges 404 16.3 IoT Vulnerabilities 405 16.4 Layer-Wise Threats in IoT Architecture 406 16.4.1 Sensing Layer Security Issues 407 16.4.2 Network Layer Security Issues 408 16.4.3 Middleware Layer Security Issues 409 16.4.4 Gateways Security Issues 410 16.4.5 Application Layer Security Issues 411 16.5 Attack Prevention Techniques 411 16.5.1 IoT Authentication 412 16.5.2 Session Establishment 413 16.6 Conclusion 414 References 414 17 Machine Learning Models in Prediction of Strength Parameters of FRP-Wrapped RC Beams 419 Aman Kumar, Harish Chandra Arora, Nishant Raj Kapoor and Ashok Kumar 17.1 Introduction 420 17.1.1 Defining Fiber-Reinforced Polymer 421 17.1.2 Types of FRP Composites 422 17.1.2.1 Carbon Fiber–Reinforced Polymer 422 17.1.2.2 Glass Fiber 423 17.1.2.3 Aramid Fiber 424 17.1.2.4 Basalt Fiber 424 17.2 Strengthening of RC Beams With FRP Systems 425 17.2.1 FRP-to-Concrete Bond 426 17.2.2 Flexural Strengthening of Beams With FRP Composite 427 17.2.3 Shear Strengthening of Beams With FRP Composite 427 17.3 Machine Learning Models 428 17.3.1 Prediction of Bond Strength 430 17.3.2 Estimation of Flexural Strength 434 17.3.3 Estimation of Shear Strength 434 17.4 Conclusion 441 References 441 18 Prediction of Indoor Air Quality Using Artificial Intelligence 447 Nishant Raj Kapoor, Ashok Kumar, Anuj Kumar, Aman Kumar and Harish Chandra Arora 18.1 Introduction 448 18.2 Indoor Air Quality Parameters 450 18.2.1 Physical Parameters 453 18.2.1.1 Humidity 453 18.2.1.2 Air Changes (Ventilation) 454 18.2.1.3 Air Velocity 454 18.2.1.4 Temperature 454 18.2.2 Particulate Matter 455 18.2.3 Chemical Parameters 456 18.2.3.1 Carbon Dioxide 456 18.2.3.2 Carbon Monoxide 456 18.2.3.3 Nitrogen Dioxide 456 18.2.3.4 Sulphur Dioxide 457 18.2.3.5 Ozone 457 18.2.3.6 Gaseous Ammonia 458 18.2.3.7 Volatile Organic Compounds 458 18.2.4 Biological Parameters 459 18.3 AI in Indoor Air Quality Prediction 459 18.4 Conclusion 464 References 465 Index 471

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Book Synopsis

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Book SynopsisSMART GRIDS for SMART CITIES Written and edited by a team of experts in the field, this first volume in a two-volume set focuses on an interdisciplinary perspective on the financial, environmental, and other benefits of smart grid technologies and solutions for smart cities. What makes a regular electric grid a smart grid? It comes down to digital technologies that enable two-way communication between a utility and its customers, as opposed to the traditional electric grid, where power flows in one direction. Based on statistics and available research, smart grids globally attract the largest investment venues in smart cities. Smart grids and city buildings that are connected in smart cities contribute to significant financial savings and improve the economy. The smart grid has many components, including controls, computers, automation, and new technologies and equipment working together. These technologies cooperate with the electrical grid to respond digitally to our quickly changingTable of ContentsPreface xvii 1 Carbon-Free Fuel and the Social Gap: The Analysis 1 Saravanan Chinnusamy, Milind Shrinivas Dangate and Nasrin I. Shaikh 1.1 Introduction 2 1.2 Objectives 3 1.3 Study Areas 3 1.3.1 Community A 4 1.3.2 Community B 4 1.3.3 community c 5 1.3.4 Community d 5 1.4 Data Collection 6 1.5 Data Analysis 9 1.6 Conclusion 10 References 13 2 Opportunities of Translating Mobile Base Transceiver Station (BTS) for EV Charging Through Energy Management Systems in DC Microgrid 15 A. Matheswaran, P. Prem, C. Ganesh Babu and K. Lakshmi 2.1 Introduction 16 2.1.1 Telecom Sector in India 16 2.1.2 Overview of Base Transceiver Station (BTS) 17 2.1.3 Electric Vehicle in India 19 2.1.4 Evolution of EV Charging Station 21 2.2 Translating Mobile Base Transceiver Station (BTS) for EV Charging 21 2.2.1 Mobile Base Transceiver Station (BTS) for EV Charging – A Substitute or Complementary Solution? 21 2.2.2 Proposed Methodology 23 2.2.3 System Description 24 2.2.3.1 Solar PV Array 24 2.2.3.2 DC-DC Boost Converter 25 2.2.3.3 Rectifier 25 2.2.3.4 Battery Backup System 26 2.2.3.5 Charge Controller 27 2.2.3.6 Bidirectional Converter 28 2.3 Implementation of Energy Management System in Base Transceiver Station (BTS) 29 2.3.1 Introduction 29 2.3.2 Control Strategies 30 2.3.2.1 MPPT Control 31 2.3.2.2 Charge Controller Control 31 2.3.2.3 Bidirectional Converter Control 32 2.3.3 Power Supervisory and Control Algorithm (PSCA) 33 2.3.3.1 Grid Available Mode 33 2.3.3.2 Grid Fault Mode 33 2.3.4 Results and Discussions 35 2.3.4.1 Grid Available Mode 35 2.3.4.2 Grid Failure Mode 35 2.4 Conclusion 35 References 38 3 A Review on Advanced Control Techniques for Multi-Input Power Converters for Various Applications 41 Kodada Durga Priyanka and Abitha Memala Wilson Duraisamy 3.1 Introduction 42 3.2 Multi-Input Magnetically Connected Power Converters 46 3.2.1 Dual-Source Power DC to DC Converter with Buck-Boost Arrangement 46 3.2.2 Bidirectional Multi-Input Arrangement 47 3.2.3 Full-Bridge Boost DC-DC Converter Formation 48 3.2.4 Multi-Input Power Converter with Half-Bridge and Full Bridge Configuration 49 3.3 Electrically Coupled Multi-Input Power DC-DC Converters 50 3.3.1 Combination of Electrically Linked Multi-Input DC/DC Power Converter 50 3.3.2 Multi-Input Power Converters in Series or Parallel Connection 51 3.3.3 Multi-Input DC/DC Fundamental Power Converters 52 3.3.4 Multiple-Input Boost Converter for RES 53 3.3.5 Multi-Input Buck-Boost/Buck/Boost-Boost Based Converter 54 3.3.6 Multi-Input Buck-Boost/Buck/Boost-Boost Based Converter 55 3.3.7 Multi-Input DC/DC Converter Using ZVS (Zero Voltage Switching) 57 3.3.8 Multi-Input DC-DC Converter Based Three Switches Leg 57 3.3.9 Multi-Input Converter Constructed on Switched Inductor/Switched Capacitor/Diode Capacitor 58 3.3.10 High/Modular VTR Multi-Input Converters 59 3.3.11 Multi/Input and Multi/Output (MIMO) Power Converter 60 3.4 Electro Magnetically Coupled Multi-Input Power DC/DC Converters 61 3.4.1 Direct Charge Multi-Input DC/DC Power Converter 61 3.4.2 Boost-Integrated Full-Bridge DC-DC Power Converter 62 3.4.3 Isolated Dual-Port Power Converter for Immediate Power Management 63 3.4.4 Dual Port Converter with Non-Isolated and Isolated Ports 63 3.4.5 Multi-Port ZVS And ZCS DC-DC Converter 64 3.4.6 Combined DC-Link and Magnetically Coupled DC/DC Power Converter 65 3.4.7 Three-Level Dual-Input DC-DC Converter 65 3.4.8 Half-Bridge Tri-Modal DC-DC Converter 66 3.4.9 Bidirectional Converter with Various Collective Battery Storage Input Sources 75 3.5 Different Control Methods Used in Multi-Input DC-DC Power Converters 75 3.5.1 Proportional Integral Derivation Controller (PID) 76 3.5.2 Model Predictive Control Method (MPC) 77 3.5.3 State Space Modelling (SSM) 78 3.5.4 Fuzzy Logic Control (FLC) 79 3.5.5 Sliding Mode Control (SMC) 80 3.6 Comparison and Future Scope of Work 82 3.6.1 Comparison and Discussion 82 3.7 Conclusion 85 References 86 4 Case Study: Optimized LT Cable Sizing for an IT Campus 101 O.V. Gnana Swathika, K. Karthikeyan, Umashankar Subramaniam and K.T.M.U. Hemapala Abbreviations 102 4.1 Introduction 102 4.2 Methodology 103 4.2.1 Algorithm for Cable Sizing 103 4.3 Results and Discussion 103 4.3.1 Feeder Schedule 104 4.3.2 Design Consideration for LT Power Cable 104 4.3.3 Cable Sizing & Voltage Drop Calculation 107 4.4 Conclusion 114 References 114 5 Advanced Control Architecture for Interlinking Converter in Autonomous AC, DC and Hybrid AC/DC Micro Grids 115 M. Padma Lalitha, S. Suresh and A. Viswa Pavani 5.1 Introduction 116 5.2 Prototype Model of IC 117 5.3 Implemented Photo Voltaic System 118 5.4 Highly Reliable and Efficient (HRE) Configurations 120 5.5 MATLAB Simulink Results 122 5.6 Conclusion 127 References 127 6 Optimal Power Flow Analysis in Distributed Grid Connected Photovoltaic Systems 131 Neenu Thomas, T.N.P. Nambiar and Jayabarathi R. 6.1 Introduction 131 6.2 System Development and Design Parameters 132 6.3 Proposed Algorithm 138 6.4 Results and Discussion 138 6.5 Conclusion 141 References 141 7 Reliability Assessment for Solar and Wind Renewable Energy in Generation System Planning 143 S. Vinoth John Prakash and P.K. Dhal 7.1 Introduction 144 7.2 Generation & Load Model 146 7.2.1 Generation Model-RBTS 146 7.2.2 Wind Power Generation Model 147 7.2.2.1 Wind Speed and Wind Turbine Output Model 147 7.2.3 Solar Power Generation Model 150 7.2.3.1 Solar Radiation and Solar Power Output Model 150 7.2.4 Load Model 152 7.3 Results and Analysis 152 7.3.1 Reliability Indices Evaluation for Different Scenario 153 7.4 Conclusion 155 References 156 8 Implementation of Savonius Blad Wind Tree Structure by Super Lift Luo Converter for Smart Grid Applications and Benefits to Smart City 159 Jency Joseph J., Anitha Mary X., Josh F. T., Vinoth Kumar K. and Vinodha K. 8.1 Introduction 160 8.2 Savonius Wind Turbine – Performance Design 160 8.3 Design Modules 163 8.4 Results and Discussion 167 8.5 Positive Output Super Lift Luo Converter 170 8.6 Conclusion 171 References 172 9 Analysis: An Incorporation of PV and Battery for DC Scattered System 175 M. Karuppiah, P. Dineshkumar, A. Arunbalaj and S. Krishnakumar 9.1 Introduction 176 9.2 Block Diagram of Proposed System 179 9.2.1 Determine the Load Profile 180 9.2.2 Duration of Autonomy and Recharge 180 9.2.3 Select the Battery Rating 181 9.2.4 Sizing the PV Array 182 9.2.5 Analysis of Boost Converter 184 9.2.5.1 To Select a Proper Inductor Value 187 9.2.5.2 To Select a Proper Capacitor Value 187 9.3 Proposed System Simulations 188 9.4 Conclusion 192 References 193 10 Dead Time Compensation Scheme Using Space Vector PWM for 3Ø Inverter 195 Sreeramula Reddy, Ravindra Prasad, Harinath Reddy and Suresh Srinivasan 10.1 Introduction 195 10.2 Concept of Space Vector PWM 197 10.3 Proteus Simulation 200 10.4 Hardware Setup 201 10.4.1 Total Harmonic Distortion 206 10.4.2 Hardware Configuration 209 10.5 Conclusion 210 References 211 11 Transformer-Less Grid Connected PV System Using TSRPWM Strategy with Single Phase 7 Level Multi-Level Inverter 213 S. Sruthi, K. Karthikumar, D. Narmitha, P. Chandra Sekhar and K. Karthi 11.1 Introduction 214 11.2 Proposed System 215 11.3 DC-DC Influence Converter 216 11.4 Controlling of 7-Level Inverter 218 11.5 Controlling for Boost Converter and Inverter 221 11.6 MATLAB Simulation Results 221 11.7 Conclusion 224 References 225 12 An Enhanced Multi-Level Inverter Topology for HEV Applications 227 Premkumar E. and Kanimozhi G. 12.1 Introduction 227 12.2 E-MLI Topology 228 12.2.1 Switching Operation of the E-MLI Topology 229 12.2.2 Diode-Clamped Multi-Level Inverter (DC-MLI) 232 12.3 PWM for the E-MLI Topology 233 12.3.1 SPWM Based Switching for the E-MLI Topology 234 12.3.2 Phase Opposition Disposition (POD) Scheme for DC-MLI 234 12.4 Simulation Results & Discussions 236 12.5 Conclusion 249 References 249 13 Improved Sheep Flock Heredity Algorithm-Based Optimal Pricing of RP 253 P. Booma Devi, Booma Jayapalan and A.P. Jagadeesan 13.1 Introduction 254 13.2 RP Flow Tracing 257 13.2.1 Intent Function 257 13.2.1.1 System’s Price Loss After RP Compensation 257 13.2.1.2 SVC Support Price for RP 258 13.2.1.3 Diesel Generator RP Production Price 258 13.2.1.4 Minimization Function 258 13.3 Existing Methodologies 259 13.3.1 Particle Swarm Optimization (PSO) 259 13.3.1.1 PSO Parameter Settings 259 13.3.2 Hybrid Particle Swarm Optimization (HPSO) 260 13.3.2.1 Flowchart for HPSO 260 13.4 Proposed Methodology 261 13.4.1 Improved Sheep Flock Heredity Algorithm 261 13.4.2 ISFHA Algorithm 263 13.5 Case Study 263 13.5.1 Realistic Seventy-Five Bus Indian System Wind Farm 263 13.6 Conclusion 266 References 267 14 Dual Axis Solar Tracking with Weather Monitoring System by Using IR and LDR Sensors with Arduino UNO 269 Rajesh Babu Damala and Rajesh Kumar Patnaik 14.1 Introduction 269 14.2 Associated Hardware Components Details 270 14.2.1 Arduino Uno 270 14.2.2 L293D Motor Driver 271 14.2.3 LDR Sensor 272 14.2.4 Solar Panel 273 14.2.5 RPM 10 Motor 274 14.2.6 Jumper Wires 274 14.2.7 16×2 LCD (Liquid Crystal Display) Module with I2C 275 14.2.8 DTH11 Sensor 276 14.2.9 Rain Drop Sensor 276 14.3 Methodology 277 14.3.1 Dual Axis Solar Tracking System Working Model 277 14.3.2 Dual Axis Solar Tracking System Schematic Diagram 279 14.4 Results and Discussion 279 14.5 Conclusion 281 References 282 15 Missing Data Imputation of an Off-Grid Solar Power Model for a Small-Scale System 285 Aadyasha Patel, Aniket Biswal and O.V. Gnana Swathika Abbreviations and Nomenclature 286 15.1 Overview 286 15.2 Literature Review 287 15.3 AI/ML for Imputation of Missing Values 288 15.3.1 Cbr 288 15.3.2 Mice 290 15.3.3 Results and Discussion 291 15.3.3.1 Data Collection 291 15.3.3.2 Error Metrics 292 15.3.3.3 Comparison Between CBR and MICE 293 15.4 Applications of MICE in Imputation 296 15.5 Summary 296 References 297 16 Power Theft in Smart Grids and Microgrids: Mini Review 299 P. Tejaswi and O.V. Gnana Swathika 16.1 Introduction 299 16.2 Smart Grids/Microgrids Security Threats and Challenges 300 16.2.1 Security Threats to Smart Grid/Microgrid by Classification of Sources 301 16.2.1.1 Smart Grid/Microgrid Threats Sources in Technical Point of View 302 16.2.2 Sources of Smart Grids/Microgrids Threats in Non-Technical Point of View 304 16.2.2.1 Security of Environment 304 16.2.2.2 Regulatory Policies of Government 304 16.3 Conclusion 304 References 304 17 Isolated SEPIC-Based DC-DC Converter for Solar Applications 309 Varun Mukesh Lal, Pranay Singh Parihar and Kanimozhi. G 17.1 Introduction 309 17.2 Converter Operation and Analysis 311 17.2.1 Mode A 311 17.2.2 Mode B 313 17.3 Design Equations 314 17.4 Simulation Results 316 17.5 Conclusion 321 References 321 18 Hybrid Converter for Stand-Alone Solar Photovoltaic System 323 R.R. Rubia Gandhi and C. Kathirvel 18.1 Introduction 324 18.2 Review on Converter Topology 324 18.3 Block Diagram 325 18.4 Existing Converter Topology 326 18.5 Proposed Tapped Boost Hybrid Converter 326 18.5.1 Novelty in the Circuit 327 18.5.2 Converter Modes of Operation 327 18.6 Derivation Part of Tapped Boost Hybrid Converter 327 18.6.1 Voltage Gain 328 18.6.2 Modulation Index 328 18.7 Design Specification of the Converter 329 18.8 Simulation Results for Both DC and AC Power Conversion 330 18.9 Hardware Results 330 18.10 TBHC Parameters for Simulation 332 18.11 Conclusion 334 References 334 19 Analysis of Three-Phase Quasi Switched Boost Inverter Based on Switched Inductor-Switched Capacitor Structure 337 P. Sriramalakshmi, Vachan Kumar, Pallav Pant and Reshab Kumar Sahoo 19.1 Introduction 337 19.1.1 Conventional Inverter (VSI) 339 19.1.2 Z-Source Inverter (ZSI) 339 19.1.3 SBI Based on SL-SC Structure 340 19.2 Working Modes of Three-Phase SL-SC Circuit 341 19.2.1 Shoot-Through State 341 19.2.2 Non-Shoot-Through State 342 19.3 Design of Three-Phase SL-SC Based Quasi Switched Boost Inverter 342 19.3.1 Steady State Analysis of SL-SC Topology 342 19.3.2 Design of Passive Elements 344 19.3.3 Design Equations 344 19.3.4 Design Specifications 344 19.4 Simulation Results and Discussions 344 19.4.1 Simulation Diagram of SBC PWM Technique 344 19.4.2 SBC PWM Technique 345 19.4.3 Switching Pulse Generated for the Power Switches 347 19.4.4 Expanded Switching Pulse 348 19.4.5 Input Current 348 19.4.6 Current in Inductor L 1 349 19.4.7 Current in Inductor L 2 349 19.4.8 Capacitor Voltage VC 2 350 19.4.9 dc Link Voltage 350 19.4.10 Output Load Voltage 351 19.4.11 Output Load Current 351 19.5 Performance Analysis 351 19.6 Conclusion 353 References 354 20 Power Quality Improvement and Performance Enhancement of Distribution System Using D-STATCOM 357 M. Sai Sandeep, N. Balaji, Muqthiar Ali and Suresh Srinivasan 20.1 Introduction 358 20.2 Distribution Static Synchronous Compensator (d-statcom) 360 20.3 Modelling of Distribution System 361 20.3.1 Single Machine System 361 20.3.2 Modeling of IEEE 14 Bus System 362 20.4 Simulation Results & Discussions 363 20.4.1 Power Flow Analysis on Single Machine System 363 20.4.2 Different Modes of Operation of D-STATCOM on Single Machine System 365 20.4.3 Step Change in Reference Value of dc Link Voltage 368 20.5 IEEE-14 Bus Systems 370 20.6 Conclusion 374 References 374 Index 377

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Book SynopsisModular Multilevel Converters Expert discussions of cutting-edge methods used in MMC control, protection, and fault detection In Modular Multilevel Converters: Control, Fault Detection, and Protection, a team of distinguished researchers delivers a comprehensive discussion of fault detection, protection, and tolerant control of modular multilevel converters (MMCs) under internal and external faults. Beginning with a description of the configuration of MMCs, their operation principles, modulation schemes, mathematical models, and component design, the authors go on to explore output control, fault detection, capacitor monitoring, and other topics of central importance in the field. The book offers summaries of centralized capacitor voltage-balancing control methods and presents several capacitor monitoring methods, like the direct and sorting-based techniques. It also describes full-bridge and half-bridge submodule-based hybrid MMC protection methods and alternative fault blocking SM-baTable of ContentsAbout the Authors xiii Preface xv 1 Modular Multilevel Converters 1 1.1 Introduction 1 1.2 MMC Configuration 2 1.2.1 Converter Configuration 2 1.2.2 Submodule Configuration 2 1.3 Operation Principles 3 1.3.1 Submodule Normal Operation 3 1.3.2 Submodule Blocking Operation 5 1.3.3 Converter Operation 6 1.4 Modulation Scheme 8 1.4.1 Phase-Disposition PWM 9 1.4.2 Phase-Shifted PWM 10 1.4.3 Nearest Level Modulation 11 1.5 Mathematical Model 12 1.5.1 Submodule Mathematical Model 12 1.5.1.1 Switching-Function Based Model 13 1.5.1.2 Reference-Based Model 13 1.5.2 Arm Mathematical Model 14 1.5.2.1 Switching-Function Based Model 14 1.5.2.2 Reference-Based Model 15 1.5.3 Three-Phase MMC Mathematical Model 16 1.5.3.1 AC-Side Mathematical Model 17 1.5.3.2 DC-Side Mathematical Model 17 1.6 Design Constraints 18 1.6.1 Power Device Design 18 1.6.1.1 Rated Voltage of Power Devices 19 1.6.1.2 Rated Current of Power Devices 19 1.6.2 Capacitor Design 21 1.6.3 Arm Inductor Design 23 1.7 Faults Overview of MMCs 24 1.7.1 Internal Faults of MMCs 24 1.7.2 External Faults of MMCs 25 1.8 Summary 25 References 26 2 Control of MMCs 29 2.1 Introduction 29 2.2 Overall Control of MMCs 30 2.3 Output Control of MMCs 31 2.3.1 Current Control 31 2.3.2 Power and DC-Link Voltage Control 33 2.3.3 Grid Forming Control 36 2.4 Centralized Capacitor Voltage Balancing Control 38 2.4.1 On-State SMs Number Based VBC 39 2.4.2 Balancing Adjusting Number Based VBC 39 2.4.2.1 Capacitor VBC 40 2.4.2.2 SM Switching Frequency 40 2.4.3 IPSC-PWM Harmonic Current Based VBC 42 2.4.3.1 IPSC-PWM Scheme 42 2.4.3.2 High-Frequency Arm Current 43 2.4.3.3 Arm Capacitor Voltage Analysis 46 2.4.3.4 Voltage Balancing Control 47 2.4.4 SHE-PWM Pulse Energy Sorting Based VBC 53 2.4.4.1 MMCs Analysis with Grid-Frequency Pulses 53 2.4.4.2 Charge Transfer of Capacitors in Lower Arm 56 2.4.4.3 Charge Transfer of Capacitors in Upper Arm 57 2.4.4.4 Voltage Balancing Control 59 2.4.5 PSC-PWM Pulse Energy Sorting Based VBC 65 2.4.5.1 MMC with PSC-PWM 65 2.4.5.2 Capacitor Charge Transfer Under Linearization Method 67 2.4.5.3 Capacitor Voltage Analysis 70 2.4.5.4 Voltage Balancing Control 72 2.5 Individual Capacitor Voltage Balancing Control 79 2.5.1 Average and Balancing Control Based VBC 79 2.5.1.1 Average Control 80 2.5.1.2 Balancing Control 80 2.5.2 Reference Modulation Index Based VBC 81 2.5.2.1 Analysis of Capacitor Voltage 82 2.5.2.2 Control of i cdc by modulation Index m 83 2.5.2.3 Voltage Balancing Control by m 84 2.5.3 Reference Phase Angle Based VBC 86 2.5.3.1 Control of i cdc by Phase Angle θ 86 2.5.3.2 Voltage Balancing Control by θ 87 2.6 Circulating Current Control 94 2.6.1 Proportional Integration Control 95 2.6.2 Multiple Proportional Resonant Control 97 2.6.3 Repetitive Control 98 2.7 Summary 100 References 100 3 Fault Detection of MMCs under IGBT Faults 103 3.1 Introduction 103 3.2 IGBT Faults 104 3.2.1 IGBT Short- Circuit Fault 105 3.2.2 IGBT Open- Circuit Fault 105 3.3 Protection and Detection Under IGBT Short- Circuit Faults 106 3.3.1 SM Under IGBT Short- Circuit Fault 106 3.3.2 Protection and Detection Under IGBT Short- Circuit Fault 107 3.4 mmc Features Under IGBT Open- Circuit Faults 109 3.4.1 Faulty SM Features Under T 1 Open- Circuit Fault 109 3.4.2 Faulty SM Features Under T 2 Open- Circuit Fault 110 3.4.2.1 Operation Mode of Faulty SM 110 3.4.2.2 Faulty SM Capacitor Voltage of MMCs in Inverter Mode 111 3.4.2.3 Faulty SM Capacitor Voltage of MMCs in Rectifier Mode 112 3.5 Kalman Filter Based Fault Detection Under IGBT Open- Circuit Faults 115 3.5.1 Kalman Filter Algorithm 117 3.5.2 Circulating Current Estimation 118 3.5.3 Faulty Phase Detection 119 3.5.4 Capacitor Voltage 120 3.5.5 Faulty SM Detection 121 3.6 Integrator Based Fault Detection Under IGBT Open- Circuit Faults 127 3.7 STW Based Fault Detection Under IGBT Open- Circuit Faults 132 3.7.1 MMC Data 132 3.7.2 Sliding- Time Windows 133 3.7.3 Feature of STW 134 3.7.4 Features Relationships Between Neighboring STWs 137 3.7.5 Features Extraction Algorithm 137 3.7.6 Energy Entropy Matrix 138 3.7.7 2D- CNN 138 3.7.8 Fault Detection Method 140 3.7.9 Selection of Sliding Interval 141 3.7.10 Analysis of Fault Localization Time 142 3.8 IF Based Fault Detection Under IGBT Open- Circuit Faults 145 3.8.1 IT for MMCs 145 3.8.2 SM Depth in IT 146 3.8.3 IF for MMCs 147 3.8.4 SM Average Depth in IF 147 3.8.5 IF Output 147 3.8.6 Fault Detection 149 3.8.7 Selection of m p 150 3.8.8 Selection of k 151 3.9 Summary 156 References 156 4 Condition Monitoring and Control of MMCs Under Capacitor Faults 161 4.1 Introduction 161 4.2 Capacitor Equivalent Circuit in MMCs 162 4.3 Capacitor Parameter Characteristics in MMCs 164 4.3.1 Capacitor Current Characteristics 164 4.3.2 Capacitor Impedance Characteristics 167 4.3.3 Capacitor Voltage Characteristics 167 4.4 Capacitor Aging 169 4.5 Capacitance Monitoring 171 4.5.1 Capacitor Voltage and Current Based Monitoring Strategy 172 4.5.2 Arm Average Capacitance Based Monitoring Method 172 4.5.2.1 Equivalent Arm Structure 172 4.5.2.2 Capacitor Monitoring Method 173 4.5.3 Reference SM based Monitoring Method 179 4.5.3.1 Principle of the RSM- Based Capacitor Monitoring Strategy 179 4.5.3.2 Capacitor Monitoring- Based Voltage- Balancing Control 180 4.5.3.3 Selection of RSM 182 4.5.3.4 Capacitor Monitoring Strategy 183 4.5.4 Sorting- Based Monitoring Strategy 189 4.5.5 Temperature Effect of Capacitance 195 4.6 ESR Monitoring 195 4.6.1 Direct ESR Monitoring Strategy 196 4.6.2 Sorting- Based ESR Monitoring Strategy 196 4.6.3 Temperature Effect of ESR 203 4.7 Capacitor Lifetime Monitoring 204 4.8 Arm Current Optimal Control Under Capacitor Aging 205 4.8.1 Equivalent Circuit of MMCs 205 4.8.2 Arm Current Characteristics 207 4.8.3 Arm Current Optimal Control 208 4.9 SM Power Losses Optimal Control Under Capacitor Aging 212 4.9.1 Equivalent SM Reference 213 4.9.2 SM Conduction Losses 215 4.9.3 SM Switching Losses 216 4.9.4 SM Power Losses Optimal Control 217 4.10 Summary 225 References 226 5 Fault-Tolerant Control of MMCs Under SM Faults 229 5.1 Introduction 229 5.2 SM Protection Circuit 229 5.3 Redundant Submodules 230 5.4 Fault- Tolerant Scheme 231 5.4.1 Cold Reserve Mode 232 5.4.2 Spinning Reserve Mode- I 233 5.4.3 Spinning Reserve Mode- II 235 5.4.4 Spinning Reserve Mode- III 235 5.4.5 Comparison of Fault- Tolerant Schemes 235 5.5 Fundamental Circulating Current Elimination Based Tolerant Control 236 5.5.1 Equivalent Circuit of MMCs 236 5.5.2 Fundamental Circulating Current 238 5.5.3 Fundamental Circulating Current Elimination Control 239 5.5.4 Control Analysis 241 5.6 Summary 247 References 247 6 Control of MMCs Under AC Grid Faults 249 6.1 Introduction 249 6.2 Mathematical Model of MMCs under AC Grid Faults 250 6.2.1 AC- Side Mathematical Model 250 6.2.1.1 MMC with AC- Side Transformer 250 6.2.1.2 MMCs without AC- Side Transformer 252 6.2.2 Instantaneous Power Mathematical Model 253 6.3 AC- Side Current Control of MMCs under AC Grid Faults 254 6.3.1 Positive- and Negative- Sequence Current Control 255 6.3.1.1 Inner Loop Current Control 255 6.3.1.2 Outer Power Control 256 6.3.2 Zero- Sequence Current Control 257 6.3.3 Proportional Resonant Based Current Control 259 6.4 Circulating Current Suppression Control of MMCs under AC Grid Faults 261 6.4.1 Circulating Current of MMCs Under AC Grid Faults 261 6.4.2 Single- Phase Vector Based Control 262 6.4.3 αβ0 Stationary Frame Based Control 264 6.4.4 Three- Phase Stationary Frame Based Control 266 6.4.4.1 Positive- and Negative- Sequence Controller 267 6.4.4.2 Zero- Sequence Controller 268 6.5 Summary 269 References 270 7 Protection Under DC Short-Circuit Fault in HVDC System 273 7.1 Introduction 273 7.2 MMC Under DC Short- Circuit Fault 274 7.2.1 System Configuration 274 7.2.2 AC Circuit Breaker 274 7.2.3 Protection Thyristor 275 7.2.4 Protection Operation 276 7.3 DC Circuit Breaker Based Protection 281 7.3.1 Mechanical Circuit Breaker 282 7.3.2 Semiconductor Circuit Breaker 283 7.3.2.1 Semi- Controlled Semiconductor Circuit Breaker 283 7.3.2.2 Fully Controlled Semiconductor Circuit Breaker 284 7.3.3 Hybrid Circuit Breaker 285 7.3.3.1 Conventional Hybrid Circuit Breaker 285 7.3.3.2 Proactive Hybrid Circuit Breaker 286 7.3.4 Multiterminal Circuit Breaker 287 7.3.4.1 Assembly CB 287 7.3.4.2 Multiport CB 288 7.3.5 Superconducting Fault Current Limiter 289 7.3.6 SFCL- Based Circuit Breaker 289 7.3.6.1 SFCL- Based Hybrid Circuit Breaker 290 7.3.6.2 SFCL- Based Self- Oscillating Circuit Breaker 291 7.3.6.3 SFCL- Based Forced Zero- Crossing Circuit Breaker 292 7.4 Fault Blocking Converter Based Protection 293 7.4.1 FB SM and HB SM Based Hybrid MMC 294 7.4.2 Fault Blocking Control 296 7.4.3 FB SM Ratio 298 7.4.4 Alternative Fault Blocking SMs 298 7.5 Bypass Thyristor MMC Based Protection 299 7.5.1 Bypass Thyristor MMC Configuration 299 7.5.2 SM Control 302 7.5.3 Current Interruption Control 303 7.5.3.1 Three- Phase Rectifier Period 304 7.5.3.2 One- Phase Current Interruption Moment 304 7.5.3.3 Single- Phase Rectifier Period 305 7.5.3.4 Three- Phase Current Interruption Moment 306 7.5.4 Protection Operation 307 7.6 CTB- HMMC Based Protection 311 7.6.1 CTB- HMMC Configuration 312 7.6.2 SM Operation Principle 313 7.6.3 Operation Principle for DC Fault Protection 314 7.6.4 DC- Side Current Interruption Operation 315 7.6.5 Capacitor Voltage Increment 317 7.6.6 AC- Side Current Interruption Operation 318 7.6.7 MMC Comparison 321 7.6.7.1 Comparison with Current Blocking SM Based MMCs 321 7.6.7.2 Comparison with Thyristor Based MMCs 323 7.7 Summary 328 References 329 Index 333

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Book SynopsisPrinciples of Laser Materials Processing Authoritative resource providing state-of-the-art coverage in the field of laser materials processing, supported with supplementary learning materials Principles of Laser Materials Processing goes over the most recent advancements and applications in laser materials processing, with the second edition providing a welcome update to the successful first edition through updated content on the important fields within laser materials processing. The text includes solved example problems and problem sets suitable for the readers' further understanding of the technology explained. Split into three parts, the text first introduces basic concepts of lasers, including the characteristics of lasers and the design of their components, to aid readers in their initial understanding of the technology. The text then reviews the engineering concepts that are needed to analyze the different processes. Finally, it delves into the bacTable of ContentsPREFACE TO THE SECOND EDITION xxi PREFACE TO THE FIRST EDITION xxiii ABOUT THE COMPANION WEBSITE xxv PART I PRINCIPLES OF INDUSTRIAL LASERS 1 1 Laser Background 3 1.1 Laser Generation 3 1.2 Optical Resonators 12 1.3 Laser Pumping 21 1.4 System Levels 24 1.5 Broadening Mechanisms 26 1.6 Beam Modification 29 1.7 Beam Characteristics 35 1.8 Summary 43 2 Types of Lasers 55 2.1 Solid-State Lasers 55 2.2 Gas Lasers 57 2.3 Semiconductor (Diode) Lasers 69 2.4 New Developments in Industrial Laser Technology 80 2.5 Summary 89 3 Beam Delivery 95 3.1 The Electromagnetic Spectrum 95 3.2 Birefringence 96 3.3 Brewster Angle 96 3.4 Polarization 98 3.5 Beam Expanders 101 3.6 Beam Splitters 102 3.7 Beam Delivery Systems 103 3.8 Beam Shaping 116 3.9 Summary 125 PART II ENGINEERING BACKGROUND 133 4 Heat and Fluid Flow 135 4.1 Energy Balance During Processing 135 4.2 Heat Flow in the Workpiece 136 4.3 Fluid Flow in Molten Pool 156 4.4 Summary 161 5 The Microstructure 175 5.1 Process Microstructure 175 5.2 Discontinuities 195 5.3 Summary 202 6 Solidification 209 6.1 Solidification Without Flow 209 6.2 Solidification with Flow 216 6.3 Rapid Solidification 221 6.4 Summary 222 7 Residual Stresses and Distortion 227 7.1 Causes of Residual Stresses 227 7.2 Basic Stress Analysis 232 7.3 Effects of Residual Stresses 237 7.4 Measurement of Residual Stresses 240 7.5 Relief of Residual Stresses and Distortion 250 7.6 Summary 252 PART III LASER MATERIALS PROCESSING 261 8 Background on Laser Processing 263 8.1 System-Related Parameters 263 8.2 Process Efficiency 272 8.3 Disturbances That Affect Process Quality 274 8.4 General Advantages and Disadvantages of Laser Processing 275 8.5 Summary 275 9 Laser Cutting and Drilling 279 9.1 Laser Cutting 279 9.2 Laser Drilling 308 9.3 New Developments 318 9.4 Summary 326 10 Laser Welding 335 10.1 Laser Welding Parameters 335 10.2 Welding Efficiency 344 10.3 Mechanism of Laser Welding 344 10.4 Material Considerations 355 10.5 Weldment Discontinuities 359 10.6 Advantages and Disadvantages of Laser Welding 360 10.7 Special Techniques 360 10.8 Specific Applications 371 10.9 Summary 382 11 Laser Surface Modification 391 11.1 Laser Surface Heat Treatment 391 11.2 Laser Surface Melting 413 11.3 Laser Direct Metal Deposition 414 11.4 Laser Physical Vapor Deposition (LPVD) 419 11.5 Laser Shock Peening 420 11.6 Laser Texturing 427 11.7 Summary 429 12 Laser Forming 437 12.1 Principle of Laser Forming 437 12.2 Process Parameters 439 12.3 Laser-Forming Mechanisms 439 12.4 Process Analysis 443 12.5 Advantages and Disadvantages 447 12.6 Applications 448 12.7 Summary 448 13 Additive Manufacturing 453 13.1 Computer-Aided Design 453 13.2 Part Building 462 13.3 Post-Processing 477 13.4 Applications 478 13.5 Advantages and Disadvantages 480 13.6 Summary 480 14 Medical and Nanotechnology Applications of Lasers 485 14.1 Medical Applications 485 14.2 Nanotechnology Applications 490 14.3 Summary 494 15 Sensors for Process Monitoring 497 15.1 Laser Beam Monitoring 497 15.2 Process Monitoring 504 15.3 Summary 522 16 Processing of Sensor Outputs 527 16.1 Signal Transformation 527 16.2 Data Reduction 532 16.3 Pattern Classification 534 16.4 Summary 550 17 Laser Safety 557 17.1 Laser Hazards 557 17.2 Laser Classification 562 17.3 Preventing Laser Accidents 563 17.4 Summary 569 Appendix 17.A 571 Problem 572 Bibliography 572 Index 573

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Book SynopsisESSENTIALS OF SEMICONDUCTOR DEVICE PHYSICS An introductory semiconductor device physics textbook that is accessible to readers without a background in statistical physics I wish this book had been available when I needed to make a Semiconductor class myself a few years ago [...] A very nice aspect is that some concepts (e.g. density of states) are explained in a way that I have not seen elsewhere. These types of unconventional approaches are very valuable for a teacher.(Bjorn Maes, University of Mons, Belgium) [...] the author offers an accessible description of statistical analysis and adopts it to explain the core properties of semiconductors. [...] [He] uses interesting metaphors and analogies to exemplify some of the most difficult notions, in an innovative and engaging way. (Andrea di Falco, University of St. Andrews, UK) The subject of this book is the physics of semiconductor devices, which is an important topic in engineering and physics because it forms the background for electronic and optoelectronic devices, including solar cells. The author aims to provide students and teachers with a concise text that focuses on semiconductor devices and covers the necessary background in statistical physics. This text introduces the key prerequisite knowledge in a simple, clear, and friendly manner. It distills the key concepts of semiconductor devices down to their essentials, enabling students to master this key subject in engineering, physics, and materials. The subject matter treated in this book is directly connected to the physics of p-n junctions and solar cells, which has become a topic of intense interest in the last decade. Sample topics covered within the text include: Chemical potential, Fermi level, Fermi-Dirac distribution, drift current and diffusion current. The physics of semiconductors, band theory and intuitive derivations of the concentration of charge carriers. The p-n junction, with qualitative analysis preceding the mathematical descriptions. A derivation of the current vs voltage relation in p-n junctions (Shockley equation). Important applications of p-n junctions, including solar cellsThe two main types of transistors: Bipolar Junction Transistors (BJT) and Metal Oxide Semiconductor Field Effect Transistors (MOSFET) For students and instructors, it may be used as a primary textbook for an introductory semiconductor device physics course and is suitable for a course of approximately 30-50 hours. Scientists studying and researching semiconductor devices in general, and solar cells in particular, will also benefit from the clear and intuitive explanations found in this book.Table of ContentsPreface 1 Concepts of Statistical Physics 1.1 Introduction 1.2 Thermal Equilibrium 1.3 Partition function - Part I 1.4 Diffusive equilibrium and the chemical potential 1.5 The partition function, Part II 1.6 Example of application: energy and number of elements of a system 1.7 The Fermi-Dirac distribution 1.8 Analogy between the systems “box” and “coins” 1.9 Concentration of electrons and Fermi level 1.10 Transport 1.11 Relationship between current and concentration of particles (continuity equation) 1.12 Suggestions for further reading 1.13 Exercises 2 – Semiconductors 2.1 Band Theory 2.2 Electrons and holes 2.3 Concentration of free electrons 2.4 Density of states 2.5 Concentration of holes and Fermi level 2.6 Extrinsic semiconductors (doping) 2. 7 Exercises 3 Introduction to semiconductor devices: the p-n junction 3.1 p-n junction in thermodynamic equilibrium – qualitative description 3.2 p-n junction in thermodynamic equilibrium – quantitative description 3.3 Systems outside thermodynamic equilibrium: the quasi-Fermi levels. 3.4 Qualitative description of the relationship between current and voltage in a p-n junction 3.5 The current vs voltage relationship in a p-n junction (Shockley’s equation) 3.6 Suggestions for further reading 3.7 Exercises 4 Photovoltaic devices (mainly solar cells) 4.1 Solar cells and photodetectors 4.2 Physical principles 4.3 The equivalent circuit 4.4 The I x V curve and the fill-factor 4.5 Efficiency of solar cells and the theoretical limit 4.6 Connections of solar cells 4.7 Suggestions for further reading 4.8 Exercises 5 Transistors 5.1 The Bipolar Junction Transistor (BJT) 5.1.1 Physical principles of the BJT 5.1.2 The beta parameter and the relationship between emitter, collector and base currents 5.1.3 Relationship between IC and VCE and the Early effect 5.1.4 The BJT as an amplifier 5.2 The MOSFET 5.2.1 Physical principles 5.2.3 Examples of applications of MOSFETS: logic inverters and logic gates 5.3 Suggestions for further reading 5.4 Exercises Appendix: Geometrical interpretation of the chemical potential and free energy

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Book SynopsisPrepare for the Azure Data Engineering certificationand an exciting new career in analyticswith this must-have study aide In the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203, accomplished data engineer and tech educator Benjamin Perkins delivers a hands-on, practical guide to preparing for the challenging Azure Data Engineer certification and for a new career in an exciting and growing field of tech. In the book, you'll explore all the objectives covered on the DP-203 exam while learning the job roles and responsibilities of a newly minted Azure data engineer. From integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions, you'll get up to speed quickly and efficiently with Sybex's easy-to-use study aids and tools. This Study Guide also offers: Career-ready advice for anyone hoping to ace their first data engineering job interview and excel in their first day in the fieldIndispensable tips and tricks to familiarize yourself with the DP-203 exam structure and help reduce test anxietyComplimentary access to Sybex's expansive online study tools, accessible across multiple devices, and offering access to hundreds of bonus practice questions, electronic flashcards, and a searchable, digital glossary of key terms A one-of-a-kind study aid designed to help you get straight to the crucial material you need to succeed on the exam and on the job, the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203 belongs on the bookshelves of anyone hoping to increase their data analytics skills, advance their data engineering career with an in-demand certification, or hoping to make a career change into a popular new area of tech.Table of ContentsIntroduction xxvii Part I Azure Data Engineer Certification and Azure Products 1 Chapter 1 Gaining the Azure Data Engineer Associate Certification 3 The Journey to Certification 7 How to Pass Exam DP- 203 8 Understanding the Exam Expectations and Requirements 9 Use Azure Daily 17 Read Azure Articles to Stay Current 17 Have an Understanding of All Azure Products 20 Azure Product Name Recognition 21 Azure Data Analytics 23 Azure Synapse Analytics 23 Azure Databricks 26 Azure HDInsight 28 Azure Analysis Services 30 Azure Data Factory 31 Azure Event Hubs 33 Azure Stream Analytics 34 Other Products 35 Azure Storage Products 36 Azure Data Lake Storage 37 Azure Storage 40 Other Products 42 Azure Databases 43 Azure Cosmos DB 43 Azure SQL Server Products 46 Additional Azure Databases 46 Other Products 47 Azure Security 48 Azure Active Directory 48 Role- Based Access Control 51 Attribute- Based Access Control 53 Azure Key Vault 53 Other Products 55 Azure Networking 56 Virtual Networks 56 Other Products 59 Azure Compute 59 Azure Virtual Machines 59 Azure Virtual Machine Scale Sets 60 Azure App Service Web Apps 60 Azure Functions 60 Azure Batch 60 Azure Management and Governance 60 Azure Monitor 61 Azure Purview 61 Azure Policy 62 Azure Blueprints (Preview) 62 Azure Lighthouse 62 Azure Cost Management and Billing 62 Other Products 63 Summary 64 Exam Essentials 64 Review Questions 66 Chapter 2 CREATE DATABASE dbName; GO 69 The Brainjammer 70 A Historical Look at Data 71 Variety 73 Velocity 74 Volume 74 Data Locations 74 Data File Formats 75 Data Structures, Types, and Concepts 83 Data Structures 83 Data Types and Management 92 Data Concepts 95 Data Programming and Querying for Data Engineers 125 Data Programming 126 Querying Data 143 Understanding Big Data Processing 169 Big Data Stages 169 Etl, Elt, Eltl 174 Analytics Types 175 Big Data Layers 176 Summary 177 Exam Essentials 177 Review Questions 179 Part II Design and Implement Data Storage 181 Chapter 3 Data Sources and Ingestion 183 Where Does Data Come From? 185 Design a Data Storage Structure 189 Design an Azure Data Lake Solution 190 Recommended File Types for Storage 198 Recommended File Types for Analytical Queries 199 Design for Efficient Querying 200 Design for Data Pruning 203 Design a Folder Structure That Represents the Levels of Data Transformation 203 Design a Distribution Strategy 205 Design a Data Archiving Solution 206 Design a Partition Strategy 207 Design a Partition Strategy for Files 209 Design a Partition Strategy for Analytical Workloads 210 Design a Partition Strategy for Efficiency and Performance 211 Design a Partition Strategy for Azure Synapse Analytics 211 Identify When Partitioning Is Needed in Azure Data Lake Storage Gen 2 212 Design the Serving/Data Exploration Layer 213 Design Star Schemas 214 Design Slowly Changing Dimensions 215 Design a Dimensional Hierarchy 219 Design a Solution for Temporal Data 220 Design for Incremental Loading 222 Design Analytical Stores 223 Design Metastores in Azure Synapse Analytics and Azure Databricks 224 The Ingestion of Data into a Pipeline 228 Azure Synapse Analytics 228 Azure Data Factory 268 Azure Databricks 275 Event Hubs and IoT Hub 301 Azure Stream Analytics 303 Apache Kafka for HDInsight 314 Migrating and Moving Data 316 Summary 317 Exam Essentials 317 Review Questions 319 Chapter 4 The Storage of Data 321 Implement Physical Data Storage Structures 322 Implement Compression 322 Implement Partitioning 325 Implement Sharding 328 Implement Different Table Geometries with Azure Synapse Analytics Pools 329 Implement Data Redundancy 331 Implement Distributions 341 Implement Data Archiving 342 Azure Synapse Analytics Develop Hub 346 Implement Logical Data Structures 360 Build a Temporal Data Solution 361 Build a Slowly Changing Dimension 365 Build a Logical Folder Structure 368 Build External Tables 369 Implement File and Folder Structures for Efficient Querying and Data Pruning 372 Implement a Partition Strategy 375 Implement a Partition Strategy for Files 376 Implement a Partition Strategy for Analytical Workloads 377 Implement a Partition Strategy for Streaming Workloads 378 Implement a Partition Strategy for Azure Synapse Analytics 378 Design and Implement the Data Exploration Layer 379 Deliver Data in a Relational Star Schema 379 Deliver Data in Parquet Files 385 Maintain Metadata 386 Implement a Dimensional Hierarchy 386 Create and Execute Queries by Using a Compute Solution That Leverages SQL Serverless and Spark Cluster 388 Recommend Azure Synapse Analytics Database Templates 389 Implement Azure Synapse Analytics Database Templates 389 Additional Data Storage Topics 390 Storing Raw Data in Azure Databricks for Transformation 390 Storing Data Using Azure HDInsight 392 Storing Prepared, Trained, and Modeled Data 393 Summary 394 Exam Essentials 395 Review Questions 396 Part III Develop Data Processing 399 Chapter 5 Transform, Manage, and Prepare Data 401 Chapter 6 Ingest and Transform Data 402 Transform Data Using Azure Synapse Pipelines 404 Transform Data Using Azure Data Factory 410 Transform Data Using Apache Spark 414 Transform Data Using Transact- SQL 429 Transform Data Using Stream Analytics 431 Cleanse Data 433 Split Data 435 Shred JSON 439 Encode and Decode Data 445 Configure Error Handling for the Transformation 450 Normalize and Denormalize Values 451 Transform Data by Using Scala 461 Perform Exploratory Data Analysis 463 Transformation and Data Management Concepts 473 Transformation 473 Data Management 480 Azure Databricks 481 Data Modeling and Usage 485 Data Modeling with Machine Learning 486 Usage 494 Summary 500 Exam Essentials 500 Review Questions 502 Create and Manage Batch Processing and Pipelines 505 Design and Develop a Batch Processing Solution 507 Design a Batch Processing Solution 510 Develop Batch Processing Solutions 512 Create Data Pipelines 538 Handle Duplicate Data 560 Handle Missing Data 569 Handle Late- Arriving Data 571 Upsert Data 572 Configure the Batch Size 578 Configure Batch Retention 581 Design and Develop Slowly Changing Dimensions 582 Design and Implement Incremental Data Loads 583 Integrate Jupyter/IPython Notebooks into a Data Pipeline 590 Chapter 7 Revert Data to a Previous State 591 Handle Security and Compliance Requirements 592 Design and Create Tests for Data Pipelines 593 Scale Resources 593 Design and Configure Exception Handling 593 Debug Spark Jobs Using the Spark UI 594 Implement Azure Synapse Link and Query the Replicated Data 594 Use PolyBase to Load Data to a SQL Pool 595 Read from and Write to a Delta Table 595 Manage Batches and Pipelines 596 Trigger Batches 597 Schedule Data Pipelines 597 Validate Batch Loads 598 Implement Version Control for Pipeline Artifacts 604 Manage Data Pipelines 607 Manage Spark Jobs in a Pipeline 609 Handle Failed Batch Loads 610 Summary 610 Exam Essentials 611 Review Questions 612 Design and Implement a Data Stream Processing Solution 615 Develop a Stream Processing Solution 617 Design a Stream Processing Solution 618 Create a Stream Processing Solution 630 Process Time Series Data 657 Design and Create Windowed Aggregates 658 Process Data Within One Partition 661 Process Data Across Partitions 663 Upsert Data 665 Handle Schema Drift 674 Configure Checkpoints/Watermarking During Processing 680 Replay Archived Stream Data 685 Design and Create Tests for Data Pipelines 688 Monitor for Performance and Functional Regressions 689 Optimize Pipelines for Analytical or Transactional Purposes 689 Scale Resources 690 Design and Configure Exception Handling 691 Handle Interruptions 694 Ingest and Transform Data 694 Transform Data Using Azure Stream Analytics 694 Monitor Data Storage and Data Processing 695 Monitor Stream Processing 695 Summary 695 Exam Essentials 696 Review Questions 697 Part IV Secure, Monitor, and Optimize Data Storage and Data Processing 699 Chapter 8 Keeping Data Safe and Secure 701 Design Security for Data Policies and Standards 702 Design a Data Auditing Strategy 711 Design a Data Retention Policy 716 Design for Data Privacy 717 Design to Purge Data Based on Business Requirements 719 Design Data Encryption for Data at Rest and in Transit 719 Design Row- Level and Column- Level Security 722 Design a Data Masking Strategy 723 Design Access Control for Azure Data Lake Storage Gen 2 724 Implement Data Security 730 Implement a Data Auditing Strategy 731 Manage Sensitive Information 739 Implement a Data Retention Policy 745 Encrypt Data at Rest and in Motion 748 Implement Row- Level and Column- Level Security 749 Implement Data Masking 753 Manage Identities, Keys, and Secrets Across Different Data Platform Technologies 755 Implement Access Control for Azure Data Lake Storage Gen 2 765 Implement Secure Endpoints (Private and Public) 772 Implement Resource Tokens in Azure Databricks 778 Load a DataFrame with Sensitive Information 779 Write Encrypted Data to Tables or Parquet Files 780 Develop a Batch Processing Solution 781 Handle Security and Compliance Requirements 782 Design and Implement the Data Exploration Layer 784 Browse and Search Metadata in Microsoft Purview Data Catalog 784 Push New or Updated Data Lineage to Microsoft Purview 785 Summary 786 Exam Essentials 787 Review Questions 789 Chapter 9 Monitoring Azure Data Storage and Processing 791 Monitoring Data Storage and Data Processing 793 Implement Logging Used by Azure Monitor 793 Configure Monitoring Services 799 Understand Custom Logging Options 821 Measure Query Performance 822 Monitor Data Pipeline Performance 823 Monitor Cluster Performance 824 Measure Performance of Data Movement 824 Interpret Azure Monitor Metrics and Logs 825 Monitor and Update Statistics about Data Across a System 828 Schedule and Monitor Pipeline Tests 830 Interpret a Spark Directed Acyclic Graph 830 Monitor Stream Processing 832 Implement a Pipeline Alert Strategy 832 Develop a Batch Processing Solution 832 Design and Create Tests for Data Pipelines 832 Develop a Stream Processing Solution 837 Monitor for Performance and Functional Regressions 837 Design and Create Tests for Data Pipelines 838 Azure Monitoring Overview 841 Azure Batch 841 Azure Key Vault 842 Azure SQL 843 Summary 844 Exam Essentials 844 Review Questions 846 Chapter 10 Troubleshoot Data Storage Processing 849 Optimize and Troubleshoot Data Storage and Data Processing 851 Optimize Resource Management 854 Compact Small Files 857 Handle Skew in Data 859 Handle Data Spill 860 Find Shuffling in a Pipeline 862 Tune Shuffle Partitions 864 Tune Queries by Using Indexers 869 Tune Queries by Using Cache 876 Optimize Pipelines for Analytical or Transactional Purposes 877 Optimize Pipeline for Descriptive versus Analytical Workloads 886 Troubleshoot a Failed Spark Job 888 Troubleshoot a Failed Pipeline Run 890 Rewrite User- Defined Functions 899 Design and Develop a Batch Processing Solution 901 Design and Configure Exception Handling 902 Debug Spark Jobs by Using the Spark UI 902 Scale Resources 902 Monitor Batches and Pipelines 904 Handle Failed Batch Loads 904 Design and Develop a Stream Processing Solution 905 Optimize Pipelines for Analytical or Transactional Purposes 905 Handle Interruptions 906 Scale Resources 908 Summary 909 Exam Essentials 910 Review Questions 912 Appendix Answers to Review Questions 915 Chapter 1: Gaining the Azure Data Engineer Associate Certification 916 Chapter 2: CREATE DATABASE dbName; GO 916 Chapter 3: Data Sources and Ingestion 917 Chapter 4: The Storage of Data 918 Chapter 5: Transform, Manage, and Prepare Data 918 Chapter 6. Create and Manage Batch Processing and Pipelines 919 Chapter 7: Design and Implement a Data Stream Processing Solution 920 Chapter 8: Keeping Data Safe and Secure 921 Chapter 9: Monitoring Azure Data Storage and Processing 921 Chapter 10: Troubleshoot Data Storage Processing 922 Index 925

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Book SynopsisControl over Communication Networks Advanced and systematic examination of the design and analysis of networked control systems and multi-agent systems Control Over Communication Networks provides a systematic and nearly self-contained description of the analysis and design of networked control systems (NCSs) and multi-agent systems (MASs) over imperfect communication networks, with a primary focus on fading channels and delayed channels. The text characterizes the effect of communication channels on the stability and performance of NCSs, and further studies the joint impact of communication channels and network topology on the consensus of MASs. By integrating communication and control theory, the four highly-qualified authors present fundamental results concerning the stabilization of NCSs over power-constrained fading channels and Gaussian finite-state Markov channels, linear-quadratic optimal control of NCSs with random input gains, optimal state estiTable of ContentsAbout the Authors xiii Preface xv Acknowledgments xvii Acronyms xix List of Symbols xxi 1 Introduction 1 1.1 Introduction and Motivation 1 1.1.1 Networked Control Systems 1 1.1.2 Multi-Agent Systems 2 1.2 Literature Review 4 1.2.1 Basics of Communication Theory 4 1.2.2 Stabilization of NCSs 6 1.2.2.1 Control over Noiseless Digital Channels 6 1.2.2.2 Control over Stochastic Digital Channels 7 1.2.2.3 Control over Analog Channels 8 1.2.3 LQ Optimal Control of NCSs over Fading Channels 9 1.2.4 Estimation of NCSs with Intermittent Communication 11 1.2.4.1 Stability of Kalman Filtering with Intermittent Observations 11 1.2.4.2 Remote State Estimation with Sensor Scheduling 12 1.2.5 Distributed Consensus of MASs 13 1.3 Preview of the Book 15 1.4 Preliminaries 18 1.4.1 Graph Theory 18 1.4.2 Hadamard Product and Kronecker Product 19 Bibliography 20 2 Stabilization over Power Constrained Fading Channels 29 2.1 Introduction 29 2.2 Problem Formulation 29 2.3 Fundamental Limitations 31 2.4 Mean-Square Stabilizability 35 2.4.1 Scalar Systems 36 2.4.2 Two-Dimensional Systems 37 2.4.2.1 Communication Structure 38 2.4.2.2 Encoder/Decoder Design 38 2.4.2.3 Scheduler Design 39 2.4.2.4 Scheduler Parameter Selection 40 2.4.2.5 Proof of Theorem 2.3 41 2.4.3 High-Dimensional Systems: TDMA Scheduler 44 2.4.4 High-Dimensional Systems: Adaptive TDMA Scheduler 45 2.4.4.1 Scheduling Algorithm 46 2.4.4.2 Scheduler Parameter Selection 46 2.4.4.3 Proof of Theorem 2.5 46 2.5 Numerical Illustrations 51 2.5.1 Scalar Systems 51 2.5.2 Vector Systems 52 2.6 Conclusions 53 Bibliography 53 3 Stabilization over Gaussian Finite-State Markov Channels 57 3.1 Introduction 57 3.2 Problem Formulation 58 3.2.1 Stability of Markov Jump Linear Systems 59 3.2.2 Sojourn Times for Markov Lossy Process 60 3.3 Fundamental Limitation 61 3.4 Stabilization over Finite-State Markov Channels 64 3.4.1 Communication Structure 65 3.4.2 Observer/Estimator/Controller Design 65 3.4.3 Encoder/Decoder/Scheduler Design 67 3.4.4 Sufficient Stabilizability Conditions 68 3.5 Stabilization over Markov Lossy Channels 71 3.5.1 Two-Dimensional Systems 71 3.5.1.1 Optimal Scheduler Design 72 3.5.1.2 Scheduler Parameter Selection 74 3.5.1.3 Sufficiency Proof of Theorem 3.4 75 3.5.2 High-Dimensional Systems 77 3.5.3 Numerical Illustrations 81 3.6 Conclusions 82 Bibliography 83 4 Linear-Quadratic Optimal Control of NCSs with Random Input Gains 85 4.1 Introduction 85 4.2 Problem Formulation 86 4.3 Finite-Horizon LQ Optimal Control 88 4.4 Solvability of Modified Algebraic Riccati Equation 91 4.4.1 Cone-Invariant Operators 91 4.4.2 Solvability 97 4.5 LQ Optimal Control 108 4.6 Conclusion 114 Bibliography 115 5 Multisensor Kalman Filtering with Intermittent Measurements 117 5.1 Introduction 117 5.2 Problem Formulation 118 5.3 Stability Analysis 120 5.3.1 Transmission Capacity 120 5.3.2 Preliminaries 120 5.3.3 Lower Bound 121 5.3.4 Upper Bound 124 5.3.5 Special Cases 130 5.4 Examples 131 5.5 Conclusions 132 Bibliography 133 6 Remote State Estimation with Stochastic Event-Triggered Sensor Schedule and Packet Drops 135 6.1 Introduction 135 6.2 Problem Formulation 135 6.3 Optimal Estimator 137 6.4 Suboptimal Estimators 143 6.4.1 Fixed Memory Estimator 143 6.4.2 Particle Filter 145 6.5 Simulations 149 6.6 Conclusions 151 Bibliography 152 7 Distributed Consensus over Undirected Fading Networks 153 7.1 Introduction 153 7.2 Problem Formulation 154 7.3 Identical Fading Networks 155 7.4 Nonidentical Fading Networks 163 7.4.1 Definition of Edge Laplacian 163 7.4.2 Sufficient Consensus Conditions 164 7.5 Simulations 168 7.6 Conclusions 170 Bibliography 170 8 Distributed Consensus over Directed Fading Networks 173 8.1 Introduction 173 8.2 Problem Formulation 174 8.3 Identical Fading Networks 174 8.3.1 Consensus Error Dynamics 175 8.3.2 Consensusability Results 177 8.3.3 Balanced Directed Graph Cases 179 8.4 Definitions and Properties of CIIM, CIM, and CEL 181 8.4.1 Definitions of CIIM, CIM, and CEL 181 8.4.2 Properties of CIIM, CIM, and CEL 182 8.5 Nonidentical Fading Networks 185 8.5.1 Λ=μI 189 8.5.1.1 Star Graphs 190 8.5.1.2 Directed Path Graphs 191 8.5.2 Λ ≠ μI 192 8.6 Simulations 192 8.7 Conclusions 194 Bibliography 195 9 Distributed Consensus over Networks with Communication Delay and Packet Dropouts 197 9.1 Introduction 197 9.2 Problem Formulation 198 9.3 Consensusability with Delay and Identical Packet Dropouts 199 9.3.1 Stability Criterion of NCSs with Delay and Multiplicative Noise 199 9.3.2 Consensusability Conditions 204 9.4 Consensusability with Delay and Nonidentical Packet Dropouts 209 9.5 Illustrative Examples 214 9.6 Conclusions 216 Bibliography 216 10 Distributed Consensus over Markovian Packet Loss Channels 219 10.1 Introduction 219 10.2 Problem Formulation 219 10.3 Identical Markovian Packet Loss 220 10.3.1 Analytic Consensus Conditions 224 10.3.2 Critical Consensus Condition for Scalar Agent Dynamics 226 10.4 Nonidentical Markovian Packet Loss 228 10.5 Numerical Simulations 232 10.6 Conclusions 234 Bibliography 235 11 Synchronization of the Delayed Vicsek Model 237 11.1 Introduction 237 11.2 Directed Graphs 238 11.3 Problem Formulation 239 11.4 Synchronization of Delayed Linear Vicsek Model 240 11.5 Synchronization of Delayed Nonlinear Vicsek Model 246 11.6 Simulations 249 11.7 Conclusions 253 Bibliography 253 Index 255

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Book SynopsisPresents the science of colour from new perspectives and outlines results obtained from the authors' work in the mathematical theory of colour This innovative volume summarizes existing knowledge in the field, attempting to present as much data as possible about colour, accumulated in various branches of science (physics, phychophysics, colorimetry, physiology) from a unified theoretical position. Written by a colour specialist and a professional mathematician, the book offers a new theoretical framework based on functional analysis and convex analysis. Employing these branches of mathematics, instead of more conventional linear algebra, allows them to provide the knowledge required for developing techniques to measure colour appearance to the standards adopted in colorimetric measurements. The authors describe the mathematics in a language that is understandable for colour specialists and include a detailed overview of all chapters to help readers not familiar with colour science. Divided into two parts, the book first covers various key aspects of light colour, such as colour stimulus space, colour mechanisms, colour detection and discrimination, light-colour perception typology, and light metamerism. The second part focuses on object colour, featuring detailed coverage of object-colour perception in single- and multiple-illuminant scenes, object-colour solid, colour constancy, metamer mismatching, object-colour indeterminacy and more. Throughout the book, the authors combine differential geometry and topology with the scientific principles on which colour measurement and specification are currently based and applied in industrial applications. Presents a unique compilation of the author's substantial contributions to colour scienceOffers a new approach to colour perception and measurement, developing the theoretical framework used in colorimetryBridges the gap between colour engineering and a coherent mathematical theory of colourOutlines mathematical foundations applicable to the colour vision of humans and animals as well as technologies equipped with artificial photosensorsContains algorithms for solving various problems in colour science, such as the mathematical problem of describing metameric lightsFormulates all results to be accessible to non-mathematicians and colour specialistsFoundations of Colour Science: From Colorimetry to Perception is an invaluable resource for academics, researchers, industry professionals and undergraduate and graduate students with interest in a mathematical approach to the science of colour.Table of Contents1 Outline for readers in a hurry 1 I Light colour 81 2 Colour stimulus space and colour mechanisms 85 2.1 Grassmann structures and Grassmann colour codes 89 2.2 Continuous Grassmann structures and continuous Grassmann colour codes 97 3 Identification of Grassmann structures based on metameric matching 101 3.1 Colourmatching functions 102 3.2 Monochromatic primaries and colour matching functions in the trichromatic case (=3) 109 3.3 Fundamental colour mechanisms in human colour vision 112 3.3.1 K¨onig’s approach to identification of the fundamental colourmechanisms 120 3.3.2 Some estimates of the cone fundamentals used in colour research and applications 123 4 Colour-signal cone 129 4.1 Strong colour-signal-cone-boundary hypothesis 133 4.2 Empirical status of the strong colour-signal-cone-boundary hypothesis 138 4.3 Colour-signal-cone-boundary hypothesis 145 4.4 The colour-signal cone of a 3-pigment Grassmann-Govardovskii structure 149 5 Colour stimulus manifold 153 5.1 Three-dimensional colour stimulusmanifold 155 5.2 Non-linear colour stimulus map Colour stimulus transformation caused by themedium 160 5.2.1 The colour stimulus shift caused by the medium variations 161 5.2.2 Colour robustness tomediumvariations 163 5.3 Causes of individual differences in trichromatic colour matching 165 5.3.1 Effect of the photopigment peak sensitivity on the-coordinates 166 5.3.2 Effect of the ocular media transmittance on -coordinates 171 5.3.3 Trade-off between the ocular media spectral transmittance and the photopigment peak sensitivity in their effect on colour 174 5.3.4 Dependence of the equivalent peak-wavelength shift on light Impossibility to overcome colour deficiency using a coloured filter 176 5.3.5 Parametric identification of fundamental colour mechanisms 180 6 Light metamerism 183 6.1 Metamer sets 184 6.2 Colour mechanisms’ transformations preserving light metamerism 188 6.3 Lightmetamerismindex 190 7 Light metamer mismatching 191 7.1 Metamer-mismatch regions 191 7.2 Indices of lightmetamer mismatching 197 7.3 Computing trichromaticmetamer-mismatch regions 202 7.3.1 Effect of the spectral positioning of photopigments onmetamer mismatching 206 7.3.2 Effect of the peak photopigment absorbance on metamer mismatching 210 7.3.3 Metamer mismatching depending on the position in the chromaticity diagram 211 7.3.4 Metamer mismatching induced by pre-receptoral filters 211 7.3.5 Differences between cone fundamentals as revealed bymetamer mismatching 217 7.3.6 Metamer mismatching for the 10◦ colour matching functions of Stiles and Burch 221 7.3.7 Metamer mismatching induced by neutral density filters 234 7.3.8 Metamer mismatching produced by camera sensors 238 8 Light-colour perception 243 8.1 Achromatic scales and achromatic codes 248 8.1.1 Ordinal brightness scales 249 8.1.2 Grassmann brightness code Luminance 254 8.2 Hue, purity, and brightness fibre bundles Cylindrical and psychophysical colour coordinates 262 8.3 Colour transformation caused by media and metamer mismatching, as expressed in the psychophysical colour coordinates 270 8.4 Light-colour perception in dichromats 273 8.5 Chromatic structures 280 8.5.1 Partial hue-matching 283 8.5.2 Experiment on partial hue-matching 289 8.5.3 Colour categories 292 8.5.4 Chromatically ordered structures 297 8.5.5 Chromatic scales and chromatic codes 299 8.5.6 Hue, purity and saturation in chromatic structures 301 8.6 Light-colour manifold 304 8.6.1 Hue cyclic order 305 8.6.2 Light-colour manifold 308 8.6.3 Circular Hering structures, their representation and experimental identification 311 8.6.4 Light-colour manifold vs colour stimulus manifold 321 9 Typology of light-colour perception Inter-individual differences 329 10 Colour matching structures and matching metamerism 341 10.1 Colourmatching structures 347 10.2 Matchingmetamerism 358 11 Identification of Grassmann structures induced by colour matching structures 363 11.1 Colour matching set, threshold set, and sensitivity function 364 11.2 Regular and strongly regular tolerance extensions 368 11.3 Identification of Grassmann structures induced by colour matching tolerance relations 371 11.3.1 Identification of the linear colour mechanism space as a subspace in the linear span of a given set of linearly independent functionals 372 11.3.2 Deriving the linear colour mechanism space from the colour matching set (the method of tangential hyperplane 378 11.3.3 Deriving the fundamental colour mechanisms from the colour matching set that they generate (the method of quadratic approximation) 383 12 Identification of indiscriminate relations Colour detection and discrimination 391 12.1 Colour detectionmodels 394 12.1.1 Single-channel detectionmodels 394 12.1.2 Fundamental colour mechanisms revisited 397 12.1.3 Multi-channel detectionmodels 399 12.2 Peak-detector model equivalent to a sublinear colour detectionmodel 400 12.2.1 Sublinear colour detectionmodels 401 12.2.2 Multi-channel sublinearmodels 402 12.2.3 Themost sensitive colour mechanisms 404 12.3 Colour discriminationmodels 409 13 In search of colour mechanisms in the eye and the brain 413 13.1 Do the cone photoreceptor responses encode the colour stimulus? 413 13.1.1 Local non-linearity of the photoreceptor response 414 13.1.2 Light adaptation in photoreceptors 415 13.1.3 Spatial interaction between the cone photoreceptors 417 13.1.4 Why the colour stimulus cannot be derived from the cone photoreceptor responses 417 13.2 Do cone-opponent neural cells encode the opponent chromatic codes? 418 13.3 Transition to a different paradigm 425 13.3.1 From symmetric to asymmetric colour matching 425 13.3.2 Fromlight stimulus to light-stimulus array 428 13.3.3 On the notion of ”neural image” 430 13.4 Spatio-chromatic processing in the visual cortex 436 13.4.1 Estimating luminance-pattern gradient using simple cortical cells 436 13.4.2 Directional gradient-encoding with double-opponent cells 446 13.4.3 Difference in spatial sensitivity of (M+L)-, (M-L)-, and S-(M+L)-cells, and its implication for colour perception 449 13.4.4 Representation of the colour-signal surface in the form of its tangent bundle 450 Object colour 458 14 Object-colour solid 465 14.1 General properties of the object-colour solid 466 14.2 Optimal object stimuli 468 14.3 Elementary step functions as optimal object stimuli 470 14.4 Optimal object stimuli for trichromatic human observers 472 14.5 Condition for all step functions of degree to be optimal object stimuli 472 15 Trichromatic regular object-colour solid 475 15.1 Meridians of the trichromatic regular object-colour solid 475 15.2 Equator of the trichromatic object-colour solid and strictly optimal object stimuli 481 16 Object-colour stimulus manifold 489 16.1 Objectmetamerism 489 16.2 Object atlas 493 16.3 Object-colour stimulus manifold Illuminant-induced nonlinear object-colour stimulusmap 496 16.4 Trichromatic object-colour stimulusmanifold 497 16.4.1 Trichromatic regular object-colour stimulus manifold and its spherical representation 497 16.4.2 Spherical representation of the trichromatic objectcolour stimulus manifold and the object-colour stimulus gamut 502 16.4.3 Object-colour stimulus shift induced by the illuminant change 504 17 Object-colour perception in a single-illuminant scene 507 17.1 Perceptual object-colour coordinates 513 17.2 Perceptual correlates of coordinates 516 17.3 Effect of illumination on object-colour in a single-illuminant scene: Object-colour shift induced by illumination 521 17.4 Object-colour perception by dichromats in a single-illuminant scene 524 18 Object metamer mismatching 535 18.1 Metamer-mismatch regions 535 18.2 Numerical evaluation ofmetamer-mismatch regions 539 18.3 Indices of objectmetamer mismatching 542 18.4 Object-metamerism-preserving transformations of colour mechanisms 545 19 Object-colour perception in a multiple-illuminant scene 549 19.1 Object/light colour equivalence and its inseparability 554 19.2 Object/light atlas 556 19.3 Object/light colour stimulusmanifold 557 19.3.1 Asymmetric colourmatching 557 19.3.2 Material colour 561 19.3.3 Lighting colour 562 19.3.4 Object/light colour stimulus manifold Material and lighting components of object/light colour stimulus manifold Material- and lighting-colour coordinates 564 19.4 Material colour shift induced by illumination change Implication for the problemof ”colour constancy” 569 20 Object-colour indeterminacy 573 20.1 Trade-off between object and light components 573 20.2 Trade-off betweenmaterial and lighting colours 579 20.2.1 Invariant relationship between lightness and lighting brightness 581 20.2.2 Invariant relationship between lightness, lighting brightness and shading brightness 586 20.2.3 Shading as a sensory basis of shape 588 20.2.4 Invariant relationship between material-colour image and lighting-colour image in the chromatic domain 590 20.3 Object-colour indeterminacy in variegated scenes Impact of articulation 591 20.4 Implication for measuring object-colour 594 21 On perception in general: An outline of an alternative approach 601 21.1 What is colour for? 603 21.2 The need for a new approach to perception: Linguistic metaphor 607 22 Epilogue 619 References 623 A Some auxiliary facts from functional analysis 649 A.1 Banach spaces of measures and functions, and stimulus spaces 649 A.2 Convex analysis 652 B Proofs 657

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Book SynopsisOpen RAN A comprehensive survey of Open RAN technology and its ecosystem In Open RAN: The Definitive Guide, a team of distinguished industry leaders deliver an authoritative guide to all four principles of the Open RAN vision: openness, virtualization, intelligence, and interoperability. Written by the industry experts currently defining the specifications, building the systems, and testing and deploying the networks, the book covers O-RAN architecture, the fronthaul interface, security, cloudification, virtualization, intelligence, certification, badging, and standardization. This critical reference on Open RAN explains how and why an open and disaggregated, intelligent, and fully virtualized network is the way networks should be designed and deployed moving forward. Readers will also find: A thorough introduction from key industry players, including AT&T, Telefonica, Mavenir, VMWare, Google and VIAVI Comprehensive explorations of Open X-Table of ContentsList of Contributors xiii Foreword xv Preface xvii About the Authors xix Definitions / Acronyms xxi 1 The Evolution of RAN 1 Sameh M. Yamany 1.1 Introduction 1 1.2 RAN Architecture Evolution 4 1.2.1 The 2G RAN 5 1.2.2 The 3G RAN 6 1.2.3 The 4G/LTE RAN 6 1.2.4 The 5G RAN 9 1.3 The Case for Open RAN 11 1.4 6G and the Road Ahead 11 1.5 Conclusion 13 Bibliography 13 2 Open RAN Overview 14 Rittwik Jana 2.1 Introduction 14 2.1.1 What is Open RAN and Why is it Important? 17 2.1.2 How Does Open RAN Accelerate Innovation? 17 2.1.3 What are the major challenges that Open RAN can help to address? 18 2.2 Open RAN Architecture 18 2.3 Open RAN Cloudification and Virtualization 19 2.4 RAN Intelligence 20 2.5 Fronthaul Interface and Open Transport 20 2.6 Securing Open RAN 21 2.7 Open Source Software 21 2.8 RAN Automation and Deployment with CI/CD 22 2.9 Open RAN Testing 22 2.10 Industry Organizations 23 2.11 Conclusion 23 Bibliography 23 3 O-RAN Architecture Overview 24 Rajarajan Sivaraj and Sridhar Rajagopal 3.1 Introduction 24 3.1.1 General Description of O-RAN Functions 24 3.1.1.1 Centralized Unit – Control Plane and User Plane Functions (CU-CP and CU-UP) 26 3.1.1.2 Distributed Unit Function (DU) 26 3.1.1.3 Radio Unit Function (RU) 26 3.1.1.4 Evolved Node B (eNB) 27 3.1.2 RAN Intelligent Controller (RIC) and Service Management and Orchestration (SMO) Functions 28 3.1.3 Interfaces 29 3.2 Near-RT RIC Architecture 30 3.2.1 Standard Functional Architecture Principles 30 3.2.2 E2 Interface Design Principles 32 3.2.3 xApp API Design Architecture 34 3.3 Non-RT RIC Architecture 37 3.3.1 Standard Functional Architecture Principles 38 3.3.2 A1 Interface Design Principles 38 3.3.3 R1 API Design Principles for rApps 41 3.4 SMO Architecture 47 3.4.1 Standard Functional Architecture Principles 47 3.4.2 O1 Interface Design Principles 48 3.4.3 Open M-Plane Fronthaul Design Principles 51 3.4.4 O2 Interface Design Principles 52 3.5 Other O-RAN Functions and Open Interfaces 54 3.5.1 O-RAN compliant Centralized Unit Control Plane (O-CU-CP) 54 3.5.1.1 Control Plane Procedures 54 3.5.1.2 Management Plane Procedures 54 3.5.2 O-CU-UP 54 3.5.2.1 Control Plane Procedures 55 3.5.2.2 User Plane Procedures 55 3.5.2.3 Management Plane Procedures 55 3.5.3 O-DU 55 3.5.3.1 Control Plane Procedures 55 3.5.3.2 User Plane Procedures 55 3.5.3.3 Management Plane Procedures 55 3.5.4 O-eNB 56 3.5.5 O-RU 56 3.6 Conclusion 57 Bibliography 57 4 Cloudification and Virtualization 59 Padma Sudarsan and Sridhar Rajagopal 4.1 Virtualization Trends 59 4.2 Openness and Disaggregation with vRAN 59 4.3 Cloud Deployment Scenarios 61 4.3.1 Private, Public, and Hybrid Cloud 61 4.3.2 Telco Features Required for “Any Cloud” Deployment 62 4.3.3 On Premise, Far Edge, Edge, and Central Deployments 63 4.3.4 Classical, Virtual Machines (VMs), Containers, and Hybrid Deployments 64 4.4 Unwinding the RAN Monolith 64 4.4.1 Adapting Cloud-Native Principles 66 4.4.2 Architectural Patterns 67 4.4.3 Software Architecture Portability and Refactoring Considerations 68 4.4.4 Compute Architecture Consideration 69 4.5 Orchestration, Management, and Automation as Key to Success 70 4.5.1 Acceleration Abstraction Layer 73 4.5.2 Cloud Deployment Workflow Automation 75 4.6 Summary 76 Bibliography 76 5 RAN Intelligence 77 Dhruv Gupta, Rajarajan Sivaraj, and Rittwik Jana 5.1 Introduction 77 5.2 Challenges and Opportunities in Building Intelligent Networks 77 5.3 Background on Machine Learning Life Cycle Management 78 5.4 ML-Driven Intelligence and Analytics for Non-RT RIC 80 5.5 ML-Driven Intelligence and Analytics for Near-RT RIC 82 5.6 E2 Service Models for Near-RT RIC 83 5.6.1 E2SM-KPM 84 5.6.2 E2SM-RC 84 5.6.3 Other E2SMs 85 5.7 ml Algorithms for Near-RT RIC 86 5.7.1 Reinforcement Learning Models 87 5.8 Near-RT RIC Platform Functions for AI/ML Training 88 5.9 RIC Use Cases 89 5.10 Conclusion 90 Bibliography 90 6 The Fronthaul Interface 91 Aditya Chopra 6.1 The Lower-Layer Split RAN 91 6.1.1 Lower Layer Fronthaul Split Options 92 6.2 Option 8 Split – CPRI and eCPRI 93 6.3 Option 6 Split – FAPI and nFAPI 94 6.3.1 Subinterfaces 97 6.3.2 Architecture Agnostic Deployment 97 6.4 Option 7 Split – O-RAN Alliance Open Fronthaul 97 6.4.1 Control (C) and User (U) Plane 98 6.4.2 Management (M) Plane 98 6.4.3 Synchronization (S) Plane 100 6.4.4 Key Features 100 6.4.4.1 Fronthaul Compression 100 6.4.4.2 Delay Management 102 6.4.4.3 Beamforming 102 6.4.4.4 Initial Access 103 6.4.4.5 License Assisted Access and Spectrum Sharing 104 6.5 Conclusions 104 Bibliography 104 7 Open Transport 105 Reza Vaez-Ghaemi and Luis Manuel Contreras Murillo 7.1 Introduction 105 7.2 Requirements 105 7.2.1 Fronthaul Requirements 106 7.2.2 Midhaul Requirements 106 7.2.3 Backhaul Requirements 107 7.2.4 Synchronization Requirements 107 7.3 WDM Solutions 108 7.3.1 Passive WDM 109 7.3.2 Active WDM 109 7.3.3 Semiactive WDM 110 7.4 Packet-Switched Solutions 111 7.4.1 Technology Overview 112 7.4.2 Deployment Patterns 112 7.4.3 Connectivity Service and Protocols 113 7.4.4 Quality of Service (QoS) 114 7.5 Management and Control Interface 114 7.5.1 Control and Management Architecture 114 7.5.2 Interaction with O-RAN Management 116 7.6 Synchronization Solutions 117 7.6.1 Synchronization Baseline 117 7.6.2 Synchronization Accuracy and Limits 118 7.7 Testing 118 7.8 Conclusion 119 Bibliography 120 8 O-RAN Security 121 Amy Zwarico 8.1 Introduction 121 8.2 Zero Trust Principles 121 8.3 Threats to O-RAN 122 8.3.1 Stakeholders 122 8.3.2 Threat Surface and Threat Actors 122 8.3.3 Overall Threats 123 8.3.4 Threats Against the Lower Layer Split (LLS) Architecture and Open Fronthaul Interface 123 8.3.5 Threats Against O-RU 124 8.3.6 Threats Against Near- and Non-Real-Time RICs, xApps, and rApps 124 8.3.7 Threats Against Physical Network Functions (PNFs) 124 8.3.8 Threats Against SMO 125 8.3.9 Threats Against O-Cloud 125 8.3.10 Threats to the Supply Chain 125 8.3.11 Physical Threats 126 8.3.12 Threats Against 5G Radio Networks 126 8.3.13 Threats to Standards Development 126 8.4 Protecting O-RAN 126 8.4.1 Securing the O-RAN-Defined Interfaces 126 8.4.1.1 A1 127 8.4.1.2 O1 127 8.4.1.3 O2 128 8.4.1.4 E2 128 8.4.1.5 Open Fronthaul 128 8.4.1.6 R1 130 8.4.1.7 3GPP Interfaces 131 8.4.2 Securing the O-Cloud 131 8.4.3 Container Security 131 8.4.4 O-RAN Software Security 131 8.4.5 Software Bill of Materials (SBOM) 132 8.5 Recommendations for Vendors and MNOs 132 8.6 Conclusion 134 Bibliography 134 9 Open RAN Software 137 David Kinsey, Padma Sudarsan, and Rittwik Jana 9.1 Introduction 137 9.2 O-RAN Software Community (OSC) 138 9.2.1 OSC Projects 138 9.2.2 The Service Management and Orchestration (SMO) Framework 138 9.2.3 Near-RT RIC (RIC) 139 9.2.4 O-CU-CP and O-CU-UP 140 9.2.5 O-DU Project 140 9.2.6 O-RU 140 9.2.7 O-Cloud 140 9.2.8 The AI/ML Framework 141 9.2.9 Support Projects 141 9.3 Open Network Automation Platform (ONAP) 141 9.3.1 Netconf/YANG Support 141 9.3.2 Configuration Persistence 142 9.3.3 VES Support 142 9.3.4 A1 Support 142 9.3.5 Optimization Support 142 9.4 Other Open-Source Communities 143 9.5 Conclusion 144 Bibliography 144 10 Open RAN Deployments 145 Sidd Chenumolu 10.1 Introduction 145 10.2 Network Architecture 146 10.2.1 Network Components 147 10.2.1.1 Antenna 147 10.2.1.2 O-RAN – Radio Unit 148 10.2.1.3 O-RAN-Distributed Unit (O-DU) 150 10.2.1.4 O-RAN-Centralized Unit (O-CU) 150 10.2.1.5 RAN Intelligent Controller (RIC) 150 10.2.2 Traditional vs. O-RAN Deployment 151 10.2.3 Typical O-RAN Deployment 152 10.2.4 Spectrum and Regulatory 153 10.3 Network Planning and Design 153 10.3.1 Cell Site Design 154 10.3.2 Network Function Placement 155 10.3.3 Dimensioning 155 10.3.3.1 Application Dimensioning 155 10.3.3.2 Platform Dimensioning 156 10.3.4 Virtualization Impact 156 10.3.4.1 Non-Uniform Memory Access 157 10.3.4.2 Hyper-Threading 157 10.3.4.3 CPU Pinning 157 10.3.4.4 Huge Page 157 10.3.4.5 Single Root Input/Output Virtualization 158 10.3.4.6 PCI Passthrough 158 10.3.4.7 Data Plane Development Kit 158 10.3.4.8 Resource Director Technology 158 10.3.4.9 Cache Allocation Technology 158 10.3.4.10 Resource Overcommitment 159 10.3.4.11 Operating System 159 10.3.4.12 K8S Impact 159 10.3.5 Networking Hardware 159 10.3.6 Hardware Type 160 10.3.7 Reliability and Availability 160 10.3.8 Impact of Network Slicing 161 10.4 Network Deployment 162 10.4.1 DU Deployment 162 10.4.1.1 DU Deployed at a Centralized Data Center 162 10.4.1.2 Timing Design When DU is at the dc 163 10.4.1.3 DU Deployed at Cell Site 164 10.4.2 CU Deployment 165 10.4.3 Radio Unit Instantiation 165 10.4.4 Radio Unit Management 166 10.4.4.1 Hierarchical Management Architecture Model 166 10.4.4.2 Hybrid Management Architecture Model 166 10.4.5 Network Management 166 10.4.6 Public Cloud Provider Overview 167 10.4.6.1 Native Services 167 10.4.6.2 CD Pipeline 167 10.4.6.3 Cluster Creation and Management 168 10.4.6.4 Transport Design 168 10.4.7 Life Cycle Management of NFs 168 10.4.8 Network Monitoring and Observability 169 10.4.8.1 Prometheus 169 10.4.8.2 Jaeger 169 10.4.8.3 Fluentd and Fluentbit 169 10.4.8.4 Probing 169 10.4.9 Network Inventory 169 10.4.10 Building the Right Team 170 10.5 Conclusion 170 Bibliography 170 11 Open RAN Test and Integration 172 Ian Wong, Ph.D. 11.1 Introduction 172 11.2 Testing Across the Network Life Cycle 174 11.3 O-RAN ALLIANCE Test and Integration Activities 175 11.3.1 Test Specifications 175 11.3.2 Conformance Test Specifications 176 11.3.2.1 A1 Interface Test Specification (O-RAN.WG2.A1TS) 178 11.3.2.2 E2 Interface Test Specification (O-RAN.WG3.E2TS) 179 11.3.2.3 Open Fronthaul Conformance Test Specification (O-RAN.WG4.CONF) 180 11.3.2.4 Xhaul Transport Testing (O-RAN.WG9.XTRP-Test.0) 181 11.3.2.5 Security Test Specifications (O-RAN.SFG.Security-Test-Specifications) 181 11.3.3 Interoperability Test Specifications 181 11.3.3.1 Fronthaul Interoperability Test Specification (O-RAN.WG4.IOT.0-09.00) 182 11.3.3.2 Open F1/W1/E1/X2/Xn Interoperability Test Specification (O-RAN.WG5.IOT.0) 183 11.3.3.3 Stack Interoperability Test Specification (O-RAN.WG8.IOT) 183 11.3.4 End-to-End Test Specifications 185 11.3.5 O-RAN Certification and Badging 186 11.3.6 Open Test and Integration Centers 187 11.3.7 O-RAN Global PlugFests 189 11.4 Conclusion 189 Bibliography 189 12 Other Open RAN Industry Organizations 191 Aditya Chopra, Manish Singh, Prabhakar Chitrapu, Luis Lopes, and Diane Rinaldo 12.1 Telecom Infra Project 191 12.1.1 Organizational Structure 192 12.1.2 Core Activities 194 12.2 Trials and Deployments 194 12.3 Small Cell Forum 195 12.3.1 A History of Openness at SCF 196 12.3.2 Alignment with the 3GPP and O-RAN Alliance Solutions 196 12.4 3rd Generation Partnership Project 197 12.5 Open RAN Policy Coalition 199 12.6 Conclusion 200 Bibliography 200 Index 201

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Book SynopsisControl and Filter Design of Single-Phase Grid-Connected Converters A state-of-the-art discussion of modern grid inverters In Control and Filter Design of Single-Phase Grid-Connected Converters, a team of distinguished researchers deliver a robust and authoritative treatment of critical distributed power generation technologies, grid-connected inverter designs, and renewable energy utilization. The book includes detailed explanations of the system structure of distributed generation (DG)-grid interface converters and the methods of controlling DG-grid interface voltage source converters (VSCs) with high-order filters. The authors also explore the challenges and obstacles associated with modern power electronic grid-connected inverter control technology and introduce some designed systems that meet these challenges, such as the grid impedance canceller. Readers will discover demonstrations of basic principles, guidelines, examples, and design and simulatTable of ContentsAuthor Biography xiii Preface xvii Part I Background 1 1 Introduction 3 1.1 Architecture of DG Grid-Connected Converter 3 1.1.1 Power Conversion Stage 5 1.1.1.1 Switching Network 5 1.1.1.2 Output Filter 6 1.1.2 Control Stage 7 1.2 Challenges for Controlling DG Grid-Connected VSCs with High-Order Power Filter 8 1.2.1 Intrinsic Challenges 8 1.2.1.1 Filter Parametric Sensitivities 9 1.2.1.2 Digital Delay 10 1.2.2 Extrinsic Challenges 10 1.2.2.1 Grid Impedance Variation 10 1.2.2.2 Disturbances at the PCC 10 1.3 Methods for Controlling DG Grid-Connected VSCs with High-Order Power Filter 12 1.3.1 Methodologies to Assess the Stability of DG Grid-Connected VSCs 12 1.3.1.1 Eigenvalue-Based Analysis 12 1.3.1.2 Impedance-Based Stability Analysis 12 1.3.1.3 Application Issue Related to Impedance-Based Stability Analysis 13 1.3.2 Methods to Mitigate Filter Resonance 14 1.3.2.1 Online Grid Impedance Estimation 14 1.3.2.2 Inherent Damping 15 1.3.2.3 Passive Damping 15 1.3.2.4 Active Damping 17 1.3.2.5 Hybrid Damping 19 1.3.3 Harmonic distortion Mitigation Methods 20 1.4 Supplementary Note 21 References 22 2 Control Structure and Modulation Techniques of Single-Phase Grid-Connected Inverter 29 2.1 Control Structure of Single-Phase Grid-Connected Inverter 29 2.1.1 Natural Frame Control 30 2.1.2 Synchronous Reference Frame Control 32 2.1.3 Grid Synchronization Methods 33 2.1.3.1 Zero-Crossing Method 33 2.1.3.2 Filtering of Grid Voltages 34 2.1.3.3 PLL Technique 34 2.2 Modulation Methods 35 2.2.1 Unipolar Modulation Method 35 2.2.1.1 Continuous Unipolar Modulation 36 2.2.1.2 Discontinuous Unipolar Modulation 36 2.2.2 Bipolar Modulation Method 39 2.3 Summary 40 References 41 Part II LCL/LLCL Power Filter 43 3 An LLCL Power Filter for Single-Phase Grid-Connected Inverter 45 3.1 Introduction 45 3.2 Principle of Traditional LCL Filter and Proposed LLCL Filter 46 3.3 Parametric Design of LCL and LLCL Filters 49 3.3.1 Constraints and Procedure of Power Filter Design 49 3.3.2 Saving Analysis on the Grid-Side Inductance 53 3.3.3 Specific Design Consideration for a Simple Passive Damping Strategy 53 3.4 Design Examples for LCL and LLCL filters 54 3.5 Experimental Results 56 3.5.1 Experimental Results 57 3.5.2 Analysis and Discussion 58 3.6 Summary 59 References 59 4 Modeling and Suppressing Conducted Electromagnetic Interference Noise for LCL/LLCL-Filtered Single-Phase Transformerless Grid-Connected Inverter 61 4.1 Introduction 61 4.2 Conducted EMI Noise Analysis 62 4.2.1 CM and DM Voltage Noises 62 4.2.2 Spectrum of DM and CM Voltage Noise for GCI Using DUPWM 64 4.2.3 Spectrum of DM Voltage Noise for GCI Using BPWM 67 4.3 Modified LLCL Filter to Fully Suppress the Conducted EMI Noise for GCI Using DUPWM 68 4.3.1 Modified Solution for LLCL Filter 68 4.3.2 Improved Parameter Design of LLCL filter 72 4.3.3 Constraints on Harmonics of the Grid-Injected Current and EMI Noise Within 150 kHz to 1 MHz 72 4.3.3.1 Constraints on Leakage Current 73 4.3.4 Experimental Verification 74 4.3.4.1 Power Spectrum of the Grid-Injected Current 75 4.3.4.2 Measured Conducted EMI Noise 75 4.3.5 Negative Dc-rail Voltage with Respect to the Earth V Dc_n and Leakage Current 78 4.4 Novel DM EMI Suppressor for LLCL-Filtered GCI without CM Noise Issue 79 4.4.1 Proposed DM EMI Suppressor 79 4.4.2 Experimental Verification 83 4.5 Summary 85 4.5.1 For Single-Phase Transformerless GCI Using DUPWM 85 4.5.2 For Single-Phase Transformerless GCI Using BPWM or a System Without cm EMI Noise Issue 85 References 86 Part III Passive Damping 89 5 Design of Passive Damper for LCL/LLCL-Filtered Grid-Connected Inverter 91 5.1 Introduction 91 5.2 Design Method for Passive Damping 92 5.2.1 Passive Damping Scheme of LCL Filter 92 5.2.2 Passive Damping Scheme of LLCL Filter 95 5.2.3 Design Example 97 5.3 Analysis of Power Loss Caused by the Filter 98 5.3.1 Passive Damping Power Loss 98 5.3.2 Power Losses in Inductors 100 5.4 Experimental Results 101 5.5 Summary 110 References 113 6 Composite Passive Damping Scheme for LLCL-Filtered Grid-Connected Inverter 115 6.1 Introduction 115 6.2 Upper and Lower Limits of the PR + HC Controller Gain 116 6.2.1 LLCL Filter-Based Grid-Connected Inverter Configuration 116 6.2.2 Lower Limit of the PR + HC Controller Gain 117 6.2.3 Upper Limit of the PR + HC Controller Gain 118 6.3 E-Q-Factor-Based Passive Damping Design 119 6.3.1 Principle of the Equivalent Q-Factor Method 119 6.3.2 E-Q-Factor-Based RC Parallel Damping Design 121 6.3.3 E-Q-Factor-Based RL Series Damping Design 124 6.4 New Composite Passive Damping Scheme for the LLCL Filter 126 6.4.1 Composite Passive Damping Scheme 126 6.4.2 Design Example 127 6.4.3 Analysis of Achieved Damping 129 6.5 Experimental Verification 134 6.6 Summary 136 References 138 Part IV Robust Control Design 139 7 Robust Hybrid Damper Design for LCL/LLCL-Filtered Grid-Connected Inverter 141 7.1 Introduction 141 7.2 Control Bandwidth Analysis of the Grid-Current Feedback Method 142 7.2.1 LCL/LLCL-Filtered Grid-Connected Inverter System 142 7.2.2 Maximum Achieved Bandwidth of the Control Method 143 7.3 Proposed Single-Loop Control with High Bandwidth 145 7.3.1 Mathematical Model of the Proposed Single-Loop Control with Hybrid Damper 145 7.3.2 System-Characteristics-Based Single-Loop Control Design Methodology 148 Step 1: Design of the RC Parallel Damper 148 Step 2: Design of the Proportionality Coefficient K p of the PR + HC Regulator 148 Step 3: Determination of the Critical Grid Inductance 149 Step 4: Determination of the Critical Frequency Region for Case 1 and the Critical Frequency (f 0 of Case 1 and f L0 of Case 2) 151 Step 5: Design of the Digital Notch Filter 152 Step 6: Checking the Phase Margin of the Entire System 153 7.4 Design Example 155 7.4.1 System Design 155 7.4.2 System Parameter Robustness Analysis 156 7.5 Experimental Verification 156 7.6 Summary 160 References 161 8 Robust Impedance-Based Design of LLCL-Filtered Grid-Connected Inverter against the Wide Variation of Grid Reactance 163 8.1 Introduction 163 8.2 Modeling of the LLCL-Type Grid-Connected Inverter 164 8.2.1 System Description 164 8.2.2 Norton Equivalent Model 165 8.3 Stability Analysis Considering Grid-Reactance Variation 166 8.3.1 Non-Passive Regions of Inverter Output Admittance 166 8.3.2 Possible Instability Under the Wide Variation of Grid Reactance 167 8.4 Proposed Measures and Design Procedure Under the Grid-Reactance Variation Condition 168 8.4.1 Proposed Measures Against Grid-Reactance Variation 168 8.4.2 Design Procedure 170 Step 1- Calculate the Minimum Grid Inductance L g_min 170 Step 2- Design L 1 ,C total , and L 2 171 Step 3- Design the Bypass Filtering Branch 172 Step 4- Design the Minimum Grid Capacitance C g_min 172 Step 5- Design the Proportional Gain K P of the PR+HC Regulator 172 Step 6- Select C EMI ,C d , and R d 173 Step 7- Check F I < F D 2 175 8.5 Design Example 177 8.6 Simulation and Experimental Verification 179 8.6.1 Simulation 179 8.6.2 Experiments 182 8.6.2.1 Experimental Results 183 8.6.2.2 Analysis and Discussion 185 8.7 Summary 187 References 187 Part V Active Damping 191 9 Active Damping of LLCL-Filter Resonance Based on LC-Trap Voltage or Current Feedback 193 9.1 Introduction 193 9.2 Control of LLCL-Filtered Grid Converter 194 9.2.1 Description and General Control 194 9.2.2 Block Diagrams of Different Active Dampers 196 9.2.3 Effects of Delay G d (s) 197 9.3 Circuit Equivalences of LLCL Active Dampers 199 9.3.1 General Virtual Impedance Model 199 9.3.2 LC-Trap Voltage Feedback 200 9.3.3 LC-Trap Current Feedback 204 9.4 Z-Domain Root-Locus Analysis 206 9.4.1 Z-Domain Transfer Functions 206 9.4.2 Root-Locus Analyses with Different Active Dampers 207 9.4.3 Comparison 209 9.5 Experimental Verification 209 9.6 Summary 212 References 213 10 Enhancement of System Stability Using Active Cancelation to Eliminate the Effect of Grid Impedance on System Stability and Injected Power Quality of Grid-Connected Inverter 217 10.1 Introduction 217 10.2 Principle of the Grid Impedance Cancelator 218 10.3 Modeling with the Grid Impedance Cancelator 221 10.3.1 System Configuration with the Grid Impedance Cancelator 221 10.3.2 AC Voltage Regulation 222 10.3.3 Active Damping Function 222 10.3.4 dc Capacitor Voltage Control 226 10.4 Modeling of the Grid Impedance Cancelator 226 10.5 Experimental Verification 231 10.6 Summary 239 References 239 Index 241

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Book SynopsisSustainability in Biofuel Production Technology Explore current challenges and the latest technologies in biofuel production In Sustainability in Biofuel Production Technology, a team of engineers and chemists delivers a thorough and accessible exploration of the source of renewable energy biofuels poised to help conserve natural resources and limit the impact of fossil fuel use. The book offers detailed information about the challenges and trends in biodiesel production and includes contributions from leading researchers in the field of biodiesel production. Readers will explore aviation biofuels, biofuel production technologies, reactor design and safety considerations, and the modelling and simulation of biofuel production as they move through the book's 14 chapters. The authors also analyze the performance of biofuels along with cost estimations and mathematical modeling of various process parameters. Readers will also find: A thorough introduction to biofuels, including their history, generation, classification, and relevant technologiesIn-depth presentations of the production technologies of biofuels, including chemical and biological production processesComprehensive explorations of the utilization of biofuels in aviation, including performance analyses and safety considerationsFulsome discussions of key issues and challenges in biofuels production pathways and the environmental effects of biofuels Perfect for academic researchers and industrial scientists working in the biofuels, bioenergy, catalysis, and materials science sectors, Sustainability in Biofuel Production Technology will also be suitable for members of regulatory bodies in the bioenergy sector.Table of Contents1. Introduction to Biofuel 2. Ethanol as the Leading “First-Generation” Biofuel 3. Advanced Biofuels – Alternative to Biofuels 4. Biofuels Production Technologies - An overview 5. Chemically Produced Biofuels 6. Microalgae - Biofuel Production Trends 7. Agro-Waste Produced Biofuels 8. Biofuels for aviation 9. State of the art design and fabrication of reactor in biofuel production 10. Modeling and simulation to predict the performance the diesel blends 11. Challenges To Biofuel Development 12. Greener catalytic processes in biofuel production 13. Life Cycle Assessment 14. Social Economic Impact of Biofuel Index

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Book SynopsisMicrogrids Understand microgrids and networked microgrid systems Microgrids are interconnected groups of energy sources that operate together, capable of connecting with a larger grid or operating independently as needed and network conditions require. They can be valuable sources of energy for geographically circumscribed areas with highly targeted energy needs, and for remote or rural areas where continuous connection with a larger grid is difficult. Microgrids' controllability makes them especially effective at incorporating renewable energy sources. Microgrids: Theory and Practice introduces readers to the analysis, design, and operation of microgrids and larger networked systems that integrate them. It brings to bear both cutting-edge research into microgrid technology and years of industry experience in designing and operating microgrids. Its discussions of core subjects such as microgrid modeling, control, and optimization make it an essential shor

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Book SynopsisARTIFICIAL INTELLIGENCE-BASED SMART POWER SYSTEMS Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies Artificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years. To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies. Artificial Intelligence-based Smart PTable of ContentsEditor Biography xv List of Contributors xvii 1 Introduction to Smart Power Systems 1Sivaraman Palanisamy, Zahira Rahiman, and Sharmeela Chenniappan 1.1 Problems in Conventional Power Systems 1 1.2 Distributed Generation (DG) 1 1.3 Wide Area Monitoring and Control 2 1.4 Automatic Metering Infrastructure 4 1.5 Phasor Measurement Unit 6 1.6 Power Quality Conditioners 8 1.7 Energy Storage Systems 8 1.8 Smart Distribution Systems 9 1.9 Electric Vehicle Charging Infrastructure 10 1.10 Cyber Security 11 1.11 Conclusion 11 References 11 2 Modeling and Analysis of Smart Power System 15Madhu Palati, Sagar Singh Prathap, and Nagesh Halasahalli Nagaraju 2.1 Introduction 15 2.2 Modeling of Equipment’s for Steady-State Analysis 16 2.2.1 Load Flow Analysis 16 2.2.1.1 Gauss Seidel Method 18 2.2.1.2 Newton Raphson Method 18 2.2.1.3 Decoupled Load Flow Method 18 2.2.2 Short Circuit Analysis 19 2.2.2.1 Symmetrical Faults 19 2.2.2.2 Unsymmetrical Faults 20 2.2.3 Harmonic Analysis 20 2.3 Modeling of Equipments for Dynamic and Stability Analysis 22 2.4 Dynamic Analysis 24 2.4.1 Frequency Control 24 2.4.2 Fault Ride Through 26 2.5 Voltage Stability 26 2.6 Case Studies 27 2.6.1 Case Study 1 27 2.6.2 Case Study 2 28 2.6.2.1 Existing and Proposed Generation Details in the Vicinity of Wind Farm 29 2.6.2.2 Power Evacuation Study for 50 MW Generation 30 2.6.2.3 Without Interconnection of the Proposed 50 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation 31 2.6.2.4 Observations Made from Table 2.6 31 2.6.2.5 With the Interconnection of Proposed 50 MW Generation from Wind Farm on 66 kV level of 220/66 kV Substation 31 2.6.2.6 Normal Condition without Considering Contingency 32 2.6.2.7 Contingency Analysis 32 2.6.2.8 With the Interconnection of Proposed 60 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation 33 2.7 Conclusion 34 References 34 3 Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications 37Marimuthu Marikannu, Vijayalakshmi Subramanian, Paranthagan Balasubramanian, Jayakumar Narayanasamy, Nisha C. Rani, and Devi Vigneshwari Balasubramanian 3.1 Introduction 37 3.2 Multilevel Cascaded Boost Converter 40 3.3 Modes of Operation of MCBC 42 3.3.1 Mode-1 Switch S A Is ON 42 3.3.2 Mode-2 Switch S A Is ON 42 3.3.3 Mode-3-Operation – Switch S A Is ON 42 3.3.4 Mode-4-Operation – Switch S A Is ON 42 3.3.5 Mode-5-Operation – Switch S A Is ON 42 3.3.6 Mode-6-Operation – Switch S A Is OFF 42 3.3.7 Mode-7-Operation – Switch S A Is OFF 42 3.3.8 Mode-8-Operation – Switch S A Is OFF 43 3.3.9 Mode-9-Operation – Switch S A Is OFF 44 3.3.10 Mode 10-Operation – Switch S A is OFF 45 3.4 Simulation and Hardware Results 45 3.5 Prominent Structures of Estimated DC–DC Converter with Prevailing Converter 49 3.5.1 Voltage Gain and Power Handling Capability 49 3.5.2 Voltage Stress 49 3.5.3 Switch Count and Geometric Structure 49 3.5.4 Current Stress 52 3.5.5 Duty Cycle Versus Voltage Gain 52 3.5.6 Number of Levels in the Planned Converter 52 3.6 Power Electronic Converters for Renewable Energy Sources (Applications of MLCB) 54 3.6.1 MCBC Connected with PV Panel 54 3.6.2 Output Response of PV Fed MCBC 54 3.6.3 H-Bridge Inverter 54 3.7 Modes of Operation 55 3.7.1 Mode 1 55 3.7.2 Mode 2 55 3.7.3 Mode 3 56 3.7.4 Mode 4 56 3.7.5 Mode 5 56 3.7.6 Mode 6 56 3.7.7 Mode 7 58 3.7.8 Mode 8 58 3.7.9 Mode 9 59 3.7.10 Mode 10 59 3.8 Simulation Results of MCBC Fed Inverter 60 3.9 Power Electronic Converter for E-Vehicles 61 3.10 Power Electronic Converter for HVDC/Facts 62 3.11 Conclusion 63 References 63 4 Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 65Naveenkumar Marati, Shariq Ahammed, Kathirvel Karuppazaghi, Balraj Vaithilingam, Gyan R. Biswal, Phaneendra B. Bobba, Sanjeevikumar Padmanaban, and Sharmeela Chenniappan 4.1 Introduction 65 4.2 Applications of Power Electronic Converters 66 4.2.1 Power Electronic Converters in Electric Vehicle Ecosystem 66 4.2.2 Power Electronic Converters in Renewable Energy Resources 67 4.3 Classification of DC-Link Topologies 68 4.4 Briefing on DC-Link Topologies 69 4.4.1 Passive Capacitive DC Link 69 4.4.1.1 Filter Type Passive Capacitive DC Links 70 4.4.1.2 Filter Type Passive Capacitive DC Links with Control 72 4.4.1.3 Interleaved Type Passive Capacitive DC Links 74 4.4.2 Active Balancing in Capacitive DC Link 75 4.4.2.1 Separate Auxiliary Active Capacitive DC Links 76 4.4.2.2 Integrated Auxiliary Active Capacitive DC Links 78 4.5 Comparison on DC-Link Topologies 82 4.5.1 Comparison of Passive Capacitive DC Links 82 4.5.2 Comparison of Active Capacitive DC Links 83 4.5.3 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation 86 4.6 Future and Research Gaps in DC-Link Topologies with Balancing Techniques 94 4.7 Conclusion 95 References 95 5 Energy Storage Systems for Smart Power Systems 99Sivaraman Palanisamy, Logeshkumar Shanmugasundaram, and Sharmeela Chenniappan 5.1 Introduction 99 5.2 Energy Storage System for Low Voltage Distribution System 100 5.3 Energy Storage System Connected to Medium and High Voltage 101 5.4 Energy Storage System for Renewable Power Plants 104 5.4.1 Renewable Power Evacuation Curtailment 106 5.5 Types of Energy Storage Systems 109 5.5.1 Battery Energy Storage System 109 5.5.2 Thermal Energy Storage System 110 5.5.3 Mechanical Energy Storage System 110 5.5.4 Pumped Hydro 110 5.5.5 Hydrogen Storage 110 5.6 Energy Storage Systems for Other Applications 111 5.6.1 Shift in Energy Time 111 5.6.2 Voltage Support 111 5.6.3 Frequency Regulation (Primary, Secondary, and Tertiary) 112 5.6.4 Congestion Management 112 5.6.5 Black Start 112 5.7 Conclusion 112 References 113 6 Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage 115Thamatapu Eswararao, Sundaram Elango, Umashankar Subramanian, Krishnamohan Tatikonda, Garika Gantaiahswamy, and Sharmeela Chenniappan 6.1 Introduction 115 6.2 Structure of Supercapacitor 117 6.2.1 Mathematical Modeling of Supercapacitor 117 6.3 Bidirectional Buck–Boost Converter 118 6.3.1 FPGA Controller 119 6.4 Experimental Results 120 6.5 Conclusion 123 References 125 7 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator 129Rania Moutchou, Ahmed Abbou, Bouazza Jabri, Salah E. Rhaili, and Khalid Chigane 7.1 Introduction 129 7.2 Proposed MPPT Control Algorithm 130 7.3 Wind Energy Conversion System 131 7.3.1 Wind Turbine Characteristics 131 7.3.2 Model of PMSG 132 7.4 Fuzzy Logic Command for the MPPT of the PMSG 133 7.4.1 Fuzzification 134 7.4.2 Fuzzy Logic Rules 134 7.4.3 Defuzzification 134 7.5 Results and Discussions 135 7.6 Conclusion 139 References 139 8 A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 141Aleena Swetapadma, Shobha Agarwal, Satarupa Chakrabarti, and Soham Chakrabarti 8.1 Introduction 141 8.2 Nearest Neighbor Searching 142 8.3 Proposed Method 144 8.3.1 Power System Network Under Study 144 8.3.2 Proposed Fault Location Method 145 8.4 Results 146 8.4.1 Performance Varying Nearest Neighbor 147 8.4.2 Performance Varying Distance Matrices 147 8.4.3 Near Boundary Faults 148 8.4.4 Far Boundary Faults 149 8.4.5 Performance During High Resistance Faults 149 8.4.6 Single Pole to Ground Faults 150 8.4.7 Performance During Double Pole to Ground Faults 151 8.4.8 Performance During Pole to Pole Faults 151 8.4.9 Error Analysis 152 8.4.10 Comparison with Other Schemes 153 8.4.11 Advantages of the Scheme 154 8.5 Conclusion 154 Acknowledgment 154 References 154 9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability 157Md. I. H. Pathan, Mohammad S. Shahriar, Mohammad M. Rahman, Md. Sanwar Hossain, Nadia Awatif, and Md. Shafiullah 9.1 Introduction 157 9.2 Power System Models 159 9.2.1 PSS Integrated Single Machine Infinite Bus Power Network 159 9.2.2 PSS-UPFC Integrated Single Machine Infinite Bus Power Network 160 9.3 Methods 161 9.3.1 Group Method Data Handling Model 161 9.3.2 Extreme Learning Machine Model 162 9.3.3 Neurogenetic Model 162 9.3.4 Multigene Genetic Programming Model 163 9.4 Data Preparation and Model Development 165 9.4.1 Data Production and Processing 165 9.4.2 Machine Learning Model Development 165 9.5 Results and Discussions 166 9.5.1 Eigenvalues and Minimum Damping Ratio Comparison 166 9.5.2 Time-Domain Simulation Results Comparison 170 9.5.2.1 Rotor Angle Variation Under Disturbance 170 9.5.2.2 Rotor Angular Frequency Variation Under Disturbance 171 9.5.2.3 DC-Link Voltage Variation Under Disturbance 173 9.6 Conclusions 173 References 174 10 Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 179Jyoti Shukla, Basanta K. Panigrahi, and Monika Vardia 10.1 Introduction 179 10.2 PV-Wind Hybrid Power Generation Configuration 180 10.3 Proposed Systems Topologies 181 10.3.1 Structure of PV System 181 10.3.2 The MPPTs Technique 183 10.3.3 NN Predictive Controller Technique 183 10.3.4 ANFIS Technique 184 10.3.5 Training Data 186 10.4 Wind Power Generation Plant 187 10.5 Pitch Angle Control Techniques 189 10.5.1 PI Controller 189 10.5.2 NARMA-L2 Controller 190 10.5.3 Fuzzy Logic Controller Technique 192 10.6 Proposed DVRs Topology 192 10.7 Proposed Controlling Technique of DVR 193 10.7.1 ANFIS and PI Controlling Technique 193 10.8 Results of the Proposed Topologies 196 10.8.1 PV System Outputs (MPPT Techniques Results) 196 10.8.2 Main PV System outputs 196 10.8.3 Wind Turbine System Outputs (Pitch Angle Control Technique Result) 198 10.8.4 Proposed PMSG Wind Turbine System Output 199 10.8.5 Performance of DVR (Controlling Technique Results) 203 10.8.6 DVRs Performance 203 10.9 Conclusion 204 References 204 11 Deep Reinforcement Learning and Energy Price Prediction 207Deepak Yadav, Saad Mekhilef, Brijesh Singh, and Muhyaddin Rawa Abbreviations 207 11.1 Introduction 208 11.2 Deep and Reinforcement Learning for Decision-Making Problems in Smart Power Systems 210 11.2.1 Reinforcement Learning 210 11.2.1.1 Markov Decision Process (MDP) 210 11.2.1.2 Value Function and Optimal Policy 211 11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings 212 11.2.3 Deep Reinforcement Learning Algorithms 212 11.3 Applications in Power Systems 213 11.3.1 Energy Management 213 11.3.2 Power Systems’ Demand Response (DR) 215 11.3.3 Electricity Market 216 11.3.4 Operations and Controls 217 11.4 Mathematical Formulation of Objective Function 218 11.4.1 Locational Marginal Prices (LMPs) Representation 219 11.4.2 Relative Strength Index (RSI) 219 11.4.2.1 Autoregressive Integrated Moving Average (ARIMA) 219 11.5 Interior-point Technique & KKT Condition 220 11.5.1 Explanation of Karush–Kuhn–Tucker Conditions 220 11.5.2 Algorithm for Finding a Solution 221 11.6 Test Results and Discussion 221 11.6.1 Illustrative Example 221 11.7 Comparative Analysis with Other Methods 223 11.8 Conclusion 224 11.9 Assignment 224 Acknowledgment 225 References 225 12 Power Quality Conditioners in Smart Power System 233Zahira Rahiman, Lakshmi Dhandapani, Ravi Chengalvarayan Natarajan, Pramila Vallikannan, Sivaraman Palanisamy, and Sharmeela Chenniappan 12.1 Introduction 233 12.1.1 Voltage Sag 234 12.1.2 Voltage Swell 234 12.1.3 Interruption 234 12.1.4 Under Voltage 234 12.1.5 Overvoltage 234 12.1.6 Voltage Fluctuations 234 12.1.7 Transients 235 12.1.8 Impulsive Transients 235 12.1.9 Oscillatory Transients 235 12.1.10 Harmonics 235 12.2 Power Quality Conditioners 235 12.2.1 STATCOM 235 12.2.2 Svc 235 12.2.3 Harmonic Filters 236 12.2.3.1 Active Filter 236 12.2.4 UPS Systems 236 12.2.5 Dynamic Voltage Restorer (DVR) 236 12.2.6 Enhancement of Voltage Sag 236 12.2.7 Interruption Mitigation 237 12.2.8 Mitigation of Harmonics 241 12.3 Standards of Power Quality 244 12.4 Solution for Power Quality Issues 244 12.5 Sustainable Energy Solutions 245 12.6 Need for Smart Grid 245 12.7 What Is a Smart Grid? 245 12.8 Smart Grid: The “Energy Internet” 245 12.9 Standardization 246 12.10 Smart Grid Network 247 12.10.1 Distributed Energy Resources (DERs) 247 12.10.2 Optimization Techniques in Power Quality Management 247 12.10.3 Conventional Algorithm 248 12.10.4 Intelligent Algorithm 248 12.10.4.1 Firefly Algorithm (FA) 248 12.10.4.2 Spider Monkey Optimization (SMO) 250 12.11 Simulation Results and Discussion 254 12.12 Conclusion 257 References 257 13 The Role of Internet of Things in Smart Homes 259Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohammad Ebrahim Shiri, Hamid Haj Seyyed Javadi, Morteza Azimi Nasab, Mohammad Zand, and Tina Samavat 13.1 Introduction 259 13.2 Internet of Things Technology 260 13.2.1 Smart House 261 13.3 Different Parts of Smart Home 262 13.4 Proposed Architecture 264 13.5 Controller Components 265 13.6 Proposed Architectural Layers 266 13.6.1 Infrastructure Layer 266 13.6.1.1 Information Technology 266 13.6.1.2 Information and Communication Technology 266 13.6.1.3 Electronics 266 13.6.2 Collecting Data 267 13.6.3 Data Management and Processing 267 13.6.3.1 Service Quality Management 267 13.6.3.2 Resource Management 267 13.6.3.3 Device Management 267 13.6.3.4 Security 267 13.7 Services 267 13.8 Applications 268 13.9 Conclusion 269 References 269 14 Electric Vehicles and IoT in Smart Cities 273Sanjeevikumar Padmanaban, Tina Samavat, Mostafa Azimi Nasab, Morteza Azimi Nasab, Mohammad Zand, and Fatemeh Nikokar 14.1 Introduction 273 14.2 Smart City 275 14.2.1 Internet of Things and Smart City 275 14.3 The Concept of Smart Electric Networks 275 14.4 IoT Outlook 276 14.4.1 IoT Three-layer Architecture 276 14.4.2 View Layer 276 14.4.3 Network Layer 277 14.4.4 Application Layer 278 14.5 Intelligent Transportation and Transportation 278 14.6 Information Management 278 14.6.1 Artificial Intelligence 278 14.6.2 Machine Learning 279 14.6.3 Artificial Neural Network 279 14.6.4 Deep Learning 280 14.7 Electric Vehicles 281 14.7.1 Definition of Vehicle-to-Network System 281 14.7.2 Electric Cars and the Electricity Market 281 14.7.3 The Role of Electric Vehicles in the Network 282 14.7.4 V2G Applications in Power System 282 14.7.5 Provide Baseload Power 283 14.7.6 Courier Supply 283 14.7.7 Extra Service 283 14.7.8 Power Adjustment 283 14.7.9 Rotating Reservation 284 14.7.10 The Connection between the Electric Vehicle and the Power Grid 284 14.8 Proposed Model of Electric Vehicle 284 14.9 Prediction Using LSTM Time Series 285 14.9.1 LSTM Time Series 286 14.9.2 Predicting the Behavior of Electric Vehicles Using the LSTM Method 287 14.10 Conclusion 287 Exercise 288 References 288 15 Modeling and Simulation of Smart Power Systems Using HIL 291Gunapriya Devarajan, Puspalatha Naveen Kumar, Muniraj Chinnusamy, Sabareeshwaran Kanagaraj, and Sharmeela Chenniappan 15.1 Introduction 291 15.1.1 Classification of Hardware in the Loop 291 15.1.1.1 Signal HIL Model 291 15.1.1.2 Power HIL Model 292 15.1.1.3 Reduced-Scaled HIL Model 292 15.1.2 Points to Be Considered While Performing HIL Simulation 293 15.1.3 Applications of HIL 293 15.2 Why HIL Is Important? 293 15.2.1 Hardware-In-The-Loop Simulation 294 15.2.2 Simulation Verification and Validation 295 15.2.3 Simulation Computer Hardware 295 15.2.4 Benefits of Using Hardware-In-The-Loop Simulation 296 15.3 HIL for Renewable Energy Systems (RES) 296 15.3.1 Introduction 296 15.3.2 Hardware in the Loop 297 15.3.2.1 Electrical Hardware in the Loop 297 15.3.2.2 Mechanical Hardware in the Loop 297 15.4 HIL for HVDC and FACTS 299 15.4.1 Introduction 299 15.4.2 Modular Multi Level Converter 300 15.5 HIL for Electric Vehicles 301 15.5.1 Introduction 301 15.5.2 EV Simulation Using MATLAB, Simulink 302 15.5.2.1 Model-Based System Engineering (MBSE) 302 15.5.2.2 Model Batteries and Develop BMS 302 15.5.2.3 Model Fuel Cell Systems (FCS) and Develop Fuel Cell Control Systems (FCCS) 303 15.5.2.4 Model Inverters, Traction Motors, and Develop Motor Control Software 304 15.5.2.5 Deploy, Integrate, and Test Control Algorithms 304 15.5.2.6 Data-Driven Workflows and AI in EV Development 305 15.6 HIL for Other Applications 306 15.6.1 Electrical Motor Faults 306 15.7 Conclusion 307 References 308 16 Distribution Phasor Measurement Units (PMUs) in Smart Power Systems 311Geethanjali Muthiah, Meenakshi Devi Manivannan, Hemavathi Ramadoss, and Sharmeela Chenniappan 16.1 Introduction 311 16.2 ComparisonofPMUsandSCADA 312 16.3 Basic Structure of Phasor Measurement Units 313 16.4 PMU Deployment in Distribution Networks 314 16.5 PMU Placement Algorithms 315 16.6 Need/Significance of PMUs in Distribution System 315 16.6.1 Significance of PMUs – Concerning Power System Stability 316 16.6.2 Significance of PMUs in Terms of Expenditure 316 16.6.3 Significance of PMUs in Wide Area Monitoring Applications 316 16.7 Applications of PMUs in Distribution Systems 317 16.7.1 System Reconfiguration Automation to Manage Power Restoration 317 16.7.1.1 Case Study 317 16.7.2 Planning for High DER Interconnection (Penetration) 319 16.7.2.1 Case Study 319 16.7.3 Voltage Fluctuations and Voltage Ride-Through Related to DER 320 16.7.4 Operation of Islanded Distribution Systems 320 16.7.5 Fault-Induced Delayed Voltage Recovery (FIDVR) Detection 322 16.8 Conclusion 322 References 323 17 Blockchain Technologies for Smart Power Systems 327A. Gayathri, S. Saravanan, P. Pandiyan, and V. Rukkumani 17.1 Introduction 327 17.2 Fundamentals of Blockchain Technologies 328 17.2.1 Terminology 328 17.2.2 Process of Operation 329 17.2.2.1 Proof of Work (PoW) 329 17.2.2.2 Proof of Stake (PoS) 329 17.2.2.3 Proof of Authority (PoA) 330 17.2.2.4 Practical Byzantine Fault Tolerance (PBFT) 330 17.2.3 Unique Features of Blockchain 330 17.2.4 Energy with Blockchain Projects 330 17.2.4.1 Bitcoin Cryptocurrency 331 17.2.4.2 Dubai: Blockchain Strategy 331 17.2.4.3 Humanitarian Aid Utilization of Blockchain 331 17.3 Blockchain Technologies for Smart Power Systems 331 17.3.1 Blockchain as a Cyber Layer 331 17.3.2 Agent/Aggregator Based Microgrid Architecture 332 17.3.3 Limitations and Drawbacks 332 17.3.4 Peer to Peer Energy Trading 333 17.3.5 Blockchain for Transactive Energy 335 17.4 Blockchain for Smart Contracts 336 17.4.1 The Platform for Smart Contracts 337 17.4.2 The Architecture of Smart Contracting for Energy Applications 338 17.4.3 Smart Contract Applications 339 17.5 Digitize and Decentralization Using Blockchain 340 17.6 Challenges in Implementing Blockchain Techniques 340 17.6.1 Network Management 341 17.6.2 Data Management 341 17.6.3 Consensus Management 341 17.6.4 Identity Management 341 17.6.5 Automation Management 342 17.6.6 Lack of Suitable Implementation Platforms 342 17.7 Solutions and Future Scope 342 17.8 Application of Blockchain for Flexible Services 343 17.9 Conclusion 343 References 344 18 Power and Energy Management in Smart Power Systems 349Subrat Sahoo 18.1 Introduction 349 18.1.1 Geopolitical Situation 349 18.1.2 Covid-19 Impacts 350 18.1.3 Climate Challenges 350 18.2 Definition and Constituents of Smart Power Systems 351 18.2.1 Applicable Industries 352 18.2.2 Evolution of Power Electronics-Based Solutions 353 18.2.3 Operation of the Power System 355 18.3 Challenges Faced by Utilities and Their Way Towards Becoming Smart 356 18.3.1 Digitalization of Power Industry 359 18.3.2 Storage Possibilities and Integration into Grid 360 18.3.3 Addressing Power Quality Concerns and Their Mitigation 362 18.3.4 A Path Forward Towards Holistic Condition Monitoring 363 18.4 Ways towards Smart Transition of the Energy Sector 366 18.4.1 Creating an All-Inclusive Ecosystem 366 18.4.1.1 Example of Sensor-Based Ecosystem 367 18.4.1.2 Utilizing the Sensor Data for Effective Analytics 368 18.4.2 Modular Energy System Architecture 370 18.5 Conclusion 371 References 373 Index 377

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Book SynopsisTable of ContentsPreface xiii Book Description xv 1 An Analysis of the Internet of Things (IoT) as the Defining Technology of a Generation 1Deepika Gupta, Asmita Singh, Anand Sharma and Gurpreet Singh 1.1 Introduction 1 1.2 Growth of IoT 2 1.3 IoT Technologies 3 1.4 Application Areas of Internet of Things 4 1.5 IoT Security and Protection Concerns 11 1.6 IoT Security 12 1.7 Conclusion 12 2 Blockchain in IoT and Limitations 17Vishal Walia, Vishu Madaan, Prateek Agrawal, Anand Mohan, Charu Gupta, Anand Sharma and Akshat Agrawal 2.1 Introduction 18 2.2 Literature Review 22 2.3 Limitations of Blockchain 23 2.4 Conclusion 24 3 IoT Protocol Security Mechanisms 29D. Karthika and Dr. K. Kalaiselvi 3.1 Introduction 30 3.2 Comparing to IoT Security and Cyber-Physical Structures 31 3.3 Potential IoT and the Need for Safety 32 3.4 Future-Cognitive Structures and IoT 32 3.5 Security Engineering for IoT Development 33 3.6 Building Security into Design and Development 34 3.7 Security in Agile Developments 35 3.8 Focusing on the IoT Device in Operation 36 3.9 IoT Security Innovation Cryptographic Basics 37 3.10 Cryptographic Primitive Forms and Implementations in the IoT 37 3.11 Encryption and Decryption 38 3.12 Hashes 41 3.13 Digital Signatures 42 3.14 Generation of Random Numbers 43 3.15 Cloud Security for IoT 44 3.16 Control of Assets/Inventories 45 4 IoT Security, Privacy, Challenges, and Solutions 53Ankit Garg, Ashima Gambhir and Prachi Goel 4.1 Introduction 54 4.1.1 Elements of Internet of Things (IoT) 55 4.2 IoT Landscape: Current and Potential Applications 56 4.3 Advantages of Internet of Things (IoT) 59 4.4 Architecture of IoT Systems 60 4.5 IoT Security 63 4.6 Security Challenges in IoT Architecture 66 4.7 Security by Design in IoT 71 4.8 Best Practices to Secure IoT Devices 71 4.9 Security Attacks in IoT System 73 4.10 Various IoT Security Challenges 78 4.11 Limitations of Available Resources 79 4.12 Solutions to Preserve Privacy in IoT Systems 83 5 CIA-CPS: Concept, Issues, and Application of IoT in Cyber Physical System 93Gaurav Jolly and Rahul Johari 5.1 Introduction 93 5.2 Cyber Physical System: Definition 95 5.3 System Interfaces 96 5.4 Communication Channel 98 5.5 Physical Interaction 100 5.6 CPS vs IoT 102 5.7 Cyber Physical System Issues 104 5.8 Literature Survey 106 5.9 Applications of Cyber Physical System 108 5.10 Future of Cyber Physical Systems 115 5.11 Conclusion 116 6 Trust Calculation in IoT Nodes Without Trusted Third Party Using PUF Methodology 119Sivasankari Narasimhan 6.1 Introduction 119 6.2 Related Works 121 6.3 Trust Calculation Basics 123 6.4 Deriving Trust Relationships 127 6.5 Trust Derivation Examples 128 6.6 Combination of Trust Relationship 130 6.7 Analysis of Attacks 131 6.8 Conclusions 132 7 Comparative Analysis of Indexing Schemes Used in Cloud Computing Data Management 135Prachi Goyal, Ankit Garg and Prakhar Jindal 7.1 Introduction 136 7.2 Literature Review 138 7.3 Overview of System Architecture 140 7.4 Experiments and Comparison 142 7.5 Database for Experiment 143 7.6 Assessment of the Index Structure 144 7.7 Performance Evaluation of Exact Search 147 7.8 Evaluation of Indexing Schemes Based on k-Nearest Neighbor Search 148 7.9 Evaluation of Data Distribution 152 7.10 Conclusion 153 8 Evolution and Insight in Industrial Internet of Things (IIoT): Importance and Impact 159Nabeela Hasan and Mansaf Alam 8.1 Introduction 160 8.2 An Efficient Approach Towards IIoT Technology 161 8.3 Evolution of IIoT 163 8.4 IIoT Architecture 165 8.5 Industrial Applications of IoT 172 8.6 Smart Manufacturing 172 8.7 Smart Healthcare 173 8.8 Smart Transportation 174 8.9 Smart Cities 174 8.10 Oil and Gas Industry 175 8.11 Logistics and Supply Chain 176 8.12 Basic Technologies of IIoT 177 8.13 Things Over Internet 178 8.14 Technology on Blockchain 178 8.15 Computing of Data Over Cloud Technology 178 8.16 Artificial Intelligence and Cyber Physical Systems 179 8.17 Analytics on Management of Big Data 179 8.18 Future Technologies: Augmented and Virtual Reality 180 8.19 Industry 4.0 180 8.20 Research Challenges 187 8.20.1 Energy Efficiency 187 8.20.2 Coexistence and Interoperability 187 8.20.3 Real-Time Performance 188 8.20.4 Security and Privacy 189 8.20.5 Fault Detection and Reconfiguration 189 8.20.6 User-Friendliness in Product Deployment and Usage 190 8.21 Conclusions 190 9 Evolving Trends of Artificial Intelligence and Robotics in Smart City Applications: Crafting Humane Built Environment 195Niva Rana Mahanta and Suvarna Lele 9.1 Fundamentals of Smart Cities 196 9.2 Case Study Analysis 209 9.3 Smart Buildings in Smart Cities: Humane Approach 225 9.4 Future Scope and Impact on Society 232 9.5 Conclusion 235 10 T-Secure IoT in Smart Home System 243Esra SIPAHI, Md Harun Rashid and Erkin ARTANTAS 10.1 Introduction 244 10.2 Literature 245 10.3 Method 254 10.4 Chematic Implementation 260 10.5 Simulation and Result 260 10.6 Conclusion 260 11 Intelligent Micro-Mobility E-Scooter: Revolutionizing Urban Transport 267Leena Wanganoo, VinodKumar Shukla and Vaishnavi Mohan 11.1 Introduction 268 11.2 Intelligent Transport System 269 11.3 Technologies Used in Intelligent Transport Systems 270 11.4 Micro Mobility 272 11.5 Case Study 276 11.6 Methodology: Value -- Steam Mapping the Existing Operations 276 11.7 Operational Challenges Faced by Arnab Micro Mobility 281 11.8 Conclusion 287 12 Automatic Booking of LPG and Leakage Detection System Using IoT 291Aishwarya Jain, Meghana H M and Annaiah H 12.1 "What is IoT?" 292 12.2 Why IoT Matters 292 12.2.1 Collecting and Sending Information 293 12.2.2 Receiving and Acting on Information 293 12.2.3 Doing Both: The Goal of an IoT System 294 12.3 The oneM2M IoT Standardized Architecture 294 12.4 The IoT World Forum (IoTWF) Standardized Architecture 296 12.5 A Simplified IoT Architecture 299 12.6 Case Study: Automatic LPG Booking and Leakage Detection System using IoT 302 12.6.1 Problem Statement 302 12.6.2 Proposed Solution 303 12.6.3 Architecture of the System 304 12.6.4 System Setup 308 12.6.5 Working of System 308 12.7 Conclusion 310 References 310 Index 313

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Book SynopsisAGILE SOFTWARE DEVELOPMENT A unique title that introduces the whole range of agile software development processes from the fundamental concepts to the highest levels of applications such as requirement analysis, software testing, quality assurance, and risk management. Agile Software Development (ASD) has become a popular technology because its methods apply to any programming paradigm. It is important in the software development process because it emphasizes incremental delivery, team collaboration, continuous planning, and learning over delivering everything at once near the end. Agile has gained popularity as a result of its use of various frameworks, methods, and techniques to improve software quality. Scrum is a major agile framework that has been widely adopted by the software development community. Metaheuristic techniques have been used in the agile software development process to improve software quality and reliability. These techniques not only improve quality and reliabili

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