Electronics and communications engineering Books

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 SynopsisTable of ContentsPREFACE ix ABOUT THE COMPANION WEBSITE xiii 1 THE CHANGING LANDSCAPE OF PROJECT MANAGEMENT 1 CHAPTER OVERVIEW 1 1.0 INTRODUCTION 1 1.1 EXECUTIVE VIEW OF PROJECT MANAGEMENT 2 1.2 COMPLEX PROJECTS 5 1.3 GLOBAL PROJECT MANAGEMENT 12 1.4 PROJECT MANAGEMENT METHODOLOGIES AND FRAMEWORKS 14 1.5 THE NEED FOR EFFECTIVE GOVERNANCE 20 1.6 ENGAGEMENT PROJECT MANAGEMENT 20 1.7 CUSTOMER RELATIONS MANAGEMENT 22 1.8 OTHER DEVELOPMENTS IN PROJECT MANAGEMENT 23 1.9 A NEW LOOK AT DEFINING PROJECT SUCCESS 24 1.10 THE GROWTH OF PAPERLESS PROJECT MANAGEMENT 30 1.11 PROJECT MANAGEMENT MATURITY AND METRICS 31 1.12 PROJECT MANAGEMENT BENCHMARKING AND METRICS 35 1.13 CONCLUSIONS 41 2 THE DRIVING FORCES FOR BETTER METRICS 43 CHAPTER OVERVIEW 43 2.0 INTRODUCTION 43 2.1 STAKEHOLDER RELATIONS MANAGEMENT 44 2.2 PROJECT AUDITS AND THE PMO 55 2.3 INTRODUCTION TO SCOPE CREEP 56 2.4 PROJECT HEALTH CHECKS 64 2.5 MANAGING DISTRESSED PROJECTS 69 3 METRICS 83 CHAPTER OVERVIEW 83 3.0 INTRODUCTION 83 3.1 PROJECT MANAGEMENT METRICS: THE EARLY YEARS 84 3.2 PROJECT MANAGEMENT METRICS: CURRENT VIEW 87 3.3 METRICS MANAGEMENT MYTHS 88 3.4 SELLING EXECUTIVES ON A METRICS MANAGEMENT PROGRAM 89 3.5 UNDERSTANDING METRICS 91 3.6 CAUSES FOR LACK OF SUPPORT FOR METRICS MANAGEMENT 95 3.7 USING METRICS IN EMPLOYEE PERFORMANCE REVIEWS 96 3.8 CHARACTERISTICS OF A METRIC 97 3.9 METRIC CATEGORIES AND TYPES 99 3.10 SELECTING THE METRICS 101 3.11 SELECTING A METRIC/KPI OWNER 105 3.12 METRICS AND INFORMATION SYSTEMS 106 3.13 CRITICAL SUCCESS FACTORS 106 3.14 METRICS AND THE PMO 109 3.15 METRICS AND PROJECT OVERSIGHT/GOVERNANCE 112 3.16 METRICS TRAPS 113 3.17 PROMOTING THE METRICS 114 3.18 CHURCHILL DOWNS INCORPORATED’S PROJECT PERFORMANCE MEASUREMENT APPROACHES 114 4 KEY PERFORMANCE INDICATORS 121 CHAPTER OVERVIEW 121 4.0 INTRODUCTION 121 4.1 THE NEED FOR KPIS 122 4.2 USING THE KPIS 126 4.3 THE ANATOMY OF A KPI 128 4.4 KPI CHARACTERISTICS 129 4.5 CATEGORIES OF KPIS 133 4.6 KPI SELECTION 134 4.7 KPI MEASUREMENT 140 4.8 KPI INTERDEPENDENCIES 142 4.9 KPIS AND TRAINING 144 4.10 KPI TARGETS 145 4.11 UNDERSTANDING STRETCH TARGETS 148 4.12 KPI FAILURES 149 4.13 KPIS AND INTELLECTUAL CAPITAL 151 4.14 KPI BAD HABITS 154 4.15 BRIGHTPOINT CONSULTING, INC.--DASHBOARD DESIGN: KEY PERFORMANCE INDICATORS AND METRICS 159 5 VALUE-BASED PROJECT MANAGEMENT METRICS 169 CHAPTER OVERVIEW 169 5.0 INTRODUCTION 169 5.1 VALUE OVER THE YEARS 171 5.2 VALUES AND LEADERSHIP 172 5.3 COMBINING SUCCESS AND VALUE 175 5.4 RECOGNIZING THE NEED FOR VALUE METRICS 178 5.5 THE NEED FOR EFFECTIVE MEASUREMENT TECHNIQUES 181 5.6 CUSTOMER/STAKEHOLDER IMPACT ON VALUE METRICS 187 5.7 CUSTOMER VALUE MANAGEMENT 188 5.8 THE RELATIONSHIP BETWEEN PROJECT MANAGEMENT AND VALUE 193 5.9 BACKGROUND OF METRICS 197 5.10 SELECTING THE RIGHT METRICS 204 5.11 THE FAILURE OF TRADITIONAL METRICS AND KPIS 207 5.12 THE NEED FOR VALUE METRICS 207 5.13 CREATING A VALUE METRIC 208 5.14 PRESENTING THE VALUE METRIC IN A DASHBOARD 215 5.15 INDUSTRY EXAMPLES OF VALUE METRICS 216 5.16 USE OF CRISIS DASHBOARDS FOR OUT-OFRANGE VALUE ATTRIBUTES 222 5.17 ESTABLISHING A METRICS MANAGEMENT PROGRAM 223 5.18 USING VALUE METRICS FOR FORECASTING 225 5.19 METRICS AND JOB DESCRIPTIONS 226 5.20 GRAPHICAL REPRESENTATION OF METRICS 227 5.21 CREATING A PROJECT VALUE BASELINE 239 6 DASHBOARDS 247 CHAPTER OVERVIEW 247 6.0 INTRODUCTION 247 6.1 DOES EVERYONE KNOW WHAT A DASHBOARD REALLY IS? 252 6.2 HOW WE PROCESS DASHBOARD INFORMATION 256 6.3 DASHBOARD CORE ATTRIBUTES 256 6.4 THE MEANING OF INFORMATION 257 6.5 TRAFFIC LIGHT DASHBOARD REPORTING 259 6.6 DASHBOARDS AND SCORECARDS 261 6.7 CREATING A DASHBOARD IS A LOT LIKE ONLINE DATING 264 6.8 BENEFITS OF DASHBOARDS 266 6.9 IS YOUR BI TOOL FLEXIBLE ENOUGH? 267 6.10 FOUR EASY STEPS TO IMPLEMENTING A SUCCESSFUL BUSINESS INTELLIGENCE SOLUTION 270 6.11 RULES FOR DASHBOARDS 275 6.12 THE SEVEN DEADLY SINS OF DASHBOARD DESIGN AND WHY THEY SHOULD BE AVOIDED 275 6.13 BRIGHTPOINT CONSULTING, INC.: DESIGNING EXECUTIVE DASHBOARDS 278 6.14 ALL THAT GLITTERS IS NOT GOLD 287 6.15 USING EMOTICONS 310 6.16 MISLEADING INDICATORS 311 6.17 AGILE AND SCRUM METRICS 313 6.18 DATA WAREHOUSES 333 6.20 TEAMQUEST CORPORATION 340 6.21 A SIMPLE TEMPLATE 360 6.22 SUMMARY OF DASHBOARD DESIGN REQUIREMENTS 360 6.23 DASHBOARD LIMITATIONS 367 6.24 THE DASHBOARD PILOT RUN 370 6.25 EVALUATING DASHBOARD VENDORS 371 6.26 NEW DASHBOARD APPLICATIONS 372 7 DASHBOARD APPLICATIONS 375 CHAPTER OVERVIEW 375 7.0 INTRODUCTION 375 7.1 DASHBOARDS IN ACTION: DUNDAS DATA VISUALIZATION 376 7.2 DASHBOARDS IN ACTION: PIE 376 7.3 PIE OVERVIEW 388 7.4 DASHBOARDS IN ACTION: INTERNATIONAL INSTITUTE FOR LEARNING 403 8 THE PORTFOLIO MANAGEMENT PMO AND METRICS 407 CHAPTER OVERVIEW 407 8.0 INTRODUCTION 407 8.1 CRITICAL QUESTIONS 408 8.2 VALUE CATEGORIES 408 8.3 PORTFOLIO METRICS 410 8.4 MEASUREMENT TECHNIQUES AND METRICS 411 8.5 THE GROWTH OF PORTFOLIO METRICS 413 8.6 METRICS FOR MEASURING INTANGIBLES 415 8.7 THE NEED FOR STRATEGIC METRICS 418 8.8 CRISIS DASHBOARDS 421 INDEX 425

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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 SynopsisTable of ContentsIntroduction xxi Assessment Test xxxiii Chapter 1 Overview of Google Cloud 1 Types of Cloud Services 2 Compute Resources 3 Storage 4 Networking 7 Specialized Services 8 Cloud Computing vs. Data Center Computing 8 Rent Instead of Own Resources 8 Pay- as- You- Go- for- What- You- Use Model 9 Elastic Resource Allocation 9 Specialized Services 10 Summary 10 Exam Essentials 10 Review Questions 12 Chapter 2 Google Cloud Computing Services 17 Computing Components of Google Cloud 18 Computing Resources 19 Storage Components of Google Cloud 23 Storage Resources 23 Databases 26 Networking Components of Google Cloud 28 Networking Services 28 Identity Management and Security 30 Development Tools 30 Additional Components of Google Cloud 31 Management and Observability Tools 31 Specialized Services 32 Summary 33 Exam Essentials 33 Review Questions 36 Chapter 3 Projects, Service Accounts, and Billing 41 How Google Cloud Organizes Projects and Accounts 42 Google Cloud Resource Hierarchy 42 Organization Policies 45 Managing Projects 46 Roles and Identities 49 Roles in Google Cloud 50 Granting Roles to Identities 50 Service Accounts 52 Billing 53 Billing Accounts 53 Billing Budgets and Alerts 56 Exporting Billing Data 57 Enabling APIs 59 Summary 60 Exam Essentials 61 Review Questions 62 Chapter 4 Introduction to Computing in Google Cloud 67 Compute Engine 68 Virtual Machine Images 68 Virtual Machines Are Contained in Projects 77 Virtual Machines Run in a Zone and Region 78 Users Need Privileges to Create Virtual Machines 79 Preemptible Virtual Machines 80 Custom Machine Types 81 Use Cases for Compute Engine Virtual Machines 82 App Engine 83 Structure of an App Engine Application 84 App Engine Standard and Flexible Environments 85 Use Cases for App Engine 86 Kubernetes Engine 87 Kubernetes Functionality 88 Kubernetes Cluster Architecture 88 Kubernetes Engine Use Cases 89 Anthos 90 Cloud Run 90 Cloud Run Use Cases 91 Cloud Functions 91 Cloud Functions Execution Environment 91 Cloud Functions Use Cases 93 Summary 93 Exam Essentials 95 Review Questions 96 Chapter 5 Computing with Compute Engine Virtual Machines 101 Creating and Configuring Virtual Machines with the Console 102 Main Virtual Machine Configuration Details 104 Advanced Configuration Details 109 Creating and Configuring Virtual Machines with Cloud SDK 117 Installing Cloud SDK 117 Example Installation on Ubuntu Linux 118 Creating a Virtual Machine with Cloud SDK 119 Creating a Virtual Machine with Cloud Shell 120 Basic Virtual Machine Management 121 Starting and Stopping Instances 121 Network Access to Virtual Machines 121 Monitoring a Virtual Machine 123 Cost of Virtual Machines 123 Guidelines for Planning, Deploying, and Managing Virtual Machines 125 Summary 125 Exam Essentials 126 Review Questions 127 Chapter 6 Managing Virtual Machines 131 Managing Single Virtual Machine Instances 132 Managing Single Virtual Machine Instances in the Console 132 Managing a Single Virtual Machine Instance with Cloud Shell and the Command Line 141 Introduction to Instance Groups 147 Creating and Removing Instance Groups and Templates 147 Instance Groups Load Balancing and Autoscaling 149 Guidelines for Managing Virtual Machines 150 Summary 150 Exam Essentials 151 Review Questions 152 Chapter 7 Computing with Kubernetes 157 Introduction to Kubernetes Engine 158 Kubernetes Cluster Architecture 159 Kubernetes Objects 159 Deploying Kubernetes Clusters 162 Deploying Kubernetes Clusters Using Cloud Console 162 Deploying Kubernetes Clusters Using Cloud Shell and Cloud SDK 167 Deploying Application Pods 168 Monitoring Kubernetes 172 Summary 172 Exam Essentials 173 Review Questions 174 Chapter 8 Managing Standard Mode Kubernetes Clusters 179 Viewing the Status of a Kubernetes Cluster 180 Viewing the Status of Kubernetes Clusters Using Cloud Console 180 Pinning Services to the Top of the Navigation Menu 182 Viewing the Status of Kubernetes Clusters Using Cloud SDK and Cloud Shell 188 Adding, Modifying, and Removing Nodes 193 Adding, Modifying, and Removing Nodes with Cloud Console 193 Adding, Modifying, and Removing Nodes with Cloud SDK and Cloud Shell 195 Adding, Modifying, and Removing Pods 196 Adding, Modifying, and Removing Pods with Cloud Console 196 Adding, Modifying, and Removing Pods with Cloud SDK and Cloud Shell 200 Adding, Modifying, and Removing Services 203 Adding, Modifying, and Removing Services with Cloud Console 203 Adding, Modifying, and Removing Services with Cloud SDK and Cloud Shell 205 Creating Repositories in the Artifact Registry 207 Viewing the Image Repository and Image Details with Cloud Console 207 Summary 209 Exam Essentials 209 Review Questions 210 Chapter 9 Computing with Cloud Run and App Engine 215 Overview of Cloud Run 216 Cloud Run Services 216 Cloud Run Jobs 217 Creating a Cloud Run Service 218 Creating a Cloud Run Job 222 App Engine Components 223 Deploying an App Engine Application 226 Deploying an App Using Cloud Shell and SDK 226 Scaling App Engine Applications 228 Splitting Traffic Between App Engine Versions 229 Summary 230 Exam Essentials 231 Review Questions 232 Chapter 10 Computing with Cloud Functions 237 Introduction to Cloud Functions 238 Events, Triggers, and Functions 238 Runtime Environments 239 Cloud Functions Receiving Events from Cloud Storage 241 Deploying a Cloud Function for Cloud Storage Events Using Cloud Console 241 Deploying a Cloud Function for Cloud Storage Events Using gcloud Commands 244 Cloud Functions Receiving Events from Pub/Sub 245 Deploying a Cloud Function for Cloud Pub/Sub Events Using Cloud Console 245 Deploying a Cloud Function for Cloud Pub/Sub Events Using gcloud Commands 246 Summary 247 Exam Essentials 247 Review Questions 249 Chapter 11 Planning Storage in the Cloud 253 Types of Storage Systems 254 Cache 255 Persistent Storage 257 Object Storage 258 Storage Types When Planning a Storage Solution 264 Storage Data Models 265 Object: Cloud Storage 266 Relational: Cloud SQL and Cloud Spanner 266 Analytical: BigQuery 268 NoSQL: Cloud Firestore and Bigtable 270 Choosing a Storage Solution: Guidelines to Consider 277 Summary 278 Exam Essentials 278 Review Questions 280 Chapter 12 Deploying Storage in Google Cloud 285 Deploying and Managing Cloud SQL 286 Creating and Connecting to a MySQL Instance 286 Creating a Database, Loading Data, and Querying Data 288 Backing Up MySQL in Cloud SQL 289 Deploying and Managing Firestore 292 Adding Data to a Firestore Database 292 Backing Up Firestore 294 Deploying and Managing BigQuery 294 Estimating the Cost of Queries in BigQuery 294 Viewing Jobs in BigQuery 296 Deploying and Managing Cloud Spanner 297 Deploying and Managing Cloud Pub/Sub 302 Deploying and Managing Cloud Bigtable 306 Deploying and Managing Cloud Dataproc 308 Managing Cloud Storage 314 Summary 316 Exam Essentials 316 Review Questions 317 Chapter 13 Loading Data into Storage 321 Loading and Moving Data to Cloud Storage 322 Loading and Moving Data to Cloud Storage Using the Console 322 Loading and Moving Data to Cloud Storage Using the Command Line 327 Importing and Exporting Data 328 Importing and Exporting Data: Cloud SQL 328 Importing and Exporting Data: Cloud Firestore 332 Importing and Exporting Data: BigQuery 332 Importing and Exporting Data: Cloud Spanner 337 Exporting Data from Cloud Bigtable 339 Importing and Exporting Data: Cloud Dataproc 340 Streaming Data to Cloud Pub/Sub 341 Summary 342 Exam Essentials 342 Review Questions 344 Chapter 14 Networking in the Cloud: Virtual Private Clouds and Virtual Private Networks 349 Creating a Virtual Private Cloud with Subnets 350 Creating a Virtual Private Cloud with Cloud Console 350 Creating a Virtual Private Cloud with gcloud 354 Creating a Shared Virtual Private Cloud Using gcloud 355 Deploying Compute Engine with a Custom Network 357 Creating Firewall Rules for a Virtual Private Cloud 359 Structure of Firewall Rules 360 Creating Firewall Rules Using Cloud Console 361 Creating Firewall Rules Using gcloud 364 Creating a Virtual Private Network 364 Creating a Virtual Private Network Using Cloud Console 364 Creating a Virtual Private Network Using gcloud 368 Summary 368 Exam Essentials 369 Review Questions 370 Chapter 15 Networking in the Cloud: DNS, Load Balancing, Google Private Access, and IP Addressing 375 Configuring Cloud DNS 376 Creating DNS Managed Zones Using Cloud Console 376 Creating DNS Managed Zones Using gcloud 381 Configuring Load Balancers 382 Types of Load Balancers 382 Configuring Load Balancers Using Cloud Console 383 Configuring Load Balancers Using gcloud 386 Google Private Access 389 Managing IP Addresses 389 Expanding CIDR Blocks 390 Reserving IP Addresses 390 Summary 391 Exam Essentials 392 Review Questions 394 Chapter 16 Deploying Applications with Cloud Marketplace and Cloud Foundation Toolkit 399 Deploying a Solution Using Cloud Marketplace 400 Browsing Cloud Marketplace and Viewing Solutions 400 Deploying Cloud Marketplace Solutions 403 Building Infrastructure Using the Cloud Foundation Toolkit 411 Deployment Manager Configuration Files 411 Deployment Manager Template Files 414 Launching a Deployment Manager Template 414 Cloud Foundation Toolkit 415 Config Connector 418 Summary 418 Exam Essentials 418 Review Questions 420 Chapter 17 Configuring Access and Security 425 Managing Identity and Access Management 426 Viewing Account IAM Assignments 426 Assigning IAM Roles to Accounts and Groups 428 Defining Custom IAM Roles 432 Managing Service Accounts 436 Managing Service Accounts with Scopes 436 Assigning a Service Account to a VM Instance 438 Viewing Audit Logs 440 Summary 441 Exam Essentials 441 Review Questions 443 Chapter 18 Monitoring, Logging, and Cost Estimating 447 Cloud Monitoring 448 Creating Dashboards 449 Using Metric Explorer 450 Creating Alerts 454 Cloud Logging 458 Log Routers and Log Sinks 458 Configuring Log Sinks 459 Viewing and Filtering Logs 459 Viewing Message Details 462 Using Cloud Diagnostics 463 Overview of Cloud Trace 463 Viewing Google Cloud Status 464 Using the Pricing Calculator 464 Summary 467 Exam Essentials 468 Review Questions 469 Appendix Answers to Review Questions 473 Chapter 1: Overview of Google Cloud 474 Chapter 2: Google Cloud Computing Services 476 Chapter 3: Projects, Service Accounts, and Billing 478 Chapter 4: Introduction to Computing in Google Cloud 480 Chapter 5: Computing with Compute Engine Virtual Machines 482 Chapter 6: Managing Virtual Machines 485 Chapter 7: Computing with Kubernetes 487 Chapter 8: Managing Standard Mode Kubernetes Clusters 489 Chapter 9: Computing with Cloud Run and App Engine 491 Chapter 10: Computing with Cloud Functions 494 Chapter 11: Planning Storage in the Cloud 496 Chapter 12: Deploying Storage in Google Cloud 498 Chapter 13: Loading Data into Storage 500 Chapter 14: Networking in the Cloud: Virtual Private Clouds and Virtual Private Networks 502 Chapter 15: Networking in the Cloud: DNS, Load Balancing, Google Private Access, and IP Addressing 504 Chapter 16: Deploying Applications with Cloud Marketplace and Cloud Foundation Toolkit 507 Chapter 17: Configuring Access and Security 509 Chapter 18: Monitoring, Logging, and Cost Estimating 511 Index 515

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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 SynopsisAn Introduction to Optimization Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB exercises and worked examples Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in machine learning. Numerous diagrams andTable of ContentsPreface xv About the Companion Website xviii Part I Mathematical Review 1 1 Methods of Proof and Some Notation 3 1.1 Methods of Proof 3 1.2 Notation 5 Exercises 5 2 Vector Spaces and Matrices 7 2.1 Vector and Matrix 7 2.2 Rank of a Matrix 11 2.3 Linear Equations 16 2.4 Inner Products and Norms 18 Exercises 20 3 Transformations 23 3.1 Linear Transformations 23 3.2 Eigenvalues and Eigenvectors 24 3.3 Orthogonal Projections 26 3.4 Quadratic Forms 27 3.5 Matrix Norms 32 Exercises 35 4 Concepts from Geometry 39 4.1 Line Segments 39 4.2 Hyperplanes and Linear Varieties 39 4.3 Convex Sets 41 4.4 Neighborhoods 43 4.5 Polytopes and Polyhedra 44 Exercises 45 5 Elements of Calculus 47 5.1 Sequences and Limits 47 5.2 Differentiability 52 5.3 The Derivative Matrix 54 5.4 Differentiation Rules 57 5.5 Level Sets and Gradients 58 5.6 Taylor Series 61 Exercises 65 Part II Unconstrained Optimization 67 6 Basics of Set-Constrained and Unconstrained Optimization 69 6.1 Introduction 69 6.2 Conditions for Local Minimizers 70 Exercises 78 7 One-Dimensional Search Methods 87 7.1 Introduction 87 7.2 Golden Section Search 87 7.3 Fibonacci Method 91 7.4 Bisection Method 97 7.5 Newton’s Method 98 7.6 Secant Method 101 7.7 Bracketing 103 7.8 Line Search in Multidimensional Optimization 103 Exercises 105 8 Gradient Methods 109 8.1 Introduction 109 8.2 Steepest Descent Method 110 8.3 Analysis of Gradient Methods 117 Exercises 126 9 Newton’s Method 133 9.1 Introduction 133 9.2 Analysis of Newton’s Method 135 9.3 Levenberg–Marquardt Modification 138 9.4 Newton’s Method for Nonlinear Least Squares 139 Exercises 142 10 Conjugate Direction Methods 145 10.1 Introduction 145 10.2 Conjugate Direction Algorithm 146 10.2.1 Basic Conjugate Direction Algorithm 146 10.3 Conjugate Gradient Algorithm 151 10.4 Conjugate Gradient Algorithm for Nonquadratic Problems 154 Exercises 156 11 Quasi-Newton Methods 159 11.1 Introduction 159 11.2 Approximating the Inverse Hessian 160 11.3 Rank One Correction Formula 162 11.4 DFP Algorithm 166 11.5 BFGS Algorithm 170 Exercises 173 12 Solving Linear Equations 179 12.1 Least-Squares Analysis 179 12.2 Recursive Least-Squares Algorithm 187 12.3 Solution to a Linear Equation with Minimum Norm 190 12.4 Kaczmarz’s Algorithm 191 12.5 Solving Linear Equations in General 194 Exercises 201 13 Unconstrained Optimization and Neural Networks 209 13.1 Introduction 209 13.2 Single-Neuron Training 211 13.3 Backpropagation Algorithm 213 Exercises 222 14 Global Search Algorithms 225 14.1 Introduction 225 14.2 Nelder–Mead Simplex Algorithm 225 14.3 Simulated Annealing 229 14.3.1 Randomized Search 229 14.3.2 Simulated Annealing Algorithm 229 14.4 Particle Swarm Optimization 231 14.4.1 Basic PSO Algorithm 232 14.4.2 Variations 233 14.5 Genetic Algorithms 233 14.5.1 Basic Description 233 14.5.1.1 Chromosomes and Representation Schemes 234 14.5.1.2 Selection and Evolution 234 14.5.2 Analysis of Genetic Algorithms 238 14.5.3 Real-Number Genetic Algorithms 243 Exercises 244 Part III Linear Programming 247 15 Introduction to Linear Programming 249 15.1 Brief History of Linear Programming 249 15.2 Simple Examples of Linear Programs 250 15.3 Two-Dimensional Linear Programs 256 15.4 Convex Polyhedra and Linear Programming 258 15.5 Standard Form Linear Programs 260 15.6 Basic Solutions 264 15.7 Properties of Basic Solutions 267 15.8 Geometric View of Linear Programs 269 Exercises 273 16 Simplex Method 277 16.1 Solving Linear Equations Using Row Operations 277 16.2 The Canonical Augmented Matrix 283 16.3 Updating the Augmented Matrix 284 16.4 The Simplex Algorithm 285 16.5 Matrix Form of the Simplex Method 291 16.6 Two-Phase Simplex Method 294 16.7 Revised Simplex Method 297 Exercises 301 17 Duality 309 17.1 Dual Linear Programs 309 17.2 Properties of Dual Problems 316 17.3 Matrix Games 321 Exercises 324 18 Nonsimplex Methods 331 18.1 Introduction 331 18.2 Khachiyan’s Method 332 18.3 Affine Scaling Method 334 18.3.1 Basic Algorithm 334 18.3.2 Two-Phase Method 337 18.4 Karmarkar’s Method 339 18.4.1 Basic Ideas 339 18.4.2 Karmarkar’s Canonical Form 339 18.4.3 Karmarkar’s Restricted Problem 341 18.4.4 From General Form to Karmarkar’s Canonical Form 342 18.4.5 The Algorithm 345 Exercises 349 19 Integer Linear Programming 351 19.1 Introduction 351 19.2 Unimodular Matrices 351 19.3 The Gomory Cutting-Plane Method 358 Exercises 366 Part IV Nonlinear Constrained Optimization 369 20 Problems with Equality Constraints 371 20.1 Introduction 371 20.2 Problem Formulation 373 20.3 Tangent and Normal Spaces 374 20.4 Lagrange Condition 379 20.5 Second-Order Conditions 387 20.6 Minimizing Quadratics Subject to Linear Constraints 390 Exercises 394 21 Problems with Inequality Constraints 399 21.1 Karush–Kuhn–Tucker Condition 399 21.2 Second-Order Conditions 406 Exercises 410 22 Convex Optimization Problems 417 22.1 Introduction 417 22.2 Convex Functions 419 22.3 Convex Optimization Problems 426 22.4 Semidefinite Programming 431 22.4.1 Linear Matrix Inequalities and Their Properties 431 22.4.2 LMI Solvers 435 22.4.2.1 Finding a Feasible Solution Under LMI Constraints 436 22.4.2.2 Minimizing a Linear Objective Under LMI Constraints 438 22.4.2.3 Minimizing a Generalized Eigenvalue Under LMI Constraints 440 Exercises 442 23 Lagrangian Duality 449 23.1 Overview 449 23.2 Notation 449 23.3 Primal–Dual Pair 450 23.4 General Duality Properties 451 23.4.1 Convexity of Dual Problem 451 23.4.2 Primal Objective in Terms of Lagrangian 451 23.4.3 Minimax Inequality Chain 452 23.4.4 Optimality of Saddle Point 452 23.4.5 Weak Duality 453 23.4.6 Duality Gap 453 23.5 Strong Duality 454 23.5.1 Strong Duality ⇔ Minimax Equals Maximin 454 23.5.2 Strong Duality ⇒ Primal Unconstrained Minimization 455 23.5.3 Strong Duality ⇒ Optimality 455 23.5.4 Strong Duality ⇒ KKT (Including Complementary Slackness) 455 23.5.5 Strong Duality ⇒ Saddle Point 456 23.6 Convex Case 456 23.6.1 Convex Case: KKT ⇒ Strong Duality 456 23.6.2 Convex Case: Regular Optimal Primal ⇒ Strong Duality 457 23.6.3 Convex Case: Slater’s Condition ⇒ Strong Duality 457 23.7 Summary of Key Results 457 Exercises 458 24 Algorithms for Constrained Optimization 459 24.1 Introduction 459 24.2 Projections 459 24.3 Projected Gradient Methods with Linear Constraints 462 24.4 Convergence of Projected Gradient Algorithms 465 24.4.1 Fixed Points and First-Order Necessary Conditions 466 24.4.2 Convergence with Fixed Step Size 468 24.4.3 Some Properties of Projections 469 24.4.4 Armijo Condition 470 24.4.5 Accumulation Points 471 24.4.6 Projections in the Convex Case 472 24.4.7 Armijo Condition in the Convex Case 474 24.4.8 Convergence in the Convex Case 480 24.4.9 Convergence Rate with Line-Search Step Size 481 24.5 Lagrangian Algorithms 483 24.5.1 Lagrangian Algorithm for Equality Constraints 484 24.5.2 Lagrangian Algorithm for Inequality Constraints 486 24.6 Penalty Methods 489 Exercises 495 25 Multiobjective Optimization 499 25.1 Introduction 499 25.2 Pareto Solutions 499 25.3 Computing the Pareto Front 501 25.4 From Multiobjective to Single-Objective Optimization 505 25.5 Uncertain Linear Programming Problems 508 25.5.1 Uncertain Constraints 508 25.5.2 Uncertain Objective Function Coefficients 511 25.5.3 Uncertain Constraint Coefficients 513 25.5.4 General Uncertainties 513 Exercises 513 Part V Optimization in Machine Learning 517 26 Machine Learning Problems and Feature Engineering 519 26.1 Machine Learning Problems 519 26.1.1 Data with Labels and Supervised Learning 519 26.1.2 Data Without Labels and Unsupervised Learning 521 26.2 Data Normalization 522 26.3 Histogram of Oriented Gradients 524 26.4 Principal Component Analysis and Linear Autoencoder 526 26.4.1 Singular Value Decomposition 526 26.4.2 Principal Axes and Principal Components of a Data Set 527 26.4.3 Linear Autoencoder 529 Exercises 530 27 Stochastic Gradient Descent Algorithms 537 27.1 Stochastic Gradient Descent Algorithm 537 27.2 Stochastic Variance Reduced Gradient Algorithm 540 27.3 Distributed Stochastic Variance Reduced Gradient 542 27.3.1 Distributed Learning Environment 542 27.3.2 SVRG in Distributed Optimization 543 27.3.3 Communication Versus Computation 545 27.3.4 Data Security 545 Exercises 546 28 Linear Regression and Its Variants 553 28.1 Least-Squares Linear Regression 553 28.1.1 A Linear Model for Prediction 553 28.1.2 Training the Model 554 28.1.3 Computing Optimal ̂w 554 28.1.4 Optimal Predictor and Performance Evaluation 555 28.1.5 Least-Squares Linear Regression for Data Sets with Vector Labels 556 28.2 Model Selection by Cross-Validation 559 28.3 Model Selection by Regularization 562 Exercises 564 29 Logistic Regression for Classification 569 29.1 Logistic Regression for Binary Classification 569 29.1.1 Least-Squares Linear Regression for Binary Classification 569 29.1.2 Logistic Regression for Binary Classification 570 29.1.3 Interpreting Logistic Regression by Log Error 572 29.1.4 Confusion Matrix for Binary Classification 573 29.2 Nonlinear Decision Boundary via Linear Regression 575 29.2.1 Least-Squares Linear Regression with Nonlinear Transformation 576 29.2.2 Logistic Regression with Nonlinear Transformation 578 29.3 Multicategory Classification 580 29.3.1 One-Versus-All Multicategory Classification 580 29.3.2 Softmax Regression for Multicategory Classification 581 Exercises 584 30 Support Vector Machines 589 30.1 Hinge-Loss Functions 589 30.1.1 Geometric Interpretation of the Linear Model 589 30.1.2 Hinge Loss for Binary Data Sets 590 30.1.3 Hinge Loss for Multicategory Data Sets 592 30.2 Classification by Minimizing Hinge Loss 593 30.2.1 Binary Classification by Minimizing Average Hinge Loss 593 30.2.2 Multicategory Classification by Minimizing E hww or E hcs 594 30.3 Support Vector Machines for Binary Classification 596 30.3.1 Hard-Margin Support Vector Machines 596 30.3.2 Support Vectors 598 30.3.3 Soft-Margin Support Vector Machines 599 30.3.4 Connection to Hinge-Loss Minimization 602 30.4 Support Vector Machines for Multicategory Classification 602 30.5 Kernel Trick 603 30.5.1 Kernels 603 30.5.2 Kernel Trick 604 30.5.3 Learning with Kernels 605 30.5.3.1 Regularized Logistic Regression with Nonlinear Transformation for Binary Classification 605 30.5.3.2 Regularized Hinge-Loss Minimization for Binary Classification 606 Exercises 607 31 K-Means Clustering 611 31.1 K-Means Clustering 611 31.2 K-Means++ forCenterInitialization 615 31.3 Variants of K-Means Clustering 617 31.3.1 K-Means Clustering Based on 1-Norm Regularization 617 31.3.2 PCA-Guided K-Means Clustering 619 31.4 Image Compression by Vector Quantization and K-Means Clustering 622 Exercises 623 References 627 Index 635

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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 SynopsisTable of ContentsForeword xxix Preface xxxi Introduction xxxv Part I Technical Foundations 1 Chapter 1 Introduction to Concepts and Relationships 3 Roles and Responsibilities 4 Network and Wireless Architects 4 Security, Risk, and Compliance Roles 5 Operations and Help Desk Roles 8 Support Roles 9 External and Third Parties 9 Security Concepts for Wireless Architecture 11 Security and IAC Triad in Wireless 11 Aligning Wireless Architecture Security to Organizational Risk 14 Factors Influencing Risk Tolerance 15 Assigning a Risk Tolerance Level 15 Considering Compliance and Regulatory Requirements 17 Compliance Regulations, Frameworks, and Audits 17 The Role of Policies, Standards, and Procedures 19 Segmentation Concepts 22 Authentication Concepts 23 Cryptography Concepts 27 Wireless Concepts for Secure Wireless Architecture 30 NAC and IEEE 802.1X in Wireless 33 SSID Security Profiles 34 Security 35 Endpoint Devices 35 Network Topology and Distribution of Users 37 Summary 43 Chapter 2 Understanding Technical Elements 45 Understanding Wireless Infrastructure and Operations 45 Management vs. Control vs. Data Planes 46 Cloud-Managed Wi-Fi and Gateways 48 Controller Managed Wi-Fi 52 Local Cluster Managed Wi-Fi 53 Remote APs 55 Summary 55 Understanding Data Paths 56 Tunneled 58 Bridged 59 Considerations of Bridging Client Traffic 59 Hybrid and Other Data Path Models 61 Filtering and Segmentation of Traffic 62 Summary 71 Understanding Security Profiles for SSIDs 72 WPA2 and WPA3 Overview 73 Transition Modes and Migration Strategies for Preserving Security 76 Enterprise Mode (802.1X) 77 Personal Mode (Passphrase with PSK/SAE) 87 Open Authentication Networks 94 Chapter 3 Understanding Authentication and Authorization 101 The IEEE 802.1X Standard 102 Terminology in 802.1X 103 High-Level 802.1X Process in Wi-Fi Authentication 105 RADIUS Servers, RADIUS Attributes, and VSAs 107 RADIUS Servers 107 RADIUS Servers and NAC Products 108 Relationship of RADIUS, EAP, and Infrastructure Devices 110 RADIUS Attributes 111 RADIUS Vendor-Specific Attributes 115 RADIUS Policies 116 RADIUS Servers, Clients and Shared Secrets 118 Other Requirements 121 Additional Notes on RADIUS Accounting 122 Change of Authorization and Disconnect Messages 123 EAP Methods for Authentication 127 Outer EAP Tunnels 129 Securing Tunneled EAP 132 Inner Authentication Methods 133 Legacy and Unsecured EAP Methods 137 Recommended EAP Methods for Secure Wi-Fi 138 MAC-Based Authentications 140 MAC Authentication Bypass with RADIUS 140 MAC Authentication Without RADIUS 147 MAC Filtering and Denylisting 147 Certificates for Authentication and Captive Portals 148 RADIUS Server Certificates for 802.1X 148 Endpoint Device Certificates for 802.1X 151 Best Practices for Using Certificates for 802.1X 152 Captive Portal Server Certificates 158 Best Practices for Using Certificates for Captive Portals 159 In Most Cases, Use a Public Root CA Signed Server Certificate 159 Understand the Impact of MAC Randomization on Captive Portals 159 Captive Portal Certificate Best Practices Recap 161 Summary 162 Captive Portal Security 163 Captive Portals for User or Guest Registration 163 Captive Portals for Acceptable Use Policies 165 Captive Portals for BYOD 166 Captive Portals for Payment Gateways 167 Security on Open vs. Enhanced Open Networks 167 Access Control for Captive Portal Processes 167 LDAP Authentication for Wi-Fi 168 The 4-Way Handshake in Wi-Fi 168 The 4-Way Handshake Operation 168 The 4-Way Handshake with WPA2-Personal and WPA3-Personal 170 The 4-Way Handshake with WPA2-Enterprise and WPA3-Enterprise 171 Summary 171 Chapter 4 Understanding Domain and Wi-Fi Design Impacts 173 Understanding Network Services for Wi-Fi 173 Time Sync Services 174 Time Sync Services and Servers 175 Time Sync Uses in Wi-Fi 175 DNS Services 177 DHCP Services 180 DHCP for Wi-Fi Clients 181 Planning DHCP for Wi-Fi Clients 184 DHCP for AP Provisioning 185 Certificates 186 Understanding Wi-Fi Design Impacts on Security 187 Roaming Protocols’ Impact on Security 188 Fast Roaming Technologies 193 System Availability and Resiliency 203 RF Design Elements 205 AP Placement, Channel, and Power Settings 205 Wi-Fi 6E 207 Rate Limiting Wi-Fi 208 Other Networking, Discovery, and Routing Elements 213 Summary 217 Part II Putting It All Together 219 Chapter 5 Planning and Design for Secure Wireless 221 Planning and Design Methodology 222 Discover Stage 223 Architect Stage 224 Iterate Stage 225 Planning and Design Inputs (Define and Characterize) 227 Scope of Work/Project 228 Teams Involved 230 Organizational Security Requirements 233 Current Security Policies 235 Endpoints 236 Users 239 System Security Requirements 239 Applications 240 Process Constraints 240 Wireless Management Architecture and Products 241 Planning and Design Outputs (Design, Optimize, and Validate) 241 Wireless Networks (SSIDs) 247 System Availability 249 Additional Software or Tools 249 Processes and Policy Updates 250 Infrastructure Hardening 251 Correlating Inputs to Outputs 252 Planning Processes and Templates 254 Requirements Discovery Template (Define and Characterize) 254 Sample Network Planning Template (SSID Planner) 261 Sample Access Rights Planning Templates 262 Notes for Technical and Executive Leadership 267 Planning and Budgeting for Wireless Projects 268 Consultants and Third Parties Can Be Invaluable 271 Selecting Wireless Products and Technologies 271 Expectations for Wireless Security 275 Summary 279 Chapter 6 Hardening the Wireless Infrastructure 281 Securing Management Access 282 Enforcing Encrypted Management Protocols 283 Eliminating Default Credentials and Passwords 293 Controlling Administrative Access and Authentication 296 Securing Shared Credentials and Keys 301 Addressing Privileged Access 303 Additional Secure Management Considerations 307 Designing for Integrity of the Infrastructure 308 Managing Configurations, Change Management, and Backups 309 Configuring Logging, Reporting, Alerting, and Automated Responses 313 Verifying Software Integrity for Upgrades and Patches 314 Working with 802.11w Protected Management Frames 316 Provisioning and Securing APs to Manager 321 Adding Wired Infrastructure Integrity 325 Planning Physical Security 331 Locking Front Panel and Console Access on Infrastructure Devices 334 Disabling Unused Protocols 337 Controlling Peer-to- Peer and Bridged Communications 339 A Note on Consumer Products in the Enterprise 339 Blocking Ad-Hoc Networks 341 Blocking Wireless Bridging on Clients 342 Filtering Inter-Station Traffic, Multicast, and mDNS 344 Best Practices for Tiered Hardening 353 Additional Security Configurations 354 Security Monitoring, Rogue Detection, and WIPS 355 Considerations for Hiding or Cloaking SSIDs 356 Requiring DHCP for Clients 359 Addressing Client Credential Sharing and Porting 360 Summary 362 Part III Ongoing Maintenance and Beyond 365 Chapter 7 Monitoring and Maintenance of Wireless Networks 367 Security Testing and Assessments of Wireless Networks 367 Security Audits 368 Vulnerability Assessments 370 Security Assessments 373 Penetration Testing 375 Ongoing Monitoring and Testing 376 Security Monitoring and Tools for Wireless 376 Wireless Intrusion Prevention Systems 377 Recommendations for WIPS 404 Synthetic Testing and Performance Monitoring 405 Security Logging and Analysis 407 Wireless-Specific Tools 410 Logging, Alerting, and Reporting Best Practices 416 Events to Log for Forensics or Correlation 417 Events to Alert on for Immediate Action 419 Events to Report on for Analysis and Trending 422 Troubleshooting Wi-Fi Security 424 Troubleshooting 802.1X/EAP and RADIUS 425 Troubleshooting MAC-based Authentication 428 Troubleshooting Portals, Onboarding, and Registration 431 Troubleshooting with Protected Management Frames Enabled 431 Training and Other Resources 432 Technology Training Courses and Providers 432 Vendor-Specific Training and Resources 435 Conferences and Community 436 Summary 437 Chapter 8 Emergent Trends and Non-Wi- Fi Wireless 439 Emergent Trends Impacting Wireless 440 Cloud-Managed Edge Architectures 440 Remote Workforce 441 Process Changes to Address Remote Work 443 Recommendations for Navigating a Remote Workforce 444 Bring Your Own Device 445 Zero Trust Strategies 455 Internet of Things 463 Enterprise IoT Technologies and Non-802.11 Wireless 465 IoT Considerations 466 Technologies and Protocols by Use Case 467 Features and Characteristics Impact on Security 502 Other Considerations for Secure IoT Architecture 507 Final Thoughts from the Book 508 Appendix A Notes on Configuring 802.1X with Microsoft NPS 513 Wi-Fi Infrastructure That Supports Enterprise (802.1X) SSID Security Profiles 513 Endpoints That Support 802.1X/EAP 514 A Way to Configure the Endpoints for the Specified Connectivity 515 An Authentication Server That Supports RADIUS 517 Appendix B Additional Resources 521 IETF RFCs 521 IEEE Standards and Documents 522 Wi-Fi Alliance 524 Blog, Consulting, and Book Materials 524 Compliance and Mappings 525 Cyber Insurance and Network Security 528 Appendix C Sample Architectures 531 Architectures for Internal Access Networks 532 Managed User with Managed Device 533 Headless/Non-User- Based Devices 539 Contractors and Third Parties 544 BYOD/Personal Devices with Internal Access 547 Guidance on WPA2-Enterprise and WPA3-Enterprise 549 Guidance on When to Separate SSIDs 550 Architectures for Guest/Internet-only Networks 551 Guest Networks 551 BYOD/Personal Devices with Internet-only Access 553 Determining Length of a WPA3-Personal Passphrase 555 Appendix D Parting Thoughts and Call to Action 559 The Future of Cellular and Wi-Fi 559 MAC Randomization 562 Index 567

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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 SynopsisTable of ContentsAcknowledgments x Preface xi About the Companion Website xiv Chapter 1 Introduction to Quantum Mechanics 1 1.1 Introduction 2 1.2 The Classical Electron 2 1.3 Two-Slit Electron Experiment 4 1.4 The Photoelectric Effect 8 1.5 Wave-Packets and Uncertainty 11 1.6 The Wavefunction 13 1.7 The Schrödinger Equation 15 1.8 The Electron in a One-Dimensional Well 19 1.9 The Hydrogen Atom 25 1.10 Electron Transmission and Reflection at Potential Energy Step 30 1.11 Spin 32 1.12 The Pauli Exclusion Principle 35 1.13 Operators and the Postulates of Quantum Mechanics 36 1.14 Expectation Values and Hermitian Operators 38 1.15 Summary 40 Problems 42 Note 45 Suggestions for Further Reading 45 Chapter 2 Semiconductor Physics 46 2.1 Introduction 47 2.2 The Band Theory of Solids 48 2.3 Bloch Functions 49 2.4 The Kronig–Penney Model 52 2.5 The Bragg Model 57 2.6 Effective Mass in Three Dimensions 59 2.7 Number of States in a Band 61 2.8 Band Filling 63 2.9 Fermi Energy and Holes 65 2.10 Carrier Concentration 66 2.11 Semiconductor Materials 78 2.12 Semiconductor Band Diagrams 80 2.13 Direct Gap and Indirect Gap Semiconductors 82 2.14 Extrinsic Semiconductors 86 2.15 Carrier Transport in Semiconductors 91 2.16 Equilibrium and Nonequilibrium Dynamics 95 2.17 Carrier Diffusion and the Einstein Relation 98 2.18 Quasi-Fermi Energies 101 2.19 The Diffusion Equation 104 2.20 Traps and Carrier Lifetimes 107 2.21 Alloy Semiconductors 111 2.23 Summary 114 Problems 116 Suggestions for Further Reading 122 Chapter 3 The p-n Junction Diode 123 3.1 Introduction 124 3.2 Diode Current 125 3.3 Contact Potential 130 3.4 The Depletion Approximation 132 3.5 The Diode Equation 141 3.6 Reverse Breakdown and the Zener Diode 153 3.7 Tunnel Diodes 156 3.8 Generation/Recombination Currents 158 3.9 Metal-Semiconductor Junctions 161 3.10 Heterojunctions 172 3.11 Alternating Current (AC) and Transient Behavior 173 3.12 Summary 176 Problems 177 Note 181 Suggestions for Further Reading 181 Chapter 4 Photon Emission and Absorption 182 4.1 Introduction to Luminescence and Absorption 183 4.2 Physics of Light Emission 184 4.3 Simple Harmonic Radiator 187 4.4 Quantum Description 188 4.5 The Exciton 192 4.6 Two-Electron Atoms and the Exchange Interaction 195 4.7 Molecular Excitons 202 4.8 Band-to-Band Transitions 205 4.9 Photometric Units 210 4.10 Summary 214 Problems 215 Note 219 Suggestions for Further Reading 219 Chapter 5 Semiconductor Devices Based on the p-n Junction 220 5.1 Introduction 221 5.2 The p-n Junction Solar Cell 222 5.3 Light Absorption 224 5.4 Solar Radiation 226 5.5 Solar Cell Design and Analysis 227 5.6 Solar Cell Efficiency Limits and Tandem Cells 234 5.7 The Light Emitting Diode 236 5.8 Emission Spectrum 239 5.9 Non-Radiative Recombination 240 5.10 Optical Outcoupling 241 5.11 GaAs LEDs 244 5.12 GaP:N LEDs 245 5.13 Double Heterojunction Al X Ga 1−x as Leds 246 5.14 AlGaInP LEDs 251 5.15 Ga 1−x in X N Leds 253 5.16 Bipolar Junction Transistor 257 5.17 Junction Field Effect Transistor 266 5.18 BJT and JFET Symbols and Applications 270 5.19 Summary 271 Problems 274 Further Reading 282 Chapter 6 The Metal Oxide Semiconductor Field Effect Transistor 283 6.1 Introduction to the MOSFET 284 6.2 MOSFET Physics 286 6.3 MOS Capacitor Analysis 288 6.4 Accumulation Layer and Inversion Layer Thicknesses 297 6.5 Capacitance of MOS Capacitor 301 6.6 Work Functions, Trapped Charges, and Ion Beam Implantation 303 6.7 Surface Mobility 304 6.8 MOSFET Transistor Characteristics 307 6.9 MOSFET Scaling 312 6.10 Nanoscale Photolithography 313 6.11 Ion Beam Implantation 321 6.12 MOSFET Fabrication 323 6.13 CMOS Structures 328 6.14 Threshold Voltage Adjustment 329 6.15 Two-Dimensional Electron Gas 331 6.16 Modeling Nanoscale MOSFETs 336 6.17 Flash Memory 338 6.18 Tunneling 340 6.19 Summary 348 Problems 350 Notes 352 Recommended Reading 352 Chapter 7 The Quantum Dot 353 7.1 Introduction and Overview 354 7.2 Quantum Dot Semiconductor Materials 356 7.3 Synthesis of Quantum Dots 357 7.4 Quantum Dot Confinement Physics 363 7.5 Franck-Condon Principle and the Stokes Shift 369 7.6 The Quantum Mechanical Oscillator 376 7.7 Vibronic Transitions 379 7.8 Surface Passivation 383 7.9 Auger Processes 389 7.10 Biological Applications of Quantum Dots 396 7.11 Summary 397 Problems 398 Recommended Reading 399 Chapter 8 Organic Semiconductor Materials and Devices 400 8.1 Introduction to Organic Electronics 401 8.2 Conjugated Systems 402 8.3 Polymer OLEDs 408 8.4 Small-Molecule OLEDs 413 8.5 Anode Materials 417 8.6 Cathode Materials 417 8.7 Hole Injection Layer 418 8.8 Electron Injection Layer 420 8.9 Hole Transport Layer 420 8.10 Electron Transport Layer 422 8.11 Light Emitting Material Processes 424 8.12 Host Materials 426 8.13 Fluorescent Dopants 428 8.14 Phosphorescent and Thermally Activated Delayed Fluorescence Dopants 430 8.15 Organic Solar Cells 434 8.16 Organic Solar Cell Materials 439 8.17 The Organic Field Effect Transistor 443 8.18 Summary 446 Problems 450 Notes 455 Suggestions for Further Reading 455 Chapter 9 One- and Two-Dimensional Semiconductor Materials and Devices 456 9.1 Introduction 457 9.2 Linear Combination of Atomic Orbitals 458 9.3 Density Functional Theory 465 9.4 Transition Metal Dichalcogenides 467 9.5 Multigate MOSFETs 472 9.6 Summary 476 Problems 477 Recommended Reading 478 Appendix 1: Physical Constants 479 Appendix 2: Derivation of the Uncertainty Principle 480 Appendix 3: Derivation of Group Velocity 484 Appendix 4: Reduced Mass 486 Appendix 5: The Boltzmann Distribution Function 488 Appendix 6: Properties of Semiconductor Materials 494 Appendix 7: Calculation of the Bonding and Antibonding Orbital Energies Versus Interproton Separation for the Hydrogen Molecular Ion 496 Index 501

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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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Book SynopsisThe must-have reference for Azure newcomers As Microsoft's Azure platform takes a larger stake in the cloud computing world, more tech pros need to know the ins-and-outs of this fast-growing platform. Microsoft Azure For Dummies is the essential guide for users who are new to the platform. Take your first steps into the world of Azure as you learn all about the core servicesstraight from a Microsoft expert. This book covers the Azure essentials you need to know, including building a virtual network on Azure, launching and scaling applications, migrating existing services, and keeping everything secure. In classic Dummies style, you'll learn the fundamentals of Azure's core services andwhen you're readyhow to move into more advanced services. Discover the basics of cloud computing with Microsoft Azure and learn what services you can access with AzureBuild your cloud network with Azure and migrate an existing network to the platformScale applications seamlessly and make sure your security is air-tightUpdated to included expanded information on data resources, machine learning, artificial intelligence, and collaboration, Microsoft Azure For Dummies, 2nd Edition answers the call for an entry-level, comprehensive guide that provides a simple-to-understand primer on core Azure services. It's an invaluable resource for IT managers and others arriving at the platform for the first time.Table of ContentsIntroduction 1 Part 1: Getting Started with Microsoft Azure 5 Chapter 1: Introducing Microsoft Azure 7 Chapter 2: Exploring Azure Resource Manager 29 Part 2: Deploying Infrastructure Services to Microsoft Azure 49 Chapter 3: Managing Storage in Azure 51 Chapter 4: Planning Your Virtual Network Topology 71 Chapter 5: Deploying and Configuring Azure Virtual Machines 99 Chapter 6: Shipping Docker Containers in Azure 129 Part 3: Deploying Platform Resources to Microsoft Azure 151 Chapter 7: Deploying and Configuring Azure App Service Apps 153 Chapter 8: Running Serverless Apps in Azure 179 Chapter 9: Managing Databases in Microsoft Azure 193 Chapter 10: Using Data Analytics and Machine Learning in Azure 215 Part 4: Providing High Availability, Scalability, and Security for Your Azure Resources 237 Chapter 11: Protecting the Azure Environment 239 Chapter 12: Managing Identity and Access with Azure Active Directory 265 Chapter 13: Implementing Azure Governance 285 Part 5: Going Beyond the Basics in Microsoft Azure 303 Chapter 14: Discovering DevOps in Microsoft Azure 305 Chapter 15: Monitoring Your Azure Environment 319 Chapter 16: Extending Your On-Premises Environment to Azure 343 Part 6: The Part of Tens 369 Chapter 17: Top Ten Azure Technology Opportunities to Watch 371 Chapter 18: Ten Ways to Optimize an Azure Environment 381 Index 389

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Book SynopsisNetwork Science Network Science offers comprehensive insight on network analysis and network optimization algorithms, with simple step-by-step guides and examples throughout, and a thorough introduction and history of network science, explaining the key concepts and the type of data needed for network analysis, ensuring a smooth learning experience for readers. It also includes a detailed introduction to multiple network optimization algorithms, including linear assignment, network flow and routing problems. The text is comprised of five chapters, focusing on subgraphs, network analysis, network optimization, and includes a list of case studies, those of which include influence factors in telecommunications, fraud detection in taxpayers, identifying the viral effect in purchasing, finding optimal routes considering public transportation systems, among many others. This insightful book shows how to apply algorithms to solve complex problems in real-life scenarios andTable of ContentsPreface x Acknowledgments xiii About the Author xiv About the Book xv 1 Concepts in Network Science 1 1.1 Introduction 1 1.2 The Connector 2 1.3 History 3 1.3.1 A History in Social Studies 4 1.4 Concepts 5 1.4.1 Characteristics of Networks 7 1.4.2 Properties of Networks 7 1.4.3 Small World 8 1.4.4 Random Graphs 11 1.5 Network Analytics 12 1.5.1 Data Structure for Network Analysis and Network Optimization 13 1.5.1.1 Multilink and Self-Link 14 1.5.1.2 Loading and Unloading the Graph 15 1.5.2 Options for Network Analysis and Network Optimization Procedures 15 1.5.3 Summary Statistics 16 1.5.3.1 Analyzing the Summary Statistics for the Les Misérables Network 17 1.6 Summary 21 2 Subnetwork Analysis 23 2.1 Introduction 23 2.1.1 Isomorphism 25 2.2 Connected Components 26 2.2.1 Finding the Connected Components 27 2.3 Biconnected Components 35 2.3.1 Finding the Biconnected Components 36 2.4 Community 38 2.4.1 Finding Communities 45 2.5 Core 58 2.5.1 Finding k-Cores 59 2.6 Reach Network 62 2.6.1 Finding the Reach Network 65 2.7 Network Projection 70 2.7.1 Finding the Network Projection 72 2.8 Node Similarity 77 2.8.1 Computing Node Similarity 82 2.9 Pattern Matching 88 2.9.1 Searching for Subgraphs Matches 91 2.10 Summary 98 3 Network Centralities 101 3.1 Introduction 101 3.2 Network Metrics of Power and Influence 102 3.3 Degree Centrality 103 3.3.1 Computing Degree Centrality 103 3.3.2 Visualizing a Network 110 3.4 Influence Centrality 114 3.4.1 Computing the Influence Centrality 115 3.5 Clustering Coefficient 121 3.5.1 Computing the Clustering Coefficient Centrality 121 3.6 Closeness Centrality 124 3.6.1 Computing the Closeness Centrality 124 3.7 Betweenness Centrality 129 3.7.1 Computing the Between Centrality 130 3.8 Eigenvector Centrality 136 3.8.1 Computing the Eigenvector Centrality 137 3.9 PageRank Centrality 144 3.9.1 Computing the PageRank Centrality 144 3.10 Hub and Authority 151 3.10.1 Computing the Hub and Authority Centralities 152 3.11 Network Centralities Calculation by Group 157 3.11.1 By Group Network Centralities 158 3.12 Summary 164 4 Network Optimization 167 4.1 Introduction 167 4.1.1 History 167 4.1.2 Network Optimization in SAS Viya 170 4.2 Clique 170 4.2.1 Finding Cliques 172 4.3 Cycle 176 4.3.1 Finding Cycles 177 4.4 Linear Assignment 179 4.4.1 Finding the Minimum Weight Matching in a Worker-Task Problem 181 4.5 Minimum-Cost Network Flow 185 4.5.1 Finding the Minimum-Cost Network Flow in a Demand–Supply Problem 188 4.6 Maximum Network Flow Problem 194 4.6.1 Finding the Maximum Network Flow in a Distribution Problem 195 4.7 Minimum Cut 199 4.7.1 Finding the Minimum Cuts 201 4.8 Minimum Spanning Tree 205 4.8.1 Finding the Minimum Spanning Tree 206 4.9 Path 208 4.9.1 Finding Paths 211 4.10 Shortest Path 220 4.10.1 Finding Shortest Paths 223 4.11 Transitive Closure 235 4.11.1 Finding the Transitive Closure 236 4.12 Traveling Salesman Problem 239 4.12.1 Finding the Optimal Tour 243 4.13 Vehicle Routing Problem 249 4.13.1 Finding the Optimal Vehicle Routes for a Delivery Problem 253 4.14 Topological Sort 265 4.14.1 Finding the Topological Sort in a Directed Graph 266 4.15 Summary 268 5 Real-World Applications in Network Science 271 5.1 Introduction 271 5.2 An Optimal Tour Considering a Multimodal Transportation System – The Traveling Salesman Problem Example in Paris 272 5.3 An Optimal Beer Kegs Distribution – The Vehicle Routing Problem Example in Asheville 285 5.4 Network Analysis and Supervised Machine Learning Models to Predict COVID-19 Outbreaks 298 5.5 Urban Mobility in Metropolitan Cities 306 5.6 Fraud Detection in Auto Insurance Based on Network Analysis 312 5.7 Customer Influence to Reduce Churn and Increase Product Adoption 320 5.8 Community Detection to Identify Fraud Events in Telecommunications 324 5.9 Summary 328 Index 329

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Book SynopsisTutorial for analytical and scientific approaches related to LEO satellites ground station performance, including math, experiments, and simulations. Ground Station Design and Analysis for LEO satellites provides complete instructions and steps for ground station performance evaluation, including stations dedicated for scientific or communication purposes, and offers the reader an enhanced learning experience by proposing 40 ideas related to ground station performance assessment. Each idea goes over the math analysis, experiment or simulation, the methodology applied, the results, and a conclusion. This approach provides the reader with the opportunity to compare theoretical results with on-site results, guiding the reader towards intelligent and practical performance evaluation and enhancement. The text also considers the future emerging developments of LEO satellites and their challenges and applications, including multimedia and other scientific applications.Table of ContentsPreface x Acknowledgments xiv 1 LEO Satellite Ground Station Design Concepts 1 1.1 An Overview of LEO Satellites 1 1.2 Satellite System Architecture 4 1.3 The Satellite Ground Station 8 1.4 Ground Station Subsystems 11 1.4.1 Antennas 11 1.4.2 Low Noise Amplifier 11 1.4.3 Converters 12 1.4.4 Safety System 13 1.5 Downlink Budget 14 1.5.1 Error-Performance 15 1.5.2 Received Signal Power 15 1.5.3 Link Budget Analyses 18 1.6 Figure of Merit and System Noise Temperature 19 1.7 Satellite and Ground Station Geometry 25 1.8 LEO MOST Satellite and Ground Stations 29 References 31 2 Rain Attenuation 35 2.1 Rain Attenuation Concepts 35 2.2 Rain Attenuation for LEO Satellite Ground Station 38 2.3 Rain Attenuation Modeling for LEO Satellite Ground Station 41 References 44 3 Downlink Performance 47 3.1 Downlink Performance Definition 47 3.2 Composite Noise Temperature at LEO Satellite Ground Station 47 3.3 Antenna Noise Temperature at LEO Satellite Ground Station 49 3.4 Downlink Performance – Figure of Merit 51 3.5 Downlink Performance: Signal-to-Noise Ratio (S/N) 54 3.6 Downlink and Uplink Antenna Separation 58 3.7 Desensibilization by Uplink Signal at LEO Satellite Ground Station 59 3.8 Downlink and Uplink Frequency Isolation 61 3.9 Sun Noise Measurement at LEO Satellite Ground Station 63 References 69 4 Horizon Plane and Communication Duration 71 4.1 LEO Satellite Tracking Principles 71 4.2 Ideal Horizon Plane and Communication Duration with LEO Satellites 78 4.3 The Range and Horizon Plane Simulation for Ground Stations of LEO Satellites 81 4.4 Practical Horizon Plane for LEO Ground Stations 83 4.5 Real Communication Duration and Designed Horizon Plane Determination 87 4.6 Ideal and Designed Horizon Plane Relation in Space 88 4.7 Savings on Transmit Power through Designed Horizon Plane at LEO Satellite Ground Stations 93 4.8 Elevation Impact on Signal-to-Noise Density Ratio for LEO Satellite Ground Stations 96 References 100 5 LEO Coverage 103 5.1 LEO Coverage Concept 103 5.2 LEO Coverage Geometry 104 5.3 The Coverage of LEO Satellites at Low Elevation 105 5.4 Coverage Belt 107 5.5 LEO Global Coverage 109 5.6 Constellation’s Coverage – Starlink Case 113 5.7 Handover-Takeover Process: Geometrical Interpretation and Confirmation 115 References 118 6 LEOs Sun Synchronization 121 6.1 Orbital Sun Synchronization Concept 121 6.2 Orbital Nodal Regression 124 6.3 LEO Sun Synchronization and Inclination Window 127 6.4 Perigee Deviation under Inclination Window for Sun-Synchronized LEOs 129 References 132 7 Launching Process 133 7.1 Introduction to the Launching Process 133 7.2 Injection Velocity and Apogee Simulation from Low Earth Orbits 137 7.3 Hohmann Coplanar Transfer from Low Earth Orbits 141 7.4 The GEO Altitude Attainment and Inclination Alignment 145 7.4.1 Circularization and the Altitude Attainment 147 7.4.2 Inclination Alignment 150 References 151 8 LEO Satellites for Search and Rescue Services 153 8.1 Introduction to LEO Satellites for Search and Rescue Services 153 8.2 SARSAT System 154 8.2.1 SARSAT Space Segment 155 8.2.2 SARSAT Ground Segment 157 8.2.3 Beacons 160 8.3 Doppler Shift 162 8.4 Local User Terminal (LUT) Simulation for LEO Satellites 165 8.5 Missed Passes for SARSAT System 170 8.6 LEOSAR Versus MEOSAR 174 References 178 9 Interference Aspects 181 9.1 General Interference Aspects 181 9.2 Intermodulation Products 183 9.3 Intermodulation by Uplink Signal at LEO Satellite Ground Stations 185 9.4 Modeling of Interference Caused by Uplink Signal for LEO Satellite Ground Stations 189 9.5 Downlink Adjacent Interference for LEO Satellites 193 9.6 Adjacent Satellites Interference (Identification/Avoiding) 195 9.6.1 Adjacent Interference Identification and Duration Interval 198 9.7 Modulation Index Application for Downlink Interference Identification 200 9.7.1 Simulation Approach of Interference Events and Timelines 202 9.8 Uplink Interference Identification for LEO Search and Rescue Satellites 205 References 207 10 Two More Challenges 209 10.1 Introduction to the Two Challenges 209 10.2 Downlink Free Space Loss Compensation 209 10.3 Horizon Plane Width: New Parameter for LEO Satellite Ground Station Geometry 214 References 217 11 Closing Remarks 219 References 222 Index 224

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Book SynopsisEnergy Smart Appliances Enables designers and manufacturers to manage real-world energy performance and expectations by covering a range of potential scenarios and challenges Energy Smart Appliances provides utilities and appliance manufacturers, and designers with new approaches to better understand real-world performance, assess actual energy benefits, and tailor each technology to the needs of their customers. With contributions from a fully international group of experts, including heads of prestigious research organizations and leading universities, and innovation managers of the main appliance manufacturers, Energy Smart Appliances includes discussion on: Enabling technologies for energy smart appliances, covering IoT devices and technology and active energy efficiency measures in residential environments Smart home and appliances, answering questions like Where are we heading in terms of the overall smart homes' future?' and Table of ContentsAbout the Editors xv List of Contributors xvii Acknowledgments xxi Introduction xxiii Antonio Moreno-Munoz and Neomar Giacomini 1 Demand-Side Flexibility in Smart Grids 1 Antonio Moreno-Munoz and Joaquin Garrido-Zafra 1.1 The Energy Sector 1 1.2 The Power Grid 2 1.3 The Smart Grid 5 1.4 Power Grid Flexibility 6 1.4.1 The Need for Flexibility 7 1.4.2 Sources of Flexibility 8 1.4.2.1 Flexible Generation 8 1.4.2.2 Flexible Transmission and Grid Interconnection 8 1.4.2.3 Control Over VRES 9 1.4.2.4 Energy Storage Facilities 9 1.4.2.5 Demand-Side Management 9 1.4.2.6 Other Sources of Flexibility 11 1.5 Power Quality, Reliability, and Resilience 12 1.5.1 Power Quality Disturbances 13 1.5.1.1 Transients 14 1.5.1.2 Short-Duration RMS Variation 16 1.5.1.3 Long-Duration RMS Variation 17 1.5.1.4 Imbalance 17 1.5.1.5 Waveform Distortion 18 1.5.1.6 Voltage Fluctuation 19 1.5.1.7 Power Frequency Variations 19 1.6 Economic Implications and Issues of Poor Power Quality 20 1.7 Internet of Things 24 1.8 The Relevance of Submetering 25 1.9 Energy Smart Appliances 26 Symbols and Abbreviations 28 References 29 2 A Deep Dive into the Smart Energy Home 35 Neomar Giacomini 2.1 Smart Home Ecosystem 35 2.2 Enabling Technologies 44 2.3 Limitations 46 2.4 A Look into a Future Anchored in the Past 51 2.5 Conclusion 59 Symbols and Abbreviations 60 Glossary 60 References 61 3 Household Energy Demand Management 65 Esther Palomar, Ignacio Bravo, and Carlos Cruz 3.1 Introduction 65 3.2 Technical Opportunities and Challenges for DSM 67 3.2.1 Software Solutions 67 3.2.2 Hardware Platforms 69 3.2.3 Communication Infrastructures 70 3.2.4 Communication Protocols 74 3.2.5 Security Concerns 79 3.3 Pilots and Experimental Settings 82 3.4 Conclusions 82 Symbols and Abbreviations 83 Glossary 84 References 86 4 Demand-Side Management and Demand Response 93 Neyre Tekbıyık-Ersoy 4.1 Introduction 93 4.2 Demand Response vs. Demand-Side Management 94 4.3 The Need for Demand Response/Demand-Side Management 94 4.4 DSM Strategies 95 4.4.1 Energy Efficiency/Energy Conservation 95 4.4.2 Peak Demand Clipping 96 4.4.3 Demand Valley Filling 96 4.4.4 Load Shifting 97 4.4.5 Flexible Load Shaping 97 4.4.6 Strategic Load Growth 97 4.5 Demand Response Programs 98 4.5.1 Types of Loads: Elastic vs. Non-elastic 98 4.5.2 General Approaches to Demand Response 98 4.5.3 Smart Pricing Models for DR 99 4.6 Smallest Communication Subsystem Enabling DSM: HAN 100 4.6.1 General Structure 100 4.6.2 Enabling Communication Technologies 101 4.7 Smart Metering 102 4.7.1 Smart Meters vs. Conventional Meters 102 4.7.2 What Should Consumers Know About the Advanced Metering Infrastructure 104 4.8 Energy Usage Patterns of Households 104 4.9 Energy Consumption Scheduling 106 4.10 Demand Response Options for Appliances 107 4.11 Bidirectional Effects of Demand Response 108 4.11.1 Value of Demand Response for Balancing Renewable Energy Generation 108 4.11.2 Value of Demand Response for Reducing Household Energy Expenses 109 4.12 Consumer Objections and Wishes Related to Smart Appliances and Demand Response 110 4.13 Costs and Benefits of Demand-Side Management 111 Symbols and Abbreviations 113 Glossary 114 References 114 5 Standardizing Demand-Side Management: The OpenADR Standard and Complementary Protocols 117 Rolf Bienert 5.1 History and Creation of OpenADR 117 5.2 Re-development of OpenADR 2.0 120 5.3 How OpenADR Works 122 5.3.1 Event Service (EiEvent) 125 5.3.2 Opt Service (EiOpt) 127 5.3.3 Report Service (EiReport) 128 5.3.4 Registration Service (EiRegister) 128 5.4 Cybersecurity 130 5.5 Other Standards and Their Interaction with OpenADR and Energy Smart Appliances 131 5.6 Energy Market Aspects for Appliances 139 5.7 Typical DR and DSM Use Cases 140 Symbols and Abbreviations 143 Glossary 144 References 144 6 Energy Smart Appliances 147 Neomar Giacomini 6.1 Energy Smart Appliances 147 6.2 Which Appliances? 148 6.3 Smart Energy Controller 150 6.4 Large Home Appliances 151 6.4.1 Dishwashers 151 6.4.2 Dryers 153 6.4.3 Grills and Smokers 155 6.4.4 Hvac 156 6.4.5 Microwaves 158 6.4.6 Refrigerators and Freezers 160 6.4.7 Stoves, Ovens, and Cooktops 162 6.4.8 Washing Machines 163 6.4.9 Water Heaters 165 6.5 Small Appliances 166 6.5.1 Coffee Machines, Blenders, Faucets, Food Processors, Mixers, and Toasters 166 6.5.2 Robotic Lawn Mowers and Electric Tools 167 6.6 Monitoring 167 6.6.1 Energy Monitors, Haptics Sensors, Weather Sensors, and Others 167 6.7 Health, Comfort, and Care 168 6.7.1 Air Purifiers, Humidifiers, Health Monitors, Sleep Sensors, and Tracking Devices 168 6.7.2 Cat Litter Robots, Pet Feeders, and Other Pet-Related Connected Devices 169 6.7.3 Hair Dryers, Brushes, and Straighteners 169 6.7.4 Treadmills, Indoor Exercise Bike, and Other Fitness Equipment 170 6.7.5 Water Filtration Systems 170 6.8 House Automation 171 6.8.1 Blinds & Shades and Light Bulbs 171 6.8.2 Garage Door Opener 172 6.8.3 Sprinklers, Gardening Sensors, and Accent Lighting 172 6.8.4 Smart Power Strips and Smart Power Switches 173 6.8.5 Presence, Proximity, and Movement Sensors 173 6.8.6 Thermostats and Temperature Sensors 174 6.8.7 Vacuum Cleaners, Vacuum Robots, Mop Robots, and Power Tools 174 6.9 Non-appliances 174 6.9.1 Electric Cars and Motorcycles 174 6.9.2 Desktop Computers 175 6.9.3 Modems and Routers 175 6.9.4 Power Banks, Uninterrupted Power Supplies 176 6.9.5 Smartphones, Tablet Computers, Smartwatches, and Video Games 176 6.10 Entertainment 177 6.10.1 Aquariums 177 6.10.2 Audio Systems 177 6.10.3 Televisions and Streaming Receivers (Cast Feature) 178 6.10.4 Virtual Assistants (Multiple Forms) 178 6.10.5 Virtual Reality Goggles and Other Gadgets 178 6.11 Security 179 6.11.1 Alarms, Cameras, Door Locks, and Doorbell Cameras 179 6.12 Conclusion 180 Symbols and Abbreviations 180 Glossary 181 References 181 7 The ETSI SAREF Ontology for Smart Applications: A Long Path of Development and Evolution 183 Raúl García-Castro, Maxime Lefrançois, María Poveda-Villalón, and Laura Daniele 7.1 Introduction 183 7.2 IoT Ontologies for Semantic Interoperability 184 7.3 The SAREF Initiative 186 7.4 Specification and Design of the SAREF Ontology 187 7.4.1 A Modular and Versioned Suite of Ontologies 187 7.4.2 Methodology 188 7.4.3 Version Control and Editing Workflow 190 7.4.4 Automatization of Requirements and Quality Checks 190 7.4.5 Continuous Integration and Deployment 191 7.5 Overview of the SAREF Ontology 191 7.5.1 Device 193 7.5.2 Feature of Interest and Property 194 7.5.3 Measurement 194 7.5.4 Service, Function, Command, and State 195 7.6 The SAREF Ontology in the Smart Home Environment 196 7.6.1 Energy 198 7.6.2 Water 200 7.6.3 Building 202 7.6.4 City 204 7.6.5 Systems 206 7.7 The SAREF Ontology in Use 207 7.8 Lessons Learnt 209 7.8.1 Specification of Ontology Requirements 209 7.8.2 Stakeholder’s Workshops 210 7.8.3 Tool Support 210 7.8.4 Ontology Modularization 211 7.8.5 Ontology Patterns 212 7.9 Conclusions and Future Work 212 Acknowledgments 213 References 213 8 Scheduling of Residential Shiftable Smart Appliances by Metaheuristic Approaches 217 Recep Çakmak 8.1 Introduction 217 8.2 Demand Response Programs in Demand-Side Management 222 8.3 Time-Shiftable and Smart Appliances in Residences 224 8.4 Smart Metaheuristic Algorithms 226 8.4.1 BAT Algorithm 226 8.4.2 Firefly Algorithm (FFA) 228 8.4.3 Cuckoo Search Algorithm 229 8.4.4 SOS Algorithm 231 8.5 Scheduling of Time-Shiftable Appliances by Smart Metaheuristic Algorithms 232 Symbols and Abbreviations 237 Glossary 238 References 238 9 Distributed Operation of an Electric Vehicle Fleet in a Residential Area 243 Alicia Triviño, Inmaculada Casaucao, and José A. Aguado 9.1 Introduction 243 9.2 EV Charging Stations 246 9.3 EV Services 248 9.3.1 Ancillary Services 248 9.3.2 Domestic Services 248 9.4 Dispatching Strategies for EVs 249 9.4.1 Classification of EV Dispatching Strategies 251 9.5 Proposed Distributed EV Dispatching Strategy 252 9.6 Conclusions 259 Acknowledgments 260 References 260 10 Electric Vehicles as Smart Appliances for Residential Energy Management 263 Indradip Mitra, Zakir Rather, Angshu Nath, and Sahana Lokesh 10.1 Introduction 263 10.2 EV Charging Standards and Charging Protocols 265 10.2.1 EV Charging Standards 265 10.2.1.1 Iec 61851 265 10.2.1.2 Sae J 1772 266 10.2.1.3 Gb/t 20234 267 10.2.2 Charging Protocols for EV Charging 267 10.2.2.1 Type 1 AC Charger 267 10.2.2.2 Type 2 AC Charger 268 10.2.2.3 CHArge de MOve (CHAdeMO) Protocol 268 10.2.2.4 Combined Charging System (CCS) 268 10.2.2.5 Tesla Charging Protocol 268 10.3 Communication Protocols Used in EV Ecosystem 268 10.3.1 Open Charge Point Protocol 268 10.3.2 Open Automated Demand Response (OpenADR) 269 10.3.3 Open Smart Charging Protocol (OSCP) 269 10.3.4 Ieee 2030.5 269 10.3.5 Iso/iec 15118 269 10.4 Residential EV Charging Infrastructure 270 10.4.1 Prerequisites to Installation of EV Charge Point 271 10.4.2 EV Charger Connection Requirements and Recommendations 271 10.4.2.1 United Kingdom 271 10.4.2.2 The Netherlands 272 10.4.2.3 Germany 275 10.5 Impacts of EV Charging 275 10.5.1 Impact on Electricity Distribution Network 275 10.5.1.1 Voltage Issues 276 10.5.1.2 Increase in Peak Load 278 10.5.1.3 Congestion 278 10.5.1.4 Losses 278 10.6 Smart Charging for Home Charging 282 10.6.1 Type of Smart Charging 283 10.6.2 Requirements for Smart Charging 286 10.6.3 Additional Smart Charging Enablers 287 10.7 Residential Smart Energy Management 289 10.7.1 Unidirectional Smart Charging 289 10.7.2 Vehicle-to-Home/Building 292 10.7.3 Vehicle-to-Grid (V2G) 296 10.8 Conclusion 297 List of Abbreviations 297 Glossary 298 References 299 11 Induction Heating Appliances: Toward More Sustainable and Smart Home Appliances 301 Óscar Lucía, Héctor Sarnago, Jesús Acero, and José M. Burdío 11.1 Introduction to Induction Heating 301 11.1.1 Induction Heating Fundamentals 301 11.1.2 Induction Heating History 304 11.2 Domestic Induction Heating Technology 306 11.2.1 Power Electronics 309 11.2.2 Electromagnetic Design 314 11.2.3 Digital Control 315 11.2.4 Efficiency 318 11.3 Advanced Features and Connectivity 319 11.3.1 High-Performance Power Electronics 319 11.3.2 Advanced Control 321 11.3.3 Flexible Cooking Surfaces 322 11.3.4 Connectivity 322 11.4 Conclusion and Future Challenges 325 Symbols and Abbreviations 325 References 326 Index 333

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Book SynopsisTable of ContentsPreface to the Second Edition xiii About the Companion Website xvii I MATLAB Programming 1 1 Getting Started 3 1.1 Running the MATLAB IDE 3 Manipulating windows 5 1.2 MATLAB variables 5 Variable assignment statements 6 Variable names 7 Variable workspace 8 1.3 Numbers and functions 8 1.4 Documentation 9 1.5 Writing simple MATLAB scripts 10 Block structure 11 Appropriate variable names 11 Useful comments 11 Units 11 Formatting for clarity 12 Basic display command 12 1.6 A few words about errors and debugging 12 Error messages are your friends 13 Sketch a plan on paper first 13 Start small and add slowly 13 1.7 Using the debugger 13 Looking ahead 14 Programming Problems 14 2 Vectors and Strings 19 2.1 Vector basics 20 2.2 Operations on vectors 21 Multiplication by a scalar 21 Addition with a scalar 21 Element-by-element operation with two vectors 21 Functions of vectors 22 Length of vectors 22 Subarrays 23 Concatenating vectors 23 2.3 Special vector functions 23 Statistical Functions 24 2.4 Using rand and randi 25 2.5 String basics 25 2.6 String operations 27 2.7 Character vectors 29 2.8 Getting information from the user 30 Looking ahead 31 Programming Problems 31 3 Plotting 35 3.1 The plot command 35 Axis scaling 38 Plot labeling 39 3.2 Tabulating and plotting a simple function 39 3.3 Bar graphs and histograms 43 Histograms 45 3.4 Drawing several plots on one graph 45 Multiple plots with a single plot command 46 Combining multiple plots with a hold command 48 Thickening plotted curves 49 3.5 Adding lines and text 50 3.6 Changing object properties 52 Looking ahead 54 Programming Problems 55 4 Matrices 57 4.1 Entering and manipulating matrices 57 Size of a matrix 59 Matrix transpose 60 4.2 Operations on matrices 60 Arithmetic operations with a scalar 60 Addition and subtraction of two matrices of the same size 61 Functions of matrices 61 Matrix multiplication 62 The identity matrix 62 The inverse of a matrix 63 The determinant of a matrix 64 Matrix–vector multiplication 64 4.3 Solving linear systems: the backslash operator 65 Extended example: solving circuit problems 65 Wire segments 66 Wire junctions 66 Voltage sources 66 Resistors 67 Ground 67 4.4 Special matrix functions 71 Looking ahead 72 Programming Problems 72 5 Control Flow Commands 75 5.1 Conditional execution: the if statement 75 5.2 Logical expressions 79 5.3 Logical variables 80 5.4 for loops 81 Good programming practice 84 5.5 while loops 84 5.6 Other control flow commands 86 Switch-case statement 86 Break statement 86 Programming Problems 87 6 Animation 93 6.1 Basic animation 94 6.2 Animating function plots 98 6.3 Kinematics of motion 101 One-dimensional motion: constant speed 101 Motion with constant acceleration 104 Time-marching dynamics: nonconstant force 106 6.4 Looking ahead 108 Programming Problems 108 7 Writing Your Own MATLAB Functions 114 7.1 MATLAB function files 115 Declaring MATLAB functions 115 7.2 Function inputs and outputs 116 7.3 Local workspaces 117 7.4 Multiple outputs 117 7.5 Function files 117 7.6 Other functional forms 118 Subfunctions 118 Nested functions 122 Anonymous functions 122 7.7 Optional arguments for functions 123 7.8 Looking forward 124 Programming Problems 125 8 More MATLAB Data Classes and Structures 132 8.1 Cell arrays 132 8.2 Structures 133 8.3 Complex numbers 134 8.4 Function handles 135 8.5 Tables 135 8.6 Other data classes and data structures 136 Programming Problems 137 II Building Gui Tools 139 9 Building GUI Tools with App Designer 141 9.1 The App Designer interface 142 9.2 Getting started: HelloTool 144 9.3 Components communicating: SliderTool 148 9.4 Transforming a MATLAB program into a GUI tool: DampedEfieldTool 150 Step0: Write and debug the program 151 Step1: Plan the GUI 152 Step 2: Create the GUI in App Designer 153 Step 3: Connect program inputs and outputs to the GUI components 155 Step 4: Add callbacks to invoke the primary model function 157 9.5 Test and improve 157 Many ways to do things 159 Key points from this chapter 159 Programming Problems 160 10 More GUI Techniques 168 10.1 Sharing data between callbacks 169 10.2 More GUI components 170 Text and Numeric Edit Fields 170 Drop Down 171 Check Box 171 Label 172 List Box 172 Radio Button Group 173 Image 173 Communicating user choices 173 Tab Group 174 Menu bar 174 Toolbar 176 Text Area 176 The uses of invisibility 176 10.3 Popups 176 Progress dialogue 176 Wait bar 178 Input dialogue 178 Confirm dialogue 179 10.4 Responding to keyboard input 181 10.5 Mouse events and object dragging 181 III Advanced Topics 187 11 More Graphics 189 11.1 Logarithmic plots 189 11.2 Plotting functions on two axes 192 11.3 Plotting surfaces 194 11.4 Plotting vector fields 199 11.5 Working with images 200 Importing and manipulating bit-mapped images 200 Placing images on surface objects 207 11.6 Rotating composite objects in three dimensions 209 12 More Mathematics 213 12.1 Derivatives 214 Derivatives of mathematical functions expressed as MATLAB functions 214 Derivatives of tabulated functions 215 12.2 Integration 218 Integrating tabulated functions 218 Integrating mathematical functions expressed as MATLAB functions 221 12.3 Zeros of a function of one variable 225 12.4 Function minimization 227 Finding a minimum of a function of one variable 227 Multidimensional minimization 229 Fitting to an arbitrary function by multidimensional minimization 229 Solving simultaneous nonlinear equations by multidimensional minimization 233 12.5 Solving ordinary differential equations 235 Plotting a slope field 238 12.6 Eigenvalues and eigenvectors 239 13 Reading and Writing Files 242 13.1 Saving and loading data in .mat files 242 13.2 Reading and writing spreadsheet files 244 13.3 Writing text files 245 The write matrix command 245 Writing formatted text files 246 Formatting a string using sprintf 249 13.4 Reading data from a text file 249 Reading into a cell array 250 Reading complicated text data files 250 13.5 A GUI interface to filenames using uiputfile and uigetfile 252 Appendix Using latex Commands 255 Index 261

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Book SynopsisDiscover the foundations of software engineering with this easy and intuitive guide In the newly updated second edition of Beginning Software Engineering, expert programmer and tech educator Rod Stephens delivers an instructive and intuitive introduction to the fundamentals of software engineering. In the book, you'll learn to create well-constructed software applications that meet the needs of users while developing the practical, hands-on skills needed to build robust, efficient, and reliable software. The author skips the unnecessary jargon and sticks to simple and straightforward English to help you understand the concepts and ideas discussed within. He also offers you real-world tested methods you can apply to any programming language. You'll also get: Practical tips for preparing for programming job interviews, which often include questions about software engineering practices A no-nonsense guide to requirements gathering, system modeliTable of ContentsIntroduction xxvii Part I: Software Engineering Step- By- Step Chapter 1: Software Engineering From 20,000 Feet 3 Requirements Gathering 4 High- Level Design 5 Low- Level Design 6 Development 6 Testing 7 Deployment 9 Maintenance 10 Wrap- Up 10 Everything All at Once 11 Summary 12 What You Learned in This Chapter 13 Chapter 2: Before the Beginning 15 Document Management 16 Historical Documents 19 Email 19 Code 22 Code Documentation 22 Application Documentation 25 Summary 26 What You Learned in This Chapter 27 Chapter 3: the Team 29 Team Features 30 Clear Roles 30 Effective Leadership 30 Clear Goals 31 Consensus 32 Open Communication 32 Support for Risk- Taking 33 Shared Accountability 33 Informal Atmosphere 34 Trust 34 Team Roles 34 Common Roles 35 More- Specialized Roles 36 Informal Roles 36 Roles Wrap- Up 37 Team Culture 37 Interviews 40 Interview Puzzles 40 The Bottom Line 41 Physical Environment 41 Creativity 41 Office Space 43 Ergonomics 43 Work- Life Balance 45 Collaboration Software 46 Searching 46 Overload 47 Outsourcing 47 Summary 48 What You Learned in This Chapter 50 Chapter 4: Project Management 53 Executive Support 54 Project Management 56 PERT Charts 57 Critical Path Methods 62 Gantt Charts 65 Scheduling Software 67 Predicting Times 68 Get Experience 69 Break Unknown Tasks into Simpler Pieces 70 Look for Similarities 71 Expect the Unexpected 71 Track Progress 73 Risk Management 74 Summary 76 What You Learned in This Chapter 79 Chapter 5: Requirements Gathering 81 Requirements Defined 82 Clear 82 Unambiguous 83 Consistent 84 Prioritized 84 Verifiable 88 Words to Avoid 89 Requirement Categories 89 Audience- Oriented Requirements 90 Business Requirements 90 User Requirements 90 Functional Requirements 91 Nonfunctional Requirements 92 Implementation Requirements 92 FURPS 92 FURPS+ 93 Common Requirements 96 Gathering Requirements 96 Listen to Customers (and Users) 97 Use the Five Ws (and One H) 98 Who 98 What 98 When 98 Where 98 Why 99 How 99 Study Users 99 Refining Requirements 100 Copy Existing Systems 101 Clairvoyance 102 Brainstorm 103 Recording Requirements 106 UML 107 User Stories 107 Use Cases 108 Prototypes 108 Requirements Specification 109 Validation and Verification 110 Changing Requirements 110 Digital Transformation 111 What to Digitize 111 How to Digitize 112 Summary 113 What You Learned in This Chapter 116 Chapter 6: High- Level Design 117 The Big Picture 118 What to Specify 119 Security 119 Hardware 120 User Interface 121 Internal Interfaces 122 External Interfaces 123 Architecture 124 Monolithic 124 Client/Server 125 Component- Based 127 Service- Oriented 128 Data- Centric 130 Event- Driven 130 Rule- Based 130 Distributed 131 MIX and Match 132 Reports 133 Other Outputs 134 Database 135 Audit Trails 136 User Access 136 Database Maintenance 137 NoSQL 137 Cloud Databases 138 Configuration Data 138 Data Flows and States 139 Training 139 UML 141 Structure Diagrams 142 Behavior Diagrams 145 Activity Diagrams 145 Use Case Diagram 146 State Machine Diagram 147 Interaction Diagrams 148 Sequence Diagram 148 Communication Diagram 150 Timing Diagram 150 Interaction Overview Diagram 151 UML Summary 151 Summary 151 What You Learned in This Chapter 152 Chapter 7: Low- Level Design 155 Design Approaches 156 Design- to- Schedule 157 Design- to- Tools 158 Process- Oriented Design 158 Data- Oriented Design 159 Object- Oriented Design 159 Hybrid Approaches 159 High, Low, and Iterative Design 160 OO Design 160 Identifying Classes 161 Building Inheritance Hierarchies 162 Refinement 163 Generalization 165 Hierarchy Warning Signs 167 Object Composition 167 Database Design 168 Relational Databases 168 First Normal Form 170 Second Normal Form 174 Third Normal Form 176 Higher Levels of Normalization 179 When to Optimize 180 Summary 180 What You Learned in This Chapter 182 Chapter 8: Security Design 185 Security Goals 186 Security Types 186 Cybersecurity 188 Shift- Left Security 189 Malware Menagerie 189 Phishing and Spoofing 193 Social Engineering Attacks 195 Crapware 197 Password Attacks 198 User Access 201 Countermeasures 201 Cyber Insurance 202 Summary 203 What You Learned in This Chapter 207 Chapter 9: User Experience Design 209 Design Mindset 210 UI vs. UX 210 UX Designers 211 Platform 212 User Skill Level 214 Beginners and Beyond 216 Configuration 217 Hidden Configuration 218 Models 219 Metaphors and Idioms 220 Case Study: Microsoft Word 221 Design Guidelines 225 Allow Exploration 225 Make the Interface Immutable 227 Support Commensurate Difficulty 227 Avoid State 228 Make Similar Things Similar 228 Provide Redundant Commands 230 Do the Right Thing 231 Show Qualitative Data, Explain Quantitative Data 232 Give Forms Purpose 232 Gather All Information at Once 233 Provide Reasonable Performance 234 Only Allow What’s Right 235 Flag Mistakes 235 Form Design 236 Use Standard Controls 236 Decorating 237 Displaying 237 Arranging 237 Commanding 238 Selecting 238 Entering 239 Display Five Things 240 Arrange Controls Nicely 241 Summary 241 What You Learned in This Chapter 242 Chapter 10: Programming 245 Tools 246 Hardware 246 Network 247 Development Environment 248 Source Code Control 249 Profilers 249 Static Analysis Tools 249 Testing Tools 249 Source Code Formatters 250 Refactoring Tools 250 Training 250 Collaboration Tools 250 Algorithms 251 Top- Down Design 252 Programming Tips and Tricks 255 Be Alert 255 Write for People, Not the Computer 255 Comment First 256 Write Self- Documenting Code 259 Keep It Small 259 Stay Focused 261 Avoid Side Effects 261 Validate Results 262 Practice Offensive Programming 264 Use Exceptions 266 Write Exception Handlers First 266 Don’t Repeat Code 267 Defer Optimization 267 Summary 269 What You Learned in This Chapter 270 Chapter 11: Algorithms 273 Algorithm Study 274 Algorithmic Approaches 275 Decision Trees 275 Knapsack 275 The Eight Queens Problem 276 Exhaustive Search 277 Backtracking 278 Pruning Trees 279 Branch and Bound 279 Heuristics 280 Greedy 281 Divide and Conquer 282 Recursion 283 Dynamic Programming 285 Caching 287 Randomization 287 Monte Carlo Algorithms 287 Las Vegas Algorithms 288 Atlantic City Algorithms 289 State Diagrams 289 Design Patterns 290 Creational Patterns 291 Structural Patterns 291 Behavioral Patterns 292 Design Pattern Summary 293 Parallel Programming 293 Artificial Intelligence 295 Definitions 295 Learning Systems 296 Natural Language Processing 297 Artificial Neural Network 297 Deep Learning 297 Expert System 298 Artificial General Intelligence 298 Algorithm Characteristics 301 Summary 302 What You Learned in This Chapter 304 Chapter 12: Programming Languages 307 The Myth of Picking a Language 308 Language Generations 311 First Generation 311 Second Generation 311 Third Generation (3GL) 312 Fourth Generation 313 Fifth Generation 314 Sixth Generation 314 IDEs 315 Language Families 316 Assembly 316 Imperative 317 Procedural 317 Declarative 318 Object- Oriented 318 Functional 319 Specialized 319 Language Family Summary 319 The Best Language 319 Summary 323 What You Learned in This Chapter 324 Chapter 13: Testing 327 Testing Goals 329 Reasons Bugs Never Die 330 Diminishing Returns 330 Deadlines 330 Consequences 330 It’s Too Soon 330 Usefulness 331 Obsolescence 331 It’s Not a Bug 331 It Never Ends 332 It’s Better Than Nothing 333 Fixing Bugs Is Dangerous 333 Which Bugs to Fix 334 Levels of Testing 334 Unit Testing 335 Integration Testing 336 Regression Testing 337 Automated Testing 337 Component Interface Testing 338 System Testing 339 Acceptance Testing 340 Other Testing Categories 341 Testing Techniques 342 Exhaustive Testing 342 Black- Box Testing 343 White- Box Testing 344 Gray- Box Testing 344 Testing Habits 345 Test and Debug When Alert 345 Test Your Own Code 346 Have Someone Else Test Your Code 346 Fix Your Own Bugs 348 Think Before You Change 349 Don’t Believe in Magic 349 See What Changed 350 Fix Bugs, Not Symptoms 350 Test Your Tests 350 How to Fix a Bug 351 Estimating Number of Bugs 351 Tracking Bugs Found 352 Seeding 353 The Lincoln Index 353 Summary 355 What You Learned in This Chapter 357 Chapter 14: Deployment 359 Scope 360 The Plan 361 Cutover 362 Staged Deployment 362 Gradual Cutover 363 Incremental Deployment 365 Parallel Testing 365 Deployment Tasks 365 Deployment Mistakes 366 Summary 368 What You Learned in This Chapter 370 Chapter 15: Metrics 371 Wrap Party 372 Defect Analysis 372 Species of Bugs 373 Discoverer 373 Severity 374 Creation Time 374 Age at Fix 374 Task Type 375 Defect Database 376 Ishikawa Diagrams 376 Software Metrics 379 Qualities of Good Attributes and Metrics 381 Using Metrics 382 Process Metrics 384 Project Metrics 384 Things to Measure 385 Size Normalization 387 Function Point Normalization 389 Count Function Point Metrics 390 Multiply by Complexity Factors 391 Calculate Complexity Adjustment Value 392 Calculate Adjusted FP 394 Summary 395 What You Learned in This Chapter 398 Chapter 16: Maintenance 401 Maintenance Costs 402 Task Categories 404 Perfective Tasks 404 Feature Improvements 406 New Features 406 The Second System Effect 407 Adaptive Tasks 408 Corrective Tasks 410 Preventive Tasks 414 Clarification 414 Code Reuse 415 Improved Flexibility 416 Bug Swarms 417 Bad Programming Practices 417 Individual Bugs 418 Not Invented Here 418 Task Execution 419 Summary 420 What You Learned in This Chapter 423 Part II: Process Models Chapter 17: Predictive Models 427 Model Approaches 428 Prerequisites 428 Predictive and Adaptive 429 Success and Failure Indicators for Predictive Models 430 Advantages and Disadvantages of Predictive Models 431 Waterfall 432 Waterfall with Feedback 433 Sashimi 434 Incremental Waterfall 436 V- model 438 Software Development Life Cycle 439 Summary 442 What You Learned in This Chapter 444 Chapter 18: Iterative Models 445 Iterative vs. Predictive 446 Iterative vs. Incremental 448 Prototypes 449 Types of Prototypes 451 Pros and Cons 451 Spiral 453 Clarifications 455 Pros and Cons 456 Unified Process 457 Pros and Cons 459 Rational Unified Process 459 Cleanroom 460 Cowboy Coding 461 Summary 461 What You Learned in This Chapter 463 Chapter 19: Rad 465 RAD Principles 467 James Martin RAD 470 Agile 471 Self- Organizing Teams 473 Agile Techniques 474 Communication 474 Incremental Development 475 Focus on Quality 478 XP 478 XP Roles 479 XP Values 480 XP Practices 481 Have a Customer On-Site 481 Play the Planning Game 482 Use Stand- Up Meetings 483 Make Frequent Small Releases 483 Use Intuitive Metaphors 484 Keep Designs Simple 484 Defer Optimization 484 Refactor When Necessary 485 Give Everyone Ownership of the Code 485 Use Coding Standards 486 Promote Generalization 486 Use Pair Programming 486 Test Constantly 486 Integrate Continuously 486 Work Sustainably 487 Use Test- Driven and Test- First Development 487 Scrum 488 Scrum Roles 489 Scrum Sprints 490 Planning Poker 491 Burndown 492 Velocity 494 Lean 494 Lean Principles 494 Crystal 495 Crystal Clear 498 Crystal Yellow 498 Crystal Orange 499 Feature- Driven Development 500 FDD Roles 501 FDD Phases 502 Develop a Model 502 Build a Feature List 502 Plan by Feature 503 Design by Feature 503 Build by Feature 504 FDD Iteration Milestones 504 Disciplined Agile Delivery 506 DAD Principles 506 DAD Roles 506 DAD Phases 507 Dynamic Systems Development Method 508 DSDM Phases 508 DSDM Principles 510 DSDM Roles 511 Kanban 512 Kanban Principles 513 Kanban Practices 513 Kanban Board 514 Summary 515 What You Learned in This Chapter 517 Part III: Advanced Topics Chapter 20: Software Ethics 523 Ethical Behavior 524 IEEE- CS/ACM 524 ACS 525 CPSR 526 Business Ethics 527 Nada 528 Hacker Ethics 529 Hacker Terms 530 Responsibility 531 Gray Areas 533 Software Engineering Dilemmas 535 Misusing Data and the Temptation of Free Data 535 Disruptive Technology 536 Algorithmic Bias 537 False Confidence 537 Lack of Oversight 538 Getting Paid 539 Thought Experiments 539 The Tunnel Problem 540 The Trolley Problem 542 Summary 544 What You Learned in This Chapter 545 Chapter 21: Future Trends 547 Security 548 UX/UI 549 Code Packaging 550 Cloud Technology 551 Software Development 552 Algorithms 553 Tech Toys 554 Summary 555 What You Learned in This Chapter 556 Appendix: Solutions to Exercises 559 Glossary 631 Index 663

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Book SynopsisGraph Database and Graph Computing for Power System Analysis Understand a new way to model power systems with this comprehensive and practical guide Graph databases have become one of the essential tools for managing large data systems. Their structure improves over traditional table-based relational databases in that it reconciles more closely to the inherent physics of a power system, enabling it to model the components and the network of a power system in an organic way. The authors' pioneering research has demonstrated the effectiveness and the potential of graph data management and graph computing to transform power system analysis. Graph Database and Graph Computing for Power System Analysis presents a comprehensive and accessible introduction to this research and its emerging applications. Programs and applications conventionally modeled for traditional relational databases are reconceived here to incorporate graph computing. The result is a detailed guide which demonstrates theTable of ContentsAbout the Authors xiii Preface xv Acknowledgments xvii Part I Theory and Approaches 1 1 Introduction 3 1.1 Power System Analysis 6 1.1.1 Power Flow Calculation 6 1.1.2 State Estimation 6 1.1.3 Contingency Analysis 7 1.1.4 Security-Constrained Automatic Generation Control 7 1.1.5 Security-Constrained ED 8 1.1.6 Electromechanical Transient Simulation 9 1.1.7 Photovoltaic Power Generation Forecast 10 1.2 Mathematical Model 10 1.2.1 Direct Methods of Solving Large-Scale Linear Equations 10 1.2.2 Iterative Methods of Solving Large-Scale Linear Equations 11 1.2.3 High-Dimensional Differential Equations 11 1.2.4 Mixed Integer-Programming Problems 11 1.3 Graph Computing 12 1.3.1 Graph Modeling Basics 13 1.3.2 Graph Parallel Computing 14 References 14 2 Graph Database 17 2.1 Database Management Systems History 17 2.2 Graph Database Theory and Method 18 2.2.1 Graph Database Principle and Concept 18 2.2.1.1 Defining a Graph Schema 19 2.2.1.2 Creating a Loading Job 20 2.2.1.3 Graph Query Language 21 2.2.2 System Architecture 25 2.2.3 Graph Computing Platform 25 2.3 Graph Database Operations and Performance 26 2.3.1 Graph Database Management System 26 2.3.1.1 Parallel Processing by MapReduce 27 2.3.1.2 Graph Partition 29 2.3.2 Graph Database Performance 35 References 38 3 Graph Parallel Computing 41 3.1 Graph Parallel Computing Mechanism 41 3.2 Graph Nodal Parallel Computing 44 3.3 Graph Hierarchical Parallel Computing 46 3.3.1 Symbolic Factorization 47 3.3.2 Elimination Tree 51 3.3.3 Node Partition 56 3.3.4 Numerical Factorization 57 3.3.5 Forward and Backward Substitution 58 References 59 4 Large-Scale Algebraic Equations 61 4.1 Iterative Methods of Solving Nonlinear Equations 61 4.1.1 Gauss–Seidel Method 61 4.1.2 PageRank Algorithm 62 4.1.2.1 PageRank Algorithm Mechanism 63 4.1.2.2 Iterative Method 66 4.1.2.3 Algebraic Method 67 4.1.2.4 Convergence Analysis 69 4.1.3 Newton–Raphson Method 72 4.2 Direct Methods of Solving Linear Equations 75 4.2.1 Introduction 75 4.2.2 Basic Concepts 76 4.2.2.1 Data Structures of Sparse Matrix 76 4.2.2.2 Matrices and Graphs 78 4.2.3 Historical Development 80 4.2.4 Direct Methods 81 4.2.4.1 Solving Triangular Systems 81 4.2.4.2 Symbolic Factorization 82 4.2.4.3 Fill-Reducing Ordering 82 4.3 Indirect Methods of Solving Linear Equations 83 4.3.1 Stationary Methods 83 4.3.1.1 Jacobi Method 83 4.3.1.2 Gauss–Seidel Method 85 4.3.1.3 SOR Method 86 4.3.1.4 SSOR Method 86 4.3.2 Nonstationary Methods 88 4.3.2.1 CG Method 88 4.3.2.2 Gmres 89 4.3.2.3 BCG (bi-CG) 90 References 91 5 High-Dimensional Differential Equations 95 5.1 Integration Methods 95 5.1.1 An Overview of Integration Methods and their Accuracy 95 5.1.1.1 One-Step Methods 96 5.1.1.2 Linear Multistep Methods 99 5.1.2 Integration Methods for Power System Transient Simulations 100 5.1.3 Transient Analysis Accuracy 100 5.1.4 Transient Analysis Stability 101 5.1.4.1 Absolute Stability 101 5.1.4.2 Stiff Stability 102 5.2 Time Step Control 103 5.2.1 Adaptive Time Step 104 5.2.1.1 Change by Iteration Number 105 5.2.1.2 Change by Estimated Truncation Error 105 5.2.1.3 Change by State Variable Derivative 106 5.2.2 Multiple Time Step 106 5.2.3 Break Points 109 5.3 Initial Operation Condition 110 5.4 Graph-Based Transient Parallel Simulation 115 5.5 Numerical Case Study 117 5.6 Summary 123 References 124 6 Optimization Problems 125 6.1 Optimization Theory 125 6.2 Linear Programming 125 6.2.1 The Simplex Method 127 6.2.1.1 Basic Feasible Solution 127 6.2.1.2 The Simplex Iteration 128 6.2.2 Interior-Point Methods 132 6.3 Nonlinear Programming 138 6.3.1 Unconstrained Optimization Approaches 139 6.3.1.1 Line Search 140 6.3.1.2 Trust Region Optimization 141 6.3.1.3 Quasi-Newton Method 141 6.3.1.4 Double Dogleg Optimization 142 6.3.1.5 Conjugate Gradient Optimization 143 6.3.2 Constrained Optimization Approaches 145 6.3.2.1 Karush–Kuhn–Tucker Conditions 145 6.3.2.2 Linear Approximations of Nonlinear Programming with Linear Constraints 145 6.3.2.3 Linear Approximations of Nonlinear Programming with Nonlinear Constraints 147 6.4 Mixed Integer Optimization Approach 147 6.4.1 Branch-and-Bound Approach 148 6.4.2 Machine Learning for Branching 150 6.5 Optimization Problems Solution by Graph Parallel Computing 151 6.5.1 Simplex Method Based on Graph Parallel Computing 151 6.5.2 Interior-Point Method Based on Graph Parallel Computing 154 References 156 7 Graph-Based Machine Learning 159 7.1 State of Art on PV Generation Forecasting 159 7.2 Graph Machine Learning Model 160 7.3 Convolutional Graph Auto-Encoder 162 7.3.1 Auto-Encoder 162 7.3.2 Auto-Encoder on Graphs 163 7.3.3 Probability Distribution Function Approximation 164 7.3.4 Convolutional Graph Auto-Encoder 167 7.3.5 Graph Feature Extraction Artificial Neural Network (R(G)) 169 7.3.6 Encoder (Q) and Decoder (P) 170 7.3.7 Estimation of P(V∗/ π) 171 References 171 Part II Implementations and Applications 175 8 Power Systems Modeling 177 8.1 Power System Graph Modeling 177 8.2 Physical Graph Model and Computing Graph Model 178 8.3 Node-Breaker Model and Graph Representation 180 8.4 Bus-Branch Model and Graph Representation 189 8.5 Graph-Based Topology Analysis 190 8.5.1 Substation-Level Topology Analysis 190 8.5.2 System-Level Network Topology Analysis 196 References 198 9 State Estimation Graph Computing 199 9.1 Power System State Estimation 199 9.2 Graph Computing-Based State Estimation 201 9.2.1 State Estimation Graph Computing Algorithm 201 9.2.1.1 Build Node-Based State Estimation 201 9.2.1.2 Graph-Based State Estimation Parallel Algorithm 203 9.2.2 Numerical Example 209 9.2.3 Graph-Based State Estimation Implementation 215 9.2.3.1 Graph-Based State Estimation Graph Schema 215 9.2.3.2 Nodal Gain Matrix Formation 216 9.2.3.3 Build RHS 219 9.2.4 Graph-Based State Estimation Computation Efficiency 220 9.3 Bad Data Detection and Identification 223 9.3.1 Chi-Squares Test 224 9.3.2 Advanced Bad Data Detection 224 9.3.3 Bad Data Identification 228 9.3.3.1 Normalized Residual 228 9.3.3.2 Largest Normalized Residual for Bad Data Identification 229 9.4 Graph-Based Bad Data Detection Implementation 229 References 231 10 Power Flow Graph Computing 233 10.1 Power Flow Mathematical Model 233 10.2 Gauss–Seidel Method 234 10.3 Newton–Raphson Method 242 10.3.1 Build Jacobian Graph 245 10.3.2 Graph-Based Symbolic Factorization 247 10.3.3 Graph-Based Elimination Tree Creation and Node Partition 249 10.3.4 Graph Numerical Factorization 251 10.3.5 Build Right-Hand Side 253 10.3.6 Graph Forward and Backward Substitution 254 10.3.7 Graph-Based Newton–Raphson Power Flow Calculation 255 10.4 Fast Decoupled Power Flow Calculation 257 10.4.1 Build B_P and B_PP Graphs 259 10.5 Ill-Conditioned Power Flow Problem Solution 261 10.5.1 Introduction 261 10.5.2 Determine the Feasibility of the Power Flow 262 10.5.3 Problem Formulation for Determining the Feasibility of Power Flow 263 10.5.4 Power Flow Feasibility Verification 264 10.5.5 Find a Feasible Solution for the Power Flow Problem 266 References 271 11 Contingency Analysis Graph Computing 273 11.1 dc Power Flow 273 11.2 Bridge Search 276 11.3 Conjugate Gradient for Postcontingency Power Flow Calculation 282 11.4 Contingency Analysis Using Convolutional Neural Networks 294 11.4.1 Convolutional Neural Network 295 11.4.2 Convolutional Neural Network Components 297 11.4.2.1 Convolutional Neural Network Input 297 11.4.2.2 Convolutional Neural Network Output 297 11.4.2.3 Convolutional Neural Network Convolutional Layer 297 11.4.2.4 CNN Pooling Layer 298 11.4.2.5 CNN Fully Connected Layer 299 11.4.3 Evaluation Metrics 299 11.4.3.1 Accuracy 299 11.4.3.2 Precision 300 11.4.3.3 Recall 300 11.4.4 Implementation of Convolutional Neural Network 300 11.5 Contingency Analysis Graph Computing Implementation 302 References 306 12 Economic Dispatch and Unit Commitment 309 12.1 Classic Economic Dispatch 309 12.1.1 Thermal Unit Economic Dispatch 309 12.1.2 Hydrothermal Power Generation System Economic Dispatch 315 12.2 Security-Constrained Economic Dispatch 320 12.2.1 Generation Shift Factor Matrix 323 12.2.2 Graph-Based SCED Modeling 325 12.2.3 Graph-Based SCED 327 12.2.3.1 Buildup Simplex Graph 328 12.2.3.2 Graph-Based Simplex Method 331 12.2.3.3 Update Power Flow 331 12.2.3.4 Graph-Based SCED Implementation 333 12.3 Security-Constrained Unit Commitment 334 12.3.1 SCUC Model 334 12.3.2 Graph-Based SCUC 335 12.4 Numerical Case Study 336 12.4.1 Graph-Based SCED Modeling 336 12.4.2 Basic Feasible Solution 340 12.4.3 Economic Dispatch Optimal Solution 342 References 342 13 Automatic Generation Control 345 13.1 Classic Automatic Generation Control 345 13.1.1 Speed Governor Control 345 13.1.2 Speed Droop Function 347 13.1.3 Frequency Supplementary Control 353 13.1.4 Fundamentals of Automatic Generation Control 355 13.2 Network Security-Constrained Automatic Generation Control 358 13.3 Security-Constrained AGC Graph Computing 361 References 364 14 Small-Signal Stability 365 14.1 Small-Signal Stability of a Dynamic System 365 14.2 System Linearization 366 14.3 Small-Signal Stability Mode 367 14.4 Single-Machine Infinite Bus System 367 14.4.1 Classical Generator Model 367 14.4.2 Third-Order Generator Model 369 14.4.3 Numerical Case Study 373 14.4.3.1 Stable Case 373 14.4.3.2 Instable Case 376 14.5 Small-Signal Oscillation Stabilization 378 14.6 Eigenvalue Calculation 379 14.6.1 Graph-Based Small-Signal Stability Analysis 382 14.6.2 Buildup Small-Signal Stability Graph 383 14.6.3 Numerical Example 383 References 388 15 Transient Stability 391 15.1 Transient Stability Theory 391 15.1.1 Stability Region and Boundary 391 15.1.2 Energy Function Method 391 15.1.2.1 Controlling UEP Method 392 15.1.2.2 Stability-Region-Based Controlling UEP Method 393 15.2 Transient Simulation Model 393 15.2.1 Generator Rotor Model 393 15.2.2 Generator Electro-Magnetic Model 394 15.2.3 Excitation System Model 394 15.2.4 Governor Model 396 15.2.5 PSS Model 397 15.3 Transient Simulation Approach 397 15.3.1 Transient Simulation Algorithm 398 15.3.2 Steady-State Equilibrium Condition 398 15.3.3 Generator Injection Current 400 15.4 Transient Simulation by Graph Parallel Computing 401 15.4.1 Transient Simulation Graph 401 15.4.2 Loading Data into Graph 403 15.4.3 Graph-Based Transient Simulation Implementation 406 15.5 Numerical Example 406 15.5.1 Power Flow Data 406 15.5.2 Dynamic Data 406 15.5.3 Power Flow Results 409 15.5.4 Steady-State Equilibrium Point 410 15.5.5 Generator Injection Current Calculation 415 15.5.6 Calculate Bus Voltage 416 15.5.7 Simulation Results 416 References 421 16 Graph-Based Deep Reinforcement Learning on Overload Control 425 16.1 Introduction 425 16.2 DDPG Algorithm 426 16.2.1 Terminology 426 16.2.2 Q Function 427 16.2.3 Q Value Approximation 427 16.2.4 Policy Gradient 428 16.3 Branch Overload Control 429 16.3.1 States 429 16.3.2 Actions 430 16.3.3 Rewards 430 16.4 Graph-Based Deep Reinforcement Learning Implementation 430 References 433 17 Conclusions 435 Appendix 437 Index 481

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Book SynopsisIoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety Enables readers to understand a broad area of state-of-the-art research in physical IoT-enabled security IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety describes new techniques in unobtrusive surveillance that enable people to act and communicate freely, while at the same time protecting them from malevolent behavior. It begins by characterizing the latest on surveillance systems deployed at smart campuses, miniatures of smart cities with more demanding frameworks that enable learning, social interaction, and creativity, and by performing a comparative assessment in the area of unobtrusive surveillance systems for smart campuses. A proposed taxonomy for IoT-enabled smart campus unfolds in five research dimensions: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology. By applying this taxonoTable of ContentsAuthor Biography xi Preface xiii 1 Introduction 1 1.1 Smart Cities Dimensions and Risks 2 1.2 Smart Campuses Components 2 1.3 Smart Campuses Unobtrusive Surveillance Systems 3 1.4 Smart Campus Safety Systems Survey 3 1.5 Smart Campuses Comparative Assessment 4 1.6 Smart Campus Systems Classification 4 1.7 Smart Campus Safety: A System Architecture 4 1.8 Human Factor as an Unobtrusive Surveillance System’s Adoption Parameter for Smart Campus Safety 5 1.9 Smart Campus Surveillance Systems Future Trends and Directions 5 2 Smart City 7 2.1 Smart Cities Dimensions 7 2.1.1 Smart Economy 8 2.1.2 Smart Governance 9 2.1.3 Smart Living 10 2.1.4 Smart Mobility 10 2.1.5 Smart People 11 2.1.6 Smart Environment 12 2.2 Risks Related to Smart Cities 12 2.2.1 Technical Risks 12 2.2.2 Nontechnical Risks 13 2.3 Mitigating Smart Cities Risks 14 2.4 Systems Beyond Smart Cities 15 3 Smart Campus 17 3.1 Smart Campus Components 17 3.1.1 Smart Grid 18 3.1.2 Smart Community Services 20 3.1.3 Smart Management 21 3.1.4 Smart Propagation Services 23 3.1.5 Smart Prosperity 24 3.2 Unobtrusive Surveillance Campus System 24 4 Unobtrusive Surveillance Systems 27 4.1 Geospatial Internet of Things 27 4.2 Smart Campus Unobtrusive Surveillance 28 4.3 Proposed Taxonomy 28 4.4 Adopted Weighted Scoring Model 34 5 Smart Campus Safety Systems Survey 39 5.1 Systems Not Classified 39 5.2 Systems That Focus on Public Spaces and Smart Parking 46 5.3 Systems That Focus on Smart Buildings, Smart Labs, Public Spaces, and Smart Lighting 48 5.4 Systems That Focus on Public Spaces and Smart Traffic Lights 51 5.5 Systems That Focus on Smart Buildings and Smart Classes 54 5.6 Systems That Focus on Smart Buildings, Public Spaces, Smart Lighting, and Smart Traffic Lights 58 5.7 Systems That Focus on Smart Buildings and Smart Labs 63 5.8 Systems That Focus on Smart Buildings and Public Spaces 67 5.9 Systems That Focus on Smart Campus Ambient Intelligence and User Context 79 5.10 Systems That Focus on Smart Campus Low-Power Wide Area Networks Technology 87 6 Comparative Assessment 97 7 Classification and Proposed Solution 103 7.1 Weighting Process 103 7.2 Classification Process 106 8 Smart Campus Spatiotemporal Authentication Unobtrusive Surveillance System for Smart Campus Safety 111 8.1 Smart Campus Spatiotemporal Authentication Unobtrusive Surveillance System 112 8.2 Smart Campus Safety: A System Architecture 116 9 Human Factor as an Unobtrusive Surveillance System’s Adoption Parameter for Smart Campus Safety 127 9.1 Ethical Dilemma of Adopting an Unobtrusive Surveillance System 127 9.2 Degree of Free Will Engagement and Negotiation with an Unobtrusive System 128 10 Smart Campus Surveillance Systems Future Trends and Directions 131 References 133 Index 143

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Book SynopsisA ROADMAP FOR ENABLING INDUSTRY 4.0 BY ARTIFICAIAL INTELLIGENCE The book presents comprehensive and up-to-date technological solutions to the main aspects regarding the applications of artificial intelligence to Industry 4.0. The industry 4.0 vision has been discussed for quite a while and the enabling technologies are now mature enough to turn this vision into a grand reality sooner rather than later. The fourth industrial revolution, or Industry 4.0, involves the infusion of technology-enabled deeper and decisive automation into manufacturing processes and activities. Several information and communication technologies (ICT) are being integrated and used towards attaining manufacturing process acceleration and augmentation. This book explores and educates the recent advancements in blockchain technology, artificial intelligence, supply chains in manufacturing, cryptocurrencies, and their crucial impact on realizing the Industry 4.0 goals. The book thus provides a conceptual framework Table of ContentsPreface xv 1 Artificial Intelligence—The Driving Force of Industry 4.0 1 Hesham Magd, Henry Jonathan, Shad Ahmad Khan and Mohamed El Geddawy 1.1 Introduction 2 1.2 Methodology 2 1.3 Scope of AI in Global Economy and Industry 4.0 3 1.3.1 Artificial Intelligence—Evolution and Implications 4 1.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy 5 1.3.3 The Driving Forces for Industry 4.0 7 1.4 Artificial Intelligence—Manufacturing Sector 8 1.4.1 AI Diversity—Applications to Manufacturing Sector 9 1.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0 12 1.5 Conclusion 13 References 14 2 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview 17 Sachi Pandey, Vijay Laxmi and Rajendra Prasad Mahapatra 2.1 Introduction 17 2.2 Industrial Transformation/Value Chain Transformation 18 2.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT 19 2.2.2 Second Scenario: Selling Outcome (User Demand)– Based Services Using IIoT 20 2.3 IIoT Reference Architecture 20 2.4 IIoT Technical Concepts 22 2.5 IIoT and Cloud Computing 26 2.6 IIoT and Security 27 References 29 3 Artificial Intelligence of Things (AIoT) and Industry 4.0– Based Supply Chain (FMCG Industry) 31 Seyyed Esmaeil Najafi, Hamed Nozari and S. A. Edalatpanah 3.1 Introduction 32 3.2 Concepts 33 3.2.1 Internet of Things 33 3.2.2 The Industrial Internet of Things (IIoT) 34 3.2.3 Artificial Intelligence of Things (AIoT) 35 3.3 AIoT-Based Supply Chain 36 3.4 Conclusion 40 References 40 4 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 43 Alireza Goli, Amir-Mohammad Golmohammadi and S. A. Edalatpanah 4.1 Introduction 44 4.2 Literature Review 45 4.2.1 Summary of the First Three Industrial Revolutions 45 4.2.2 Emergence of Industry 4.0 45 4.2.3 Some of the Challenges of Industry 4.0 47 4.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting 48 4.4 Proposed Approach 50 4.4.1 Mathematical Model 50 4.4.2 Advantages of the Proposed Model 51 4.5 Discussion and Conclusion 52 References 53 5 Integrating IoT and Deep Learning—The Driving Force of Industry 4.0 57 Muhammad Farrukh Shahid, Tariq Jamil Saifullah Khanzada and Muhammad Hassan Tanveer 5.1 Motivation and Background 58 5.2 Bringing Intelligence Into IoT Devices 60 5.3 The Foundation of CR-IoT Network 62 5.3.1 Various AI Technique in CR-IoT Network 63 5.3.2 Artificial Neural Network (ANN) 63 5.3.3 Metaheuristic Technique 64 5.3.4 Rule-Based System 64 5.3.5 Ontology-Based System 65 5.3.6 Probabilistic Models 65 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network 65 5.5 Realization of CR-IoT Network in Daily Life Examples 69 5.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study 70 5.7 Conclusion 75 References 75 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment 79 Mahendra Prasad Nath, Sushree Bibhuprada B. Priyadarshini, Debahuti Mishra and Brojo Kishore Mishra 6.1 Introduction 80 6.2 Overview of Blockchain 83 6.3 Components of Blockchain 85 6.3.1 Data Block 85 6.3.2 Smart Contracts 87 6.3.3 Consensus Algorithms 87 6.4 Safety Issues in Blockchain Technology 88 6.5 Usage of Big Data Framework in Dynamic Supply Chain System 91 6.6 Machine Learning and Big Data 94 6.6.1 Overview of Shallow Models 95 6.6.1.1 Support Vector Machine (SVM) 95 6.6.1.2 Artificial Neural Network (ANN) 95 6.6.1.3 K-Nearest Neighbor (KNN) 95 6.6.1.4 Clustering 96 6.6.1.5 Decision Tree 96 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems 96 6.7.1 Replenishment Planning 96 6.7.2 Optimizing Orders 97 6.7.3 Arranging and Organizing 97 6.7.4 Enhanced Demand Structuring 97 6.7.5 Real-Time Management of the Supply Chain 97 6.7.6 Enhanced Reaction 98 6.7.7 Planning and Growth of Inventories 98 6.8 IoT-Enabled Blockchains 98 6.8.1 Securing IoT Applications by Utilizing Blockchain 99 6.8.2 Blockchain Based on Permission 101 6.8.3 Blockchain Improvements in IoT 101 6.8.3.1 Blockchain Can Store Information Coming from IoT Devices 101 6.8.3.2 Secure Data Storage with Blockchain Distribution 101 6.8.3.3 Data Encryption via Hash Key and Tested by the Miners 102 6.8.3.4 Spoofing Attacks and Data Loss Prevention 102 6.8.3.5 Unauthorized Access Prevention Using Blockchain 103 6.8.3.6 Exclusion of Centralized Cloud Servers 103 6.9 Conclusions 103 References 104 7 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet 111 Yash Gupta, Shilpi Sharma, Naveen Rajan P. and Nadia Mohamed Kunhi 7.1 Introduction 112 7.2 Related Work 113 7.3 Methodology 114 7.3.1 Splitting of Data (Test/Train) 116 7.3.2 Prophet Model 116 7.3.3 Data Cleaning 119 7.3.4 Model Implementation 119 7.4 Results 120 7.4.1 Comparing Forecast to Actuals 121 7.4.2 Adding Holidays 122 7.4.3 Comparing Forecast to Actuals with the Cleaned Data 122 7.5 Conclusion and Future Scope 122 References 125 8 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems 127 Manas Kumar Yogi and A.S.N. Chakravarthy 8.1 Introduction 128 8.2 Related Work 131 8.3 Proposed Mechanism 133 8.4 Experimental Results 135 8.5 Future Directions 137 8.6 Conclusion 138 References 138 9 Environmental and Industrial Applications Using Internet of Things (IoT) 141 Manal Fawzy, Alaa El Din Mahmoud and Ahmed M. Abdelfatah 9.1 Introduction 142 9.2 IoT-Based Environmental Applications 146 9.3 Smart Environmental Monitoring 147 9.3.1 Air Quality Assessment 147 9.3.2 Water Quality Assessment 148 9.3.3 Soil Quality Assessment 150 9.3.4 Environmental Health-Related to COVID- 19 Monitoring 150 9.4 Applications of Sensors Network in Agro-Industrial System 151 9.5 Applications of IoT in Industry 153 9.5.1 Application of IoT in the Autonomous Field 153 9.5.2 Applications of IoT in Software Industries 155 9.5.3 Sensors in Industry 156 9.6 Challenges of IoT Applications in Environmental and Industrial Applications 157 9.7 Conclusions and Recommendations 159 Acknowledgments 159 References 159 10 An Introduction to Security in Internet of Things (IoT) and Big Data 169 Sushree Bibhuprada B. Priyadarshini, Suraj Kumar Dash, Amrit Sahani, Brojo Kishore Mishra and Mahendra Prasad Nath 10.1 Introduction 170 10.2 Allusion Design of IoT 172 10.2.1 Stage 1—Edge Tool 172 10.2.2 Stage 2—Connectivity 172 10.2.3 Stage 3—Fog Computing 173 10.2.4 Stage 4—Data Collection 173 10.2.5 Stage 5—Data Abstraction 173 10.2.6 Stage 6—Applications 173 10.2.7 Stage 7—Cooperation and Processes 174 10.3 Vulnerabilities of IoT 174 10.3.1 The Properties and Relationships of Various IoT Networks 174 10.3.2 Device Attacks 175 10.3.3 Attacks on Network 175 10.3.4 Some Other Issues 175 10.3.4.1 Customer Delivery Value 175 10.3.4.2 Compatibility Problems With Equipment 176 10.3.4.3 Compatibility and Maintenance 176 10.3.4.4 Connectivity Issues in the Field of Data 176 10.3.4.5 Incorrect Data Collection and Difficulties 177 10.3.4.6 Security Concern 177 10.3.4.7 Problems in Computer Confidentiality 177 10.4 Challenges in Technology 178 10.4.1 Skepticism of Consumers 178 10.5 Analysis of IoT Security 179 10.5.1 Sensing Layer Security Threats 180 10.5.1.1 Node Capturing 180 10.5.1.2 Malicious Attack by Code Injection 180 10.5.1.3 Attack by Fake Data Injection 180 10.5.1.4 Sidelines Assaults 181 10.5.1.5 Attacks During Booting Process 181 10.5.2 Network Layer Safety Issues 181 10.5.2.1 Attack on Phishing Page 181 10.5.2.2 Attacks on Access 182 10.5.2.3 Attacks on Data Transmission 182 10.5.2.4 Attacks on Routing 182 10.5.3 Middleware Layer Safety Issues 182 10.5.3.1 Attack by SQL Injection 183 10.5.3.2 Attack by Signature Wrapping 183 10.5.3.3 Cloud Attack Injection with Malware 183 10.5.3.4 Cloud Flooding Attack 183 10.5.4 Gateways Safety Issues 184 10.5.4.1 On-Boarding Safely 184 10.5.4.2 Additional Interfaces 184 10.5.4.3 Encrypting End-to-End 184 10.5.5 Application Layer Safety Issues 185 10.5.5.1 Theft of Data 185 10.5.5.2 Attacks at Interruption in Service 185 10.5.5.3 Malicious Code Injection Attack 185 10.6 Improvements and Enhancements Needed for IoT Applications in the Future 186 10.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) 189 10.8 Conclusion 192 References 193 11 Potential, Scope, and Challenges of Industry 4.0 201 Roshan Raman and Aayush Kumar 11.1 Introduction 202 11.2 Key Aspects for a Successful Production 202 11.3 Opportunities with Industry 4.0 204 11.4 Issues in Implementation of Industry 4.0 206 11.5 Potential Tools Utilized in Industry 4.0 207 11.6 Conclusion 210 References 210 12 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges 215 Roshan Raman and Aditya Ranjan 12.1 Introduction 216 12.2 Changing Market Demands 217 12.2.1 Individualization 218 12.2.2 Volatility 218 12.2.3 Efficiency in Terms of Energy Resources 218 12.3 Recent Technological Advancements 219 12.4 Industrial Revolution 4.0 221 12.5 Challenges to Industry 4.0 224 12.6 Conclusion 225 References 226 13 The Role of Multiagent System in Industry 4.0 227 Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan and Rudra Pratap Ojha 13.1 Introduction 228 13.2 Characteristics and Goals of Industry 4.0 Conception 228 13.3 Artificial Intelligence 231 13.3.1 Knowledge-Based Systems 232 13.4 Multiagent Systems 234 13.4.1 Agent Architectures 234 13.4.2 Jade 238 13.4.3 System Requirements Definition 239 13.4.4 HMI Development 240 13.5 Developing Software of Controllers Multiagent Environment Behavior Patterns 240 13.5.1 Agent Supervision 240 13.5.2 Documents Dispatching Agents 241 13.5.3 Agent Rescheduling 242 13.5.4 Agent of Executive 242 13.5.5 Primary Roles of High-Availability Agent 243 13.6 Conclusion 244 References 244 14 An Overview of Enhancing Encryption Standards for Multimedia in Explainable Artificial Intelligence Using Residue Number Systems for Security 247 Akeem Femi Kadri, Micheal Olaolu Arowolo, Ayisat Wuraola Yusuf-Asaju, Kafayat Odunayo Tajudeen and Kazeem Alagbe Gbolagade 14.1 Introduction 248 14.2 Reviews of Related Works 250 14.3 Materials and Methods 258 14.3.1 Multimedia 258 14.3.2 Artificial Intelligence and Explainable Artificial Intelligence 261 14.3.3 Cryptography 262 14.3.4 Encryption and Decryption 265 14.3.5 Residue Number System 266 14.4 Discussion and Conclusion 268 References 268 15 Market Trends with Cryptocurrency Trading in Industry 4.0 275 Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal and Vivek Kumar Verma 15.1 Introduction 276 15.2 Industry Overview 276 15.2.1 History (From Barter to Cryptocurrency) 276 15.2.2 In the Beginning Was Bitcoin 278 15.3 Cryptocurrency Market 279 15.3.1 Blockchain 279 15.3.1.1 Introduction to Blockchain Technology 279 15.3.1.2 Mining 280 15.3.1.3 From Blockchain to Cryptocurrency 281 15.3.2 Introduction to Cryptocurrency Market 281 15.3.2.1 What is a Cryptocurrency? 281 15.3.2.2 Cryptocurrency Exchanges 283 15.4 Cryptocurrency Trading 283 15.4.1 Definition 283 15.4.2 Advantages 283 15.4.3 Disadvantages 284 15.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain 285 15.5.1 Need for a Fee-Driven System 285 15.5.2 Ethereum Structure 286 15.5.3 How is the Gas Fee Calculated? 287 15.5.3.1 Why are Ethereum Gas Prices so High? 287 15.5.3.2 Carbon Neutrality 287 15.6 Conclusion 291 References 292 16 Blockchain and Its Applications in Industry 4.0 295 Ajay Sudhir Bale, Tarun Praveen Purohit, Muhammed Furqaan Hashim and Suyog Navale 16.1 Introduction 296 16.2 About Cryptocurrency 296 16.3 History of Blockchain and Cryptocurrency 298 16.4 Background of Industrial Revolution 300 16.4.1 The First Industrial Revolution 301 16.4.2 The Second Industrial Revolution 301 16.4.3 The Third Industrial Revolution 302 16.4.4 The Fourth Industrial Revolution 302 16.5 Trends of Blockchain 303 16.6 Applications of Blockchain in Industry 4.0 304 16.6.1 Blockchain and the Government 304 16.6.2 Blockchain in the Healthcare Sector 304 16.6.3 Blockchain in Logistics and Supply Chain 306 16.6.4 Blockchain in the Automotive Sector 307 16.6.5 Blockchain in the Education Sector 308 16.7 Conclusion 309 References 310 Index 315

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