Electronics and communications engineering Books

Book SynopsisProvides detailed mathematical exposition of the fundamentals of fuzzy set theory, including intuitionistic fuzzy sets This book examines fuzzy and intuitionistic fuzzy mathematics and unifies the latest existing works in literature. It enables readers to fully understand the mathematics of both fuzzy set and intuitionistic fuzzy set so that they can use either one in their applications. Each chapter of Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set begins with an introduction, theory, and several examples to guide readers along. The first one starts by laying the groundwork of fuzzy/intuitionistic fuzzy sets, fuzzy hedges, and fuzzy relations. The next covers fuzzy numbers and explains Zadeh''s extension principle. Then comes chapters looking at fuzzy operators; fuzzy similarity measures and measures of fuzziness; and fuzzy/intuitionistic fuzzy measures and fuzzy integrals. The book also: discusses the definition and properties of fuzzy measurTable of ContentsPreface xiii Organization of the Book xv 1 Fuzzy/Intuitionistic Fuzzy Set Theory 1 1.1 Introduction to Fuzzy Set 1 1.2 Mathematical Representation of Fuzzy Sets 3 1.3 Membership Function 6 1.4 Fuzzy Relations 10 1.5 Projection 13 1.6 Composition of Fuzzy Relation 14 1.7 Fuzzy Binary Relation 19 1.8 Transitive Closure of Fuzzy Binary Relation 21 1.9 Fuzzy Equivalence Relation 23 1.10 Intuitionistic Fuzzy Set 24 1.11 Construction of Intuitionistic Fuzzy Set 26 1.12 Intuitionistic Fuzzy Relations 29 1.13 Composition of Intuitionistic Fuzzy Relation 31 1.13.1 Composition of IFR Using T-norms and T-conorms 32 1.14 Intuitionistic Fuzzy Binary Relation 34 1.14.1 Reflexive Property 34 1.14.2 Symmetric Property 37 1.14.3 Transitive Property 38 1.15 Summary 39 References 39 2 Playing with Fuzzy/Intuitionistic Fuzzy Numbers 41 2.1 Introduction 41 2.2 Fuzzy Numbers 41 2.3 Fuzzy Intervals 42 2.4 Zadeh’s Extension Principle 43 2.4.1 Extension Principle for Two Variables 44 2.5 Fuzzy Numbers with α-Levels 48 2.6 Operations on Fuzzy Numbers with Intervals 52 2.7 Operations with Fuzzy Numbers based on α-Levels 54 2.8 Operations on Fuzzy Numbers Using Extension Principle 62 2.8.1 Operations 63 2.8.2 Examples on Operations of Fuzzy Numbers Using Extension Principle 64 2.9 L–R Representation of Fuzzy Numbers 66 2.10 Intuitionistic Fuzzy Numbers 73 2.11 Triangular Intuitionistic Fuzzy Number 74 2.12 Operations Using Triangular Intuitionistic Fuzzy Numbers 75 2.13 Trapezoidal Intuitionistic Fuzzy Numbers 77 2.14 Cut Set of Intuitionistic Fuzzy Number 78 2.15 Distances Between Two Intuitionistic Fuzzy Numbers 80 2.16 Summary 80 References 80 3 Similarity Measures and Measures of Fuzziness 83 3.1 Introduction 83 3.2 Distance and Similarity Measures 83 3.2.1 Distance Measure 84 3.2.2 Similarity Measure 84 3.3 Types of Distance Measure Between Fuzzy Sets 84 3.4 Types of Similarity Measures Between Fuzzy Sets 85 3.5 Generalized Fuzzy Number 85 3.6 Similarity Measures Between Two Fuzzy Numbers 88 3.7 Inclusion Measure 94 3.8 Measures of Fuzziness 95 3.8.1 Index of Fuzziness 95 3.8.2 Yager’s Measure 96 3.8.3 Fuzzy Entropy 96 3.9 Intuitionistic Fuzzy Distance and Similarity Measures 98 3.10 Intuitionistic Fuzzy Entropy 105 3.11 Different Types of Intuitionistic Fuzzy Entropies 106 3.12 Summary 107 References 107 4 Fuzzy/Intuitionistic Fuzzy Measures and Fuzzy Integrals 111 4.1 Introduction 111 4.2 Definition of Fuzzy Measure 111 4.3 Fuzzy Measures 112 4.3.1 Sugeno λ-Fuzzy Measure 112 4.3.2 Belief Measure 115 4.3.3 Plausibility Measure 116 4.3.4 Possibility Measure and Necessity Measure 116 4.3.4.1 Possibility Measure 117 4.3.4.2 Necessity Measure 119 4.4 Fuzzy Integrals 121 4.4.1 Sugeno Integral 122 4.4.2 Choquet Integral 125 4.4.3 Sipos Integral 129 4.5 Intuitionistic Fuzzy Integral 130 4.5.1 Intuitionistic Fuzzy Choquet Integral 130 4.6 Summary 131 References 131 5 Operations on Fuzzy/Intuitionistic Fuzzy Sets and Application in Decision Making 133 5.1 Introduction 133 5.2 Fuzzy Operations 133 5.2.1 Fuzzy Union 134 5.2.2 Fuzzy Intersection 134 5.2.3 Fuzzy Complements 134 5.2.4 Algebraic Product 136 5.2.5 Algebraic Sum 137 5.2.6 Simple Difference 137 5.2.7 Bounded Sum 137 5.2.8 Bounded Difference 137 5.2.9 Bounded Product 137 5.3 Fuzzy Other Operators: Fuzzy T-Norms and T-Conorms 138 5.3.1 Definition of T-Norm 138 5.3.2 Definition of T-Conorm 139 5.4 Implication Operator 142 5.5 Aggregation Operator with Application in Decision Making 144 5.5.1 Fuzzy Weighted Averaging Operator (FWA) 144 5.5.2 Fuzzy Ordered Weighted Averaging Operator (FOWA) 145 5.5.3 Fuzzy Generalized Ordered Weighted Averaging Operator (GOWA) 146 5.5.4 Fuzzy Hybrid Averaging Operator (FHA) 146 5.5.5 Fuzzy Quasi-Arithmetic Weighted Averaging Operator 146 5.5.6 Induced Generalized Fuzzy Averaging Operator (IGOWA) 147 5.5.7 Choquet Aggregation Operator 149 5.5.8 Induced Choquet Ordered Aggregation Operator 150 5.6 Intuitionistic Fuzzy Operators 152 5.7 Intuitionistic Fuzzy Aggregation Operator 153 5.7.1 Generalized Intuitionistic Fuzzy Aggregation Operator 153 5.7.2 Generalized Intuitionistic Fuzzy Ordered Weighting Operator (GIFOWA) 155 5.7.3 Generalized Intuitionistic Fuzzy Hybrid Operator 157 5.7.4 Intuitionistic Fuzzy Weighted Geometric Operator (IFWG) 160 5.7.5 Intuitionistic Fuzzy Ordered Weighted Geometric Operator 161 5.7.6 Induced Generalized Intuitionistic Fuzzy Ordered Averaging Operator 161 5.7.7 Intuitionistic Fuzzy Choquet Integral Operator 162 5.7.8 Induced Intuitionistic Fuzzy Choquet Integral Operator 162 5.8 Example on Decision-making Problems 164 5.9 Summary 168 References 168 6 Fuzzy Linear Equations 171 6.1 Introduction 171 6.2 Fuzzy Linear Equation 172 6.2.1 Problem of Finding an Unknown Number 173 6.3 Solving Linear Equation Using Cramer’s Rule 177 6.4 Inverse of a Fuzzy Matrix 182 6.5 Summary 189 References 189 7 Fuzzy Matrices and Determinants 191 7.1 Basic Matrix Theory 191 7.1.1 Matrix Addition 192 7.1.2 Matrix Multiplication 193 7.1.3 Transpose of a Matrix 193 7.2 Fuzzy Matrices 194 7.2.1 Matrix Addition, Multiplication, Max, Min Operations 197 7.2.2 Identity Matrix 202 7.3 Determinant of a Square Fuzzy Matrix 202 7.3.1 Examples of Fuzzy Determinants 203 7.4 Adjoint of a Square Fuzzy Matrix 206 7.4.1 Few Proposition of Adjoint of Fuzzy Matrices 207 7.5 Properties of Reflexive Matrices 212 7.6 Generalized Inverse of a Fuzzy Matrix 215 7.7 Intuitionistic Fuzzy Matrix 216 7.7.1 Identity Matrix 217 7.7.2 Null Matrix 218 7.7.3 Generalized Inverse of Intuitionistic Fuzzy Matrix 218 7.8 Summary 218 References 218 8 Fuzzy Subgroups 221 8.1 Introduction 221 8.2 Theorems of Fuzzy Subgroup 222 8.3 Fuzzy-level Subgroup 226 8.4 Fuzzy Normal Subgroup 228 8.5 Fuzzy Subgroups Using T-norms 229 8.6 Product of Fuzzy Subgroups 231 8.7 Summary 234 References 235 9 Application of Fuzzy/Intuitionistic Fuzzy Set in Image Processing 237 9.1 Introduction 237 9.2 Digital Images 237 9.3 Image Enhancement 238 9.3.1 Fuzzy Enhancement Method 238 9.3.2 Intuitionistic Fuzzy Enhancement Method 239 9.4 Thresholding 240 9.4.1 Intuitionistic Fuzzy Thresholding Method 242 9.4.2 Fuzzy Thresholding Method 244 9.5 Edge Detection 244 9.5.1 Fuzzy Edge-detection Method 245 9.5.2 Intuitionistic Fuzzy Edge Detection 246 9.6 Clustering 248 9.6.1 Fuzzy c Means Clustering (FCM) 248 9.6.2 Intuitionistic Fuzzy Clustering 249 9.6.3 Kernel Clustering 250 9.7 Mathematical Morphology 252 9.7.1 Fuzzy Approach 254 9.7.2 Intuitionistic Fuzzy Approach 254 9.8 Summary 256 References 256 10 Type-2 Fuzzy Set 259 10.1 Introduction 259 10.2 Type-2 Fuzzy Set 260 10.3 Operations on Type-2 Fuzzy Set 263 10.4 Inclusion Measure and Similarity Measure 267 10.4.1 Similarity Measure 268 10.5 Interval Type-2 Fuzzy Set 270 10.6 Application of Interval Type-2 Fuzzy Set in Image Segmentation 271 10.7 Summary 273 References 273 Beyond Your Doubts 275 Index 281

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Book SynopsisThis book is about constructing models from experimental data. It covers a range of topics, from statistical data prediction to Kalman filtering, from black-box model identification to parameter estimation, from spectral analysis to predictive control. Written for graduate students, this textbook offers an approach that has proven successful throughout the many years during which its author has taught these topics at his University. The book: Contains accessible methods explained step-by-step in simple termsOffers an essential tool useful in a variety of fields, especially engineering, statistics, and mathematicsIncludes an overview on random variables and stationary processes, as well as an introduction to discrete time models and matrix analysisIncorporates historical commentaries to put into perspective the developments that have brought the discipline to its current stateProvides many examples and solved problems to complement the presentation and facilitate comprehension of Table of ContentsIntroduction xi Acknowledgments xv 1 Stationary Processes and Time Series 1 1.1 Introduction 1 1.2 The Prediction Problem 1 1.3 Random Variable 4 1.4 Random Vector 5 1.4.1 Covariance Coefficient 7 1.5 Stationary Process 9 1.6 White Process 11 1.7 MA Process 12 1.8 AR Process 16 1.8.1 Study of the AR(1) Process 16 1.9 Yule–Walker Equations 20 1.9.1 Yule–Walker Equations for the AR(1) Process 20 1.9.2 Yule–Walker Equations for the AR(2) and AR(n) Process 21 1.10 ARMA Process 23 1.11 Spectrum of a Stationary Process 24 1.11.1 Spectrum Properties 24 1.11.2 Spectral Diagram 25 1.11.3 Maximum Frequency in Discrete Time 25 1.11.4 White Noise Spectrum 25 1.11.5 Complex Spectrum 26 1.12 ARMA Model: Stability Test and Variance Computation 26 1.12.1 Ruzicka Stability Criterion 28 1.12.2 Variance of an ARMA Process 32 1.13 FundamentalTheorem of Spectral Analysis 35 1.14 Spectrum Drawing 38 1.15 Proof of the FundamentalTheorem of Spectral Analysis 43 1.16 Representations of a Stationary Process 45 2 Estimation of Process Characteristics 47 2.1 Introduction 47 2.2 General Properties of the Covariance Function 47 2.3 Covariance Function of ARMA Processes 49 2.4 Estimation of the Mean 50 2.5 Estimation of the Covariance Function 53 2.6 Estimation of the Spectrum 55 2.7 Whiteness Test 57 3 Prediction 61 3.1 Introduction 61 3.2 Fake Predictor 62 3.2.1 Practical Determination of the Fake Predictor 64 3.3 Spectral Factorization 66 3.4 Whitening Filter 70 3.5 Optimal Predictor from Data 71 3.6 Prediction of an ARMA Process 76 3.7 ARMAX Process 77 3.8 Prediction of an ARMAX Process 78 4 Model Identification 81 4.1 Introduction 81 4.2 Setting the Identification Problem 82 4.2.1 Learning from Maxwell 82 4.2.2 A General Identification Problem 84 4.3 Static Modeling 85 4.3.1 Learning from Gauss 85 4.3.2 Least Squares Made Simple 86 4.3.2.1 Trend Search 86 4.3.2.2 Seasonality Search 86 4.3.2.3 Linear Regression 87 4.3.3 Estimating the Expansion of the Universe 90 4.4 Dynamic Modeling 92 4.5 External RepresentationModels 92 4.5.1 Box and Jenkins Model 92 4.5.2 ARX and AR Models 93 4.5.3 ARMAX and ARMA Models 94 4.5.4 MultivariableModels 96 4.6 Internal RepresentationModels 96 4.7 The Model Identification Process 100 4.8 The Predictive Approach 101 4.9 Models in Predictive Form 102 4.9.1 Box and Jenkins Model 103 4.9.2 ARX and AR Models 103 4.9.3 ARMAX and ARMA Models 104 5 Identification of Input–Output Models 107 5.1 Introduction 107 5.2 Estimating AR and ARX Models: The Least Squares Method 107 5.3 Identifiability 110 5.3.1 The ̄R Matrix for the ARX(1, 1) Model 111 5.3.2 The ̄R Matrix for a General ARX Model 112 5.4 Estimating ARMA and ARMAX Models 115 5.4.1 Computing the Gradient and the Hessian from Data 117 5.5 Asymptotic Analysis 123 5.5.1 Data Generation SystemWithin the Class of Models 125 5.5.2 Data Generation System Outside the Class of Models 127 5.5.2.1 Simulation Trial 132 5.5.3 General Considerations on the Asymptotics of Predictive Identification 132 5.5.4 Estimating the Uncertainty in Parameter Estimation 132 5.5.4.1 Deduction of the Formula of the Estimation Covariance 134 5.6 Recursive Identification 138 5.6.1 Recursive Least Squares 138 5.6.2 Recursive Maximum Likelihood 143 5.6.3 Extended Least Squares 145 5.7 Robustness of IdentificationMethods 147 5.7.1 Prediction Error and Model Error 147 5.7.2 Frequency Domain Interpretation 148 5.7.3 Prefiltering 149 5.8 Parameter Tracking 149 6 Model Complexity Selection 155 6.1 Introduction 155 6.2 Cross-validation 157 6.3 FPE Criterion 157 6.3.1 FPE Concept 157 6.3.2 FPE Determination 158 6.4 AIC Criterion 160 6.4.1 AIC Versus FPE 161 6.5 MDL Criterion 161 6.5.1 MDL Versus AIC 162 6.6 Durbin–Levinson Algorithm 164 6.6.1 Yule–Walker Equations for Autoregressive Models of Orders 1 and 2 165 6.6.2 Durbin–Levinson Recursion: From AR(1) to AR(2) 166 6.6.3 Durbin–Levinson Recursion for Models of Any Order 169 6.6.4 Partial Covariance Function 171 7 Identification of State Space Models 173 7.1 Introduction 173 7.2 Hankel Matrix 175 7.3 Order Determination 176 7.4 Determination of Matrices G and H 177 7.5 Determination of Matrix F 178 7.6 Mid Summary: An Ideal Procedure 179 7.7 Order Determination with SVD 179 7.8 Reliable Identification of a State Space Model 181 8 Predictive Control 187 8.1 Introduction 187 8.2 Minimum Variance Control 188 8.2.1 Determination of the MV Control Law 190 8.2.2 Analysis of the MV Control System 192 8.2.2.1 Structure 193 8.2.2.2 Stability 193 8.3 Generalized Minimum Variance Control 196 8.3.1 Model Reference Control 198 8.3.2 Penalized Control Design 200 8.3.2.1 Choice A for Q(z) 201 8.3.2.2 Choice B for Q(z) 203 8.4 Model-Based Predictive Control 204 8.5 Data-Driven Control Synthesis 205 9 Kalman Filtering and Prediction 209 9.1 Introduction 209 9.2 Kalman Approach to Prediction and Filtering Problems 210 9.3 The Bayes Estimation Problem 212 9.3.1 Bayes Problem – Scalar Case 213 9.3.2 Bayes Problem – Vector Case 215 9.3.3 Recursive Bayes Formula – Scalar Case 215 9.3.4 Innovation 217 9.3.5 Recursive Bayes Formula – Vector Case 219 9.3.6 Geometric Interpretation of Bayes Estimation 220 9.3.6.1 Geometric Interpretation of the Bayes Batch Formula 220 9.3.6.2 Geometric Interpretation of the Recursive Bayes Formula 222 9.4 One-step-ahead Kalman Predictor 223 9.4.1 The Innovation in the State Prediction Problem 224 9.4.2 The State Prediction Error 224 9.4.3 Optimal One-Step-Ahead Prediction of the Output 225 9.4.4 Optimal One-Step-Ahead Prediction of the State 226 9.4.5 Riccati Equation 228 9.4.6 Initialization 231 9.4.7 One-step-ahead Optimal Predictor Summary 232 9.4.8 Generalizations 236 9.4.8.1 System 236 9.4.8.2 Predictor 236 9.5 Multistep Optimal Predictor 237 9.6 Optimal Filter 239 9.7 Steady-State Predictor 240 9.7.1 Gain Convergence 241 9.7.2 Convergence of the Riccati Equation Solution 244 9.7.2.1 Convergence Under Stability 244 9.7.2.2 ConvergenceWithout Stability 246 9.7.2.3 Observability 250 9.7.2.4 Reachability 251 9.7.2.5 General Convergence Result 256 9.8 Innovation Representation 265 9.9 Innovation Representation Versus Canonical Representation 266 9.10 K-Theory Versus K–W Theory 267 9.11 Extended Kalman Filter – EKF 271 9.12 The Robust Approach to Filtering 273 9.12.1 Norm of a Dynamic System 274 9.12.2 Robust Filtering 276 10 Parameter Identification in a Given Model 281 10.1 Introduction 281 10.2 Kalman Filter-Based Approaches 281 10.3 Two-Stage Method 284 10.3.1 First Stage – Data Generation and Compression 285 10.3.2 Second Stage – Compressed Data Fitting 287 11 Case Studies 291 11.1 Introduction 291 11.2 Kobe Earthquake Data Analysis 291 11.2.1 Modeling the Normal Seismic Activity Data 294 11.2.2 Model Validation 296 11.2.3 Analysis of the Transition Phase via Detection Techniques 299 11.2.4 Conclusions 300 11.3 Estimation of a Sinusoid in Noise 300 11.3.1 Frequency Estimation by Notch Filter Design 301 11.3.2 Frequency Estimation with EKF 305 Appendix A Linear Dynamical Systems 309 A.1 State Space and Input–Output Models 309 A.1.1 Characteristic Polynomial and Eigenvalues 309 A.1.2 Operator Representation 310 A.1.3 Transfer Function 310 A.1.4 Zeros, Poles, and Eigenvalues 310 A.1.5 Relative Degree 311 A.1.6 Equilibrium Point and System Gain 311 A.2 Lagrange Formula 312 A.3 Stability 312 A.4 Impulse Response 313 A.4.1 Impulse Response from a State Space Model 314 A.4.2 Impulse Response from an Input–Output Model 314 A.4.3 Quadratic Summability of the Impulse Response 315 A.5 Frequency Response 315 A.6 Multiplicity of State Space Models 316 A.6.1 Change of Basis 316 A.6.2 Redundancy in the System Order 317 A.7 Reachability and Observability 318 A.7.1 Reachability 318 A.7.2 Observability 320 A.7.3 PBH Test of Reachability and Observability 321 A.8 System Decomposition 323 A.8.1 Reachability and Observability Decompositions 323 A.8.2 Canonical Decomposition 324 A.9 Stabilizability and Detectability 328 Appendix B Matrices 331 B.1 Basics 331 B.2 Eigenvalues 335 B.3 Determinant and Inverse 337 B.4 Rank 340 B.5 Annihilating Polynomial 342 B.6 Algebraic and Geometric Multiplicity 345 B.7 Range and Null Space 345 B.8 Quadratic Forms 346 B.9 Derivative of a Scalar Function with Respect to a Vector 349 B.10 Matrix Diagonalization via Similarity 350 B.11 Matrix Diagonalization via Singular Value Decomposition 351 B.12 Matrix Norm and Condition Number 353 Appendix C Problems and Solutions 357 Bibliography 391 Index 397

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Book SynopsisA guide to the application of the theory and practice of computing to develop and maintain software that economically solves real-world problem How to Engineer Software is a practical, how-to guide that explores the concepts and techniques ofmodel-based software engineering using the Unified Modeling Language. The authora noted expert on the topicdemonstrates how software can be developed and maintained under a true engineering discipline. He describes the relevant software engineering practices that are grounded in Computer Science and Discrete Mathematics. Model-based software engineering uses semantic modeling to reveal as many precise requirements as possible. This approach separates business complexities from technology complexities, and gives developers the most freedom in finding optimal designs and code. The book promotes development scalability through domain partitioning and subdomain partitioning. It also explores software documentation that spTable of ContentsForeword xi Preface xvii Acknowledgments xxv Online Resources xxvii Part I Introduction and Foundations 1 1 Introduction 3 2 The Nature of Code 39 3 Fundamental Principles 67 4 Functional and Nonfunctional Requirements 91 5 UML Overview 115 6 Partitioning Systems into Domains 125 Part II Semantic Modeling: Model-based Functional Requirements 151 7 Use Case Diagrams: Scope and Context 153 8 Class Models: Policies to Enforce 183 9 Interaction Diagrams: Process at a Mid-Level 237 10 State Models: Process at a Fine-Grained Level 261 11 Partitioning Domains into Subdomains 305 12 Wrapping Up Semantic Modeling 323 Part III Model-based Design and Code 369 13 Introduction to Design and Code 371 14 Designing Interfaces: Specifying Real-World Interaction 379 15 High-Level Design: Classes and Operations 407 16 High-Level Design: Contracts and Signatures 447 17 Detailed Design and Code 503 18 Formal Disciplines of Design and Code 539 19 Optimization 583 20 Model Compilation 633 21 Advanced Open Model Compilation 675 22 Wrapping Up Model-Based Design and Code 705 Part IV Related Topics 723 23 Estimation 725 24 Development and Maintenance Processes 759 25 Economics of Error Handling 787 26 Arguments Against Model-Based Software Engineering 815 Part V Summary 827 27 Closing Remarks 829 Part VI Appendices 843 Appendix A: Documentation Principles 845 Appendix B: WebBooks 2.0 Background 849 Appendix C: WebBooks 2.0 Domains 853 Appendix D: Semantic Model for Order fulfillment 857 Appendix E: (Pro Forma) Order fulfillment Design 885 Appendix F: Semantic Model for Payment 905 Appendix G: (Pro Forma) Payment Design 927 Appendix H: Semantic Model for Scalability 943 Appendix I: (Pro Forma) Scalability Design 969 Appendix J: Semantic Model for High availability 985 Appendix K: (Pro Forma) High availability Design 1001 Appendix L: Semantics of Semantic Modeling 1011 Appendix M: Sample Production Rules 1049 Appendix N: Software Structural Complexity Metrics 1061 References 1081 Index 1091

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Book SynopsisProvides students with an understanding of the modeling and practice in power system stability analysis and control design, as well as the computational tools used by commercial vendors Bringing together wind, FACTS, HVDC, and several other modern elements, this book gives readers everything they need to know about power systems. It makes learning complex power system concepts, models, and dynamics simpler and more efficient while providing modern viewpoints of power system analysis. Power System Modeling, Computation, and Control provides students with a new and detailed analysis of voltage stability; a simple example illustrating the BCU method of transient stability analysis; and one of only a few derivations of the transient synchronous machine model. It offers a discussion on reactive power consumption of induction motors during start-up to illustrate the low-voltage phenomenon observed in urban load centers. Damping controller designs using power system stabilizer, HVDC systemTable of ContentsPreface xvii About the Companion Website xxi 1 Introduction 1 1.1 Electrification 1 1.2 Generation, Transmission, and Distribution Systems 2 1.2.1 Central Generating Station Model 2 1.2.2 Renewable Generation 4 1.2.3 Smart Grids 5 1.3 Time Scales 5 1.3.1 Dynamic Phenomena 5 1.3.2 Measurements and Data 5 1.3.3 Control Functions and System Operation 7 1.4 Organization of the Book 7 Part I System Concepts 9 2 Steady-State Power Flow 11 2.1 Introduction 11 2.2 Power Network Elements and Admittance Matrix 12 2.2.1 Transmission Lines 12 2.2.2 Transformers 13 2.2.3 Per Unit Representation 14 2.2.4 Building the Network Admittance Matrix 14 2.3 Active and Reactive Power Flow Calculations 16 2.4 Power Flow Formulation 19 2.5 Newton-Raphson Method 21 2.5.1 General Procedure 21 2.5.2 NR Solution of Power Flow Equations 22 2.6 Advanced Power Flow Features 27 2.6.1 Load Bus Voltage Regulation 27 2.6.2 Multi-area Power Flow 28 2.6.3 Active Line Power Flow Regulation 29 2.6.4 Dishonest Newton-Raphson Method 30 2.6.5 Fast Decoupled Loadflow 30 2.6.6 DC Power Flow 31 2.7 Summary and Notes 31 Appendix 2.A Two-winding Transformer Model 32 Appendix 2.B LU Decomposition and Sparsity Methods 36 Appendix 2.C Power Flow and Dynamic Data for the 2-area, 4-machine System 39 Problems 42 3 Steady-State Voltage Stability Analysis 47 3.1 Introduction 47 3.2 Voltage Collapse Incidents 48 3.2.1 Tokyo, Japan: July 23, 1987 48 3.2.2 US Western Power System: July 2, 1996 48 3.3 Reactive Power Consumption on Transmission Lines 49 3.4 Voltage Stability Analysis of a Radial Load System 55 3.4.1 Maximum Power Transfer 59 3.5 Voltage Stability Analysis of Large Power Systems 61 3.6 Continuation Power Flow Method 64 3.6.1 Continuation Power Flow Algorithm 66 3.7 An AQ-Bus Method for Solving Power Flow 67 3.7.1 Analytical Framework for the AQ-Bus Method 69 3.7.2 AQ-Bus Formulation for Constant-Power-Factor Loads 70 3.7.3 AQ-Bus Algorithm for Computing Voltage Stability Margins 71 3.8 Power System Components Affecting Voltage Stability 73 3.8.1 Shunt Reactive Power Supply 74 3.8.2 Under-Load Tap Changer 76 3.9 Hierarchical Voltage Control 79 3.10 Voltage Stability Margins and Indices 80 3.10.1 Voltage Stability Margins 80 3.10.2 Voltage Sensitivities 81 3.10.3 Singular Values and Eigenvalues of the Power Flow Jacobian Matrix 82 3.11 Summary and Notes 82 Problems 83 4 Power System Dynamics and Simulation 87 4.1 Introduction 87 4.2 Electromechanical Model of Synchronous Machines 88 4.3 Single-Machine Infinite-Bus System 90 4.4 Power System Disturbances 94 4.4.1 Fault-On Analysis 94 4.4.2 Post-Fault Analysis 96 4.4.3 Other Types of Faults 98 4.5 Simulation Methods 98 4.5.1 Modified Euler Methods 99 4.5.1.1 Euler Full-Step Modification Method 100 4.5.1.2 Euler Half-Step Modification Method 101 4.5.2 Adams-Bashforth Second-Order Method 101 4.5.3 Selecting Integration Stepsize 102 4.5.4 Implicit Integration Methods 104 4.5.4.1 Integration of DAEs 105 4.6 Dynamic Models of Multi-Machine Power Systems 106 4.6.1 Constant-Impedance Loads 107 4.6.2 Generator Current Injections 108 4.6.3 Network Equation Extended to the Machine Internal Node 108 4.6.4 Reduced Admittance Matrix Approach 109 4.6.5 Method for Dynamic Simulation 109 4.7 Multi-Machine Power System Stability 114 4.7.1 Reference Frames for Machine Angles 115 4.8 Power System Toolbox 117 4.9 Summary and Notes 119 Problems 119 5 Direct Transient Stability Analysis 123 5.1 Introduction 123 5.2 Equal-Area Analysis of a Single-Machine Infinite-Bus System 124 5.2.1 Power-Angle Curve 124 5.2.2 Fault-On and Post-Fault Analysis 126 5.3 Transient Energy Functions 127 5.3.1 Lyapunov Functions 128 5.3.2 Energy Function for Single-Machine Infinite-Bus Electromechanical Model 128 5.4 Energy Function Analysis of a Disturbance Event 131 5.5 Single-Machine Infinite-Bus Model Phase Portrait and Region of Stability 135 5.6 Direct Stability Analysis using Energy Functions 138 5.7 Energy Functions for Multi-Machine Power Systems 139 5.7.1 Direct Stability Analysis for Multi-Machine Systems 142 5.7.2 Computation of Critical Energy 143 5.8 Dynamic Security Assessment 146 5.9 Summary and Notes 146 Problems 147 6 Linear Analysis and Small-Signal Stability 149 6.1 Introduction 149 6.2 Electromechanical Modes 150 6.3 Linearization 151 6.3.1 State-Space Models 151 6.3.2 Input-Output Models 152 6.3.3 Modal Analysis and Time-Domain Solutions 152 6.3.4 Time Response of Linear Systems 154 6.3.5 Participation Factors 156 6.4 Linearized Models of Single-Machine Infinite-Bus Systems 157 6.5 Linearized Models of Multi-Machine Systems 160 6.5.1 Synchronizing Torque Matrix and Eigenvalue Properties 162 6.5.2 Modeshapes and Participation Factors 162 6.6 Developing Linearized Models of Large Power Systems 164 6.6.1 Analytical Partial Derivatives 165 6.6.2 Numerical Linearization 169 6.7 Summary and Notes 171 Problems 171 Part II Synchronous Machine Models and their Control Systems 175 7 Steady-State Models and Operation of Synchronous Machines 177 7.1 Introduction 177 7.2 Physical Description 177 7.2.1 Amortisseur Bars 179 7.3 Synchronous Machine Model 179 7.3.1 Flux Linkage and Voltage Equations 181 7.3.2 Stator (Armature) Self and Mutual Inductances 183 7.3.3 Mutual Inductances between Stator and Rotor 183 7.3.4 Rotor Self and Mutual Inductances 184 7.4 Park Transformation 185 7.4.1 Electrical Power in dq0 Variables 188 7.5 Reciprocal, Equal Lad Per-Unit System 189 7.5.1 Stator Base Values 189 7.5.2 Stator Voltage Equations 190 7.5.3 Rotor Base Values 191 7.5.4 Rotor Voltage Equations 191 7.5.5 Stator Flux-Linkage Equations 192 7.5.6 Rotor Flux-Linkage Equations 192 7.5.7 Equal Mutual Inductance 192 7.6 Equivalent Circuits 196 7.6.1 Flux-Linkage Circuits 196 7.6.2 Voltage Equivalent Circuits 197 7.7 Steady-State Analysis 199 7.7.1 Open-Circuit Condition 199 7.7.2 Loaded Condition 201 7.7.3 Drawing Voltage-Current Phasor Diagrams 202 7.8 Saturation Effects 204 7.8.1 Representations of Magnetic Saturation 205 7.9 Generator Capability Curves 207 7.10 Summary and Notes 209 Problems 209 8 Dynamic Models of Synchronous Machines 213 8.1 Introduction 213 8.2 Machine Dynamic Response During Fault 213 8.2.1 DC Offset and Stator Transients 215 8.3 Transient and Subtransient Reactances and Time Constants 216 8.4 Subtransient Synchronous Machine Model 221 8.5 Other Synchronous Machine Models 227 8.5.1 Flux-Decay Model 227 8.5.2 Classical Model 228 8.6 dq-axes Rotation Between a Generator and the System 229 8.7 Power System Simulation using Detailed Machine Models 230 8.7.1 Power System Simulation Algorithm 231 8.8 Linearized Models 232 8.9 Summary and Notes 234 Problems 235 9 Excitation Systems 237 9.1 Introduction 237 9.2 Excitation System Models 238 9.3 Type DC Exciters 239 9.3.1 Separately Excited DC exciter 239 9.3.2 Self-Excited DC Exciter 243 9.3.3 Voltage Regulator 244 9.3.4 Initialization of DC Type Exciters 245 9.3.5 Transfer Function Analysis 246 9.3.6 Generator and Exciter Closed-Loop System 248 9.3.7 Excitation System Response Ratios 251 9.4 Type AC Exciters 252 9.5 Type ST Excitation Systems 254 9.6 Load Compensation Control 257 9.7 Protective Functions 259 9.8 Summary and Notes 259 Appendix 9.A Anti-Windup Limits 260 Problems 261 10 Power System Stabilizers 265 10.1 Introduction 265 10.2 Single-Machine Infinite-Bus System Model 266 10.3 Synchronizing and Damping Torques 271 10.3.1 ΔTe2 Under Constant Field Voltage 272 10.3.2 ΔTe2 With Excitation System Control 273 10.4 Power System Stabilizer Design using Rotor Speed Signal 275 10.4.1 PSS Design Requirements 276 10.4.2 PSS Control Blocks 277 10.4.3 PSS Design Methods 279 10.4.4 Torsional Filters 284 10.4.5 PSS Field Tuning 287 10.4.6 Interarea Mode Damping 287 10.5 Other PSS Input Signals 288 10.5.1 Generator Terminal Bus Frequency 288 10.5.2 Electrical Power Output ΔPe 288 10.6 Integral-of-Accelerating-Power or Dual-Input PSS 289 10.7 Summary and Notes 293 Problems 293 11 Load and Induction Motor Models 295 11.1 Introduction 295 11.2 Static Load Models 296 11.2.1 Exponential Load Model 296 11.2.2 Polynomial Load Model 297 11.3 Incorporating ZIP Load Models in Dynamic Simulation and Linear Analysis 298 11.4 Induction Motors: Steady-State Models 303 11.4.1 Physical Description 304 11.4.2 Mathematical Description 304 11.4.2.1 Modeling Equations 304 11.4.2.2 Reference Frame Transformation 306 11.4.3 Equivalent Circuits 308 11.4.4 Per-Unit Representation 310 11.4.5 Torque-Slip Characteristics 311 11.4.6 Reactive Power Consumption 313 11.4.7 Motor Startup 314 11.5 Induction Motors: Dynamic Models 315 11.5.1 Initialization 318 11.5.2 Reactive Power Requirement during Motor Stalling 320 11.6 Summary and Notes 323 Problems 324 12 Turbine-Governor Models and Frequency Control 327 12.1 Introduction 327 12.2 Steam Turbines 328 12.2.1 Turbine Configurations 328 12.2.2 Steam Turbine-Governors 331 12.3 Hydraulic Turbines 333 12.3.1 Hydraulic Turbine-Governors 337 12.3.2 Load Rejection of Hydraulic Turbines 338 12.4 Gas Turbines and Co-Generation Plants 339 12.5 Primary Frequency Control 342 12.5.1 Isolated Turbine-Generator Serving Local Load 343 12.5.2 Interconnected Units 347 12.5.3 Frequency Response in US Power Grids 349 12.6 Automatic Generation Control 351 12.7 Turbine-Generator Torsional Oscillations and Subsynchronous Resonance 356 12.7.1 Torsional Modes 356 12.7.2 Electrical Network Modes 363 12.7.3 SSR Occurrence and Countermeasures 365 12.8 Summary and Notes 366 Problems 367 Part III Advanced Power System Topics 371 13 High-Voltage Direct Current Transmission Systems 373 13.1 Introduction 373 13.1.1 HVDC System Installations and Applications 375 13.1.2 HVDC System Economics 377 13.2 AC/DC and DC/AC Conversion 377 13.2.1 AC-DC Conversion using Ideal Diodes 378 13.2.2 Three-Phase Full-Wave Bridge Converter 379 13.3 Line-Commutation Operation in HVDC Systems 383 13.3.1 Rectifier Operation 383 13.3.1.1 Thyristor Ignition Delay Angle 383 13.3.1.2 Commutation Overlap 385 13.3.2 Inverter Operation 388 13.3.3 Multiple Bridge Converters 389 13.3.4 Equivalent Circuit 389 13.4 Control Modes 391 13.4.1 Mode 1: Normal Operation 392 13.4.2 Mode 2: Reduced-Voltage Operation 393 13.4.3 Mode 3: Transitional Mode 394 13.4.4 System Operation Under Fault Conditions 396 13.4.5 Communication Requirements 396 13.5 Multi-terminal HVDC Systems 397 13.6 Harmonics and Reactive Power Requirement 398 13.6.1 Harmonic Filters 398 13.6.2 Reactive Power Support 399 13.7 AC-DC Power Flow Computation 401 13.8 Dynamic Models 406 13.8.1 Converter Control 406 13.8.2 DC Line Dynamics 408 13.8.3 AC-DC Network Solution 409 13.9 Damping Control Design 411 13.10 Summary and Notes 416 Problems 416 14 Flexible AC Transmission Systems 421 14.1 Introduction 421 14.2 Static Var Compensator 422 14.2.1 Circuit Configuration and Thyristor Switching 422 14.2.2 Steady-State Voltage Regulation and Stability Enhancement 423 14.2.2.1 Voltage Stability Enhancement 424 14.2.2.2 Transient Stability Enhancement 427 14.2.3 Dynamic Voltage Control and Droop Regulation 429 14.2.4 Dynamic Simulation 433 14.2.5 Damping Control Design using SVC 435 14.3 Thyristor-Controlled Series Compensator 441 14.3.1 Fixed Series Compensation 442 14.3.2 TCSC Circuit Configuration and Switching 442 14.3.3 Voltage Reversal Control 444 14.3.4 Mitigation of Subsynchronous Oscillations 445 14.3.5 Dynamic Model and Damping Control Design 446 14.4 Shunt VSC Controllers 451 14.4.1 Voltage-Sourced Converters 451 14.4.1.1 Three-Phase Full-Wave VSCs 453 14.4.1.2 Three-Level Converters 455 14.4.1.3 Harmonics 455 14.4.2 Static Compensator 458 14.4.2.1 Steady-State Analysis 458 14.4.2.2 Dynamic Model 459 14.4.3 VSC HVDC Systems 463 14.4.3.1 Steady-State Operation 463 14.4.3.2 Dynamic Model 466 14.5 Series and Coupled VSC Controllers 469 14.5.1 Static Synchronous Series Compensation 469 14.5.1.1 Steady-State Analysis 469 14.5.2 Unified Power Flow Controller 471 14.5.2.1 Steady-State Analysis 471 14.5.3 Interline Power Flow Controller 475 14.5.3.1 Steady-State Analysis 475 14.5.4 Dynamic Model 478 14.5.4.1 Series Voltage Insertion 479 14.5.4.2 Line Active and Reactive Power Flow Control 480 14.6 Summary and Notes 480 Problems 481 15 Wind Power Generation and Modeling 487 15.1 Background 487 15.2 Wind Turbine Components 489 15.3 Wind Power 491 15.3.1 Blade Angle Orientation 492 15.3.2 Power Coefficient 494 15.4 Wind Turbine Types 496 15.4.1 Type 1 496 15.4.2 Type 2 497 15.4.3 Type 3 498 15.4.4 Type 4 498 15.5 Steady-State Characteristics 499 15.5.1 Type-1Wind Turbine 499 15.5.2 Type-2Wind Turbine 501 15.5.3 Type-3Wind Turbine 502 15.6 Wind Power Plant Representation 505 15.7 Overall Control Criteria for Variable-Speed Wind Turbines 510 15.8 Wind Turbine Model for Transient Stability Planning Studies 513 15.8.1 Overall Model Structure 513 15.8.2 Generator/Converter Model 514 15.8.3 Electrical Control Model 515 15.8.4 Drive-Train Model 517 15.8.5 Torque Control Model 519 15.8.6 Aerodynamic Model 520 15.8.7 Pitch Controller 522 15.9 Plant-Level Control Model 526 15.9.1 Simulation Example 526 15.10 Summary and Notes 527 Problems 528 16 Power System Coherency and Model Reduction 531 16.1 Introduction 531 16.2 Interarea Oscillations and Slow Coherency 532 16.2.1 Slow Coherency 534 16.2.2 Slow Coherent Areas 536 16.2.3 Finding Coherent Groups of Machines 541 16.3 Generator Aggregation and Network Reduction 544 16.3.1 Generator Aggregation 545 16.3.2 Dynamic Aggregation 548 16.3.3 Load Bus Elimination 551 16.4 Simulation Studies 555 16.4.1 Singular Perturbations Method 556 16.5 Linear Reduced Model Methods 557 16.5.1 Modal Truncation 558 16.5.2 Balanced Model Reduction Method 559 16.6 Dynamic Model Reduction Software 559 16.7 Summary and Notes 560 Problems 560 References 563 Index 577

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Book SynopsisCovers significant changes in GPS/INS technology, and includes new material on GPS, GNSSs including GPS, Glonass, Galileo, BeiDou, QZSS, and IRNSS/NAViC, and MATLAB programs on square root information filtering (SRIF) This book provides readers with solutions to real-world problems associated with global navigation satellite systems, inertial navigation, and integration. It presents readers with numerous detailed examples and practice problems, including GNSS-aided INS, modeling of gyros and accelerometers, and SBAS and GBAS. This revised fourth edition adds new material on GPS III and RAIM. It also provides updated information on low cost sensors such as MEMS, as well as GLONASS, Galileo, BeiDou, QZSS, and IRNSS/NAViC, and QZSS. Revisions also include added material on the more numerically stable square-root information filter (SRIF) with MATLAB programs and examples from GNSS system state filters such as ensemble time filter with square-root covariance Table of ContentsPreface to the Fourth Edition xxv Acknowledgments xxix About the Authors xxx Acronyms xxxi About the Companion Website xxxix 1 Introduction 1 1.1 Navigation 1 1.1.1 Navigation-Related Technologies 1 1.1.2 Navigation Modes 2 1.2 GNSS Overview 3 1.2.1 GPS 4 1.2.2 Global Orbiting Navigation Satellite System (GLONASS) 6 1.2.3 Galileo 7 1.2.4 BeiDou 9 1.2.5 Regional Satellite Systems 10 1.3 Inertial Navigation Overview 10 1.3.1 History 11 1.3.2 Development Results 12 1.4 GNSS/INS Integration Overview 16 1.4.1 The Role of Kalman Filtering 16 1.4.2 Implementation 17 Problems 17 References 18 2 Fundamentals of Satellite Navigation Systems 21 2.1 Chapter Focus 21 2.2 Satellite Navigation Systems Considerations 21 2.2.1 Systems Other than GNSS 21 2.2.2 Comparison Criteria 22 2.3 Satellite Navigation 22 2.3.1 GNSS Orbits 23 2.3.2 Navigation Solution (Two-Dimensional Example) 25 2.3.3 User Solution and Dilution of Precision (DOP) 28 2.3.4 Example Calculation of DOPs 32 2.4 Time and GPS 33 2.4.1 Coordinated Universal Time (UTC) Generation 33 2.4.2 GPS System Time 33 2.4.3 Receiver Computation of UTC 34 2.5 Example: User Position Calculations with No Errors 35 2.5.1 User Position Calculations 35 2.5.2 User Velocity Calculations 37 Problems 39 References 41 3 Fundamentals of Inertial Navigation 43 3.1 Chapter Focus 43 3.2 Terminology 44 3.3 Inertial Sensor Technologies 50 3.3.1 Gyroscopes 50 3.3.2 Accelerometers 53 3.3.3 Sensor Errors 55 3.3.4 Inertial Sensor Assembly (ISA) Calibration 57 3.3.5 Carouseling and Indexing 60 3.4 Inertial Navigation Models 60 3.4.1 Geoid Models 61 3.4.2 Terrestrial Navigation Coordinates 61 3.4.3 Earth Rotation Model 63 3.4.4 Gravity Models 63 3.4.5 Attitude Models 68 3.5 Initializing the Navigation Solution 70 3.5.1 Initialization from an Earth-fixed Stationary State 70 3.5.2 Initialization on the Move 73 3.6 Propagating the Navigation Solution 73 3.6.1 Attitude Propagation 73 3.6.2 Position and Velocity Propagation 82 3.7 Testing and Evaluation 86 3.7.1 Laboratory Testing 86 3.7.2 Field Testing 86 3.7.3 Performance Qualification Testing 87 3.8 Summary 89 3.8.1 Further Reading 89 Problems 90 References 92 4 GNSS Signal Structure, Characteristics, and Information Utilization 93 4.1 Legacy GPS Signal Components, Purposes, and Properties 93 4.1.1 Signal Models for the Legacy GPS Signals 94 4.1.2 Navigation Data Format 98 4.1.3 GPS Satellite Position Calculations 102 4.1.4 C/A-Code and Its Properties 108 4.1.5 P(Y)-Code and Its Properties 115 4.1.6 L1 and L2 Carriers 116 4.1.7 Transmitted Power Levels 117 4.1.8 Free Space and Other Loss Factors 117 4.1.9 Received Signal Power 118 4.2 Modernization of GPS 118 4.2.1 Benefits from GPS Modernization 119 4.2.2 Elements of the Modernized GPS 120 4.2.3 L2 Civil Signal (L2C) 122 4.2.4 L5 Signal 123 4.2.5 M-Code 125 4.2.6 L1C Signal 126 4.2.7 GPS Satellite Blocks 128 4.2.8 GPS Ground Control Segment 129 4.3 GLONASS Signal Structure and Characteristics 129 4.3.1 Frequency Division Multiple Access (FDMA) Signals 130 4.3.2 CDMA Modernization 131 4.4 Galileo 132 4.4.1 Constellation and Levels of Services 132 4.4.2 Navigation Data and Signals 132 4.5 BeiDou 134 4.6 QZSS 135 4.7 IRNSS/NAVIC 138 Problems 138 References 141 5 GNSS Antenna Design and Analysis 145 5.1 Applications 145 5.2 GNSS Antenna Performance Characteristics 145 5.2.1 Size and Cost 145 5.2.2 Frequency and Bandwidth Coverage 146 5.2.3 Radiation Pattern Characteristics 147 5.2.4 Antenna Polarization and Axial Ratio 149 5.2.5 Directivity, Efficiency, and Gain of a GNSS Antenna 152 5.2.6 Antenna Impedance, Standing Wave Ratio, and Return Loss 153 5.2.7 Antenna Bandwidth 154 5.2.8 Antenna Noise Figure 155 5.3 Computational Electromagnetic Models (CEMs) for GNSS Antenna Design 157 5.4 GNSS Antenna Technologies 159 5.4.1 Dipole-Based GNSS Antennas 159 5.4.2 GNSS Patch Antennas 160 5.4.3 Survey-Grade/Reference GNSS Antennas 169 5.5 Principles of Adaptable Phased-Array Antennas 173 5.5.1 Digital Beamforming Adaptive Antenna Array Formulations 176 5.5.2 STAP 179 5.5.3 SFAP 179 5.5.4 Configurations of Adaptable Phased-Array Antennas 179 5.5.5 Relative Merits of Adaptable Phased-Array Antennas 180 5.6 Application Calibration/Compensation Considerations 181 Problems 183 References 184 6 GNSS Receiver Design and Analysis 189 6.1 Receiver Design Choices 189 6.1.1 Global Navigation Satellite System (GNSS) Application to Be Supported 189 6.1.2 Single or Multifrequency Support 189 6.1.3 Number of Channels 191 6.1.4 Code Selections 191 6.1.5 Differential Capability 192 6.1.6 Aiding Inputs 194 6.2 Receiver Architecture 195 6.2.1 Radio Frequency (RF) Front End 195 6.2.2 Frequency Down-Conversion and IF Amplification 197 6.2.2.1 SNR 198 6.2.3 Analog-to-Digital Conversion and Automatic Gain Control 199 6.2.4 Baseband Signal Processing 200 6.3 Signal Acquisition and Tracking 200 6.3.1 Hypothesize About the User Location 201 6.3.2 Hypothesize About Which GNSS Satellites Are Visible 201 6.3.3 Signal Doppler Estimation 202 6.3.4 Search for Signal in Frequency and Code Phase 202 6.3.5 Signal Detection and Confirmation 207 6.3.6 Code Tracking Loop 210 6.3.7 Carrier Phase Tracking Loops 215 6.3.8 Bit Synchronization 219 6.3.9 Data Bit Demodulation 219 6.4 Extraction of Information for User Solution 220 6.4.1 Signal Transmission Time Information 220 6.4.2 Ephemeris Data for Satellite Position and Velocity 221 6.4.3 Pseudorange Measurements Formulation Using Code Phase 221 6.4.4 Measurements Using Carrier Phase 223 6.4.5 Carrier Doppler Measurement 225 6.4.6 Integrated Doppler Measurements 226 6.5 Theoretical Considerations in Pseudorange, Carrier Phase, and Frequency Estimations 228 6.5.1 Theoretical Error Bounds for Code Phase Measurement 229 6.5.2 Theoretical Error Bounds for Carrier Phase Measurements 230 6.5.3 Theoretical Error Bounds for Frequency Measurement 231 6.6 High-Sensitivity A-GPS Systems 232 6.6.1 How Assisting Data Improves Receiver Performance 233 6.6.2 Factors Affecting High-Sensitivity Receivers 237 6.7 Software-Defined Radio (SDR) Approach 239 6.8 Pseudolite Considerations 240 Problems 242 References 244 7 GNSS Measurement Errors 249 7.1 Source of GNSS Measurement Errors 249 7.2 Ionospheric Propagation Errors 249 7.2.1 Ionospheric Delay Model 251 7.2.2 GNSS SBAS Ionospheric Algorithms 253 7.3 Tropospheric Propagation Errors 262 7.4 The Multipath Problem 263 7.4.1 How Multipath Causes Ranging Errors 264 7.5 Methods of Multipath Mitigation 266 7.5.1 Spatial Processing Techniques 266 7.5.2 Time-Domain Processing 269 7.5.3 Multipath Mitigation Technology (MMT) 271 7.5.4 Performance of Time-Domain Methods 281 7.6 Theoretical Limits for Multipath Mitigation 283 7.6.1 Estimation-Theoretic Methods 283 7.6.2 Minimum Mean-Squared Error (MMSE) Estimator 284 7.6.3 Multipath Modeling Errors 284 7.7 Ephemeris Data Errors 285 7.8 Onboard Clock Errors 285 7.9 Receiver Clock Errors 286 7.10 Error Budgets 287 Problems 289 References 291 8 Differential GNSS 293 8.1 Introduction 293 8.2 Descriptions of Local-Area Differential GNSS (LADGNSS), Wide-Area Differential GNSS (WADGNSS), and Space-Based Augmentation System (SBAS) 294 8.2.1 LADGNSS 294 8.2.2 WADGNSS 294 8.2.3 SBAS 294 8.3 GEO with L1L5 Signals 299 8.3.1 GEO Uplink Subsystem (GUS) Control Loop Overview 302 8.4 GUS Clock Steering Algorithm 307 8.4.1 Receiver Clock Error Determination 309 8.4.2 Clock Steering Control Law 311 8.5 GEO Orbit Determination (OD) 312 8.5.1 OD Covariance Analysis 313 8.6 Ground-Based Augmentation System (GBAS) 318 8.6.1 Local-Area Augmentation System (LAAS) 318 8.6.2 Joint Precision Approach and Landing System (ALS) 318 8.6.3 Enhanced Long-Range Navigation (eLORAN) 319 8.7 Measurement/Relative-Based DGNSS 320 8.7.1 Code Differential Measurements 320 8.7.2 Carrier Phase Differential Measurements 322 8.7.3 Positioning Using Double-Difference Measurements 324 8.8 GNSS Precise Point Positioning Services and Products 325 8.8.1 The International GNSS Service (IGS) 325 8.8.2 Continuously Operating Reference Stations (CORSs) 326 8.8.3 GPS Inferred Positioning System (GIPSY) and Orbit Analysis Simulation Software (OASIS) 326 8.8.4 Scripps Coordinate Update Tool (SCOUT) 327 8.8.5 The Online Positioning User Service (OPUS) 327 8.8.6 Australia’s Online GPS Processing System (AUPOS) 328 8.8.7 National Resources Canada (NRCan) 328 Problems 328 References 328 9 GNSS and GEO Signal Integrity 331 9.1 Introduction 331 9.1.1 Range Comparison Method 332 9.1.2 Least-Squares Method 332 9.1.3 Parity Method 334 9.2 SBAS and GBAS Integrity Design 334 9.2.1 SBAS Error Sources and Integrity Threats 336 9.2.2 GNSS-Associated Errors 337 9.2.3 GEO-Associated Errors 339 9.2.4 Receiver and Measurement Processing Errors 340 9.2.5 Estimation Errors 341 9.2.6 Integrity-Bound Associated Errors 342 9.2.7 GEO Uplink Errors 343 9.2.8 Mitigation of Integrity Threats 344 9.3 SBAS Example 349 9.4 Summary 351 9.5 Future: GIC 351 Problems 352 References 352 10 Kalman Filtering 355 10.1 Chapter Focus 355 10.2 Frequently Asked Questions 356 10.3 Notation 360 10.3.1 Real Vectors and Matrices 360 10.3.2 Probability Essentials 363 10.3.3 Discrete Time Notation 365 10.4 Kalman Filter Genesis 366 10.4.1 Measurement Update (Corrector) 366 10.4.2 Time Update (Predictor) 373 10.4.3 Basic Kalman Filter Equations 378 10.4.4 The Time-Invariant Case 378 10.4.5 Observability and Stability Issues 378 10.5 Alternative Implementations 380 10.5.1 Implementation Issues 380 10.5.2 Conventional Implementation Improvements 381 10.5.3 James E. Potter (1937–2005) and Square Root Filtering 383 10.5.4 Square Root Matrix Manipulation Methods 384 10.5.5 Alternative Square Root Filter Implementations 386 10.6 Nonlinear Approximations 388 10.6.1 Linear Approximation Errors 389 10.6.2 Adaptive Kalman Filtering 392 10.6.3 Taylor–Maclauren Series Approximations 392 10.6.4 Trajectory Perturbation Modeling 393 10.6.5 Structured Sampling Methods 394 10.7 Diagnostics and Monitoring 397 10.7.1 Covariance Matrix Diagnostics 397 10.7.2 Innovations Monitoring 398 10.8 GNSS-Only Navigation 401 10.8.1 GNSS Dynamic Models 402 10.8.2 GNSS Measurement Models 406 10.9 Summary 410 Problems 412 References 414 11 Inertial Navigation Error Analysis 419 11.1 Chapter Focus 419 11.2 Errors in the Navigation Solution 420 11.2.1 Navigation Error Variables 421 11.2.2 Coordinates Used for INS Error Analysis 421 11.2.3 Model Variables and Parameters 421 11.2.4 Dynamic Coupling Mechanisms 427 11.3 Navigation Error Dynamics 430 11.3.1 Error Dynamics Due to Velocity Integration 431 11.3.2 Error Dynamics Due to Gravity Miscalculations 432 11.3.3 Error Dynamics Due to Coriolis Acceleration 433 11.3.4 Error Dynamics Due to Centrifugal Acceleration 434 11.3.5 Error Dynamics Due to Earthrate Leveling 435 11.3.6 Error Dynamics Due to Velocity Leveling 436 11.3.7 Error Dynamics Due to Acceleration and IMU Alignment Errors 437 11.3.8 Composite Model from All Effects 438 11.3.9 Vertical Navigation Instability 439 11.3.10 Schuler Oscillations 444 11.3.11 Core Model Validation and Tuning 445 11.4 Inertial Sensor Noise Propagation 447 11.4.1 1∕f Noise 447 11.4.2 White Noise 447 11.4.3 Horizontal CEP Rate Versus Sensor Noise 449 11.5 Sensor Compensation Errors 450 11.5.1 Sensor Compensation Error Models 450 11.5.2 Carouseling and Indexing 456 11.6 Chapter Summary 456 11.6.1 Further Reading 457 Problems 458 References 459 12 GNSS/INS Integration 461 12.1 Chapter Focus 461 12.2 New Application Opportunities 462 12.2.1 Integration Advantages 462 12.2.2 Enabling New Capabilities 463 12.2.3 Economic Factors 464 12.3 Integrated Navigation Models 468 12.3.1 Common Navigation Models 468 12.3.2 GNSS Error Models 470 12.3.3 INS Error Models 473 12.3.4 GNSS/INS Error Model 474 12.4 Performance Analysis 476 12.4.1 The Influence of Trajectories 476 12.4.2 Performance Metrics 477 12.4.3 Dynamic Simulation Model 479 12.4.4 Sample Results 480 12.5 Summary 485 Problems 486 References 487 Appendix A Software 489 A.1 Software Sources 489 A.2 Software for Chapter 2 490 A.3 Software for Chapter 3 490 A.4 Software for Chapter 4 490 A.5 Software for Chapter 7 491 A.6 Software for Chapter 10 491 A.7 Software for Chapter 11 492 A.8 Software for Chapter 12 493 A.9 Software for Appendix B 494 A.10 Software for Appendix C 494 A.11 GPS Almanac/Ephemeris Data Sources 495 Appendix B Coordinate Systems and Transformations 497 B.1 Coordinate Transformation Matrices 497 B.1.1 Notation 497 B.1.2 Definitions 498 B.1.3 Unit Coordinate Vectors 498 B.1.4 Direction Cosines 499 B.1.5 Composition of Coordinate Transformations 500 B.2 Inertial Reference Directions 500 B.2.1 Earth’s Polar Axis and the Equatorial Plane 500 B.2.2 The Ecliptic and the Vernal Equinox 500 B.2.3 Earth-Centered Inertial (ECI) Coordinates 501 B.3 Application-dependent Coordinate Systems 501 B.3.1 Cartesian and Polar Coordinates 501 B.3.2 Celestial Coordinates 502 B.3.3 Satellite Orbit Coordinates 503 B.3.4 Earth-Centered Inertial (ECI) Coordinates 504 B.3.5 Earth-Centered, Earth-Fixed (ECEF) Coordinates 505 B.3.6 Ellipsoidal Radius of Curvature 512 B.3.7 Local Tangent Plane (LTP) Coordinates 513 B.3.8 Roll–Pitch–Yaw (RPY) Coordinates 516 B.3.9 Vehicle Attitude Euler Angles 516 B.3.10 GPS Coordinates 518 B.4 Coordinate Transformation Models 520 B.4.1 Euler Angles 521 B.4.2 Rotation Vectors 522 B.4.3 Direction Cosines Matrix 538 B.4.4 Quaternions 542 B.5 Newtonian Mechanics in Rotating Coordinates 547 B.5.1 Rotating Coordinates 547 B.5.2 Time Derivatives of Matrix Products 548 B.5.3 Solving for Centrifugal and Coriolis Accelerations 548 Appendix C PDF Ambiguity Errors in Nonlinear Kalman Filtering 551 C.1 Objective 551 C.2 Methodology 552 C.2.1 Computing Expected Values 552 C.2.2 Representative Sample of PDFs 553 C.2.3 Parametric Class of Nonlinear Transformations Used 556 C.2.4 Ambiguity Errors in Nonlinearly Transformed Means and Variances 558 C.3 Results 558 C.3.1 Nonlinearly Transformed Means 558 C.3.2 Nonlinearly Transformed Variances 559 C.4 Mitigating Application-specific Ambiguity Errors 563 References 564 Index 565

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Book SynopsisDiscover how the NG-RAN architecture is, and isn''t, ready for the challenges introduced by 5G 5G Radio Access Network Architecture: The Dark Side of 5G explores foundational and advanced topics in Radio Access Network (RAN) architecture and why a re-thinking of that architecture is necessary to support new 5G requirements. The distinguished engineer and editor Sasha Sirotkin has included numerous works written by industry insiders with state of the art research at their disposal. The book explains the relevant standards and technologies from an academic perspective, but also explains why particular standards decisions were made and how a variety of NG-RAN architecture options could be deployed in real-life networks. All major standards and technologies associated with the NG-RAN architecture are discussed in this book, including 3GPP, O-RAN, Small Cell Forum, IEEE, and IETF. Readers will learn about how a re-design of the RAN architecture would ensure that 5G neTable of ContentsPreface xv Acknowledgments xvii List of Contributors xix Acronyms and Abbreviations xxi 1 Introduction 1 2 Market Drivers 5Reza Arefi and Sasha Sirotkin 2.1 Introduction 5 2.2 Key Ideas 7 2.3 Spectrum 9 2.3.1 Spectrum Needs 9 2.3.2 Target Spectrum 12 2.3.3 Spectrum Implications 13 2.4 New Spectrum Models 14 2.4.1 New Ways of Sharing Spectrum 15 2.4.2 Localized Licensing 17 2.5 Regulations Facilitating 5G Applications 18 2.6 Network Deployment Models 19 2.7 Technical Requirements of 5G Radio Interfaces 20 2.8 Business Drivers 23 2.9 Role of Standards 25 2.10 Role of Open Source 29 2.11 Competition 31 2.12 Challenges 32 2.13 Summary 34 References 35 3 5G System Overview 37 3.1 Introduction 37 3.2 5G Core Network 37Sebastian Speicher 3.2.1 Introduction 37 3.2.2 Service-Based Architecture 39 3.2.2.1 Fostering Functional Reuse 39 3.2.2.2 Overview of 5GC Control-Plane Functions 41 3.2.3 Control-User Plane Separation (CUPS) 43 3.2.4 Common Access-Agnostic Core Network 44 3.2.5 Enablers for Concurrent and Efficient Access to Local and Centralized Services 46 3.2.5.1 Overview 46 3.2.5.2 Single PDU Session-Based Access to Local Services 47 3.2.5.3 Multiple PDU Session-Based Access to Local Services 48 3.2.6 Network Slicing 50 3.2.7 Private Networks 53 3.2.7.1 Overview 53 3.2.7.2 Stand-Alone Non-public Networks 54 3.2.7.3 Public-Network-Integrated Non-public Network 55 References 57 3.3 NG Radio Access Network 59Sasha Sirotkin 3.3.1 Introduction 59 3.3.2 Network Protocol Stacks 62 3.3.2.1 Control-Plane Protocol Stack 62 3.3.2.2 User-Plane Protocol Stack 62 3.3.2.3 Standards 63 3.3.3 NG Interface 63 3.3.3.1 NG-C Interface 64 3.3.3.2 NG-U Interface 69 3.3.4 Xn Interface 70 3.3.4.1 Xn Control Plane (Xn-C) Interface 70 3.3.4.2 Xn User Plane (Xn-U) Interface 75 3.3.5 Additional NG-RAN Features 76 3.3.5.1 RAN Sharing 76 3.3.5.2 Slicing 77 3.3.5.3 Virtualization 78 3.3.5.4 Non-3GPP Access 78 References 79 3.4 NR Protocol Stack 80Sudeep Palat 3.4.1 Introduction 80 3.4.2 NG-RAN Architecture 81 3.4.3 NR User Plane 81 3.4.4 Supporting QoS with 5GC 86 3.4.5 NR Control Plane 88 3.4.5.1 RRC States 88 3.4.5.2 RRC Procedures and Functions 89 3.4.6 Summary 97 References 98 3.5 NR Physical Layer 99Alexei Davydov 3.5.1 Introduction 99 3.5.2 Waveform and Numerology 100 3.5.3 Frame Structure 101 3.5.4 Synchronization and Initial Access 104 3.5.4.1 Downlink Synchronization Signals 104 3.5.4.2 Random Access Channel 106 3.5.5 Downlink Control Channel 107 3.5.6 Uplink Control Channel 109 3.5.7 Reference Signals 112 3.5.7.1 CSI-RS 112 3.5.7.2 DM-RS 114 3.5.7.3 PT-RS 115 3.5.7.4 SRS 116 3.5.8 Beam Management 116 3.5.9 Channel Coding and Modulation 118 3.5.10 Co-Existence with LTE, Forward Compatibility and Uplink Coverage Enhancement 121 References 122 4 NG-RAN Architecture 123Colby Harper and Sasha Sirotkin 4.1 Introduction 123 4.1.1 Monolithic gNB Architecture 124 4.1.2 Common Public Radio Interface (CPRI) 125 4.1.3 Antenna Interface 129 4.1.3.1 Before 5G: WhereWe Have Been 130 4.1.3.2 New 5G Era: WhereWe Are 131 4.1.3.3 Release-17 and Beyond: WhereWe Are Going 132 4.1.4 gNB Functional Split(s) 133 4.1.5 Conclusions 138 4.1.6 Further Reading 138 References 138 4.2 High-Level gNB-CU/DU Split 140 4.2.1 Key Ideas 140 4.2.2 Market Drivers 141 4.2.3 Functional Description 143 4.2.3.1 F1 Control-Plane Protocol 144 4.2.3.2 User-Plane Protocol 154 4.2.3.3 OAM Aspects 154 4.2.4 Further Reading 154 References 155 4.3 Multi-Radio Dual Connectivity 156Sergio Parolari 4.3.1 Key Ideas 157 4.3.2 MR-DC Options 157 4.3.3 Market Drivers 158 4.3.4 Functional Description 160 4.3.4.1 Control Plane 160 4.3.4.2 User Plane 164 4.3.4.3 Procedures 169 4.3.5 Further Reading 174 References 175 4.4 Control–User Plane Separation 176Feng Yang 4.4.1 Key Ideas 176 4.4.2 Market Drivers 177 4.4.3 Functional Description 179 4.4.3.1 Control Plane 180 4.4.3.2 OAM Aspects 187 4.4.3.3 Relation to SDN 188 4.4.3.4 Relation to 5GC 188 4.4.4 Further Reading 189 References 190 4.5 Lower-Layer Split 191 4.5.1 Key Ideas 191 4.5.2 Market Drivers 192 4.5.3 Functional Split 194 4.5.3.1 Fronthaul Bandwidth Requirements 195 4.5.3.2 Low-Level Functional Split Details 196 4.5.3.3 Latency Management 198 4.5.4 Fronthaul Interface 200 4.5.4.1 Messages 201 4.5.4.2 Scheduling Procedure 207 4.5.4.3 Beamforming Methods 209 4.5.5 Fronthaul Timing Synchronization 209 4.5.6 Operation, Administration and Maintenance (OAM) 210 4.5.7 Further Reading 211 References 212 4.6 Small Cells 213Clare Somerville 4.6.1 Key Ideas 213 4.6.2 Market Drivers 214 4.6.3 Barriers and Solutions 215 4.6.3.1 Site Locations 215 4.6.3.2 Scaling Up Deployment 215 4.6.3.3 Backhaul 216 4.6.3.4 Edge Compute 216 4.6.4 Small Cell Variants 216 4.6.4.1 Disaggregation Architectures 216 4.6.4.2 Platform Architectures 218 4.6.4.3 Operating Frequency Impacts on Architecture 220 4.6.4.4 Operational Models 221 4.6.5 Key Interfaces for Small Cells 222 4.6.5.1 FAPI 222 4.6.5.2 nFAPI 226 4.6.5.3 Management Plane 228 4.6.6 Worked Examples 229 4.6.6.1 Indoor Enterprise Example 229 4.6.6.2 Outdoor Urban Example 230 4.6.6.3 Private Network Example 231 4.6.7 Further Reading 232 References 232 4.7 Summary 233 5 NG-RAN Evolution 235 5.1 Introduction 235 5.2 Wireless Relaying in 5G 235Georg Hampel 5.2.1 Key Ideas 236 5.2.2 Market Drivers 237 5.2.3 Functional Description 239 5.2.3.1 IAB Architecture 239 5.2.3.2 Backhaul Transport and QoS 242 5.2.3.3 Resource Coordination 247 5.2.3.4 Plug-and-Play Network Integration 250 5.2.4 Outlook 255 References 255 5.3 Non-terrestrial Networks 257Leszek Raschkowski, Eiko Seidel, Nicolas Chuberre, Stefano Cioni, Thibault Deleu, and Thomas Heyn 5.3.1 Key Ideas 258 5.3.2 Market Drivers 260 5.3.3 NTN Based NG-RAN Architecture 261 5.3.3.1 Access Network with Transparent NTN Payload 261 5.3.3.2 Access Network with Regenerative NTN Payload 262 5.3.3.3 Transport network based on NTN 262 5.3.4 NTN radio protocol 262 5.3.4.1 Scheduling and Link Adaptation 264 5.3.4.2 NR Layer 2 Enhancements for NTN 264 5.3.4.3 NR Control-Plane Procedure Adaptations for NTN 265 5.3.4.4 NR Mobility within NTN 266 5.3.5 NR Physical Layer Adaptations for NTN 267 5.3.5.1 Timing and Frequency Acquisition and Tracking 267 5.3.5.2 HARQ 268 5.3.5.3 Timing Advance (TA) 271 5.3.5.4 Physical Layer Control Loops 272 5.3.6 NTN Channel Model 272 5.3.7 Outlook 274 References 274 6 Enabling Technologies 277 6.1 Introduction 277 6.2 Virtualization 277Sridhar Rajagopal 6.2.1 Key Ideas 278 6.2.2 Market Drivers 279 6.2.3 Architecture Evolution Toward Virtualization 280 6.2.4 Containers and Microservices 280 6.2.5 NFV Evolution 284 6.2.6 RAN Virtualization Platform 285 6.2.6.1 gNB-DU and gNB-CU Virtualization 286 6.2.6.2 Standardization of Orchestration and Cloudification in O-RAN 288 6.2.7 Virtualization Challenges 289 6.2.7.1 Accelerator Integration 289 6.2.7.2 Timing and Synchronization 290 6.2.7.3 RAN Scaling withWorkload 290 6.2.7.4 Inter-Process Communication 291 6.2.7.5 Virtualization Overhead 291 6.2.7.6 SCTP/GTP Interface Support 291 6.2.7.7 High Availability 292 6.2.7.8 Power Consumption 292 6.2.7.9 Distributed Cloud Deployments for RAN Nodes 292 6.2.8 Further Reading 293 References 293 6.3 Open Source 294Sasha Sirotkin 6.3.1 Key Ideas 295 6.3.2 Market Drivers 296 6.3.3 Open Source License 296 6.3.4 Software-Defined Radio 298 6.3.5 Open Source RAN Projects 299 6.3.5.1 srsLTE 299 6.3.5.2 OpenLTE 300 6.3.5.3 OpenBTS 300 6.3.5.4 Open Air Interface 300 6.3.5.5 TIP 301 6.3.5.6 O-RAN 301 6.3.6 Summary 302 References 302 6.4 Multi-Access Edge Computing 303Miltiadis Filippou and Dario Sabella 6.4.1 Key Ideas 304 6.4.2 Market Drivers 304 6.4.3 MEC Standard 305 6.4.3.1 ETSI MEC System Architecture 305 6.4.3.2 ETSI MEC APIs 308 6.4.3.3 Location API 308 6.4.4 ETSI MEC Deployment in 3GPP 5G Systems 310 6.4.4.1 MEC Deployment in a 5G Network 311 6.4.5 Inter-MEC System Communication 313 6.4.5.1 Possible Implementation 315 6.4.6 Flexible MEC Service Consumption 316 6.4.6.1 Edge Host Zoning in Multi-Vendor Environments 316 6.4.7 High Mobility Automotive Scenarios 321 6.4.7.1 MEC-Supported Cooperative Information 321 6.4.8 Further Reading 323 References 323 6.5 Operations, Administration, and Management 326Vladimir Yanover 6.5.1 Introduction 326 6.5.2 Key Ideas 326 6.5.3 Service-Based Management Architecture 327 6.5.3.1 Examples of Management Services 328 6.5.3.2 Management Service Exposure 329 6.5.4 NG-RAN and 5GC Information Models 330 6.5.5 Performance Management 330 6.5.6 Management of Split NG-RAN 332 6.5.6.1 Background 332 6.5.6.2 Information Object Classes 332 6.5.7 O-RAN Alliance Management Architecture 333 6.5.8 Management of Network Slicing 334 6.5.8.1 Basic Concepts of Slicing Management 334 6.5.8.2 Support of Slicing Management in RAN Provisioning Service 336 6.5.8.3 Configuration and LCM of NSSI and NSI 337 6.5.8.4 NSI and NSSI Information Models (NRMs) 338 6.5.9 SON in 5G 338 6.5.9.1 SON Evolution 338 6.5.9.2 “Legacy” SON Use Cases 339 6.5.9.3 Multi-Domain SON with E2E Optimization 340 6.5.9.4 SON Enablers in 5G System 342 6.5.9.5 Distributed SON 342 6.5.9.6 Hybrid SON 343 6.5.10 Further Reading 343 References 345 6.6 Transport Network 346Yaakov (J.) Stein, Yuri Gittik, and Ron Insler 6.6.1 Key Ideas 346 6.6.2 Market Drivers 347 6.6.3 Defining the Problem 349 6.6.4 The Physical Layer 350 6.6.4.1 Achieving the Required Data Rates 351 6.6.4.2 Achieving the Required Latencies 352 6.6.4.3 Achieving the Required Reliability 355 6.6.4.4 Frequency and Time Synchronization 357 6.6.4.5 Energy Efficiency 360 6.6.5 Higher Layers 360 6.6.5.1 xHaul Network Topology 362 6.6.5.2 Transport Protocols 363 6.6.5.3 Protocol Stacks for User Traffic 366 6.6.5.4 Technology Comparison 367 6.6.6 Conclusions 374 References 374 7 NG-RAN Deployment Considerations 379Andreas Neubacher and Vishwanath Ramamurthi 7.1 Introduction 379 7.2 Key Ideas 381 7.3 Deployment Objectives and Challenges 381 7.3.1 Where to Provide Coverage 381 7.3.2 Network Capacity and Compute Resource Planning 383 7.3.2.1 Air Interface Capacity 383 7.3.2.2 Compute Resources for Edge Computing Services 384 7.3.2.3 Reliability Considerations 385 7.3.3 Service Fulfillment Criteria 386 7.4 Deployment Considerations 387 7.4.1 Deployment Cost 387 7.4.2 Spectrum and Radio Propagation Considerations 388 7.4.3 5G Frequency Ranges 390 7.4.4 Transport Considerations 391 7.4.5 Baseband Pooling 393 7.4.6 Choice of a NG-RAN Split Architecture 394 7.4.6.1 Sub-6 GHz Case 394 7.4.6.2 High-Band (mmWave) Case 394 7.5 Conclusions 395 References 395 Index 397

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Book SynopsisCombines the latest trends in spectrum sharing, both from a research and a standards/regulation/experimental standpoint Written by noted professionals from academia, industry, and research labs, this unique book provides a comprehensive treatment of the principles and architectures for spectrum sharing in order to help with the existing and future spectrum crunch issues. It presents readers with the most current standardization trends, including CEPT / CEE, eLSA, CBRS, MulteFire, LTE-Unlicensed (LTE-U), LTE WLAN integration with Internet Protocol security tunnel (LWIP), and LTE/Wi-Fi aggregation (LWA), and offers substantial trials and experimental results, as well as system-level performance evaluation results. The book also includes a chapter focusing on spectrum policy reinforcement and another on the economics of spectrum sharing. Beginning with the historic form of cognitive radio, Spectrum Sharing: The Next Frontier in Wireless Networks continues wiTable of ContentsAbout the Editors xvii List of Contributors xxi Preface xxv Abbreviations xxix 1 Introduction: From Cognitive Radio to Modern Spectrum Sharing 1Constantinos B. Papadias, Tharmalingam Ratnarajah, and Dirk T.M. Slock 1.1 A Brief History of Spectrum Sharing 1 1.2 Background 3 1.3 Book overview 5 1.4 Summary 14 2 Regulation and Standardization Activities Related to Spectrum Sharing 17Markus Mueck, María Dolores (Lola) Pérez Guirao, Rao Yallapragada, and Srikathyayani Srikanteswara 2.1 Introduction 17 2.2 Standardization 19 2.2.1 Licensed Shared Access 19 2.2.2 Evolved Licensed Shared Access 21 2.2.3 Citizen Broadband Radio System 24 2.2.4 CBRS Alliance 25 2.3 Regulation 28 2.3.1 European Conference of Postal and Telecommunications Administrations 28 2.3.2 Federal Communications Commission 29 2.3.3 A Comparison: (e)LSA vs CBRS Regulation Framework 30 2.3.4 Conclusion 31 References 32 3 White Spaces and Database-assisted Spectrum Sharing 35Andrew Stirling 3.1 Introduction 35 3.2 Demand for Spectrum Outstrips Supply 36 3.2.1 Making Room for New Wireless Technology 36 3.2.2 Unused Spectrum 37 3.3 Three-tier Access Model 38 3.3.1 Secondary Users: Exploiting Gaps left by Primary Users 39 3.3.2 Passive Users: Vulnerable to Transmissions in White Space Frequencies 39 3.3.3 Opportunistic Spectrum Users 40 3.4 What is Efficient Use of Spectrum? 40 3.4.1 Broadcasters prefer Large Coverage Areas with Lower Spectrum Reuse 41 3.4.2 ISPs Respond to Growing Bandwidth Demand from Subscribers 41 3.4.3 Protection of Primary Users Defines the Scope for Sharing 42 3.5 Tapping Unused Capacity: the Evolution of Spectrum Sharing 43 3.5.1 Traditional Coordination is a Slow and Expensive Process 44 3.5.2 License-exempt Access as the Default Spectrum Sharing Mechanism 44 3.5.3 DSA offers Lower Friction and more Scalability 45 3.5.3.1 Early days of DSA 46 3.5.3.2 CR: Towards Flexible, Adaptive, Ad Hoc Access 46 3.5.4 Spectrum Databases are Preferred by Regulators 47 3.6 Determining which Frequencies are Available to Share: Technology 48 3.6.1 CR: Its Original Sense 48 3.6.2 DSA is more Pragmatic and Immediately Applicable 48 3.6.3 Spectrum Sensing 48 3.6.3.1 Hidden Nodes: Limiting the Scope/Certainty of Sensing 49 3.6.3.2 Overcoming the Hidden Node Problem: a Cooperative Approach 49 3.6.4 Beacons 50 3.6.5 Spectrum Databases used with Device Geolocation 51 3.7 Implementing Flexible Spectrum Access 53 3.7.1 Software-defined Radio Underpins Flexibility 53 3.7.2 Regulation Needs to Adapt to the New Flexibility in Radio Devices 54 3.8 Foundations for More Flexible Access in the Future 54 3.8.1 Finer-grained Spectrum Access Management 54 3.8.2 More Flexible License Exemption 54 3.8.2.1 Towards a UHF Spectrum Commons or Superhighway 55 References 56 Further Reading 57 4 Evolving Spectrum Sharing Methods, Standards and Trials: TVWS, CBRS, MulteFire and More 59Dani Anderson, K.A. Shruthi, David Crawford, and Robert W. Stewart 4.1 Introduction 59 4.2 TV White Space 59 4.2.1 Overview 59 4.2.2 Operating Standards 61 4.2.3 Overview of TVWS Trials and Projects 63 4.3 Emerging Shared Spectrum Technologies 66 4.3.1 Introduction 66 4.3.2 CBRS 67 4.3.3 Other Shared Spectrum LTE Solutions 70 4.4 Conclusion 73 References 73 5 Spectrum Above Radio Bands 75Abhishek K. Gupta and Adrish Banerjee 5.1 Introduction and Motivation for mmWave 75 5.2 mmWave Communication: What is Different? 76 5.2.1 Distinguishing Features 76 5.2.2 Implications 76 5.2.3 Opportunity and Need for Sharing 77 5.3 Bands in Above-6GHz Spectrum 78 5.3.1 26-GHz band: 24.25–27.5GHz 79 5.3.2 28-GHz band: 27.5–29.5GHz 79 5.3.3 32-GHz band: 31.8–33.4GHz 79 5.3.4 40-GHz band: 37–43.5GHz 79 5.3.4.1 40-GHz lower band 80 5.3.4.2 40-GHz upper band 80 5.3.5 64–71-GHz band 80 5.4 Spectrum Sharing over mmWave Bands 80 5.4.1 Factors Determining Sharing vs No Sharing 80 5.4.1.1 Directionality 81 5.4.1.2 Deployment and Blockage Density 81 5.4.1.3 Traffic Characteristics 82 5.4.1.4 Amount of Sharing 82 5.4.1.5 Inter-operator Coordination 82 5.4.1.6 Sharing of Other Resources 83 5.4.1.7 Multi-user Communication 84 5.4.1.8 Technical vs Financial Gains 84 5.5 Spectrum Sharing Options for mmWave Bands 84 5.5.1 Exclusive Licensing 84 5.5.2 Unlicensed Spectrum 85 5.5.2.1 Hybrid Spectrum Access 86 5.5.3 Spectrum License Sharing 87 5.5.3.1 Uncoordinated Sharing of Spectrum Licenses 87 5.5.3.2 Restricted Sharing of Spectrum Licenses 88 5.5.4 Shared Licenses 90 5.5.4.1 Spectrum Pooling 90 5.5.4.2 Partial or Fully Coordinated 90 5.5.4.3 Common Database 91 5.5.4.4 Sensing/D2D Communication-based Coordination 91 5.5.5 Secondary Licenses and Markets 91 5.5.5.1 Primary/Secondary Markets 92 5.5.5.2 Third-party Markets 92 5.5.6 Increasing the utilization of spectrum 92 5.6 Conclusions 93 References 93 6 The Licensed Shared Access Approach 97António J. Morgado 6.1 Introduction to Spectrum Management 97 6.2 The Dawn of Licensed Shared Access 98 6.2.1 The LSA Regulatory Environment 99 6.2.2 LSA/ASA in the 2300–2400 MHz band 101 6.3 An Improved LSA Network Architecture 103 6.4 Operation of the Improved Architecture in Dynamic LSA Use Cases 106 6.4.1 Railway Scenario 107 6.4.2 Macro-cellular Scenario 109 6.4.3 Small Cell Scenario 112 6.5 Summary 115 References 116 7 Collaborative Sensing Techniques 121Christian Steffens and Marius Pesavento 7.1 Sparse Signal Representation 123 7.2 Sparse Sensing 125 7.3 Collaborative Sparse Sensing 128 7.3.1 Coherent Sparse Reconstruction 129 7.3.2 Non-Coherent Sparse Reconstruction 131 7.4 Estimation Performance 134 7.4.1 Comparison of Centralized, Distributed, and Collaborative Sensing 134 7.4.2 Source Localization 136 7.5 Concluding Remarks 138 References 139 8 Cooperative Communication Techniques for Spectrum Sharing 147Faheem Khan, Miltiades C. Filippou, and Mathini Sellathurai 8.1 Introduction 147 8.2 Distributed Precoding Exploiting Commonly Available Statistical CSIT for Efficient Coordination 149 8.2.1 Problem Formulation 150 8.2.2 Distributed Statistically Coordinated Precoding 151 8.2.3 Performance Evaluation 153 8.3 A Statistical Channel and Primary Traffic-aware Cooperation Framework for Optimal Service Coexistence 155 8.3.1 Joint Design of Spectrum Sensing and Reception for a SIMO Hybrid CR System 156 8.3.1.1 Problem Formulation and Solution Framework 158 8.3.1.2 Performance Evaluation 159 8.3.2 Throughput Performance of Sensing-optimized Hybrid MIMO CR Systems 161 8.3.2.1 Problem Formulation and Solution Framework 161 8.3.2.2 Performance Evaluation 162 8.4 Summary 164 References 165 9 Reciprocity-Based Beamforming Techniques for Spectrum Sharing in MIMO Networks 169Kalyana Gopala and Dirk T.M. Slock 9.1 Multi-antenna Cognitive Radio Paradigms 169 9.1.1 Spatial Overlay: MISO/MIMO Interference Channel 170 9.1.2 Spatial Underlay 170 9.1.3 Spatial Interweave 170 9.2 From Multi-antenna Underlay to LSA Coordinated Beamforming 171 9.2.1 CoBF and CSIT Discussion 171 9.2.2 Some LoS Results 173 9.2.3 Noncoherent Multi-user MIMO Communications using Covariance CSIT 174 9.3 TDD Reciprocity Calibration 175 9.3.1 Fundamentals 175 9.3.2 Diagonality of the Calibration Matrix 178 9.3.3 Coherent and Non-coherent Calibration Scheme 178 9.3.4 UE-aided vs Internal Calibration 179 9.3.5 Group Calibration System Model 179 9.3.6 Least-squares Solution 181 9.3.7 A Bilinear Model 181 9.4 MIMO IBC Beamformer Design 182 9.4.1 System Model 182 9.4.2 WSR Optimization via WSMSE 182 9.4.3 Naive UL/DL Duality-based Beamformer Exploiting Reciprocity 183 9.5 Experimental Validation 184 9.6 Conclusions 188 References 188 10 Spectrum Sharing with Full Duplex 191Sudip Biswas, Ali Cagatay Cirik, Miltiades C. Filippou, and Tharmalingam Ratnarajah 10.1 Introduction 191 10.2 Transceiver Design for an FD MIMO CR Cellular Network 192 10.2.1 System Model 192 10.2.1.1 Signal and Channel Model 192 10.2.1.2 SI Cancellation 194 10.2.1.3 MSE of the Received Data Stream 195 10.2.2 Joint Transceiver Design 196 10.2.3 Imperfect CSI and Robust Design 197 10.2.3.1 CSI Acquisition 197 10.2.3.2 CSI Modeling 198 10.2.3.3 Robust Transceiver Design 198 10.2.4 Numerical Results 200 10.3 Transceiver Design for an FD MIMO IoT Network 203 10.3.1 System Model 204 10.3.1.1 Signal and Channel Model 204 10.3.1.2 SI Cancellation 205 10.3.1.3 MSE of the Received Data Stream 206 10.3.2 Joint Transceiver Design 206 10.3.3 Imperfect CSI and Robust Design 207 10.3.4 Numerical Results 208 10.4 Summary 209 References 210 Appendix for Chapter 10 211 10.A.1 Useful lemmas 211 11 Communication and Radar Systems: Spectral Coexistence and Beyond 213Fan Liu and Christos Masouros 11.1 Background and Applications 213 11.1.1 Civilian Applications 213 11.1.2 Military Applications 214 11.2 Radar Basics 214 11.3 Radar Communication Coexistence 216 11.3.1 Opportunistic Access 216 11.3.2 Precoding Designs 216 11.3.2.1 Interfering Channel Estimation 216 11.3.2.2 Closed-form Precoding 218 11.3.2.3 Optimization-based Precoding 219 11.4 Dual-functional Radar Communication Systems 221 11.4.1 Temporal and Spectral Processing 221 11.4.2 Spatial Processing 222 11.5 Summary and Open Problems 225 References 226 12 The Role of Antenna Arrays in Spectrum Sharing 229Constantinos B. Papadias, Konstantinos Ntougias, and Georgios K. Papageorgiou 12.1 Introduction 229 12.2 Spectrum Sharing 229 12.2.1 Spectrum Sharing from a Physical Viewpoint 229 12.2.2 Spectrum Sharing from a Regulatory Viewpoint 231 12.3 Attributes of Antenna Arrays 233 12.4 Impact of Arrays on Spectrum Sharing 234 12.4.1 Spectrum Sensing 234 12.4.2 Shared Spectrum Access 234 12.5 Antenna-Array-Aided Spectrum Sharing 235 12.5.1 System Setup 235 12.5.2 Assumptions 236 12.5.3 System Model 237 12.5.3.1 Secondary System 237 12.5.3.2 Primary System 238 12.5.4 Problem Formulation 238 12.5.4.1 Sum-SE, SE, and SINR 238 12.5.4.2 Transmission Constraints 239 12.5.4.3 Original Optimization Problem 239 12.5.4.4 Relaxed Optimization Problem 240 12.5.5 Solution and Algorithm 242 12.5.5.1 Solution for Other Linear Precoding Schemes 242 12.5.6 Performance Evaluation via Numerical Simulations 243 12.6 Antenna-Array-Aided Spectrum Sensing 245 12.6.1 Printed Yagi–Uda Arrays and Hex-Antenna Nodes 246 12.6.2 Test Setup 248 12.6.3 Collaborative Spectrum Sensing Techniques 249 12.6.4 Experimental Results 250 12.6.4.1 Detection in High SNR 253 12.6.4.2 Detection in Low SNR 253 12.7 Summary and Conclusions 253 Acknowledgments 253 References 254 13 Resource Allocation for Shared Spectrum Networks 257Eduard A. Jorswieck and M. Majid Butt 13.1 Introduction 257 13.2 Information-theoretic Background 259 13.3 Types of Spectrum Sharing 261 13.4 Resource Allocation for Efficient Spectrum Sharing 263 13.4.1 Multi-objective Programming 263 13.4.2 Resource Allocation Games 265 13.4.3 Resource Matching for Spectrum Sharing 267 13.5 Resource and Spectrum Trading 270 13.6 Conclusions and Future Work 275 References 275 14 Unlicensed Spectrum Access in 3GPP 279Daniela Laselva, David López Pérez, Mika Rinne, Tero Henttonen, Claudio Rosa, Markku Kuusela 14.1 Introduction 279 14.2 LTE-WLAN Aggregation at the PDCP Layer 280 14.2.1 User Plane Radio Protocol Architecture 281 14.2.2 Bearer Type and Aggregation 282 14.2.3 Flow Control Schemes 283 14.3 LTE-WLAN Integration at IP Layer 284 14.3.1 User Plane Radio Protocol Architecture 284 14.3.2 Flow Control Schemes 286 14.4 LTE in Unlicensed Band 287 14.4.1 Spectrum and Regulations 287 14.4.2 Channel Access 288 14.4.3 Frame Structure 289 14.4.4 Discovery Reference Signal and RRM 290 14.4.5 Uplink Enhancements 291 14.5 Performance Evaluation 294 14.5.1 Aggregation Gains of LWA and LWIP 294 14.5.2 Performance Advantages of LAA 298 14.6 Future Technologies 301 14.6.1 5G New Radio in Unlicensed Band 301 14.6.2 The Role of WLAN in the 5G System 302 14.7 Conclusions 302 References 303 15 Performance Analysis of Spatial Spectrum Reuse in Ultradense Networks 305Youjia Chen, Ming Ding, and David López-Pérez 15.1 Introduction 305 15.2 Network Scenario and System Model 306 15.2.1 Network Scenario 306 15.2.2 Wireless System Model 307 15.3 Performance Analysis of Full Spectrum Reuse Network 308 15.3.1 The Coverage Probability 308 15.3.2 The Area Spectral Efficiency 311 15.4 Performance with Multi-channel Spectrum Reuse 312 15.5 Simulation and Discussion 312 15.5.1 Performance with Full Spectrum Reuse Strategy 313 15.5.2 Performance with Multi-channel Spectrum Reuse Strategy 314 15.6 Conclusion 316 Appendix for Chapter 15 316 15.A.1 Proof of Lemma 15.1 316 15.A.2 Proof of Lemma 15.2 317 15.A.3 Proof of Theorem 15.1 318 References 318 16 Large-scale Wireless Spectrum Monitoring: Challenges and Solutions based on Machine Learning 321Sreeraj Rajendran and Sofie Pollin 16.1 Challenges 321 16.2 Crowdsourcing 323 16.3 Wireless Spectrum Analysis 324 16.3.1 Anomaly Detection 324 16.3.2 Performance Comparisons 328 16.3.3 Wireless Signal Classification 331 16.3.3.1 Fully Supervised Models 331 16.3.3.2 Semi-supervised Models 332 16.3.3.3 Performance-friendly Models 333 16.4 Future Research Directions 335 16.4.1 Machine Learning 336 16.4.2 Anomaly Geo-localization 336 16.4.3 Crowd Engagement and Sustainability 336 16.5 Conclusion 337 References 337 17 Policy Enforcement in Dynamic Spectrum Sharing 341Jung-Min (Jerry) Park, Vireshwar Kumar, and Taiwo Oyedare 17.1 Introduction 341 17.2 Technical Background 342 17.3 Security and Privacy Threats 343 17.3.1 Sensing-driven Spectrum Sharing 343 17.3.1.1 PHY-layer Threats 344 17.3.1.2 MAC-layer Threats 344 17.3.1.3 Cross-layer Threats 345 17.3.2 Database-driven Spectrum Sharing 345 17.3.2.1 PHY-layer Threats 346 17.3.2.2 Threats to the Database Access Protocol 346 17.3.2.3 Threats to the Privacy of Users 346 17.4 Enforcement Approaches 347 17.4.1 Ex Ante (Preventive) Approaches 348 17.4.1.1 Device Hardening 348 17.4.1.2 Network Hardening 350 17.4.1.3 Privacy Preservation 351 17.4.2 Ex Post (Punitive) Approaches 352 17.4.2.1 Spectrum Monitoring 352 17.4.2.2 Spectrum Forensics 352 17.4.2.3 Localization 353 17.4.2.4 Punishment 353 17.5 Open Problems 354 17.5.1 Research Challenges 354 17.5.2 Regulatory Challenges 354 17.6 Summary 355 References 355 18 Economics of Spectrum Sharing, Valuation, and Secondary Markets 361William Lehr 18.1 Introduction 361 18.2 Spectrum Scarcity, Regulation, and Market Trends 363 18.3 Estimating Spectrum Values 370 18.4 Growing Demand for Spectrum 373 18.5 5G Future and Spectrum Economics 375 18.6 Secondary Markets and Sharing 381 18.7 Conclusion 384 References 385 19 The Future Outlook for Spectrum Sharing 389Richard Womersley 19.1 Introduction 389 19.2 Share and Share Alike 390 19.3 Regulators Recognize the Value of Shared Access 393 19.4 The True Demand for Spectrum 395 19.5 The Impact of Sharing on Spectrum Demand 397 19.6 General Authorization needed to Encourage Sharing 399 19.7 The Long-term Outlook for Spectrum Sharing 401 References 403 Index 405

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Book SynopsisSummarizes the current state and upcoming trends within the area of fog computing Written by some of the leading experts in the field, Fog Computing: Theory and Practicefocuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth. Presented in two partsFog Computing Systems and Architectures, and Fog Computing Techniques and Applicationthis book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricinTable of ContentsList of Contributors xxiii Acronyms xxix Part I Fog Computing Systems and Architectures 1 1 Mobile Fog Computing 3Chii Chang, Amnir Hadachi, Jakob Mass, and Satish Narayana Srirama 1.1 Introduction 3 1.2 Mobile Fog Computing and Related Models 5 1.3 The Needs of Mobile Fog Computing 6 1.3.1 Infrastructural Mobile Fog Computing 7 1.3.2 Land Vehicular Fog 9 1.3.3 Marine Fog 11 1.3.4 Unmanned Aerial Vehicular Fog 12 1.3.5 User Equipment-Based Fog 13 1.4 Communication Technologies 15 1.4.1 IEEE 802.11 15 1.4.2 4G, 5G Standards 16 1.4.3 WPAN, Short-Range Technologies 17 1.4.4 LPWAN, Other Medium- and Long-Range Technologies 18 1.5 Nonfunctional Requirements 18 1.5.1 Heterogeneity 20 1.5.2 Context-Awareness 23 1.5.3 Tenant 25 1.5.4 Provider 27 1.5.5 Security 29 1.6 Open Challenges 31 1.6.1 Challenges in Land Vehicular Fog Computing 31 1.6.2 Challenges in Marine Fog Computing 32 1.6.3 Challenges in Unmanned Aerial Vehicular Fog Computing 32 1.6.4 Challenges in User Equipment-based Fog Computing 33 1.6.5 General Challenges 33 1.7 Conclusion 35 Acknowledgment 36 References 36 2 Edge and Fog: A Survey, Use Cases, and Future Challenges 43Cosmin Avasalcai, Ilir Murturi, and Schahram Dustdar 2.1 Introduction 43 2.2 Edge Computing 44 2.2.1 Edge Computing Architecture 46 2.3 Fog Computing 47 2.3.1 Fog Computing Architecture 49 2.4 Fog and Edge Illustrative Use Cases 50 2.4.1 Edge Computing Use Cases 50 2.4.2 Fog Computing Use Cases 54 2.5 Future Challenges 57 2.5.1 Resource Management 57 2.5.2 Security and Privacy 58 2.5.3 Network Management 61 2.6 Conclusion 61 Acknowledgment 62 References 62 3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities 67Mi Zhang, Faen Zhang, Nicholas D. Lane, Yuanchao Shu, Xiao Zeng, Biyi Fang, Shen Yan, and Hui Xu 3.1 Introduction 67 3.2 Challenges and Opportunities 68 3.2.1 Memory and Computational Expensiveness of DNN Models 68 3.2.2 Data Discrepancy in Real-world Settings 70 3.2.3 Constrained Battery Life of Edge Devices 71 3.2.4 Heterogeneity in Sensor Data 72 3.2.5 Heterogeneity in Computing Units 73 3.2.6 Multitenancy of Deep Learning Tasks 73 3.2.7 Offloading to Nearby Edges 75 3.2.8 On-device Training 76 3.3 Concluding Remarks 76 References 77 4 Caching, Security, and Mobility in Content-centric Networking 79Osman Khalid, Imran Ali Khan, Rao Naveed Bin Rais, and Assad Abbas 4.1 Introduction 79 4.2 Caching and Fog Computing 81 4.3 Mobility Management in CCN 82 4.3.1 Classification of CCN Contents and their Mobility 83 4.3.2 User Mobility 83 4.3.3 Server-side Mobility 84 4.3.4 Direct Exchange for Location Update 84 4.3.5 Query to the Rendezvous for Location Update 84 4.3.6 Mobility with Indirection Point 84 4.3.7 Interest Forwarding 85 4.3.8 Proxy-based Mobility Management 85 4.3.9 Tunnel-based Redirection (TBR) 86 4.4 Security in Content-centric Networks 88 4.4.1 Risks Due to Caching 90 4.4.2 DOS Attack Risk 90 4.4.3 Security Model 91 4.5 Caching 91 4.5.1 Cache Allocation Approaches 91 4.5.2 Data Allocation Approaches 93 4.6 Conclusions 101 References 101 5 Security and Privacy Issues in Fog Computing 105Ahmad Ali, Mansoor Ahmed, Muhammad Imran, and Hasan Ali Khattak 5.1 Introduction 105 5.2 Trust in IoT 107 5.3 Authentication 109 5.3.1 Related Work 109 5.4 Authorization 113 5.4.1 Related Work 114 5.5 Privacy 117 5.5.1 Requirements of Privacy in IoT 118 5.6 Web Semantics and Trust Management for Fog Computing 120 5.6.1 Trust Through Web Semantics 120 5.7 Discussion 123 5.7.1 Authentication 124 5.7.2 Authorization 125 5.8 Conclusion 130 References 130 6 How Fog Computing Can Suppor Latency/Reliability-sensitive IoT Applications: An Overview and a Taxonomy of State-of-the-art Solutions 139Paolo Bellavista, Javier Berrocal, Antonio Corradi, Sajal K. Das, Luca Foschini, Isam Mashhour Al Jawarneh, and Alessandro Zanni 6.1 Introduction 139 6.2 Fog Computing for IoT: Definition and Requirements 142 6.2.1 Definitions 142 6.2.2 Motivations 144 6.2.3 Fog Computing Requirements When Applied to Challenging IoTs Application Domains 148 6.2.4 IoT Case Studies 152 6.3 Fog Computing: Architectural Model 154 6.3.1 Communication 154 6.3.2 Security and Privacy 156 6.3.3 Internet of Things 156 6.3.4 Data Quality 156 6.3.5 Cloudification 157 6.3.6 Analytics and Decision-Making 157 6.4 Fog Computing for IoT: A Taxonomy 158 6.4.1 Communication 159 6.4.2 Security and Privacy Layer 165 6.4.3 Internet of Things 170 6.4.4 Data Quality 173 6.4.5 Cloudification 179 6.4.6 Analytics and Decision-Making Layer 183 6.5 Comparisons of Surveyed Solutions 189 6.5.1 Communication 189 6.5.2 Security and Privacy 191 6.5.3 Internet of Things 193 6.5.4 Data Quality 194 6.5.5 Cloudification 195 6.5.6 Analytics and Decision-Making Layer 197 6.6 Challenges and Recommended Research Directions 198 6.7 Concluding Remarks 201 References 202 7 Harnessing the Computing Continuum for Programming Our World 215Pete Beckman, Jack Dongarra, Nicola Ferrier, Geoffrey Fox, Terry Moore, Dan Reed, and Micah Beck 7.1 Introduction and Overview 215 7.2 Research Philosophy 217 7.3 A Goal-oriented Approach to Programming the Computing Continuum 219 7.3.1 A Motivating Continuum Example 219 7.3.2 Goal-oriented Annotations for Intensional Specification 221 7.3.3 A Mapping and Run-time System for the Computing Continuum 222 7.3.4 Building Blocks and Enabling Technologies 224 7.4 Summary 228 References 228 8 Fog Computing for Energy Harvesting-enabled Internet of Things 231S. A. Tegos, P. D. Diamantoulakis, D. S. Michalopoulos, and G. K. Karagiannidis 8.1 Introduction 231 8.2 System Model 232 8.2.1 Computation Model 233 8.2.2 Energy Harvesting Model 235 8.3 Tradeoffs in EH Fog Systems 238 8.3.1 Energy Consumption vs. Latency 238 8.3.2 Execution Delay vs. Task Dropping Cost 239 8.4 Future Research Challenges 240 Acknowledgment 241 References 241 9 Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control 245Delaram Amiri, Arman Anzanpour, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, Nikil Dutt, and Marco Levorato 9.1 Introduction 245 9.2 Background 247 9.3 Related Topics 249 9.4 Design Challenges 250 9.5 IoT System Architecture 251 9.5.1 Fog Computing and its Benefits 252 9.6 Fog-assisted Runtime Energy Management in Wearable Sensors 253 9.6.1 Computational Self-Awareness 255 9.6.2 Energy Optimization Algorithms 255 9.6.3 Myopic Strategy 258 9.6.4 MDP Strategy 259 9.7 Conclusions 263 Acknowledgment 264 References 264 10 Latency Minimization Through Optimal Data Placement in Fog Networks 269Ning Wang and Jie Wu 10.1 Introduction 269 10.2 RelatedWork 272 10.2.1 Long-Term and Short-Term Placement 272 10.2.2 Data Replication 272 10.3 Problem Statement 273 10.3.1 Network Model 273 10.3.2 Multiple Data Placement with Budget Problem 274 10.3.3 Challenges 274 10.4 Delay Minimization Without Replication 275 10.4.1 Problem Formulation 275 10.4.2 Min-Cost Flow Formulation 276 10.4.3 Complexity Reduction 277 10.5 Delay Minimization with Replication 279 10.5.1 Hardness Proof 279 10.5.2 Single Request in Line Topology 279 10.5.3 Greedy Solution in Multiple Requests 280 10.5.4 Rounding Approach in Multiple Requests 282 10.6 Performance Evaluation 285 10.6.1 Trace Information 285 10.6.2 Experimental Setting 285 10.6.3 Algorithm Comparison 286 10.6.4 Experimental Results 287 10.7 Conclusion 289 Acknowledgement 289 References 290 11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim++ 293Tariq Qayyum, Asad Waqar Malik, Muazzam A. Khan, and Samee U. Khan 11.1 Introduction 293 11.2 Modeling and Simulation 294 11.3 FogNetSim++: Architecture 296 11.4 FogNetSim++: Installation and Environment Setup 298 11.4.1 OMNeT++ Installation 298 11.4.2 FogNetSim++ Installation 300 11.4.3 Sample Fog Simulation 300 11.5 Conclusion 305 References 305 Part II Fog Computing Techniques and Applications 309 12 Distributed Machine Learning for IoT Applications in the Fog 311Aluizio F. Rocha Neto, Flavia C. Delicato, Thais V. Batista, and Paulo F. Pires 12.1 Introduction 311 12.2 Challenges in Data Processing for IoT 314 12.2.1 Big Data in IoT 315 12.2.2 Big Data Stream 318 12.2.3 Data Stream Processing 319 12.3 Computational Intelligence and Fog Computing 322 12.3.1 Machine Learning 322 12.3.2 Deep Learning 326 12.4 Challenges for Running Machine Learning on Fog Devices 328 12.4.1 Solutions Available on the Market to Deploy ML on Fog Devices 331 12.5 Approaches to Distribute Intelligence on Fog Devices 334 12.6 Final Remarks 340 Acknowledgments 341 References 341 13 Fog Computing-Based Communication Systems for Modern Smart Grids 347Miodrag Forcan and Mirjana Maksimović 13.1 Introduction 347 13.2 An Overview of Communication Technologies in Smart Grid 349 13.3 Distribution Management System (DMS) Based on Fog/Cloud Computing 356 13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and ThingSpeak 359 13.5 Conclusion 366 References 367 14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems 371Chu-ge Wu and Ling Wang 14.1 Introduction 371 14.2 Estimation of Distribution Algorithm 372 14.3 Related Work 373 14.4 Problem Statement 374 14.5 Details of Proposed Algorithm 376 14.5.1 Encoding and Decoding Method 376 14.5.2 uEDA Scheme 377 14.5.3 Local Search Method 378 14.6 Simulation 378 14.6.1 Comparison Algorithm 378 14.6.2 Simulation Environment and Experiment Settings 379 14.6.3 Compared with the Heuristic Method 381 14.7 Conclusion 383 References 383 15 Reliable and Power-Efficient Machine Learning in Wearable Sensors 385Parastoo Alinia and Hassan Ghasemzadeh 15.1 Introduction 385 15.2 Preliminaries and Related Work 386 15.2.1 Gold Standard MET Computation 386 15.2.2 Sensor-based MET Estimation 387 15.2.3 Unreliability Mitigation 388 15.2.4 Transfer Learning 388 15.3 System Architecture and Methods 389 15.3.1 Reliable MET Calculation 390 15.3.2 The Reconfigurable MET Estimation System 392 15.4 Data Collection and Experimental Procedures 394 15.4.1 Exergaming Experiment 394 15.4.2 Treadmill Experiment 395 15.5 Results 396 15.5.1 Reliable MET Calculation 396 15.5.2 Reconfigurable Design 402 15.6 Discussion and Future Work 404 15.7 Summary 405 References 406 16 Insights into Software-Defined Networking and Applications in Fog Computing 411Osman Khalid, Imran Ali Khan, and Assad Abbas 16.1 Introduction 411 16.2 OpenFlow Protocol 414 16.2.1 OpenFlow Switch 414 16.3 SDN-Based Research Works 416 16.4 SDN in Fog Computing 419 16.5 SDN in Wireless Mesh Networks 421 16.5.1 Challenges in Wireless Mesh Networks 421 16.5.2 SDN Technique in WMNs 421 16.5.3 Benefits of SDN in WMNs 423 16.5.4 Fault Tolerance in SDN-based WMNs 424 16.6 SDN in Wireless Sensor Networks 424 16.6.1 Challenges in Wireless Sensor Networks 424 16.6.2 SDN in Wireless Sensor Networks 425 16.6.3 Sensor Open Flow 426 16.6.4 Home Networks Using SDWN 426 16.6.5 Securing Software Defined Wireless Networks (SDWN) 426 16.7 Conclusion 427 References 427 17 Time-Critical Fog Computing for Vehicular Networks 431Ahmed Chebaane, Abdelmajid Khelil, and Neeraj Suri 17.1 Introduction 431 17.2 Applications and Timeliness Guarantees and Perturbations 434 17.2.1 Application Scenarios 434 17.2.2 Application Model 436 17.2.3 Timeliness Guarantees 436 17.2.4 Benchmarking Vehicular Applications Concerning Timeliness Guarantees 437 17.2.5 Building Blocks to Reach Timeliness Guarantees 440 17.2.6 Timeliness Perturbations 441 17.3 Coping with Perturbation to Meet Timeliness Guarantees 443 17.3.1 Coping with Constraints 443 17.3.2 Coping with Failures 448 17.3.3 Coping with Threats 448 17.4 Research Gaps and Future Research Directions 449 17.4.1 Mobile Fog Computing 449 17.4.2 Fog Service Level Agreement (SLA) 450 17.5 Conclusion 451 References 451 18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks 459Shuja Mughal, Kamran Sattar Awaisi, Assad Abbas, Inayat ur Rehman, Muhammad Usman Shahid Khan, and Mazhar Ali 18.1 Introduction 459 18.2 Proposed Methodology 461 18.3 Hypothesis Formulation 463 18.4 Simulation Design 464 18.4.1 Results and Discussions 464 18.4.2 Hypothesis Testing 467 18.5 Conclusions 469 References 470 19 Fog Computing to Enable Geospatial Video Analytics for Disaster-incident Situational Awareness 473Dmitrii Chemodanov, Prasad Calyam, and Kannappan Palaniappan 19.1 Introduction 473 19.1.1 How Can Geospatial Video Analytics Help with Disaster-Incident Situational Awareness? 473 19.1.2 Fog Computing for Geospatial Video Analytics 474 19.1.3 Function-Centric Cloud/Fog Computing Paradigm 475 19.1.4 Function-Centric Fog/Cloud Computing Challenges 476 19.1.5 Chapter Organization 477 19.2 Computer Vision Application Case Studies and FCC Motivation 478 19.2.1 Patient Tracking with Face Recognition Case Study 478 19.2.2 3-D Scene Reconstruction from LIDAR Scans 480 19.2.3 Tracking Objects of Interest in WAMI 482 19.3 Geospatial Video Analytics Data Collection Using Edge Routing 484 19.3.1 Network Edge Geographic Routing Challenges 484 19.3.2 Artificial Intelligence Relevance in Geographic Routing 486 19.3.3 AI-Augmented Geographic Routing Implementation 487 19.4 Fog/Cloud Data Processing for Geospatial Video Analytics Consumption 490 19.4.1 Geo-Distributed Latency-Sensitive SFC Challenges 491 19.4.2 Metapath-Based Composite Variable Approach 492 19.4.3 Metapath-Based SFC Orchestration Implementation 495 19.5 Concluding Remarks 496 19.5.1 What Have We Learned? 496 19.5.2 The Road Ahead and Open Problems 497 References 498 20 An Insight into 5G Networks with Fog Computing 505Osman Khalid, Imran Ali Khan, Rao Naveed Bin Rais, and Asad Waqar Malik 20.1 Introduction 505 20.2 Vision of 5G 507 20.3 Fog Computing with 5G Networks 508 20.3.1 Fog Computing 508 20.3.2 The Need of Fog Computing in 5G Networks 508 20.4 Architecture of 5G 508 20.4.1 Cellular Architecture 508 20.4.2 Energy Efficiency 510 20.4.3 Two-Tier Architecture 512 20.4.4 Cognitive Radio 512 20.4.5 Cloud-Based Architecture 513 20.5 Technology and Methodology for 5G 514 20.5.1 HetNet 515 20.5.2 Beam Division Multiple Access (BDMA) 516 20.5.3 Mixed Bandwidth Data Path 516 20.5.4 Wireless Virtualization 516 20.5.5 Flexible Duplex 518 20.5.6 Multiple-Input Multiple-Output (MIMO) 518 20.5.7 M2M 519 20.5.8 Multibeam-Based Communication System 520 20.5.9 Software-Defined Networking (SDN) 520 20.6 Applications 521 20.6.1 Health Care 521 20.6.2 Smart Grid 521 20.6.3 Logistic and Tracking 521 20.6.4 Personal Usage 521 20.6.5 Virtualized Home 522 20.7 Challenges 522 20.8 Conclusion 524 References 524 21 Fog Computing for Bioinformatics Applications 529Hafeez Ur Rehman, Asad Khan, and Usman Habib 21.1 Introduction 529 21.2 Cloud Computing 531 21.2.1 Service Models 532 21.2.2 Delivery Models 532 21.3 Cloud Computing Applications in Bioinformatics 533 21.3.1 Bioinformatics Tools Deployed as SaaS 533 21.3.2 Bioinformatics Platforms Deployed as PaaS 535 21.3.3 Bioinformatics Tools Deployed as IaaS 535 21.4 Fog Computing 537 21.5 Fog Computing for Bioinformatics Applications 539 21.5.1 Real-Time Microorganism Detection System 541 21.6 Conclusion 543 References 543 Index 547

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Book SynopsisFrequency Variations in Power Systems: Modeling, State Estimation and Control presents the Frequency Divider Formula (FDF); a unique approach that defines, calculates and estimates the frequency in electrical energy systems. This authoritative book is written by two noted researchers on the topic. They define the meaning of frequency of an electrical quantity (such as voltage and current) in non-stationary conditions (for example the frequency is not equal to the nominal one) and pose the foundation of the frequency divider formula. The book describes the consequences of using a variable frequency in power system modelling and simulations, in state estimation and frequency control applications. In addition, the authors include a discussion on the applications of the frequency divider in systems where part of the generation is not based on synchronous machines, but rather on converter-interfaced energy resources, such as wind and solar power plants. This important book:Table of ContentsList of Figures xiii List of Tables xix Preface xxi Acknowledgments xxvii Acronyms and Abbreviations xxix Notation xxxiii Part I Background 1 1 Frequency in Power Systems 3 1.1 Conventional Definitions 3 1.2 Alternating Current 6 1.3 Reference Frequency 8 1.4 Transforms 13 2 Power System Model 39 2.1 Time Scales 39 2.2 Quasi-Steady-State Model 41 2.3 Differential Algebraic Equations 45 2.4 Conventional Devices 48 3 Dynamic State Estimation 77 3.1 Basic Concepts 77 3.2 Introducing Dynamics 84 3.3 Estimation of Bus Frequencies 89 4 Frequency Control 105 4.1 Introduction 105 4.2 Power Balancing 106 4.3 Power Oscillation Damping 115 4.4 Nonsynchronous Devices 125 Part II Theory 137 5 Frequency Divider Formula 139 5.1 Rationale 139 5.2 Derivation 141 5.3 Equivalent Networks 155 5.4 Inclusion of Measurements 159 5.5 Frequency Participation Factors 163 6 Frequency Makers and Frequency Takers 175 6.1 Introduction 175 6.2 Derivation 176 6.3 Taxonomy 178 6.4 Examples 187 Part III Applications 205 7 Frequency Control 207 7.1 Impact of Frequency Signals 207 7.2 Synthesis of Frequency Signals 216 8 Dynamic State Estimation 227 8.1 Machine Rotor Speeds 227 8.2 Center of Inertia 247 8.3 Applications of the RoCoP 256 9 Power System Model 261 9.1 Introduction 261 9.2 Frequency Dependent Model 262 9.3 Example 265 10 Frequency in Power Systems 269 10.1 Definitions 269 10.2 Final Remarks 269 Appendix A Data 273 A.1 Three-Bus System 273 A.2 WSCC System 273 A.3 IEEE 14-Bus System 278 A.4 New England System 281 Appendix B Irish Transmission System 291 Bibliography 293 Index 313

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Book SynopsisOffers a one-stop reference on the application of advanced modeling and simulation (M&S) in cyber physical systems (CPS) engineering This book provides the state-of-the-art in methods and technologies that aim to elaborate on the modeling and simulation support to cyber physical systems (CPS) engineering across many sectors such as healthcare, smart grid, or smart home. It presents a compilation of simulation-based methods, technologies, and approaches that encourage the reader to incorporate simulation technologies in their CPS engineering endeavors, supporting management of complexity challenges in such endeavors. Complexity Challenges in Cyber Physical Systems: Using Modeling and Simulation (M&S) to Support Intelligence, Adaptation and Autonomy is laid out in four sections. The first section provides an overview of complexities associated with the application of M&S to CPS Engineering. It discusses M&S in the context of autonomous systems involvement wTable of ContentsPreface xi Foreword xvBarry Martin Horowitz About the Editors xvii List of Contributors xix Author Biography xxiii Part I Introduction 1 1 The Complexity in Application of Modeling and Simulation for Cyber Physical Systems Engineering 3Saurabh Mittal and Andreas Tolk 2 Challenges in the Operation and Design of Intelligent Cyber‑Physical Systems 27Sebastian Castro, Pieter J. Mosterman, Akshay H. Rajhans, and Roberto G. Valenti 3 NATO Use of Modeling and Simulation to Evolve Autonomous Systems 53Jan Mazal, Agostino Bruzzone, Michele Turi, Marco Biagini, Fabio Corona, and Jason Jones Part II Modeling Support to CPS Engineering 81 4 Multi‑Perspective Modeling and Holistic Simulation: A System‑Thinking Approach to Very Complex Systems Analysis 83Mamadou K. Traoré 5 A Unifying Framework for the Hierarchical Co-Simulation of Cyber-Physical Systems 111Fernando J. Barros 6 Model-Based Systems of Systems Engineering Trade-off Analytics 131Aleksandra Markina-Khusid, Ryan Jacobs, and Judith Dahmann 7 Taming Complexity and Risk in Internet of Things (IoT) Ecosystem Using System Entity Structure (SES) Modeling 163Saurabh Mittal, Sheila A. Cane, Charles Schmidt, Richard B. Harris, and John Tufarolo Part III Simulation‐Based CPS Engineering 191 8 Simulation Model Continuity for Efficient Development of Embedded Controllers in Cyber-Physical Systems 193Rodrigo Castro, Ezequiel Pecker Marcosig, and Juan I. Giribet 9 Cyber-Physical Systems Design Methodology for the Prediction of Symptomatic Events in Chronic Diseases 223Kevin Henares, Josué Pagán, José L. Ayala, Marina Zapater, and José L. Risco-Martín 10 Model‐Based Engineering with Application to Autonomy 255Rahul Bhadani, Matt Bunting, and Jonathan Sprinkle Part IV The Cyber Element 287 11 Perspectives on Securing Cyber Physical Systems 289Zach Furness 12 Cyber-Physical System Resilience: Frameworks, Metrics, Complexities, Challenges, and Future Directions 301Md Ariful Haque, Sachin Shetty, and Bheshaj Krishnappa 13 The Cyber Creation of Social Structures 339E. Dante Suarez and Loren Demerath Part V Way Forward 371 14 A Research Agenda for Complexity in Application of Modeling and Simulation for Cyber Physical Systems Engineering 373Andreas Tolk and Saurabh Mittal Cyber Physical Systems – Modeling and Simulation to Balance Enthusiasm and Caution 391Kris Rosfjord Index 395

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Book SynopsisIs a literature review looming in your future? Are you procrastinating on writing a literature review at this very moment? If so, this is the book for you. Writing often causes trepidation and procrastination for engineering studentsissues that compound while writing a literature review, a type of academic writing most engineers are never formally taught. Consider this workbook as a couch-to-5k program for engineering writers rather than runners: if you complete the activities in this book from beginning to end, you will have a literature review draft ready for revision and content editing by your research advisor. So, You Have to Write a Literature Review presents a dynamic and practical method in which engineering studentstypically late-career undergraduates or graduate studentscan learn to write literature reviews, and translate genre-based writing instruction into easy-to-follow, bite-sized activities and content. Written in a refreshingly conversational style whilTable of ContentsA Note from the Series Editor ix About the Authors xiii Acknowledgments xv About the Book xvii How to Use This Book xix 1 Why Is Writing So Hard? 1 Overcoming Barriers to Writing and Making Time to Write 1 1.1 Writing as a Cognitive, Social, and Affective Activity 2 1.2 Time Management, Self-Discipline, and the Writing/Research Timeline 4 1.3 Accountability Is an Essential Part of Writing 5 1.3.1 Shut Up & Write Groups 5 1.3.2 Accountability Partners 6 1.3.3 Schedule Your Writing in a Scientific Way 7 1.3.4 Deliberate and Distributed Practice 7 1.3.5 Bribery 8 References 9 2 What Is the Point of a Literature Review, Anyway?! 11 2.1 The Literature Review Serves as an Argument to Establish a Gap in Prior Research 12 2.2 The Literature Review Establishes the Author’s Credibility 13 2.3 The Literature Review Prepares Readers to Interpret and Appreciate Your Findings 15 2.4 Envisioning Your Audience 15 2.5 Deliberate Language Choices Support the Functions of the Literature Review 16 Reference 17 3 Gathering and Storing Literature 19 3.1 What to Cite? The Difference Between Types of Academic Publications 19 3.2 What NOT to Cite: Types of Documents to Avoid Citing 21 3.3 Searching for Literature 23 3.4 Saving and Storing Your Literature 23 3.5 Reference Managers 27 3.6 Your Turn: Collecting Literature 27 3.7 But How Many References Do I Need in My Literature Review?! 30 4 Reading Strategies and Remembering What You Read 33 4.1 Deciding Whether to Skim or Read 33 4.2 What Are You Focusing On? 34 4.3 Effective Methods for Skimming Literature 36 4.4 Reading Scholarly Literature 38 4.5 Taking Notes and Starting an Annotated Bibliography (or: Helping Your “Future Self”) 39 References 42 5 Finding Connections Between Literature 43 5.1 Identifying Overarching Themes and Topics in Literature 44 5.2 Identifying “Synthesis” of Literature in Action 46 5.3 Drawing Connections Between Literature 48 5.4 Justifying “Gaps in the Literature” 49 6 Organizing Your Literature Review 53 6.1 Envisioning the Macrostructure of Your Literature Review 54 6.2 A Discussion on Topic Sentences: The First Sentence of the Paragraph 61 6.3 Creating a Macrostructure for Your Own Literature Review 61 7 Writing the “Ugly Draft” 65 7.1 Twelve Steps to Building Your Literature Review 65 7.2 Strategies to Help You Build and Sculpt Paragraphs: Introducing Rhetorical Moves and Steps in Genre Maps 69 7.3 If You Are Not into Outlines … Leverage Who You Are as a Writer to Get that Ugly Draft on Paper! 73 References 73 8 Using Citations to Connect Ideas 75 8.1 The Proximity of the Citation to the Reference Carries Meaning 75 8.1.1 String Citations 76 8.1.2 Topic Citations 77 8.1.3 End-of-Sentence Citations 78 8.1.4 Direct Citation 79 8.2 Literature Occurs in the Past, but a Literature Synthesis Points to YOUR Future 81 8.3 The “Accordion Stage” of Writing a Literature Review Will Hone the Density of Citations and Conciseness 83 8.4 The Literature Review Is a Political Document 84 9 Revising the “BIG Four” Literature Review Faux Pas 87 9.1 Ineffective or Missing Topic Sentences 87 9.1.1 All Sentences That Follow the Topic or Umbrella Sentence Should Directly Support That First Sentence 88 9.1.2 There Is Rarely a Need to Directly Cite One Article or Author in the Topic Sentence 89 9.2 Fluffy Writing 91 9.3 Globalisms 91 9.4 Lack of Connection or Synthesis Between Topics or Articles 93 Reference 96 10 Am I Done Yet? 97 10.1 Self-Check Yourself Before You Wreck Yourself 97 11 Interpreting Advisor Feedback 101 11.1 Conclusion: Our Wishes for You 103 12 Theory Behind the Practice 105 For Instructors and Advisors 105 12.1 On Genre Studies and Moves-Steps Analysis 106 12.2 On Technical Writing for Engineers 109 12.3 On Writing Literature Reviews 111 12.4 On Grammar Editing and Revision Strategies 113 12.5 Last Thoughts 116 References 116 Index 119

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Book SynopsisENERGY STORAGE Written and edited by a team of well-known and respected experts in the field, this new volume on energy storage presents the state-of-the-art developments and challenges in the field of renewable energy systems for sustainability and scalability for engineers, researchers, academicians, industry professionals, consultants, and designers. The world's energy landscape is very complex. Fossil fuels, especially because of hydraulic fracturing, are still a mainstay of global energy production, but renewable energy sources, such as wind, solar, and others, are increasing in importance for global energy sustainability. Experts and non-experts agree that the next game-changer in this area will be energy storage. Energy storage is crucial for continuous operation of power plants and can supplement basic power generation sources over a stand-alone system. It can enhance capacity and leads to greater security, including continuous electricity supply and other applications. A depenTable of ContentsList of Contributors xi Preface xiii 1 Thermal Energy Storage Systems for Concentrating Solar Power Plants 1 Dr. Pratibha Biswal 1.1 Introduction 2 1.2 Concentrating Solar Power (CSP) Technology 2 1.2.1 CSP Receiver Concepts 4 1.2.1.1 Parabolic Trough System 4 1.2.1.2 Linear Fresnel Reflector Systems 5 1.2.1.3 Central Receiver Plants 6 1.2.1.4 Dish System 7 1.3 Thermal Energy Storage in CSP 7 1.3.1 Active Two-Tank System 9 1.3.1.1 Active Two-Tank Direct 9 1.3.2 Active Single-Tank Thermocline 20 1.3.3 Other TES Systems 21 1.3.3.1 Packed-Bed Storage System 21 1.3.3.2 Passive Thermal Storage System 22 1.3.4 Types of Thermal Energy Storage (TES) 22 1.3.4.1 Sensible Energy Storage 22 1.3.4.2 Latent Heat Storage 24 1.3.4.3 Thermochemical Energy Storage 25 1.4 Corrosion Problem in TES-CSP System 26 1.5 Conclusion 26 References 27 2 Solar Thermal Power Plant with Thermal Energy Storage 31 Anil Kumar, Umakanta Sahoo and BK Jayasimha Rathod 2.1 Introduction 32 2.2 Literature Review 39 2.2.1 Power Installed Capacity of India 39 2.2.2 Energy Storage Systems 40 2.2.3 Thermal Storage Systems 40 2.3 Energy Demand of World 44 2.4 Experimental Set Up 48 2.4.1 Description of Experimental Set Ups 49 2.5 Experimental Data Analysis, Results and Discussions 55 2.5.1 Performance of Reflector Round the Year (Experimental Set up I) 58 2.5.1.1 Simulation Results 63 2.5.1.2 Typical PID of a Solar Module from ‘India One’ Solar Power Plant 66 2.5.1.3 Quantity of Steam to Turbine 67 2.6 Experimental Data Analysis, Results and Discussions 69 2.7 Conclusions 75 Symbols 76 Acknowledgement 77 References 77 3 Efficient Energy Storage Systems for Wind Power Application 81 Pradeep Kumar Sahu, Satyaranjan Jena and Umakanta Sahoo 3.1 Introduction 82 3.2 Energy Storage Devices 84 3.2.1 Electrical Energy Storage 84 3.2.1.1 Superconducting Magnetic Energy Storage (SMES) 85 3.2.1.2 Supercapacitors 86 3.2.2 Mechanical Energy Storage 87 3.2.2.1 Flywheel Energy Storage (FES) 87 3.2.2.2 Pumped Hydroelectric Storage (PHS) 88 3.2.2.3 Compressed Air Energy Storage 89 3.2.3 Chemical Energy Storage 89 3.2.3.1 Battery Storage System (BSS) 90 3.2.3.2 Fuel Cells 90 3.2.3.3 Solar Fuel 90 3.2.4 Thermal Energy Storage 93 3.3 Hybrid Energy Storage System (HESS) 93 3.4 Power Converter Topologies for Hybrid Energy Storage 95 3.4.1 Passive Topology 95 3.4.2 Semi-Active Topology 97 3.4.3 Active Topology 97 3.4.4 Comparison of Different Topologies 98 3.5 HESS Energy Management and Control 99 3.5.1 HESS Control Schemes 99 3.5.1.1 Classical Control Scheme 100 3.5.1.2 Intelligent Control Schemes 102 3.5.2 Comparison of Different Control Schemes 103 3.6 Applications of the Storage Technologies in Wind Power 104 3.6.1 Power Fluctuation Mitigation 104 3.6.2 Low Voltage Ride Through (LVRT) 105 3.6.3 Voltage Control Support 105 3.6.4 Oscillation Damping 106 3.6.5 Peak Shaving 106 3.6.6 Spinning Reserve 107 3.6.7 Time Shifting 108 3.6.8 Transmission Line Curtailment 108 3.6.9 Load Following 109 3.6.10 Unit Commitment 110 3.7 Conclusion 110 References 112 4 Advances in Electrochemical Energy Storage Device: Supercapacitor 119 Swagatika Kamila, Bikash Kumar Jena and Suddhasatwa Basu 4.1 Introduction 120 4.2 Types of Energy Storage Devices 120 4.3 Overview of Supercapacitor and Its Global Scenario 122 4.4 Status of Supercapacitor in India 125 4.5 Types of Supercapacitor According to the Energy Storage Mechanism 126 4.5.1 Electrical Double-Layer Capacitor (EDLC) 126 4.5.2 Pseudocapacitor 128 4.5.3 Hybrid Supercapacitor 129 4.5.3.1 Composite Supercapacitor 129 4.5.3.2 Asymmetric Supercapacitor 130 4.5.3.3 Battery Type 130 4.6 Basic Components of Supercapacitor 130 4.6.1 Current Collector 130 4.6.2 Electrode Materials 131 4.6.2.1 EDLC Materials 131 4.6.2.2 Pseudocapacitive Materials 132 4.6.3 Electrolytes 138 4.6.4 Binders 138 4.6.5 Separators 139 4.7 Conclusion 140 References 140 5 Thermal Energy Storage Systems for Cooling and Heating Applications 149 Pankaj Kalita, Debangsu Kashyap and Urbashi Bordoloi 5.1 Introduction 150 5.2 Classification of Storage Systems 151 5.3 Sensible Heat Storage 151 5.3.1 Water-Based Storage 153 5.3.2 Packed Beds 156 5.3.3 Aquifers 158 5.3.4 Borehole 160 5.4 Latent Heat Storage 163 5.4.1 Enhancement Methods for Thermal Conductivity Enhancement 164 5.4.1.1 Macro and Microencapsulation 165 5.4.1.2 Addition of Fins 166 5.4.1.3 Multiple PCM Technology 167 5.4.1.4 Immersion Through Material Pores 167 5.5 Thermochemical Heat Storage 168 5.5.1 Absorption Cycle 172 5.5.2 Adsorption Cycles 173 5.5.3 Chemical Reaction 174 5.6 Application of Thermal Energy Storage Systems 176 5.6.1 Absorption Refrigeration System 176 5.6.2 Solar Pumps Application in Space Cooling/Heating 177 5.6.3 Solar Pond Integrated Packed-Bed TES System for Space Heating 178 5.6.4 Solar FPC 179 5.6.5 Solar PV/T 181 5.6.6 Solar Air Heater 183 5.7 Design Problems 184 5.8 Conclusion 196 References 196 6 Optimistic Technological Approaches for Sustainable Energy Storage Devices/Materials 201 Benjamin Raj, Arya Das, Suddhasatwa Basu and Mamata Mohapatra 6.1 Introduction 202 6.2 Advancements in Supercapacitor Technology 202 6.2.1 The Current Global Supercapacitor Market 205 6.2.2 Challenges: From Lab to Market 207 6.2.3 Current Trends and Opportunities 209 6.2.4 Composites and Novel Architectures 209 6.2.5 Microsupercapacitors 210 6.2.6 Hybrid Supercapacitors 211 6.2.7 Flexible, Wearable and Smart Supercapacitors 211 6.3 Advancements in Battery Technology 212 6.3.1 Challenges 213 6.3.2 Nickel-Cadmium Batteries 213 6.3.3 Nickel-Metal Hydride Batteries 214 6.3.4 Lead Storage Battery 214 6.3.5 Sodium Sulphur Battery 215 6.3.6 Flow Batteries 217 6.3.7 Lithium Ion Batteries (LIBs) 218 6.4 Conclusion and Outlook 221 References 222 7 Electro-Chemical Battery Energy Storage Systems - A Comprehensive Overview 229 Nikhil P G and G Sivaramakrishnan 7.1 Introduction 229 7.2 Electro-Chemical Storage Devices 231 7.2.1 Definition and Types 231 7.2.2 Energy Storage Landscape and Benefits of Electro-Chemical Storage 235 7.2.3 Drivers and Barriers in Implementation of Energy Storage Systems 240 7.3 Design and Performance Parameters for Electro-Chemical Storage 240 7.3.1 Design Basis for Large Storage Application 240 7.4 Case Study From Industry 243 7.5 Best Practices in Battery Maintenance 245 7.6 End of Life Cycle of Batteries 247 7.6.1 Major Recyclable Products from the Process 248 7.6.2 Disposal Measures 248 7.7 India Energy Storage Mission 249 7.8 Conclusion 251 References 251 8 Simulation of Charging and Discharging a Thermal Energy Storage System Involving Phase Change Material 253 S. Sanyal, A. Borgohain and S.P. Gupta 8.1 Introduction 253 8.2 Design of Latent Heat Storage (LHS) System 256 8.2.1 Identification of Suitable PCM 256 8.2.2 Design of Heat Exchanger 260 8.2.3 Performance Evaluation 261 8.3 Analysis of Phase Change Systems 261 8.4 Simulation 263 8.4.1 Equations Involved 263 8.4.2 Modelling 265 8.4.3 Transient Analysis 269 8.5 Results and Discussion 269 8.5.1 Scalability of Mesh 269 8.5.2 Melting 270 8.5.3 Solidification 271 8.5.4 Performance 273 8.6 Conclusion 274 Acknowledgement 274 Abbreviation 275 References 275 Index 277

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Book SynopsisThe energy scene in the world is a complex picture of a variety of energy sources being used to meet the world's growing energy needs. There is, however, a gap in the demand and supply. It is recognized that decentralized power generation based on the various renewable energy technologies can, to some extent, help in meeting the growing energy needs. The renewable energy landscape has witnessed tremendous changes in the policy framework with accelerated and ambitious plans to increase the contribution of renewable energy such as solar, wind, bio-power, and others. Hybrid renewable energy systems are important for continuous operation and supplements each form of energy seasonally, offering several benefits over a stand-alone system. It can enhance capacity and lead to greater security of continuous electricity supply, among other applications. This book provides a platform for researchers, academics, industry professionals, consultants and designers to discover state-of-the-art deveTable of Contents1 Resource Assessment and Implementation of Hybrid Renewable Energy Systems for Food Preservation in Agro-Tropical Areas: A Techno-Economic Approach 1 M. Edwin, M. Saranya Nair and S. Joseph Sekhar 1.1 Introduction 2 1.1.1 Objectives 4 1.2 Materials and Methods 5 1.2.1 Resource Assessment 6 1.2.1.1 Definition of the Study Region 6 1.2.1.2 Field Survey from Households 6 1.2.1.3 Existing Collection and Preservation Methods for Milk 7 1.2.1.4 Potential of Renewable Energy Sources 8 1.2.1.5 Identification of Influential Parameters 10 1.2.1.6 Load/Demand Assessment 10 1.2.2 Modelling and Simulation of a Hybrid Renewable Energy–Based Cooling System 13 1.2.2.1 System Description 13 1.2.2.2 Energy Modelling 14 1.2.2.3 Economic Modelling 15 1.2.2.4 Simulation and Performance Evaluation 15 1.3 Results and Discussion 19 1.3.1 Overall Efficiency of the System 19 1.3.2 Evaluation of Economic Parameters 22 1.3.3 Techno-Economic Study 29 1.3.4 Sensitivity Analysis 29 1.4 Conclusions 32 References 33 2 Implementation of Hybrid Renewable Energy Projects in Rural India—A Case Study 37 Utpal Goswami and Arvind Kumar 2.1 Introduction 37 2.2 Overview of Microgrid 40 2.3 Basic Structure of Hybrid System 40 2.4 Hybrid Microgrid Control 41 2.5 Project Location 42 2.6 Load Profile Study of Proposed Location 42 2.7 Operation of Hybrid Microgrid System Considered for Current Study 44 2.8 Technical Specification of Hybrid System 46 2.9 Modeling of Hybrid Microgrid System 46 2.10 Last One Year Output of Hybrid Microgrid Plant 53 2.11 Financial Analysis 55 2.12 Tariff Calculation 55 2.13 Conclusion 59 References 60 3 Techno-Economic Analysis of Hybrid Renewable Energy System with Energy Storage for Rural Electrification 63 Pradeep Kumar Sahu, Satyaranjan Jena and Umakanta Sahoo 3.1 Introduction 64 3.2 HES Components 65 3.3 Energy Storage Systems 66 3.3.1 Pumped Hydro Storage (PHS) 68 3.3.2 Compressed Air Energy Storage (CAES) 68 3.3.3 Flywheel Energy Storage (FES) 69 3.3.4 Chemical Energy Storage 70 3.3.4.1 Hydrogen-Based ESS 70 3.3.4.2 Battery Energy Storage (BESS) 71 3.3.5 Electromagnetic Energy Storage 72 3.3.5.1 Super Capacitors (SC) 72 3.3.5.2 Superconducting Magnet Energy Storage (SMES) 73 3.4 Hybrid Energy System Configuration 74 3.4.1 Integration Schemes 74 3.4.2 DC-Coupled Systems 76 3.4.3 AC-Coupled Systems 76 3.4.4 Hybrid-Coupled Systems 77 3.5 Component Sizing of Hybrid RE Systems 78 3.6 Techno-Economical Analysis 78 3.6.1 Selection of Study Area for the Proposed Study 81 3.6.2 Load Assessment of the Study Area 81 3.6.3 Resources Assessment 81 3.6.4 Economic Analysis 85 3.6.4.1 Net Present Cost (NPC) 86 3.6.4.2 Cost of Energy (COE) 87 3.6.5 Results and Discussion 87 3.7 Conclusion 91 References 91 4 Modeling and Energy Optimization of Hybrid Energy Storage System 97 Hemavathi S. 4.1 Introduction 97 4.2 Modeling of Proposed Topology 98 4.2.1 Modeling of Photovoltaic System 99 4.2.2 Modeling of Li-Ion Battery Module 100 4.2.3 Modeling of Ultracapacitor Module 103 4.3 Control Strategies 104 4.3.1 PV-MPPT Technique and DC/DC Converter Model 105 4.3.2 Hybrid Active Power Control of Energy Storage Systems 107 4.4 Energy Optimization Strategy and Simulation Results 109 4.4.1 Energy Optimization Strategy 109 4.4.2 Simulation Results 110 4.5 Conclusion 112 Acknowledgment 112 References 113 5 Techno Commercial Study of Hybrid Systems for the Agriculture Farm Using Homer Software 115 Sanjay Kumar C, Karthikeyan M, Prasannakumaran K M and V. Kirubakaran 5.1 Introduction 116 5.2 Electricity Consumption by Agricultural Sector 117 5.3 Literature Review 117 5.4 Study Location 118 5.4.1 Solar Energy Potential in Dindigul District 118 5.5 Load Estimation of the Farm 120 5.5.1 Daily Power Consumption by the Farm 120 5.6 Renewable Energy Technology Used in the Hybrid System 121 5.6.1 Solar PV System 121 5.6.1.1 PV Module 121 5.6.1.2 Storage Batteries 121 5.6.1.3 Converter 122 5.6.2 Biogas Energy Potential in Farm 122 5.6.2.1 Volume Calculation of Digester 123 5.6.2.2 Volume of Gas Collecting Chamber (Vc) 123 5.6.2.3 Generator Sizing 124 5.6.3 Biomass Potential in the Particular Site 124 5.6.3.1 Syn Gas Generation Rate 125 5.6.3.2 Fuel Consumption Rate (FCR) 125 5.7 System Design and Analysis 125 5.7.1 Result Analysis 126 5.7.1.1 Case-1 PV/Biomass Hybrid System 127 5.7.1.2 Case 2 – Hybrid PV/Biogas System 128 5.8 Conclusion 131 References 132 6 Experimental Investigation of Solar Photovoltaic Cold Storage With Thermal Energy Storage 135 K. Sahoo, V. Yadav, N. Goyal, S. Kumar, Y. Singh, S. Mukhopadhyay, U. Sahoo, A.K. Tripathi and C. Banerjee 6.1 Introduction 136 6.2 Scope of Cold Storage in India 137 6.3 Materials and Method 138 6.3.1 Experimental Setup 138 6.4 Economic Analysis 141 6.4.1 Payback Period 149 6.5 Different Business Models for SPV Cold Storage With Thermal Energy Storage 149 6.6 Result and Discussions 153 6.7 Conclusions 164 Acknowledgements 165 Abbreviations 165 References 166 7 Estimation of Fault Voltages in Renewable Energy–Based Microgrid 169 Golla Anand, Chinmoy Basak, Rishabh Anand, Sourav Sahoo and Prof. Sarita Nanda 7.1 Introduction 170 7.2 Problem Formulation 173 7.2.1 Taylor Series Based Voltage Signal Formulation 173 7.2.2 Recursive Least Square (RLS) Algorithm 175 7.3 Pseudo Code/Algorithm for Taylor-RLS 176 7.4 Experimental Validation 177 7.5 Conclusion 181 References 181 8 Optimization of PV-Wind Hybrid Renewable Energy System for Health Care Buildings in Smart City 183 A. Karthick, V. Kumar Chinnaiyan, J. Karpagam, V.S. Chandrika and P. Ravi Kumar 8.1 Introduction 184 8.2 Objectives and Methodology 186 8.3 Description of the HE 188 8.4 Results and Discussion 189 8.5 Conclusion 195 Nomenclatures 196 References 196 9 Hybrid Solar-Biomass Gasifier System for Electricity and Cold Storage Applications for Rural Areas of India 199 Nasir ul Rasheed Rather and Umakanta Sahoo 9.1 Introduction 200 9.2 Literature Review 202 9.2.1 Gasification of Biomass 202 9.2.2 Solar Energy Cooling and Heating 203 9.2.3 Engine Exhaust and Waste Heat Recovery 204 9.3 Materials and Methods 205 9.3.1 System Components 205 9.3.1.1 Biomass Gasifier 207 9.3.1.2 Gas-Engine Generator 209 9.3.1.3 Waste Heat Recovery Unit 210 9.3.1.4 Scheffler Dish Collector 213 9.3.1.5 Vapor Absorption Machine (VAM) 224 9.3.1.6 Cold Storage Unit 230 9.4 Performance Evaluation 233 9.4.1 Thermodynamic Analysis 234 9.5 Results and Discussion 235 9.6 Conclusion & Suggestions for Future Work 244 Suggestions for Future Work 244 References 245 Index 247

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Book SynopsisEnergy is one of the most important topics of our time, and renewable energy has been a long and still-unfolding story that has taken decades to bring us to where we are today. Even after so much progress, engineers and scientists are always still developing new and innovative techniques, processes, equipment, and materials to further the science and fulfill the mission of generating cleaner, renewable energy for the world's consumption. This new groundbreaking series, Advances in Renewable Energy, covers these topics across the spectrum, including solar, wind, and other renewable energy sources. This first volume in the series focuses on solar energy, probably the fastest-growing and developing area of renewable energy. With new materials and processes constantly coming online, it is important for engineers and scientists to stay abreast of the state-of-the-art in the field, and this volume does just that. Covering not just the basics of the technology and technological advaTable of ContentsAbout the Editor xi Contributors xii 1 Reliability Testing of PV Module in the Outdoor Condition 1Birinchi Bora, O.S. Sastry, Som Mondal and B. Prasad 1.1 Introduction 1 1.2 Indoor Testing of Reliability of PV Module 4 1.3 Basics of Measurement Methods used to Identify Failures in the PV Module in the Field after Installation 7 1.3.1 Visual Inspection 8 1.3.2 I-V Tracer 11 1.3.3 Temperature Coefficient 13 1.3.4 Series Resistance 15 1.3.5 Curve Correction Factor 16 1.3.6 Dark I-V 17 1.3.7 Degradation Analysis 18 1.3.8 IR Thermography 19 1.3.9 Insulation Resistance Tester 22 1.3.10 EL Camera 23 1.3.11 Interconnect Breakage Tester 25 1.3.12 Current, Voltage and Continuity Checking 25 1.3.13 Environmental Parameter Checking 25 1.4 Quantification of Reliability 26 1.5 Procedure for Performance and Reliability Testing of PV Module in Outdoor Conditions 33 1.5.1 Selection Procedure of PV Modules for Testing in the Field 33 1.5.2 Testing Report Format of Performance Guarantee Test 33 1.6 Conclusion 35 Abbreviation 35 References 36 2 Solar Energy Technologies and Water Potential for Distillation: A Pre-Feasibility Investigation for Rajasthan, India 39Nikhil Gakkhar, Manoj Kumar Soni and Sanjeev Jakhar 2.1 Introduction 40 2.2 Solar Assisted Technologies for Water Purification 41 2.3 Resource Availability in Rajasthan, India, for Solar Distillation 45 2.3.1 Availability of Solar Irradiance 47 2.3.2 Land Availability in Rajasthan 47 2.3.3 Water Availability from Various Sources 51 2.3.3.1 Surface Water Resources of Rajasthan 51 2.3.3.2 Rainfall 54 2.3.3.3 Domestic Wastewater 54 2.3.3.4 Groundwater 58 2.4 Estimation of Solar Potential and Water Availability 58 2.4.1 Solar PV Potential 59 2.4.2 Solar CSP Potential 60 2.4.3 Water Potential Estimation for Distillation 61 2.5 Choice of Distillation Technology 65 2.5.1 PV-Assisted RO Plants 65 2.5.2 CSP-Assisted MSF Plants 71 2.6 Conclusion 75 Nomenclature 77 References 77 3 Design Analysis of Solar Photovoltaic Power Plants for Northern and Southern Regions of India 83Sanjay Kumar 3.1 Introduction 83 3.1.1 Solar Power in India 88 3.2 Site Selection 90 3.2.1 Geography 90 3.2.2 Specification of Locations 100 3.2.3 Location Dedicated for Power Plant Setup 100 3.2.4 Load Profile of INA 116 3.3 Technology 124 3.3.1 Solar PV Systems 124 3.3.2 Major Components 125 3.3.2.1 Module 126 3.3.2.2 Inverters 127 3.3.2.3 Auxiliary Components 128 3.4 BOM for 3MW Power Plant 134 3.5 Quality, Testing and Standard Certification 140 3.6.1 Modules selection 146 3.6.1.1 Installation of Module 147 3.6.2 Inverter Selection 148 3.7 Financial Analysis 150 3.8 Plant Layout with Electrical and Civil Engineering Aspects 151 3.8.1 Land Requirement 151 3.8.2 Plant Layout 151 3.8.3 Civil Works 152 3.8.4 Module Mounting Structures 152 3.8.5 Operation and Maintenance 152 3.9 Monitoring System 153 3.9.1 SCADA 153 3.9.2 Control and Instrumentation System 154 3.10 Environmental Aspects 155 3.10.1 State Pollution Control Board Clearances 156 3.11 Project Management 156 3.11.1 Project Contracting 156 3.11.2 Quality Management 157 3.11.3 Construction Management 157 3.11.4 Health, Safety and Environment 158 3.11.5 Commissioning and Testing 159 3.11.6 Operation and Maintenance (O & M) 160 3.11.7 Training 161 3.12 Solar Business Models for Megawatt-Scale Projects in India 161 3.12.1 Power Purchase Agreement (PPA) Model 161 3.12.2 Captive Model 161 3.12.3 REC Model 162 3.12.4 REC Formalities and Procedures 163 3.12.5 Business Models under the REC Mechanism 165 3.12.6 Risk Factors of REC 166 3.13 Concepts toward Net Zero Energy Solar Building 167 3.14 Strategy Implementation 168 3.15 Conclusion 176 Abbreviations 177 References 179 4 Cold Storage with Backup Thermal Energy Storage System 181K. Sahoo, B. Bandhyopadhyay, S. Mukhopadhyay, U. Sahoo, T. S. Kumar, V. Yadav and Y. Singh 4.1 Introduction 181 4.1.1 Recommended Condition for Fruits and Vegetables 183 4.1.2 Incompatibility 183 4.2 Solar Energy Scenario 184 4.2.1 Overview of Solar Radiation 187 4.2.1.1 Basic Principles 187 4.2.1.2 Diffuse and Direct Solar Radiation 188 4.2.1.3 Global Solar Radiation 188 4.3 Refrigeration Technology Overview 190 4.3.1 Brier Introduction of Refrigeration 190 4.3.2 Carnot Cycle 191 4.3.3 Reverse Carnot Cycle 192 4.3.4 Air Refrigeration Cycle 193 4.3.5 Vapour Compression Refrigeration System 194 4.3.6 Actual Vapour Compression Refrigeration System 195 4.4 Literature Review 195 4.5 Designing of Solar PV Cold Storage 196 4.5.1 Determining the Size of Cold Room 197 4.5.2 Cooling Load Calculation 197 4.5.2.1 Transmission Load 197 4.5.2.2 Heat Transmission through Door 198 4.5.2.3 Equipment Load 199 4.5.2.4 Product Heat Load 199 4.5.2.5 Heat of Respiration 199 4.5.2.6 Human Occupancy Load 200 4.5.2.7 Cooling Load Due to Thermal Energy Storage 200 4.5.3 Cooling Load Summary for 10 MT Storage Capacities 200 4.5.4 Solar Photovoltaic Plant Design 202 4.5.4.1 Photovoltaic Module Design 202 4.5.4.2 Inverter Sizing 202 4.5.4.3 Battery Sizing 203 4.5.4.4 Solar Charge Controller Sizing 203 4.6 Design of Cold Room Mechanical System 203 4.7 Designing of Thermal Energy Storage System (TES) 206 4.8 Battery Storage 208 4.9 Refrigerant 208 4.10 Specification of Cold Storage and Thermal Energy Storage System 209 4.11 Design of Solar Thermal Based Cold Storage 210 4.11.1 Technology Selection 211 4.11.2 Energy and Collector Area Required from Solar Thermal Technology 212 4.12 Economic Analysis 213 4.12.1 Net Present Value (NPV) 213 4.12.2 Internal Rate of Return (IRR) 214 4.12.3 Payback Period 214 4.13 Economic Analysis of Solar PV Cold Storage 215 4.13.1 NPV and IRR Calculation of Solar PV Cold Storage 215 4.13.2 Payback Period of Solar PV Cold Storage 221 4.14 Economic Analysis of Solar Thermal System Based Cold Storage 223 4.14.1 NPV and IRR Calculation 223 4.14.2 Payback Period of Solar Thermal Cold Storage 229 4.15 Conclusion 231 References 231 5 Development of Parabolic Trough Collector Based Power and Ejector Refrigeration System Using Eco-Friendly Refrigerants 233D.K. Gupta, R. Kumar and N. Kumar 5.1 Introduction 234 5.2 Literature Review 236 5.3 Solar Operated Ejector Cooling and Power Cycle 244 5.3.1 Working of Proposed Cycle 245 5.3.2 First and Second Law Analysis of Proposed Cycle 247 5.4 Ejector Cooling and Power Cycle with Various Ecofriendly Refrigerants 250 5.4.1 System Description 250 5.4.2 Properties of Refrigerants 251 5.4.3 Thermodynamic Analysis 251 5.4.4 Parameters considered for Operation of Proposed System 253 5.5 Ejector Organic Rankine Cycle Integrated with a Triple Pressure Level Vapour Absorption System 253 5.5.1 Working of Proposed System 253 5.5.2 Energy and Exergy Analysis of the Proposed System 258 5.6 Combined Organic Rankine Cycle with Double Ejector 261 5.6.1 Working of Proposed Cycle 262 5.6.2 First and Second Law Analysis of Proposed Cycle 264 5.7 Result and Discussions 267 5.8 Conclusion 297 Nomenclatures 298 Greek symbols 299 Subscript 300 References 300 6 Unlocking the Design of Stand-Alone and Grid-Connected Rooftop Solar PV Systems 309Tanmay Bishnoi 6.1 Introduction 310 6.2 Stand-Alone Solar PV System 312 6.2.1 Types of Stand-Alone PV System Configurations 312 6.2.2 Design Methodology 313 6.2.3 Detailed Steps for Designing a Solar PV System 314 6.2.4 Stand-Alone Solar PV System Design and Safety Standards 330 6.3 Grid-Connected Solar PV System 330 6.3.1 Step by Step Procedure for Designing a Rooftop Grid-Connected Solar PV System 331 6.3.2 Grid-Tied Solar PV System Standards 333 6.3.3 Performance Analysis of a Solar PV System 334 6.4 Costing Analysis for a Solar PV System 337 6.5 Conclusion 359 References 360 Index 363

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Book SynopsisA comprehensive guide to antenna design, manufacturing processes, antenna integration, and packaging Antenna-in-Package Technology and Applicationscontains an introduction to the history of AiP technology. It explores antennas and packages, thermal analysis and design, as well as measurement setups and methods for AiP technology. The authorswell-known experts on the topicexplain why microstrip patch antennas are the most popular and describe the myriad constraints of packaging, such as electrical performance, thermo-mechanical reliability, compactness, manufacturability, and cost. The book includes information on how the choice of interconnects is governed by JEDEC for automatic assembly and describes low-temperature co-fired ceramic, high-density interconnects, fan-out wafer level packagingbased AiP, and 3D-printing-based AiP. The book includes a detailed discussion of the surface laminar circuitbased AiP designs for large-scale mm-wave phased arraTable of ContentsList of Contributors xiii Preface xv Abbreviations xix 1 Introduction 1Yueping Zhang 1.1 Background 1 1.2 The Idea 3 1.3 Exploring the Idea 4 1.3.1 Bluetooth Radio and Other RF Applications 4 1.3.2 60-GHz Radio and Other Millimeter-wave Applications 7 1.4 Developing the Idea into a Mainstream Technology 8 1.5 Concluding Remarks 11 Acknowledgements 12 References 12 2 Antennas 17Yueping Zhang 2.1 Introduction 17 2.2 Basic Antennas 17 2.2.1 Dipole 17 2.2.2 Monopole 18 2.2.3 Loop 18 2.2.4 Slot 19 2.3 Unusual Antennas 19 2.3.1 Laminated Resonator Antenna 19 2.3.2 Dish-like Reflector Antenna 19 2.3.3 Slab Waveguide Antenna 20 2.3.4 Differentially Fed Aperture Antenna 20 2.3.5 Step-profiled Corrugated Horn Antenna 21 2.4 Microstrip Patch Antennas 21 2.4.1 Basic Patch Antennas 21 2.4.2 Stacked Patch Antennas 25 2.4.3 Patch Antenna Arrays 27 2.5 Microstrip Grid Array Antennas 30 2.5.1 Basic Configuration 31 2.5.2 Principle of Operation 31 2.5.3 Design Formulas with an Example 32 2.6 Yagi-Uda Antennas 37 2.6.1 Horizontal Yagi-Uda Antenna 38 2.6.2 Vertical Yagi-Uda Antenna 38 2.6.3 Yagi-Uda Antenna Array 39 2.7 Magneto-Electric Dipole Antennas 41 2.7.1 Single-polarized Microstrip Magneto-electric Dipole Antenna 42 2.7.2 Dual-polarized Microstrip Magneto-electric Dipole Antenna 42 2.7.3 Simulated and Measured Results 45 2.8 Performance Improvement Techniques 45 2.8.1 Single-layer Spiral AMC 49 2.8.2 Design Guidelines 49 2.8.3 A Design Example 50 2.9 Summary 50 Acknowledgements 50 References 51 3 Packaging Technologies 57Ning Ye 3.1 Introduction 57 3.2 Major Packaging Milestones 57 3.3 Packaging Taxonomy 58 3.3.1 Routing Layer in Packages 58 3.3.1.1 Lead Frame 58 3.3.1.2 Laminate 59 3.3.1.3 Redistribution Layer 61 3.3.2 Die to Routing Layer Interconnect 62 3.3.2.1 Wire Bonds 62 3.3.2.2 Flip Chips 63 3.4 Packaging Process for Several Major Packages 64 3.4.1 Wire Bond Plastic Ball Grid Array 64 3.4.1.1 Die Preparation 66 3.4.1.2 Die Attach 66 3.4.1.3 Wire Bonding 67 3.4.1.4 Molding 69 3.4.1.5 Ball Mounting 71 3.4.1.6 Package Singulation 71 3.4.2 Wire Bond Quad Flat No-Lead Packages 71 3.4.3 Flip-chip Plastic Ball Grid Arrays 73 3.4.3.1 Flip-chip Bumping 73 3.4.3.2 Flip-chip Attach 75 3.4.3.3 Underfill 76 3.4.4 Wafer Level Packaging 77 3.4.5 Fan Out Wafer Level Packaging 78 3.5 Summary and Emerging Trends 79 References 84 4 Electrical, Mechanical, and Thermal Co-Design 89Xiaoxiong Gu and Pritish Parida 4.1 Introduction 89 4.2 Electrical, Warpage, and Thermomechanical Analysis for AiP Co-design 92 4.2.1 28-GHz Phased Array Antenna Module Overview 92 4.2.2 Thermomechanical Test Vehicle Overview 94 4.2.3 Antenna Prototyping and Interconnect Characterization 96 4.2.4 Warpage Analysis and Test 96 4.2.5 Thermal Simulation and Characterization 98 4.3 Thermal Management Considerations for Next-generation Heterogeneous Integrated Systems 102 4.3.1 AiP Cooling Options Under Different Power Dissipation Conditions 102 4.3.2 Thermal Management for Heterogeneous Integrated High-power Systems 108 Acknowledgment 110 References 110 5 Antenna-in-Package Measurements 115A.C.F. Reniers, U. Johannsen, and A.B. Smolders 5.1 General Introduction and Antenna Parameters 115 5.1.1 Antenna Measurement Concepts 115 5.1.2 Field Regions 116 5.1.3 Radiation Characteristics 118 5.1.4 Polarization Properties of Antennas 120 5.2 Impedance Measurements 123 5.2.1 Circuit Representation of Antennas 123 5.3 Anechoic Measurement Facility for Characterizing AiPs 128 5.3.1 Design of the mmWave Anechoic Chamber 128 5.3.2 Defining Antenna Measurement Uncertainty 129 5.3.3 Uncertainty in the mmWave Antenna Test Facility 132 5.3.4 Case Study AiP: Characterization of a mmWave Circularly Polarized Rod Antenna 132 5.4 Over-the-air System-level Testing 139 5.5 Summary and Conclusions 142 References 142 6 Antenna-in-package Designs in Multilayered Low-temperature Co-fired Ceramic Platforms 147Atif Shamim and Haoran Zhang 6.1 Introduction 147 6.2 LTCC Technology 148 6.2.1 Introduction 149 6.2.2 LTCC Fabrication Process 150 6.2.3 LTCC Material Suppliers and Manufacturing Foundries 151 6.3 LTCC-based AiP 153 6.3.1 SIW AiP 153 6.3.2 mmWave AiP 156 6.3.2.1 5G AiP 157 6.3.2.2 WPAN (60-GHz) AiP 158 6.3.2.3 Automotive Radar (79-GHz) AiP 159 6.3.2.4 Imaging and Radar (94-GHz) AiP 160 6.3.2.5 Sub-THz (Above-100-GHz) AiP 161 6.3.3 Active Antenna in LTCC 162 6.3.4 Gain Enhancement Techniques in LTCC 164 6.3.5 Ferrite LTCC-based Antenna 167 6.4 Challenges and Upcoming Trends in LTCC AiP 171 References 172 7 Antenna Integration in Packaging Technology operating from 60 GHz up to 300 GHz (HDI-based AiP) 179Frédéric Gianesello, Diane Titz, and Cyril Luxey 7.1 Organic Packaging Technology for AiP 179 7.1.1 Organic Package Overview 179 7.1.2 Buildup Architecture 180 7.1.3 Industrial Material 182 7.1.4 HDI Design Rules 183 7.1.5 Assembly Constraints and Body Size 185 7.2 Integration of AiP in Organic Packaging Technology Below 100 GHz 187 7.2.1 Integration Strategy of the Antenna 187 7.2.2 60-GHz AiP Modules 189 7.2.3 94-GHz AiP Module 197 7.3 Integration of AiP in Organic Packaging Technology in the 120–140-GHz Band 203 7.3.1 120–140-GHz AiP Module 203 7.3.2 Link Demonstration Using a BiCMOS Chip with the 120-GHz BGA Module 208 7.4 Integration of AiP in Organic Packaging Technology Beyond 200 GHz 210 7.5 Conclusion and Perspectives 214 References 215 8 Antenna Integration in eWLB Package 219Maciej Wojnowski and Klaus Pressel 8.1 Introduction 219 8.2 The Embedded Wafer Level BGA Package 220 8.2.1 Process Flow for the eWLB 222 8.2.2 Vertical Interconnections in the eWLB 223 8.2.3 Embedded Z-Line Technology 225 8.3 Toolbox Elements for AiP in eWLB 227 8.3.1 Transmission Lines 227 8.3.2 Passive Components and Distributed RF Circuits 231 8.3.3 RF Transition to PCB 238 8.3.4 Vertical RF Transitions 239 8.4 Antenna Integration in eWLB 243 8.4.1 Single Antenna 244 8.4.2 Antenna Array 245 8.4.3 3D Antenna and Antenna Arrays 246 8.5 Application Examples 249 8.5.1 Two-channel 60-GHz Transceiver Module 249 8.5.2 Four-channel 77-GHz Transceiver Module 253 8.5.3 Six-channel 60-GHz Transceiver Module 258 8.6 Conclusion 263 Acknowledgement 263 References 264 9 Additive Manufacturing AiP Designs and Applications 267Tong-Hong Lin, Ryan A. Bahr, and Manos M. Tentzeris 9.1 Introduction 267 9.2 Additive Manufacturing Technologies 269 9.2.1 Inkjet Printing 269 9.2.2 FDM 3D Printing 269 9.2.3 SLA 3D Printing 270 9.3 Material Characterization 272 9.3.1 Resonator-based Material Characterization 273 9.3.2 Transmissive-based Material Characterization 274 9.4 Recent Advances in AM for Packaging 275 9.4.1 Interconnects 276 9.4.2 AiP 277 9.5 Fabrication Process 278 9.5.1 3D Printing Process 278 9.5.2 Inkjet Printing Process 280 9.5.3 AiP Fabrication Process 281 9.6 AiP and SoP using AM Technologies 282 9.6.1 AiP Design 282 9.6.2 SoP Design 284 9.7 Summary and Prospect 287 References 289 10 SLC-based AiP for Phased Array Applications 293Duixian Liu and Xiaoxiong Gu 10.1 Introduction 293 10.2 SLC Technology 296 10.3 AiP for 5G Base Station Applications 297 10.3.1 Package and Antenna Structure 298 10.3.2 AiP Design Considerations 299 10.3.2.1 Surface Wave Effects 299 10.3.2.2 Vertical Transitions 300 10.3.3 Aperture-coupled Patch Antenna Design 302 10.3.4 28-GHz Aperture-coupled Cavity-backed Patch Array Design 307 10.3.5 Passive Antenna Element Characterization 309 10.3.6 Active Module Characterization of 64-element Beams 310 10.3.7 28-GHz AiP Phased-array Conclusion 314 10.4 94-GHz Scalable AiP Phased-array Applications 315 10.4.1 Scalable Phased-array Concept 317 10.4.2 94-GHz Antenna Prototype Designs 320 10.4.3 94-GHz Antenna Prototype Evaluation 322 10.4.4 94-GHz AiP Array Design 322 10.4.5 Package Modeling and Simulation 326 10.4.6 Package Assembly and Test 328 10.4.7 Antenna Pattern and Radiated Power Measurement 330 Acknowledgment 333 References 334 11 3D AiP for Power Transfer, Sensor Nodes, and IoT Applications 341Amin Enayati, Karim Mohammadpour-Aghdam, and Farbod Molaee-Ghaleh 11.1 Introduction 341 11.2 Small Antenna Design and Miniaturization Techniques 342 11.2.1 Physical Bounds on the Radiation Q-factor for Antenna Structures 342 11.2.1.1 Lower Bounds on Antenna Enclosed in a Sphere: Chu, McLean, and Thal Limits 342 11.2.1.2 Lower Bounds on Antenna Enclosed in an Arbitrary Structure: Gustafsson–Yaghjian Limit 343 11.2.2 Figure of Merit for Antenna Miniaturization 345 11.2.2.1 Relation between Q-factor and Antenna Input Impedance 345 11.2.2.2 Antenna Efficiency Effect on the Radiation Q 346 11.2.2.3 Cross-polarization Effect on Antenna Radiation Q 346 11.2.2.4 Figure of Merit Definition 346 11.2.3 Antenna Miniaturization Techniques 346 11.2.3.1 Miniaturization through Geometrical Shaping of the Antenna 347 11.2.3.2 Miniaturization through Material Loading 349 11.3 Multi-mode Capability: A Way to Achieve Wideband Antennas 354 11.4 Miniaturized Antenna Solutions for Power Transfer and Energy Harvesting Applications 355 11.4.1 Integrated Antenna Design Challenges for WPT and Scavenging Systems 356 11.4.1.1 Conjugate Impedance Matching 356 11.4.1.2 Antenna Structure Selection 357 11.4.2 Small Antenna Structure that can be Optimized for Arbitrary Input Impedance 357 11.4.2.1 Basic Antenna Structure 357 11.4.2.2 Antenna Size Reduction by Folding 358 11.4.2.3 Final Antenna Structure and Parameter Analysis 358 11.4.3 Example of an AiP Solution for On-chip Scavenging/UWB Applications 360 11.5 AiP Solutions in Low-cost PCB Technology 364 11.5.1 Introduction to Wireless Sensor Networks and IoT 364 11.5.1.1 Examples of Antennas for IoT Devices 365 11.5.2 3D System-in-Package Solutions for Microwave Wireless Devices 365 11.5.3 E-CUBE: A 3D SiP Solution 368 11.5.3.1 Multilayer Flex-rigid PCB for Antenna Element Design 369 11.5.3.2 Modular Design of the Antenna Array and Power Distribution Network 371 11.5.3.3 Construction and Measurement Results 374 References 377 Index 385

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Book SynopsisPresents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and Table of ContentsList of Figures xiii List of Tables xix Preface xxi Acknowledgments xxv Part I Neural Networks for Serial Robot Arm Control 1 1 Zeroing Neural Networks for Control 3 1.1 Introduction 3 1.2 Scheme Formulation and ZNN Solutions 4 1.2.1 ZNN Model 4 1.2.2 Nonconvex Function Activated ZNN Model 8 1.3 Theoretical Analyses 9 1.4 Computer Simulations and Verifications 12 1.4.1 ZNN for Solving (1.13) at t = 1 12 1.4.2 ZNN for Solving (1.13) with Different Bounds 15 1.5 Summary 16 2 Adaptive Dynamic Programming Neural Networks for Control 17 2.1 Introduction 17 2.2 Preliminaries on Variable Structure Control of the Sensor–Actuator System 18 2.3 Problem Formulation 19 2.4 Model-Free Control of the Euler–Lagrange System 20 2.4.1 Optimality Condition 21 2.4.2 Approximating the Action Mapping and the Critic Mapping 21 2.5 Simulation Experiment 23 2.5.1 The Model 23 2.5.2 Experiment Setup and Results 24 2.6 Summary 25 3 Projection Neural Networks for Robot Arm Control 27 3.1 Introduction 27 3.2 Problem Formulation 29 3.3 A Modified Controller without Error Accumulation 30 3.3.1 Existing RNN Solutions 30 3.3.2 Limitations of Existing RNN Solutions 32 3.3.3 The Presented Algorithm 33 3.3.4 Stability 34 3.4 Performance Improvement Using Velocity Compensation 36 3.4.1 A Control Law with Velocity Compensation 36 3.4.2 Stability 37 3.5 Simulations 41 3.5.1 Regulation to a Fixed Position 41 3.5.2 Tracking of Time-Varying References 42 3.5.3 Comparisons 47 3.6 Summary 50 4 Neural Learning and Control Co-Design for Robot Arm Control 51 4.1 Introduction 51 4.2 Problem Formulation 52 4.3 Nominal Neural Controller Design 53 4.4 A Novel Dual Neural Network Model 54 4.4.1 Neural Network Design 54 4.4.2 Stability 56 4.5 Simulations 62 4.5.1 Simulation Setup 62 4.5.2 Simulation Results 63 4.5.2.1 Tracking Performance 63 4.5.2.2 With vs.Without Excitation Noises 64 4.6 Summary 66 5 Robust Neural Controller Design for Robot Arm Control 67 5.1 Introduction 67 5.2 Problem Formulation 68 5.3 Dual Neural Networks for the Nominal System 69 5.3.1 Neural Network Design 69 5.3.2 Convergence Analysis 71 5.4 Neural Design in the Presence of Noises 72 5.4.1 Polynomial Noises 72 5.4.1.1 Neural Dynamics 73 5.4.1.2 Practical Considerations 77 5.4.2 Special Cases 78 5.4.2.1 Constant Noises 78 5.4.2.2 Linear Noises 80 5.5 Simulations 81 5.5.1 Simulation Setup 81 5.5.2 Nominal Situation 81 5.5.3 Constant Noises 82 5.5.4 Time-Varying Polynomial Noises 86 5.6 Summary 86 6 Using Neural Networks to Avoid Robot Singularity 87 6.1 Introduction 87 6.2 Preliminaries 89 6.3 Problem Formulation 90 6.3.1 Manipulator Kinematics 90 6.3.2 Manipulability 90 6.3.3 Optimization Problem Formulation 91 6.4 Reformulation as a Constrained Quadratic Program 91 6.4.1 Equation Constraint: Speed Level Resolution 91 6.4.2 Redefinition of the Objective Function 92 6.4.3 Set Constraint 93 6.4.4 Reformulation and Convexification 94 6.5 Neural Networks for Redundancy Resolution 95 6.5.1 Conversion to a Nonlinear Equation Set 95 6.5.2 Neural Dynamics for Real-Time Redundancy Resolution 96 6.5.3 Convergence Analysis 96 6.6 Illustrative Examples 98 6.6.1 Manipulability Optimization via Self Motion 98 6.6.2 Manipulability Optimization in Circular Path Tracking 99 6.6.3 Comparisons 102 6.6.4 Summary 104 Part II Neural Networks for Parallel Robot Control 105 7 Neural Network Based Stewart Platform Control 107 7.1 Introduction 107 7.2 Preliminaries 108 7.3 Robot Kinematics 109 7.3.1 Geometric Relation 109 7.3.2 Velocity Space Resolution 111 7.4 Problem Formulation as Constrained Optimization 112 7.5 Dynamic Neural Network Model 113 7.5.1 Neural Network Design 113 7.6 Theoretical Results 115 7.6.1 Optimality 115 7.6.2 Stability 116 7.6.3 Comparison with Other Control Schemes 117 7.7 Numerical Investigation 118 7.7.1 Simulation Setups 118 7.7.2 Circular Trajectory 122 7.7.3 Infinity-Sign Trajectory 127 7.7.4 Square Trajectory 127 7.8 Summary 129 8 Neural Network Based Learning and Control Co-Design for Stewart Platform Control 131 8.1 Introduction 131 8.2 Kinematic Modeling of Stewart Platforms 133 8.2.1 Geometric Relation 133 8.2.2 Velocity Space Resolution 135 8.3 Recurrent Neural Network Design 136 8.3.1 Problem Formulation from an Optimization Perspective 136 8.3.2 Neural Network Dynamics 138 8.3.3 Stability 138 8.3.4 Optimality 139 8.4 Numerical Investigation 142 8.4.1 Setups 142 8.4.2 Circular Trajectory 143 8.4.3 Square Trajectory 143 8.5 Summary 145 Part III Neural Networks for Cooperative Control 147 9 Zeroing Neural Networks for Robot Arm Motion Generation 149 9.1 Introduction 149 9.2 Preliminaries 151 9.2.1 Problem Definition and Assumption 151 9.2.1.1 Assumption 151 9.2.2 Manipulator Kinematics 151 9.3 Problem Formulation and Distributed Scheme 152 9.3.1 Problem Formulation and Neural-Dynamic Design 152 9.3.2 Distributed Scheme 153 9.4 NTZNN Solver and Theoretical Analyses 153 9.4.1 ZNN for Real-Time Redundancy Resolution 154 9.4.2 Theoretical Analyses and Results 157 9.5 Illustrative Examples 160 9.5.1 Consensus to a Fixed Configuration 160 9.5.2 Cooperative Motion Generation Perturbed by Noises 161 9.5.3 ZNN-Based Solution Perturbed by Noises 162 9.6 Summary 165 10 Zeroing Neural Networks for Robot Arm Motion Generation 167 10.1 Introduction 167 10.2 Preliminaries, Problem Formulation, and Distributed Scheme 168 10.2.1 Definition and Robot Arm Kinematics 168 10.2.2 Problem Formulation 168 10.2.3 Distributed Scheme 169 10.3 NANTZNN Solver and Theoretical Analyses 169 10.3.1 NANTZNN for Real-Time Redundancy Resolution 170 10.3.2 Theoretical Analyses and Results 171 10.4 Illustrative Examples 172 10.4.1 Cooperative Motion Planning without Noises 174 10.4.2 Cooperative Motion Planning with Noises 174 10.5 Summary 175 Reference 177 Index 185

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Book SynopsisOffers an Interdisciplinary approach to the engineering of functional materials for efficient solar cell technology Written by a collection of experts in the field of solar cell technology, this book focuses on the engineering of a variety of functional materials for improving photoanode efficiency of dye-sensitized solar cells (DSSC). The first two chapters describe operation principles of DSSC, charge transfer dynamics, as well as challenges and solutions for improving DSSCs. The remaining chapters focus on interfacial engineering of functional materials at the photoanode surface to create greater output efficiency. Interfacial Engineering in Functional Materials for Dye-Sensitized Solar Cells begins by introducing readers to the history, configuration, components, and working principles of DSSC It then goes on to cover both nanoarchitectures and light scattering materials as photoanode. Function of compact (blocking) layer in the photoanode and of TiClTable of ContentsList of Contributors xi Preface xv 1 Dye-Sensitized Solar Cells: History, Components, Configuration, and Working Principle 1S.N. Karthick, K.V. Hemalatha, Suresh Kannan Balasingam, F. Manik Clinton, S. Akshaya, and Hee-Je Kim 1.1 Introduction 1 1.2 History of Dye-sensitized Solar Cells 3 1.3 Components of DSSCs 4 1.3.1 Conductive Glass Substrate 4 1.3.2 Photoanode 4 1.3.3 Counter Electrode 4 1.3.4 Electrolytes 6 1.3.4.1 Types of Solvents Used in Electrolytes 6 1.3.4.2 Alternative Redox Mediators 7 1.3.5 Dyes 8 1.4 Configuration of DSSCs 8 1.4.1 Metal Substrates for Photoanode and Glass/TCO for Counter Electrode 8 1.4.2 Metal Substrates for Counter Electrode and Glass/TCO for Photoanode 10 1.4.3 Metal Substrate for Photoanode and Polymer Substrate for Counter Electrode 10 1.4.4 Polymer Substrates for Flexible DSSCs 10 1.4.5 Glass/TCO-Free Metal Substrates for Flexible DSSCs 11 1.4.6 Glass/TCO-Free Metal Wire Substrates for Flexible DSSCs 11 1.5 Working Principle of DSSCs 11 1.5.1 Electron Transfer Mechanism in DSSCs 14 1.5.2 Photoelectric Performance 14 Acknowledgments 15 References 15 2 Function of Photoanode: Charge Transfer Dynamics, Challenges, and Alternative Strategies 17A. Dennyson Savariraj and R.V. Mangalaraja 2.1 Introduction 17 2.2 The General Composition of DSSC 18 2.3 Selection of Substrate for DSSCs 18 2.4 Photoanode 19 2.4.1 Coating Procedure 20 2.4.2 Significance of Using Mesoporous Structure 20 2.5 Sensitizer 20 2.6 Charge Transfer Mechanism 21 2.7 Interfaces 21 2.8 Significance of Dye/Metal Oxide Interface 22 2.9 Factors That Influence Efficiency in DSSC 23 2.9.1 Dye Aggregation 23 2.9.2 Effect of Metal Oxide on the Performance of Metal Oxide/Dye Interface 24 2.9.3 Role of Electronic Structure of Metal Oxides 25 2.10 Kinetics of Operation in DSSCs 26 2.11 Strategies to Improve the Photoanode Performance 28 2.11.1 TiCl4 Treatment 28 2.11.2 Composites 28 2.11.3 Light Scattering 29 2.11.4 Nanoarchitectures 29 2.11.5 Doping 30 2.11.6 Interfacial Engineering 30 2.12 Conclusion 30 Acknowledgments 31 References 31 3 Nanoarchitectures as Photoanodes 35Hari Murthy 3.1 Introduction 35 3.2 DSSC Operation 36 3.3 Nanoarchitectures for Improved Device Performance of Photoanodes 39 3.3.1 TiO2 39 3.3.2 ZnO 51 3.3.3 SnO2 53 3.3.4 Nb2O5 55 3.3.5 Graphene 55 3.3.6 Other Photoanode Materials 56 3.4 Future Outlook and Challenges 65 3.5 Conclusion 66 References 66 4 Light Scattering Materials as Photoanodes 79Rajkumar C and A. Arulraj 4.1 Introduction 79 4.2 Introduction to Light Scattering 79 4.3 Materials for Light Scattering in DSSCs 80 4.4 Early Theoretical Predictions of Light Scattering in DSSCs 82 4.5 Different Light Scattering Materials 85 4.5.1 Mixing of Large Particles into Small Particles 85 4.5.2 Voids as Light Scatters 87 4.5.3 Nano-Composites for Light Scattering 87 4.5.3.1 Nanowire–Nanoparticle Composite 87 4.5.3.2 Nanofiber–Nanoparticle Composite 87 4.5.3.3 SrTiO3 Nanocubes–ZnO Nanoparticle Composite 88 4.5.3.4 Silica Nanosphere–ZnO Nanoparticle Composite 88 4.5.3.5 SnO2 Aggregate–SnO2 Nanosheet Composite 88 4.5.3.6 Ag (4,4′-Dicyanamidobiphenyl) Complex–TiO2 NP Composite 88 4.6 Light Scattering Layers 88 4.6.1 Surface Modified TiO2 Particles in Scattering Layer 88 4.6.2 Dual Functional Materials in DSSC 89 4.6.3 Double-Light Scattering Layer 89 4.6.4 Large Particles as Scattering Layers 89 4.6.4.1 TiO2 Nanotubes 90 4.6.4.2 TiO2 Nanowires 90 4.6.4.3 TiO2 Nanospindles 90 4.6.4.4 TiO2 Nanofibers 90 4.6.4.5 TiO2 Rice Grain Nanostructures 90 4.6.4.6 Nest-Shaped TiO2 Structures 91 4.6.4.7 Nano-Embossed Hollow Spherical TiO2 91 4.6.4.8 Hexagonal TiO2 Plates 91 4.6.4.9 TiO2 Photonic Crystals 91 4.6.4.10 Cubic CeO2 Nanoparticles 94 4.6.4.11 Spherical TiO2 Aggregates 94 4.6.4.12 Hierarchical TiO2 Submicroflowers 94 4.6.4.13 SnO2 Aggregates 94 4.6.4.14 ZnO Nanoflowers 95 4.6.5 Core–Shell Nanoparticles for Light Scattering in DSSCs 95 4.6.6 Double-Layer Photoanode 95 4.6.6.1 TiO2 Aggregates 96 4.6.6.2 Morphology-Controlled 1D–3D Bilayer TiO2 Nanostructures 96 4.6.6.3 Quintuple-Shelled SnO2 Hollow Microspheres 96 4.6.6.4 Carbon-Based Materials for Light Scattering 96 4.6.6.5 3D N-Doped TiO2 Microspheres Used as Scattering Layers 96 4.6.6.6 ZnO Hollow Spheres and Urchin-like TiO2 Microspheres 96 4.6.6.7 SnO2 as Light-Scattering Layer 97 4.6.7 Three-Layer Photoanode 97 4.6.8 Four-Layer Photoanode 97 4.6.9 Surface Plasmon Effect in DSSC 97 4.7 Conclusion 99 References 99 5 Function of Compact (Blocking) Layer in Photoanode 107Su Pei Lim 5.1 Introduction 107 5.2 Titanium Dioxide (TiO2) and Titanium (Ti)-Based Material as a Compact Layer 107 5.3 Zinc Oxide (ZnO) as a Compact Layer 112 5.4 Less Common Metal Oxide as a Compact Layer 117 5.5 Conclusion 118 References 121 6 Function of TiCl4 Posttreatment in Photoanode 125T.S. Senthil and C.R. Kalaiselvi 6.1 Introduction 125 6.2 Role of TiCl4 Posttreatment in Photo-Anode 126 6.3 Effect of Posttreatment of TiCl4 on Various Perspectives 126 6.3.1 TiO2 Morphology, Porosity, and Surface Area 126 6.3.2 Dye Adsorption and Photocurrent Generation 129 6.3.3 Electron Transport and Diffusion Coefficient 132 6.3.4 Recombination Losses at Short Circuit 134 6.3.5 Concentration and Dipping Time of TiCl4 135 6.4 Conclusion 136 References 137 7 Doped Semiconductor as Photoanode 139K. S. Rajni and T. Raguram 7.1 Introduction 139 7.2 Photoanode 140 7.3 Characterization 141 7.4 Doped TiO2 Photoanodes 141 7.4.1 Alkali Earth Metals-doped TiO2 141 7.4.1.1 Lithium-doped TiO2 141 7.4.1.2 Magnesium-doped TiO2 143 7.4.1.3 Calcium-doped TiO2 143 7.4.2 Metalloids-doped TiO2 143 7.4.2.1 Boron-doped TiO2 145 7.4.2.2 Silicon-doped TiO2 145 7.4.2.3 Germanium-doped TiO2 145 7.4.2.4 Antimony-doped TiO2 146 7.4.3 Nonmetals-doped TiO2 146 7.4.3.1 Carbon-doped TiO2 146 7.4.3.2 Nitrogen-doped TiO2 147 7.4.3.3 Fluorine-doped TiO2 147 7.4.3.4 Sulfur-doped TiO2 147 7.4.3.5 Iodine-doped TiO2 148 7.4.4 Transition Metals-doped TiO2 148 7.4.4.1 Scandium-doped TiO2 148 7.4.4.2 Vanadium, Niobium, and Tantalum-doped TiO2 148 7.4.4.3 Chromium-doped TiO2 148 7.4.4.4 Manganese and Cobalt-doped TiO2 150 7.4.4.5 Iron-doped TiO2 150 7.4.4.6 Nickel-doped TiO2 151 7.4.4.7 Copper-doped TiO2 152 7.4.4.8 Zinc-doped TiO2 153 7.4.4.9 Yttrium-doped TiO2 153 7.4.4.10 Zirconium-doped TiO2 154 7.4.4.11 Molybdenum-doped TiO2 154 7.4.4.12 Silver-doped TiO2 155 7.4.5 Post-Transition Metals 155 7.4.5.1 Aluminum-doped TiO2 155 7.4.5.2 Gallium-doped TiO2 155 7.4.5.3 Indium-doped TiO2 155 7.4.5.4 Tin-doped TiO2 156 7.4.6 Lanthanides-doped TiO2 156 7.4.6.1 Lanthanum-doped TiO2 156 7.4.6.2 Cerium-doped TiO2 156 7.4.6.3 Neodymium-doped TiO2 157 7.4.6.4 Samarium-doped TiO2 157 7.4.6.5 Europium-doped TiO2 157 7.4.7 Co-doped TiO2 158 7.4.8 Tri-doped TiO2 158 7.5 Conclusion 158 References 159 8 Binary Semiconductor Metal Oxide as Photoanodes 163S.S. Kanmani, I. John Peter, A. Muthu Kumar, P. Nithiananthi, C. Raja Mohan, and K. Ramachandran 8.1 Why Metal Oxide Semiconductors? 163 8.2 Development of MOS-Based DSSC 164 8.2.1 TiO2/ZnO Core/Shell Configuration 165 8.2.2 Preparation of TiO2/ZnO Core/Shell Nanomaterials 165 8.2.3 TiO2/ZnO Core/Shell Nanomaterials 165 8.2.4 DSSC Performance of TiO2/ZnO Core/Shell Configuration 167 8.3 Importance of Heterostructures 170 8.4 I–V Characteristics 171 8.5 Matching of Bandgaps 171 8.6 Conclusion 189 References 189 9 Plasmonic Nanocomposite as Photoanode 193Su Pei Lim 9.1 Introduction 193 9.2 Plasmonic Nanocomposite Modified TiO2 as Photoanode 193 9.3 Plasmonic Nanocomposite Modified ZnO as Photoanode 197 9.4 Plasmonic Nanocomposite Modified with Less Common Metal Oxide as Photoanode 203 9.5 Conclusion 206 References 206 10 Carbon Nanotubes-Based Nanocomposite as Photoanode 213Giovana R. Cagnani, Nirav Joshi, and Flavio M. Shimizu 10.1 Introduction 213 10.2 Recent Advances on DSSC Photoanodes 215 10.3 Structure and Properties of Carbon Nanotubes 216 10.4 CNT-Based Photoanode Material 218 10.5 Effect of the Morphology and Interface of the CNT Photoanodes on the Efficiency of the DSSC 221 10.6 Summary and Future Prospect 223 Acknowledgment 223 References 223 11 Graphene-Based Nanocomposite as Photoanode 231Subhendu K. Panda, G. Murugadoss, and R. Thangamuthu 11.1 Introduction 231 11.2 Graphene–TiO2 Nanocomposite for Photoanode 232 11.3 Conclusion and Remarks 241 References 242 12 Graphitic Carbon Nitride Based Nanocomposites as Photoanodes 247T.S. Shyju, S. Anandhi, P. Vengatesh, C. Karthik Kumar, and M. Paulraj 12.1 Introduction 247 12.2 Importance of Graphitic Carbon Nitride 248 12.3 Photoanodes for DSSC 250 12.4 Preparation of Graphitic Carbon Nitride 252 12.4.1 Bulk Graphitic Carbon Nitride 253 12.4.2 Mesoporous Graphitic Carbon Nitrides 253 12.4.3 Doping in Graphitic Carbon Nitride 254 12.4.4 Ag Deposited g-C3N4 254 12.4.5 Chemical Doping 254 12.5 Operation Principles of DSSC 255 12.5.1 Nanostructured Graphitic Carbon Nitride in DSSC 257 12.6 Graphitic Carbon Nitride in Polymer Films Solar Cell 259 12.7 Preparation of Carbon Nitride Counter Electrode 259 12.8 Quantum Dot Graphitic Carbon Nitride 260 12.9 Porous Graphitic Carbon Nitride 260 12.10 Summary 260 Acknowledgment 261 References 261 Index 265

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Book SynopsisA comprehensive and up-to-date reference on holographic recording Photorefractive Materials for Dynamic Optical Recording offers a comprehensive overview of the physics, technology, and characterization of photorefractive materials that are used for optical recording. The author, a noted expert on the topic, offers an exploration of both transient and permanent holographic information storage methods. The text is written in clear terms with coherent explanations of the different methods that allows for easy access to the most appropriate method for a specific need. The book provides an analysis of the fundamental properties of the materials and explores the dynamic recording of a spatial electric charge distribution and the associated spatial electric ?eld distribution. The text also includes information on the characterization of photorefractive materials using holographic and nonholographic optical methods and electrical techniques, reporting a large nuTable of ContentsList of Figures xi List of Tables xxxiii Preface xxxv Acknowledgments xxxvii Part I Fundamentals 1 1 Electro-Optic Effect 5 1.1 Light Propagation in Crystals 5 1.2 Tensorial Analysis 8 1.3 Electro-Optic Effect 8 1.4 Perovskite Crystals 11 1.5 Sillenite Crystals 11 1.6 Concluding Remarks 17 2 Photoactive Centers and Photoconductivity 19 2.1 Photoactive Centers: Deep and Shallow Traps 20 2.2 Luminescence 28 2.3 Photoconductivity 29 2.4 Photovoltaic Effect 40 2.5 Nonlinear Photovoltaic Effect 44 2.6 Light-Induced Absorption or Photochromic Effect 48 2.7 Dember or Light-Induced Schottky Effect 51 Part II Holographic Recording 55 3 Recording a Space-Charge Electric Field 57 3.1 Index-of-Refraction Modulation 60 3.2 General Formulation 63 3.3 First Spatial Harmonic Approximation 66 3.4 Steady-State Nonstationary Process: Running Holograms 72 3.5 Photovoltaic Materials 84 4 Volume HologramwithWave Mixing 89 4.1 CoupledWaveTheory: Fixed Grating 89 4.2 Dynamic CoupledWaveTheory 92 4.3 Phase Modulation 115 4.4 Four-Wave Mixing 119 4.5 Conclusions 120 5 Anisotropic Diffraction 121 5.1 Coupled-Wave with Anisotropic Diffraction 121 5.2 Anisotropic Diffraction and Optical Activity 122 6 Stabilized Holographic Recording 125 6.1 Introduction 125 6.2 Mathematical Formulation 127 6.3 Self-Stabilized Recording in Actual Materials 135 Part III Materials Characterization 151 7 General Electrical and Optical Techniques 155 7.1 Electro-Optic Coefficient 155 7.2 Light-Induced Absorption 157 7.3 Dark Conductivity 161 7.4 Photoconductivity 162 7.5 Photo-Electric Conversion 173 7.6 Modulated Photoconductivity 175 7.7 Photo-Electromotive-Force Techniques (PEMF) 178 8 Holographic Techniques 189 8.1 Holographic Recording and Erasing 189 8.2 Direct Holographic Techniques 189 8.3 Hologram Recording 195 8.4 Hologram Erasure 195 8.5 Materials 197 8.6 Phase Modulation Techniques 205 8.7 Holographic Photo-Electromotive-Force (HPEMF) Techniques 218 9 Self-Stabilized Holographic Techniques 229 9.1 Holographic Phase Shift 229 9.2 Fringe-Locked Running Holograms 232 9.3 Characterization of LiNbO3:Fe 239 Part IV Applications 243 10 Vibrations and Deformations 245 10.1 Measurement of Vibration and Deformation 245 10.2 Experimental Setup 246 11 Fixed Holograms 257 11.1 Introduction 257 11.2 Fixed Holograms in LiNbO3 257 12 Photoelectric Conversion 263 12.1 Photoelectric Conversion Efficiency: Dember and Photovoltaic Effects 263 Part V Appendix 265 Introduction 266 Appendix A Reversible Real-Time Holograms 267 A.1 Naked-Eye Detection 267 A.2 Instrumental Detection 268 Appendix B Diffraction EfficiencyMeasurement 271 B.1 Angular Bragg Selectivity 271 B.2 Reversible Holograms 274 B.3 High Index-of-Refraction Material 275 Appendix C Effectively Applied Electric Field 279 Appendix D PhysicalMeaning of Some Parameters 281 D.1 Temperature 281 D.2 Diffusion and Mobility 284 Appendix E Photodiodes 287 E.1 Photovoltaic Regime 288 E.2 Photoconductive Regime 289 E.3 Operational Amplifier 290 Bibliography 291 Index 305

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Book SynopsisAddresses the importance of EM wave absorbers and details pertinent theory, design, and applications Demands for various EM-wave absorbers are rapidly increasing along with recent trends toward complicated electromagnetic environments and development of higher-frequency communication equipment, including AI technology. This book provides a broad perspective on electromagnetic wave absorbers, as well as discussion of specific types of absorbers, their advantages and disadvantages, their applications, and performance verification. Electromagnetic Wave Absorbers: Detailed Theories and Applications presents the theory behind wave absorbers and their practical usage in design of EM-wave absorber necessary particularly for EMC environments, and similar applications. The first half of the book contains the foundations of electromagnetic wave engineering, specifically the transmission line theories necessary for EM-wave absorber analysis, the basic knowledge of rTable of ContentsPreface xi 1 Fundamentals of Electromagnetic Wave Absorbers 1 1.1 Introduction to Electromagnetic-Wave Absorbers 2 1.2 Fundamentals of Absorber Characteristics 3 1.3 Classifications of Absorbers 4 1.3.1 Classifications by Appearance 4 1.3.1.1 Single-layer-type Absorber 4 1.3.1.2 Quarter-wavelength-type Absorber 7 1.3.1.3 Multilayered Absorber 7 1.3.1.4 Jaumann Absorber 7 1.3.1.5 Sawtooth-shape Absorber 7 1.3.1.6 Pyramidal Wave Absorber 7 1.3.1.7 Absorbers by Artificial Materials and Special Materials 8 1.3.2 Classifications of Material 8 1.3.2.1 Conductive Absorber Material 8 1.3.2.2 Dielectric Absorber Material 8 1.3.2.3 Magnetic Absorber Material 8 1.3.2.4 Metamaterial 8 1.3.3 Classifications by Configuration Forms 9 1.3.3.1 Classification from Layered Numbers 9 1.3.4 Classifications by Frequency Characteristics 10 1.3.4.1 Narrowband-type Absorber 10 1.3.4.2 Broadband-type Absorber 10 1.3.4.3 Ultra-wideband-type Absorber 11 1.4 Application Examples of Wave Absorbers 11 References 13 2 Fundamental Theory of EM-Wave Absorbers 17 2.1 Transmission Line Theory 17 2.1.1 Transmission Line Equation 18 2.1.2 Reflection Coefficient 23 2.1.2.1 Reflection Coefficient at Load Terminal End 23 2.1.2.2 Reflection Coefficient on Transmission Line 24 2.1.2.3 Reflection Coefficient and Standing-Wave Ratio 25 2.1.3 Transmission Line with Loss 26 2.1.4 Reflection Coefficient in Transmission Line with Loss 27 2.2 Smith Chart 28 2.2.1 Principle of Smith Chart 28 2.2.2 Admittance Chart 34 2.2.3 Examples of Smith Chart Application 35 2.2.3.1 Impedance of Transmission Line with Short-circuit Termination 35 2.2.3.2 Matching Method with a Single Movable Stub 36 2.2.3.3 Matching Method Using Fixed Multiple Stubs 38 2.3 Fundamentals of Electromagnetic Wave Analysis 40 2.3.1 Derivation of Maxwell’s Equations 40 2.3.1.1 Maxwell’s First Electromagnetic Equation 41 2.3.1.2 Maxwell’s Second Electromagnetic Equation 43 2.3.2 Wave Equations 45 2.3.3 Reflection from Perfect Conductor in Normal Incidence 47 2.3.4 Reflection and Transmission in Two Medium Interfaces 50 2.3.4.1 Normal Incidence Cases 50 2.3.4.2 Oblique Incidence 53 2.3.5 Theory of Multiple Reflections 59 2.3.5.1 Reflection and Transmission Coefficients 59 2.A Appendix 62 2.A.1 Appendix to Section 2.3.2 (1) 62 References 63 3 Methods of Absorber Analysis 65 3.1 Normal Incidence to Single-layer Flat Absorber 65 3.2 Oblique Incidence to Single-layer Flat Absorber 68 3.3 Characteristics of the Multilayered Absorber 71 3.3.1 Normal Incidence Case 71 3.3.2 Case of Oblique Incidence 73 3.3.2.1 Case of the TE Wave 73 3.3.2.2 Case of the TM Wave 73 3.4 Case of Multiple Reflected and Scattered Waves 74 3.4.1 Standing Wave Ratio in Beat Generation 78 3.A Appendix 80 3.A.1 Appendix to Section 3.4.1 (1) 80 References 82 4 Basic Theory of Computer Analysis 83 4.1 FDTD Analysis Method 84 4.1.1 Basis of FDTD 84 4.1.2 Methods of Time and Space Difference 86 4.1.3 Relationship of Time Arrangement of the Electromagnetic Field 87 4.1.4 Relationship of Spatial Arrangement of the Electromagnetic Field 89 4.1.5 General Expressions of FDTD Analysis 91 4.1.6 Absorbing Boundary Conditions 95 4.1.7 Analysis Model and Boundary Conditions 95 4.1.7.1 Behavior of the Periodic Boundary 97 4.1.7.2 Behavior of the PLM Absorbing Boundary 98 4.1.7.3 Behaviors at Variable Cell Size 99 4.1.7.4 Convergence by Configuration Dimensions and Number of Cells 101 4.2 Finite Element Method 102 4.2.1 Foundation of the Finite Element Method 102 4.2.1.1 Outline of the Finite Element Method 102 4.2.1.2 History of FEM 102 4.2.1.3 Variational Method as FEM Foundation 102 4.2.1.4 Relationship Between Functional and Laplace Equation 104 4.2.2 Summary of Analytical Procedures 105 4.2.3 Example of Electrostatic Field Analysis 106 4.2.4 Application of Electrostatic Field Analysis 112 4.3 Three-Dimensional Electric Current Potential Method 112 4.3.1 Outline of the Electric Current Vector Potential Method 112 4.3.2 Basic Equation and Auxiliary Equation 113 4.3.3 Formulations of the Basic and Auxiliary Equations 116 4.3.4 Derivation of the Approximate Potential Function 118 4.3.5 Discretization of the Basic Equation 122 4.3.5.1 The First Term on the Right Side of Eq. (4.110) 122 4.3.5.2 x Component in the First Term of the Basic Equation (4.110) 124 4.3.5.3 y Component in the First Term of Basic Equation (4.110) 125 4.3.5.4 z Component in the First Term of the Basic Equation (4.110) 125 4.3.5.5 The Second Term on the Right Side of Eq. (4.110) 125 4.3.5.6 x Component of the Second Term on the Right Side of the Basic Equation (4.110) 126 4.3.5.7 The First Term of x Component in Eq. (4.133) 126 4.3.5.8 The Second Term of the x Component in Eq. (4.133) 126 4.3.5.9 The Third Term of the x Component in Eq. (4.133) 127 4.3.6 Discretization of the Auxiliary Equation 128 4.3.6.1 x Component in Eq. (4.144) 129 4.3.7 General Potential Equation in Elements 130 4.3.8 Example of the Analytical Model 132 4.3.9 Unnecessary Current Absorber Analysis 134 4.A Appendix 139 4.A.1 Appendix to Section 4.3.4 (1) 139 4.A.2 Appendix to Section 4.3.5 (1) 142 4.A.3 Appendix to Section 4.3.5 (2) 143 References 143 5 Fundamental EM-Wave Absorber Materials 145 5.1 Carbon Graphite 145 5.2 Ferrite 148 5.2.1 Soft Magnetic Material 148 5.2.2 Spinel-type Magnetic Oxide 149 5.2.2.1 Crystal Structure of Oxide 149 5.2.2.2 Crystal Structure of Ferrite 151 5.3 Hexagonal Ferrite 152 References 154 6 Theory of Special Mediums 155 6.1 Chiral Medium 156 6.1.1 Electromagnetic Fields in Chiral Medium 158 6.1.2 Electromagnetic-Field Reflection by Chiral Medium 160 6.2 Theory of Magnetized Ferrite 166 6.2.1 Foundation of Equation of Magnetization Motion 167 6.2.2 Tensor Susceptibility 170 6.2.2.1 Lossless Medium Case 170 6.2.2.2 Loss Medium Case 174 6.3 MW-Propagation of Circular Waveguide with Ferrite 179 6.3.1 Derivation of Fundamental Equations 179 6.3.2 Derivation of Electromagnetic-Field Components 182 6.3.3 Circular Waveguide with Ferrite 185 6.3.3.1 Ferrite Fully Filled Case 185 6.3.3.2 Ferrite Partially Filled Case 186 6.3.4 Coaxial Waveguide with Ferrite 188 6.4 Metamaterial 192 6.4.1 Metamaterial Outlines 192 6.4.2 Metamaterial Theories 195 6.4.2.1 Left-Handed and Right-Handed Systems 195 6.4.2.2 Conversion from Material to Transmission Line Concept 196 6.4.3 Negative Permittivity and Permeability 198 6.4.4 Negative Refractive Index Medium 202 6.4.5 Metamaterial as a Medium 204 6.4.6 Metamaterial Absorber 205 6.A Appendix 206 6.A.1 Appendix to Section 6.1.2 (1) 206 6.A.2 Appendix to Section 6.2.2 (1) 207 6.A.3 Appendix to Section 6.2.2 (2) 208 6.A.4 Appendix to Section 6.3.1 (1) 209 6.A.5 Appendix to Section 6.3.1 (2) 210 6.A.6 Appendix to Section 6.3.1 (3) 212 References 213 7 Measurement Methods on EM-Wave Absorbers 217 7.1 Material Constant Measurement Methods 217 7.1.1 Standing-Wave Method 218 7.1.1.1 Case of Using Waveguide 218 7.1.1.2 Method of Using Coaxial Waveguides 221 7.1.2 Cavity Resonator Method 225 7.1.2.1 Method of Micro-sample Insertion 225 7.1.2.2 Complex Permittivity Measurement 230 7.1.2.3 Complex Permeability Measurement 233 7.2 Measurement of EM-Wave Absorption Characteristics 235 7.2.1 Method of Using TEM Mode Transmission Line 235 7.2.1.1 Coaxial Waveguide Method 236 7.2.1.2 Strip Line Method 237 7.2.1.3 TEM Cell Method 238 7.2.2 Waveguide Method 239 7.2.3 Space Standing-Wave Method 240 7.A Appendix 242 7.A.1 Appendix to Section 7.1.2 (1) 242 7.A.2 Appendix to Section 7.1.2 (2) 243 7.A.3 Appendix to Section 7.1.2 (3) 243 7.A.4 Appendix to Section 7.1.2 (4) 244 References 244 8 Configuration Examples of the EM-wave Absorber 247 8.1 Quarter-wave-Type Absorber 247 8.2 Single-Layer-Type Absorber 252 8.2.1 Ferrite Absorber 252 8.3 Two-Layered Absorber 253 8.4 Applications as Building Material 255 8.4.1 TV Ghost Prevention Measures 255 8.4.2 Ferrite Core-Embedded PC Board 258 8.5 Low-Reflective Shield Building Materials 260 References 262 9 Absorber Characteristic Control by Equivalent Transformation Method of Material Constants 265 9.1 Basic Concepts and Means 265 9.2 Examples of ETMMC Absorbers 266 9.2.1 Microchip Integrated -type Absorber 266 9.2.2 Absorber with Small Holes 269 9.2.2.1 Effect of Square Hole Size 271 9.2.2.2 Effect of Adjacent Hole Space 272 9.2.2.3 Relation of Absorber Thickness and Hole Dimensions 272 9.2.3 Absorber with Square Conductive Elements 274 9.2.3.1 Effect of Conductor Dimensions 276 9.2.3.2 Input Admittance Characteristics 279 9.2.4 Absorber with Line-Shaped Conductive Elements 281 9.2.4.1 Lattice Type 282 9.2.4.2 Cross Type 283 9.2.4.3 Square Conductive Line Frame 284 9.2.4.4 Double-Layered PCLF Type 288 9.2.5 Absorber Based on Integrated Circuit Concept 291 9.2.5.1 Configuration of Absorber 291 9.2.5.2 Space Experiment Characteristic 295 References 296 10 Autonomous Controllable-Type Absorber 299 10.1 Autonomous Control-type Metamaterial 299 10.2 Configurations of the ACMM Absorber 301 10.3 The Main Point as the Technical Breakthrough 302 10.4 Characteristics as the EM-Wave Absorber 304 10.4.1 Complicated Wiring Problems 305 10.4.2 Controlling the Problem of Absorber Characteristics 305 10.4.3 Stability of Wave Absorption Characteristics 306 10.4.4 Oblique Incidence Characteristics 306 10.4.5 Controllability of Frequency Characteristic 308 10.4.6 Broadband Characteristic 308 10.5 Input Impedance Characteristic 311 10.6 Examples of Application Fields 313 References 314 Index 317

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Book SynopsisProvides an introduction to the Finite Difference Time Domain method and shows how Python code can be used to implement various simulations This book allows engineering students and practicing engineers to learn the finite-difference time-domain (FDTD) method and properly apply it toward their electromagnetic simulation projects. Each chapter contains a concise explanation of an essential concept and instruction on its implementation into computer code. Included projects increase in complexity, ranging from simulations in free space to propagation in dispersive media. This third edition utilizes the Python programming language, which is becoming the preferred computer language for the engineering and scientific community. Electromagnetic Simulation Using the FDTD Method with Python, Third Edition is written with the goal of enabling readers to learn the FDTD method in a manageable amount of time. Some basic applications of signal processing theory are expTable of ContentsAbout the Authors ix Preface xi Guide to the Book xiii 1 One-Dimensional Simulation with the FDTD Method 1 1.1 One-Dimensional Free-Space Simulation 1 1.2 Stability and the FDTD Method 5 1.3 The Absorbing Boundary Condition in One Dimension 6 1.4 Propagation in a Dielectric Medium 7 1.5 Simulating Different Sources 9 1.6 Determining Cell Size 10 1.7 Propagation in a Lossy Dielectric Medium 11 1.A Appendix 14 References 15 2 More on One-Dimensional Simulation 25 2.1 Reformulation Using the Flux Density 25 2.2 Calculating the Frequency Domain Output 28 2.3 Frequency-Dependent Media 31 2.3.1 Auxiliary Differential Equation Method 35 2.4 Formulation Using Z Transforms 37 2.4.1 Simulation of Unmagnetized Plasma 38 2.5 Formulating a Lorentz Medium 41 2.5.1 Simulation of Human Muscle Tissue 45 References 47 3 Two-Dimensional Simulation 59 3.1 FDTD in Two Dimensions 59 3.2 The Perfectly Matched Layer (PML) 62 3.3 Total/Scattered Field Formulation 72 3.3.1 A Plane Wave Impinging on a Dielectric Cylinder 74 3.3.2 Fourier Analysis 76 References 78 4 Three-Dimensional Simulation 99 4.1 Free-Space Simulation 99 4.2 The PML in Three Dimensions 103 4.3 Total/Scattered Field Formulation in Three Dimensions 105 4.3.1 A Plane Wave Impinging on a Dielectric Sphere 107 References 111 5 Advanced Python Features 129 5.1 Classes 129 5.1.1 Named Tuples 131 5.2 Program Structure 133 5.2.1 Code Repetition 133 5.2.2 Overall Structure 135 5.3 Interactive Widgets 136 6 Deep Regional Hyperthermia Treatment Planning 159 6.1 Introduction 160 6.2 FDTD Simulation of the Sigma 60 161 6.2.1 Simulation of the Applicator 161 6.2.2 Simulation of the Patient Model 163 6.3 Simulation Procedure 165 6.4 Discussion 168 References 170 Appendix A The Z Transform 171 Appendix B Analytic Solution to Calculating the Electric Field 183 Index 195

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Book SynopsisPresents the latest developments in switchgear and DC/DC converters for DC grids, and includes substantially expanded material on MMC HVDC This newly updated edition covers all HVDC transmission technologies including Line Commutated Converter (LCC) HVDC; Voltage Source Converter (VSC) HVDC, and the latest VSC HVDC based on Modular Multilevel Converters (MMC), as well as the principles of building DC transmission grids. Featuring new material throughout, High Voltage Direct Current Transmission: Converters, Systems and DC Grids, 2nd Edition offers several new chapters/sections including one on the newest MMC converters. It also provides extended coverage of switchgear, DC grid protection and DC/DC converters following the latest developments on the market and in research projects. All three HVDC technologies are studied in a wide range of topics, including: the basic converter operating principles; calculation of losses; system modelling, including dynamiTable of ContentsPreface xvii Part I HVDC with Current Source Converters 1 1 Introduction to Line Commutated HVDC 3 1.1 HVDC Applications 3 1.2 Line Commutated HVDC Components 4 1.3 DC Cables and Overhead Lines 7 1.3.1 Introduction 7 1.3.2 Mass-impregnated Cables 7 1.3.3 Low-pressure Oil-filled Cables 7 1.3.4 Extruded Cross-linked Polyethylene Cables 8 1.4 LCC HVDC Topologies 8 1.5 Losses in LCC HVDC Systems 10 1.6 Conversion of AC Lines to DC 10 1.7 Ultra High Voltage HVDC 12 2 Thyristors 13 2.1 Operating Characteristics 13 2.2 Switching Characteristics 14 2.3 Losses in HVDCThyristors 18 2.4 Valve Structure andThyristor Snubbers 20 2.5 Thyristor Rating Selection and Overload Capability 22 3 Six-pulse Diode and Thyristor Converter 25 3.1 Three-phase Uncontrolled Bridge 25 3.2 Three-phase Thyristor Rectifier 27 3.3 Analysis of Commutation Overlap in a Thyristor Converter 28 3.4 Active and Reactive Power in a Three-phase Thyristor Converter 32 3.5 Inverter Operation 33 4 HVDC Rectifier Station Modelling, Control and Synchronisation with AC System 37 4.1 HVDC Rectifier Controller 37 4.2 Phase-locked Loop 38 4.3 Master-level HVDC Control 40 5 HVDC Inverter Station Modelling and Control 43 5.1 Inverter Controller 43 5.1.1 Control Structure 43 5.1.2 Extinction Angle Control 43 5.1.3 DC Voltage Control 44 5.1.4 DC Current Control at Inverter 45 5.2 Commutation Failure 45 6 HVDC System V–I Diagrams and Operating Modes 49 6.1 HVDC Equivalent Circuit 49 6.2 HVDC V–I Operating Diagram 49 6.3 HVDC Power Reversal 51 7 HVDC Analytical Modelling and Stability 57 7.1 Introduction to Converter and HVDC Modelling 57 7.1.1 Detailed Switching Transients Modelling 57 7.1.2 Modelling with Switchings 57 7.1.3 Analytical Dynamic Modelling of Converters 58 7.1.4 Phasor Modelling 58 7.2 HVDC Analytical Model 58 7.3 CIGRE HVDC Benchmark Model 60 7.4 Converter Modelling, Linearisation, and Gain Scheduling 60 7.5 AC System Modelling for HVDC Stability Studies 64 7.6 LCC Converter Transformer Model 67 7.7 DC System Including DC Cable 68 7.7.1 DC Cable/Line Modelling as a Single 𝜋 Section 68 7.7.2 Controller Model 69 7.7.3 Complete DC System Model 69 7.8 Accurate DC Cable Modelling 70 7.8.1 Wideband Cable Model 70 7.8.2 Cable Higher-order Analytical Model in State Space 72 7.9 HVDC–HVAC System Model 76 7.10 Analytical Dynamic Model Verification 77 7.11 Basic HVDC Dynamic Analysis 77 7.11.1 Eigenvalue Analysis 77 7.11.2 Eigenvalue Sensitivity Study 77 7.11.3 Influence of PLL Gains 79 7.12 HVDC Second Harmonic Instability 80 7.13 100 Hz Oscillations on the DC Side 82 8 HVDC Phasor Modelling and Interactions with AC System 83 8.1 Converter and DC System Phasor Model 83 8.2 Phasor AC System Model and Interaction with DC System 84 8.3 Inverter AC Voltage and Power Profile as DC Current is Increasing 86 8.4 Influence of Converter Extinction Angle 88 8.5 Influence of Shunt Reactive Power Compensation 88 8.6 Influence of Load at the Converter Terminals 88 8.7 Influence of Operating Mode (DC Voltage Control Mode) 88 8.8 Rectifier Operating Mode 90 9 HVDC Operation with Weak AC Systems 95 9.1 Introduction 95 9.2 Short Circuit Ratio and Equivalent Short Circuit Ratio 95 9.2.1 Definition of SCR and ESCR 95 9.2.2 Operating Difficulties with Low SCR Systems 98 9.3 Background on Power Transfer Between Two AC Systems 99 9.4 Phasor Study of Converter Interactions with Weak AC Systems 101 9.5 System Dynamics (Small Signal Stability) with Low SCR 101 9.6 Control and Main Circuit Solutions for Weak AC Grids 102 9.7 LCC HVDC with SVC 103 9.8 Capacitor Commutated Converters for HVDC 104 9.9 AC System with Low Inertia 106 10 Fault Management and HVDC System Protection 111 10.1 Introduction 111 10.2 DC Line Faults 111 10.3 AC System Faults 113 10.3.1 Rectifier AC Faults 113 10.3.2 Inverter AC Faults 114 10.4 Internal Faults 115 10.5 System Reconfiguration for Permanent Faults 116 10.6 Overvoltage Protection 119 11 LCC HVDC System Harmonics 121 11.1 Harmonic Performance Criteria 121 11.2 Harmonic Limits 122 11.3 Thyristor Converter Harmonics 123 11.4 Harmonic Filters 124 11.4.1 Introduction 124 11.4.2 Tuned Filters 126 11.4.3 Damped Filters 128 11.5 Non-characteristic Harmonic Reduction Using HVDC Controls 132 Bibliography Part I: Line Commutated Converter HVDC 133 Part II HVDC with Voltage Source Converters 137 12 VSC HVDC Applications and Topologies, Performance and Cost Comparison with LCC HVDC 139 12.1 Application of Voltage Source Converters in HVDC 139 12.2 Comparison with LCC HVDC 141 12.3 HVDC Technology Landscape 142 12.4 Overhead and Subsea/Underground VSC HVDC Transmission 143 12.5 DC Cable Types with VSC HVDC 147 12.6 Monopolar and Bipolar VSC HVDC Systems 147 12.7 VSC HVDC Converter Topologies 148 12.7.1 HVDC with Two-level Voltage Source Converter 148 12.7.2 HVDC with Neutral Point Clamped Converter 150 12.7.3 MMC VSC HVDC Transmission Systems 151 12.7.4 MMC HVDC Based on FB Topology 153 12.8 VSC HVDC Station Components 155 12.8.1 AC CB 155 12.8.2 VSC Converter Transformer 155 12.8.3 VSC Converter AC Harmonic Filters 156 12.8.4 DC Capacitors 156 12.8.5 DC Filter 157 12.8.6 Two-level VSC HVDC Valves 158 12.8.7 MMC Valves and Cells 159 12.9 AC Inductors 160 12.10 DC Inductors 161 13 IGBT Switches and VSC Converter Losses 165 13.1 Introduction to IGBT and IGCT 165 13.2 General VSC Converter Switch Requirements 166 13.3 IGBT Technology 166 13.3.1 IGBT Operating Characteristics 167 13.3.2 Fast Recovery Anti-parallel Diode 171 13.4 High Power IGBT Devices 171 13.5 IEGT Technology 172 13.6 Losses Calculation 173 13.6.1 Conduction Loss Modelling 173 13.6.2 Switching Loss Modelling 174 13.7 Balancing Challenges in Two-level IGBT Valves 178 13.8 Snubbers Circuits 179 14 Single-phase and Three-phase Two-level VSC Converters 181 14.1 Introduction 181 14.2 Single-phase VSC 181 14.3 Three-phase VSC 184 14.4 Square-wave, Six-pulse Operation 185 14.4.1 180∘ Conduction 185 14.4.2 120∘ Conduction 188 15 Two-level PWM VSC Converters 193 15.1 Introduction 193 15.2 PWM Modulation 193 15.2.1 Multipulse with Constant Pulse Width 193 15.2.2 Modulating Signal 194 15.3 Sinusoidal Pulse Width Modulation 195 15.4 Third Harmonic Injection 197 15.5 Selective Harmonic Elimination Modulation 198 15.6 Converter Losses for Two-level SPWMVSC 198 15.7 Harmonics with PWM 201 15.8 Comparison of PWM Modulation Techniques 203 16 Multilevel VSC Converters in HVDC Applications 205 16.1 Introduction 205 16.2 Modulation Techniques for Multilevel Converters 207 16.3 Neutral Point Clamped Multilevel Converter 208 16.4 Half Bridge MMC 210 16.4.1 Operating Principles of Half-bridge MMC 210 16.4.2 Capacitor Voltage Balancing 212 16.4.3 MMC Cell Capacitance 214 16.4.4 MMC Arm Inductance 215 16.4.5 MMC with Fundamental Frequency Modulation 218 16.4.6 MMC with PWM Modulation 218 16.5 Full Bridge MMC 222 16.5.1 Operating Principles 222 16.6 Comparison of Multilevel Topologies 224 17 Two-level VSC HVDC Modelling, Control, and Dynamics 227 17.1 PWM Two-level Converter Average Model 227 17.1.1 Converter Model in an ABC Frame 227 17.1.2 Converter Model in the ABC Frame Including Blocked State 229 17.2 Two-level PWM Converter Model in DQ Frame 230 17.3 VSC Converter Transformer Model 231 17.4 Two-level VSC Converter and AC Grid Model in the ABC Frame 231 17.5 Two-level VSC Converter and AC Grid Model in a DQ Rotating Coordinate Frame 232 17.6 VSC Converter Control Principles 233 17.7 The Inner Current Controller Design 234 17.7.1 Control Strategy 234 17.7.2 Decoupling Control 234 17.7.3 Current Feedback Control 235 17.7.4 Controller Gains 236 17.8 Outer Controller Design 237 17.8.1 AC Voltage Control 237 17.8.2 Power Control 238 17.8.3 DC Voltage Control 239 17.8.4 AC Grid Support 240 17.9 Complete Two-level VSC Converter Controller 240 17.10 Small Signal Linearised VSC HVDC Model 242 17.11 Small Signal Dynamic Studies 242 17.11.1 Dynamics of Weak AC Systems 242 17.11.2 Impact of PLL Gains on Robustness 244 18 Two-level VSC HVDC Phasor-domain Interaction with AC Systems and PQ Operating Diagrams 247 18.1 Power Exchange Between Two AC Voltage Sources 247 18.2 Converter Phasor Model and Power Exchange with an AC System 249 18.3 Phasor Study of VSC Converter Interaction with AC System 252 18.3.1 Test System 252 18.3.2 Assumptions and Converter Limits 252 18.3.3 Case 1: Converter Voltages Are Known 253 18.3.4 Case 2: Converter Currents are Known 254 18.3.5 Case 3: PCC Voltage is Known 254 18.4 Operating Limits 254 18.5 Design Point Selection 255 18.6 Influence of AC System Strength 258 18.7 Influence of AC System Impedance Angle (Xs/Rs) 258 18.8 Influence of Transformer Reactance 258 18.9 Influence of Converter Control Modes 262 18.10 Operation with Very Weak AC Systems 262 19 Half Bridge MMC: Dimensioning, Modelling, Control, and Interaction with AC System 269 19.1 Basic Equations and Steady-state Control 269 19.2 Steady-state Dimensioning 272 19.3 Half Bridge MMC Non-linear Average Dynamic Model 275 19.4 Non-linear Average Value Model Including Blocked State 276 19.5 HB MMC HVDC Start-up and Charging MMC Cells 278 19.6 HB MMC Dynamic DQ Frame Model and Phasor Model 279 19.6.1 Assumptions 279 19.6.2 Zero Sequence Model 282 19.6.3 Fundamental Frequency Model in DQ Frame 282 19.6.4 Second Harmonic Model in the D2Q2 Coordinate Frame 284 19.7 Second Harmonic of Differential Current 286 19.8 Complete MMC Converter DQ Model in Matrix Form 286 19.9 Second-harmonic Circulating Current Suppression Controller 287 19.10 Simplified DQ Frame Model with Circulating Current Controller 290 19.11 Phasor Model of MMC with Circulating Current Suppression Controller 295 19.12 Simplified Dynamic MMC Model Using Equivalent Series Capacitor CMMC 296 19.13 Full Dynamic Analytical HB MMC Model 300 19.14 HB MMC Controller and Arm Voltage Control 301 19.15 MMC Total Series Reactance and Comparison with Two-level VSC 304 19.16 MMC Interaction with AC System and PQ Operating Diagrams 306 20 Full Bridge MMC Converter: Dimensioning, Modelling, and Control 309 20.1 FB MMC Arm Voltage Range 309 20.2 Full Bridge MMC Converter Non-linear Average Model 309 20.3 FB MMC Non-linear Average Model Including Blocked State 310 20.4 Full Bridge MMC Cell Charging 312 20.5 Hybrid MMC Design 313 20.5.1 Operation Under Low DC Voltage 313 20.5.2 Overmodulation Requirements 314 20.5.3 Cell Voltage Balancing Under Low DC Voltage 315 20.5.4 Optimal Design of Full Bridge MMC 315 20.6 Full Bridge MMC DC Voltage Variation Using a Detailed Model 318 20.7 FB MMC Analytical Dynamic DQ Model 320 20.7.1 Zero Sequence Model 320 20.7.2 Fundamental Frequency Model 321 20.8 Simplified FB MMC Model 321 20.9 FB MMC Converter Controller 322 21 MMC Converter Under Unbalanced Conditions 325 21.1 Introduction 325 21.2 MMC Balancing Controller Structure 326 21.3 Balancing Between Phases (Horizontal Balancing) 326 21.4 Balancing Between Arms (Vertical Balancing) 328 21.5 Simulation of Balancing Controls 330 21.6 Operation with Unbalanced AC Grid 332 21.6.1 Detecting Positive and Negative Sequence Components 332 21.6.2 Controlling Grid Current Sequence Components with MMC 336 22 VSC HVDC Under AC and DC Fault Conditions 339 22.1 Introduction 339 22.2 Faults on the AC System 339 22.3 DC Faults with Two-level VSC 340 22.4 Influence of DC Capacitors 345 22.5 VSC Converter Modelling Under DC Faults and VSC Diode Bridge 345 22.5.1 VSC Diode Bridge Average Model 345 22.5.2 Phasor Model of VSC Diode Bridge Under DC Fault 348 22.5.3 Simple Expression for VSC Diode Bridge Steady-state Fault Current Magnitude 351 22.6 VSC Converter Mode Transitions as DC Voltage Reduces 352 22.7 DC Faults with Half Bridge Modular Multilevel Converter 354 22.8 Full Bridge MMC Under DC Faults 356 23 VSC HVDC Application For AC Grid Support and Operation with Passive AC Systems 359 23.1 VSC HVDC High Level Controls and AC Grid Support 359 23.2 HVDC Embedded Inside an AC Grid 360 23.3 HVDC Connecting Two Separate AC Grids 361 23.4 HVDC in Parallel with AC 361 23.5 Operation with a Passive AC System and Black Start Capability 362 23.6 VSC HVDC Operation with Offshore Wind Farms 362 23.7 VSC HVDC Supplying Power Offshore and Driving a MW-Size Variable Speed Motor 365 Bibliography Part II: Voltage Source Converter HVDC 366 Part III DC Transmission Grids 371 24 Introduction to DC Grids 373 24.1 DC versus AC Transmission 373 24.2 Terminology 374 24.3 DC Grid Planning, Topology, and Power Transfer Security 375 24.4 Technical Challenges 376 24.5 DC Grid Building by Multiple Manufacturers – Interoperability 376 24.6 Economic Aspects 377 25 DC Grids with Line Commutated Converters 379 25.1 Multiterminal LCC HVDC 379 25.2 Italy–Corsica–Sardinia Multiterminal HVDC Link 380 25.3 Connecting the LCC Converter to a DC Grid 381 25.3.1 Power Reversal 381 25.3.2 DC Faults 382 25.3.3 AC Faults 383 25.4 Control of LCC Converters in DC Grids 383 25.5 Control of LCC DC Grids Through DC Voltage Droop Feedback 384 25.6 Managing LCC DC Grid Faults 385 25.7 Reactive Power Issues 387 25.8 Employing LCC Converter Stations in Established DC Grids 387 26 DC Grids with Voltage Source Converters and Power Flow Model 389 26.1 Connecting a VSC Converter to a DC Grid 389 26.1.1 Power Reversal and Control 389 26.1.2 DC Faults 389 26.1.3 AC Faults 389 26.2 Multiterminal VSC HVDC Operating in China 390 26.3 DC Grid Power Flow Model 390 26.4 DC Grid Power Flow Under DC Faults 395 27 DC Grid Control 399 27.1 Introduction 399 27.2 Fast Local VSC Converter Control in DC Grids 399 27.3 DC Grid Dispatcher with Remote Communication 401 27.4 Primary, Secondary, and Tertiary DC Grid Control 402 27.5 DC Voltage Droop Control for VSC Converters in DC Grids 403 27.6 Three-level Control for VSC Converters with Dispatcher Droop 405 27.6.1 Three-level Control for VSC Converters 405 27.6.2 Dispatcher Controller 406 27.7 Power Flow Algorithm When DC Powers are Regulated 406 27.8 Power Flow and Control Study of CIGRE DC Grid Test System 411 27.8.1 CIGRE DC Grid Test System 411 27.8.2 Power Flow After Outage of the Largest Terminal 413 28 DC Circuit Breakers 417 28.1 Introduction 417 28.2 Challenges with DC Circuit Opening 417 28.2.1 DC Current Commutation 417 28.2.2 DC Current Suppression and Dissipation of Energy 418 28.3 DC CB Operating Principles and a Simple Model 418 28.4 DC CB Performance Requirements 420 28.4.1 Opening Speed 420 28.4.2 DC CB Ratings and Series Inductors 420 28.4.3 Bidirectional Current Interruption 421 28.4.4 Multiple Open/close Operations in a Short Time 421 28.4.5 Losses, Size, and Weight 421 28.4.6 Standardisation 421 28.5 Practical HV DC CBs 422 28.6 Mechanical DC CB 422 28.6.1 Operating Principles and Construction 422 28.6.2 Mathematical Model and Design Principles 424 28.6.3 Test Circuit for DC CB Simulation 426 28.6.4 Simulation of DC Fault Clearing 427 28.6.5 Negative Fault Current Interruption 427 28.6.6 Multiple Open/close Operations in a Short Time 428 28.6.7 Mechanical DC CB for High Voltages 429 28.7 Semiconductor-based DC CB 430 28.7.1 Topology and Design 430 28.7.2 Self-protection of Semiconductor Valves 432 28.7.3 Simulation of Fault Current Interruption 432 28.8 Hybrid DC CB 434 28.8.1 Topology and Design 434 28.8.2 Hybrid DC CB for High Voltages 435 28.8.3 Simulation of Fault Current Interruption 436 28.8.4 Bidirectional Operation 437 28.8.5 Fault Current Limiting 438 29 DC Grid Fault Management and Protection System 441 29.1 Introduction 441 29.2 Fault Current Components in DC Grids 442 29.3 DC System Protection Coordination with AC System Protection 444 29.4 DC Grid Protection System Development 445 29.5 DC Grid Protection System Based on Local Measurements 446 29.5.1 Protection Based on DC Current and Current Differential 446 29.5.2 Rate of Change of Voltage Protection 447 29.6 Blocking MMC Converters Under DC Faults 450 29.7 Differential DC Grid Protection Strategy 452 29.8 Selective Protection for Star-topology DC Grids 455 29.9 DC Grids with DC Fault-tolerant VSC Converters 456 29.9.1 Grid Topology and Strategy 456 29.9.2 VSC Converter with Increased AC Coupling Reactors 457 29.9.3 LCL VSC Converter 459 29.9.4 VSC Converter with Fault Current Limiter 461 29.10 DC Grids with Full Bridge MMC Converters 461 30 High Power DC/DC Converters and DC Power Flow Controlling Devices 465 30.1 Introduction 465 30.2 Power Flow Control Using Series Resistors 466 30.3 Low-stepping-ratio DC/DC Converters (DC Choppers) 469 30.3.1 Converter Topology 469 30.3.2 Converter Controller 470 30.3.3 DC/DC Chopper Average Value Model 471 30.3.4 H-Bridge DC/DC Chopper 473 30.4 Non-isolated MMC-based DC/DC Converter (M2DC) 473 30.4.1 Introduction 473 30.4.2 Modelling and Design 474 30.4.3 Design Example and Comparison with MMC AC/DC 477 30.4.4 Controller Design 479 30.4.5 Simulation Responses 480 30.5 DC/DC Converters with DC Polarity Reversal 484 30.6 High-stepping-ratio Isolated DC/DC Converter (Dual Active Bridge DC/DC) 484 30.6.1 Introduction 484 30.6.2 Modelling and Control 486 30.6.3 Simulated Responses 487 30.7 High-stepping-ratio LCL DC/DC Converter 490 30.8 Building DC Grids with DC/DC Converters 492 30.9 DC Hubs 495 30.10 Developing DC Grids Using DC Hubs 496 30.11 North Sea DC Grid Topologies 496 Bibliography Part III: DC Transmission Grids 500 Appendix A Variable Notations 503 Appendix B Analytical Background to Rotating DQ Frame 505 B.1 Transforming AC Variables to a DQ Frame 505 B.2 Derivative of an Oscillating Signal in a DQ Frame 507 B.3 Transforming an AC System Dynamic Equation to a DQ Frame 507 B.4 Transforming an n-Order State Space AC System Model to a DQ Frame 509 B.5 Static (Steady-state) Modeling in a Rotating DQ Coordinate Frame 510 B.6 Representing the Product of Oscillating Signals in a DQ Frame 511 B.7 Representing Power in DQ Frame 512 Appendix C System Modeling Using Complex Numbers and Phasors 515 Appendix D Simulink Examples 517 D.1 Chapter 3 Examples 517 D.2 Chapter 5 Examples 517 D.3 Chapter 6 Examples 519 D.4 Chapter 8 Examples 521 D.5 Chapter 14 Examples 523 D.6 Chapter 16 Examples 524 D.7 Chapter 17 Examples 527 Index 535

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Book SynopsisThe definitive guide to problem-solving in the design of communications systems In Algorithms for Communications Systems and their Applications, 2nd Edition, authors Benvenuto, Cherubini, and Tomasin have delivered the ultimate and practical guide to applying algorithms in communications systems. Written for researchers and professionals in the areas of digital communications, signal processing, and computer engineering, Algorithms for Communications Systems presents algorithmic and computational procedures within communications systems that overcome a wide range of problems facing system designers. New material in this fully updated edition includes: MIMO systems (Space-time block coding/Spatial multiplexing /Beamforming and interference management/Channel Estimation) OFDM and SC-FDMA (Synchronization/Resource allocation (bit and power loading)/Filtered OFDM) Improved radio channel model (Doppler and shadowing/mmWave) PTable of ContentsPreface 3 Acknowledgments 3 1 Elements of signal theory 7 1.1 Continuous-time linear systems 7 1.2 Discrete-time linear systems 10 Discrete Fourier transform 13 The DFT operator 14 Circular and linear convolution via DFT 15 Convolution by the overlap-save method 17 IIR and FIR filters 19 1.3 Signal bandwidth 22 The sampling theorem 24 Heaviside conditions for the absence of signal distortion 26 1.4 Passband signals and systems 26 Complex representation 26 Relation between a signal and its complex representation 28 Baseband equivalent of a transformation 36 Envelope and instantaneous phase and frequency 37 1.5 Second-order analysis of random processes 38 1.5.1 Correlation 39 Properties of the autocorrelation function 40 1.5.2 Power spectral density 40 Spectral lines in the PSD 40 Cross power spectral density 42 Properties of the PSD 42 PSD through filtering 43 1.5.3 PSD of discrete-time random processes 43 Spectral lines in the PSD 44 PSD through filtering 45 Minimum-phase spectral factorization 46 1.5.4 PSD of passband processes 47 PSD of in-phase and quadrature components 47 Cyclostationary processes 50 1.6 The autocorrelation matrix 56 Properties 56 Eigenvalues 56 Other properties 57 Eigenvalue analysis for Hermitian matrices 58 1.7 Examples of random processes 60 1.8 Matched filter 66 White noise case 68 1.9 Ergodic random processes 69 1.9.1 Mean value estimators 71 Rectangular window 74 Exponential filter 74 General window 75 1.9.2 Correlation estimators 75 Unbiased estimate 76 Biased estimate 76 1.9.3 Power spectral density estimators 77 Periodogram or instantaneous spectrum 77 Welch periodogram 78 Blackman and Tukey correlogram 79 Windowing and window closing 79 1.10 Parametric models of random processes 82 ARMA 82 MA 84 AR 84 Spectral factorization of AR models 87 Whitening filter 87 Relation between ARMA, MA, and AR models 87 1.10.1 Autocorrelation of AR processes 89 1.10.2 Spectral estimation of an AR process 91 Some useful relations 92 AR model of sinusoidal processes 94 1.11 Guide to the bibliography 95 Bibliography 95 Appendixes 97 1.A Multirate systems 98 1.A.1 Fundamentals 98 1.A.2 Decimation 100 1.A.3 Interpolation 102 1.A.4 Decimator filter 104 1.A.5 Interpolator filter 105 1.A.6 Rate conversion 108 1.A.7 Time interpolation 109 Linear interpolation 110 Quadratic interpolation 112 1.A.8 The noble identities 112 1.A.9 The polyphase representation 113 Efficient implementations 114 1.B Generation of a complex Gaussian noise 121 1.C Pseudo-noise sequences 122 Maximal-length 122 CAZAC 124 Gold 125 2 The Wiener filter 129 2.1 The Wiener filter 129 Matrix formulation 130 Optimum filter design 132 The principle of orthogonality 134 Expression of the minimum mean-square error 135 Characterization of the cost function surface 136 The Wiener filter in the z-domain 137 2.2 Linear prediction 140 Forward linear predictor 141 Optimum predictor coefficients 141 Forward prediction error filter 142 Relation between linear prediction and AR models 143 First and second order solutions 144 2.3 The least squares method 145 Data windowing 146 Matrix formulation 146 Correlation matrix 147 Determination of the optimum filter coefficients 147 2.3.1 The principle of orthogonality 148 Minimum cost function 149 The normal equation using the data matrix 149 Geometric interpretation: the projection operator 150 2.3.2 Solutions to the LS problem 151 Singular value decomposition 152 Minimum norm solution 154 2.4 The estimation problem 155 Estimation of a random variable 155 MMSE estimation 155 Extension to multiple observations 157 Linear MMSE estimation of a random variable 158 Linear MMSE estimation of a random vector 158 2.4.1 The Cramér-Rao lower bound 160 Extension to vector parameter 162 2.5 Examples of application 164 2.5.1 Identification of a linear discrete-time system 164 2.5.2 Identification of a continuous-time system 166 2.5.3 Cancellation of an interfering signal 169 2.5.4 Cancellation of a sinusoidal interferer with known frequency 170 2.5.5 Echo cancellation in digital subscriber loops 171 2.5.6 Cancellation of a periodic interferer 172 Bibliography 173 Appendixes 174 2.A The Levinson-Durbin algorithm 175 Lattice filters 176 The Delsarte-Genin algorithm 177 3 Adaptive transversal filters 179 3.1 The MSE design criterion 180 3.1.1 The steepest descent or gradient algorithm 181 Stability 181 Conditions for convergence 183 Adaptation gain 184 Transient behaviour of the MSE 185 3.1.2 The least mean square algorithm 186 Implementation 187 Computational complexity 188 Conditions for convergence 188 3.1.3 Convergence analysis of the LMS algorithm 190 Convergence of the mean 191 Convergence in the mean-square sense: real scalar case 192 Convergence in the mean-square sense: general case 193 Fundamental results 196 Observations 197 Final remarks 199 3.1.4 Other versions of the LMS algorithm 199 Leaky LMS 199 Sign algorithm 200 Normalized LMS 200 Variable adaptation gain 201 3.1.5 Example of application: the predictor 202 3.2 The recursive least squares algorithm 208 Normal equation 209 Derivation 210 Initialization 212 Recursive form of the minimum cost function 212 Convergence 214 Computational complexity 214 Example of application: the predictor 215 3.3 Fast recursive algorithms 215 3.3.1 Comparison of the various algorithms 216 3.4 Examples of application 216 3.4.1 Identification of a linear discrete-time system 217 Finite alphabet case 219 3.4.2 Cancellation of a sinusoidal interferer with known frequency 220 Bibliography 221 4 Transmission channels 223 4.1 Radio channel 223 4.1.1 Propagation and used frequencies in radio transmission 224 Basic propagation mechanisms 224 Frequency ranges 224 4.1.2 Analog front-end architectures 226 Radiation masks 226 Conventional superheterodyne receiver 227 Alternative architectures 227 Direct conversion receiver 228 Single conversion to low-IF 229 Double conversion and wideband IF 229 4.1.3 General channel model 230 High power amplifier 230 Transmission medium 233 Additive noise 234 Phase noise 234 4.1.4 Narrowband radio channel model 235 Equivalent circuit at the receiver 237 Multipath 238 Path loss as a function of distance 240 4.1.5 Fading effects in propagation models 243 Macroscopic fading or shadowing 243 Microscopic fading 245 4.1.6 Doppler shift 245 4.1.7 Wideband channel model 247 Multipath channel parameters 249 Statistical description of fading channels 250 4.1.8 Channel statistics 252 Power delay profile 252 Coherence bandwidth 253 Doppler spectrum 254 Coherence time 255 Doppler spectrum models 256 Power angular spectrum 256 Coherence distance 256 On fading 257 4.1.9 Discrete-time model for fading channels 258 Generation of a process with a preassigned spectrum 259 4.1.10 Discrete-space model of shadowing 261 4.1.11 Multiantenna systems 264 Discrete-time model 266 4.2 Telephone channel 268 Distortion 270 Noise sources 270 Echo 270 Appendixes 272 4.A Discrete-time NB model for mmWave channels 273 Angular domain representation 273 Bibliography 274 5 Vector quantization 277 5.1 Basic concept 277 5.2 Characterization of VQ 278 Parameters determining VQ performance 278 Comparison between VQ and scalar quantization 280 5.3 Optimum quantization 281 Generalized Lloyd algorithm 282 5.4 The Linde, Buzo, and Gray algorithm 284 Choice of the initial codebook 285 Splitting procedure 286 Selection of the training sequence 287 5.4.1 k-means clustering 288 5.5 Variants of VQ 288 Tree search VQ 288 Multistage VQ 289 Product code VQ 291 5.6 VQ of channel state information 292 MISO channel quantization 292 Channel feedback with feedforward information 294 5.7 Principal component analysis 295 5.7.1 PCA and k-means clustering 297 Bibliography 299 6 Digital transmission model and channel capacity 301 6.1 Digital transmission model 301 6.2 Detection 305 6.2.1 Optimum detection 306 ML 307 MAP 307 6.2.2 Soft detection 309 LLRs associated to bits of BMAP 309 Simplified expressions 312 6.2.3 Receiver strategies 314 6.3 Relevant parameters of the digital transmission model 314 Relations among parameters 315 6.4 Error probability 317 6.5 Capacity 320 6.5.1 Discrete-time AWGN channel 321 6.5.2 SISO narrowband AWGN channel 322 6.5.3 SISO dispersive AGN channel 322 6.5.4 MIMO discrete-time NB AWGN channel 325 6.6 Achievable rates of modulations in AWGN channels 326 6.6.1 Rate as a function of the SNR per dimension 327 6.6.2 Coding strategies depending on the signal-to-noise ratio 329 Coding gain 330 6.6.3 Achievable rate of an AWGN channel using PAM 331 Bibliography 333 Appendixes 334 6.A Gray labelling 335 6.B The Gaussian distribution and Marcum functions 336 6.B.1 The Q function 336 6.B.2 Marcum function 338 7 Single-carrier modulation 341 7.1 Signals and systems 341 7.1.1 Baseband digital transmission (PAM) 341 Modulator 342 Transmission channel 343 Receiver 343 Power spectral density 344 7.1.2 Passband digital transmission (QAM) 346 Modulator 346 Power spectral density 347 Three equivalent representations of the modulator 348 Coherent receiver 349 7.1.3 Baseband equivalent model of a QAM system 349 Signal analysis 349 7.1.4 Characterization of system elements 353 Transmitter 353 Transmission channel 354 Receiver 355 7.2 Intersymbol interference 356 Discrete-time equivalent system 356 Nyquist pulses 357 Eye diagram 361 7.3 Performance analysis 365 Signal-to-noise ratio 365 Symbol error probability in the absence of ISI 366 Matched filter receiver 367 7.4 Channel equalization 367 7.4.1 Zero-forcing equalizer 367 7.4.2 Linear equalizer 368 Optimum receiver in the presence of noise and ISI 369 Alternative derivation of the IIR equalizer 370 Signal-to-noise ratio at detector 374 7.4.3 LE with a finite number of coefficients 375 Adaptive LE 376 Fractionally spaced equalizer 378 7.4.4 Decision feedback equalizer 381 Design of a DFE with a finite number of coefficients 384 Design of a fractionally spaced DFE 387 Signal-to-noise ratio at the decision point 389 Remarks 390 7.4.5 Frequency domain equalization 390 DFE with data frame using a unique word 390 7.4.6 LE-ZF 394 7.4.7 DFE-ZF with IIR filters 394 DFE-ZF as noise predictor 400 DFE as ISI and noise predictor 400 7.4.8 Benchmark performance of LE-ZF and DFE-ZF 402 Comparison 402 Performance for two channel models 403 7.4.9 Passband equalizers 404 Passband receiver structure 405 Optimization of equalizer coefficients and carrier phase offset 407 Adaptive method 408 7.5 Optimum methods for data detection 410 7.5.1 Maximum-likelihood sequence detection 412 Lower bound to error probability using MLSD 413 The Viterbi algorithm 414 Computational complexity of the VA 419 7.5.2 Maximum a posteriori probability detector 419 Statistical description of a sequential machine 420 The forward-backward algorithm 421 Scaling 425 The log likelihood function and the Max-Log-MAP criterion 426 LLRs associated to bits of BMAP 427 Relation between Max-Log-MAP and Log-MAP 428 7.5.3 Optimum receivers 428 7.5.4 The Ungerboeck’s formulation of MLSD 430 7.5.5 Error probability achieved by MLSD 433 Computation of the minimum distance 437 7.5.6 The reduced-state sequence detection 441 Trellis diagram 442 The RSSE algorithm 444 Further simplification: DFSE 446 7.6 Numerical results obtained by simulations 447 QPSK over a minimum-phase channel 447 QPSK over a non minimum phase channel 448 8-PSK over a minimum phase channel 449 8-PSK over a non minimum phase channel 449 7.7 Precoding for dispersive channels 451 7.7.1 Tomlinson-Harashima precoding 452 7.7.2 Flexible precoding 454 7.8 Channel estimation 456 7.8.1 The correlation method 456 7.8.2 The LS method 458 Formulation using the data matrix 459 7.8.3 Signal-to-estimation error ratio 460 7.8.4 Channel estimation for multirate systems 464 7.8.5 The LMMSE method 465 7.9 Faster-than-Nyquist Signalling 467 Bibliography 467 Appendixes 470 7.A Simulation of a QAM system 471 7.B Description of a finite-state machine 477 7.C Line codes for PAM systems 478 7.C.1 Line codes 478 Non-return-to-zero format 478 Return-to-zero format 479 Biphase format 480 Delay modulation or Miller code 481 Block line codes 481 Alternate mark inversion 481 7.C.2 Partial response systems 482 The choice of the PR polynomial 485 Symbol detection and error probability 489 Precoding 491 Error probability with precoding 492 Alternative interpretation of PR systems 493 7.D Implementation of a QAM transmitter 497 8 Multicarrier modulation 499 8.1 MC systems 499 8.2 Orthogonality conditions 500 Time domain 501 Frequency domain 501 z-transform domain 501 8.3 Efficient implementation of MC systems 502 MC implementation employing matched filters 502 Orthogonality conditions in terms of the polyphase components 505 MC implementation employing a prototype filter 505 8.4 Non-critically sampled filter banks 510 8.5 Examples of MC systems 515 OFDM or DMT 515 Filtered multitone 516 8.6 Analog signal processing requirements in MC systems 517 8.6.1 Analog filter requirements 517 Interpolator filter and virtual subchannels 517 Modulator filter 519 8.6.2 Power amplifier requirements 520 8.7 Equalization 521 8.7.1 OFDM equalization 521 8.7.2 FMT equalization 524 Per-subchannel fractionally-spaced equalization 524 Per-subchannel T -spaced equalization 524 Alternative per-subchannel T -spaced equalization 525 8.8 Orthogonal time frequency space modulation 526 OTFS equalization 527 8.9 Channel estimation in OFDM 527 Instantaneous estimate or LS method 528 LMMSE 530 The LS estimate with truncated impulse response 531 8.9.1 Channel estimate and pilot symbols 532 8.10 Multiuser access schemes 532 8.10.1 OFDMA 533 8.10.2 SC-FDMA or DFT-spread OFDM 534 8.11 Comparison between MC and SC systems 535 8.12 Other MC waveforms 536 Bibliography 537 9 Transmission over multiple input multiple output channels 539 9.1 The MIMO NB channel 539 Spatial multiplexing and spatial diversity 544 Interference in MIMO channels 544 9.2 CSI only at the receiver 545 9.2.1 SIMO combiner 545 Equalization and diversity 548 9.2.2 MIMO combiner 548 Zero-forcing 549 MMSE 550 9.2.3 MIMO nonlinear detection and decoding 550 V-BLAST system 550 Spatial modulation 552 9.2.4 Space-time coding 553 The Alamouti code 553 The Golden code 555 9.2.5 MIMO channel estimation 556 The least squares method 556 The LMMSE method 557 9.3 CSI only at the transmitter 558 9.3.1 MISO linear precoding 558 MISO antenna selection 559 9.3.2 MIMO linear precoding 560 ZF precoding 561 9.3.3 MIMO nonlinear precoding 562 Dirty paper coding 562 TH precoding 564 9.3.4 Channel estimation for CSIT 564 9.4 CSI at both the transmitter and the receiver 565 9.5 Hybrid beamforming 566 Hybrid beamforming and angular domain representation 567 9.6 Multiuser MIMO: broadcast channel 568 9.6.1 CSI at both the transmitter and the receivers 569 Block diagonalization 570 User selection 571 Joint spatial division and multiplexing 572 9.6.2 Broadcast channel estimation 573 9.7 Multiuser MIMO: multiple-access channel 573 9.7.1 CSI at both the transmitters and the receiver 574 Block diagonalization 575 9.7.2 Multiple-access channel estimation 575 9.8 Massive MIMO 575 9.8.1 Channel hardening 576 9.8.2 Multiuser channel orthogonality 576 Bibliography 576 10 Spread-spectrum systems 581 10.1 Spread-spectrum techniques 581 10.1.1 Direct sequence systems 581 Classification of CDMA systems 589 Synchronization 590 10.1.2 Frequency hopping systems 590 Classification of FH systems 592 10.2 Applications of spread-spectrum systems 593 10.2.1 Anti-jamming 594 10.2.2 Multiple access 596 10.2.3 Interference rejection 597 10.3 Chip matched filter and rake receiver 597 Number of resolvable rays in a multipath channel 597 Chip matched filter 598 10.4 Interference 601 Detection strategies for multiple-access systems 603 10.5 Single-user detection 603 Chip equalizer 603 Symbol equalizer 605 10.6 Multiuser detection 606 10.6.1 Block equalizer 606 10.6.2 Interference cancellation detector 608 Successive interference cancellation 608 Parallel interference cancellation 610 10.6.3 ML multiuser detector 610 Correlation matrix 611 Whitening filter 611 10.7 Multicarrier CDMA systems 612 Bibliography 613 Appendixes 615 10.A Walsh codes 616 11 Channel codes 619 11.1 System model 620 11.2 Block codes 622 11.2.1 Theory of binary codes with group structure 622 Properties 622 Parity check matrix 625 Code generator matrix 628 Decoding of binary parity check codes 628 Cosets 629 Two conceptually simple decoding methods 630 Syndrome decoding 631 11.2.2 Fundamentals of algebra 633 modulo-q arithmetic 634 Polynomials with coefficients from a field 637 Modular arithmetic for polynomials 638 Devices to sum and multiply elements in a finite field 640 Remarks on finite fields 642 Roots of a polynomial 646 Minimum function 648 Methods to determine the minimum function 650 Properties of the minimum function 652 11.2.3 Cyclic codes 653 The algebra of cyclic codes 653 Properties of cyclic codes 654 Encoding by a shift register of length r 658 Encoding by a shift register of length k 661 Hard decoding of cyclic codes 662 Hamming codes 663 Burst error detection 666 11.2.4 Simplex cyclic codes 666 Relation to PN sequences 668 11.2.5 BCH codes 669 An alternative method to specify the code polynomials 669 Bose-Chaudhuri-Hocquenhemcodes 671 Binary BCH codes 674 Reed-Solomon codes 675 Decoding of BCH codes 676 Efficient decoding of BCH codes 681 11.2.6 Performance of block codes 689 11.3 Convolutional codes 690 11.3.1 General description of convolutional codes 693 Parity check matrix 695 Generator matrix 696 Transfer function 696 Catastrophic error propagation 700 11.3.2 Decoding of convolutional codes 702 Interleaving 702 Two decoding models 703 Decoding by the Viterbi algorithm 704 Decoding by the forward-backward algorithm 705 Sequential decoding 706 11.3.3 Performance of convolutional codes 710 11.4 Puncturing 711 11.5 Concatenated codes 711 The soft-output Viterbi algorithm 711 11.6 Turbo codes 713 Encoding 713 The basic principle of iterative decoding 718 FBA revisited 719 Iterative decoding 728 Performance evaluation 730 11.7 Iterative detection and decoding 730 11.8 Low-density parity check codes 734 11.8.1 Representation of LDPC codes 735 Matrix representation 735 Graphical representation 736 11.8.2 Encoding 737 Encoding procedure 737 11.8.3 Decoding 738 Hard decision decoder 738 The sum-product algorithm decoder 741 The LR-SPA decoder 744 The LLR-SPA or log-domain SPA decoder 745 The min-sum decoder 747 Other decoding algorithms 748 11.8.4 Example of application 748 Performance and coding gain 748 11.8.5 Comparison with turbo codes 749 11.9 Polar codes 751 11.9.1 Encoding 752 Internal CRC 753 LLRs associated to code bits 754 11.9.2 Tanner graph 755 11.9.3 Decoding algorithms 757 Successive cancellation decoding - the principle 758 Successive cancellation decoding - the algorithm 760 Successive cancellation list decoding 763 Other decoding algorithms 765 11.9.4 Frozen set design 765 Genie-aided SC decoding 766 Design based on density evolution 767 Channel polarisation 770 11.9.5 Puncturing and shortening 770 Puncturing 771 Shortening 772 Frozen set design 774 11.9.6 Performance 774 11.10Milestones in channel coding 775 Bibliography 775 Appendixes 781 11.A Nonbinary parity check codes 782 Linear codes 783 Parity check matrix 784 Code generator matrix 785 Decoding of nonbinary parity check codes 786 Coset 786 Two conceptually simple decoding methods 787 Syndrome decoding 787 12 Trellis coded modulation 789 12.1 Linear TCM for one and two-dimensional signal sets 790 12.1.1 Fundamental elements 790 Basic TCM scheme 792 Example 792 12.1.2 Set partitioning 795 12.1.3 Lattices 797 12.1.4 Assignment of symbols to the transitions in the trellis 802 12.1.5 General structure of the encoder/bit-mapper 807 Computation of dfree 809 12.2 Multidimensional TCM 811 Encoding 812 Decoding 815 12.3 Rotationally invariant TCM schemes 817 Bibliography 817 13 Techniques to achieve capacity 819 13.1 Capacity achieving solutions for multicarrier systems 819 13.1.1 Achievable bit rate of OFDM 819 13.1.2 Waterfilling solution 820 Iterative solution 821 13.1.3 Achievable rate under practical constraints 821 Effective SNR and system margin in MC systems 822 Uniform power allocation and minimum rate per subchannel 823 13.1.4 The bit and power loading problem revisited 824 Transmission modes 824 Problem formulation 825 Some simplifying assumptions 826 On loading algorithms 826 The Hughes-Hartogs algorithm 827 The Krongold-Ramchandran Jones algorithm 827 The Chow-Cioffi Bingham algorithm 830 Comparison 832 13.2 Capacity achieving solutions for single carrier systems 833 Achieving capacity 837 Bibliography 838 14 Synchronization 839 14.1 The problem of synchronization for QAM systems 839 14.2 The phase-locked loop 841 14.2.1 PLL baseband model 843 Linear approximation 844 14.2.2 Analysis of the PLL in the presence of additive noise 846 Noise analysis using the linearity assumption 847 14.2.3 Analysis of a second order PLL 848 14.3 Costas loop 852 14.3.1 PAM signals 852 14.3.2 QAM signals 854 14.4 The optimum receiver 856 Timing recovery 858 Carrier phase recovery 862 14.5 Algorithms for timing and carrier phase recovery 863 14.5.1 ML criterion 863 Assumption of slow time varying channel 863 14.5.2 Taxonomy of algorithms using the ML criterion 863 Feedback estimators 865 Early-late estimators 866 14.5.3 Timing estimators 867 Non data aided 867 NDA synchronization via spectral estimation 869 Data aided and data directed 871 Data and phase directed with feedback: differentiator scheme 874 Data and phase directed with feedback: Mueller & Muller scheme 874 Non data aided with feedback 877 14.5.4 Phasor estimators 878 Data and timing directed 878 Non data aided forM-PSK signals 878 Data and timing directed with feedback 879 14.6 Algorithms for carrier frequency recovery 880 14.6.1 Frequency offset estimators 881 Non data aided 881 Non data aided and timing independent with feedback 882 Non data aided and timing directed with feedback 883 14.6.2 Estimators operating at the modulation rate 883 Data aided and data directed 884 Non data aided forM-PSK 885 14.7 Second-order digital PLL 885 14.8 Synchronization in spread-spectrum systems 885 14.8.1 The transmission system 885 Transmitter 885 Optimum receiver 886 14.8.2 Timing estimators with feedback 887 Non data aided: non coherent DLL 888 Non data aided modified code tracking loop 888 Data and phase directed: coherent DLL 891 14.9 Synchronization in OFDM 891 14.9.1 Frame synchronization 891 Effects of STO 891 Schmidl and Cox algorithm 893 14.9.2 Carrier frequency synchronization 894 Estimator performance 895 Other synchronization solutions 895 14.10Synchronization in SC-FDMA 896 Bibliography 899 15 Self-training equalization 901 15.1 Problem definition and fundamentals 901 Minimization of a special function 904 15.2 Three algorithms for PAM systems 908 The Sato algorithm 908 Benveniste-Goursat algorithm 909 Stop-and-go algorithm 909 Remarks 910 15.3 The contour algorithm for PAM systems 910 Simplified realization of the contour algorithm 912 15.4 Self-training equalization for partial response systems 913 The Sato algorithm 914 The contour algorithm 915 15.5 Self-training equalization for QAM systems 917 The Sato algorithm 918 15.5.1 Constant-modulus algorithm 919 The contour algorithm 921 Joint contour algorithm and carrier phase tracking 922 15.6 Examples of applications 924 Bibliography 928 Appendixes 930 15.A On the convergence of the contour algorithm 931 16 Low-complexity demodulators 933 16.1 Phase-shift keying 933 16.1.1 Differential PSK 935 Error probability ofM-DPSK 936 16.1.2 Differential encoding and coherent demodulation 937 Differentially encoded BPSK 937 Multilevel case 938 16.2 (D)PSK non-coherent receivers 940 16.2.1 Baseband differential detector 940 16.2.2 IF-band (1 Bit) differential detector 942 Signal at detection point 944 16.2.3 FM discriminator with integrate and dump filter 945 16.3 Optimum receivers for signals with random phase 946 ML criterion 948 Implementation of a non coherentML receiver 951 Error probability for a non coherent binary FSK system 953 Performance comparison of binary systems 956 16.4 Frequency-based modulations 957 16.4.1 Frequency shift keying 957 Coherent demodulator 959 Non coherent demodulator 959 Limiter-discriminator FM demodulator 961 16.4.2 Minimum-shift keying 961 Power spectral density of CPFSK 963 Performance 963 MSK with differential precoding 967 16.4.3 Remarks on spectral containment 968 16.5 Gaussian MSK 968 PSD of GMSK 972 16.5.1 Implementation of a GMSK scheme 973 Configuration I 973 Configuration II 974 Configuration III 975 16.5.2 Linear approximation of a GMSK signal 977 Performance of GMSK 978 Performance in the presence of multipath 983 Bibliography 985 Appendixes 985 16.A Continuous phase modulation 986 Alternative definition of CPM 986 Advantages of CPM 988 17 Applications of interference cancellation 989 17.1 Echo and near–end crosstalk cancellation for PAM systems 990 Crosstalk cancellation and full duplex transmission 991 Polyphase structure of the canceller 992 Canceller at symbol rate 993 Adaptive canceller 994 Canceller structure with distributed arithmetic 995 17.2 Echo cancellation for QAM systems 998 17.3 Echo cancellation for OFDM systems 1001 17.4 Multiuser detection for VDSL 1004 17.4.1 Upstream power back-off 1009 17.4.2 Comparison of PBO methods 1011 Bibliography 1014 18 Examples of communication systems 1019 18.1 The 5G cellular system 1019 18.1.1 Cells in a wireless system 1019 18.1.2 The release 15 of the 3GPP standard 1020 18.1.3 Radio access network 1021 Time-frequency plan 1022 NR data transmission chain 1023 OFDM numerology 1023 Channel estimation 1024 18.1.4 Downlink 1024 Synchronization 1026 Initial access or beam sweeping 1027 Channel estimation 1028 Channel state information reporting 1028 18.1.5 Uplink 1029 Transform precoding numerology 1029 Channel estimation 1029 Synchronization 1030 Timing advance 1031 18.1.6 Network slicing 1031 18.2 GSM 1032 Radio subsystem 1034 18.3 Wireless local area networks 1036 Medium access control protocols 1036 18.4 DECT 1037 18.5 Bluetooth 1040 18.6 Transmission over unshielded twisted pairs 1041 18.6.1 Transmission over UTP in the customer service area 1041 18.6.2 High speed transmission over UTP in local area networks 1045 18.7 Hybrid fibre/coaxial cable networks 1048 Ranging and power adjustment in OFDMA systems 1051 Ranging and power adjustment for uplink transmission 1052 Bibliography 1053 Appendixes 1057 18.A Duplexing 1058 Three methods 1058 18.B Deterministic access methods 1059 19 High-speed communications over twisted-pair cables 1063 19.1 Quaternary partial response class-IV system 1063 Analog filter design 1064 Received signal and adaptive gain control 1064 Near-end crosstalk cancellation 1065 Decorrelation filter 1065 Adaptive equalizer 1065 Compensation of the timing phase drift 1066 Adaptive equalizer coefficient adaptation 1066 Convergence behaviour of the various algorithms 1067 19.1.1 VLSI implementation 1069 Adaptive digital NEXT canceller 1069 Adaptive digital equalizer 1071 Timing control 1075 Viterbi detector 1077 19.2 Dual duplex system 1077 Dual duplex transmission 1077 Physical layer control 1080 Coding and decoding 1080 19.2.1 Signal processing functions 1083 The 100BASE-T2 transmitter 1083 The 100BASE-T2 receiver 1084 Computational complexity of digital receive filters 1086 Bibliography 1087 Appendixes 1087 19.A Interference suppression 1088

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Book SynopsisHigh Voltage and Electrical Insulation Engineering A comprehensive graduate-level textbook on high voltage insulation engineering, updated to reflect emerging trends and techniques in the field High Voltage and Electrical Insulation Engineering presents systematic coverage of the behavior of dielectric materials. This classic textbook opens with clear explanations of fundamental terminology, electric-field classification, and field estimation techniques. Subsequent chapters describe the field dependent performance of gaseous, vacuum, liquid, and solid dielectrics under different classified field conditions, and illustrate the monitoring of electrical insulation conditions by both single and continuous online methods. Throughout the text, numerous tables, figures, diagrams, and images are provided to strengthen understanding of all material. Fully revised to incorporate the most current technological application techniques, the second edition offers an entirely new section on conditionTable of ContentsAuthor Biographies xv Preface xix Acknowledgments xxiii 1 Introduction 1 1.1 Electric Charge, Discharge, Current, and Potential 2 1.2 Electric and Magnetic Fields 4 1.3 Electromagnetism 4 1.4 Dielectric and Electrical Insulation 6 1.5 Electrical Breakdown 6 1.5.1 Global Breakdown 7 1.5.2 Local Breakdown or Partial Breakdown 7 1.5.3 Breakdown Strength or Electric Strength 7 1.6 Corona, Streamer, Star, and Leader 7 1.6.1 Aurora 9 1.6.2 Electric Arc 10 1.7 Capacitance and Capacitor 10 1.7.1 Stray Capacitance 11 1.8 Forms of Voltages and Currents 12 1.8.1 TravelingWaves 13 1.8.2 Neutral and Ground 13 References 13 2 Electric Fields, Their Control and Estimation 15 2.1 Electric Field Intensity, “E” 15 2.2 Breakdown and Electric Strength of Dielectrics, “Eb” 18 2.2.1 Partial Breakdown in Dielectrics 18 2.3 Classification of Electric Fields 19 2.3.1 Degree of Uniformity of Electric Fields 21 2.3.1.1 Effect of Grounding on Field Configuration 23 2.4 Control of Electric Field Intensity (Stress Control) 25 2.5 Estimation of Electric Field Intensity 30 2.5.1 Basic Equations for Potential and Field Intensity in Electrostatic Fields 31 2.5.2 Analytical Methods for the Estimation of Electric Field Intensity in Homogeneous Isotropic Single Dielectric 34 2.5.2.1 Direct Solution of Laplace Equation 35 2.5.2.2 “Gaussian Surface” Enclosed Charge Techniques for the Estimation and Optimization of Field 39 2.5.3 Analysis of Electric Field Intensity in Isotropic Multidielectric System 46 2.5.3.1 Field with Longitudinal Interface 46 2.5.3.2 Field with Perpendicular Interface 48 2.5.3.3 Field with Diagonal Interface 53 2.5.4 Numerical Methods for the Estimation of Electric Field Intensity 54 2.5.4.1 Finite Element Method (FEM) 55 2.5.4.2 Charge Simulation Method (CSM) 62 2.5.5 Numerical Optimization of Electric Fields 69 2.5.5.1 Optimization by Displacement of Contour Points 70 2.5.5.2 Optimization by Changing the Positions of Optimization Charges and Contour Points 71 2.5.5.3 Optimization by Modification of “Contour Elements” 73 2.6 Conclusion 75 References 76 3 Field Dependent Behavior of Air and Other Gaseous Dielectrics 79 3.1 Fundamental Process of Field Assisted Generation of Charge Carriers 83 3.1.1 Impact Ionization 85 3.1.2 Thermal Ionization 86 3.1.3 Photoionization and Interaction of Metastables with Molecules 86 3.2 Breakdown of Atmospheric Air in Uniform andWeakly Nonuniform Fields 88 3.2.1 Uniform Field with Space Charge 89 3.2.2 Development of Electron Avalanche 91 3.2.3 Development of Streamer or “Kanal Discharge” 96 3.2.4 Breakdown Mechanisms 99 3.2.4.1 Breakdown in Uniform Fields with Small Gap Distances (Townsend Mechanism) 99 3.2.4.2 Breakdown with Streamer (Streamer or Kanal Mechanism) 106 3.2.5 Breakdown Voltage Characteristics in Uniform Fields (Paschen’s Law) 111 3.2.6 Breakdown Voltage Characteristics inWeakly Nonuniform Fields 122 3.3 Breakdown in Extremely Nonuniform Fields and Corona 123 3.3.1 Development of Avalanche Discharge of Below Critical Amplification 124 3.3.1.1 Positive Needle–Plane Electrode Configuration (Positive or Anode Star Corona) 125 3.3.1.2 Negative Needle–Plane Electrode Configuration (Negative or Cathode Star Corona) 127 3.3.2 Development of Streamer or Kanal Discharge 129 3.3.2.1 Positive Rod–Plane Electrode (Positive Streamer Corona) 129 3.3.2.2 Negative Rod–Plane Electrode (Negative Streamer Corona) 134 3.3.2.3 Symmetrical Positive and Negative Electrode Configurations in Extremely Nonuniform Fields 136 3.3.3 Development of Stem and Leader Corona 137 3.3.3.1 Development and Propagation of Positive Leader Corona 141 3.3.3.2 Development and Propagation of Negative Leader Corona and the Phenomenon of Space Leader 144 3.3.3.3 Electromagnetic Interference (EMI) Produced by Corona 147 3.3.4 Summary of the Development of Breakdown in Extremely Nonuniform Fields 148 3.3.5 Breakdown Voltage Characteristics of Air in Extremely Nonuniform Fields 150 3.3.5.1 Breakdown Preceded with Stable Star Corona 152 3.3.5.2 Breakdown Preceded with Stable Streamer Corona 156 3.3.5.3 Breakdown Preceded with Stable Streamer and Leader Coronas (Long Air Gaps) 163 3.3.5.4 The Requirement of Time for the Formation of Spark Breakdown with Impulse Voltages 168 3.3.5.5 Effect of Wave Shape on Breakdown with Impulse Voltages 171 3.3.5.6 Conclusions from Measured Breakdown Characteristics in Extremely Nonuniform Fields 175 3.3.5.7 Estimation of Breakdown Voltage in Extremely Nonuniform Fields in Long Air Gaps 176 3.3.6 Effects of Partial Breakdown or Corona in Atmospheric Air 178 3.3.6.1 Chemical Decomposition of Air by Corona 179 3.3.6.2 Corona Power Loss in Transmission Lines 182 3.3.6.3 Electromagnetic Interference (EMI) and Audible Noise (AN) Produced by Power System Network 184 3.3.6.4 Other Effects of High Voltage Transmission Lines and Corona on the Environment 187 3.4 Electric Arcs and Their Characteristics 188 3.4.1 Static Voltage–Current, U–I, Characteristics of Arcs in Air 189 3.4.2 Dynamic U–I Characteristics of Arcs 192 3.4.3 Extinction of Arcs 194 3.5 Properties of Sulfurhexafluoride, SF6, Gas, and Its Application in Electrical Installations 194 3.5.1 Properties of Sulfurhexafluoride, SF6 Gas 197 3.5.1.1 Physical Properties 199 3.5.1.2 Property of Electron Attachment 199 3.5.2 Breakdown in Uniform and Weakly Nonuniform Fields with SF6 Insulation 201 3.5.3 External Factors Affecting Breakdown Characteristics in Compressed Gases 210 3.5.3.1 Effect of Electrode Materials and Their Surface Roughness on Breakdown 210 3.5.3.2 Effect of Particle Contaminants in Gas Insulated Systems (GIS) 212 3.5.3.3 Particle Initiated PB and Breakdown Measurements in GIS 219 3.5.3.4 Preventive Measures for the Effect of Particles in GIS 222 3.5.4 Breakdown in Extremely Nonuniform and Distorted Weakly Nonuniform Fields with Stable PB in SF6 Gas Insulation 222 3.5.5 Electrical Strength of Mixtures of SF6 with Other Gases 226 3.5.6 Decomposition of SF6 and Its Mixtures in Gas Insulated Equipment 230 3.5.7 SF6 Gas and Environment 234 3.5.8 Development in Gas Insulated Power Apparatus 236 3.5.9 Mineral Oils Versus SF6 Gas 236 3.5.10 Basic Electrical Insulation Requirements for GITs 238 3.5.11 SF6 Gas Insulation, a Replacement for Oils 239 3.5.12 Basic Cooling Requirements Met by Gas for GITs 240 3.5.13 Environment Concerns and Future Trends 241 3.6 Investigations for the Requirement of Optimum Clearance for 25 kV Electric Traction: A Case Study 242 3.6.1 Field Estimation for the Traction Overhead Conductor at 25 kV 243 3.6.2 Measurement of Breakdown/Withstand Voltage Characteristics 247 3.6.3 Measurements with ac Power Frequency Voltage 247 3.6.4 Measurements Under FairWeather, Natural Fog, and Natural Rain Conditions 248 3.6.5 Measurements Under Artificial Rain 249 3.6.6 Investigation of the Performance of Air-Gap Under System Overvoltages 250 3.6.7 Measurements with Impulse Voltages 252 3.6.8 Measurements with Insulating-Barrier in the Gap 253 3.6.9 Choice of Solid Insulating Barrier 253 3.6.10 Positioning and Fastening of the Solid Insulating Barrier in the Gap 254 3.6.11 Measurement Results with Teflon Sheet as a Barrier 254 3.7 Conclusions and Recommendations 255 References 257 4 Lightning and Ball Lightning, Development Mechanisms, Deleterious Effects, Protection 267 4.1 The Globe, a Capacitor 268 4.1.1 The Earth’s Atmosphere and the Clouds 269 4.1.1.1 The Troposphere 270 4.1.1.2 The Stratosphere 270 4.1.1.3 The Ionosphere 271 4.1.2 Clouds and Their Important Role 271 4.1.2.1 Classification of Clouds 271 4.1.3 Static Electric Charge in the Atmosphere 273 4.1.3.1 External Source of Electric Charge 273 4.1.3.2 Charges Due to Ionization Within the Atmospheric Air 275 4.1.3.3 Charging Mechanisms and Thunderstorms 276 4.2 Mechanisms of Lightning Strike 278 4.2.1 Mechanism of Breakdown in Long Air Gap 278 4.2.2 Mechanisms of Lightning Strike on the Ground 280 4.2.3 Preference of Locations for the Lightning to Strike 282 4.3 Deleterious Effects of Lightning 284 4.3.1 Loss of Life of the Living Beings 284 4.3.2 Fire Hazards Due to Lightning 284 4.3.3 Blast Created by Lightning 285 4.3.4 Development of Transient Over-Voltage Due to Lightning Strike on the Electric Power System Network and Its Protection 286 4.4 Protection from Lightning 288 4.4.1 Protection of Lives 289 4.4.2 Protection of Buildings and Structures 290 4.4.2.1 Air Termination Network 291 4.4.2.2 Down Conductor 292 4.4.2.3 Earth Termination System 292 4.4.3 The Protected Area 292 4.4.3.1 Protected Volume Determined by a Cone 292 4.4.3.2 Protected Volume Evolved by Rolling a Sphere 293 4.5 Ball Lightning 295 4.5.1 The Phenomenon of Ball Lightning 295 4.5.2 Injurious Effects of Ball Lightning 296 4.5.3 Models and Physics of Ball Lightning 296 4.5.4 Ball Lightning Without Lightning Strike 298 4.5.4.1 TheWeather and Climatic Conditions 299 4.5.4.2 The Man Made Sources of Charge/Current 299 4.5.5 Ball Lightning, a Mythological Legend in India 300 4.6 Lightning, a Truthful Myth 301 4.6.1 Examples of Known and Widely Accepted Myths 301 4.6.2 The Mythology of “Bijli Mahadev” 302 4.6.3 Geographical Location and the Construction of the Temple 302 4.6.4 The Mechanism of Destruction of the Deity 304 References 304 5 Electrical Properties of Vacuum as High Voltage Insulation 307 5.1 Pre-breakdown Electron Emission in Vacuum 308 5.1.1 Mechanism of Electron Emission from Metallic Surfaces 308 5.1.2 Non-metallic Electron Emission Mechanisms 311 5.2 Pre-breakdown Conduction and Spark Breakdown in Vacuum 316 5.2.1 Electrical Breakdown in Vacuum Interrupters 324 5.2.1.1 High Current Arc Quenching in Vacuum 324 5.2.1.2 Delayed Re-ignition of Arcs 325 5.2.1.3 Effect of Insulator Surface Phenomena 326 5.2.2 Effect of Conditioning of Electrodes on Breakdown Voltage 326 5.2.3 Effect of Area of Electrodes on Breakdown in Vacuum 328 5.3 Vacuum as Insulation in Space Applications 329 5.3.1 Vacuum-Insulated Power Supplies for Space 329 5.3.2 Vacuum Related Problems in Low Earth Orbit Plasma Environment 330 5.4 Development in Vacuum Technology Applications in Power System Switchgears 331 5.4.1 Development in Actuator Mechanism for the Interrupter Units 333 5.4.2 Development of 245 kV Vacuum Circuit Breaker 334 5.5 Conclusion 335 References 336 6 Liquid Dielectrics, Their Classification, Properties, and Breakdown Strength 339 6.1 Classification of Liquid Dielectrics 340 6.1.1 Mineral Insulating Oils 341 6.1.1.1 Mineral Insulating Oil in Transformers 342 6.1.2 Vegetable Oils 344 6.1.3 Synthetic Liquid Dielectrics, the Chlorinated Diphenyls 344 6.1.3.1 Halogen-Free Synthetic Oils 345 6.1.4 Inorganic Liquids as Insulation 346 6.1.5 Polar and Nonpolar Dielectrics 347 6.2 Dielectric Properties of Insulating Materials 347 6.2.1 Insulation Resistance Offered by Dielectrics 347 6.2.2 Permittivity of Insulating Materials 349 6.2.3 Polarization in Insulating Materials 350 6.2.3.1 Effect of Time on Polarization 352 6.2.3.2 Polarization Under Alternating Voltages and the Eigen-Frequency of Dielectrics 355 6.2.3.3 High Frequency High Voltage Application of Dielectrics 358 6.2.4 Dielectric Power Losses in Insulating Materials 360 6.3 Breakdown in Liquid Dielectrics 363 6.3.1 Electric Conduction in Insulating Liquids 364 6.3.1.1 Liquid Dielectrics in Motion and Electrohydrodynamics (EHD) 367 6.3.2 Intrinsic Breakdown Strength 369 6.3.3 Practical Breakdown Strength Measurement at Near Uniform Fields 370 6.3.3.1 Effect of Moisture and Temperature on Breakdown Strength 372 6.3.4 Breakdown in Extremely Nonuniform Fields and the Development of Streamer 376 6.4 Aging in Mineral Insulating Oils 382 References 384 7 Solid Dielectrics, Their Sources, Properties, and Behavior in Electric Fields 387 7.1 Classification of Solid Insulating Materials 388 7.1.1 Inorganic Insulating Materials 388 7.1.1.1 Ceramic Insulating Materials 388 7.1.1.2 Glass as an Insulating Material 392 7.1.2 Polymeric Organic Materials 392 7.1.2.1 Thermoplastic Polymers 393 7.1.2.2 Thermoset Polymers 393 7.1.2.3 Polymer Compounds 394 7.1.2.4 Polyvinylchloride (PVC) 394 7.1.2.5 Polyethylene (PE) 395 7.1.2.6 Epoxy Resins (EP-Resins) 400 7.1.2.7 Natural and Synthetic Rubber 402 7.1.3 Composite Insulating System 403 7.1.3.1 Impregnated Paper as a Composite Insulation System 403 7.1.3.2 Insulating Board Materials 407 7.1.3.3 Fiber Reinforced Plastics (FRP) 407 7.2 Partial Breakdown in Solid Dielectrics 408 7.2.1 Internal Partial Breakdown 409 7.2.2 Surface Discharge (Tracking) 416 7.2.3 Degradation of Solid Dielectrics Caused by PB 419 7.2.3.1 Inhibition of Partial Breakdown/Treeing in Solid Dielectrics 420 7.2.4 Partial Breakdown Detection and Measurement 422 7.2.4.1 Indirect Methods of PB Detection 422 7.2.4.2 Direct Methods of PB Detection and Measurement 423 7.3 Breakdown and Pre-breakdown Phenomena in Solid Dielectrics 424 7.3.1 Intrinsic Breakdown Strength of Solid Dielectrics 426 7.3.2 Thermal Breakdown 429 7.3.3 Mechanism of Breakdown in Extremely Nonuniform Fields 433 7.3.4 “Treeing” a Pre-breakdown Phenomenon in Polymeric Dielectrics 434 7.3.4.1 Forms of Treeing Patterns 434 7.3.4.2 Classification of Treeing Process 434 7.3.5 Requirement of Time for Breakdown 437 7.3.6 Estimation of Life Expectancy Characteristics 440 7.3.7 Practical Breakdown Strength and Electric Stress in Service of Solid Dielectrics 443 7.4 Development and Application of Solid Dielectric Line Insulators in High Voltage Power System 444 7.4.1 Polymeric, also known as Composite Dielectric Insulators 445 7.4.2 Design and Construction of Polymeric Insulators 446 7.4.2.1 The Core or the Rod 446 7.4.2.2 Metallic End Fittings 446 7.4.2.3 The Weather Sheds 447 7.4.3 Hollow Core Polymer Insulators 449 7.4.4 Properties of Silicone Rubber and Fiber-Glass Reinforced Polymers 450 7.4.4.1 Hydrophobicity 450 7.4.5 Electrical Properties and Specified Tests 452 7.4.5.1 Water Diffusion Test 452 7.4.5.2 Water Immersion Test 452 7.5 Condition Monitoring of Electrical Insulation 453 7.5.1 Offline Single Measurement Techniques 454 7.5.2 Online Continuous Measurement Techniques 455 7.5.3 Construction of Large Rotating Electrical Machines 456 7.5.3.1 Typical Nature of Insulation in Electrical Machines 456 7.5.4 Partial Breakdown (PB) Monitoring Techniques Applied on Large Rotating Machines 458 7.5.5 PB Measurements with VHF and UHF Sensors/Couplers 460 7.5.5.1 Capacitive PB Couplers 461 7.5.5.2 Inductive PB Couplers 461 7.5.5.3 Electro-Magnetic (EM) PB Couplers 462 References 464 Index 469

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Book SynopsisA comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal Data Processing contains a review of the most recent applications of data processing. The Editors and contributors ? noted experts on the topic ? offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-Table of ContentsList of contributors xv Series Preface xix Preface xxi About the Companion Website xxv 1 Introduction 1Soham Sarkar, Abhishek Basu, and Siddhartha Bhattacharyya 1.1 Areas of Application for Multimodal Signal 1 1.1.1 Implementation of the Copyright Protection Scheme 1 1.1.2 Saliency Map Inspired Digital Video Watermarking 1 1.1.3 Saliency Map Generation Using an Intelligent Algorithm 2 1.1.4 Brain Tumor Detection Using Multi-Objective Optimization 2 1.1.5 Hyperspectral Image Classification Using CNN 2 1.1.6 Object Detection for Self-Driving Cars 2 1.1.7 Cognitive Radio 2 1.2 Recent Challenges 2 References 3 2 Progressive Performance of Watermarking Using Spread Spectrum Modulation 5Arunothpol Debnath, Anirban Saha, Tirtha Sankar Das, Abhishek Basu, and Avik Chattopadhyay 2.1 Introduction 5 2.2 Types of Watermarking Schemes 9 2.3 Performance Evaluation Parameters of a Digital Watermarking Scheme 10 2.4 Strategies for Designing the Watermarking Algorithm 11 2.4.1 Balance of Performance Evaluation Parameters and Choice of Mathematical Tool 11 2.4.2 Importance of the Key in the Algorithm 13 2.4.3 Spread Spectrum Watermarking 13 2.4.4 Choice of Sub-band 14 2.5 Embedding and Detection of a Watermark Using the Spread Spectrum Technique 15 2.5.1 General Model of Spread Spectrum Watermarking 15 2.5.2 Watermark Embedding 17 2.5.3 Watermark Extraction 18 2.6 Results and Discussion 18 2.6.1 Imperceptibility Results for Standard Test Images 20 2.6.2 Robustness Results for Standard Test Images 20 2.6.3 Imperceptibility Results for Randomly Chosen Test Images 22 2.6.4 Robustness Results for Randomly Chosen Test Images 22 2.6.5 Discussion of Security and the key 24 2.7 Conclusion 31 References 36 3 Secured Digital Watermarking Technique and FPGA Implementation 41Ranit Karmakar, Zinia Haque, Tirtha Sankar Das, and Rajeev Kamal 3.1 Introduction 41 3.1.1 Steganography 41 3.1.2 Cryptography 42 3.1.3 Difference between Steganography and Cryptography 43 3.1.4 Covert Channels 43 3.1.5 Fingerprinting 43 3.1.6 Digital Watermarking 43 3.1.6.1 Categories of Digital Watermarking 44 3.1.6.2 Watermarking Techniques 45 3.1.6.3 Characteristics of Digital Watermarking 47 3.1.6.4 Different Types of Watermarking Applications 48 3.1.6.5 Types of Signal Processing Attacks 48 3.1.6.6 Performance Evaluation Metrics 49 3.2 Summary 50 3.3 Literary Survey 50 3.4 System Implementation 51 3.4.1 Encoder 52 3.4.2 Decoder 53 3.4.3 Hardware Realization 53 3.5 Results and Discussion 55 3.6 Conclusion 57 References 64 4 Intelligent Image Watermarking for Copyright Protection 69Subhrajit Sinha Roy, Abhishek Basu, and Avik Chattopadhyay 4.1 Introduction 69 4.2 Literature Survey 72 4.3 Intelligent Techniques for Image Watermarking 75 4.3.1 Saliency Map Generation 75 4.3.2 Image Clustering 77 4.4 Proposed Methodology 78 4.4.1 Watermark Insertion 78 4.4.2 Watermark Detection 81 4.5 Results and Discussion 82 4.5.1 System Response for Watermark Insertion and Extraction 83 4.5.2 Quantitative Analysis of the Proposed Watermarking Scheme 85 4.6 Conclusion 90 References 92 5 Video Summarization Using a Dense Captioning (DenseCap) Model 97Sourav Das, Anup Kumar Kolya, and Arindam Kundu 5.1 Introduction 97 5.2 Literature Review 98 5.3 Our Approach 101 5.4 Implementation 102 5.5 Implementation Details 108 5.6 Result 110 5.7 Limitations 127 5.8 Conclusions and Future Work 127 References 127 6 A Method of Fully Autonomous Driving in Self-Driving Cars Based on Machine Learning and Deep Learning 131Harinandan Tunga, Rounak Saha, and Samarjit Kar 6.1 Introduction 131 6.2 Models of Self-Driving Cars 131 6.2.1 Prior Models and Concepts 132 6.2.2 Concept of the Self-Driving Car 133 6.2.3 Structural Mechanism 134 6.2.4 Algorithm for theWorking Procedure 134 6.3 Machine Learning Algorithms 135 6.3.1 Decision Matrix Algorithms 135 6.3.2 Regression Algorithms 135 6.3.3 Pattern Recognition Algorithms 135 6.3.4 Clustering Algorithms 137 6.3.5 Support Vector Machines 137 6.3.6 Adaptive Boosting 138 6.3.7 TextonBoost 139 6.3.8 Scale-Invariant Feature Transform 140 6.3.9 Simultaneous Localization and Mapping 140 6.3.10 Algorithmic Implementation Model 141 6.4 Implementing a Neural Network in a Self-Driving Car 142 6.5 Training and Testing 142 6.6 Working Procedure and Corresponding Result Analysis 143 6.6.1 Detection of Lanes 143 6.7 Preparation-Level Decision Making 146 6.8 Using the Convolutional Neural Network 147 6.9 Reinforcement Learning Stage 147 6.10 Hardware Used in Self-Driving Cars 148 6.10.1 LIDAR 148 6.10.2 Vision-Based Cameras 149 6.10.3 Radar 150 6.10.4 Ultrasonic Sensors 150 6.10.5 Multi-Domain Controller (MDC) 150 6.10.6 Wheel-Speed Sensors 150 6.10.7 Graphics Processing Unit (GPU) 151 6.11 Problems and Solutions for SDC 151 6.11.1 Sensor Disjoining 151 6.11.2 Perception Call Failure 152 6.11.3 Component and Sensor Failure 152 6.11.4 Snow 152 6.11.5 Solutions 152 6.12 Future Developments in Self-Driving Cars 153 6.12.1 Safer Transportation 153 6.12.2 Safer Transportation Provided by the Car 153 6.12.3 Eliminating Traffic Jams 153 6.12.4 Fuel Efficiency and the Environment 154 6.12.5 Economic Development 154 6.13 Future Evolution of Autonomous Vehicles 154 6.14 Conclusion 155 References 155 7 The Problem of Interoperability of Fusion Sensory Data from the Internet of Things 157Doaa Mohey Eldin, Aboul Ella Hassanien, and Ehab E. Hassanein 7.1 Introduction 157 7.2 Internet of Things 158 7.2.1 Advantages of the IoT 159 7.2.2 Challenges Facing Automated Adoption of Smart Sensors in the IoT 159 7.3 Data Fusion for IoT Devices 160 7.3.1 The Data Fusion Architecture 160 7.3.2 Data Fusion Models 161 7.3.3 Data Fusion Challenges 161 7.4 Multi-Modal Data Fusion for IoT Devices 161 7.4.1 Data Mining in Sensor Fusion 162 7.4.2 Sensor Fusion Algorithms 163 7.4.2.1 Central Limit Theorem 163 7.4.2.2 Kalman Filter 163 7.4.2.3 Bayesian Networks 164 7.4.2.4 Dempster-Shafer 164 7.4.2.5 Deep Learning Algorithms 165 7.4.2.6 A Comparative Study of Sensor Fusion Algorithms 168 7.5 A Comparative Study of Sensor Fusion Algorithms 170 7.6 The Proposed Multimodal Architecture for Data Fusion 175 7.7 Conclusion and Research Trends 176 References 177 8 Implementation of Fast, Adaptive, Optimized Blind Channel Estimation for Multimodal MIMO-OFDM Systems Using MFPA 183Shovon Nandi, Narendra Nath Pathak, and Arnab Nandi 8.1 Introduction 183 8.2 Literature Survey 185 8.3 STBC-MIMO-OFDM Systems for Fast Blind Channel Estimation 187 8.3.1 Proposed Methodology 187 8.3.2 OFDM-Based MIMO 188 8.3.3 STBC-OFDM Coding 188 8.3.4 Signal Detection 189 8.3.5 Multicarrier Modulation (MCM) 189 8.3.6 Cyclic Prefix (CP) 190 8.3.7 Multiple Carrier-Code Division Multiple Access (MC-CDMA) 191 8.3.8 Modified Flower Pollination Algorithm (MFPA) 192 8.3.9 Steps in the Modified Flower Pollination Algorithm 192 8.4 Characterization of Blind Channel Estimation 193 8.5 Performance Metrics and Methods 195 8.5.1 Normalized Mean Square Error (NMSE) 195 8.5.2 Mean Square Error (MSE) 196 8.6 Results and Discussion 196 8.7 Relative Study of Performance Parameters 198 8.8 Future Work 201 References 201 9 Spectrum Sensing for Cognitive Radio Using a Filter Bank Approach 205Srijibendu Bagchi and Jawad Yaseen Siddiqui 9.1 Introduction 205 9.1.1 Dynamic Exclusive Use Model 206 9.1.2 Open Sharing Model 206 9.1.3 Hierarchical Access Model 206 9.2 Cognitive Radio 207 9.3 Some Applications of Cognitive Radio 208 9.4 Cognitive Spectrum Access Models 209 9.5 Functions of Cognitive Radio 210 9.6 Cognitive Cycle 211 9.7 Spectrum Sensing and Related Issues 211 9.8 Spectrum Sensing Techniques 213 9.9 Spectrum Sensing in Wireless Standards 216 9.10 Proposed Detection Technique 218 9.11 Numerical Results 221 9.12 Discussion 222 9.13 Conclusion 223 References 223 10 Singularity Expansion Method in Radar Multimodal Signal Processing and Antenna Characterization 231Nandan Bhattacharyya and Jawad Y. Siddiqui 10.1 Introduction 231 10.2 Singularities in Radar Echo Signals 232 10.3 Extraction of Natural Frequencies 233 10.3.1 Cauchy Method 233 10.3.2 Matrix Pencil Method 233 10.4 SEM for Target Identification in Radar 234 10.5 Case Studies 236 10.5.1 Singularity Extraction from the Scattering Response of a Circular Loop 236 10.5.2 Singularity Extraction from the Scattering Response of a Sphere 237 10.5.3 Singularity Extraction from the Response of a Disc 238 10.5.4 Result Comparison with Existing Work 239 10.6 Singularity Expansion Method in Antennas 239 10.6.1 Use of SEM in UWB Antenna Characterization 240 10.6.2 SEM for Determining Printed Circuit Antenna Propagation Characteristics 241 10.6.3 Method of Extracting the Physical Poles from Antenna Responses 241 10.6.3.1 Optimal Time Window for Physical Pole Extraction 241 10.6.3.2 Discarding Low-Energy Singularities 242 10.6.3.3 Robustness to Signal-to-Noise Ratio (SNR) 243 10.7 Other Applications 243 10.8 Conclusion 243 References 243 11 Conclusion 249Soham Sarkar, Abhishek Basu, and Siddhartha Bhattacharyya References 250 Index 253

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Book SynopsisPersuasive Communication for Science and Technology Leaders Explore this insightful guide to the development of persuasive leadership skills perfect for students and managers in technical fields Many technical managers receive little or no training in the persuasive arts. Though technically skilled, they often lack the ability to engage effectively with an audiences outside their field. Persuasive Communication for Science and Technology Leaders: Writing and Speaking with Confidence delivers a thorough treatment of how to connect with audiences whose knowledge, values, personal experiences, ethnic background, gender, and worldview may differ from their own. Written in a highly readable and entertaining style, this book goes beyond the scope of a standard textbook on persuasive communication. Its practical lessons illustrate the techniques of effective scientific and technical writing while emphasizing values-based leadership for a more just,Table of ContentsAbout the Author xv Acknowledgments xvii How This Book Differs From Other Communication Guides xix Previously Published Material xxi Also By Stephen Wilbers xxiii Welcome xxv Introduction 1 Who can benefit from this book 2 How this book differs from other textbooks & communication guides 2 How this book evolved from my writing & teaching 4 How to read this book using the SQ4R method 6 How this book is organized 7 Questions to ask yourself as you read this book 9 Part ONE—Writing 11 Chapter 1 Explaining Complex Technologies Clearly 13 Writing in stages 14 Think of yourself as a translator 15 Adopt the seven habits of highly effective writers 15 Approach writing as a process 17 Don’t be blocked by writer’s block 18 Communicating internationally without ambiguity 20 Don’t confuse non-native speakers of American English 20 Limit your use of prepositionalized English 22 Don’t assume that American & British English are identical 22 Don’t be too quick to laugh at ESL or ELL errors 23 Connecting your thoughts with sentence & paragraph structure 24 Write in sentences, but think in three-part paragraphs 25 Break sprawling sentences & paragraphs into shorter units 27 Use introductory elements & transitions to connect your thoughts 27 Emphasizing key points with sentence variety 29 Use trailing elements & asides for variety, emphasis, & elaboration 29 Invert your sentences for variety, transitions, & coherence 30 Just for Fun: How Charles the Great changed Latin to our benefit today 34 Get Out of Jail Free: e.g. for i.e. 36 Chapter 2 Breathing Life into Scientific & Technical Writing 37 Supporting your explanations with detail 39 Support your argument with colorful, specific detail 39 Evoke the five senses to make your descriptions come alive 41 Don’t neglect smell, the most evocative of the five senses 42 Use graphs, tables, figures, & equations to highlight, illustrate, & explain 43 Animating your sentences & descriptions with verbs 44 Use verb-driven clauses to convey information succinctly & emphatically 44 Use colorful, action verbs to animate your descriptions 46 Working with verbs, noun stacks, & sentence variety 49 Keep your verbs within sight of their subjects 49 Unstack those noun stacks 51 Just for Fun: Nominalize your verbs to inflict pain on your reader 58 Get Out of Jail Free: It’s for its 59 Chapter 3 Expanding Your Expressive Range 61 Using your first person subjective voice 63 When appropriate, write in the first person for a more engaging style 64 Know the difference between transitive & intransitive, active & passive 65 Use the first person in theses & dissertations when appropriate 66 Know when not to write in the first person 67 Use an overtly subjective voice to convey honesty, personality, & warmth 69 Going beyond “Plain English” to more varied expression 72 Know the value of “Plain English,” but recognize its limitations 72 Vary your sentence structure & length 74 Punctuate your beat with pauses 75 Place key words at the beginning & ending of your sentences 75 Expanding your vocabulary to convey nuance, beauty, & complexity 76 Collect good words 77 Look up & learn new words as you read, starting with this book 78 Just for Fun: American poet runs afoul of Plain English guidelines 82 Get Out of Jail Free: Principle for principal 84 Chapter 4 Connecting with a Wider Audience 85 Getting your reader’s attention 88 Know how to write a good lead (or lede) 88 Use colorful quotes to enliven your writing 90 Collect examples of good leads (or ledes) for ideas & inspiration 91 Structuring your articles, blogs, messages, texts, & tweets 94 Follow a newsletter checklist to meet a tight deadline 94 Use a three-step structure in your email messages 96 Tweet short & sweet – and with integrity 98 Base your level of formality and correctness on four touchstones: Purpose, audience, subject, & occasion 100 Communicating correctly 102 Know the rules & know when to break ’em 103 Proofread for eight errors of hurry & haste 105 Communicating inclusively 107 Recognize all genders, ages, & ethnicities 107 Be aware of gender differences in communication patterns 112 Avoid ambiguity when writing to non-native English speakers 113 Just for Fun: SlumberWrite software guaranteed to produce soporific writing 116 Get Out of Jail Free: Complementary for complimentary 118 Part Two —Speaking 119 Chapter 5 Mastering the Physical & Behavioral Skills of Public Speaking 121 Connecting with your posture, dress, & appearance 123 Stand & sit tall 123 Dress appropriately for the audience & the occasion 124 Connecting with your eyes 124 Look directly into their eyes 125 Expect less audience feedback when presenting online or on camera 125 Connecting with your voice 126 Don’t underestimate the power of your speaking voice 126 Play your voice like a musical instrument it is 127 Be proud of your accent 128 Enunciate your words 129 Connecting with your gestures, facial expressions, & movement 130 Expand your gestural range 131 Make your face interesting 131 Claim your space early & hold your ground 132 Just for Fun: Sailing, writing, & speaking 134 Get Out of Jail Free: There’s for there’re & subject-verb nonagreement 136 Chapter 6 Feeling & Projecting Confidence 137 Feeling confident 139 Get control of your mind & your body by breathing 139 Prepare, release tension, & adjust expectations 139 Take a six-step approach to feeling confident 140 Projecting confidence 140 Speak at full volume 141 Don’t rush your delivery 141 Take the twelve-step approach to projecting confidence 142 Recovering from mental lapses & technical glitches 142 Prepare a safety net 143 Remember that the audience is on your side 144 Be prepared to be challenged 145 Aim for good, not perfect 146 Just for Fun: Speaking your mind & breaking the rules like Jesse Ventura 148 Get Out of Jail Free: Myself for I, Me, and Bobby McGee 150 Chapter 7 Connecting with Content, Conviction, & Humor 153 Opening, holding, & closing well 154 Get their attention 154 Give an overview & emphasize transitions 156 Prepare a good closing 156 Playing your part convincingly 158 Play it for all it’s worth 158 Show them the real you 159 Underscore key points with visuals 159 Making it fun by having fun 161 Know which types of humor work best 162 Play it safe with self-deprecating humor 165 Just for Fun: Papa says to maintain parallel structure 168 Get Out of Jail Free: Nonparallel structure 169 Chapter 8 Practicing, Delivering, & Evaluating Your Presentation 173 Creating muscle memory by practicing 175 Rehearse your words out loud 176 Practice your gestures & expressions 176 Practice working within your allotted time 176 For most presentations, don’t read your text 177 Handling difficult questions & inappropriate questioners 177 Decide whether and when to take questions 178 Answer the question when you can 178 Know how to manage an interview & how to talk to the media 180 Evaluating presentations with a score sheet 182 Enforce a strict time limit 182 Offer timely feedback & constructive criticism 183 Concentrate on strengths & note areas for improvement 183 Use a score sheet to identify and evaluate skills 183 Just for Fun: Even Eliza Doolittle trips over the rules of English grammar 187 Get Out of Jail Free: Who or whom do you trust? 188 Epilogue 191 Appendix A Words Every Educated Person Should Know 195 Appendix B Sixteen Common Language Errors 199 Remember the eight language errors that got you out of jail 199 Avoid eight additional common language errors 202 Appendix C Key Physical & Behavioral Skills of Public Speaking 207 Appendix D Key Themes & Strategies 209 Key themes & highlights from Chapter Summaries 209 Part One: Writing 209 Part Two: Speaking 210 Appendix E Works Cited, Recommended Reading, & Style Guides 213 Works cited 213 Recommended reading 216 Style guides 217 Index 219

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Book SynopsisOptimize your dynamic spectrum access approach using the latest applications and techniques Dynamic Spectrum Access Decisions: Local, Distributed, Centralized and Hybrid Designs prepares engineers to build optimum communications systems by describing at the outset what type of spectrum sensing capabilities are needed. Meant for anyone who has a basic understanding of wireless communications and networks and an interest in the physical and MAC layers of communication systems, this book has a tremendous range of civilian and military applications. Dynamic Spectrum Access Decisions provides fulsome discussions of cognitive radios and networks, but also DSA technologies that operate outside the context of cognitive radios. DSA has applications in: Licensed spectrum bands Unlicensed spectrum bands Civilian communications Military communications Consisting of a set of techniques derived from network iTable of ContentsAbout the Author xxix Preface xxxi List of Acronyms xxxiii About the Companion Website xxxvii Part I DSA Basic Design Concept 1 1 Introduction 3 2 Spectrum Sensing Techniques 15 3 Receiver Operating Characteristics and Decision Fusion 35 4 Designing a Hybrid DSA System 57 Part II Case Studies 77 5 DSA as a Set of Cloud Services 79 6 Dynamic Spectrum Management for Cellular 5G Systems 93 7 DSA and 5G Adaptation to Military Communications 117 8 DSA and Co-site Interference Mitigation 127 Part III United States Army’s Techniques for Spectrum Management Operations 139 9 Overview 141 10 Tactical Staff Organization and Planning 149 11 Support to the Warfighting Functions 159 12 Joint Task Force Considerations 163 13 Spectrum Management Operations Tools 171 Appendix A Spectrum Management Task List 183 Appendix B Capabilities and Compatibility between Tools 195 Appendix C Spectrum Physics 201 Appendix D Spectrum Management Lifecycle 205 Appendix E Military Time Zone Designators 217 References 221 Part IV The IEEE Standards 1900x – 2019 – Dynamic Spectrum Access Networks Standards Committee (DySPAN-SC) 223 14 IEEE Standard for Definitions and Concepts for Dynamic Spectrum Access: Terminology Relating to Emerging Wireless Networks, System Functionality, and Spectrum Management 225 15 IEEE Recommended Practice for the Analysis of In-Band and Adjacent Band Interference and Coexistence Between Radio Systems 283 16 IEEE Standard for Architectural Building Blocks Enabling Network-Device Distributed Decision Making for Optimized Radio Resource Usage in Heterogeneous Wireless Access Networks 373 17 IEEE Standard for Policy Language Requirements and System Architectures for Dynamic Spectrum Access Systems 481 18 IEEE Standard for Spectrum Sensing Interfaces and Data Structures for Dynamic Spectrum Access and Other Advanced Radio Communication Systems 519 19 IEEE Standard for Radio Interface for White Space Dynamic Spectrum Access Radio Systems Supporting Fixed and Mobile Operation 657 Index 703

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Book SynopsisA concise and practical introduction to the foundations and engineering principles of self-adaptation Though it has recently gained significant momentum, the topic of self-adaptation remains largely under-addressed in academic and technical literature. This book changes that. Using a systematic and holistic approach, An Introduction to Self-adaptive Systems: A Contemporary Software Engineering Perspective provides readers with an accessible set of basic principles, engineering foundations, and applications of self-adaptation in software-intensive systems. It places self-adaptation in the context of techniques like uncertainty management, feedback control, online reasoning, and machine learning while acknowledging the growing consensus in the software engineering community that self-adaptation will be a crucial enabling feature in tackling the challenges of new, emerging, and future systems. The author combines cutting-edge technical research with bTable of ContentsForeword xi Acknowledgments xv Acronyms xvii Introduction xix 1 Basic Principles of Self-Adaptation and Conceptual Model 1 1.1 Principles of Self-Adaptation 2 1.2 Other Adaptation Approaches 4 1.3 Scope of Self-Adaptation 5 1.4 Conceptual Model of a Self-Adaptive System 5 1.4.1 Environment 5 1.4.2 Managed System 7 1.4.3 Adaptation Goals 8 1.4.4 Feedback Loop 8 1.4.5 Conceptual Model Applied 10 1.5 A Note on Model Abstractions 11 1.6 Summary 11 1.7 Exercises 12 1.8 Bibliographic Notes 14 2 Engineering Self-Adaptive Systems: A Short Tour in Seven Waves 17 2.1 Overview of the Waves 18 2.2 Contributions Enabled by the Waves 20 2.3 Waves Over Time with Selected Work 20 2.4 Summary 22 2.5 Bibliographic Notes 23 3 Internet-of-Things Application 25 3.1 Technical Description 25 3.2 Uncertainties 28 3.3 Quality Requirements and Adaptation Problem 29 3.4 Summary 29 3.5 Exercises 30 3.6 Bibliographic Notes 31 4 Wave I: Automating Tasks 33 4.1 Autonomic Computing 34 4.2 Utility Functions 35 4.3 Essential Maintenance Tasks for Automation 37 4.3.1 Self-Optimization 37 4.3.2 Self-Healing 38 4.3.3 Self-Protection 40 4.3.4 Self-Configuration 42 4.4 Primary Functions of Self-Adaptation 43 4.4.1 Knowledge 44 4.4.2 Monitor 46 4.4.3 Analyzer 47 4.4.4 Planner 49 4.4.5 Executor 51 4.5 Software Evolution and Self-Adaptation 52 4.5.1 Software Evolution Management 53 4.5.2 Self-Adaptation Management 54 4.5.3 Integrating Software Evolution and Self-Adaptation 55 4.6 Summary 56 4.7 Exercises 59 4.8 Bibliographic Notes 60 5 Wave II: Architecture-based Adaptation 63 5.1 Rationale for an Architectural Perspective 64 5.2 Three-Layer Model for Self-Adaptive Systems 66 5.2.1 Component Control 67 5.2.2 Change Management 67 5.2.3 Goal Management 68 5.2.4 Three-Layer Model Applied to DeltaIoT 68 5.2.5 Mapping Between the Three-Layer Model and the Conceptual Model for Self-Adaptation 70 5.3 Reasoning about Adaptation using an Architectural Model 70 5.3.1 Runtime Architecture of Architecture-based Adaptation 71 5.3.2 Architecture-based Adaptation of the Web-based Client-Server System 73 5.4 Comprehensive Reference Model for Self-Adaptation 75 5.4.1 Reflection Perspective on Self-Adaptation 76 5.4.2 MAPE-K Perspective on Self-Adaptation 78 5.4.3 Distribution Perspective on Self-Adaptation 79 5.5 Summary 83 5.6 Exercises 84 5.7 Bibliographic Notes 87 6 Wave III: Runtime Models 89 6.1 What is a Runtime Model? 90 6.2 Causality and Weak Causality 90 6.3 Motivations for Runtime Models 91 6.4 Dimensions of Runtime Models 92 6.4.1 Structural versus Behavioral 93 6.4.2 Declarative versus Procedural 94 6.4.3 Functional versus Qualitative 95 6.4.3.1 Functional Models 95 6.4.3.2 Quality Models 95 6.4.4 Formal versus Informal 98 6.5 Principal Strategies for Using Runtime Models 101 6.5.1 MAPE Components Share K Models 101 6.5.2 MAPE Components Exchange K Models 103 6.5.2.1 Runtime Models 103 6.5.2.2 Components of the Managing System 104 6.5.3 MAPE Models Share K Models 105 6.6 Summary 108 6.7 Exercises 109 6.8 Bibliographic Notes 114 7 Wave IV: Requirements-driven Adaptation 115 7.1 Relaxing Requirements for Self-Adaptation 116 7.1.1 Specification Language to Relax Requirements 116 7.1.1.1 Language Operators for Handling Uncertainty 116 7.1.1.2 Semantics of Language Primitives 118 7.1.2 Operationalization of Relaxed Requirements 118 7.1.2.1 Handing Uncertainty 118 7.1.2.2 Requirements Reflection and Mitigation Mechanisms 119 7.1.2.3 A Note on the Realization of Requirements Reflection 121 7.2 Meta-Requirements for Self-Adaptation 122 7.2.1 Awareness Requirements 123 7.2.2 Evolution Requirements 124 7.2.3 Operationalization of Meta-requirements 126 7.3 Functional Requirements of Feedback Loops 127 7.3.1 Design and Verify Feedback Loop Model 128 7.3.2 Deploy and Execute Verified Feedback Loop Model 130 7.4 Summary 131 7.5 Exercises 132 7.6 Bibliographic Notes 134 8 Wave V: Guarantees Under Uncertainties 137 8.1 Uncertainties in Self-Adaptive Systems 139 8.2 Taming Uncertainty with Formal Techniques 141 8.2.1 Analysis of Adaptation Options 141 8.2.2 Selection of Best Adaptation Option 143 8.3 Exhaustive Verification to Provide Guarantees for Adaptation Goals 144 8.4 Statistical Verification to Provide Guarantees for Adaptation Goals 149 8.5 Proactive Decision-Making using Probabilistic Model Checking 154 8.6 A Note on Verification and Validation 160 8.7 Integrated Process to Tame Uncertainty 160 8.7.1 Stage I: Implement and Verify the Managing System 161 8.7.2 Stage II: Deploy the Managing System 162 8.7.3 Stage III: Verify Adaptation Options, Decide, and Adapt 163 8.7.4 Stage IV: Evolve Adaptation Goals and Managing System 163 8.8 Summary 164 8.9 Exercises 165 8.10 Bibliographic Notes 168 9 Wave VI: Control-based Software Adaptation 171 9.1 A Brief Introduction to Control Theory 173 9.1.1 Controller Design 174 9.1.2 Control Properties 175 9.1.3 SISO and MIMO Control Systems 176 9.1.4 Adaptive Control 177 9.2 Automatic Construction of SISO Controllers 177 9.2.1 Phases of Controller Construction and Operation 178 9.2.2 Model Updates 179 9.2.3 Formal Guarantees 181 9.2.4 Example: Geo-Localization Service 183 9.3 Automatic Construction of MIMO Controllers 184 9.3.1 Phases of Controller Construction and Operation 184 9.3.2 Formal Guarantees 186 9.3.3 Example: Unmanned Underwater Vehicle 186 9.4 Model Predictive Control 189 9.4.1 Controller Construction and Operation 189 9.4.2 Formal Assessment 191 9.4.3 Example: Video Compression 192 9.5 A Note on Control Guarantees 194 9.6 Summary 194 9.7 Exercises 196 9.8 Bibliographic Notes 199 10 Wave VII: Learning from Experience 201 10.1 Keeping Runtime Models Up-to-Date Using Learning 203 10.1.1 Runtime Quality Model 204 10.1.2 Overview of Bayesian Approach 205 10.2 Reducing Large Adaptation Spaces Using Learning 208 10.2.1 Illustration of the Problem 208 10.2.2 Overview of the Learning Approach 210 10.3 Learning and Improving Scaling Rules of a Cloud Infrastructure 213 10.3.1 Overview of the Fuzzy Learning Approach 214 10.3.1.1 Fuzzy Logic Controller 214 10.3.1.2 Fuzzy Q-learning 217 10.3.1.3 Experiments 221 10.4 Summary 223 10.5 Exercises 225 10.6 Bibliographic Notes 226 11 Maturity of the Field and Open Challenges 227 11.1 Analysis of the Maturity of the Field 227 11.1.1 Basic Research 227 11.1.2 Concept Formulation 228 11.1.3 Development and Extension 229 11.1.4 Internal Enhancement and Exploration 229 11.1.5 External Enhancement and Exploration 230 11.1.6 Popularization 230 11.1.7 Conclusion 231 11.2 Open Challenges 231 11.2.1 Challenges Within the Current Waves 231 11.2.1.1 Evidence for the Value of Self-Adaptation 231 11.2.1.2 Decentralized Settings 232 11.2.1.3 Domain-Specific Modeling Languages 232 11.2.1.4 Changing Goals at Runtime 233 11.2.1.5 Complex Types of Uncertainties 233 11.2.1.6 Control Properties versus Quality Properties 234 11.2.1.7 Search-based Techniques 234 11.2.2 Challenges Beyond the Current Waves 235 11.2.2.1 Exploiting Artificial Intelligence 235 11.2.2.2 Dealing with Unanticipated Change 236 11.2.2.3 Trust and Humans in the Loop 236 11.2.2.4 Ethics for Self-Adaptive Systems 237 11.3 Epilogue 239 Bibliography 241 Index 263

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Book SynopsisExplore foundational and advanced issues in UAV cellular communications with this cutting-edge and timely new resource UAV Communications for 5G and Beyond delivers a comprehensive overview of the potential applications, networking architectures, research findings, enabling technologies, experimental measurement results, and industry standardizations for UAV communications in cellular systems. The book covers both existing LTE infrastructure, as well as future 5G-and-beyond systems. UAV Communications covers a range of topics that will be of interest to students and professionals alike. Issues of UAV detection and identification are discussed, as is the positioning of autonomous aerial vehicles. More fundamental subjects, like the necessary tradeoffs involved in UAV communication are examined in detail. The distinguished editors offer readers an opportunity to improve their ability to plan and design for the near-future, explosive growth in the number of UAVs, as well as the correspondTable of ContentsList of Contributors xvii Acronyms xxi Part I Fundamentals of UAV Communications 1 1 Overview 3Qingqing Wu, Yong Zeng, and Rui Zhang 1.1 UAV Definitions, Classes, and Global Trend 3 1.2 UAV Communication and Spectrum Requirement 4 1.3 Potential Existing Technologies for UAV Communications 6 1.3.1 Direct Link 6 1.3.2 Satellite 7 1.3.3 Ad-Hoc Network 8 1.3.4 Cellular Network 8 1.4 Two Paradigms in Cellular UAV Communications 9 1.4.1 Cellular-Connected UAVs 9 1.4.2 UAV-Assisted Wireless Communications 10 1.5 New Opportunities and Challenges 11 1.5.1 High Altitude 11 1.5.2 High LoS Probability 12 1.5.3 High 3D Mobility 12 1.5.4 SWAP Constraints 13 1.6 Chapter Summary and Main Organization of the Book 13 References 15 2 A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles 17Wahab Khawaja, Ismail Guvenc, David W. Matolak, Uwe-Carsten Fiebig, and Nicolas Schneckenberger 2.1 Introduction 17 2.2 Literature Review 20 2.2.1 Literature Review on Aerial Propagation 20 2.2.2 Existing Surveys on UAV AG Propagation 21 2.3 UAV AG Propagation Characteristics 22 2.3.1 Comparison of UAV AG and Terrestrial Propagation 22 2.3.2 Frequency Bands for UAV AG Propagation 23 2.3.3 Scattering Characteristics for AG Propagation 24 2.3.4 Antenna Configurations for AG Propagation 24 2.3.5 Doppler Effects 25 2.4 AG Channel Measurements: Configurations, Challenges, Scenarios, and Waveforms 25 2.4.1 Channel Measurement Configurations 26 2.4.2 Challenges in AG Channel Measurements 29 2.4.3 AG Propagation Scenarios 29 2.4.3.1 Open Space 31 2.4.3.2 Hilly/Mountainous 31 2.4.3.3 Forest 32 2.4.3.4 Water/Sea 32 2.4.4 Elevation Angle Effects 32 2.5 UAV AG Propagation Measurement and Simulation Results in the Literature 33 2.5.1 Path Loss/Shadowing 33 2.5.2 Delay Dispersion 36 2.5.3 Narrowband Fading and Ricean K-factor 36 2.5.4 Doppler Spread 37 2.5.5 Effects of UAV AG Measurement Environment 37 2.5.5.1 Urban/Suburban 38 2.5.5.2 Rural/Open Field 38 2.5.5.3 Mountains/Hilly, Over Sea, Forest 39 2.5.6 Simulations for Channel Characterization 40 2.6 UAV AG Propagation Models 41 2.6.1 AG Propagation Channel Model Types 41 2.6.2 Path-Loss and Large-Scale Fading Models 42 2.6.2.1 Free-Space Path-Loss Model 43 2.6.2.2 Floating-Intercept Path-Loss Model 43 2.6.2.3 Dual-Slope Path-Loss Model 43 2.6.2.4 Log-Distance Path-Loss Model 45 2.6.2.5 Modified FSPL Model 45 2.6.2.6 Two-Ray PL Model 45 2.6.2.7 Log-Distance FI Model 45 2.6.2.8 LOS/NLOS Mixture Path-Loss Model 46 2.6.3 Airframe Shadowing 47 2.6.4 Small-Scale Fading Models 47 2.6.5 Intermittent MPCs 48 2.6.6 Effect of Frequency Bands on Channel Models 51 2.6.7 MIMO AG Propagation Channel Models 52 2.6.8 Comparison of Different AG Channel Models 54 2.6.8.1 Large-Scale Fading Models 54 2.6.8.2 Small-Scale Fading Models 54 2.6.9 Comparison of Traditional Channel Models with UAV AG Propagation Channel Models 55 2.6.10 Ray Tracing Simulations 56 2.6.11 3GPP Channel Models for UAVs 58 2.7 Conclusions 60 References 60 3 UAV Detection and Identification 71Martins Ezuma, Fatih Erden, Chethan Kumar Anjinappa, Ozgur Ozdemir, Ismail Guvenc, and David Matolak 3.1 Introduction 71 3.2 RF-Based UAV Detection Techniques 75 3.2.1 RF Fingerprinting Technique 76 3.2.2 WiFi Fingerprinting Technique 76 3.3 Multistage UAV RF Signal Detection 77 3.3.1 Preprocessing Step: Multiresolution Analysis 78 3.3.2 The Naive Bayesian Decision Mechanism for RF Signal Detection 82 3.3.3 Detection of WiFi and Bluetooth Interference 84 3.4 UAV Classification Using RF Fingerprints 89 3.4.1 Feature Selection Using Neighborhood Components Analysis (NCA) 91 3.5 Experimental Results 92 3.5.1 Experimental Setup 92 3.5.2 Detection Results 94 3.5.3 UAV Classification Results 95 3.6 Conclusion 100 Acknowledgments 100 References 100 Part II Cellular-Connected UAV Communications 103 4 Performance Analysis for Cellular-Connected UAVs 105M. Mahdi Azari, Fernando Rosas, and Sofie Pollin 4.1 Introduction 105 4.1.1 Motivation 105 4.1.2 Related Works 107 4.1.3 Contributions and Chapter Structure 108 4.2 Modelling Preliminaries 109 4.2.1 Stochastic Geometry 109 4.2.2 Network Architecture 110 4.2.3 Channel Model 111 4.2.4 Blockage Modeling and LoS Probability 112 4.2.5 User Association Strategy and Link SINR 112 4.3 Performance Analysis 112 4.3.1 Exact Coverage Probability 113 4.3.2 Approximations for UAV Coverage Probability 115 4.3.2.1 Discarding NLoS and Noise Effects 116 4.3.2.2 Moment Matching 116 4.3.3 Achievable Throughput and Area Spectral Efficiency Analysis 118 4.4 System Design: Study Cases and Discussion 119 4.4.1 Analysis of Accuracy 119 4.4.2 Design Parameters 120 4.4.2.1 Impact of UAV Altitude 120 4.4.2.2 Impact of UAV Antenna Beamwidth 121 4.4.2.3 Impact of UAV Antenna Tilt 123 4.4.2.4 Impact of Different Types of Environment 123 4.4.3 Heterogeneous Networks – Tier Selection 125 4.4.4 Network Densification 127 4.5 Conclusion 129 References 136 5 Performance Enhancements for LTE-Connected UAVs: Experiments and Simulations 139Rafhael Medeiros de Amorim, Jeroen Wigard, István Z. Kovács, and Troels B. Sørensen 5.1 Introduction 139 5.2 LTE Live Network Measurements 140 5.2.1 Downlink Experiments 141 5.2.2 Path-Loss Model Characterization 145 5.2.3 Uplink Experiments 145 5.3 Performance in LTE Networks 149 5.4 Reliability Enhancements 150 5.4.1 Interference Cancellation 151 5.4.2 Inter-Cell Interference Control 152 5.4.3 CoMP 152 5.4.4 Antenna Beam Selection 153 5.4.5 Dual LTE Access 155 5.4.6 Dedicated Spectrum 158 5.4.7 Discussion 158 5.5 Summary and Outlook 159 References 160 6 3GPP Standardization for Cellular-Supported UAVs 163Helka-Liina Määttänen 6.1 Short Introduction to LTE and NR 163 6.1.1 LTE Physical Layer and MIMO 165 6.1.2 NR Physical Layer and MIMO 166 6.2 Drones Served by Mobile Networks 167 6.2.1 Interference Detection and Mitigation 168 6.2.2 Mobility for Drones 170 6.2.3 Need for Drone Identification and Authorization 171 6.3 3GPP Standardization Support for UAVs 172 6.3.1 Measurement Reporting Based on RSRP Level of Multiple Cells 172 6.3.2 Height, Speed, and Location Reporting 174 6.3.3 Uplink Power Control Enhancement 175 6.3.4 Flight Path Signalling 175 6.3.5 Drone Authorization and Identification 176 6.4 Flying Mode Detection in Cellular Networks 177 References 179 7 Enhanced Cellular Support for UAVs with Massive MIMO 181Giovanni Geraci, Adrian Garcia-Rodriguez, Lorenzo Galati Giordano, and David López-Pérez 7.1 Introduction 181 7.2 System Model 181 7.2.1 Cellular Network Topology 183 7.2.2 System Model 184 7.2.3 Massive MIMO Channel Estimation 186 7.2.4 Massive MIMO Spatial Multiplexing 186 7.3 Single-User Downlink Performance 187 7.3.1 UAV Downlink C&C Channel 187 7.4 Massive MIMO Downlink Performance 190 7.4.1 UAV Downlink C&C Channel 190 7.4.2 UAV–GUE Downlink Interplay 192 7.5 Enhanced Downlink Performance 194 7.5.1 UAV Downlink C&C Channel 195 7.5.2 UAV–GUE Downlink Interplay 196 7.6 Uplink Performance 197 7.6.1 UAV Uplink C&C Channel and Data Streaming 197 7.6.2 UAV–GUE Uplink Interplay 198 7.7 Conclusions 199 References 200 8 High-Capacity Millimeter Wave UAV Communications 203Nuria González-Prelcic, Robert W. Heath, Cristian Rusu, and Aldebaro Klautau 8.1 Motivation 203 8.2 UAV Roles and Use Cases Enabled by Millimeter Wave Communication 206 8.2.1 UAV Roles in Cellular Networks 206 8.2.2 UAV Use Cases Enabled by High-Capacity Cellular Networks 207 8.3 Aerial Channel Models at Millimeter Wave Frequencies 208 8.3.1 Propagation Considerations for Aerial Channels 208 8.3.1.1 Atmospheric Considerations 208 8.3.1.2 Blockages 210 8.3.2 Air-to-Air Millimeter Wave Channel Model 211 8.3.3 Air-to-Ground Millimeter Wave Channel Model 212 8.3.4 Ray Tracing as a Tool to Obtain Channel Measurements 214 8.4 Key Aspects of UAV MIMO Communication at mmWave Frequencies 215 8.5 Establishing Aerial mmWave MIMO Links 219 8.5.1 Beam Training and Tracking for UAV Millimeter Wave Communication 219 8.5.2 Channel Estimation and Tracking in Aerial Environments 219 8.5.3 Design of Hybrid Precoders and Combiners 221 8.6 Research Opportunities 222 8.6.1 Sensing at the Tower 222 8.6.2 Joint Communication and Radar 222 8.6.3 Positioning and Mapping 223 8.7 Conclusions 223 References 223 Part III UAV-Assisted Wireless Communications 231 9 Stochastic Geometry-Based Performance Analysis of Drone Cellular Networks 233Morteza Banagar, Vishnu V. Chetlur, and Harpreet S. Dhillon 9.1 Introduction 233 9.2 Overview of the System Model 235 9.2.1 Spatial Model 235 9.2.2 3GPP-Inspired Mobility Model 236 9.2.3 Channel Model 237 9.2.4 Metrics of Interest 237 9.3 Average Rate 238 9.4 Handover Probability 242 9.5 Results and Discussion 246 9.5.1 Density of Interfering DBSs 247 9.5.2 Average Rate 247 9.5.3 Handover Probability 249 9.6 Conclusion 250 Acknowledgment 251 References 251 10 UAV Placement and Aerial–Ground Interference Coordination 255Abhaykumar Kumbhar and Ismail Guvenc 10.1 Introduction 255 10.2 Literature Review 256 10.3 UABS Use Case for AG-HetNets 259 10.4 UABS Placement in AG-HetNet 260 10.5 AG-HetNet Design Guidelines 264 10.5.1 Path-Loss Model 265 10.5.1.1 Log-Distance Path-Loss Model 265 10.5.1.2 Okumura–Hata Path-Loss Model 266 10.6 Inter-Cell Interference Coordination 266 10.6.1 UE Association and Scheduling 269 10.7 Simulation Results 270 10.7.1 5pSE with UABSs Deployed on Hexagonal Grid 270 10.7.1.1 5pSE with Log-Normal Path-Loss Model 270 10.7.1.2 5pSE with Okumura–Hata Path-Loss Model 271 10.7.2 5pSE with GA-Based UABS Deployment Optimization 273 10.7.2.1 5pSE with Log-Normal Path-Loss Model 273 10.7.2.2 5pSE with Okumura–Hata Path-Loss model 275 10.7.3 Performance Comparison Between Fixed (Hexagonal) and Optimized UABS Deployment with eICIC and FeICIC 276 10.7.3.1 Influence of LDPLM on 5pSE 277 10.7.3.2 Influence of OHPLM on 5pSE 277 10.7.4 Comparison of Computation Time for Different UABS Deployment Algorithms 277 10.8 Concluding remarks 279 References 279 11 Joint Trajectory and Resource Optimization 283Yong Zeng, Qingqing Wu, and Rui Zhang 11.1 General Problem Formulation 283 11.2 Initial Path Planning via the Traveling Salesman and Pickup-and-Delivery Problems 285 11.2.1 TSP without Return 286 11.2.2 TSP with Given Initial and Final Locations 287 11.2.3 TSP with Neighborhood 287 11.2.4 Pickup-and-Delivery Problem 288 11.3 Trajectory Discretization 290 11.3.1 Time Discretization 290 11.3.2 Path Discretization 291 11.4 Block Coordinate Descent 291 11.5 Successive Convex Approximation 292 11.6 Unified Algorithm 295 11.7 Summary 296 References 296 12 Energy-Efficient UAV Communications 299Yong Zeng and Rui Zhang 12.1 UAV Energy Consumption Model 299 12.1.1 Fixed-Wing Energy Model 300 12.1.1.1 Forces on a UAV 300 12.1.1.2 Straight and Level Flight 301 12.1.1.3 Circular Flight 302 12.1.1.4 Arbitrary Level Flight 303 12.1.1.5 Arbitrary 3D Flight 304 12.1.2 Rotary-Wing Energy Model 304 12.2 Energy Efficiency Maximization 306 12.3 Energy Minimization with Communication Requirement 310 12.4 UAV–Ground Energy Trade-off 312 12.5 Chapter Summary 312 References 313 13 Fundamental Trade-Offs for UAV Communications 315Qingqing Wu, Liang Liu, Yong Zeng, and Rui Zhang 13.1 Introduction 315 13.2 Fundamental Trade-offs 317 13.2.1 Throughput–Delay Trade-Off 317 13.2.2 Throughput–Energy Trade-Off 318 13.2.3 Delay–Energy Trade-Off 319 13.3 Throughput–Delay Trade-Off 319 13.3.1 Single-UAV-Enabled Wireless Network 319 13.3.2 Multi-UAV-Enabled Wireless Network 321 13.4 Throughput–Energy Trade-Off 323 13.4.1 UAV Propulsion Energy Consumption Model 323 13.4.2 Energy-Constrained Trajectory Optimization 324 13.5 Further Discussions and Future Work 325 13.6 Chapter Summary 327 References 327 14 UAV–Cellular Spectrum Sharing 329Chiya Zhang and Wei Zhang 14.1 Introduction 329 14.1.1 Cognitive Radio 329 14.1.1.1 Overlay Spectrum Sharing 329 14.1.1.2 Underlay Spectrum Sharing 330 14.1.2 Drone Communication 330 14.1.2.1 UAV Spectrum Sharing 331 14.1.2.2 UAV Spectrum Sharing with Exclusive Regions 332 14.1.3 Chapter Overview 333 14.2 SNR Meta-Distribution of Drone Networks 333 14.2.1 Stochastic Geometry Analysis 333 14.2.2 Characteristic Function of the SNR Meta-Distribution 334 14.2.3 LOS Probability 338 14.3 Spectrum Sharing of Drone Networks 338 14.3.1 Spectrum Sharing in Single-Tier DSCs 339 14.3.2 Spectrum Sharing with Cellular Network 342 14.4 Summary 345 References 346 Part IV Other Advanced Technologies for UAV Communications 349 15 Non-Orthogonal Multiple Access for UAV Communications 351Tianwei Hou, Yuanwei Liu, and Xin Sun 15.1 Introduction 351 15.1.1 Motivation 352 15.2 User-Centric Strategy for Emergency Communications 352 15.2.1 System Model 354 15.2.1.1 Far user case 354 15.2.1.2 Near user case 355 15.2.2 Coverage Probability of the User-Centric Strategy 356 15.3 UAV-Centric Strategy for Offloading Actions 359 15.3.1 SINR Analysis 360 15.3.2 Coverage Probability of the UAV-Centric Strategy 361 15.4 Numerical Results 364 15.4.1 User-Centric Strategy 365 15.4.2 UAV-Centric Strategy 367 15.5 Conclusions 369 References 369 16 Physical Layer Security for UAV Communications 373Nadisanka Rupasinghe, Yavuz Yapici, Ismail Guvenc, Huaiyu Dai, and Arupjyoti Bhuyan 16.1 Introduction 373 16.2 Breaching Security in Wireless Networks 374 16.2.1 Denial-of-Service Attacks 374 16.2.2 Masquerade Attacks 374 16.2.3 Message Modification Attacks 374 16.2.4 Eavesdropping Intruders 375 16.2.5 Traffic Analysis 375 16.3 Wireless Network Security Requirements 375 16.3.1 Authenticity 375 16.3.2 Confidentiality 376 16.3.3 Integrity 376 16.3.4 Availability 376 16.4 Physical Layer Security 376 16.4.1 Physical Layer versus Upper Layers 377 16.4.2 Physical Layer Security Techniques 377 16.4.2.1 Artificial Noise 378 16.4.2.2 Cooperative Jamming 378 16.4.2.3 Protected Zone 378 16.5 Physical Layer Security for UAVs 379 16.5.1 UAV Trajectory Design to Enhance PLS 379 16.5.2 Cooperative Jamming to Enhance PLS 381 16.5.3 Spectral- and Energy-Efficient PLS Techniques 382 16.6 A Case Study: Secure UAV Transmission 383 16.6.1 System Model 383 16.6.1.1 Location Distribution and mmWave Channel Model 385 16.6.2 Protected Zone Approach for Enhancing PLS 385 16.6.3 Secure NOMA for UAV BS Downlink 386 16.6.3.1 Secrecy Outage and Sum Secrecy Rates 386 16.6.3.2 Shape Optimization for Protected Zone 388 16.6.3.3 Numerical Results 389 16.6.3.4 Location of the Most Detrimental Eavesdropper 389 16.6.3.5 Impact of the Protected Zone Shape on Secrecy Rates 390 16.6.3.6 Variation of Secrecy Rates with Altitude 391 Summary 392 References 393 17 UAV-Enabled Wireless Power Transfer 399Jie Xu, Yong Zeng, and Rui Zhang 17.1 Introduction 399 17.2 System Model 401 17.3 Sum-Energy Maximization 402 17.4 Min-Energy Maximization under Infinite Charging Duration 403 17.4.1 Multi-Location-Hovering Solution 404 17.5 Min-Energy Maximization Under Finite Charging Duration 407 17.5.1 Successive Hover-and-Fly Trajectory Design 407 17.5.1.1 Flying Distance Minimization to Visit Γ Hovering Locations 407 17.5.1.2 Hovering Time Allocation When T ≥ Tfly 408 17.5.1.3 Trajectory Refinement When T < Tfly 409 17.5.2 SCA-Based Trajectory Design 409 17.6 Numerical Results 411 17.7 Conclusion and Future Research Directions 413 References 415 18 Ad-Hoc Networks in the Sky 417Kamesh Namuduri 18.1 Communication Support for UAVs 417 18.1.1 Satellite Connectivity 418 18.1.2 Cellular Connectivity 420 18.1.3 Aerial Connectivity 420 18.2 The Mobility Challenge 421 18.2.1 UAS-to-UAS Communication 421 18.2.2 Mobility Models 422 18.3 Establishing an Ad-Hoc Network 423 18.3.1 Network Addressing 424 18.3.2 Routing 425 18.4 Standards 426 18.4.1 ASTM: Remote ID for UAS 426 18.4.2 EUROCAE: Safe, Secure, and Efficient UAS Operations 426 18.4.3 3GPP: 4G LTE and 5G Support for Connected UAS Operations 426 18.4.4 IEEE P1920.1: Aerial Communications and Networking Standards 427 18.4.5 IEEE P1920.2: Vehicle-to-Vehicle Communications Standard for UAS 427 18.5 Technologies and Products 427 18.5.1 Silvus Streamcaster 427 18.5.2 goTenna 427 18.5.3 MPU5 and Wave Relay from Persistent Systems 428 18.5.4 Kinetic Mesh Networks from Rajant 428 18.6 Software-Defined Network as a Solution for UAV Networks 428 18.7 Summary 429 References 429 Index 433

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Book SynopsisSustainable Manufacturing Systems Learn more about energy efficiency in traditional and advanced manufacturing settings with this leading and authoritative resource Sustainable Manufacturing Systems: An Energy Perspective delivers a comprehensive analysis of energy efficiency in sustainable manufacturing. The book presents manufacturing modeling methods and energy efficiency evaluation and improvement methods for different manufacturing systems. It allows industry professionals to understand the methodologies and techniques being embraced around the world that lead to advanced energy management. The book offers readers a comprehensive and systematic theoretical foundation for novel manufacturing system modeling, analysis, and control. It concludes with a summary of the insights and applications contained within and a discussion of future research issues that have yet to be grappled with. Sustainable Manufacturing Systems answers the questions that energy customers, managers, decision mTable of ContentsAuthor Biography xv Preface xvii Acknowledgments xxiii List of Figures xxv Part I Introductions to Energy Efficiency in Manufacturing Systems 1 1 Introduction 3 1.1 Definitions and Practices of Sustainable Manufacturing 3 1.1.1 Current Status of Manufacturing Industry 3 1.1.2 Sustainability in the Manufacturing Sector and Associated Impacts 5 1.1.3 Sustainable Manufacturing Practices 10 1.2 Fundamental of Manufacturing Systems 12 1.2.1 Stages of Product Manufacturing 12 1.2.2 Classification of Manufacturing Systems 13 1.2.2.1 Job Shop 13 1.2.2.2 Project Shop 14 1.2.2.3 Cellular System 15 1.2.2.4 Flow Line 15 1.2.2.5 Continuous System 15 1.3 Problem Statement and Scope 18 Problems 19 References 19 2 Energy Efficiency in Manufacturing Systems 23 2.1 Energy Consumption in Manufacturing Systems 23 2.1.1 Energy and Power Basics 23 2.1.2 Energy Generation 24 2.1.2.1 Primary Energy 25 2.1.2.2 Secondary Energy 27 2.1.3 Energy Distribution 27 2.1.3.1 Electricity 28 2.1.3.2 Steam 30 2.1.3.3 Compressed Air 30 2.1.4 Energy Consumption 31 2.1.4.1 Indirect End Use 33 2.1.4.2 Direct Process End Use 33 2.1.4.3 Direct Non-process End Use 34 2.2 Energy Saving Potentials and Energy Management Strategies for Manufacturing Systems 35 2.2.1 Machine Level 39 2.2.1.1 Intrinsic Characteristics of Machine Tools 41 2.2.1.2 Processing Conditions 42 2.2.2 System Level 43 2.2.2.1 Inhomogeneous System 44 2.2.2.2 Machine Maintenance 45 2.2.3 Plant Level 46 2.2.3.1 Indirect End Use 46 2.2.3.2 Direct Non-process End Use 47 2.3 Demand-side Energy Management 49 2.3.1 Electricity Bill Components 50 2.3.1.1 Electricity Cost 51 2.3.1.2 Demand Cost 51 2.3.1.3 Fixed Cost 52 2.3.2 Energy Efficiency Programs 52 2.3.3 Demand Response Programs 55 2.3.3.1 Incentive-based Programs 56 2.3.3.2 Price Base Options 57 Problems 59 References 59 Part II Mathematical Tools and Modeling Basics 65 3 Mathematical Tools 67 3.1 Probability 67 3.1.1 Fundamentals of Probability Theory 67 3.1.1.1 Basics of Probability Theory 67 3.1.1.2 Axioms of Probability Theory 69 3.1.1.3 Conditional Probability and Independence 72 3.1.1.4 Total Probability Theorem 73 3.1.1.5 Bayes’ Law 74 3.1.2 Random Variables 74 3.1.2.1 Discrete Random Variables 75 3.1.2.2 Continuous Random Variables 82 3.1.3 Random Process 88 3.1.3.1 Discrete-time Markov Chain 89 3.1.3.2 Continuous-time Markov Chain 92 3.2 Petri Net 94 3.2.1 Formal Definition of Petri Net 95 3.2.1.1 Definition of Petri Net 95 3.2.2 Classical Petri Net 99 3.2.2.1 State Machine Petri Net 101 3.2.2.2 Marked Graph 102 3.2.2.3 Systematic Modeling Methods 105 3.2.3 Deterministic Timed Petri Net 106 3.2.4 Stochastic Petri Net 109 3.3 Optimization Methods 113 3.3.1 Fundamentals of Optimization 113 3.3.1.1 Objective Function 114 3.3.1.2 Decision Variables 114 3.3.1.3 Constraints 115 3.3.1.4 Local and Global Optimum 116 3.3.1.5 Near-optimal Solutions 117 3.3.1.6 Single-objective and Multi-objective Optimization 117 3.3.1.7 Deterministic and Stochastic Optimization 118 3.3.2 Genetic Algorithms 119 3.3.2.1 Initialization 119 3.3.2.2 Evaluation 121 3.3.2.3 Selection 121 3.3.2.4 Crossover 123 3.3.2.5 Mutation 124 3.3.2.6 Termination Criteria 125 3.3.3 Particle Swarm Optimizer (PSO) 126 3.3.3.1 Initialization 126 3.3.3.2 Evaluation 128 3.3.3.3 Personal and Global Best Positions 128 3.3.3.4 Updating Velocity and Position 129 3.3.3.5 Termination Criteria 132 Problems 132 References 134 4 Mathematical Modeling of Manufacturing Systems 139 4.1 Basics in Manufacturing System Modeling 139 4.1.1 Structure of Manufacturing Systems 139 4.1.1.1 Basic Components 139 4.1.1.2 Structural Modeling 140 4.1.1.3 Types of Manufacturing Systems 141 4.1.2 Mathematical Models of Machines and Buffers 142 4.1.2.1 Timing Issues for Machines 143 4.1.2.2 Machine Reliability Models 143 4.1.2.3 Parameters of Aggregated Machines 145 4.1.2.4 Mathematical Model of Buffers 146 4.1.2.5 Interaction Between Machines and Buffers 147 4.1.2.6 Buffer State Transition 147 4.1.2.7 Blockage and Starvation 148 4.1.3 Performance Measures 150 4.1.3.1 Blockage and Starvation 150 4.1.3.2 Production Rate and Throughput 151 4.1.3.3 Work-in-process 151 4.2 Two-machine Production Lines 152 4.2.1 Conventions and Notations 152 4.2.1.1 Assumptions 152 4.2.1.2 Notations 152 4.2.2 State Transition 154 4.2.2.1 State Transition Probabilities 155 4.2.2.2 System Dynamics 157 4.2.3 Steady-state Probabilities 157 4.2.3.1 Identical Machines 159 4.2.3.2 Nonidentical Machines 160 4.2.4 Performance Measures 161 4.2.4.1 Blockage and Starvation 161 4.2.4.2 Production Rate 161 4.2.4.3 Work-in-process 162 4.3 Multi-machine Production Lines 162 4.3.1 Assumptions and Notations 163 4.3.1.1 Assumptions 163 4.3.1.2 Notations 163 4.3.2 State Transition 164 4.3.2.1 State Transition Probabilities 165 4.3.2.2 System Dynamics 167 4.3.3 Performance Measures 167 4.3.3.1 Blockage and Starvation 167 4.3.3.2 Production Rate 168 4.3.3.3 Work-in-process 169 4.3.4 System Modeling with Iteration-based Method 169 4.4 Production Lines Coupled with Material Handling Systems 174 4.4.1 Assumptions and Notations 174 4.4.1.1 Assumptions 175 4.4.1.2 Notations 175 4.4.2 State Transition and Performance 175 4.4.2.1 Blockage and Starvation 175 4.4.2.2 Production Rate 176 Problems 179 References 180 5 Energy Efficiency Characterization in Manufacturing Systems 181 5.1 Energy Consumption Modeling 181 5.1.1 Operation-based Energy Modeling 182 5.1.2 Component-based Energy Modeling 185 5.1.3 System-level Energy Modeling 188 5.2 Energy Cost modeling 191 5.2.1 Energy Cost Under Flat Rate 192 5.2.1.1 Energy Consumption Cost 192 5.2.1.2 Demand Cost 192 5.2.2 Energy Cost Under Time-of-use Rate 196 5.2.2.1 Energy Consumption Cost 196 5.2.2.2 Demand Cost 198 5.2.3 Energy Cost Under Critical Peak Price (CPP) 199 5.2.3.1 Energy Consumption Cost 199 5.2.3.2 Demand Cost 200 Problems 203 References 203 Part III Energy Management in Typical Manufacturing Systems 205 6 Electricity Demand Response for Manufacturing Systems 207 6.1 Time-of-use Pricing for Manufacturing Systems 208 6.1.1 Introduction to TOU 208 6.1.2 Survey of TOU Pricing in US Utilities 209 6.1.3 Comparison of Energy Cost Between Flat Rate and TOU Rates 210 6.2 TOU-Based Production Scheduling for Manufacturing Systems 216 6.2.1 Manufacturing Systems Modeling 216 6.2.2 Energy Consumption and Energy Cost Modeling 218 6.2.3 Production Scheduling for TOU-based Demand Response 219 6.2.3.1 Production Scheduling Problem Formulation 219 6.2.3.2 PSO Algorithm for Near-optimal Solutions 220 6.2.3.3 Case Study Setup 221 6.2.3.4 Optimal Production Schedules 222 6.3 Critical Peak Pricing for Manufacturing Systems 228 6.3.1 Introduction to Critical Peak Pricing (CPP) 228 6.3.2 Comparison of Energy Cost Between TOU and CPP Rates 229 Problems 234 Appendix 3.A Supplementary Information of Demand Response Tariffs 235 References 255 7 Energy Control and Optimization for Manufacturing Systems Utilizing Combined Heat and Power System 257 7.1 Introduction to Combined Heat and Power System 257 7.2 Problem Definition and Modeling 258 7.2.1 Objective Function 260 7.2.1.1 Electricity Cost 260 7.2.1.2 Operation Cost for the CHP System and Boiler 261 7.2.2 Constraints 262 7.3 Solution Approach 263 7.3.1 Initialization 263 7.3.2 Evaluation 264 7.3.3 Updating Process 265 7.4 Case Study 266 7.4.1 Case Study Settings 267 7.4.2 Results and Discussions 269 Problems 270 References 271 8 Plant-level Energy Management for Combined Manufacturing and HVAC System 273 8.1 Definition and Modeling 273 8.1.1 Objective Function 274 8.1.1.1 Calculate TEL(t) 276 8.1.1.2 Estimate q(t) 278 8.1.2 Constraints 279 8.2 Solution Approach 281 8.2.1 Initialization 281 8.2.2 Evaluation 282 8.2.3 Updating Process 282 8.3 Case Study 283 8.3.1 Model Settings 284 8.3.2 Results and Discussions 287 Problems 289 References 290 Part IV Energy Management in Advanced Manufacturing Systems 291 9 Energy Analysis of Stereolithography-based Additive Manufacturing 293 9.1 Introduction to Additive Manufacturing 293 9.1.1 Illustration of MIP SL-based AM Process 294 9.2 Energy Consumption Modeling 296 9.2.1 Energy Consumption of UV Curing Process 297 9.2.2 Energy Consumption of Building Platform Movement 298 9.2.3 Energy Consumption of Cooling System 298 9.3 Experimentation 298 9.3.1 Experiment Design Methodology 298 9.3.2 Experiment Apparatus 299 9.4 Results and Discussions 300 9.4.1 Baseline Case Results Using Default Conditions 300 9.4.2 Factorial Analysis Results 302 9.4.3 Product Quality Comparison 305 Problems 308 References 308 10 Energy Efficiency Modeling and Optimization of Cellulosic Biofuel Manufacturing System 311 10.1 Introduction to Cellulosic Biofuel Manufacturing 311 10.2 Energy Modeling of Cellulosic Biofuel Production 313 10.2.1 Energy Modeling of Biomass Size Reduction Process 314 10.2.2 Energy Modeling of Biofuel Chemical Conversion Processes 314 10.2.2.1 Heating Energy 315 10.2.2.2 Energy Loss 316 10.2.2.3 Reaction Energy 317 10.2.2.4 Energy Recovery 320 10.2.2.5 Total Energy Consumption 321 10.3 Energy Consumption Optimization Using PSO 321 10.3.1 Problem Formulation 321 10.3.2 Solution Procedures 322 10.3.2.1 Initialization 322 10.3.2.2 Evaluation 323 10.3.2.3 Updating Process 323 10.4 Case Study 323 10.4.1 Case Settings 324 10.4.2 Energy Analysis of Baseline Case 324 10.4.2.1 Energy Consumption Breakdown 324 10.4.3 Energy Analysis of Optimal Results 327 Problems 328 References 329 11 Energy-consumption Minimized Scheduling of Flexible Manufacturing Systems 333 11.1 Introduction 334 11.2 Construction of Place-timed PN for FMS Scheduling 335 11.2.1 Basic Definitions of PN 335 11.2.2 Place-timed PN Scheduling Models of FMS 336 11.3 Energy Consumption Functions 338 11.3.1 Calculating the Earliest Firing Time of Transitions 339 11.3.2 Two Energy Consumption Functions 340 11.3.2.1 Energy Consumption Function E1 341 11.3.2.2 Energy Consumption Function E2 341 11.4 Dynamic Programming for Scheduling FMS 344 11.4.1 Formulation of DP for FMSs 344 11.4.1.1 States and Stages 344 11.4.1.2 State Transition Equation 344 11.4.1.3 Bellman Equation 345 11.4.2 Reachability Graph of PNS 345 11.4.3 DP Implementation for Scheduling FMS 347 11.5 Modified Dynamic Programming for Scheduling FMS 348 11.5.1 Evaluation Function of Transition Sequences 349 11.5.2 Heuristic Function 350 11.5.3 MDP Algorithm for FMS Scheduling 351 11.6 Case Study 353 11.7 Summary 358 Problems 358 References 359 Part V Summaries and Conclusions 363 12 Research Trends and Future Directions in Sustainable Industrial Development 365 12.1 Insights into Sustainable Industrial Development 365 12.2 Energy and Resource Efficiency in Manufacturing 366 12.2.1 Equipment Design 366 12.2.2 Smart Manufacturing 367 12.3 Industrial Symbiosis 369 12.4 Supply Chain Management 371 12.5 Circular Economy 373 12.6 Life Cycle Assessment 376 References 378 Glossary 387 Acronyms 391 Index 393

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Book SynopsisPhotovoltaic Solar Energy Thoroughly updated overview of photovoltaic technology, from materials to modules and systems Volume 2 of Photovoltaic Solar Energy provides fundamental and contemporary knowledge about various photovoltaic technologies in the framework of material science, device physics of solar cells, chemistry for manufacturing, engineering of PV modules, and the design aspects of photovoltaic applications, with the aim of informing the reader about the basic knowledge of each aspect of photovoltaic technologies and applications in the context of the most recent advances in science and engineering. The text is written by leading specialists for each topic in a concise manner and includes the most recent references for deeper study. Moreover, the book gives insights into possible future developments in the field of photovoltaics. The book builds on the success of Volume 1 of Photovoltaic Solar Energy, which was published by Wiley in

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Book SynopsisA concise and up-to-date introduction to mathematical methods for students in the physical sciences Mathematical Methods in Physics, Engineering and Chemistry offers an introduction to the most important methods of theoretical physics. Written by two physics professors with years of experience, the text puts the focus on the essential math topics that the majority of physical science students require in the course of their studies. This concise text also contains worked examples that clearly illustrate the mathematical concepts presented and shows how they apply to physical problems. This targeted text covers a range of topics including linear algebra, partial differential equations, power series, Sturm-Liouville theory, Fourier series, special functions, complex analysis, the Green's function method, integral equations, and tensor analysis. This important text: Provides a streamlined approach to the subject by putting the focus on the mathematical topics that physical science studeTable of ContentsPreface xi 1 Vectors and linear operators 1 1.1 The linearity of physical phenomena 1 1.2 Vector spaces 2 1.2.1 A word on notation 4 1.2.2 Linear independence, bases, and dimensionality 5 1.2.3 Subspaces 7 1.2.4 Isomorphism of N-dimensional spaces 8 1.2.5 Dual spaces 8 1.3 Inner products and orthogonality 10 1.3.1 Inner products 10 1.3.2 The Schwarz inequality 11 1.3.3 Vector norms 12 1.3.4 Orthonormal bases and the Gram–Schmidt process 12 1.3.5 Complete sets of orthonormal vectors 15 1.4 Operators and matrices 16 1.4.1 Linear operators 17 1.4.2 Representing operators with matrices 18 1.4.3 Matrix algebra 20 1.4.4 Rank and nullity 22 1.4.5 Bounded operators 23 1.4.6 Inverses 24 1.4.7 Change of basis and the similarity transformation 25 1.4.8 Adjoints and Hermitian operators 27 1.4.9 Determinants and the matrix inverse 29 1.4.10 Unitary operators 33 1.4.11 The trace of a matrix 35 1.5 Eigenvectors and their role in representing operators 36 1.5.1 Eigenvectors and eigenvalues 36 1.5.2 The eigenproblem for Hermitian and unitary operators 39 1.5.3 Diagonalizing matrices 40 1.6 Hilbert space: Infinite-dimensional vector space 43 Exercises 47 2 Sturm–Liouville theory 51 2.1 Second-order differential equations 52 2.1.1 Uniqueness and linear independence 52 2.1.2 The adjoint operator 55 2.1.3 Self-adjoint operator 56 2.2 Sturm–Liouville systems 57 2.3 The Sturm–Liouville eigenproblem 60 2.4 The Dirac delta function 64 2.5 Completeness 66 2.6 Recap 68 Summary 68 Exercises 69 3 Partial differential equations 71 3.1 A survey of partial differential equations 71 3.1.1 The continuity equation 71 3.1.2 The diffusion equation 72 3.1.3 The free-particle Schrödinger equation 73 3.1.4 The heat equation 73 3.1.5 The inhomogeneous diffusion equation 74 3.1.6 Schrödinger equation for a particle in a potential field 74 3.1.7 The Poisson equation 74 3.1.8 The Laplace equation 75 3.1.9 The wave equation 75 3.1.10 Inhomogeneous wave equation 76 3.1.11 Summary of PDEs 76 3.2 Separation of variables and the Helmholtz equation 76 3.2.1 Rectangular coordinates 78 3.2.2 Cylindrical coordinates 80 3.2.3 Spherical coordinates 82 3.3 The paraxial approximation 83 3.4 The three types of linear PDEs 84 3.4.1 Hyperbolic PDEs 85 3.4.2 Parabolic PDEs 87 3.4.3 Elliptic PDEs 87 3.5 Outlook 88 Summary 88 Exercises 89 4 Fourier analysis 91 4.1 Fourier series 91 4.2 The exponential form of Fourier series 96 4.3 General intervals 98 4.4 Parseval’s theorem 103 4.5 Back to the delta function 105 4.6 Fourier transform 107 4.7 Convolution integral 111 Summary 115 Exercises 116 5 Series solutions of ordinary differential equations 121 5.1 The Frobenius method 122 5.1.1 Power series 122 5.1.2 Introductory example 123 5.1.3 Ordinary points 125 5.1.4 Regular singular points 130 5.2 Wronskian method for obtaining a second solution 137 5.3 Bessel and Neumann functions 137 5.4 Legendre polynomials 142 Summary 144 Exercises 145 6 Spherical harmonics 147 6.1 Properties of the Legendre polynomials, Pl(x) 148 6.1.1 Rodrigues formula 148 6.1.2 Orthogonality 150 6.1.3 Completeness 151 6.1.4 Generating function 152 6.1.5 Recursion relations 155 6.2 Associated Legendre functions, Pm l (x) 157 6.3 Spherical harmonic functions, Yml (θ, φ) 158 6.4 Addition theorem for Ym l (θ, φ) 160 6.5 Laplace equation in spherical coordinates 166 Summary 167 Exercises 168 7 Bessel functions 173 7.1 Small-argument and asymptotic forms 173 7.1.1 Limiting forms for small argument 173 7.1.2 Asymptotic forms for large argument 174 7.1.3 Hankel functions 174 7.2 Properties of the Bessel functions, Jn(x) 175 7.2.1 Series associated with the generating function 175 7.2.2 Recursion relations 177 7.2.3 Integral representation 178 7.3 Orthogonality 180 7.4 Bessel series 182 7.5 The Fourier-Bessel transform 185 7.6 Spherical Bessel functions 186 7.6.1 Reduction to elementary functions 186 7.6.2 Small-argument forms 188 7.6.3 Asymptotic forms 188 7.6.4 Orthogonality and completeness 189 7.7 Expansion of plane waves in spherical harmonics 190 Summary 192 Exercises 192 8 Complex analysis 195 8.1 Complex functions 195 8.2 Analytic functions: differentiable in a region 197 8.2.1 Continuity, differentiability, and analyticity 197 8.2.2 Cauchy–Riemann conditions 198 8.2.3 Analytic functions are functions only of z = x + iy 201 8.2.4 Useful definitions 201 8.3 Contour integrals 202 8.4 Integrating analytic functions 206 8.5 Cauchy integral formulas 210 8.5.1 Derivatives of analytic functions 211 8.5.2 Consequences of the Cauchy formulas 212 8.6 Taylor and Laurent series 213 8.6.1 Taylor series 213 8.6.2 The zeros of analytic functions are isolated 215 8.6.3 Laurent series 215 8.7 Singularities and residues 217 8.7.1 Isolated singularities, residue theorem 217 8.7.2 Multivalued functions, branch points, and branch cuts 220 8.8 Definite integrals 221 8.8.1 Integrands containing cos θ and sin θ 222 8.8.2 Infinite integrals 223 8.8.3 Poles on the contour of integration 226 8.9 Meromorphic functions 228 8.10 Approximation of integrals 230 8.10.1 The method of steepest descent 233 8.10.2 The method of stationary phase 235 8.11 The analytic signal 236 8.11.1 The Hilbert transform 237 8.11.2 Paley–Wiener and Titchmarsh theorems 239 8.11.3 Is the analytic signal, analytic? 241 8.12 The Laplace transform 242 Summary 245 Exercises 245 9 Inhomogeneous differential equations 251 9.1 The method of Green functions 251 9.1.1 Boundary conditions 252 9.1.2 Reciprocity relation: G(x, x’) = G(x’, x) 253 9.1.3 Matching conditions 254 9.1.4 Direct construction of G(x, x’) 255 9.1.5 Eigenfunction expansions 257 9.2 Poisson equation 260 9.2.1 Boundary conditions and reciprocity relations 261 9.2.2 So, what’s the Green function? 263 9.3 Helmholtz equation 266 9.3.1 Green function for two-dimensional problems 267 9.3.2 Free-space Green function for three dimensions 270 9.3.3 Expansion in spherical harmonics 270 9.4 Diffusion equation 272 9.4.1 Boundary conditions, causality, and reciprocity 272 9.4.2 Solution to the diffusion equation 274 9.4.3 Free-space Green function 275 9.5 Wave equation 279 9.6 The Kirchhoff integral theorem 283 Summary 284 Exercises 284 10 Integral equations 287 10.1 Introduction 287 10.1.1 Equivalence of integral and differential equations 287 10.1.2 Role of coordinate systems in capturing boundary data 288 10.2 Classification of integral equations 290 10.3 Neumann series 291 10.4 Integral transform methods 293 10.4.1 Difference kernels 293 10.4.2 Fourier kernels 294 10.5 Separable kernels 295 10.6 Self-adjoint kernels 297 10.7 Numerical approaches 302 10.7.1 Matrix form 302 10.7.2 Measurement space 303 10.7.3 The generalized inverse 306 Summary 314 Exercises 315 11 Tensor analysis 319 11.1 Once over lightly: A quick intro to tensors 319 11.2 Transformation properties 327 11.2.1 The two types of vector: Contravariant and covariant 327 11.2.2 Coordinate transformations 328 11.2.3 Contravariant vectors and tensors 332 11.2.4 Covariant vectors and tensors 336 11.2.5 Mixed tensors 339 11.2.6 Covariant equations 339 11.3 Contraction and the quotient theorem 340 11.4 The metric tensor 342 11.5 Raising and lowering indices 344 11.6 Geometric properties of covariant vectors 347 11.7 Relative tensors 350 11.8 Tensors as operators 353 11.9 Symmetric and antisymmetric tensors 356 11.10 The Levi-Civita tensor 357 11.11 Pseudotensors 360 11.12 Covariant differentiation of tensors 363 Summary 373 Exercises 374 A Vector calculus 377 A.1 Scalar fields 377 A.1.1 The directional derivative 377 A.1.2 The gradient 378 A.2 Vector fields 379 A.2.1 Divergence 379 A.2.2 Curl 380 A.2.3 The Laplacian 380 A.2.4 Vector operator formulae 381 A.3 Integration 382 A.3.1 Line integrals 382 A.3.2 Surface integrals 383 A.4 Important integral theorems in vector calculus 384 A.4.1 Green’s theorem in the plane 384 A.4.2 The divergence theorem 386 A.4.3 Stokes’ theorem 386 A.4.4 Conservative fields 387 A.4.5 The Helmholtz theorem 389 A.5 Coordinate systems 390 A.5.1 Orthogonal curvilinear coordinates 390 A.5.2 Unit vectors 391 A.5.3 Differential displacement 392 A.5.4 Differential surface and volume elements 393 A.5.5 Transformation of vector components 393 A.5.6 Cylindrical coordinates 394 B Power series 401 C The gamma function, Γ(x) 403 Recursion relation 403 Limit formula 404 Reflection formula 405 Digamma function 405 D Boundary conditions for Partial Differential Equations 409 Summary 417 References 419 Index 421

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Book SynopsisOffers an understanding of the theoretical principles in electronic engineering, in clear and understandable terms Introductory Electrical Engineering With Math Explained in Accessible Language offers a text that explores the basic concepts and principles of electrical engineering. The authora noted expert on the topicexplains the underlying mathematics involved in electrical engineering through the use of examples that help with an understanding of the theory. The text contains clear explanations of the mathematical theory that is needed to understand every topic presented, which will aid students in engineering courses who may lack the necessary basic math knowledge. Designed to breakdown complex math concepts into understandable terms, the book incorporates several math tricks and knowledge such as matrices determinant and multiplication. The author also explains how certain mathematical formulas are derived. In addition, the text includes tables of integrals and other tables to helTable of ContentsAbout the Author xix Preface xxi Acknowledgement xxiii Introduction xxv Conventions: Used by this Book xxvii 1 Scientific Method: General Concepts 1 1.1 Introduction 1 1.2 Powers of 10 1 1.3 Roots 2 1.4 Scientific Notation as a Tool 2 2 Infinitesimal Calculus: A Brief Introduction 9 2.1 Introduction 9 2.2 The Concept Behind Calculus 9 3 Atom: Quarks, Protons, and Electrons 19 3.1 Introduction 19 3.2 Atoms and Quarks 19 3.3 Electrons 20 3.4 Strong Force and Weak Force 21 3.5 Conductors and Electricity 22 3.6 The Shells 23 3.7 Electric Potential 24 3.8 Current 25 3.9 Electric Resistance 25 4 Voltage and Current: Direct and Alternating Current and Voltage 27 4.1 Introduction 27 4.2 Terminology 27 4.3 Batteries 27 4.4 Danger Will Robison, Danger! 30 4.5 Direct Current 31 4.6 Relative Voltages 31 4.6.1 Mountains 32 4.7 Ground 33 4.8 Alternating Current 34 Exercises 38 Solutions 39 5 Resistors: The Most Fundamental Component 41 5.1 Introduction 41 5.2 Resistor 41 5.3 Electric Resistance 41 5.4 Symbols 41 5.5 Types of Resistor 42 5.6 Power 42 5.7 Color Code 42 5.8 Potentiometer 44 5.9 Trimpots 44 5.10 Practical Usage 45 5.11 Electric Characteristics 45 5.12 Resistors in Series 45 5.13 Resistors in Parallel 46 5.14 DC and AC Analysis 46 5.15 Input and Output Synchronism 47 Exercises 48 Solutions 48 6 Ohm’s Laws: Circuit Analysis 51 6.1 Introduction 51 6.2 Basic Rules of Electricity 51 6.3 First Ohm’s Law 52 6.4 Second Ohm’s Law 53 6.5 Examples 53 Exercises 58 Solutions 59 7 Delta–Wye Conversions: Circuit Analysis 63 7.1 Introduction 63 7.2 Delta Circuit 63 7.3 Delta–Wye Conversion 63 7.4 Wye–Delta Conversion 65 7.5 Examples 65 Exercises 69 Solutions 69 8 Capacitors: And Electric Charges 73 8.1 Introduction 73 8.2 History 73 8.3 How It Works 73 8.4 Electric Characteristics 77 8.5 Electric Field 78 8.6 Capacitance 78 8.7 Stored Energy 79 8.8 Voltage and Current 81 8.9 Examples 84 8.10 AC Analysis 87 8.11 Capacitive Reactance 88 8.12 Phase 88 8.13 Electrolytic Capacitor 91 8.14 Variable Capacitors 93 8.15 Capacitors in Series 93 8.16 Capacitors in Parallel 94 8.17 Capacitor Color Code 95 8.18 Capacitor Markings 96 Exercises 98 Solutions 98 9 Electromagnetism: And the World Revolution 103 9.1 Introduction 103 9.2 The Theory 103 9.3 Hans Christian Ørsted 103 9.4 The Right-Hand Rule 105 9.5 Faraday First Experiment 105 9.6 Faraday Second Experiment 106 9.7 Conclusion 107 10 Inductors: Temperamental Devices 109 10.1 Introduction 109 10.2 The Inductor 109 10.3 Coils and Magnets 110 10.4 Inductance 111 10.5 Variable Inductor 111 10.6 Series Inductance 112 10.7 Parallel Inductance 112 10.8 DC Analysis 113 10.9 Electromotive Force 116 10.10 Current Across an Inductor 116 10.11 AC Analysis 116 10.12 Out of Sync 119 Exercises 120 Solutions 120 11 Transformers: Not the Movie 123 11.1 Introduction 123 11.2 Connected by the Magnetic Field 124 11.3 Faraday’s Law 124 11.4 Primary and Secondary 124 11.5 Real-Life Transformer 125 11.6 Multiple Secondaries 125 11.7 Center Tap 126 11.8 Law of Conservation of Energy 127 11.9 Leakage Flux 127 11.10 Internal Resistance 128 11.11 Direct Current 128 12 Generators: And Motors 129 12.1 Introduction 129 12.2 Electric Generators 129 12.3 Electric Motor 131 13 Semiconductors: And Their Junctions 133 13.1 Introduction 133 13.2 It All Started with a Light Bulb 133 13.3 Semiconductors 135 14 Diodes and Transistors: Active Components 143 14.1 Introduction 143 14.2 Diodes 143 14.3 NPN Junction 143 14.4 Biasing 144 14.5 The Transistor, Finally! 144 15 Voltage and Current Sources: Circuit Analysis 147 15.1 Introduction 147 15.2 Independent DC Voltage Sources 147 15.3 Independent AC Voltage Sources 147 15.4 Dependent Voltage Sources 148 15.5 Independent Current Sources 149 15.6 Dependent Current Sources 149 16 Source Transformations: Circuit Analysis 151 16.1 Introduction 151 16.2 The Technique 151 16.3 Example 153 Exercises 160 Solutions 161 17 Impedance and Phase: Circuit Analysis 165 17.1 Introduction 165 17.2 This is Just a Phase 165 17.3 Impedance 166 17.4 Capacitive Impedance 167 17.5 Inductive Impedance 169 17.6 Examples 169 17.7 The Importance of Impedances in Real Life 173 Exercises 177 Solutions 177 18 Power: And Work 181 18.1 Introduction 181 18.2 Electric Power and Work 181 18.3 Powers in Parallel 182 18.4 Powers in Series 183 18.5 “Alternating” Power 184 18.6 Real, Apparent, and Reactive Power 188 Exercises 191 Solutions 192 19 Kirchhoff’s Laws: Circuit Analysis 197 19.1 Introduction 197 19.2 Kirchhoff’s Laws 197 19.3 Examples 199 Exercises 210 Solutions 211 20 Nodal Analysis: Circuit Analysis 215 20.1 Introduction 215 20.2 Examples 215 Exercises 226 Solutions 227 21 Thévenin’s Theorem: Circuit Analysis 235 21.1 Introduction 235 21.2 The Theorem 235 Exercises 250 Solutions 251 22 Norton’ Theorem: Circuit Analysis 257 22.1 Introduction 257 22.2 Norton’s Theorem 257 Exercises 263 Solutions 264 23 Superposition Theorem: Circuit Analysis 269 23.1 Introduction 269 23.2 The Theorem 269 23.3 Methodology 269 23.4 Example 270 Exercises 281 Solutions 282 24 Millman’s Theorem: Circuit Analysis 287 24.1 Introduction 287 24.2 Millman’s Theorem 287 24.3 Examples 291 Exercises 295 Solutions 295 25 RC Circuits: Voltage and Current Analysis in Circuits Containing Resistors and Capacitors in Series 297 25.1 Introduction 297 25.2 Charging a Capacitor 297 25.3 RC Time Constant 308 25.4 Examples 315 Exercises 328 Solutions 330 26 RL Circuits: Voltage and Current Analysis in Circuits Containing Resistors and Inductors in Series 341 26.1 Introduction 341 26.2 Energizing 341 26.3 De-energizing 349 26.4 Examples 354 Exercises 362 Solutions 365 27 RLC Circuits: Part 1: Voltage Analysis in Circuits Containing Resistors, Capacitors, and Inductors in Series 377 27.1 Introduction 377 27.2 A Basic RLC Series Circuit 377 27.3 Examples 408 Exercises 418 Solutions 419 28 RLC Circuits: Part 2: Current Analysis in Circuits Containing Resistors, Capacitors, and Inductors in Series 427 28.1 Introduction 427 28.2 The Circuit 427 28.3 Current Equations 430 28.4 Examples 432 Exercises 442 Solutions 443 29 Transistor Amplifiers: The Magic Component 451 29.1 Introduction 451 29.2 Transistor as Amplifiers 451 29.3 The Water Storage Tank 451 29.4 Current Gain 452 29.5 Power Supply Rails 452 29.6 Amplifying 452 29.7 Quiescent Operating Point 453 29.8 Amplifier Classes 454 Exercises 477 Solutions 479 30 Operational Amplifiers: A Brief Introduction 485 30.1 Introduction 485 30.2 Operational Amplifiers 485 30.3 How Op-Amp Works 486 30.4 Op-Amp Characteristics 488 30.5 Typical Configurations 488 31 Instrumentation and Bench: A Brief Introduction 509 31.1 Introduction 509 31.2 Multimeter 509 31.3 Voltmeter 510 31.4 Ammeter 511 31.5 Ohmmeter 512 31.6 Oscilloscope 513 31.7 Breadboards 513 31.8 Wire Diameter 515 31.9 Power Supply 516 31.10 Soldering Station 517 31.11 Soldering Fume Extractors 517 31.12 Lead-Free Solder 517 31.13 A Few Images of Real Products 518 Appendix A: International System of Units (SI) 521 Appendix B: Color Code: Resistors 523 Appendix C: Root Mean Square (RMS) Value 525 Appendix D: Complex Numbers 529 Appendix E: Table of Integrals 537 Appendix F: AWG Versus Metric System: Wire Cross Sections 539 Appendix G: Resistors: Commercial Values 541 Appendix H: Capacitors: Commercial Values 543 Appendix I: Inductors: Commercial Values 549 Appendix J: Simulation Tools 557 Appendix K: Glossary 559 Index 563

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Book SynopsisA comprehensive overview of current developments and applications in biofuels production Process Systems Engineering for Biofuels Development brings together the latest and most cutting-edge research on the production of biofuels. As the first book specifically devoted to process systems engineering for the production of biofuels, Process Systems Engineering for Biofuels Development covers theoretical, computational and experimental issues in biofuels process engineering. Written for researchers and postgraduate students working on biomass conversion and sustainable process design, as well as industrial practitioners and engineers involved in process design, modeling and optimization, this book is an indispensable guide to the newest developments in areas including: Enzyme-catalyzed biodiesel productionProcess analysis of biodiesel production (including kinetic modeling, simulation and optimization)The use of ultrasonification in biodiesel productionThermochemical processes for biomTable of ContentsList of Contributors xiii Series Preface xv Preface xvii 1 Introduction 1Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah 1.1 Importance of Biofuels and Overview of their Production 1 1.2 Significance of Process Systems Engineering for Biofuels Production 3 1.2.1 Modeling of Physicochemical Properties of Thermodynamic Systems Related to Biofuels 4 1.2.2 Intensification of the Biomass Transformation Routes for the Production of Biofuels 5 1.2.3 Computer-Aided Methodologies for Process Modeling, Design, Optimization, and Control Including Supply Chain and Life Cycle Analyses 7 1.3 Overview of this Book 9 References 11 2 Waste Biomass Suitable as Feedstock for Biofuels Production 15Maria Papadaki 2.1 Introduction 15 2.1.1 The Need for Biofuels 15 2.1.2 Problem Definition 17 2.1.3 The Biomass Pool 18 2.2 Kinds of Feedstock 20 2.2.1 Spent Coffee Grounds 21 2.2.2 Lignocellulose Biomass 22 2.2.3 Palm, Olive, Coconut, Avocado, and Argan Oil Production Residues 25 2.2.4 Citrus 33 2.2.5 Grape Marc 36 2.2.6 Waste Oil and Cooking Oil 37 2.2.7 Additional Sources 38 2.3 Conclusions 40 Acknowledgment 40 References 40 3 Multiscale Analysis for the Exploitation of Bioresources: From Reactor Design to Supply Chain Analysis 49Antonio Sánchez, Borja Hernández, and Mariano Martín 3.1 Introduction 49 3.2 Unit Level 50 3.2.1 Short Cut Methods 50 3.2.2 Mechanistic Models 51 3.2.3 Rules of Thumb 56 3.2.4 Dimensionless Analysis 56 3.2.5 Surrogate Models 56 3.2.6 Experimental Correlations 59 3.3 Process Synthesis 60 3.3.1 Heuristic Based 60 3.3.2 Supestructure Optimization 61 3.3.3 Environmental Impact Metrics 65 3.3.4 Safety Considerations 66 3.4 The Product Design Problem 66 3.4.1 Product Design: Engineering Biomass 66 3.4.2 Blending Problems 68 3.5 Supply Chain Level 68 3.5.1 Introduction 68 3.5.2 Modeling Issues 70 3.6 Multiscale Links and Considerations 71 Acknowledgment 74 Nomenclature 74 References 75 4 Challenges in the Modeling of Thermodynamic Properties and Phase Equilibrium Calculations for Biofuels Process Design 85Roumiana P. Stateva and Georgi St. Cholakov 4.1 Introduction 85 4.2 Thermodynamic Modeling Framework: Elements, Structure, and Organization 86 4.3 Thermodynamics of Biofuel Systems 88 4.3.1 Phase Equilibria 88 4.3.2 Thermodynamic Models 90 4.4 Sources of Data for Biofuels Process Design 98 4.5 Methods for Predicting Data for Biofuels Process Design 102 4.5.1 Group Contribution Methods for Biofuels Process Design 103 4.5.2 Quantitative Structure–Property Relationships for Biofuels Process Design 105 4.6 Challenges for the Biofuels Process Design Methods 109 4.7 Influence of Uncertainties in Thermophysical Properties of Pure Compounds on the Phase Behavior of Biofuel Systems 112 4.8 Conclusions 114 Acknowledgment 114 Exercises 114 References 115 5 Up-grading ofWaste Oil: A Key Step in the Future of Biofuel Production 121Luigi di Bitonto and Carlo Pastore 5.1 Introduction 121 5.2 Physicochemical Pretreatments of Waste Oils: Removal of Contaminants 124 5.3 Direct Treatment and Conversion of FFAs into Methyl Esters 125 5.3.1 Homogeneous Catalysis: Brønsted and Lewis Acids 125 5.3.2 Heterogeneous Catalysis 127 5.3.3 Enzymatic Biodiesel Production 128 5.3.4 ILs Biodiesel Production 130 5.3.5 Use of Metal Hydrated Salts 133 5.4 Future Trends of the Pretreatments of Waste Oils 139 5.5 Conclusions 140 Acknowledgment 141 Abbreviations 141 References 142 6 Production of Biojet Fuel from Waste Raw Materials: A Review 149Ana Laura Moreno-Gómez, Claudia Gutiérrez-Antonio, Fernando Israel Gómez-Castro, and Salvador Hernández 6.1 Introduction 149 6.2 Waste Triglyceride Feedstock 150 6.3 Waste Lignocellulosic Feedstock 159 6.4 Waste Sugar and Starchy Feedstock 164 6.5 Main Challenges and Future Trends 165 6.6 Conclusions 167 Acknowledgments 167 References 167 7 Computer-Aided Design for Genetic Modulation to Improve Biofuel Production 173 Feng-Sheng Wang and Wu-Hsiung Wu 7.1 Introduction 173 7.2 Method 175 7.2.1 Flux Balance Analysis 175 7.2.2 Flux Variability Analysis 176 7.2.3 Minimization of Metabolic Adjustment 176 7.2.4 Regulatory On-Off Minimization 177 7.2.5 Optimal Strain Design Problem 177 7.3 Computer-Aided Strain Design Tool 179 7.4 Examples 181 7.4.1 E. coli Core Model 181 7.4.2 Genome-Scale Metabolic Model of E. coli iAF1260 183 7.5 Conclusions 185 Appendix 7.A: The SBP Program 187 References 187 8 Implementation of Biodiesel Production Process Using Enzyme-Catalyzed Routes 191Thalles Allan Andrade, Massimiliano Errico, and Knud Villy Christensen 8.1 Introduction 191 8.2 Biodiesel Production Routes: Chemical versus Enzymatic Catalysts 194 8.2.1 Chemical Catalysts 195 8.2.2 Enzymatic Catalysts 196 8.3 Optimal Reaction Conditions and Kinetic Modeling 198 8.3.1 Evaluation of the Reaction Conditions 199 8.3.2 Kinetic Modeling 201 8.4 Process Simulation and Economic Evaluation 205 8.5 Reuse of Enzyme for the Transesterification Reaction 210 8.5.1 Recovery of Eversa Transform by Means of Centrifugation 210 8.5.2 Recovery of Eversa Transform by Means of Ceramic Membranes 211 8.6 Environmental Impact and Final Remarks 215 Acknowledgments 217 Nomenclature 217 References 217 9 Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design 221Bruna Ricetti Margarida, Wanderson Rogerio Giacomin-Junior, Luiz Fernando de Lima Luz Junior, Fernando Augusto Pedersen Voll, and Marcos Lucio Corazza 9.1 Introduction 221 9.1.1 Homogeneous-Based Reactions 222 9.1.2 Heterogeneous-Based Reactions 223 9.1.3 Enzyme-Catalyzed Reactions 224 9.1.4 Supercritical Route Reactions 224 9.1.5 Methanol or Ethanol for Biodiesel Synthesis 224 9.2 Getting Started with Aspen Plus V10 224 9.2.1 Pure Compounds 225 9.2.2 Mixture Parameters 229 9.3 Kinetic Study 232 9.3.1 Esterification Reaction 232 9.3.2 Experimental Reaction Data Regression 234 9.3.3 Transesterification Reaction 236 9.3.4 Supercritical Route 238 9.4 Process Design 239 9.4.1 Esterification Reaction 239 9.4.2 Methanol Recycling 243 9.4.3 Transesterification Reaction 244 9.4.4 Biodiesel Purification 245 9.4.5 Additional Resources 248 9.5 Energy and Economic Analysis 252 9.6 Concluding Remarks 254 Acknowledgment 255 Exercises 255 References 256 10 Process Development, Design and Analysis of Microalgal Biodiesel Production Aided by Microwave and Ultrasonication 259Dipesh S. Patle, Savyasachi Shrikhande, and Gade Pandu Rangaiah 10.1 Introduction 259 10.2 Process Development and Modeling 262 10.3 Sizing and Cost Analysis 272 10.4 Comparison with the WCO-Based Process of the Same Capacity 277 10.4.1 Biodiesel Process Using WCO as Raw Material 277 10.4.2 Comparative Analysis 277 10.5 Comparison with the Microalgae-Based Processes 280 10.6 Conclusions 280 Acknowledgment 281 Appendix 10.A 281 Exercises 282 References 282 11 Thermochemical Processes for the Transformation of Biomass into Biofuels 285Carlos J. Durán-Valle 11.1 Introduction 285 11.2 Biomass and Biofuels 288 11.3 Combustion 289 11.4 Gasification 290 11.4.1 Fixed Bed Gasification 291 11.4.2 Fluidized Bed Gasification 292 11.4.3 Dual Fluidized Bed Gasification 292 11.4.4 Hydrothermal Gasification 293 11.4.5 Supercritical Water Gasification 294 11.4.6 Plasma Gasification 294 11.4.7 Catalyzed Gasification 295 11.4.8 Fischer–Tropsch Synthesis 295 11.5 Liquefaction 296 11.6 Pyrolysis 296 11.6.1 Slow Pyrolysis 297 11.6.2 Fast Pyrolysis 297 11.6.3 Flash Pyrolysis 297 11.6.4 Catalytic Biomass Pyrolysis 303 11.6.5 Microwave Heating 304 11.6.6 Product Separation 304 11.7 Carbonization 305 11.8 Conclusions 308 Acknowledgments 309 References 309 12 Intensified Purification Alternative for Methyl Ethyl Ketone Production: Economic, Environmental, Safety and Control Issues 311Eduardo Sánchez-Ramírez, Juan José Quiroz-Ramírez, and Juan Gabriel Segovia-Hernández 12.1 Introduction 311 12.2 Problem Statement and Case Study 316 12.3 Evaluation Indexes and Optimization Problem 317 12.3.1 Total Annual Cost Calculation 319 12.3.2 Environmental Index Calculation 319 12.3.3 Individual Risk Index 320 12.3.4 Controllability Index Calculation 322 12.3.5 Multi-Objective Optimization Problem 323 12.4 Global Optimization Methodology 324 12.5 Results 325 12.6 Conclusions 335 Acknowledgments 335 Notation 335 References 336 13 Present and Future of Biofuels 341Juan Gabriel Segovia-Hernández, César Ramírez-Márquez, and Eduardo Sánchez-Ramírez 13.1 Introduction 341 13.2 Some Representative Biofuels 344 13.2.1 Bioethanol 344 13.2.2 Biodiesel 347 13.2.3 Biobutanol 348 13.2.4 Biojet Fuel 349 13.2.5 Biogas 351 13.3 Perspectives and Future of Biofuels 352 References 354 Index 357

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Book SynopsisPhosphors for Radiation Detector Phosphors for Radiation Detectors Discover a comprehensive overview of luminescence phosphors for radiation detection In Phosphors for Radiation Detection, accomplished researchers Takayuki Yanagida and Masanori Koshimizu deliver a state-of-the-art exploration of the use of phosphors in radiation detection. The internationally recognized contributors discuss the fundamental physics and detector functions associated with the technology with a focus on real-world applications. The book discusses all forms of luminescence phosphors for radiation detection used in a variety of fields, including medicine, security, resource exploration, environmental monitoring, and high energy physics. Readers will discover discussions of dosimeter materials, including thermally stimulated luminescent materials, optically stimulated luminescent materials, and radiophotoluminescence materials. The book also covers transparent ceramics and glasses and a broad range of devicesTable of ContentsList of Contributors xi Preface xiii Series Preface xv 1 Ionizing Radiation Induced Luminescence 1Takayuki Yanagida 1.1 Introduction 1 1.2 Interactions of Ionizing Radiation with Matter 3 1.3 Scintillation 4 1.3.1 Energy Conversion Mechanism 4 1.3.2 Emission Mechanism 5 1.3.3 Scintillation Light Yield and Energy Resolution 8 1.3.4 Timing Properties 14 1.3.5 Radiation Hardness 17 1.3.6 Temperature Dependence 18 1.4 Ionizing Radiation Induced Storage Luminescence 18 1.4.1 General Description 18 1.4.2 Analytical Description of TSL 19 1.4.3 Analytical Description of OSL 24 1.5 Relationship of Scintillation and Storage Luminescence 26 1.6 Common Characterization Techniques of Ionizing Radiation Induced Luminescence Properties 29 References 35 2 Organic Scintillators 39Masanori Koshimizu 2.1 Introduction 39 2.2 Basic Electronic Processes in Organic Scintillators 40 2.2.1 Electronic States and Excited States Dynamics of Organic Molecules 40 2.2.2 Excitation Energy Transfer 43 2.2.3 Scintillation Dynamics in Organic Scintillators at High Linear Energy Transfer 50 2.3 Liquid Scintillators 51 2.4 Organic Crystalline Scintillators 54 2.5 Plastic Scintillators 55 2.6 Organic–Inorganic Hybrid Scintillators 59 2.6.1 Loaded Organic Scintillators 59 2.6.2 Organic–Inorganic Nanocomposite Scintillators 60 References 61 3 Inorganic Oxide Scintillators 67Daisuke Nakauchi, Noriaki Kawaguchi, and Takayuki Yanagida 3.1 Introduction 67 3.2 Crystal Growth 67 3.3 Outlines of Oxide Scintillators 70 3.4 Silicate Materials 73 3.4.1 Ce:Gd2SiO5 (Ce:GSO) 73 3.4.2 Ce:Lu2SiO5 (Ce:LSO) 74 3.4.3 Ce:Gd2Si2O7 (Ce:GPS) 76 3.4.4 LPS 77 3.5 Garnet Materials 77 3.5.1 Ce:Y3Al5O12 (Ce:YAG) 77 3.5.2 Ce:Lu3Al5O12 (Ce:LuAG), Pr:Lu3Al5O12 (Pr:LuAG) 79 3.5.3 Ce:Gd3Al2Ga3O12 (Ce:GAGG) 79 3.5.4 Ce:Tb3Al5O12 (Ce:TAG) 80 3.6 Perovskite Materials 82 3.6.1 Ce:YAlO3 (Ce:YAP) 82 3.6.2 Ce:LuAlO3 (Ce:LuAP) 82 3.7 Materials with Intrinsic Luminescence 83 3.7.1 CdWO4 83 3.7.2 Bi4Ge3O12 (BGO) 84 3.7.3 PbWO4 85 References 85 4 Inorganic Fluoride Scintillators 91Noriaki Kawaguchi, Hiromi Kimura, Daisuke Nakauchi, Takumi Kato, and Takayuki Yanagida 4.1 Introduction 91 4.2 Crystal Growth of Fluorides 94 4.2.1 Classification of Methods for Crystal Growth 94 4.2.2 Furnace Materials, Atmosphere, and Scavengers for Fluoride Crystal Growth 95 4.2.3 Fluoride Crystal Growth Methods by Pulling Out from the Melt 96 4.2.4 Fluoride Crystal Growth Methods by Solidifying the Melt in the Crucible 98 4.2.5 Fluoride Crystal Growth Methods Without Using Crucibles 99 4.3 Outline of Fluoride Scintillators 100 4.4 Fluoride Scintillators for γ-Ray Detection 101 4.4.1 Fluoride Scintillators Based on Luminescence from 5d-4f Transitions of Ce3+ Ions 101 4.4.2 Fluoride Scintillators Based on Core-Valence Luminescence 102 4.4.3 VUV Emitting Fluoride Scintillators Doped with Nd3+, Er3+, and Tm3+ Ions 105 4.5 Fluoride Scintillators for Neutron Detection 106 4.5.1 Review for Neutron Scintillators 106 4.5.2 LiCaAlF6 Single Crystals 108 4.5.3 LiF/CaF2 Eutectic Composites 111 4.6 Fluoride Scintillators for Charged Particle Detection 113 4.6.1 Methods for Charged Particle Detection 113 4.6.2 CaF2 Based Scintillators for Charged Particle Detection 115 References 117 5 Inorganic Halide Scintillators 121Yutaka Fujimoto 5.1 Introduction: History of Inorganic Halide Scintillator Research and Development 121 5.2 Characteristics of Halide Materials 122 5.2.1 Formation of Color Center and Self-Trapped Exciton 122 5.2.2 Hygroscopicity 123 5.3 Basic Techniques for Halide Scintillation Crystal Growth 125 5.4 Novel Ternary and Quaternary Halide Scintillators 127 5.4.1 Alkali Halide-Rare Earth Halide (AX–REX3) 127 5.4.2 Alkali Halide-Alkalin Earth Halide (AX–AEX2) 130 5.4.3 Elpasolite 134 5.5 Mixed-Anion Halide Scintillators 135 5.6 Next Generation of Halide Scintillators 137 5.6.1 Hf-and Tl-Based Halide Scintillators 137 References 141 6 Semiconductor Scintillators 147Naoki Kawano 6.1 Introduction 147 6.2 Photoluminescence and Scintillation Mechanisms in Semiconductors 149 6.3 Various Semiconductor Scintillators 154 6.3.1 Undoped Semiconductor Scintillator 155 6.3.2 Doped Semiconductor Scintillator 158 6.4 Quantum Size Effect 161 6.5 Organic–Inorganic Perovskite-Type Compounds 165 6.5.1 Introduction 165 6.5.2 Materials and Structures 166 6.5.3 Sample Preparation 167 6.5.4 Fundamental Optical Property 169 6.5.5 Scintillation 173 References 178 7 Thermally Stimulated Luminescent (TSL) Materials 181Kiyomitsu Shinsho 7.1 Introduction 181 7.2 TSL Phenomenon 184 7.2.1 Basic Principles of TSL 184 7.2.2 Theory and Measurement of Glow Curves 185 7.3 TSL Materials: Fluoride, Oxides, Sulfates, and Borate 190 7.3.1 Fluorides 190 7.3.2 Oxides 198 7.3.3 Sulfates 202 7.3.4 Borates 204 7.4 TSL Dosimetric Properties for Photons, Charged Particles, and Neutrons 206 7.4.1 TSL Dosimetric Properties for Photons 206 7.4.2 TSL Dosimetric Properties for Charged Particles 211 7.4.3 TSL Dosimetric Properties for Neutrons 214 7.5 Two-Dimensional (2-D) TSL Dosimetry 214 7.5.1 Introduction 214 7.5.2 Types of 2-D TSLDs 215 7.5.3 Measurement Systems 216 7.5.4 Application of 2-D TSLDs in Photon Beam Radiotherapy 218 7.5.5 Outlook for 2-D TSLDs 220 References 220 8 Optically-Stimulated Luminescent Dosimeters 225Hidehito Nanto and Go Okada 8.1 Introduction 225 8.2 Principles of OSL Phenomenon 226 8.3 OSL Materials and Dosimeters 235 8.4 Applications of OSL 239 8.5 Future Perspective 242 References 243 9 Radiophotoluminescence (RPL) 247Go Okada, Takayuki Yanagida, Hidehito Nanto, and Safa Kasap 9.1 Introduction 247 9.2 RPL Phenomenon and the Definition 248 9.3 RPL Materials and Applications 249 9.3.1 Introduction 249 9.3.2 Ag-Doped Sodium-Aluminophosphate Glasses 252 9.3.3 Al2O3:C,Mg 260 9.3.4 LiF 264 9.3.5 Sm-Doped Compounds 268 9.3.6 Other RPL Materials 276 9.4 Conclusions 278 References 278 10 New Materials for Radiation Detectors: Transparent Ceramics 283Takumi Kato, Noriaki Kawaguchi, and Takayuki Yanagida 10.1 Introduction of Transparent Ceramic Materials 283 10.1.1 Light Scattering Sources in Ceramics 283 10.1.2 History and Applications on Transparent Ceramics 285 10.2 Preparation Methodology 287 10.2.1 Sintering Mechanism of Ceramics 287 10.2.2 Effect of Residual Pores 290 10.2.3 Preparation Methods of Transparent Ceramics 291 10.3 Transparent Materials 292 10.4 Transparent Ceramic Scintillator 293 10.4.1 Sesquioxide (Such as Y2O3, Gd2O3, and Lu2O3) 293 10.4.2 Gd2O2S (GOS) 294 10.4.3 Garnet Materials (Such as YAG, LuAG, and GAGG) 294 10.4.4 Lu2SiO5 (LSO) 296 10.4.5 SrHfO3 296 10.4.6 La2Zr2O7 and La2Hf2O7 296 10.4.7 ZnO 296 10.4.8 BaF2 297 10.4.9 CeF3 298 10.4.10 CsBr 299 10.4.11 LaBr3 299 10.4.12 SrI2 300 10.5 Transparent Ceramics for Dosimeter 300 10.5.1 Al2O3 300 10.5.2 CaF2 302 10.5.3 MgO 302 10.5.4 MgF2 303 10.5.5 CsBr 304 10.5.6 Y3Al5-xGaxO12 (YAGG) 305 References 306 11 Luminescence in Glass-Based Materials by Ionizing Radiation 311Hirokazu Masai and Kenji Shinozaki 11.1 Introduction 311 11.2 Structural and Physical Properties of Glass 312 11.3 Attenuation of Quantum Beam as Shielding Materials 320 11.4 Defect Formation in Oxide Glass by Quantum Beam Irradiation 320 11.5 Scintillation in Oxide Glass 323 11.5.1 Glass Scintillators for X-Ray and γ-Ray 323 11.5.2 Glass Scintillators for Neutrons 325 11.5.3 Storage Luminescence in Glass 328 11.6 Scintillation and Dosimetry in Non-oxide Glass 329 11.7 Preparation of Glass 335 11.7.1 Melt Process 335 11.7.2 Vapor Process and Fiber Drawing 337 11.7.3 Liquid Process 338 11.8 Future Prospectives for Glass-Based Materials 338 Acknowledgement 339 References 339 12 Detectors Using Radiation Induced Luminescence 347Kenichi Watanabe 12.1 Introduction 347 12.2 General Issues to Manufacturing the Detector 349 12.3 Scintillation Detectors for Gamma-Rays and X-Rays 352 12.3.1 Gamma-Ray Spectrometer 352 12.3.2 Survey Meter and Area Monitor 356 12.3.3 Scintillation Detectors for Medical Applications 358 12.3.4 Scintillation Detectors for Other Applications 364 12.4 Scintillation Detectors for Charged Particles 366 12.5 Scintillation Detectors for Neutrons 368 12.5.1 Thermal Neutron Detectors 368 12.5.2 Fast Neutron Detectors 377 12.6 Personal Dosimeters 380 12.6.1 TL-Based Dosimetry System 380 12.6.2 OSL-Based Dosimetry System 381 12.6.3 RPL-Based Dosimetry System 382 12.7 OSL-Based Imaging System 383 References 384 Index 387

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Book SynopsisWireless Coexistence Explore a comprehensive review of the motivation for wireless coexistence and the standards and technology used to achieve it Wireless Coexistence: Standards, Challenges, and Intelligent Solutions delivers a thorough exploration of wireless ecosystems sharing the spectrum, including the multiple standards and key requirements driving the current state of wireless technology. The book surveys several standards, including IEEE 802.22, 802.15.2, and 802.19.1 and expands upon recent advances in machine learning and artificial intelligence to demonstrate how these technologies might be used to meet or exceed the challenges of wireless coexistence. The text discusses cognitive radio in the context of spectrum coexistence and provides a comparison and assessment of using artificial intelligence in place of, or in addition to, current techniques. It also considers applications to communication theory, learning algorithms for passive wireless cTable of ContentsAuthor biographies – to follow Preface – to follow 1 Introduction A Primer on Wireless Coexistence: The Electromagnetic Spectrum as a Shared Resource The Role of Standardization in Wireless Coexistence An Overview of Wireless Coexistence Strategies Standards Covered in this Book 1900.1 as a baseline taxonomy Organization of this Work 2 Regulation for Wireless Coexistence Traditional frequency assignment  Policies and Regulations Bands for unlicensed Use 3. Concepts in Communication Theory Types of Channels and Related Terminology Types of Interference and Related Terminology Types of Networks and Related Terminology Primer on Noise Primer on Propagation Primer on Orthogonal Frequency Division Multiplexing Direct-Conversion Transceivers 4 Mitigating Contention in Equal-Priority Access Designating Spectrum Resources Interference, Conflict, and Collisions What is a Primary User? Tiers of Users Unlicensed Users Contention in Spectrum Access and Mitigation Techniques Division of Responsibility among the Protocol Layers Duplexing Multiple Access and Multiplexing Frequency and Time Division Multiple Access Spectral Masks Defined in Standards Spread Spectrum Techniques Carrier Sense Multiple Access Orthogonal Frequency Division Multiple Access Final Thoughts 5 Signal Detection Introduction Definitions and Taxonomy Generic Framework for Signal Detection Noise Floor Estimation and Threshold Setting Matched Filter Detection Energy Detection Cyclic Spectral Analysis Final Thoughts 6 Intelligent Radio Concepts Introducton Intelligent Radio Use-Cases Making Radios Intelligent Intelligent Radio Architectures Learning Algorithms Looking Forward 7 Coexistence Standards in IEEE 1900 DySPAN Standards Committee (IEEE P1900) 8 Coexistence Standards in IEEE 802 The Standards to be addressed in this Chapter Types and Spatial Scope of Wireless Networks Stacks: The Structure of Wireless Protocol Standards IEEE 802.22 IEEE 802.11 TVWS Geolocation Databases in the United States IEEE 802.19.1 IEEE 802.15.2 9 LTE Carrier Aggregation and Unlicensed Access Introduction 3G to LTE LAA Motivation LTE Overview Carrier Aggregation License Assisted Access Conclusions 10 Conclusion and Future Trends Summary of the Preceding Chapters Nonorthogonal Multiple Access and Underlaying Intelligent Collaborative Radio Networks Validation and Verification of Intelligent Radios Spectrum Sharing Utopia Conclusion

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Book SynopsisA guide to drives essential to electric vehicles, wind turbines, and other motor-driven systems Analysis and Control of Electric Drives is a practical and comprehensive text that offers a clear understanding of electric drives and their industrial applications in the real-world including electric vehicles and wind turbines. The authorsnoted experts on the topicreview the basic knowledge needed to understand electric drives and include the pertinent material that examines DC and AC machines in steady state using a unique physics-based approach. The book also analyzes electric machine operation under dynamic conditions, assisted by Space Vectors. The book is filled with illustrative examples and includes information on electric machines with Interior Permanent Magnets. To enhance learning, the book contains end-of-chapter problems and all topics covered use computer simulations with MATLAB Simulink and Sciamble Workbench software that is available free onliTable of ContentsPreface xix Acknowledgment xxi About the Companion Site xxii Part I Fundamentals of Electric Drives 1 1 Electric Drives: Introduction and Motivation 3 2 Understanding Mechanical System Requirements for Electric Drives 21 3 Basic Concepts in Magnetics and Electromechanical Energy Conversion 51 4 Basic Understanding of Switch-Mode Power Electronic Converters 95 5 Control in Electric Drives 129 Part II Steady-State Operation of ac Machines 163 6 Using Space Vectors to Analyze ac Machines 165 7 Space Vector Pulse-Width-Modulated (SV-PWM) Inverters 203 8 Sinusoidal Permanent-Magnet ac (PMAC) Drives in Steady State 217 9 Induction Motors in Sinusoidal Steady-State 241 10 Induction-Motor Drives: Speed Control 285 Part III Vector Control of ac Machines 315 11 Induction Machine Equations in Phase Quantities: Assisted by Space Vectors 317 12 Dynamic Analysis of Induction Machines in Terms of dq-Windings 341 13 Mathematical Description of Vector Control in Induction Machines 377 14 Speed-Sensorless Vector Control of Induction Motor 401 14-A Appendix 423 15 Analysis of Doubly Fed Generators (DFIGs) in Steady State and Their Vector Control 427 16 Direct Torque Control (DTC) and Encoder-Less Operation of Induction Motor Drives 453 17 Vector Control of Permanent-Magnet Synchronous Motor Drives 473 18 Reluctance Drives: Stepper-Motors and Switched-Reluctance Drives 501 Index 527

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Book SynopsisLearn about the latest in cognitive and autonomous network management Towards Cognitive Autonomous Networks: Network Management Automation for 5G and Beyond delivers a comprehensive understanding of the current state-of-the-art in cognitive and autonomous network operation. Authors Mwanje and Bell fully describe today?s capabilities while explaining the future potential of these powerful technologies. This book advocates for autonomy in new 5G networks, arguing that the virtualization of network functions render autonomy an absolute necessity. Following that, the authors move on to comprehensively explain the background and history of large networks, and how we come to find ourselves in the place we?re in now. Towards Cognitive Autonomous Networks describes several novel techniques and applications of cognition and autonomy required for end-to-end cognition including: ? Configuration of autonomous networks ? Operation of autonomous networks ? Optimization of autonomous networks ? SelfTable of ContentsList of Contributors xix Foreword I xxi Foreword II xxv Preface xxvii 1 The Need for Cognitive Autonomy in Communication Networks 1Stephen S. Mwanje, Christian Mannweiler and Henning Sanneck 1.1 Complexity in Communication Networks 2 1.1.1 The Network as a Graph 2 1.1.2 Planes, Layers, and Cross-Functional Design 4 1.1.3 New Network Technology – 5G 6 1.1.4 Processes, Algorithms, and Automation 9 1.1.5 Network State Changes and Transitions 9 1.1.6 Multi-RAT Deployments 10 1.2 Cognition in Network Management Automation 11 1.2.1 Business, Service and Network Management Systems 11 1.2.2 The FCAPS Framework 13 1.2.3 Classes/Areas of NMA Use Cases 15 1.2.4 SON – The First Generation of NMA in Mobile Networks 17 1.2.5 Cognitive Network Management – Second Generation NMA 18 1.2.6 The Promise of Cognitive Autonomy 18 1.3 Taxonomy for Cognitive Autonomous Networks 19 1.3.1 Automation, Autonomy, Self-Organization, and Cognition 19 1.3.2 Data Analytics, Machine Learning, and AI 21 1.3.3 Network Autonomous Capabilities 22 1.3.4 Levels of Network Automation 23 1.3.5 Content Outline 25 References 27 2 Evolution of Mobile Communication Networks 29Christian Mannweiler, Cinzia Sartori, Bernhard Wegmann, Hannu Flinck, Andreas Maeder, Jürgen Goerge and Rudolf Winkelmann 2.1 Voice and Low-Volume Data Communications 30 2.1.1 Service Evolution – From Voice to Mobile Internet 31 2.1.2 2G and 3G System Architecture 33 2.1.3 GERAN – 2G RAN 35 2.1.4 UTRAN – 3G RAN 36 2.2 Mobile Broadband Communications 38 2.2.1 Mobile Broadband Services and System Requirements 38 2.2.2 4G System Architecture 39 2.2.3 E-UTRAN – 4G RAN 40 2.3 Network Evolution – Towards Cloud-Native Networks 42 2.3.1 System-Level Technology Enablers 42 2.3.2 Challenges and Constraints Towards Cloud-Native Networks 46 2.3.3 Implementation Aspects of Cloud-Native Networks 47 2.4 Multi-Service Mobile Communications 49 2.4.1 Multi-Tenant Networks for Vertical Industries 50 2.4.2 5G System Architecture 51 2.4.3 Service-Based Architecture in the 5G Core 54 2.4.4 5G RAN 56 2.4.5 5G New Radio 59 2.4.6 5G Mobile Network Deployment Options 63 2.5 Evolution of Transport Networks 69 2.5.1 Architecture of Transport Networks 69 2.5.2 Transport Network Technologies 70 2.6 Management of Communication Networks 72 2.6.1 Basic Principles of Network Management 72 2.6.2 Network Management Architectures 76 2.6.3 The Role of Information Models in Network Management 79 2.6.4 Dimensions of Describing Interfaces 80 2.6.5 Network Information Models 82 2.6.6 Limitations of Common Information Models 85 2.7 Conclusion – Cognitive Autonomy in 5G and Beyond 87 2.7.1 Management of Individual 5G Network Features 87 2.7.2 End-to-End Operation of 5G Networks 88 2.7.3 Novel Operational Stakeholders in 5G System Operations 88 References 89 3 Self-Organization in Pre-5G Communication Networks 93Muhammad Naseer-ul-Islam, Janne Ali-Tolppa, Stephen S. Mwanje and Guillaume Decarreau 3.1 Automating Network Operations 94 3.1.1 Traditional Network Operations 94 3.1.2 SON-Based Network Operations 95 3.1.3 SON Automation Areas and Use Cases 97 3.2 Network Deployment and Self-Configuration 98 3.2.1 Plug and Play 98 3.2.2 Automatic Neighbour Relations (ANR) 101 3.2.3 LTE Physical Cell Identity (PCI) Assignment 103 3.3 Self-Optimization 108 3.3.1 Mobility Load Balancing (MLB) 108 3.3.2 Mobility Robustness Optimization (MRO) 111 3.3.3 Energy Saving Management 115 3.3.4 Coverage and Capacity Optimization (CCO) 117 3.3.5 Random Access Channel (RACH) Optimization 120 3.3.6 Inter-Cell Interference Coordination (ICIC) 122 3.4 Self-Healing 124 3.4.1 The General Self-Healing Process 125 3.4.2 Cell Degradation Detection 125 3.4.3 Cell Degradation Diagnosis 127 3.4.4 Cell Outage Compensation 128 3.5 Support Function for SON Operation 129 3.5.1 SON Coordination 129 3.5.2 Minimization of Drive Test (MDT) 133 3.6 5G SON Support and Trends in 3GPP 136 3.6.1 Critical 5G RAN Features 136 3.6.2 SON Standardization for 5G 137 3.7 Concluding Remarks 140 References 141 4 Modelling Cognitive Decision Making 145Stephen S. Mwanje and Henning Sanneck 4.1 Inspirations from Bio-Inspired Autonomy 146 4.1.1 Distributed, Efficient Equilibria 146 4.1.2 Distributed, Effective Management 147 4.1.3 Robustness Amidst Self-Organization 147 4.1.4 Adaptability 147 4.1.5 Natural Stochasticity 148 4.1.6 From Simplicity Emerges Complexity 148 4.2 Self-Organization as Visible Cognitive Automation 148 4.2.1 Attempts at Definition 149 4.2.2 Bio-Chemical Examples of Self-Organizing Systems 149 4.2.3 Human Social-Economic Examples of Self-Organizing Systems 151 4.2.4 Features of Self-Organization – As Evidenced by Ant Foraging 152 4.2.5 Self-Organization or Cognitive Autonomy? – The Case of Ants 154 4.3 Human Cognition 154 4.3.1 Basic Cognitive Processes 155 4.3.2 Higher, Complex Cognitive Processes 156 4.3.3 Cognitive Processes in Learning 158 4.4 Modelling Cognition: A Perception-Reasoning Pipeline 159 4.4.1 Conceptualization 160 4.4.2 Contextualization 160 4.4.3 Organization 161 4.4.4 Inference 161 4.4.5 Memory Operations 162 4.4.6 Concurrent Processing and Actioning 162 4.4.7 Attention and the Higher Processes 163 4.4.8 Comparing Models of Cognition 164 4.5 Implications for Network Management Automation 167 4.5.1 Complexity of the PRP Processes 167 4.5.2 How Cognitive Is SON? 168 4.5.3 Expectations from Cognitive Autonomous Networks 168 4.6 Conclusions 169 References 170 5 Classic Artificial Intelligence: Tools for Autonomous Reasoning 173Stephen Mwanje, Marton Kajo, Benedek Schultz, Kimmo Hatonen and Ilaria Malanchini 5.1 Classical AI: Expectations and Limitations 174 5.1.1 Caveat: The Common-Sense Knowledge Problem 174 5.1.2 Search and Planning for Intelligent Decision Making 175 5.1.3 The Symbolic AI Framework 176 5.2 Expert Systems 177 5.2.1 System Components 177 5.2.2 Cognitive Capabilities and Application of Expert Systems 177 5.2.3 Rule-Based Handover-Events Root Cause Analysis 178 5.2.4 Limitations of Expert Systems 179 5.3 Closed-Loop Control Systems 180 5.3.1 The Controller 180 5.3.2 Cognitive Capabilities and Application of Closed-Loop Control 181 5.3.3 Example: Handover Optimization Loop 181 5.4 Case-Based Reasoning 182 5.4.1 The CBR Execution Cycle 183 5.4.2 Cognitive Capabilities and Applications of CBR Systems 184 5.4.3 CBR Example for RAN Energy Savings Management 185 5.4.4 Limitations of CBR Systems 185 5.5 Fuzzy Inference Systems 186 5.5.1 Fuzzy Sets and Membership Functions 186 5.5.2 Fuzzy Logic and Fuzzy Rules 187 5.5.3 Fuzzy Interference System Components 188 5.5.4 Cognitive Capabilities and Applications of FIS 189 5.5.5 Example Application: Selecting Handover Margins 190 5.6 Bayesian Networks 192 5.6.1 Definitions 193 5.6.2 Example Application: Diagnosis in Mobile Networks 193 5.6.3 Selecting and Training Bayesian Networks 194 5.6.4 Cognitive Capabilities and Applications of Bayesian Networks 195 5.7 Time Series Forecasting 196 5.7.1 Time Series Modelling 196 5.7.2 Auto Regressive and Moving Average Models 198 5.7.3 Cognitive Capabilities and Applications of Time Series Models 198 5.8 Conclusion 199 References 199 6 Machine Learning: Tools for End-to-End Cognition 203Stephen Mwanje, Marton Kajoa and Benedek Schultz 6.1 Learning from Data 204 6.1.1 Definitions 205 6.1.2 Training Using Numerical Optimization 207 6.1.3 Over- and Underfitting, Regularization 209 6.1.4 Supervised Learning in Practice – Regression 211 6.1.5 Supervised Learning in Practice – Classification 212 6.1.6 Unsupervised Learning in Practice – Dimensionality Reduction 213 6.1.7 Unsupervised Learning in Practice – Clustering Using K-Means 215 6.1.8 Cognitive Capabilities and Limitations of Machine Learning 216 6.1.9 Example Application: Temporal-Spatial Load Profiling 218 6.2 Neural Networks 219 6.2.1 Neurons and Activation Functions 220 6.2.2 Neural Network Computational Model 221 6.2.3 Training Through Gradient Descent and Backpropagation 222 6.2.4 Overfitting and Regularization 224 6.2.5 Cognitive Capabilities of Neural Networks 226 6.2.6 Application Areas in Communication Networks 226 6.3 A Dip into Deep Neural Networks 227 6.3.1 Deep Learning 227 6.3.2 The Vanishing Gradients Problem 228 6.3.3 Drivers, Enablers, and Computational Constraints 229 6.3.4 Convolutional Networks for Image Recognition 231 6.3.5 Recurrent Neural Networks for Sequence Processing 235 6.3.6 Combining LSTMs with Convolutional Networks 237 6.3.7 Autoencoders for Data Compression and Cleaning 238 6.3.8 Cognitive Capabilities and Application of Deep Neural Networks 240 6.4 Reinforcement Learning 241 6.4.1 Learning Through Exploration 241 6.4.2 RL Challenges and Framework 242 6.4.3 Value Functions 243 6.4.4 Model-Based Learning Through Value and Policy Iteration 244 6.4.5 Q-Learning Through Dynamic Programming 245 6.4.6 Linear Function Approximation 246 6.4.7 Generalized Approximators and Deep Q-Learning 247 6.4.8 Policy Gradient and Actor-Critic Methods 248 6.4.9 Cognitive Capabilities and Application of Reinforcement Learning 252 6.5 Conclusions 253 References 253 7 Cognitive Autonomy for Network Configuration 255Stephen S. Mwanje, Rashid Mijumbi and Lars Christoph Schmelz 7.1 Context Awareness for Auto-Configuration 256 7.1.1 Environment, Network, and Function Contexts 257 7.1.2 NAF Context-Aware Configuration 259 7.1.3 Objective Model 260 7.1.4 Context Model – Context Regions and Classes 263 7.1.5 Deriving the Context Model 265 7.1.6 Deriving Network and Function Configuration Policies 266 7.2 Multi-Layer Co-Channel PCI Auto-Configuration 267 7.2.1 Automating PCI Assignment in LTE and 5G Radio 268 7.2.2 PCI Assignment Objectives 269 7.2.3 Blind PCI Auto Configuration 270 7.2.4 Initial Blind Assignment 271 7.2.5 Learning Pico-Macro NRs 272 7.2.6 Predicting Macro-Macro NRs 272 7.2.7 PCI Update/Optimization and New Cells Configuration 273 7.2.8 Performance Expectations 273 7.3 Energy Saving Management in Multi-Layer RANs 274 7.3.1 The HetNet Energy Saving Management Challenge 275 7.3.2 Power Saving Groups 276 7.3.3 Cell Switch-On Switch-Off Order 277 7.3.4 PSG Load and ESM Triggering 278 7.3.5 Static Cell Activation and Deactivation Sequence 279 7.3.6 Reference-Cell-Based ESM 280 7.3.7 ESM with Multiple Reference Cells 281 7.3.8 Distributed Cell Activation and Deactivation 283 7.3.9 Improving ESM Solutions Through Cognition 285 7.4 Dynamic Baselines for Real-Time Network Control 285 7.4.1 DARN System Design 286 7.4.2 Data Pre-Processing 288 7.4.3 Prediction 288 7.4.4 Decomposition 289 7.4.5 Learning Augmentation 290 7.4.5.1 Knowledge Base 291 7.4.5.2 Alarm Generation 292 7.4.5.3 Metric Clustering 293 7.4.6 Evaluation 294 7.5 Conclusions 297 References 298 8 Cognitive Autonomy for Network-Optimization 301Stephen S. Mwanje, Mohammad Naseer Ul-Islam and Qi Liao 8.1 Self-Optimization in Communication Networks 302 8.1.1 Characterization of Self-Optimization 302 8.1.2 Open- and Closed-Loop Self-Optimization 304 8.1.3 Reactive and Proactive Self-Optimization 305 8.1.4 Model-Based and Statistical Learning Self-Optimization 306 8.2 Q-Learning Framework for Self-Optimization 306 8.2.1 Self-Optimization as a Learning Loop 307 8.2.2 Homogeneous Multi-Agent Q-Learning 308 8.2.3 The Heterogeneous Multi-Agent Q-Learning SO Framework 309 8.2.4 Fuzzy Q-Learning 310 8.3 QL for Mobility Robustness Optimization 314 8.3.1 HO Performance and Parameters Sensitivity 314 8.3.2 Q-Learning Based MRO (QMRO) 315 8.3.3 Parameter Search Strategy 317 8.3.4 Optimization Algorithm 318 8.3.5 Evaluation 318 8.4 Fuzzy Q-Learning for Tilt Optimization 322 8.4.1 Fuzzy Q-Learning Controller (FQLC) Components 322 8.4.2 The FQLC Algorithm 324 8.4.3 Homogeneous Multi-Agent Learning Strategies 325 8.4.4 Coverage and Capacity Optimization 327 8.4.5 Self-Healing and eNB Deployment 327 8.5 Interference-Aware Flexible Resource Assignment in 5G 329 8.5.1 Muting in Wireless Networks 330 8.5.2 Notations, Definitions, and Preliminaries 331 8.5.3 System Model and Problem Formulation 332 8.5.4 Optimal Resource Allocation and Performance Limits 334 8.5.5 Successive Approximation of Fixed Point (SAFP) 335 8.5.6 Partial Resource Muting 335 8.5.7 Evaluation 337 8.6 Summary and Open Challenges 340 References 341 9 Cognitive Autonomy for Network Self-Healing 345Janne Ali-Tolppa, Marton Kajo, Borislava Gajic, Ilaria Malanchini, Benedek Schultz and Qi Liao 9.1 Resilience and Self-Healing 346 9.1.1 Resilience by Design 347 9.1.2 Holistic Self-Healing 348 9.2 Overview on Cognitive Self-Healing 349 9.2.1 The Basic Building Blocks of Self-Healing 350 9.2.2 Profiling and Anomaly Detection 351 9.2.3 Diagnosis 353 9.2.4 Remediation Action 354 9.2.5 Advanced Self-Healing Concepts 354 9.2.6 Feature Reduction and Context Selection for Anomaly Detection 356 9.3 Anomaly Detection in Radio Access Networks 358 9.3.1 Use Cases 359 9.3.2 An Overview of the RAN Anomaly Detection Process 360 9.3.3 Profiling the Normal Behaviour 361 9.3.4 The New Normal – Adapting to Changes 362 9.3.5 Anomaly-Level Calculation 364 9.3.6 Anomaly Event Detection 365 9.4 Diagnosis and Remediation in Radio Access Networks 366 9.4.1 Symptom Collection 367 9.4.2 Diagnosis 367 9.4.3 Augmented Diagnosis 369 9.4.4 Deploying Corrective Actions 371 9.5 Knowledge Sharing in Cognitive Self-Healing 371 9.5.1 Information Sharing in Mobile Networks 371 9.5.2 Transfer Learning and Self-Healing for Mobile Networks 373 9.5.3 Applying Transfer Learning to Self-Healing 374 9.5.4 Prognostic Cross-Domain Anomaly Detection and Diagnosis 374 9.5.5 Cognitive Slice Lifecycle Management 375 9.5.6 Diagnosis Knowledge Cloud 376 9.5.7 Diagnosis Cloud Components 377 9.5.8 Diagnosis Cloud Evaluation 378 9.6 The Future of Self-Healing in Cognitive Mobile Networks 379 9.6.1 Predictive and Preventive Self-Healing 379 9.6.2 Predicting the Black Swan – Ludic Fallacy and Self-Healing 380 References 382 10 Cognitive Autonomy in Cross-Domain Network Analytics 385Szabolcs Nováczki, Péter Szilágyi and Csaba Vulkán 10.1 System State Modelling for Cognitive Automation 386 10.1.1 Cognitive Context-Aware Assessment and Actioning 386 10.1.2 State Modelling and Abstraction 387 10.1.3 Deriving the System-State Model 389 10.1.4 Symptom Attribution and Interpretation 392 10.1.5 Remediation and Self-Monitoring of Actions 394 10.2 Real-Time User-Plane Analytics 396 10.2.1 Levels of User Behaviour and Traffic Patterns 396 10.2.2 Monitoring and Insight Collection 398 10.2.3 Sources of U-Plane Insight 400 10.2.4 Insight Analytics from Correlated Measurements 401 10.2.5 Insight Analytics from Packet Patterns 402 10.3 Real-Time Customer Experience Management 405 10.3.1 Intent Contextualization and QoE Policy Automation 406 10.3.2 QoE Descriptors and QoE Target Definition 408 10.3.3 QoE Enforcement 410 10.4 Mobile Backhaul Automation 411 10.4.1 The Opportunities of MBH Automation 412 10.4.2 Architecture of the Automated MBH Management 413 10.4.3 MBH Automation Use Cases 416 10.5 Summary 417 References 418 11 System Aspects for Cognitive Autonomous Networks 419Stephen S. Mwanje, Janne Ali-Tolppa and Ilaria Malanchini 11.1 The SON Network Management Automation System 420 11.1.1 SON Framework for Network Management Automation 420 11.1.2 SON as Closed-Loop Control 421 11.1.3 SON Operation – The Rule-Based Multi-Agent Control 422 11.2 NMA Systems as Multi-Agent Systems 423 11.2.1 Single-Agent System (SAS) Decomposition 423 11.2.2 Single Coordinator or Multi-Agent Team Learning 424 11.2.3 Team Modelling 425 11.2.4 Concurrent Games/Concurrent Learning 425 11.3 Post-Action Verification of Automation Functions Effects 426 11.3.1 Scope Generation 427 11.3.2 Performance Assessment 428 11.3.3 Degradation Detection, Scoring and Diagnosis 429 11.3.4 Deploying Corrective Actions – The Deployment Plan 431 11.3.5 Resolving False Verification Collisions 433 11.4 Optimistic Concurrency Control Using Verification 436 11.4.1 Optimistic Concurrency Control in Distributed Systems 436 11.4.2 Optimistic Concurrency Control in SON Coordination 437 11.4.3 Extending the Coordination Transaction with Verification 437 11.5 A Framework for Cognitive Automation in Networks 440 11.5.1 Leveraging CFs in the Functional Decomposition of CAN Systems 440 11.5.2 Network Objectives and Context 442 11.5.3 Decision Applications (DApps) 443 11.5.4 Coordination and Control 444 11.5.4.1 Configuration Management Engine (CME) 444 11.5.4.2 Coordination Engine (CE) 445 11.5.5 Interfacing Among Functions 446 11.6 Synchronized Cooperative Learning in CANs 446 11.6.1 The SCL Principle 448 11.6.2 Managing Concurrency: Spatial-Temporal Scheduling (STS) 449 11.6.3 Aggregating Peer Information 451 11.6.4 SCL for MRO-MLB Conflicts 452 11.7 Inter-Function Coopetition – A Game Theoretic Opportunity 456 11.7.1 A Distributed Intelligence Challenge 457 11.7.2 Game Theory and Bayesian Games 458 11.7.3 Learning in Bayesian Games 461 11.7.4 CF Coordination as Learning Over Bayesian Games 463 11.8 Summary and Open Challenges 464 11.8.1 System Supervision 464 11.8.2 The New Paradigm 465 11.8.3 Old Problems with New Faces? 466 References 466 12 Towards Actualizing Network Autonomy 469Stephen S. Mwanje, Jürgen Goerge, Janne Ali-Tolppa, Kimmo Hatonen, Harald Bender, Csaba Rotter, Ilaria Malanchini and Henning Sanneck 12.1 Cognitive Autonomous Networks – The Vision 470 12.1.1 Cognitive Techniques in Network Automation 471 12.1.2 Success Factors in Implementing CAN Projects 475 12.1.3 Implications on KPI Design and Event Logging 476 12.1.4 Network Function Centralization and Federation 477 12.1.5 CAN Outlook on Architecture and Technology Evolution 478 12.1.6 CAN Outlook on NM System Evolution 483 12.2 Modelling Networks: The System View 486 12.2.1 System Description of a Mobile Network 486 12.2.2 Describing Performance 488 12.2.3 Implications on Automation 489 12.2.4 Control Strategies 490 12.2.5 Two-Dimensional Continuum of Control 495 12.2.6 Levels of Policy Abstraction 497 12.2.7 Implications on Optimization 500 12.2.8 The Promise of Intent-Based Network Control 502 12.3 The Development – Operations Interface in CANs 506 12.3.1 The DevOps Paradigm 506 12.3.2 Requirements for Successful Adoption of DevOps 508 12.3.3 Benefits of DevOps for CAN 509 12.4 CAN as Data Intensive Network Operations 510 12.4.1 Network Data: A New Network Asset 510 12.4.2 From Network Management to Data Management 511 12.4.3 Managing Failure in CANs 512 References 514 Index 517

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Book SynopsisProvides an introduction to the topic of smart chemical sensors, along with an overview of the state of the art based on potential applications This book presents a comprehensive overview of chemical sensors, ranging from the choice of material to sensor validation, modeling, simulation, and manufacturing. It discusses the process of data collection by intelligent techniques such as deep learning, multivariate analysis, and others. It also incorporates different types of smart chemical sensors and discusses each under a common set of sub-sections so that readers can fully understand the advantages and disadvantages of the relevant transducersdepending on the design, transduction mode, and final applications. Smart Sensors for Environmental and Medical Applications covers all major aspects of the field of smart chemical sensors, including working principle and related theory, sensor materials, classification of respective transducer type, relevant fabrication processes, methods for dataTable of ContentsList of Contributors xi Preface xiii About the Editors xvii 1 Introduction 1Hamida Hallil and Hadi Heidari 1.1 Overview 1 1.2 Sensors: History and Terminology 2 1.2.1 Definitions and General Characteristics 3 1.2.2 Influence Quantities 5 1.3 Smart Sensors for Environmental and Medical Applications 6 1.4 Outline 8 Reference 9 2 Field Effect Transistor Technologies for Biological and Chemical Sensors 11Anne-Claire Salaün, France Le Bihan, and Laurent Pichon 2.1 Introduction 11 2.2 FET Gas Sensors 12 2.2.1 Materials 12 2.2.1.1 Inorganic Semiconductors 12 2.2.1.2 Semiconductor Polymers 12 2.2.1.3 Nanostructured Materials 13 2.2.2 FET as Gas Sensors 13 2.2.2.1 Pioneering FET Gas Sensors 13 2.2.2.2 OFET Gas Sensors 13 2.2.2.3 Nanowires-Based FET Gas Sensors 14 2.3 Ion-Sensitive Field Effect Transistors Based Devices 18 2.3.1 Classical ISFET 18 2.3.2 Other Technologies 19 2.3.2.1 EGFET: Extended Gate FET 20 2.3.2.2 SGFET: Suspended Gate FFETs 20 2.3.2.3 DGFET: Dual-Gate FETs 20 2.3.2.4 Water Gating FET or Electrolyte Gated FET 21 2.3.2.5 Other FETs 23 2.3.3 BioFETs 23 2.3.3.1 General Considerations 23 2.3.3.2 DNA BioFET 23 2.3.3.3 Protein BioFET 25 2.3.3.4 Cells 25 2.4 Nano-Field Effect Transistors 25 2.4.1 Fabrication of Nano-Devices 25 2.4.1.1 Silicon Nano-Devices 25 2.4.1.2 Carbon Nanotubes Nano-Devices 28 2.4.2 Detection of Biochemical Particles by Nanostructures-Based FET 28 2.4.2.1 SiNW pH Sensor 29 2.4.2.2 DNA Detection Using SiNW-Based Sensor 30 2.4.2.3 Protein Detection 32 2.4.2.4 Detection of Bacteria and Viruses 33 References 34 3 Mammalian Cell-Based Electrochemical Sensor for Label-Free Monitoring of Analytes 43Md. Abdul Kafi, Mst. Khudishta Aktar, and Hadi Heidari 3.1 Introduction 43 3.2 State-of-the-Art Cell Chip Design and Fabrication 45 3.3 Substrate Functionalization Strategies at the Cell–Electrode Interface 48 3.4 Electrochemical Characterization of Cellular Redox 49 3.5 Application of Cell-Based Sensor 51 3.6 Prospects and Challenges of Cell-Based Sensor 54 3.7 Conclusion 56 References 56 4 Electronic Tongues 61Flavio M. Shimizu, Maria Luisa Braunger, Antonio Riul, Jr., and Osvaldo N. Oliveira, Jr. 4.1 Introduction 61 4.2 General Applications of E-tongues 63 4.3 Bioelectronic Tongues (bETs) 65 4.4 New Design of Electrodes or Measurement Systems 66 4.5 Challenges and Outlook 73 Acknowledgments 73 References 74 5 Monitoring of Food Spoilage Using Polydiacetylene‐ and Liposome‐Based Sensors 81Max Weston, Federico Mazur, and Rona Chandrawati 5.1 Introduction 81 5.2 Polydiacetylene for Visual Detection of Food Spoilage 82 5.2.1 Contaminant Detection 83 5.2.2 Freshness Indicators 85 5.2.3 Challenges, Trends, and Industrial Applicability in the Food Industry 87 5.3 Liposomes 88 5.3.1 Pathogen Detection 88 5.3.1.1 Escherichia coli 88 5.3.1.2 Salmonella spp. 90 5.3.1.3 Other Bacterium 90 5.3.1.4 Viruses, Pesticides, and Toxins 91 5.3.2 Stability of Liposome‐Based Sensors 93 5.3.3 Industrial Applicability of Liposomes 93 5.4 Conclusions 94 References 94 6 Chemical Sensors Based on Metal Oxides 103K. S. Shalini Devi, Aadhav Anantharamakrishnan, Uma Maheswari Krishnan, and Jatinder Yakhmi 6.1 Introduction 103 6.2 Classes of MOx-Based Chemical Sensors 104 6.3 Synthesis of MOx Structures 104 6.4 Mechanism of Sensing by MOx 105 6.5 Factors Influencing Sensing Performance 106 6.6 Applications of MOx-Based Chemical Sensors 109 6.6.1 MOx Sensors for Environmental Monitoring 109 6.6.2 MOx Sensors in Clinical Diagnosis 112 6.6.3 MOx Sensors in Pharmaceutical Analysis 113 6.6.4 MOx-Based Sensors in Food Analysis 116 6.6.5 MOx Sensors in Agriculture 117 6.6.6 MOx Sensors for Hazard Analysis 117 6.6.7 Flexible Sensors Based on MOx 118 6.6.8 MOx-Based Lab-on-a-Chip Sensors 118 6.7 Concluding Remarks 119 Acknowledgment 119 References 120 7 Metal Oxide Gas Sensor Electronic Interfaces 129Zeinab Hijazi, Daniele D. Caviglia, and Maurizio Valle 7.1 General Introduction 129 7.1.1 Gas Sensing System 129 7.1.2 Gas Sensing Technologies 130 7.2 MOX Gas Sensors 131 7.2.1 Principle of Operation 131 7.2.2 Assessment of Available MOX-Based Gas Sensors 132 7.3 System Requirements and Literature Review 134 7.3.1 System Requirements 134 7.3.2 Wide Range Resistance Interface Review 136 7.4 Resistance to Time/Frequency Conversion Architecture 137 7.4.1 Electronic Circuit Description 137 7.4.2 Specifications for Each Building Block to Preserve High Linearity 138 7.4.2.1 Resistance to Current Conversion (R-to-I) 138 7.4.2.2 Switches 141 7.4.2.3 Current to Voltage Conversion (I-to-V) 141 7.4.2.4 Voltage to Time/Period (V-to-T) Conversion 141 7.5 Power Consumption 141 7.5.1 Power Consumption of MOX Gas Sensor 141 7.5.2 Low Power Operating Mode 142 7.5.3 Power Consumption at Circuit Level 142 7.6 Conclusion 143 References 143 8 Smart and Intelligent E-nose for Sensitive and Selective Chemical Sensing Applications 149Saakshi Dhanekar 8.1 Introduction 149 8.1.1 The Human Olfactory System 150 8.1.2 The Artificial Olfactory System 150 8.1.2.1 Sensor Array 151 8.1.2.2 Multivariate Data Analysis 152 8.1.2.3 Pattern Recognition Methods 153 8.2 What is an Electronic Nose? 154 8.3 Applications of E-nose 155 8.3.1 Key Applications of E-nose 155 8.3.2 E-nose for Chemical Sensing 155 8.4 Types of E-nose 157 8.5 Examples of E-nose 158 8.6 Improvements and Challenges 165 8.7 Conclusion 165 References 166 9 Odor Sensing System 173Takamichi Nakamoto and Muis Muthadi 9.1 Introduction 173 9.2 Odor Biosensor 174 9.3 Prediction of Odor Impression Using Deep Learning 176 9.4 Establishment of Odor‐Source Localization Strategy Using Computational Fluid Dynamics 181 9.4.1 Background of Odor‐Source Localization 181 9.4.2 Sensor Model with Response Delay 182 9.4.3 Simulation of Testing Environment Using CFD 183 9.4.4 Simulation of Biologically Inspired Odor‐Source Localization 185 9.4.4.1 Odor Plume Tracking Strategy 185 9.4.4.2 Result 186 9.4.5 Summary of Odor Source Localization Strategy 187 9.5 Conclusion 188 Acknowledgments 189 References 189 10 Microwave Chemical Sensors 193Hamida Hallil and Corinne Dejous 10.1 Interests of Electromagnetic Transducer Gas Sensors at Microwave Frequencies 193 10.2 Operating Principle 193 10.2.1 Electromagnetic Transducers 193 10.2.2 The Case of Microwave Transducers 195 10.3 Theory of Microwave Transducers: Design, Methodology, and Approach 196 10.4 Microwave Structure‐Based Chemical Sensor 200 10.4.1 Manufacturing Techniques 200 10.4.2 Chemical Microwave Sensors 200 10.4.3 Wireless Interrogation Schemes 204 10.5 Multivariate Data Analysis and Machine Learning for Targeted Species Identification 207 10.6 Conclusion and Prospects 209 Acknowledgments 210 References 210 Index 217

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Book SynopsisThis book covers almost all of the diverse aspects of utilizing lignocellulosic biomass for valuable biorefinery product development of chemicals, alternative fuels and energy. The world has shifted towards sustainable development for the generation of energy and industrially valuable chemicals. Biorefinery plays an important role in the integration of conversion process with high-end equipment facilities for the generation of energy, fuels and chemicals. The book is divided into four parts. The first part, Basic Principles of Biorefinery, covers the concept of biorefinery, its application in industrial bioprocessing, the utilization of biomass for biorefinery application, and its future prospects and economic performance. The second part, Biorefinery for Production of Chemicals, covers the production of bioactive compounds, gallic acid, C4, C5, and C6 compounds, etc., from a variety of substrates. The third part, Biorefinery for Production of Alternative Fuel and Energy, covers Table of ContentsPreface xv Part 1: Biorefinery Basic Principles 1 1 Principles of Sustainable Biorefinery 3Samakshi Verma and Arindam Kuila 1.1 Introduction 3 1.2 Biorefinery 5 1.3 Conversion Technologies of Biorefineries 6 1.4 Some Outlooks Toward Biorefinery Technologies 7 1.5 Principles of Sustainable Biorefineries 9 1.6 Advantages of Biorefineries 10 1.7 Classification of Biorefineries 10 1.8 Conclusion 12 References 12 2 Sustainable Biorefinery Concept for Industrial Bioprocessing 15Mohd Asyraf Kassim, Tan Kean Meng, Noor Aziah Serri, Siti Baidurah Yusoff, Nur Artikah Muhammad Shahrin, Khok Yong Seng, Mohamad Hafizi Abu Bakar and Lee Chee Keong 2.1 Sustainable Industrial Bioprocess 15 2.2 Biorefinery 16 2.2.1 Starch Biorefinery 18 2.2.2 Lignocellulosic Biorefinery 19 2.3 Microalgal Biorefinery 22 2.3.1 Upstream Processing 23 2.3.2 Downstream Processing 24 2.3.2.1 Lipid-Extracted Microalgae 24 2.4 Value Added Products 27 2.4.1 Biofuel 27 2.4.1.1 Bioethanol 30 2.4.1.2 Biobutanol 31 2.4.1.3 Biodiesel 34 2.4.1.4 Short Alkane 36 2.4.2 Polyhydroxyalkanoates (PHA) 36 2.4.3 Bioactive Compounds From Food Waste Residues 39 2.5 Novel Immobilize Carrier From Biowaste 42 2.5.1 Waste Cassava Tuber Fiber 42 2.5.2 Corn Silk 43 2.5.3 Sweet Sorghum Bagasse 43 2.5.4 Coconut Shell Activated Carbon 44 2.5.5 Sugar Beet Pulp 44 2.5.6 Eggshells 45 2.6 Conclusion 45 References 46 3 Biomass Resources for Biorefinery Application 55Varsha Upadhayay, Ritika Joshi and Arindam Kuila 3.1 Introduction 55 3.2 Concept of Biorefinery 56 3.3 Biomass Feedstocks 57 3.3.1 Types of Biomass Feedstocks 57 3.3.1.1 Biomass of Sugar Industry 57 3.3.1.2 Biomass Waste 58 3.3.1.3 Sugar and Starch Biomass 59 3.3.1.4 Algal Biomass 59 3.3.1.5 Lignocelluloses Feedstock 59 3.3.1.6 Oil Crops for Biodiesel 60 3.4 Processes 60 3.4.1 Thermo Chemical Processes 62 3.4.2 Biochemical Processes 63 3.4.3 Biobased Products and the Biorefinery Concept 64 3.5 Conclusions 64 References 65 4 Evaluation of the Refinery Efficiency and Indicators for Sustainability and Economic Performance 67Rituparna Saha and Mainak Mukhopadhyay 4.1 Introduction 67 4.2 Biofuels and Biorefineries: Sustainability Development and Economic Performance 69 4.3 Future Developments Required for Building a Sustainable Biorefinery System 72 4.4 Conclusion 72 References 73 5 Biorefinery: A Future Key of Potential Energy 77Anirudha Paul, Sampad Ghosh, Saptarshi Konar and Anirban Ray 5.1 Introduction 77 5.2 Biorefinery: Definitions and Descriptions 78 5.3 Modus Operandi of Different Biorefineries 79 5.3.1 Thermochemical Processing 79 5.3.2 Mechanical Processing 79 5.3.3 Biochemical Processing 79 5.3.4 Chemical Processing 79 5.4 Types of Biorefineries 80 5.4.1 Lignocellulose Feedstock Biorefinery 80 5.4.2 Syngas Platform Biorefinery 81 5.4.3 Marine Biorefinery 81 5.4.4 Oleochemical Biorefinery 81 5.4.5 Green Biorefinery 81 5.4.6 Whole Crop Biorefinery 82 5.5 Some Biorefinery Industries 82 5.5.1 European Biorefinery Companies 82 5.5.2 Biorefinery Companies in USA 82 5.5.3 Biorefinery Companies in Asia 83 5.6 Conclusion and Future of Biorefinery 83 References 84 Part 2: Biorefinery for Production of Chemicals 89 6 Biorefinery for Innovative Production of Bioactive Compounds from Vegetable Biomass 91Massimo Lucarini, Alessandra Durazzo, Ginevra Lombardi-Boccia, Annalisa Romani, Gianni Sagratini, Noemi Bevilacqua, Francesca Ieri, Pamela Vignolini, Margherita Campo and Francesca Cecchini 6.1 Introduction 91 6.2 Waste From Grape and During Vinification: Bioactive Compounds and Innovative Production 92 6.2.1 Grape 92 6.2.2 Polyphenols 92 6.2.3 Antioxidant Activity and Health Properties of Grape 94 6.2.4 Winemaking Technologies 96 6.2.5 Winemaking By-Products 96 6.2.6 Extraction Technologies 97 6.3 Waste from Olive and During Oil Production: Bioactive Compounds and Innovative Process 99 6.3.1 Olive Oil Quality, its Components, and Beneficial Properties 100 6.3.2 Olive Oil By-Products 108 6.3.3 Olive Oil, Tradition, Biodiversity, Territory, and Sustainability 113 6.4 Bioactive Compounds in Legume Residues 115 6.4.1 Polyphenols 116 6.4.2 Phytosterols and Squalene 116 6.4.3 Dietary Fiber and Resistant Starch 117 6.4.4 Soyasaponins 117 6.4.5 Bioactive Peptides 118 References 120 7 Prospects of Bacterial Tannase Catalyzed Biotransformation of Agro and Industrial Tannin Waste to High Value Gallic Acid 129Sunny Dhiman and Gunjan Mukherjee 7.1 Introduction 129 7.2 Bacterial Tannase Producers 131 7.3 Bacterial Tannase Production 131 7.4 Hydrolyzable Tannins: A Substrate for Gallic Acid Production 133 7.5 Tannins as Waste 133 7.5.1 Agro-Waste 133 7.5.2 Industrial Waste 134 7.6 Bacterial Biotransformation of Tannins 134 7.7 Applications of Gallic Acid 136 7.7.1 Therapeutic Applications 136 7.7.2 Industrial Applications 137 7.8 Conclusions 138 References 138 8 Biorefinery Approach for Production of Industrially Important C4, C5, and C6 Chemicals 145Shritoma Sengupta and Aparna Sen 8.1 Introduction 145 8.2 Role of Biorefinery in Industrially Important Chemical Production 147 8.3 Production of C4 Chemicals 149 8.4 Production of C5 Chemicals 152 8.5 Production of C6 Chemicals 155 8.6 Concluding Remarks 157 References 158 9 Value-Added Products from Guava Waste by Biorefinery Approach 163Pranav D. Pathak, Sachin A. Mandavgane and Bhaskar D. Kulkarni 9.1 Introduction 163 9.2 Physicochemical Characterization 164 9.3 Valorization of GW 165 9.3.1 Medicinal Uses 165 9.3.1.1 GL, GB, and GF in Medicines 166 9.3.1.2 GP in Medicines 169 9.3.2 Extraction of Chemicals 171 9.3.2.1 Extraction from GL 171 9.3.2.2 Extraction from GP 176 9.3.2.3 Extraction from GS 176 9.3.3 Food Supplements 177 9.3.4 Extraction of Pectin 178 9.3.5 Animal Feed 178 9.3.6 As Insecticide 179 9.3.7 Synthesis of Nanomaterials 180 9.3.8 In Fermentations 180 9.3.9 As a Water Treatment Agent 181 9.3.10 Production of Enzymes 181 9.4 Sustainability of Value-Added Products From GW 181 9.5 Conclusion 189 References 189 10 Case-Studies Towards Sustainable Production of Value-Added Compounds in Agro-Industrial Wastes 197Massimo Lucarini, Alessandra Durazzo, Ginevra Lombardi-Boccia, Annalisa Romani, Gianni Sagratini, Noemi Bevilacqua, Francesca Ieri, Pamela Vignolini, Margherita Campo and Francesca Cecchini 10.1 Introduction 197 10.2 Experimental Pilot Plant 199 10.2.1 Chestnut 199 10.2.2 Soy 204 10.2.3 Olive Oil By-Products Case Studies 213 10.2.3.1 Olive Oil Wastewater 213 10.2.3.2 Olea europaea L. leaves 214 References 216 11 Biorefining of Lignocellulosics for Production of Industrial Excipients of Varied Functionalities 221UpadrastaLakshmishri Roy, DebabrataBera, Sreemoyee Chakraborty and Ronit Saha 11.1 Introduction 221 11.2 Structure and Composition 222 11.3 Lignocellulosic Residues: A Bioreserve for Fermentable Sugars and Polyphenols 222 11.3.1 Biorefining of Lignocellulosic Residues 223 11.4 Pre-Treatment of Lignocellulosics 224 11.4.1 Physico-Chemical Process 224 11.4.1.1 Acid Refining 224 11.4.1.2 Alcohol Refining 225 11.4.1.3 Alkali Refining 225 11.4.2 Thermo-Physical Process 226 11.4.2.1 Steam Explosion Process 226 11.4.2.2 Supercritical and Subcritical Water Treatment 226 11.4.2.3 Hot-Compressed Water Treatment 227 11.4.3 Biological Process 227 11.4.3.1 Lignin Degrading Enzymes 227 11.4.3.2 Cellulose Degrading Enzymes 229 11.4.3.3 Hemicellulose Degrading Enzymes 229 11.4.4 Phenols as By-Products of Lignocellulosic Pre-Treatment Process 230 11.5 Methods of Extraction of Polyphenols From Lignocellulosic Biomass 231 11.5.1 Solvent Affiliated Extraction 231 11.5.2 Enzyme Affiliated Extraction 231 11.5.3 Advanced Technological Methods Adopted for Recovery of Phenolics: (Pulsed-Electric-Field Pre-Treatment) 232 11.5.4 Catalytic Microwave Pyrolysis 233 11.5.5 Multifaceted Applications of Phenolics 233 11.6 Conclusion 235 References 235 12 Bioactive Compounds Production from Vegetable Biomass: A Biorefinery Approach 241Shritoma Sengupta, Debalina Bhattacharya and Mainak Mukhopadhyay 12.1 Introduction 241 12.2 Production of Bioactive Compounds 243 12.3 Bioactive Compounds From Vegetable Biomass 246 12.4 Role of Biorefinery in Production of Bioactive Compounds 248 12.5 Concluding Remarks 252 References 253 Part 3: Biorefinery for Production of Alternative Fuel and Energy 259 13 Potential Raw Materials and Production Technologies for Biorefineries 261Shilpi Bansal, Lokesh Kumar Narnoliya and Ankit Sonthalia 13.1 Introduction 261 13.2 Bioresources 264 13.2.1 First-Generation Feedstock 264 13.2.2 Second-Generation Feedstock 264 13.2.3 Third-Generation Feedstock 270 13.3 Chemicals Produced from Biomass 270 13.3.1 Ethylene 270 13.3.2 Propylene 273 13.3.3 Propylene Glycol 273 13.3.4 Butadiene 274 13.3.5 2,3-Butanediol and 2-Butanone Methyl Ethyl Ketone (MEK) 274 13.3.6 Acrylic Acid 274 13.3.7 Aromatic Compounds 275 13.4 Production Technologies 275 13.4.1 Pre-Treatment 275 13.4.2 Hydrolysis 276 13.4.3 Fermentation 277 13.4.4 Pyrolysis 278 13.4.5 Gasification 278 13.4.6 Supercritical Water 279 13.4.7 Algae Biomass 280 13.5 Conclusion 280 References 281 14 Sustainable Production of Biofuels Through Synthetic Biology Approach 289Dulam Sandhya, Phanikanth Jogam, Lokesh Kumar Narnoliya, Archana Srivastava and Jyoti Singh Jadaun 14.1 Introduction 289 14.2 Types of Biofuel 291 14.2.1 First-Generation Biofuels (Conventional Biofuels) 291 14.2.1.1 Biogas 291 14.2.1.2 Biodiesel and Bioethanol 291 14.2.2 Second-Generation Biofuels 292 14.2.2.1 Cellulosic Ethanol 293 14.2.2.2 Biomethanol 293 14.2.2.3 Dimethylformamide 293 14.2.3 Third-Generation Biofuels 293 14.2.4 Fourth-Generation Biofuels 293 14.2.5 Advantages of Biofuels 294 14.2.6 Disadvantages of Biofuels 294 14.3 Sources of Biofuel 294 14.3.1 Bacterial Source 294 14.3.2 Algal Source 296 14.3.3 Fungal Source 296 14.3.4 Plant Source 297 14.3.4.1 Plant Materials Utilized for the Production of Biofuels 298 14.3.5 Animal Source 299 14.4 Possible Routes of Biofuel Production Through Synthetic Biology 299 14.4.1 Metabolic Engineering 299 14.4.2 Tissue Culture/Genetic Engineering 300 14.4.3 CRISPR-Cas 300 14.5 Synthetic Biology and Its Application for Biofuels Production 301 14.5.1 Case Study 1: Production of Isobutanol by Engineered Saccharomyces cerevisiae 301 14.5.2 Case Study 2: Generation of Biofuel From Ionic Liquid Pretreated Plant Biomass Using Engineered E. coli 302 14.5.3 Case Study 3: CRISPRi-Mediated Metabolic Pathway Modulation for Isopentenol Production in E. coli 302 14.6 Current Status of Biofuel 302 14.7 Future Aspects 303 14.8 Conclusion 304 References 304 15 Biorefinery Approach for Bioethanol Production 313Rituparna Saha, Debalina Bhattacharya and Mainak Mukhopadhyay 15.1 Introduction 313 15.2 Bioethanol 315 15.3 Classification of Biorefineries 315 15.3.1 Agricultural Biorefinery 316 15.3.2 Lignocellulosic Biorefinery 317 15.4 Types of Pre-Treatments 318 15.4.1 Physical Pre-Treatments 318 15.4.2 Chemical Pre-Treatments 319 15.4.3 Physico-Chemical Pre-Treatments 320 15.4.4 Biological Pre-Treatments 321 15.5 Enzymatic Hydrolysis of Biomass 323 15.6 Fermentation 324 15.7 Future Prospects for the Production of Bioethanol Through Biorefineries 325 15.8 Conclusion 326 References 326 16 Biorefinery Approach for Production of Biofuel From Algal Biomass 335Bhasati Uzir and Amrita Saha 16.1 Introduction 335 16.2 Algal Biomass: The Third-Generation Biofuel 336 16.2.1 Algae as a Raw Material for Biofuels Production 338 16.2.2 Algae as Best Feedstock for Biorefinery 339 16.3 Microalgal Biomass Cultivation/Production 340 16.3.1 Open Pond Production 341 16.3.2 Closed Bioreactors/Enclosed PBRs 341 16.3.3 Hybrid Systems 341 16.4 Strain Selection and Microalgae Genetic Engineering Method Strain Selection Process for Biorefining of Microalgae 342 16.5 Harvesting Methods 343 16.6 Cellular Disruption 343 16.7 Extraction 344 16.8 Conclusion 344 References 344 17 Biogas Production and Uses 347Anirudha Paul, Saptarshi Konar, Sampad Ghosh and Anirban Ray 17.1 Introduction 347 17.2 Potential Use of Biogas 348 17.2.1 Anarobic Digestion 348 17.2.2 Biogas from Energy Crops and Straw 349 17.2.3 Biogas from Fish Waste 349 17.2.4 Biogas from Food Waste 349 17.2.5 Biogas from Sewage Sludge 350 17.2.6 Biogas from Algae 350 17.2.7 Some Biogas Biorefinery 350 17.3 Pre-Treatment 350 17.3.1 Physical Pre-Treatment 350 17.3.2 Physiochemical Pre-Treatment 351 17.3.3 Chemical Pre-Treatment 351 17.3.4 Biological Pre-Treatment 351 17.4 Process and Technology 351 17.5 Biogas Purification and Upgradation 352 17.5.1 Removal of CO2 352 17.5.2 Removal of H2S 353 17.5.3 Removal of Water 353 17.6 Conclusion 353 References 353 18 Use of Different Enzymes in Biorefinery Systems 357A.N. Anoopkumar, Sharrel Rebello, Embalil Mathachan Aneesh, Raveendran Sindhu, Parameswaran Binod, Ashok Pandey and Edgard Gnansounou 18.1 Introduction 357 18.2 Perspectives of the Biorefinery Concept 360 18.3 Starch Degradation 361 18.4 Biodegradation and Modification of Lignocellulose and Hemicellulose 361 18.5 Conversion of Pectins 363 18.6 Microbial Fermentation and Biofuel and Biodiesel Aimed Biorefinery 363 18.7 Conclusion 365 Acknowledgement 365 References 365 Part 4: Conclusion 369 19 Wheat Straw Valorization: Material Balance and Biorefinery Approach 371Sachin A. Mandavgane and Bhaskar D. Kulkarni 19.1 Introduction 371 19.2 Wax Extraction Process 372 19.3 Combustion Process 373 19.4 Mass Balance for Combustion 375 19.5 Pyrolysis of Wheat Straw 376 19.6 Mass Balance of Pyrolysis 377 19.7 Separation of Valuable Chemicals From Bio-Oil 377 19.8 Production of Biodeisel From Wheat Straw 378 19.9 Conclusion 380 Acknowledgment 381 References 381 Index 383

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Book SynopsisSecurity concerns around the rapid growth and variety of devices that are controlled and managed over the Internet is an immediate potential threat to all who own or use them. This book examines the issues surrounding these problems, vulnerabilities, what can be done to solve the problems, investigating the roots of the problems and how programming and attention to good security practice can combat the threats today that are a result of lax security processes on the Internet of Things, cloud computing and social media.Table of ContentsList of Figures xv List of Tables xix Foreword xxi Preface xxiii Acknowledgments xxv Acronyms xxvii Part I Security Designs for the Cloud Network 1 Encryption Algorithm for Data Security in Cloud Computing 3Anindita Desarkar, Ajanta Das 1.1 Introduction 4 1.2 Related Work 4 1.3 Cloud Computing - A Brief Overview 5 1.3.1 Essential Characteristics 5 1.3.2 Layers of Cloud Computing 6 1.3.3 Cloud Deployment Models 7 1.4 Data Security in Cloud Storage 7 1.4.1 Security Issues in Cloud 7 1.4.2 Symmetric Encryption Algori 8 1.4.3 Asymmetric Encryption Algorithms 12 1.4.4 Security Enhancement in Cloud Using Encryption Algorithms: Observations 15 1.5 Comparison of Encryption Algorithms 16 1.6 Performance Analysis of Encryption Algorithms in Cloud 16 1.7 Conclusion 17 References 17 2 Analysis of Security Issues in Cloud Environment 19Sushruta Mishra, Nitin Tripathy, Brojo Kishore Mishra, Chandrakanta Mahanty 2.1 An Insight into Cloud Computing 20 2.2 Critical Challenges Concerning Cloud Computing 21 2.2.1 Data Protection 21 2.2.2 Data Recovery and Availability 22 2.2.3 Management Capacities 22 2.2.4 Regulatory and Compliance Restrictions 22 2.3 Basic Models Governing Cloud Computing 22 2.3.1 Cloud Computing Models 23 2.3.2 Security Concerns of Cloud Computing 23 2.4 Security Countermeasures in Cloud Computing 26 2.4.1 Countermeasures for Communication Issues 26 2.4.2 Countermeasures for Architecture Security 26 2.4.3 Countermeasures for Challenges Inherited from Network Concepts 27 2.4.4 Countermeasures for CAS Proposed Threats 28 2.5 Discussion of an Implemented SDN Security Framework 29 2.5.1 System Design 29 2.5.2 Phase 1: User Authentication Phase 30 2.5.3 Phase 2: Controller Assignment Phase 31 2.5.4 Phase 3: Communication Phase 33 2.6 Result Analysis 35 2.6.1 Simulation Environment 35 2.6.2 Analysis of Different Attacks 35 2.6.3 Comparative Analysis 36 2.7 Conclusion 40 References 40 3 Security and Challenges in Mobile Cloud Computing 43Ankur Dumka, Minakshi Memoria, Alaknanda Ashok 3.1 Introduction 44 3.1.1 Mobile Cloud Computing 44 3.1.2 Internet of Things and Cloud Computing 46 3.2 Literature Review 46 3.3 Architecture of Integration of Mobile Cloud Computing with IoT 46 3.3.1 Infrastructural or Architectural Issues 49 3.3.2 Privacy Issues 52 3.3.3 Compliance Issues 53 3.4 Proposed Preventive Measure for Security in MCC 54 3.5 Conclusion 55 References 55 4 Fog Computing and Its Security Issues 59Jyotir Moy Chatterjee, Ishaani Priyadarshini, Shankeys, and DacNhuong Le 4.1 Introduction 60 4.2 Current Fog Applications 62 4.2.1 Why Do We Need Fog? 62 4.2.2 What Can We Do with Fog? 63 4.3 Security and Privacy in Fog Computing 66 4.3.1 Trust and Authentication 66 4.3.2 Man-in-the-Middle Attacks (MITM) 66 4.3.3 Network Security 68 4.3.4 Secure Data Storage 69 4.4 Secure and Private Data Computation 69 4.4.1 Privacy 70 4.4.2 Access Control 71 4.4.3 Intrusion Detection 71 4.5 Conclusion 71 References 73 5 Application Safety and Service Vulnerability in Cloud Network 77Sudipta Sahana, Debabrata Sarddar 5.1 Introduction 78 5.1.1 Introduction to Security Issues in Cloud Service Models 78 5.1.2 Security Issues in SaaS 78 5.1.3 Security Issues in PaaS 79 5.1.4 Security Issues in IaaS 79 5.2 Security Concerns of Cloud Computing 80 5.2.1 Data Breaches 80 5.2.2 Hijacking of Accounts 81 5.2.3 Insider Threat 81 5.2.4 Malware Injection 82 5.2.5 Abuse of Cloud Services 82 5.2.6 Insecure APIs 82 5.2.7 Denial of Service Attacks 83 5.2.8 Insufficient Due Diligence 83 5.2.9 Shared Vulnerabilities 84 5.2.10 Data Loss 84 5.3 Security Tools in Cloud 84 5.3.1 Qualys 85 5.3.2 CipherCloud 85 5.3.3 Okta 86 5.3.4 Skyline Networks 86 5.3.5 Bitglass 86 5.3.6 WhiteHat Security 87 5.3.7 Proofpoint 87 5.3.8 docTrackr 87 5.3.9 Centrify 87 5.3.10 Vaultive 88 5.3.11 Zscaler 88 5.3.12 SilverSky 88 5.4 Cloud Service Vulnerabilities 89 5.4.1 Visibility and Control Reduction at the Consumer End 89 5.4.2 On-Demand SelfService Simplifies Unauthorized Use 89 5.4.3 Web-Based Organization APIs Can Be Compromised 90 5.4.4 Separation among Multi-Tenant Fails 90 5.4.5 Incomplete Data Deletion 90 5.4.6 Stolen Credentials 90 5.4.7 Increased Complexity Strains IT Staff 91 5.4.8 Vendor Lock-In Complicates Moving to Other CSPs 91 5.4.9 Insiders Abuse Authorized Access 91 5.4.10 Stored Data is Lost 92 5.4.11 CSP Supply Chain Can Be Compromised 92 5.4.12 Inadequate Due Diligence Amplifies Cyber Threat 92 5.5 Cloud Computing Security Best Practices 92 5.5.1 Cloud Data Encryption 92 5.5.2 Identity and Access Management 93 5.5.3 Network Segmentation 93 5.5.4 Disaster Recovery 93 5.5.5 Vulnerability Management 93 5.5.6 Monitoring, Altering and Reporting 94 5.6 Conclusion 94 References 94 Part II Security Designs for the Internet of Things and Social Networks 6 IoT Security and Privacy Preservation 99Bright Keswan, Tarini Ch. Mishra, Ambarish G. Mohapatra, Poonam Keswani 6.1 Introduction 100 6.2 Review of Existing Technology 101 6.3 Research Design 101 6.4 Methodology 103 6.4.1 AWS IoT 103 6.4.2 ARM Mbed IoT 104 6.4.3 Azure IoT Suite 106 6.5 Implication and Findings 106 6.5.1 Ethical 106 6.5.2 Legal 107 6.5.3 Social 107 6.6 Future Scope 108 6.7 Conclusion 108 References 109 7 Automation Movie Recommender System Based on Internet of Things and Clustering 113Lalit Mohan Goyal, Mamta Mittal, Asheesh Sharma 7.1 Introduction 114 7.2 Background 115 7.2.1 Characteristics of IoT 115 7.2.2 Evolution of IoT 115 7.2.3 Trends in IoT 116 7.2.4 Requirements of IoT 116 7.2.5 IoT Elements 116 7.2.6 Architecture of IoT 117 7.2.7 Application Domain of IoT 117 7.2.8 IoT Technology 119 7.2.9 The Present and Future of IoT 121 7.2.10 IoT Challenges 121 7.2.11 Scope of IoT 122 7.3 Related Works 122 7.4 Proposed System 123 7.5 Implementation 124 7.6 Conclusion 127 References 127 8 Societal Implications of Emerging Technologies (SMAC) and Related Privacy Challenges 129Manikant Roy, Amar Singh, Sukanta Ghosh, Nisha Sethi 8.1 Introduction to Data Analytics 130 8.1.1 Descriptive Analytics 131 8.1.2 Diagnostic Analytics 131 8.1.3 Prescriptive Analytics 131 8.1.4 Exploratory Analytics 132 8.1.5 Predictive Analytics 133 8.1.6 Mechanistic, Causal and Inferential Analytics 133 8.2 Privacy Concerns Related to Use of Data Analytics 133 8.2.1 Immoral Actions Based on Analyses 133 8.2.2 Discrimination 134 8.2.3 Privacy Breaches 134 8.2.4 Inaccuracy of Data Analytics 134 8.2.5 E-Discovery Angst 134 8.2.6 Understanding Cloud Basics 134 8.3 Issues 137 8.3.1 Challenges 137 8.3.2 Services of Cloud 137 8.4 Social Media 138 8.4.1 Introduction 138 8.4.2 Societal Implication of Social Network 139 8.5 Conclusion 139 References 140 9 Implementation of REST Architecure-Based Energy-Efficient Home Automation System 143Shankey Garg, Jyotir Moy Chatterjee, Dac-Nhuong Le 9.1 Introduction 144 9.2 Related Work 144 9.3 RESTful Web Server 144 9.4 Why and How REST is More Suitable for IoT 145 9.5 Architecture of Arduino-Based Home Automation System 146 9.6 Implementation Details 146 9.7 Why Arduino? 147 9.8 Result Analysis 147 9.8.1 Power Consumption without Automation 148 9.8.2 Power Consumption with IoT 148 9.8.3 Total Power Consumption Analysis 149 9.9 Conclusion and Future Scope 150 References 151 10 The Vital Role of Fog Computing in Internet of Things 153Vikram Puri, Jolanda G Tromp, Chung Van Le, Nhu Gia Nguyen, Dac-Nhuong Le 10.1 Introduction 154 10.2 Related Studies 155 10.3 IoT Principles and Applications 156 10.4 Different IoT Domains 157 10.4.1 Autonomous Cars 157 10.4.2 Healthcare 157 10.4.3 Smart Home 158 10.4.4 Industry 4.0 158 10.5 Issues in Fog Computing Regarding Security and Privacy 158 10.5.1 Authentication 159 10.5.2 Trust 160 10.5.3 Attacks 160 10.5.4 End User Privacy 160 10.5.5 Secure Communication between Fog Nodes 161 10.6 Conclusion 161 References 161 Part III Security Designs for Solutions and Applications 11 The Role of Information-Centric Security in the Modern Arena of Information Technology 167Sushree Bibhuprada, Dac-Nhuong Le, B. Priyadarshini 11.1 Introduction 168 11.2 Complete Solution to Data Security 169 11.2.1 Confidentiality 169 11.2.2 Integrity 169 11.2.3 Availability 170 11.3 Intrusion Detection and Security 170 11.3.1 Divergent Type of Intrusion Detection System 170 11.3.2 Potentiality of Intrusion Detection Systems 172 11.3.3 Advantage of Intrusion Detection Systems 173 11.4 IPS vs. IDS 173 11.5 Relevant Methods to Increase Data Safety 174 11.5.1 Limit Data Access 174 11.5.2 Identification of Sensitive Data 174 11.5.3 Pre-Planned Data Security Policy 175 11.5.4 Strong and Different Passwords for Every Department 175 11.5.5 Regular Data Backup and Update 175 11.6 Conclusion 175 References 176 12 Enabling Mobile Technology for Healthcare Service Improvements 179Bhumi Dobaria, Chintan Bhatt 12.1 Introduction 180 12.1.1 Healthcare System in India 180 12.1.2 What is mHealth? 180 12.1.3 Worldwide mHealth Scenario 181 12.1.4 mHealth and Its Scope in India 181 12.2 System Design 182 12.2.1 Application Server 183 12.2.2 File System 183 12.2.3 Client 183 12.3 Result Analysis 183 12.4 Conclusion 188 References 189 13 Optimization of Ontology-Based Clinical Pathways and Incorporating Differential Privacy in the Healthcare System 191Soumya Banerjee, Rachid Benlamri, Samia Bouzefrane 13.1 Introduction 192 13.2 Ontological Structure of Clinical Pathways 194 13.3 Proposed Model 195 13.3.1 Elements of Optimization in CP 196 13.3.2 Functional Model of Differential Privacy 196 13.3.3 About the Data Visualization 199 13.3.4 Validation of Results 199 13.4 Conclusion and Further Scope of Research 202 References 203 14 Advancements and Applications in Fog Computing 207Sumit Bansal, Mayank Aggarwal, Himanshu Aggarwal 14.1 Introduction 208 14.1.1 Cloud Computing 208 14.1.2 Internet of Things 208 14.1.3 Fog Computing 209 14.2 Fog Computing Architecture 210 14.2.1 Features of Fog Computing 210 14.2.2 Architecture of Fog Computing 211 14.2.3 Components of Fog Computing 212 14.3 Communication in Fog Computing 214 14.3.1 Communication Steps 214 14.3.2 Discovery and Detection of ICOs 214 14.3.3 Models of Communication 215 14.3.4 Communication Protocols 215 14.3.5 Communication Protocol Requirements 216 14.3.6 Methods of Data Collection 216 14.4 Application or Programming Models 218 14.4.1 Sense-Process-Actuate Model 218 14.4.2 Stream Processing Model 218 14.4.3 Benefits of Fog over Cloud Computing 219 14.4.4 Simulator Tool 220 14.5 Simulation-Based Experiments 221 14.6 Scheduling 225 14.6.1 Classification of Scheduling 225 14.6.2 Need for Scheduling 225 14.6.3 Existing Scheduling Algorithms 226 14.7 Challenges in Fog Computing 227 14.7.1 Connectivity Challenges 227 14.7.2 Context Awareness 227 14.7.3 Data Handling 228 14.7.4 Security 228 14.7.5 Privacy 229 14.7.6 Pluggable Architecture 229 14.7.7 Sustainability 229 14.7.8 Network and Storage 230 14.8 Use Case Scenarios 230 14.8.1 Smart Home 230 14.8.2 Smart Rail 232 14.8.3 Smart Healthcare 233 14.8.4 Smart Agriculture 234 14.8.5 Future Applications 235 14.9 Emerging Trends 236 14.10 Conclusion 236 References 237 15 Taxonomy of Cyber-Physical Social Systems in Intelligent Transportation 241Dhiraj, Anil Saini 15.1 Introduction 242 15.2 General Overview of CPSS in Intelligent Transportation 243 15.2.1 What is CPS? 243 15.2.2 Transition from CPS to CPSS 243 15.2.3 CPSS in Transportation 244 15.3 Conceptual Framework of CPSS in Transportation 244 15.4 Research Challenges 248 15.5 Discussion and Conclusion 248 References 249 16 Cyberspace for Smart Parenting with Sensors 253Alok Ranjan Prusty 16.1 Background 254 16.2 Internet of Things 254 16.2.1 Machine to Machine 255 16.2.2 Smart Wearables 255 16.2.3 Smart Parenting 256 16.2.4 Accelerometer Sensor 257 16.2.5 Pulse Sensor 257 16.3 Project 257 16.4 Steps and Working Principle 259 16.5 Result and Analysis 260 16.6 Conclusions 262 References 262

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Book SynopsisINTELLIGENT IOT FOR THE DIGITAL WORLD DISCOVER HOW THE INTELLIGENT INTERNET OF THINGS WILL CHANGE THE INFORMATION AND COMMUNICATION TECHNOLOGY INDUSTRY IN THE NEXT DECADE In the digital world, most data and Internet of Things (IoT) services need to be efficiently processed and executed by intelligent algorithms using local or regional computing resources, thus greatly saving and reducing communication bandwidth, end-to-end service delay, long-distance data transmissions, and potential privacy breaches. This book proposes a pyramid model, where data, computing and algorithm jointly constitute the triangular base to support a variety of user-centric intelligent IoT services at the spire by using different kinds of smart terminals or devices.This book provides a state-of-the-art review of intelligent IoT technologies and applications, discusses the key challenges and opportunities facing the digital world, and answers the following five critical questTable of ContentsPreface ix Acknowledgments xvii Acronyms xix 1 IoT Technologies and Applications 1 1.1 Introduction 1 1.2 Traditional IoT Technologies 3 1.2.1 Traditional IoT System Architecture 3 1.2.2 IoT Connectivity Technologies and Protocols 7 1.3 Intelligent IoT Technologies 27 1.3.1 Data Collection Technologies 29 1.3.2 Computing Power Network 36 1.3.3 Intelligent Algorithms 39 1.4 Typical Applications 42 1.4.1 Environmental Monitoring 42 1.4.2 Public Safety Surveillance 42 1.4.3 Military Communication 44 1.4.4 Intelligent Manufacturing and Interactive Design 46 1.4.5 Autonomous Driving and Vehicular Networks 47 1.5 Requirements and Challenges for Intelligent IoT Services 48 1.5.1 A Generic and Flexible Multi-tier Intelligence IoT Architecture 48 1.5.2 Lightweight Data Privacy Management in IoT Networks 49 1.5.3 Cross-domain Resource Management for Intelligent IoT Services 50 1.5.4 Optimization of Service Function Placement, QoS, and Multi-operator Network Sharing for Intelligent IoT Services 50 1.5.5 Data Time stamping and Clock Synchronization Services for Wide-area IoT Systems 51 1.6 Conclusion 52 References 52 2 Computing and Service Architecture for Intelligent IoT 61 2.1 Introduction 61 2.2 Multi-tier Computing Networks and Service Architecture 62 2.2.1 Multi-tier Computing Network Architecture 63 2.2.2 Cost Aware Task Scheduling Framework 65 2.2.3 Fog as a Service Technology 69 2.3 Edge-enabled Intelligence for Industrial IoT 74 2.3.1 Introduction and Background 74 2.3.2 Boomerang Framework 79 2.3.3 Performance Evaluation 83 2.4 Fog-enabled Collaborative SLAM of Robot Swarm 85 2.4.1 Introduction and Background 85 2.4.2 A Fog-enabled Solution 87 2.5 Conclusion 93 References 94 3 Cross-Domain Resource Management Frameworks 97 3.1 Introduction 97 3.2 Joint Computation and Communication Resource Management for Delay-Sensitive Applications 99 3.2.1 2C Resource Management Framework 101 3.2.2 Distributed Resource Management Algorithm 104 3.2.3 Delay Reduction Performance 107 3.3 Joint Computing, Communication, and Caching Resource Management for Energy-efficient Applications 113 3.3.1 Fog-enabled 3C Resource Management Framework 116 3.3.2 Fog-enabled 3C Resource Management Algorithm 121 3.3.3 Energy Saving Performance 127 3.4 Case Study: Energy-efficient Resource Management in Tactile Internet 131 3.4.1 Fog-enabled Tactile Internet Architecture 133 3.4.2 Response Time and Power Efficiency Trade-off 135 3.4.3 Cooperative Fog Computing 137 3.4.4 Distributed Optimization for Cooperative Fog Computing 139 3.4.5 A City-wide Deployment of Fog Computing-supported Self-driving Bus System 140 3.5 Conclusion 144 References 145 4 Dynamic Service Provisioning Frameworks 149 4.1 Online Orchestration of Cross-edge Service Function Chaining 149 4.1.1 Introduction 149 4.1.2 Related Work 151 4.1.3 System Model for Cross-edge SFC Deployment 152 4.1.4 Online Optimization for Long-term Cost Minimization 157 4.1.5 Performance Analysis 162 4.1.6 Performance Evaluation 165 4.1.7 Future Directions 169 4.2 Dynamic Network Slicing for High-quality Services 170 4.2.1 Service and User Requirements 170 4.2.2 Related Work 173 4.2.3 System Model and Problem Formulation 174 4.2.4 Implementation and Numerical Results 176 4.3 Collaboration of Multiple Network Operators 180 4.3.1 Service and User Requirements 181 4.3.2 System Model and Problem Formulation 182 4.3.3 Performance Analysis 187 4.4 Conclusion 189 References 190 5 Lightweight Privacy-Preserving Learning Schemes 197 5.1 Introduction 197 5.2 System Model and Problem Formulation 199 5.3 Solutions and Results 200 5.3.1 A Lightweight Privacy-preserving Collaborative Learning Scheme 200 5.3.2 A Differentially Private Collaborative Learning Scheme 213 5.3.3 A Lightweight and Unobtrusive Data Obfuscation Scheme for Remote Inference 218 5.4 Conclusion 233 References 233 6 Clock Synchronization for Wide-area Applications 239 6.1 Introduction 239 6.2 System Model and Problem Formulation 240 6.2.1 Natural Timestamping for Wireless IoT Devices 240 6.2.2 Clock Synchronization forWearable IoT Devices 241 6.3 Natural Timestamps in Powerline Electromagnetic Radiation 243 6.3.1 Electrical Network Frequency Fluctuations and Powerline Electromagnetic Radiation 243 6.3.2 Electromagnetic Radiation-based Natural Timestamping 244 6.3.3 Implementation and Benchmark 251 6.3.4 Evaluation in Office and Residential Environments 254 6.3.5 Evaluation in a Factory Environment 259 6.3.6 Applications 261 6.4 Wearables Clock Synchronization Using Skin Electric Potentials 269 6.4.1 Motivation 269 6.4.2 Measurement Study 271 6.4.3 TouchSync System Design 276 6.4.4 TouchSync with Internal Periodic Signal 285 6.4.5 Implementation 288 6.4.6 Evaluation 290 6.5 Conclusion 297 References 297 7 Conclusion 301 Index 305

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Book SynopsisConversion of Water and CO2 to Fuels using Solar Energy Comprehensive Resource for Understanding the Emerging Solar Technologies for Hydrogen Generation via Water Splitting and Carbon-based Fuel Production via CO2 Recycling Fossil fuel burning is the primary source of carbon in the atmosphere. The realization that such burning can harm the life on our planet, has led to a surge in research activities that focus on the development of alternative strategies for energy conversion. Fuel generation using solar energy is one of the most promising approaches that has received widespread attention. The fuels produced using sunlight are commonly referred to as solar fuels. This book provides researchers interested in solar fuel generation a comprehensive understanding of the emerging solar technologies for hydrogen generation via water splitting and carbon-based fuel production via CO2 recycling. The book presents the fundamental sTable of ContentsList of Contributors xiii Preface xvii 1 Solar Fuel Generation: The Relevance and Approaches 1 Ingrid Rodriguez-Gutierrez, Flavio L. Souza, and Oomman K. Varghese 1.1 Introduction 1 1.2 The Nexus Between Fossil Fuels, Global Warming, and Climate Change 2 1.3 The Energy System Transformation 4 1.4 Solar Fuels 5 1.5 Solar Reduction of CO 2 forFuelProduction 6 1.6 Solar Water Splitting for H 2 Generation 7 1.7 Solar to Fuel Conversion Pathways 8 1.7.1 Bioconversion 8 1.7.2 Thermoconversion 9 1.7.3 Electroconversion 10 1.7.4 Photoconversion 12 1.8 Conclusion 13 References 13 Section 1 Solar Fuel Generation Processes: Science and Technology 19 2 Introduction to Photocatalytic/Photoelectrochemical Fuel Generation: Science and Technology Perspective 21 Ke Fan, Lei Wang, and Lianpeng Tong 2.1 Introduction 21 2.2 The Natural Photosynthetic Water Splitting and CO 2 Reduction 22 2.2.1 Oxygen-Evolving Complex (OEC) 22 2.2.2 Hydrogenase 23 2.2.3 Enzymes that Reduce CO 2 24 2.3 Artificial Systems for Solar-Driven Chemical Fuels Production 25 2.3.1 Bioinspired Synthetic Systems 25 2.3.1.1 Synthetic Molecular Catalysts 25 2.3.1.2 Application of Synthetic Model Compounds in PEC Cells 26 2.3.2 Bioinorganic Hybrid Systems 26 2.3.3 Photoelectrochemical Water Splitting and CO 2 Reduction 27 2.3.3.1 Some Basic Concepts of Semiconductors 27 2.3.3.2 Photoelectrochemical (PEC) Water Splitting 29 2.3.3.3 Configurations of PEC Cell for Water Splitting 33 2.3.3.4 A Few Semiconductors Extensively Studied for Water Splitting 34 2.3.3.5 Photoelectrochemical (PEC) CO 2 reduction 35 2.3.3.6 Particulate Photocatalytic Systems for Water Splitting/CO 2 Reduction 37 2.4 Challenges and Outlook 39 References 40 3 Solar Thermochemical Fuels 47 Christoph Falter Nomenclature 47 3.1 Thermodynamics 48 3.2 Solar Thermochemical Processes and Reactor Concepts 49 3.2.1 Thermolysis of H 2 O 49 3.2.2 H 2 /CO From H 2 O/CO 2 Using Thermochemical Cycles 50 3.3 Energy and Mass Balance 54 3.3.1 Thermochemical Reactor 54 3.3.2 Energy and Mass Balance of Solar Thermochemical Fuel Plant 55 3.3.3 Possibilities of Enhancing Plant Efficiency 57 3.4 Techno-Economic Analysis 58 3.4.1 System Description 59 3.4.2 Economic Model 59 3.4.3 Production Costs 60 3.4.4 Comparison with Other Alternative Fuel Pathways 62 3.5 Life-Cycle Analysis 63 3.5.1 Goal and Scope 63 3.5.2 Inventory Analysis 64 3.5.3 Impact Assessment 64 3.5.4 Interpretation 65 3.5.4.1 Scenario Analysis–CO 2 From Natural Gas Combustion 65 3.5.4.2 Scenario Analysis–Grid Electricity 65 3.5.4.3 Comparison with Published GWP Values of Other Fuel Pathways 66 3.6 Land and Water Demand 67 3.6.1 Water Footprint 67 3.6.2 Land Demand 69 3.7 Geographical Potential 71 3.7.1 Determination of Suitable Areas for Solar Thermochemical Fuel Production 71 3.7.2 Determination of Life-Cycle Production Costs 73 3.7.3 Production Cost 74 3.8 Conclusions 76 References 77 4 Principles, Operations, and Techno-Economics of Photovoltaic-Electrolysis and Photoelectrochemical Water Splitting Processes 83 Nicolas Gaillard 4.1 Introduction 83 4.2 The Solar-to-Hydrogen Conversion Process 85 4.2.1 Fundamental Concepts 85 4.2.2 Material and Device Considerations 86 4.3 PV-Electrolysis Water Splitting 88 4.3.1 The Photovoltaic Process 88 4.3.2 Fundamentals of Water Electrolysis 91 4.3.3 PV-E Operating Principles 93 4.3.4 Evolution of PV-E Systems and Current State-of-the-Art 94 4.3.4.1 PV-E Systems with Planar Photovoltaics 94 4.3.4.2 PV-E Systems with Concentrated Photovoltaics 96 4.4 Photoelectrochemical Water Splitting 97 4.4.1 Energetics of the Semiconductor/Liquid Junction 97 4.4.2 Charge Transfer Dynamics at the Semiconductor/Liquid Junction 99 4.4.3 Current–Potential Behavior of a Photoelectrode 100 4.4.4 Spontaneous Water Splitting with Multi-Junction PEC Devices 103 4.5 Techno-Economics of PV-E and PEC Water Splitting 107 4.5.1 Similarities and Differences Between PV-E and PEC Water Splitting Technologies 107 4.5.2 Independent Assessments of PEC Technologies 108 4.5.3 Independent Assessments of PV-E Technology 110 4.5.4 Comparative Assessments of PV-E and PEC Technologies 110 4.6 Conclusion and Outlook 111 Acknowledgments 112 References 113 5 A Brief History of Molecular Photosynthesis: The Quest for the Bridge Between Light and Chemistry 119 Liniquer A. Fontana, Vitor H. Rigolin, Catia Ornelas, and Jackson D. Megiatto Jr. 5.1 Introduction 119 5.2 Historical Context and Early Findings 119 5.3 The Beginning of the Modern Understanding of Photosynthesis 121 5.4 Molecular Photosynthesis: Human Ingenuity Enters the Game 123 5.4.1 Biomimetic Reaction Centers 123 5.4.2 Artificial Reaction Centers with Nonnatural Electron Donors and Acceptors 126 5.4.3 Supramolecular Assembly of Artificial Reaction Centers 128 5.4.4 Artificial Antenna 131 5.4.5 Photo-Regulation 132 5.4.6 Artificial Reaction Centers Thermodynamically Poised to Oxidize Water 134 5.5 Harvesting the Energy of Charge-Separated States for Solar Fuel Production 137 5.5.1 Solar-Sensitized Photoelectrochemical Cells 137 5.5.2 Artificial Leaf 138 5.6 Conclusions 139 References 139 6 The Competitive Kinetics of Solar-Driven CO 2 Reduction 143 Mark T. Spitler 6.1 Introduction 143 6.2 Photosynthetic Systems 144 6.2.1 General 144 6.2.2 PSII Coupling to the OEC 146 6.2.3 PSI Coupling to PSII and RuBisCO 148 6.2.4 LHC Coupling 149 6.2.5 Indirect Coupling to RuBisCo 149 6.2.6 Photostability 150 6.3 Water Oxidation 151 6.3.1 Molecular Water Oxidation 152 6.3.2 Dye-Sensitized Photoelectrosynthesis Cell (DSPEC) 154 6.3.3 Photoelectrochemical (PEC) Water Splitting 158 6.3.4 Particles 160 6.4 CO 2 Reduction 163 6.4.1 Recycling Applications 163 6.4.2 Metals as Catalysts 164 6.4.3 PV-Driven CO 2 Reduction 166 6.4.4 Solar Fuel Harvesting 167 6.4.5 Semiconductor Photoanode-Driven Reduction of CO 2 at Metals 167 6.4.6 Semiconductor Electrodes 167 6.4.7 Reduction of CO 2 at Semiconductor Surfaces 169 6.4.8 Molecular Catalysts 171 6.4.9 Particles for CO 2 Reduction 172 6.5 Conclusions 174 References 175 7 Utilizing the Band Diagram Framework to Interpret the Operation of Photoelectrochemical Cells 183 Kirk H. Bevan, Botong Miao, and Asif Iqbal 7.1 Semiconductor Concepts 183 7.2 Semiconductor–Liquid Junctions in the Dark 186 7.2.1 Charge Equilibration in the Dark 187 7.2.2 Semiconductor–Liquid Junctions Under Bias in the Dark 188 7.2.3 Biasing with Respect to Reference Electrodes 190 7.3 Illuminated Semiconductor–Liquid Junctions 190 7.3.1 Gartner’s Model 190 7.3.2 Peter’s Model 193 7.4 The Role of Numerical Modeling 194 7.4.1 Semiclassical Approach 194 7.4.2 Insights from Semiclassical Modeling 197 7.5 Outlook 200 References 200 Section 2 Materials for Solar Fuel Generation 203 8 Materials Used for Solar Thermal/Thermochemical Processes for CO 2 /H 2 O Dissociation/Conversion 205 Heng Pan, Youjun Lu, and Bingchan Hu 8.1 Introduction 205 8.2 Solar Thermolysis of H 2 OorCO 2 205 8.3 Redox Pairs for Two-Step Thermochemical Cycles 206 8.3.1 Volatile Redox Pairs 207 8.3.1.1 ZnO/Zn Pair 207 8.3.1.2 SnO 2 /SnO Pair 209 8.3.2 Nonvolatile Redox Pairs 209 8.3.2.1 Fe 3 O 4 /FeO Pair 209 8.3.2.2 CeO 2 /CeO 2−δ Pairs 210 8.3.2.3 CoFe 2 O 4 /FeAl 2 O 4 Pairs 211 8.3.2.4 Perovskites 211 8.3.3 Redox Pairs: New Discoveries 212 8.4 Materials for Sulfur–Iodine (S–I) Cycle 213 8.4.1 Corrosion-Resistant Materials 214 8.4.2 The Catalysts of HI Decomposition 214 8.4.3 The Catalysts for H 2 SO 4 Decomposition 217 8.5 Other Multi-Step Thermochemical Cycles 218 8.6 Catalysts for Solar Gasification and Reforming 220 8.6.1 Catalysts for Solar Gasification 220 8.6.2 Catalysts for Solar Reforming of Methane 220 8.6.3 Catalysts for Solar Reforming of Methanol 221 8.7 Summary and Outlook 222 Acknowledgment 222 Conflict of Interest 222 References 222 9 Electrocatalytic Reduction of CO 2 to Value-Added Chemicals and Fuels 233 Qian Sun, Kamran Dastafkan, and Chuan Zhao 9.1 Introduction 233 9.2 Fundamentals of CO 2 Electroreduction (CO 2 RR) 235 9.2.1 Reaction Mechanism of CO 2 RR 235 9.2.2 Electrochemical Cells 237 9.2.2.1 H-Cell 237 9.2.2.2 Flow Cell 240 9.2.2.3 Mea 241 9.2.2.4 High-Temperature Molten Salt Cell 242 9.2.2.5 Solid Oxide Cell 242 9.2.3 Electrolytes 243 9.3 Electrocatalysts for CO 2 RR 244 9.3.1 Metals 245 9.3.1.1 Noble Metals 245 9.3.1.2 Transition Metals 247 9.3.1.3 Oxide-Derived Metals 248 9.3.1.4 Metal Alloys 248 9.3.2 Metal Compounds 250 9.3.2.1 Metal Chalcogenides 250 9.3.2.2 Metal Oxides 252 9.3.2.3 Metal Nitrides 253 9.3.2.4 Metal Hydroxides 254 9.3.3 Single-Atom Catalysts 254 9.3.3.1 Noble Metal SACs 254 9.3.3.2 Transition Metal SACs 255 9.3.3.3 Other Metal SACs 256 9.3.4 Molecular Catalysts 257 9.3.4.1 Organometallic Complexes 257 9.3.4.2 MOF and COF Catalysts 258 9.3.4.3 Metal-Free and Polymerized Catalysts 259 9.4 In Situ Characterizations of Electrocatalysts for CO 2 RR 260 9.4.1 In Situ Raman 260 9.4.2 In Situ UV–vis Spectroscopy 262 9.4.3 In Situ FTIR Spectroscopy 262 9.4.4 Operando XAS 263 9.5 Summary and Perspectives 264 9.5.1 Challenges for CO 2 RR 265 9.5.2 Comparison with HER 265 9.5.3 Perspectives for CO 2 RR 265 References 269 10 Ceramic Materials for Photocatalytic/Photoelectrochemical Fuel Generation 285 Appu V. Raghu and Takashi Tachikawa 10.1 Introduction 285 10.2 Photocatalytic/Photoelectrochemical Fuel Generation 285 10.2.1 Photon Absorption 288 10.2.2 Requirements of Materials Useful as Photocatalysts 289 10.3 Metal Oxides as Photocatalysts 290 10.3.1 Doping and Surface Treatments 291 10.3.2 Long-Term Stability 292 10.3.3 Heterostructures 292 10.4 Other Ceramic Materials 295 10.4.1 Nitrides 295 10.4.2 Oxynitrides 296 10.4.3 Carbides 296 10.4.4 MXenes 297 10.5 Challenges 301 10.6 Conclusion 301 References 301 11 Gallium Nitride-Based Artificial Photosynthesis Integrated Devices for Solar Hydrogen Generation and Carbon Dioxide Reduction 309 Baowen Zhou, Peng Zhou, Wanjae Dong, and Zetian mi 11.1 Introduction 309 11.2 Merits of III-Nitride Nanostructures for Artificial Photosynthesis 310 11.3 Recent Advances in III-Nitrides for Artificial Photosynthesis 311 11.3.1 Solar Water Splitting 311 11.3.1.1 Photoelectrochemical Water Splitting 312 11.3.1.2 Photocatalytic Overall Water Splitting 316 11.3.2 Long-Term Stability Studies 322 11.4 GaN-Based APID for CO 2 Reduction 324 11.4.1 Photochemical CO 2 RR Toward CH 4 Production 324 11.4.2 Photochemical CO 2 RR Reduction Toward CH 3 OH Production 325 11.4.3 Photoelectrochemical CO 2 Reduction 326 11.4.3.1 Photoelectrochemical CO 2 RR Toward CO/H 2 Production 326 11.4.3.2 Photoelectrochemical CO 2 RR Toward HCOOH Production 327 11.4.3.3 Photoelectrochemical CO 2 RR Toward CH 4 Production 329 11.5 Gallium Nitride-Catalyzed Organic Transformations and N 2 Fixation 330 11.6 Summary and Prospects 332 Acknowledgment 333 Conflict of Interest 333 Additional Note 333 References 333 12 Low-Dimensional Materials for Direct Fuel Generation Assisted by Sunlight 341 Muhammad Shuaib Khan and Shaohua Shen 12.1 Introduction 341 12.2 Unique Properties of Low-Dimensional Materials 344 12.2.1 Electronic Properties 344 12.2.2 Surface Plasmon Resonance 344 12.2.2.1 Charge Transfer Mechanism 345 12.2.2.2 Local Electric Field 346 12.2.3 Crystal Facets, Kinks, and Edges 346 12.2.4 Large Surface Area and Abundant Surface-Active Sites 347 12.2.5 Heterostructure Construction 347 12.3 Applications of Low-Dimensional Materials 348 12.3.1 Water Splitting 348 12.3.1.1 0D Materials 350 12.3.1.2 1D Materials 352 12.3.1.3 2D Materials 354 12.3.1.4 Low-Dimensional Heterostructures 355 12.3.2 CO 2 Reduction 359 12.3.2.1 0D Materials 359 12.3.2.2 1D Materials 361 12.3.2.3 2D Materials 363 12.3.2.4 Low-Dimensional Heterostructures 365 12.4 Summary and Future Perspective 368 Acknowledgments 368 References 368 Index 377

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Book SynopsisA guide to a holistic approach to healthcare measurement aimed at improving access and outcomes Healthcare System Access is an important resource that bridges two areas of researchaccess modeling and healthcare system engineering. The book's mathematical modeling approach highlights fundamental approaches on measurement of and inference on healthcare access. This mathematical modeling facilitates translating data into knowledge in order to make data-driven estimates and projections about parameters, patterns, and trends in the system. The complementary engineering approach uses estimates and projections about the system to better inform efforts to design systems that will yield better outcomes. The authora noted expert on the topicoffers an in-depth exploration of the concepts of systematic disparities, reviews measures for systematic disparities, and presents a statistical framework for making inference on disparities with application to disparities in aTable of ContentsPreface vii 1 Introduction 1 2 A Multidimensional Framework for Measuring Access 13 3 Disparities in Healthcare Access 61 4 Linking Access to Health Outcomes 99 5 Healthcare Interventions for Improving Access 137 6 Data Analytics 195 Index 249

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Book Synopsis

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Book SynopsisReviews state-of-the-art technologies in modern heuristic optimization techniques and presents case studies showing how they have been applied in complex power and energy systems problems Written by a team of international experts, this book describes the use of metaheuristic applications in the analysis and design of electric power systems. This includes a discussion of optimum energy and commitment of generation (nonrenewable & renewable) and load resources during day-to-day operations and control activities in regulated and competitive market structures, along with transmission and distribution systems. Applications of Modern Heuristic Optimization Methods in Power and Energy Systems begins with an introduction and overview of applications in power and energy systems before moving on to planning and operation, control, and distribution. Further chapters cover the integration of renewable energy and the smart grid and electricity markets. The book finishes with final conclusions draTable of ContentsPreface xv Contributors xvii List of Figures xxi List of Tables xxxiii Chapter 1 Introduction 1 1.1 Background 1 1.2 Evolutionary Computation: A Successful Branch of CI 3 1.2.1 Genetic Algorithm 6 1.2.2 Non-dominated Sorting Genetic Algorithm II 8 1.2.3 Evolution Strategies and Evolutionary Programming 8 1.2.4 Simulated Annealing 9 1.2.5 Particle Swarm Optimization 10 1.2.6 Quantum Particle Swarm Optimization 10 1.2.7 Multi-objective Particle Swarm Optimization 11 1.2.8 Particle Swarm Optimization Variants 12 1.2.9 Artificial Bee Colony 13 1.2.10 Tabu Search 14 References 15 Chapter 2 Overview Of Applications In Power And Energy Systems 21 2.1 Applications to Power Systems 21 2.1.1 Unit Commitment 23 2.1.2 Economic Dispatch 24 2.1.3 Forecasting in Power Systems 25 2.1.4 Other Applications in Power Systems 27 2.2 Smart Grid Application Competition Series 28 2.2.1 Problem Description 29 2.2.2 Best Algorithms and Ranks 30 2.2.3 Further Information and How to Download 32 References 32 Chapter 3 Power System Planning And Operation 39 3.1 Introduction 39 3.2 Unit Commitment 40 3.2.1 Introduction 40 3.2.2 Problem Formulation 40 3.2.3 Advancement in UCP Formulations and Models 42 3.2.4 Solution Methodologies, State-of-the-Art, History, and Evolution 46 3.2.5 Conclusions 56 3.3 Economic Dispatch Based on Genetic Algorithms and Particle Swarm Optimization 56 3.3.1 Introduction 56 3.3.2 Fundamentals of Genetic Algorithms and Particle Swarm Optimization 58 3.3.3 Economic Dispatch Problem 60 3.3.4 GA Implementation to ED 63 3.3.5 PSO Implementation to ED 71 3.3.6 Numerical Example 79 3.3.7 Conclusions 87 3.4 Differential Evolution in Active Power Multi-Objective Optimal Dispatch 87 3.4.1 Introduction 87 3.4.2 Differential Evolution for Multi-Objective Optimization 88 3.4.3 Multi-Objective Model of Active Power Optimization for Wind Power Integrated Systems 97 3.4.4 Case Studies 100 3.4.5 Analyses of Dispatch Plan 105 3.4.6 Conclusions 106 3.5 Hydrothermal Coordination 106 3.5.1 Introduction 106 3.5.2 Hydrothermal Coordination Formulation 107 3.5.3 Problem Decomposition 110 3.5.4 Case Studies 111 3.5.5 Conclusions 114 3.6 Meta-Heuristic Method for Gms Based on Genetic Algorithm 115 3.6.1 History 115 3.6.2 Meta-heuristic Search Method 116 3.6.3 Flexible GMS 119 3.6.4 User-Friendly GMS System 131 3.6.5 Conclusion 141 3.7 Load Flow 143 3.7.1 Introduction 143 3.7.2 Load Flow Analysis in Electrical Power Systems 144 3.7.3 Particle Swarm Optimization and Mutation Operation 148 3.7.4 Load Flow Computation via Particle Swarm Optimization with Mutation Operation 150 3.7.5 Numerical Results 153 3.7.6 Conclusions 160 3.8 Artificial Bee Colony Algorithm for Solving Optimal Power Flow 161 3.8.1 Optimization in Power System Operation 162 3.8.2 The Optimal Power Flow Problem 162 3.8.3 Artificial Bee Colony 166 3.8.4 ABC for the OPF Problem 168 3.8.5 Case Studies 170 3.8.6 Conclusions 176 3.9 OPF Test Bed and Performance Evaluation of Modern Heuristic Optimization 176 3.9.1 Introduction 176 3.9.2 Problem Definition 177 3.9.3 OPF Test Systems 178 3.9.4 Differential Evolutionary Particle Swarm Optimization: DEEPSO 183 3.9.5 Enhanced Version of Mean–Variance Mapping Optimization Algorithm: MVMO-PHM 187 3.9.6 Evaluation Results 193 3.9.7 Conclusions 196 3.10 Transmission System Expansion Planning 197 3.10.1 Introduction 197 3.10.2 Transmission System Expansion Planning Models 198 3.10.3 Mathematical Modeling 199 3.10.4 Challenges 201 3.10.5 Application of Meta-heuristics to TEP 202 3.10.6 Conclusions 210 3.11 Conclusion 210 References 210 Chapter 4 Power System And Power Plant Control 227 4.1 Introduction 227 4.2 Load Frequency Control – Optimization and Stability 228 4.2.1 Introduction 228 4.2.2 Load Frequency Control 229 4.2.3 Components of Active Power Control System 230 4.2.4 Designing LFC Structure for an Interconnected Power System 232 4.2.5 Parameter Optimization and System Performance 237 4.2.6 System Stability in the Presence of Communication Delay 242 4.2.7 Conclusions 244 4.3 Control of Facts Devices 244 4.3.1 Introduction 244 4.3.2 Role of FACTS 246 4.3.3 Static Modeling of FACTS devices 247 4.3.4 Power Flow Control using FACTS 255 4.3.5 Optimal Power Flow Using Suitability FACTS devices 259 4.3.6 Use of Particle Swarm Optimization 281 4.3.7 Conclusions 283 4.4 Hybrid of Analytical and Heuristic Techniques for facts Devices 284 4.4.1 Introduction 284 4.4.2 Heuristic Algorithms 285 4.4.3 SVC and Voltage Instability Improvement 288 4.4.4 FACTS Devices and Angle Stability Improvement 293 4.4.5 Selection of Supplementary Input Signals for Damping Inter-area Oscillations 295 4.4.6 TCSC and Improvement of Total Transfer Capability 302 4.4.7 Conclusions 305 4.5 Power System Automation 305 4.5.1 Introduction 305 4.5.2 Application of PSO on Power System’s Corrective Control 307 4.5.3 Genetic Algorithm-aided DTs for Load Shedding 322 4.5.4 Power System-Controlled Islanding 324 4.5.5 Application of the method on the IEEE – 30 buses test system 326 4.5.6 Application of the method on the IEEE – 118 buses test system 327 4.5.7 Conclusions 327 4.5.8 Appendix 328 4.6 Power Plant Control 334 4.6.1 Introduction 334 4.6.2 Coal Mill Modeling 335 4.6.3 Nonlinear Model Predictive Control of Reheater Steam Temperature 340 4.6.4 Multi-objective Optimization of Boiler Combustion System 345 4.6.5 Conclusions 355 4.7 Predictive Control in Large-Scale Power Plant 355 4.7.1 Introduction 355 4.7.2 Particle Swarm Optimization Algorithm 356 4.7.3 Performance Prediction Model Development Based on NARMA Model 357 4.7.4 Design of Intelligent MPOC Scheme 361 4.7.5 Control Simulation Tests 364 4.7.6 Conclusions 367 4.8 Conclusion 368 References 369 Chapter 5 Distribution System 381 5.1 Introduction 381 5.2 Active Distribution Network Planning 382 5.2.1 Introduction 382 5.2.2 Problem Formulation 382 5.2.3 Overview of the Solution Techniques for Distribution Network Planning 385 5.2.4 Genetic Algorithm Solution to Active Distribution Network Planning Problem 385 5.2.5 Numerical Results 388 5.2.6 Conclusions 392 5.3 Optimal Selection of Distribution System Architecture 392 5.3.1 Introduction 392 5.3.2 Deterministic Optimization Techniques 393 5.3.3 Stochastic Optimization Techniques 394 5.3.4 Multi-Objective Optimization 400 5.3.5 Mathematical Modeling for Power System Components 401 5.3.6 AC/DC Power Flow in Hybrid Networks 405 5.3.7 Pareto-Based Multi-Objective Optimization Problem 409 5.4 Conservation Voltage Reduction Planning 418 5.4.1 Introduction 418 5.4.2 Conservation Voltage Reduction 418 5.4.3 CVR Based on PSO 420 5.4.4 CVR Based on AHP 423 5.4.5 Case Studies for CVR in Korean Power System 424 5.4.6 Conclusion 427 5.5 Dynamic Distribution Network Expansion Planning with Demand Side Management 427 5.5.1 Introduction 427 5.5.2 Expansion Options 431 5.5.3 Problem Formulation 436 5.5.4 Optimization Algorithm 442 5.5.5 Case Studies 450 5.5.6 Conclusions 460 5.6 GA-Guided Trust-Tech Methodology for Capacitor Placement in Distribution Systems 467 5.6.1 Introduction 467 5.6.2 Overview of the Trust-Tech Method 469 5.6.3 Computing Tier-One Local Optimal Solutions 472 5.6.4 The GA-Guided Trust-Tech Method 474 5.6.5 Applications to Capacitor Placement Problems 478 5.6.6 Numerical Study 481 5.6.7 Conclusions 488 5.7 Network Reconfiguration 489 5.7.1 Introduction 489 5.7.2 Modern Distribution Systems: A Concept 490 5.7.3 Distribution System Reconfiguration 493 5.7.4 Distribution System Service Restoration 496 5.7.5 Multi-Agent System for Distribution System Reconfiguration 501 5.7.6 Conclusions 510 5.8 Distribution System Restoration 510 5.8.1 Introduction 510 5.8.2 Power System Restoration Process 511 5.9 Group-based PSO for System Restoration 531 5.9.1 Introduction 531 5.9.2 Group-Based PSO Method 533 5.9.3 Overview of the Service Restoration Problem 539 5.9.4 Application to the Service Restoration Problem 542 5.9.5 Numerical Results 545 5.9.6 Conclusions 552 5.10 MVMO for Parameter Identification of Dynamic Equivalents for Active Distribution Networks 553 5.10.1 Introduction 553 5.10.2 Active Distribution System 553 5.10.3 Need for Aggregation and the Concept of Dynamic Equivalents 554 5.10.4 Proposed Approach with MVMO 556 5.10.5 Adaptation of MVMO for Identification Problem 558 5.10.6 Case Study 562 5.10.7 Application to Test Case 568 5.10.8 Analysis 569 5.10.9 Reflections 572 5.10.10 Conclusions 572 5.11 Parameter Estimation of Circuit Model for Distribution Transformers 573 5.11.1 Introduction 573 5.11.2 Transformer Winding Equivalent Circuit 574 5.11.3 Signal Comparison Indicators 576 5.11.4 Coefficients Estimation Using Heuristic Optimization 578 5.11.5 Coefficients Estimation Results and Conclusion 582 5.11.6 Conclusions 586 References 590 Chapter 6 Integration Of Renewable Energy In Smart Grid 613 6.1 Introduction 613 6.2 Renewable Energy Sources 613 6.2.1 Renewable Energy Sources Management Overview 613 6.2.2 Energy Resource Scheduling – Problem Formulation 615 6.2.3 Energy Resources Scheduling – Particle Swarm Optimization 617 6.2.4 Energy Resources Scheduling – Simulated Annealing 618 6.2.5 Practical Case Study 621 6.2.6 Appendix 632 6.2.7 Conclusions 634 6.3 Operation and Control of Smart Grid 635 6.3.1 Introduction 635 6.3.2 Problems for Systems Configuration or Systems Design 636 6.3.3 Systems Operation and Systems Control 638 6.3.4 System’s Management 640 6.3.5 Conclusion 645 6.4 Compliance of Reactive Power Requirements in Wind Power Plants 645 6.4.1 Introduction 645 6.4.2 Problem Definition 646 6.4.3 NN-Based Wind Speed Forecasting Method 648 6.4.4 Mean Variance Mapping Optimization Algorithm 650 6.4.5 Case Studies 654 6.4.6 Conclusions 665 6.5 Photovoltaic Controller Design 667 6.5.1 Introduction 667 6.5.2 Maximum Power Point Tracking in PV System 668 6.5.3 Particle Swarm Optimization 674 6.5.4 Application of Particle Swarm Optimization in MPPT 674 6.5.5 Illustration of PSO Technique for MPPT During Different Irradiance Conditions 676 6.5.6 Conclusion 678 6.6 Demand Side Management and Demand Response 680 6.6.1 Introduction 680 6.6.2 Methodology for Consumption Shifting and Generation Scheduling 683 6.6.3 Quantum PSO 685 6.6.4 Numeric Example 687 6.6.5 Conclusions 691 6.7 EPSO-Based Solar Power Forecasting 691 6.7.1 Introduction 691 6.7.2 General Radial Basis Function Network 693 6.7.3 k-Means 695 6.7.4 Deterministic Annealing Clustering 695 6.7.5 Evolutionary Particle Swarm Optimization 697 6.7.6 Hybrid Intelligent Method 698 6.7.7 Case Studies 699 6.7.8 Conclusion 704 6.8 Load Demand and Solar Generation Forecast for PV Integrated Smart Buildings 704 6.8.1 Introduction 704 6.8.2 Literature Review of Forecasting Techniques 714 6.8.3 Ensemble Forecast Methodology for Load Demand and PV Output Power 717 6.8.4 Numerical Results and Discussion 722 6.8.5 Conclusions 728 6.9 Multi-Objective Planning of Public Electric Vehicle Charging Stations 729 6.9.1 Introduction 729 6.9.2 Multi-Objective Electric Vehicle Charging Station Layout Planning Model 730 6.9.3 An Improved SPEA2 for Solving EVCSLP Problem 733 6.9.4 Case Study 737 6.9.5 Conclusion 740 6.10 Dispatch Modeling Incorporating Maneuver Components, Wind Power, and Electric Vehicles 741 6.10.1 Introduction 741 6.10.2 Proposed Economic Dispatch Formulation 743 6.10.3 Population-Based Optimization Algorithms 751 6.10.4 Test System and Results Analysis 753 6.10.5 Conclusion 756 6.11 Conclusions 757 References 757 Chapter 7 Electricity Markets 775 7.1 Introduction 775 7.2 Bidding Strategies 777 7.2.1 Introduction 777 7.2.2 Context Analysis 779 7.2.3 Strategic Bidding 780 7.3 Market Analysis and Clearing 781 7.3.1 Introduction 781 7.3.2 Electricity Market Simulators 782 7.3.3 Didactic Example 785 7.4 Electricity Market Forecasting 793 7.4.1 Introduction 793 7.4.2 Artificial Neural Networks for Electricity Market Price Forecasting 794 7.4.3 Support Vector Machines for Electricity Market Price Forecasting 795 7.4.4 Illustrative Results 796 7.5 Simultaneous Bidding of V2G In Ancillary Service Markets Using Fuzzy Optimization 798 7.5.1 Introduction 798 7.5.2 Fuzzy Optimization 799 7.5.3 FO-based Simultaneous Bidding of Ancillary Services Using V2G 801 7.5.4 Case Study 806 7.5.5 Results and Discussions 807 7.5.6 Conclusion 811 7.6 Conclusions 812 References 812 Index 819

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Book SynopsisLearn how to improve the quality of health care offered by your institution using data you already have Improving Health Care Quality: Case Studies with JMP teaches readers how to systematically identify problems, collect and interpret data, and solve issues in the real world. Relying on JMP software, the authors walk readers through the process of applying quality improvement techniques to real-life health care problems. The case studies provided in the book vary significantly and provide a wide-ranging view of the application of quality improvement techniques in the health care field. Studies regarding length of stay of diabetes patients to benchmarking the costs of hip replacement all serve to illuminate and explain the underlying concepts of statistical analysis. The authors break each case study down into several sections, including: Background and Task Data and Data Management Analysis Summary Table of ContentsForeword xv Preface xvii Acknowledgments xix Acronyms and Synonyms xxi About the Companion Website xxiii 1 Introduction 1 1.1 Key Concepts 1 1.2 Quality Improvement in Healthcare 1 1.3 Understanding Variability: The Key to QI 2 1.4 Quality Improvement Frameworks 3 1.4.1 Define–Measure–Analyze–Improve–Control (DMAIC) 4 1.4.2 Plan–Do–Check–Act (PDCA) 4 1.4.3 Choosing a Framework 5 1.5 Statistical Tools for Quality Improvement 6 1.5.1 Data Visualization 8 1.5.2 Subgrouping Data 8 1.5.3 Control Charts 9 1.5.4 The Importance of Assumptions 10 1.6 Using this Casebook 11 1.7 Summary 12 1.7.1 Exercises 13 1.7.2 Discussion Questions 14 References 14 2 Improving Patient Satisfaction 17 2.1 Key Concepts 17 2.2 DMAIC 17 2.3 PDCA 17 2.4 Background 17 2.5 The Task 18 2.6 The Data: ComplaintData.xlsx and PatientFeedback.jmp 18 2.7 Data Management 19 2.8 Analysis 20 2.8.1 Complaint Data 20 2.8.2 Patient Satisfaction Data 21 2.9 Summary 26 2.9.1 Statistical Insights 26 2.9.2 Implications and Next Steps 27 2.9.3 Summary of Tools and JMP Features 27 2.9.4 Exercises 27 2.9.5 Discussion Questions 28 Reference 29 3 Length of Stay and Readmission for Hospitalized Diabetes Patients 31 3.1 Key Concepts 31 3.2 DMAIC 31 3.3 PDCA 31 3.4 Background 31 3.5 The Task 32 3.6 The Data: HospitalReadmission.jmp 32 3.7 Data Management 32 3.8 Analysis 32 3.9 Summary 39 3.9.1 Statistical Insights 39 3.9.2 Implications and Next Steps 39 3.9.3 Summary of Tools and JMP Features 40 3.9.4 Exercises 40 3.9.5 Discussion Questions 41 4 Identify and Communicate Opportunities for Reducing Hospital Length of Stay Using JMP® Dashboards 43 4.1 Key Concepts 43 4.2 DMAIC 43 4.3 PDCA 43 4.4 Background 43 4.5 The Task 44 4.6 The Data: HospitalReadmission.jmp 44 4.7 Data Management 44 4.8 Analysis 44 4.8.1 Creating Dashboards with Combine Windows 44 4.8.2 Creating Dashboards with Dashboard Builder 45 4.8.3 Saving and Sharing JMP Dashboards 48 4.9 Summary 48 4.9.1 Statistical Insights 48 4.9.2 Implications and Next Steps 52 4.9.3 Summary of Tools and JMP Features 52 4.9.4 Exercises 53 4.9.5 Discussion Questions 53 References 53 5 Variability in the Cost of Hip Replacement 55 5.1 Key Concepts 55 5.2 DMAIC 55 5.3 PDCA 55 5.4 Background 55 5.5 The Task 56 5.6 The Data: SouthernTier_HipReplacement.csv 56 5.7 Data Management 56 5.7.1 Initial Data Review 57 5.7.2 Adjusting JMP Column Properties 58 5.7.3 Deleting Unneeded Columns 59 5.7.4 Shortening Character Columns 60 5.8 Analysis 61 5.8.1 Descriptive Analysis 62 5.8.2 Assessing Variability 63 5.9 Summary 67 5.9.1 Statistical Insights 67 5.9.2 Implications and Next Steps 67 5.9.3 Summary of Tools and JMP Features 68 5.9.4 Exercises 68 5.9.5 Discussion Questions 69 References 70 6 Benchmarking the Cost of Hip Replacement 71 6.1 Key Concepts 71 6.2 DMAIC 71 6.3 PDCA 71 6.4 Background 71 6.5 The Task 72 6.6 The Data: HipNYSPARCS_SouthernTier.jmp 72 6.7 Data Management 72 6.8 Analysis 73 6.8.1 Descriptive Analysis 73 6.8.2 Statistical Test of Hypothesis 73 6.8.3 Confidence Interval for Mean Total Cost 75 6.9 Summary 75 6.9.1 Statistical Insights 75 6.9.2 Implications and Next Steps 76 6.9.3 Summary of Tools and JMP Features 76 6.9.4 Exercises 76 6.9.5 Discussion Questions 77 References 78 7 Nursing Survey 79 7.1 Key Concepts 79 7.2 DMAIC 79 7.3 PDCA 79 7.4 Background 79 7.5 The Task 80 7.6 The Data: NursingResearch_Survey_Responses.jmp 80 7.7 Data Management 81 7.7.1 Initial Data Review 81 7.7.2 Recoding the Primary Role Column 83 7.8 Analysis 85 7.8.1 Descriptive Analysis 85 7.8.2 One-Sample Test of Proportion 87 7.8.3 Test for Difference of Two Proportions 88 7.9 Summary 90 7.9.1 Statistical Insights 90 7.9.2 Implications and Next Steps 90 7.9.3 Summary of Tools and JMP Features 91 7.9.4 Exercises 91 7.9.5 Discussion Questions 92 References 93 8 Determining the Sample Size for a Nursing Research Study 95 8.1 Key Concepts 95 8.2 DMAIC 95 8.3 PDCA 95 8.4 Background 95 8.5 The Task 96 8.6 The Data 96 8.7 Study Design and Data Collection Methodology 96 8.8 Analysis 97 8.8.1 Analysis Plan 97 8.8.2 The Basics of Sample Size Determination 98 8.8.3 Sample Size Determination for the Bee Sting Study 99 8.9 Summary 101 8.9.1 Statistical Insights 101 8.9.2 Implications and Next Steps 102 8.9.3 Summary of Tools and JMP Features 103 8.9.4 Exercises 104 8.9.5 Discussion Questions 104 References 105 9 Mapping California Ambulance Diversion 107 9.1 Key Concepts 107 9.2 DMAIC 107 9.3 PDCA 107 9.4 Background 107 9.5 The Task 108 9.6 The Data: ED_ambulance_diversion_trend.xlsx and CA_healthcare_facility_locations.xlsx 108 9.7 Data Management 108 9.7.1 Merging the Data Tables 109 9.7.2 Reviewing the Merged File 109 9.7.3 Extracting General Acute Care Hospital Data 112 9.8 Analysis 112 9.8.1 Descriptive Analysis 112 9.8.2 Geographic Distribution of Total Diversion Hours 113 9.9 Summary 116 9.9.1 Statistical Insights 116 9.9.2 Implications and Next Steps 116 9.9.3 Summary of Tools and JMP Features 117 9.9.4 Exercises 117 9.9.5 Discussion Questions 118 References 118 10 Monitoring Ambulance Diversion Hours 119 10.1 Key Concepts 119 10.2 DMAIC 119 10.3 PDCA 119 10.4 Background 119 10.5 The Task 120 10.6 The Data: CedarsSinai_Diversion_Hours.jmp 120 10.7 Data Management 121 10.8 Analysis 121 10.8.1 Descriptive Analysis 121 10.8.2 Control Chart Basics 122 10.8.3 Ambulance Diversion Process 123 10.8.4 Setting the Control Limits 123 10.8.5 Monitoring Ambulance Diversion with IR Charts 126 10.9 Summary 130 10.9.1 Statistical Insights 130 10.9.2 Implications and Next Steps 130 10.9.3 Summary of Tools and JMP Features 131 10.9.4 Exercises 131 10.9.5 Discussion Questions 132 References 132 11 Ambulatory Surgery Start Times 133 11.1 Key Concepts 133 11.2 DMAIC 133 11.3 PDCA 133 11.4 Background 133 11.5 The Task 134 11.6 The Data: ASU.jmp 134 11.7 Data Management 134 11.8 Analysis 135 11.8.1 Case 1 Analysis 138 11.8.2 Case 2 Analysis 140 11.9 Summary 141 11.9.1 Statistical Insights 141 11.9.2 Implications and Next Steps 143 11.9.3 Summary of Tools and JMP Features 144 11.9.4 Exercises 144 11.9.5 Discussion Questions 145 Reference 145 12 Pre-Op TJR Process Improvement – Part 1 147 12.1 Key Concepts 147 12.2 DMAIC 147 12.3 PDCA 147 12.4 Background 147 12.5 The Task 148 12.6 The Data: TJR.xlsx 148 12.7 Data Management 150 12.8 Analysis 153 12.9 Summary 159 12.9.1 Statistical Insights 159 12.9.2 Implications and Next Steps 161 12.9.3 Summary of Tools and JMP Features 161 12.9.4 Exercises 161 12.9.5 Discussion Questions 162 Reference 163 13 Pre-Op TJR Process Improvement – Part 2 165 13.1 Key Concepts 165 13.2 DMAIC 165 13.3 PDCA 165 13.4 Background 165 13.5 The Task 166 13.6 The Data: TJR.jmp 166 13.7 Data Management 166 13.8 Analysis 167 13.9 Summary 173 13.9.1 Statistical Insights 173 13.9.2 Implications and Next Steps 174 13.9.3 Summary of Tools and JMP Features 174 13.9.4 Exercises 174 13.9.5 Discussion Questions 175 References 175 14 Pre-Op TJR Process Improvement – Part 3 177 14.1 Key Concepts 177 14.2 DMAIC 177 14.3 PDCA 177 14.4 Background 177 14.5 The Task 178 14.6 The Data: TJR.jmp 178 14.7 Data Management 179 14.8 Analysis 179 14.9 Summary 187 14.9.1 Statistical Insights 187 14.9.2 Implications and Next Steps 188 14.9.3 Summary of Tools and JMP Features 190 14.9.4 Exercises 190 14.9.5 Discussion Questions 191 References 191 Index 193

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