Electronics and communications engineering Books

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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Book SynopsisThe new edition of the popular introductory textbook on numerical approximation methods and mathematical analysis, with a unique emphasis on real-world application An Introduction to Numerical Methods and Analysis helps students gain a solid understanding of a wide range of numerical approximation methods for solving problems of mathematical analysis. Designed for entry-level courses on the subject, this popular textbook maximizes teaching flexibility by first covering basic topics before gradually moving to more advanced material in each chapter and section. Throughout the text, students are provided clear and accessible guidance on a wide range of numerical methods and analysis techniques, including root-finding, numerical integration, interpolation, solution of systems of equations, and many others. This fully revised third edition contains new sections on higher-order difference methods, the bisection and inertia method for computing eigenvalues of a Table of ContentsPreface xiii 1 Introductory Concepts and Calculus Review 1 1.1 Basic Tools of Calculus 2 1.1.1 Taylor’s Theorem 2 1.1.2 Mean Value and Extreme Value Theorems 9 1.2 Error, Approximate Equality, and Asymptotic Order Notation 14 1.2.1 Error 14 1.2.2 Notation: Approximate Equality 15 1.2.3 Notation: Asymptotic Order 16 1.3 A Primer on Computer Arithmetic 20 1.4 A Word on Computer Languages and Software 29 1.5 A Brief History of Scientific Computing 32 1.6 Literature Review 36 References 36 2 A Survey of Simple Methods and Tools 39 2.1 Horner’s Rule and Nested Multiplication 39 2.2 Difference Approximations to the Derivative 44 2.3 Application: Euler’s Method for Initial Value Problems 52 2.4 Linear Interpolation 58 2.5 Application—The Trapezoid Rule 64 2.6 Solution of Tridiagonal Linear Systems 75 2.7 Application: Simple Two-Point Boundary Value Problems 81 3 Root-Finding 87 3.1 The Bisection Method 88 3.2 Newton’s Method: Derivation and Examples 95 3.3 How to Stop Newton’s Method 101 3.4 Application: Division Using Newton’s Method 104 3.5 The Newton Error Formula 108 3.6 Newton’s Method: Theory and Convergence 113 3.7 Application: Computation of the Square Root 117 3.8 The Secant Method: Derivation and Examples 120 3.9 Fixed-Point Iteration 124 3.10 Roots of Polynomials, Part 1 133 3.11 Special Topics in Root-finding Methods 141 3.11.1 Extrapolation and Acceleration 141 3.11.2 Variants of Newton’s Method 145 3.11.3 The Secant Method: Theory and Convergence 149 3.11.4 Multiple Roots 153 3.11.5 In Search of Fast Global Convergence: Hybrid Algorithms 157 3.12 Very High-order Methods and the Efficiency Index 162 3.13 Literature and Software Discussion 166 References 166 4 Interpolation and Approximation 169 4.1 Lagrange Interpolation 169 4.2 Newton Interpolation and Divided Differences 175 4.3 Interpolation Error 185 4.4 Application: Muller’s Method and Inverse Quadratic Interpolation 190 4.5 Application: More Approximations to the Derivative 194 4.6 Hermite Interpolation 196 4.7 Piecewise Polynomial Interpolation 200 4.8 An Introduction to Splines 207 4.8.1 Definition of the Problem 207 4.8.2 Cubic B-Splines 208 4.9 Tension Splines 223 4.10 Least Squares Concepts in Approximation 229 4.10.1 An Introduction to Data Fitting 229 4.10.2 Least Squares Approximation and Orthogonal Polynomials 233 4.11 Advanced Topics in Interpolation and Approximation 246 4.11.1 Stability of Polynomial Interpolation 247 4.11.2 The Runge Example 249 4.11.3 The Chebyshev Nodes 253 4.11.4 Spectral Interpolation 257 4.12 Literature and Software Discussion 265 References 266 5 Numerical Integration 269 5.1 A Review of the Definite Integral 270 5.2 Improving the Trapezoid Rule 272 5.3 Simpson’s Rule and Degree of Precision 277 5.4 The Midpoint Rule 288 5.5 Application: Stirling’s Formula 292 5.6 Gaussian Quadrature 294 5.7 Extrapolation Methods 306 5.8 Special Topics in Numerical Integration 313 5.8.1 Romberg Integration 313 5.8.2 Quadrature with Non-smooth Integrands 318 5.8.3 Adaptive Integration 323 5.8.4 Peano Estimates for the Trapezoid Rule 329 5.9 Literature and Software Discussion 335 References 335 6 Numerical Methods for Ordinary Differential Equations 337 6.1 The Initial Value Problem: Background 338 6.2 Euler’s Method 343 6.3 Analysis of Euler’s Method 346 6.4 Variants of Euler’s Method 350 6.4.1 The Residual and Truncation Error 352 6.4.2 Implicit Methods and Predictor–Corrector Schemes 355 6.4.3 Starting Values and Multistep Methods 360 6.4.4 The Midpoint Method and Weak Stability 362 6.5 Single-Step Methods: Runge–Kutta 367 6.6 Multistep Methods 374 6.6.1 The Adams Families 374 6.6.2 The BDF Family 378 6.7 Stability Issues 380 6.7.1 Stability Theory for Multistep Methods 380 6.7.2 Stability Regions 384 6.8 Application to Systems of Equations 385 6.8.1 Implementation Issues and Examples 385 6.8.2 Stiff Equations 389 6.8.3 A-Stability 390 6.9 Adaptive Solvers 394 6.10 Boundary Value Problems 407 6.10.1 Simple Difference Methods 407 6.10.2 Shooting Methods 414 6.10.3 Higher Order Difference Methods for BVPs 417 6.10.4 Finite Element Methods for BVPs 424 6.11 Literature and Software Discussion 432 References 433 7 Numerical Methods for the Solution of Systems of Equations 435 7.1 Linear Algebra Review 436 7.2 Linear Systems and Gaussian Elimination 438 7.3 Operation Counts 445 7.4 The LU Factorization 447 7.5 Perturbation, Conditioning, and Stability 459 7.5.1 Vector and Matrix Norms 459 7.5.2 The Condition Number and Perturbations 461 7.5.3 Estimating the Condition Number 468 7.5.4 Iterative Refinement 471 7.6 SPD Matrices and the Cholesky Decomposition 475 7.7 Application: Numerical Solution of Linear Least Squares Problems 478 7.8 Sparse and Structured Matrices 484 7.9 Iterative Methods for Linear Systems: A Brief Survey 485 7.10 Nonlinear Systems: Newton’s Method and Related Ideas 493 7.10.1 Newton’s Method 494 7.10.2 Fixed-Point Methods 497 7.11 Application: Numerical Solution of Nonlinear Boundary Value Problems 499 7.12 Literature and Software Discussion 501 References 502 8 Approximate Solution of the Algebraic Eigenvalue Problem 503 8.1 Eigenvalue Review 503 8.2 Reduction to Hessenberg Form 509 8.3 Power Methods 515 8.4 Bisection and Inertia to Compute Eigenvalues of Symmetric Matrices 533 8.5 An Overview of the QR Iteration 539 8.6 Application: Roots of Polynomials, Part II 548 8.7 Application: Computation of Gaussian Quadrature Rules 549 8.8 Literature and Software Discussion 557 References 557 9 A Survey of Numerical Methods for Partial Differential Equations 559 9.1 Difference Methods for the Diffusion Equation 559 9.1.1 The Basic Problem 559 9.1.2 The Explicit Method and Stability 560 9.1.3 Implicit Methods and the Crank–Nicolson Method 565 9.2 Finite Element Methods for the Diffusion Equation 574 9.3 Difference Methods for Poisson Equations 578 9.3.1 Discretization and Examples 578 9.3.2 Higher Order Methods 588 9.3.3 Iteration and the Method of Conjugate Gradients 593 9.4 Literature and Software Discussion 605 References 605 10 More on Spectral Methods 607 10.1 Spectral Methods for Two-Point Boundary Value Problems 608 10.2 Spectral Methods in Two Dimensions 621 10.3 Spectral Methods for Time-Dependent Problems 631 10.4 Clenshaw–Curtis Quadrature 635 10.5 Literature and Software Discussion 637 References 637 Appendix A: Proofs of Selected Theorems, and Additional Material 639 A.1 Proofs of the Interpolation Error Theorems 639 A.2 Proof of the Stability Result for ODEs 641 A.3 Stiff Systems of Differential Equations and Eigenvalues 642 A.4 The Matrix Perturbation Theorem 644 Index 646

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Book SynopsisUnderstanding the potential synergies between computer simulation and wargamingBased on the insights of experts in both domains, Simulation and Wargaming comprehensively explores the intersection between computer simulation and wargaming. This book shows how the practice of wargaming can be augmented and provide more detail-oriented insights using computer simulation, particularly as the complexity of military operations and the need for computational decision aids increases. The distinguished authors have hit upon two practical areas that have tremendous applications to share with one another but do not seem to be aware of that fact. The book includes insights into: The application of the data-driven speed inherent to computer simulation to wargamesThe application of the insight and analysis gained from wargames to computer simulationThe areas of concern raised by the combination of these two disparate yet related fieldsNew research and application opportunities emerging from the inteTable of ContentsForeword xv Preface xxiii List of Contributors xxv Author Biography xxix Prologue xli Part I Introduction 1 1 An Introduction to Wargaming and Modeling and Simulation 3Jeffrey Appleget Introduction 3 Terminology 3 An Abbreviated History of Wargames and Simulations 5 Wargames and Computer-Based Combat Simulations: From the Cold War to Today 6 Wargames Today 10 Simulations Today 13 Introduction 13 Simulation Types 13 Aggregate Simulations 13 Entity Simulations 14 Simulations and Prediction 14 Standard Assumptions 14 Data 15 Simulating the Reality of Combat 16 The Capability and Capacity of Modern Computing to Represent Combat 16 Finite Size 17 Number of Pieces/Entities 17 Terrain 18 Rules 18 Movement 18 Attack 19 Adjudication 19 Victory Conditions 19 Summary 20 Campaign Analysis 20 Conclusion 21 Part II Historical Context 23 2 A School for War – A Brief History of the Prussian Kriegsspiel 25Jorit Wintjes Introduction 25 Kriegsspiel Prehistory 29 A School for War – the Prussian Kriegsspiel 36 The Prussian Kriegsspiel 1824/28 – 1862 42 The Golden Age – 1862 to c. 1875 46 The Changing Kriegsspiel – c. 1875 to 1914 50 Kriegsspiel Beyond Borders – 1871 to 1914 54 Conclusion 59 3 Using Combat Models for Wargaming 65Joseph M. Saur The Nature of Combat Models 67 Europe’s Plan to Simulate the Entire Planet 77 China Exclusive: China’s “Magic Cube” Computer Unlocks the Future 77 A Model to Predict War 78 Afghanistan Stability/COIN Dynamics – Security 79 The Nature of Wargames 81 The Players – Who Might Be Involved? 85 The CRT – How Do We Adjudicate Political, Economic, Information and Other Non-Kinetic Actions? How DO WE ADJUDICATE KINETIC INTERACTIONS (Which, in This Case, We Hope Do Not Occur!)? 86 Organizational Behaviors 88 Issue in Wargames (and Combat Models) 89 Yyyyn 90 Part III Wargaming and Operations Research 91 4 An Analysis-Centric View of Wargaming, Modeling, Simulation, and Analysis 93Paul K. Davis Background and Structure 93 Relationships, Definitions, and Distinctions 94 Different Purposes for Wargaming 94 Backdrop 94 A Common Critique of M&S 94 Humans and M&S 98 Distinctions 98 A Model-Game-Model Paradigm 100 The Core Idea 100 Can Human Gaming Truly Serve as “Testing”? 101 Case Study: Deterrence and Stability on the Korean Peninsula 103 Background 103 Model Building 104 Ideal Methods and Practical Expedients 104 Modernizing the Escalation Ladder 106 Cognitive Decision Models 108 Top-Level Structure 109 Lower Level Structure 109 Designing and Executing a Human Game 111 Reflections and Conclusions 114 Implications for Simulation 117 5 Wargaming, Automation, and Military Experimentation to Quantitatively and Qualitatively Inform Decision-Making 123Jan Hodicky and Alejandro Hernandez Introduction 123 Military Methods to Knowledge Discovery 124 Technology: Knowledge Enablers 126 Wargaming Automation Challenges in M&S Perspective 128 Wargaming Relation to M&S 128 Wargaming Elements 129 Constructive Simulation Building Blocks 131 Wargaming Elements Not Supported by Constructive Simulation 131 Challenges to Combined Methodologies for Knowledge Discovery 132 Constructive Simulation Constrains in the Context of Automation and Wargaming 133 Stage- Wise Experimentation in CAW 139 A Progression of Mixed Methods to Grand Innovation 139 A Complete Application of ACAW and SWE for Future Capability Insights 144 Computer- Assisted Wargaming Classification 148 Conclusion 151 6 Simulation and Artificial Intelligence Methods for Wargames: Case Study – “European Thread” 157Andrzej Najgebauer, Sławomir Wojciechowski, Ryszard Antkiewicz, and Dariusz Pierzchała Introduction 157 Assumptions and Research Tools 159 Modeling of Complex Activities 161 Network Model of Complex Activities 161 The MCA Software Package for Wargaming 166 Wargame – Course of Action Evaluation 169 Assumptions 169 Situation 170 Model of Operation 173 A Collection of Values of the Function h(g) 173 Deterrence Phase 175 Parameters Value – Deterrence Phase 175 COA Evaluation 179 Summary 180 7 Combining Wargaming and Simulation Analysis 183Mark Sisson Introduction 183 Current Efforts Underway 184 Methodology 185 Frameworks or Schemas to Support Portfolios 186 Comparability 188 Emergence 190 Triangulation 190 Exercises 191 Artificial Intelligence 192 Wargames 193 Computer Simulation Models 194 Mathematical Models 195 Experimentation 196 Building Portfolios 196 Conclusion 199 8 The Use of M&S and Wargaming to Address Wicked Problems 203Phillip Pournelle Why Are We Doing This? 205 Framing the Problem 207 M&S Support to Wargames 212 Pathologies and How to Avoid Them 213 Combining Wargaming and M&S 219 Part IV Wargaming and Concept Developing and Testing 223 9 Simulation Support to Wargaming for Tactical Operations Planning 225Karsten Brathen, Rikke Amilde Seehuus, and Ole Martin Mevassvik Introduction 225 Operational Planning and Wargaming 226 What are the Benefits of Simulation Support to COA Wargaming? 231 Principles of Technology Support to Wargaming for Operations Planning 232 Enabling Technologies 234 Models 235 System Implementation 237 SWAP 238 SWAP Experiment 241 Conclusion and Way Forward 243 10 Simulation-Based Cyber Wargaming 249Ambrose Kam Motivation and Overview 249 Introduction 250 Cyber Simulation 253 Mission Analysis Tool 258 Wargames 261 Commercial Wargames 265 Future Work 267 Summary 269 11 Using Computer-Generated Virtual Realities, Operations Research, and Board Games for Conflict Simulations 273Armin Fügenschuh, Sönke Marahrens, Leonie Marguerite Johannsmann, Sandra Matuszewski, Daniel Müllenstedt, and Johannes Schmidt Introduction 273 Public Software (C:MA/NO) 275 User- Tailored Software (VBS3) 277 Artificial Intelligence for Solving Tactical Planning Problems 278 Wargaming Support 282 Conclusion 285 Part V Emerging Technologies 289 12 Virtual Worlds and the Cycle of Research: Enhancing Information Flow Between Simulationists and Wargamers 291Paul Vebber and Steven Aguiar The Cycle of Research as a Communications Framework 293 Bridging the Wargaming – Simulation Gap 297 Virtual World Beginnings 299 Elgin Marbles – An Analytic Game 301 Analytical vs. Narrative Games 303 Virtual Worlds as a Virtual Reality 307 Operational Wargames 308 Distributed LVC Wargames 312 The Future 315 13 Visualization Support to Strategic Decision-Making 317Richard J. Haberlin and Ernest H. Page Introduction 317 Impact/Capabilities 318 Strategic Planning 318 Acquisitions 318 Spectrum of Visualizations 319 Interactive Visualizations 320 Commercial Interactive Data Visualization 320 Custom Data and Analytics Visualization 320 Methodology 322 Model Elicitation 322 Framework 323 Considerations 323 Data 324 Analytic Tools 324 Colors of Money 324 Courses of Action 325 Model Construction 325 Strategic 326 Budget 327 Risk Identification and Mitigation 328 Example: The MITRE Simulation, Experimentation and Analytics Lab (SEAL) 329 Audio Visual Support 329 Multi-Level Security 331 Enterprise Integration 331 Community of Practice 332 Summary 333 14 Using an Ontology to Design a Wargame/Simulation System 335Dean S. Hartley, III Motivation and Overview 335 Introduction 336 A Modern Conflict Ontology 337 An Introduction to the MCO 337 Actors 338 Objects 339 Actions 340 Metrics or State Variables 342 MCO Examples 343 Provenance of the MCO 346 Knowledge of Warfare 346 Knowledge of OOTWs 346 Modeling Issues 347 Precursor Ontologies 348 Early Versions of the MCO 349 Creating a Simulation/Wargame from the Ontology 349 Model Building Steps 350 Moving from the Ontology to the Conceptual Model 352 Building Block Concept 354 Agendas and Implicit Metric Models 356 Theoretical Metric Models 357 VV&A 358 Constructing the Scenario 361 Model Infrastructure 361 Conclusion 362 15 Agent-Driven End Game Analysis for Air Defense 367M. Fatih Hocaogl̆ u Motivation and Overview 367 Introduction 367 Related Studies 369 Agent- Directed Simulation and AdSiF 371 AdSiF: Agent Driven Simulation Framework 373 End Game Agent 374 Command and Control Agent 374 C2 Architecture and Information Sharing 379 Target Evaluation 379 Fire Decision 380 Fire Doctrine 381 Decision-Level Data Fusion 382 Aims and Performance Measurement 384 Types of End Game Analysis 388 Footprint Analysis 390 Operating Area 394 Defended Area Analysis 395 Scenario View 397 Online Analysis and Scenario Replication Design 397 An Air Defense Scenario: Scenario View 398 Discussions 402 Epilogue 407 Index 411

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Book SynopsisThis is the first and most comprehensive guide on the modeling, engineering and reliable design of indoor photovoltaics which currently is the most promising and energy efficient power supply for edge nodes for the Internet of Things and other indoor devices. Indoor photovoltaics (IPV) has grown in importance over recent years. This can in part be attributed to the creation of the Internet of Things (IoT) and Artificial Intelligence (AI) along with the vast amounts of data being processed in the field, which has been a massive accelerator for this development. Moreover, since energy conservation is being imposed as the national strategy of many countries and is being set as a top priority throughout the world, understanding and promoting IPV as the most promising indoor energy harvesting source is considered by many to be essential these days. The book provides the engineer and researcher with guidelines, and presents a comprehensive overview of theoretical modeTable of ContentsPreface xi 1 Will Photovoltaics Stay Out of the Shadows? 1Joseph A. Paradiso 1.1 Introduction 1 References 6 2 Introduction to Micro Energy Harvesting 9Monika Freunek (Müller) 2.1 Introduction and History 9 2.1.1 Brief History of Electric Generators and Loads 10 2.1.2 Forms of Energies and Energy Converters 10 2.2 Kinetic Energy 11 2.2.1 Oscillating Solid Objects 12 2.2.1.1 Human Motion 13 2.2.1.2 Vibrations 13 2.2.1.3 Flow of Gas and Fluids 14 2.2.1.4 Acoustic Vibrations 15 2.2.1.5 Elastic Energy 16 2.3 Thermoelectric Conversion 16 2.4 Electrochemical Potential 18 2.5 Electromagnetic Transmission 19 2.6 Atomic Batteries 19 2.7 Challenges 20 2.8 Conclusions and Outlook 20 Acknowledgment 21 References 21 3 Introduction to Indoor Photovoltaics 25Monika Freunek (Müller) 3.1 Introduction 25 3.2 Indoor Spectra and Efficiencies 28 3.3 State of IPV Design, Issues, Approaches 31 3.4 Fields of Application 32 3.4.1 Customer and Office Applications 32 3.4.2 Ambient Assisted Living and Building Automatization 32 3.4.3 Industry, Agriculture, Horticulture, Retail, and Logistics 33 3.4.4 Relation of IPV to Outdoor Applications – Hiking, Emergency Kits 34 3.5 Degradation and Lifetime Issues 34 3.6 Conclusions and Outlook 35 References 35 4 Modeling Indoor Irradiance 39Monika Freunek (Müller) 4.1 Introduction 39 4.2 Fundamentals 40 4.2.1 Photometry and Its Impact on IPV 41 4.2.2 Comparison Measurements of Different Luxmeter Products and Settings 44 4.2.3 Conclusions for Indoor Irradiance Measurements 45 4.2.4 Available Data on Indoor Irradiance 45 4.3 Radiometric Solutions 47 4.3.1 Structure 47 4.3.2 Settings of the Studied Rooms 48 4.3.3 Investigated Installation Points 49 4.4 Analytical Model 52 4.4.1 Solar Radiation 52 4.4.2 Artifical Lighting 56 4.4.3 Interaction with Objects 60 4.4.4 Indirect Contributions of Solar Radiation 61 4.4.5 Final Results and Limits of Analytical Models 62 4.5 Simulations 62 4.5.1 Ray Tracing: Fundamental Principles 62 4.5.2 Radiance 64 4.5.3 DAYSIM 65 4.5.4 Calculation Methods and Parameters 66 4.5.5 Daylight Coefficient in DAYSIM 68 4.5.6 Environmental Parameters 69 4.5.7 Model Parameters 71 4.5.8 Results 73 4.5.9 Summary and Conclusion 85 4.6 Measurements 86 4.6.1 Available Measurement Methods 86 4.6.2 Long-Term Measurements Reference Year 89 4.6.3 Validating Simulation 94 4.6.4 Comparison Measurement Methods under Controlled Conditions 100 4.7 Discussion and Recommendation 103 4.8 Conclusion and Outlook 104 4.8.1 Autarky Factors 105 4.9 Acknowledgements 106 4.10 Symbols and Abbreviations 106 4.11 Constants 109 4.12 Abbreviations 109 Appendix 110 References 112 5 Characterization and Power Measurement of IPV Cells 115Stefan Winter 5.1 Features of IPV Compared to Outdoor PV 115 5.1.1 Irradiance 116 5.1.2 Spectrum 116 5.1.2.1 Consequences of the Different Spectra Regarding Efficiency 117 5.1.3 Incident Angle Distribution 117 5.1.4 Modulated Light Sources 117 5.1.5 Further Effects 118 5.1.6 Standardization 118 5.2 Calibration Chain and Quality Management 119 5.2.1 Basic Laboratory Measurement Methods for the Secondary Calibration of IPV Cells 119 5.3 Flexible and Precise Method for Comprehensive and Primary Calibration of IPV Devices 122 5.3.1 Lamp-Based Facility 124 5.3.2 Laser-Based Facility 124 5.4 DSR Calibration of IPV Cells 128 5.4.1 Self-Referenced IV Characteristic 129 Acknowledgment 130 References 131 6 Luminescent Solar Concentrators 133Evert P.J. Merkx and Erik van der Kolk 6.1 Introduction 134 6.2 A Crash Course in Luminescence 135 6.2.1 Luminescence in Organic Dyes 136 6.2.2 Luminescence in Rare Earth Ions 138 6.2.3 Luminescence in Quantum Dots 142 6.2.4 Hybrid Combinations 143 6.3 Principle of Operation 144 6.3.1 Absorption of Light 144 6.3.2 Emission within the LSC 145 6.3.3 Effects of Self-Absorption 146 6.3.4 Influence of the Waveguide 147 6.3.5 Conversion of Concentrated Light to Electricity 147 6.4 Calculating LSC Performance 148 6.4.1 Figures of Merit 148 6.4.2 Upper Bound for LSC Efficiency 149 6.4.3 Analytical Approach for Simple Geometries 152 6.4.4 Semi-Analytical Optimization Calculations for Arbitrary Geometries 153 6.4.5 Monte Carlo Simulations for Ray-Traced Complex Geometries 157 6.4.6 Considerations for Thin-Film LSCs 162 6.5 State-of-the-Art LSC Materials 163 6.5.1 Measures for the Visual Performance of LSC Materials 163 6.5.2 Evaluating the Performance of State-of-the-Art LSCs 165 6.5.3 Dye-Based Luminescent Solar Concentrators 167 6.5.4 Rare Earth-Based Luminescent Solar Concentrators 168 6.5.5 Quantum Dot and Doped Quantum Dot-Based Luminescent Solar Concentrators 169 6.6 Tm2+-Doped Halide Luminescent Solar Concentrators 174 6.7 LSC for an IPV Perspective 177 6.7.1 Performance Assessment 177 6.7.2 Application Examples 179 6.8 Conclusion 180 Acknowledgements 181 References 181 7 Organic Photovoltaic Cells and Modules for Applications under Indoor Lighting Conditions 189Birger Zimmermann and Uli Würfel 7.1 Introduction 190 7.2 Implications of Indoor Lighting 192 7.3 OPV Modules 198 7.4 OPV Devices and Applications 201 7.5 Acceptance and Safety Considerations 202 References 203 8 High-Efficiency Indoor Photovoltaic Energy Harvesting 213Matthias Kauer and Mathieu Bellanger 8.1 Introduction 214 8.2 Approaches for Efficient Indoor PV Energy Harvesting 216 8.2.1 PV Energy Harvesting Technologies 216 8.2.2 Commercial PV Energy Harvesting Devices 217 8.2.3 Recent Research Results for PV Energy Harvesting Devices 217 8.3 Lightricity’s PV Energy Harvesting Technology 221 8.3.1 Introduction 221 8.3.2 Energy Harvester Device Fabrication and Device Characteristics 222 8.4 High-Efficiency PV Energy Harvesting Power Supplies 225 8.4.1 Introduction 225 8.4.2 Energy Harvesting Power Management Solutions 226 8.4.3 System Integration and Performance Testing 230 8.5 Applications of Light Indoor Energy Harvesting 233 8.5.1 Watches and Wearable Devices 233 8.5.2 Wireless Building Automation Sensors 233 8.5.3 Wireless Beacons 236 8.6 Summary and Concluding Remarks 237 Acknowledgments 238 References 238 9 Indoor Photovoltaics Based on AlGaAs 241Jamie Phillips, Eunseong Moon and Alan Teran 9.1 Importance of AlGaAs for Indoor Photovoltaics 242 9.2 Design Consideration for AlGaAs III-V Photovoltaic Cells 245 9.2.1 Base/Absorber 246 9.2.2 Contact 247 9.2.3 Window 248 9.2.4 Emitter 248 9.2.5 Back Surface Field 248 9.3 Large-Area AlGaAs III-V Photovoltaics 249 9.4 Small-Area AlGaAs Photovoltaics 252 9.4.1 Model of J-V Characteristics 254 9.4.2 Performance of mm-Scale AlGaAs Photovoltaics 257 9.4.3 Dark Current Limitations 260 9.5 Monolithic GaAs PV Cell Arrays 262 9.6 Conclusion 267 References 268 Index 273

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Book SynopsisExamines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems Cyber-attacks on SCADA systems?the control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory management?can lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning. Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents Table of ContentsForeword ix Preface xi Acronyms xv 1. Introduction 1 2. Background 15 3. SCADA-Based Security Testbed 25 4. Efficient k-Nearest Neighbour Approach Based on Various-Widths Clustering 63 5. SCADA Data-Driven Anomaly Detection 87 6. A Global Anomaly Threshold to Unsupervised Detection 119 7. Threshold Password-Authenticated Secret Sharing Protocols 151 8. Conclusion 179 References 185 Index 195

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Book SynopsisA guide to the practical issues and applications in database programming with updated Visual Basic.NET SQL Server Database Programming with Visual Basic.NET offers a guide to the fundamental knowledge and practical techniques for the design and creation of professional database programs that can be used for real-world commercial and industrial applications. The authora noted expert on the topicuses the most current version of Visual Basic.NET, Visual Basic.NET 2017 with Visual Studio.NET 2017. In addition, he introduces the updated SQL Server database and Microsoft SQL Server 2017 Express. All sample program projects can be run in the most updated version, Visual Basic.NET 2019 with Visual Studio.NET 2019. Written in an accessible, down-to-earth style, the author explains how to build a sample database using the SQL Server management system and Microsoft SQL Server Management Studio 2018. The latest version of ASP.NET, ASP.NET 4.7, is also discussed to prTable of ContentsAbout the Author xix Preface xxi Acknowledgment xxiii About the Companion Website xxiv Chapter 1 Introduction 1 1.1 Outstanding Features About This Book 2 1.2 This Book is For 2 1.3 What This Book Covers 2 1.4 How This Book is Organized and How to Use This Book 5 1.5 How to Use Source Codes and Sample Database 6 1.6 Instructors and Customers Supports 8 Chapter 2 Introduction to Databases 9Ying Bai and Satish Bhalla 2.1 What are Databases and Database Programs? 10 2.1.1 File Processing System 10 2.1.2 Integrated Databases 11 2.2 Develop a Database 12 2.3 Sample Database 13 2.3.1 Relational Data Model 13 2.3.2 Entity-Relationship Model (ER) 17 2.4 Identifying Keys 18 2.5 Define Relationships 18 2.6 ER Notation 22 2.7 Data Normalization 23 2.7.1 First Normal Form (1NF) 23 2.7.2 Second Normal Form (2NF) 24 2.7.3 Third Normal Form (3NF) 26 2.8 Database Components in Some Popular Databases 28 2.8.1 Microsoft Access Databases 28 2.8.2 SQL Server Databases 29 2.8.3 Oracle Databases 32 2.9 Create Microsoft SQL Server 2017 Express Sample Database 35 2.9.1 Create the LogIn Table 36 2.9.2 Create the Faculty Table 37 2.9.3 Create Other Tables 39 2.9.4 Create Relationships Among Tables 45 2.9.4.1 Create Relationship Between the LogIn and the Faculty Tables 46 2.9.4.2 Create Relationship Between the LogIn and the Student Tables 49 2.9.4.3 Create Relationship Between the Faculty and the Course Tables 50 2.9.4.4 Create Relationship Between the Student and the StudentCourse Tables 50 2.9.4.5 Create Relationship Between the Course and the StudentCourse Tables 51 2.9.5 Store Images to the SQL Server 2017 Express Database 53 2.10 Chapter Summary 61 Homework 63 Chapter 3 Introduction to ADO.NET 67 3.1 The ADO and ADO.NET 67 3.2 Overview of the ADO.NET 69 3.3 The Architecture of the ADO.NET 70 3.4 The Components of ADO.NET 71 3.4.1 The Data Provider 72 3.4.1.1 The ODBC Data Provider 73 3.4.1.2 The OLEDB Data Provider 73 3.4.1.3 The SQL Server Data Provider 74 3.4.1.4 The Oracle Data Provider 74 3.4.2 The Connection Class 74 3.4.2.1 The Open() Method of the Connection Class 77 3.4.2.2 The Close() Method of the Connection Class 77 3.4.2.3 The Dispose() Method of the Connection Class 78 3.4.3 The Command and the Parameter Classes 78 3.4.3.1 The Properties of the Command Class 79 3.4.3.2 The Constructors and Properties of the Parameter Class 79 3.4.3.3 Parameter Mapping 80 3.4.3.4 The Methods of the ParameterCollection Class 82 3.4.3.5 The Constructor of the Command Class 83 3.4.3.6 The Methods of the Command Class 84 3.4.4 The DataAdapter Class 87 3.4.4.1 The Constructor of the DataAdapter Class 87 3.4.4.2 The Properties of the DataAdapter Class 87 3.4.4.3 The Methods of the DataAdapter Class 88 3.4.4.4 The Events of the DataAdapter Class 88 3.4.5 The DataReader Class 90 3.4.6 The DataSet Component 92 3.4.6.1 The DataSet Constructor 94 3.4.6.2 The DataSet Properties 94 3.4.6.3 The DataSet Methods 94 3.4.6.4 The DataSet Events 94 3.4.7 The DataTable Component 97 3.4.7.1 The DataTable Constructor 98 3.4.7.2 The DataTable Properties 98 3.4.7.3 The DataTable Methods 99 3.4.7.4 The DataTable Events 100 3.4.8 ADO.NET Entity Framework 102 3.4.8.1 Advantages of Using the Entity Framework 6 104 3.4.8.2 The ADO.NET 4.3 Entity Data Model 106 3.4.8.3 Using Entity Framework 6 Entity Data Model Wizard 110 3.5 Chapter Summary 118 Homework 120 Chapter 4 Introduction to Language Integrated Query (LINQ) 123 4.1 Overview of Language Integrated Query 123 4.1.1 Some Special Interfaces Used in LINQ 124 4.1.1.1 The IEnumerable and IEnumerable(Of T) Interfaces 124 4.1.1.2 The IQueryable and IQueryable(Of T) Interfaces 125 4.1.2 Standard Query Operators 126 4.1.3 Deferred Standard Query Operators 127 4.1.4 Non-Deferred Standard Query Operators 131 4.2 Introduction to LINQ Query 135 4.3 The Architecture and Components of LINQ 137 4.3.1 Overview of LINQ to Objects 138 4.3.2 Overview of LINQ to DataSet 139 4.3.3 Overview of LINQ to SQL 139 4.3.4 Overview of LINQ to Entities 140 4.3.5 Overview of LINQ to XML 140 4.4 LINQ to Objects 141 4.4.1 LINQ and ArrayList 142 4.4.2 LINQ and Strings 143 4.4.2.1 Query a String to Determine the Number of Numeric Digits 144 4.4.2.2 Sort Lines of Structured Text By any Field in the Line 145 4.4.3 LINQ and File Directories 147 4.4.3.1 Query the Contents of Files in a Folder 148 4.4.4 LINQ and Reflection 150 4.5 LINQ to DataSet 152 4.5.1 Operations to DataSet Objects 152 4.5.1.1 Query Expression Syntax 153 4.5.1.2 Method-Based Query Syntax 154 4.5.1.3 Query the Single Table 157 4.5.1.4 Query the Cross Tables 159 4.5.1.5 Query Typed DataSet 162 4.5.2 Operations to DataRow Objects Using the Extension Methods 165 4.5.3 Operations to DataTable Objects 169 4.6 LINQ to SQL 170 4.6.1 LINQ to SQL Entity Classes and DataContext Class 171 4.6.1.1 Add LINQ to Data Reference 171 4.6.1.2 Add LINQ To SQL Tools 171 4.6.2 LINQ to SQL Database Operations 175 4.6.2.1 Data Selection Query 175 4.6.2.2 Data Insertion Query 177 4.6.2.3 Data Updating Query 178 4.6.2.4 Data Deletion Query 179 4.6.3 LINQ to SQL Implementations 182 4.7 LINQ to Entities 182 4.7.1 The Object Services Component 183 4.7.2 The ObjectContext Component 183 4.7.3 The ObjectQuery Component 184 4.7.4 LINQ to Entities Flow of Execution 184 4.7.5 Implementation of LINQ to Entities 186 4.8 LINQ to XML 187 4.8.1 LINQ to XML Class Hierarchy 187 4.8.2 Manipulate XML Elements 188 4.8.2.1 Creating XML from Scratch 188 4.8.2.2 Insert XML 190 4.8.2.3 Update XML 191 4.8.2.4 Delete XML 192 4.8.3 Manipulate XML Attributes 192 4.8.3.1 Add XML Attributes 192 4.8.3.2 Get XML Attributes 193 4.8.3.3 Delete XML Attributes 193 4.8.4 Query XML with LINQ to XML 194 4.8.4.1 Standard Query Operators and XML 194 4.8.4.2 XML Query Extensions 195 4.8.4.3 Using Query Expressions with XML 196 4.8.4.4 Using XPath and XSLT with LINQ to XML 196 4.8.4.5 Mixing XML and Other Data Models 197 4.9 Visual Basic.NET Language Enhancement for LINQ 199 4.9.1 Lambda Expressions 199 4.9.2 Extension Methods 201 4.9.3 Implicitly Typed Local Variables 205 4.9.4 Query Expressions 206 4.10 Chapter Summary 208 Homework 209 Chapter 5 Data Selection Query with Visual Basic.NET 215 Part I Data Query with Visual Studio.NET Design Tools and Wizards 216 5.1 A Completed Sample Database Application Example 216 5.2 Visual Studio.NET Design Tools and Wizards 219 5.2.1 Data Components in the Toolbox Window 220 5.2.1.1 The DataSet 220 5.2.1.2 DataGridView 221 5.2.1.3 BindingSource 222 5.2.1.4 BindingNavigator 222 5.2.1.5 TableAdapter 223 5.2.1.6 TableAdapter Manager 223 5.2.2 Data Source Window 223 5.2.2.1 Add New Data Sources 224 5.2.2.2 Data Source Configuration Wizard 224 5.2.2.3 DataSet Designer 228 5.3 Query Data from SQL Server Database Using Design Tools and Wizards 231 5.3.1 Application User Interface 231 5.3.1.1 The LogIn Form 232 5.3.1.2 The Selection Form 232 5.3.1.3 The Faculty Form 232 5.3.1.4 The Course Form 234 5.3.1.5 The Student Form 234 5.4 Use Visual Studio Wizards and Design Tools to Query and Display Data 236 5.4.1 Query and Display Data using the DataGridView and Detail Controls 236 5.4.1.1 View the Entire Table 238 5.4.1.2 View Each Record or the Specified Columns with Detail View 241 5.4.2 Use DataSet Designer to Edit the Structure of the DataSet 243 5.4.3 Bind Data to the Associated Controls in LogIn Form 245 5.4.4 Develop Codes to Query Data Using the Fill() Method 249 5.4.5 Use Return a Single Value to Query Data for LogIn Form 251 5.4.6 Develop the Codes for the Selection Form 254 5.4.7 Query Data from the Faculty Table for the Faculty Form 256 5.4.8 Develop Codes to Query Data from the Faculty Table 258 5.4.8.1 Develop Codes to Query Data Using the TableAdapter Method 258 5.4.8.2 Develop Codes to Query Data Using the LINQ to DataSet Method 261 5.4.9 Query Data from the Course Table for the Course Form 262 5.4.9.1 Build the Course Queries Using the Query Builder 263 5.4.9.2 Bind Data Columns to the Associated Controls in the Course Form 265 5.4.9.3 Develop Codes to Query Data for the Course Form 267 Part II Data Query with Runtime Objects 271 5.5 Introduction to Runtime Objects 272 5.5.1 Procedure of Building a Data-Driven Application Using Runtime Object 274 5.6 Query Data from SQL Server Database Using Runtime Object 274 5.6.1 Access to SQL Server Database 274 5.6.2 Declare Global Variables and Runtime Objects 276 5.6.3 Query Data Using Runtime Objects for the LogIn Form 278 5.6.3.1 Connect to the Data Source with the Runtime Object 278 5.6.3.2 Coding for Method 1: Using the TableAdapter to Query Data 279 5.6.3.3 Coding for Method 2: Using the DataReader to Query Data 281 5.6.4 The Coding for the Selection Form 283 5.6.5 Query Data Using Runtime Objects for the Faculty Form 284 5.6.5.1 Using Three Query Methods to Retrieve Images from SQL Server Database 290 5.6.6 Query Data Using Runtime Objects for the Course Form 290 5.6.6.1 Retrieve Data from Multiple Tables Using Tables JOINS 293 5.6.7 Query Data Using Runtime Objects for the Student Form 301 5.6.7.1 Query Student Data Using Stored Procedures 302 5.6.7.2 Query Data Using Stored Procedures for Student Form 306 5.6.7.3 Query Data Using More Complicated Stored Procedures 315 5.7 Chapter Summary 320 Homework 321 Chapter 6 Data Inserting with Visual Basic.NET 327 Part I Insert Data with Visual Basic.NET Design Tools and Wizards 328 6.1 Insert Data Into a Database 328 6.1.1 Insert New Records into a Database Using the TableAdapter.Insert Method 329 6.1.2 Insert New Records into a Database Using the TableAdapter.Update Method 329 6.2 Insert Data into the SQL Server Database Using a Sample Project InsertWizard 330 6.2.1 Create InsertWizard Project Based on the SelectWizard Project 330 6.2.2 Application User Interfaces 331 6.2.3 Validate Data Before the Data Insertion 331 6.2.3.1 Visual Basic Collection and .NET Framework Collection Classes 331 6.2.3.2 Validate Data Using the Generic Collection 332 6.2.4 Initialization Coding Process for the Data Insertion 335 6.2.5 Build the Insert Query 336 6.2.5.1 Configure the TableAdapter and Build the Data Inserting Query 336 6.2.6 Develop Codes to Insert Data Using the TableAdapter.Insert Method 337 6.2.7 Develop Codes to Insert Data Using the TableAdapter.Update Method 341 6.2.8 Insert Data into the Database Using the Stored Procedures 345 6.2.8.1 Create the Stored Procedure Using the TableAdapter Query Configuration Wizard 346 6.2.8.2 Modify the Codes to Perform the Data Insertion Using the Stored Procedure 346 Part II Data Insertion with Runtime Objects 350 6.3 The General Run Time Objects Method 351 6.4 Insert Data into the SQL Server Database Using the Run Time Object Method 352 6.4.1 Insert Data into the Faculty Table for the SQL Server Database 353 6.4.1.1 Validate Data Before the Data Insertion 353 6.4.1.2 Insert Data into the Faculty Table 355 6.4.1.3 Validate Data After the Data Insertion 357 6.5 Insert Data into the Database Using Stored Procedures 360 6.5.1 Insert Data into the SQL Server Database Using Stored Procedures 360 6.5.1.1 Develop Stored Procedures in SQL Server Database 361 6.5.1.2 Develop Codes to Call Stored Procedures to Insert Data into the Course Table 363 6.6 Insert Data into the Database Using the LINQ To SQL Method 368 6.6.1 Insert Data Into the SQL Server Database Using the LINQ to SQL Queries 369 6.7 Chapter Summary 369 Homework 370 Chapter 7 Data Updating and Deleting with Visual Basic.NET 377 Part I Data Updating and Deleting with Visual Studio.NET Design Tools and Wizards 378 7.1 Update or Delete Data Against Databases 378 7.1.1 Updating and Deleting Data from Related Tables in a DataSet 379 7.1.2 Update or Delete Data Against Database Using TableAdapter DBDirect Methods - TableAdapter.Update and TableAdapter.Delete 379 7.1.3 Update or Delete Data Against Database Using TableAdapter.Update Method 380 7.2 Update and Delete Data For Microsoft SQL Server Database 381 7.2.1 Create a New Project Based on the InsertWizard Project 381 7.2.2 Application User Interfaces 382 7.2.3 Validate Data Before the Data Updating and Deleting 382 7.2.4 Build the Update and Delete Queries 382 7.2.4.1 Configure the TableAdapter and Build the Data Updating Query 383 7.2.4.2 Build the Data Deleting Query 384 7.2.5 Develop Codes to Update Data Using the TableAdapter DBDirect Method 385 7.2.5.1 Modifications of the Codes 385 7.2.5.2 Creations of the Codes 385 7.2.6 Develop Codes to Update Data Using the TableAdapter.Update Method 387 7.2.7 Develop Codes to Delete Data Using the TableAdapter DBDirect Method 388 7.2.8 Develop Codes to Delete Data Using the TableAdapter.Update Method 390 7.2.9 Validate the Data After the Data Updating and Deleting 391 Part II Data Updating and Deleting with Runtime Objects 395 7.3 The Run Time Objects Method 395 7.4 Update and Delete Data for SQL Server Database Using the Run Time Objects 396 7.4.1 Update Data Against the Faculty Table in the SQL Server Database 397 7.4.1.1 Develop Codes to Update the Faculty Data 397 7.4.1.2 Validate the Data Updating 399 7.4.2 Delete Data from the Faculty Table in the SQL Server Database 399 7.4.2.1 Develop Codes to Delete Data 399 7.4.2.2 Validate the Data Deleting 401 7.5 Update and Delete Data against SQL Server Database Using Stored Procedures 404 7.5.1 Modify an Existing Project to Create Our New Project 405 7.5.2 Create the Codes to Update and Delete Data from the Course Table 405 7.5.2.1 Develop Two Stored Procedures in the SQL Server Database 407 7.5.2.2 Call the Stored Procedures to Perform the Data Updating and Deleting 409 7.5.3 Update and Delete Data against Databases Using the LINQ to SQL Query 412 7.5.3.1 Update and Delete Data Using LINQ to SQL Query for Student Table 413 7.5.3.2 Create a New Object of the DataContext Class for Student Form 414 7.5.3.3 Develop the Codes for the Select Button Click Event Procedure 415 7.5.3.4 Develop the Codes for the Insert Button Click Event Procedure 416 7.5.3.5 Develop the Codes for the Update Button Click Event Procedure 419 7.5.3.6 Develop the Codes for the Delete Button Click Event Procedure 419 7.5.3.7 Run the Project to Test Data Updating and Deleting Actions for Student Table 421 7.6 Chapter Summary 423 Homework 423 Chapter 8 Accessing Data in ASP.NET 429 8.1 What is .NET Framework? 430 8.2 What is ASP.NET? 431 8.2.1 ASP.NET Web Application File Structure 433 8.2.2 ASP.NET Execution Model 433 8.2.3 What is Really Happened When a Web Application is Executed? 434 8.2.4 The Requirements to Test and Run the Web Project 435 8.3 Develop ASP.NET Web Application to Select Data from SQL Server Databases 435 8.3.1 Create the User Interface – LogIn Form 436 8.3.2 Develop the Codes to Access and Select Data from the Database 438 8.3.3 Validate the Data in the Client Side 442 8.3.4 Create the Second User Interface – Selection Page 443 8.3.5 Develop the Codes to Open the Other Page 444 8.3.6 Modify the Codes in the LogIn Page to Transfer to the Selection Page 446 8.3.7 Create the Third User Interface – Faculty Page 447 8.3.8 Develop the Codes to Select the Desired Faculty Information 448 8.3.8.1 Develop the Codes for the Page_Load Event Procedure 449 8.3.8.2 Develop the Codes for the Select Button Click Event Procedure 450 8.3.8.3 Develop the Codes for Other Procedures 452 8.3.9 Create the Fourth User Interface – Course Page 454 8.3.9.1 The AutoPostBack Property of the List Box Control 457 8.3.10 Develop the Codes to Select the Desired Course Information 457 8.3.10.1 Coding for the Course Page Loading and Ending Event Procedures 458 8.3.10.2 Coding for the Select Button’s Click Event Procedure 459 8.3.10.3 Coding for the SelectedIndexChanged Event Procedure of the CourseList Box 461 8.3.10.4 Coding for Other User Defined Subroutine Procedures 463 8.4 Develop ASP.NET Web Application to Insert Data Into SQL Server Databases 465 8.4.1 Develop the Codes to Perform the Data Insertion Function 466 8.4.2 Develop the Codes for the Insert Button Click Event Procedure 466 8.4.3 Validate the Data Insertion 473 8.5 Develop Web Applications to Update and Delete Data in SQL Server Databases 473 8.5.1 Modify the Codes for the Faculty Page 474 8.5.2 Develop the Codes for the Update Button Click Event Procedure 475 8.5.3 Develop the Codes for the Delete Button Click Event Procedure 479 8.5.3.1 Relationships Between Five Tables in Our Sample Database 480 8.5.3.2 Data Deleting Sequence 481 8.5.3.3 Use the Cascade Deleting Option to Simplify the Data Deleting 481 8.5.3.4 Create the Stored Procedure to Perform the Data Deleting 483 8.5.3.5 Develop the Codes to Call the Stored Procedure to Perform the Data Deleting 486 8.6 Develop ASP.NET Web Applications with LINQ to SQL Query 489 8.6.1 Create a New Object of the DataContext Class 491 8.6.2 Develop the Codes for the Data Selection Query 492 8.6.3 Develop the Codes for the Data Insertion Query 493 8.6.4 Develop the Codes for the Data Updating and Deleting Queries 496 8.7 Chapter Summary 500 Homework 500 Chapter 9 ASP.NET Web Services 505 9.1 What are Web Services and Their Components? 506 9.2 Procedures to Build a Web Service 508 9.2.1 The Structure of a Typical Web Service Project 508 9.2.2 The Real Considerations When Building a Web Service Project 509 9.2.3 Introduction to Windows Communication Foundation (WCF) 509 9.2.3.1 What is the WCF? 510 9.2.3.2 WCF Data Services 510 9.2.3.3 WCF Services 511 9.2.3.4 WCF Clients 511 9.2.3.5 WCF Hosting 512 9.2.3.6 WCF Visual Studio Templates 512 9.2.4 Procedures to Build an ASP.NET Web Service 513 9.3 Build ASP.NET Web Service Project to Access SQL Server Database 514 9.3.1 Files and Items Created in the New Web Service Project 515 9.3.2 A Feeling of the Hello World Web Service Project As it Runs 518 9.3.3 Modify the Default Namespace 520 9.3.4 Create a Base Class to Handle Error Checking for Our Web Service 522 9.3.5 Create a Customer Returned Class to Hold All Retrieved Data 522 9.3.6 Add Web Methods into Our Web Service Class 524 9.3.7 Develop the Codes for Web Methods to Perform the Web Services 524 9.3.7.1 Web Service Connection Strings 524 9.3.7.2 Modify the Existing HelloWorld Web Method 527 9.3.7.3 Develop the Codes to Perform the Database Queries 528 9.3.7.4 Develop the Codes for Subroutines Used in the Web Method 530 9.3.8 Develop the Stored Procedure to Perform the Data Query 533 9.3.8.1 Develop the Stored Procedure WebSelectFacultySP 533 9.3.8.2 Add Another Web Method to Call the Stored Procedure 534 9.3.9 Use DataSet as the Returning Object for the Web Method 536 9.3.10 Build Windows-based Web Service Clients to Consume the Web Services 538 9.3.10.1 Create a Web Service Proxy Class 539 9.3.10.2 Develop the Graphic User Interface for the Windows-based Client Project 541 9.3.10.3 Develop the Code to Consume the Web Service 541 9.3.11 Build Web-based Web Service Clients to Consume the Web Service 548 9.3.11.1 Create a New Web Site Project and Add an Existing Web Page 548 9.3.11.2 Add a Web Service Reference and Modify the Web Form Window 549 9.3.11.3 Modify the Designer and Codes for the Related Event Procedures 550 9.3.12 Deploy the Completed Web Service to Production Servers 555 9.3.12.1 Publish the Desired Web Service 557 9.4 Build ASP.NET Web Service Project to Insert Data Into SQL Server Database 559 9.4.1 Create a New Web Service Project WebServiceSQLInsert 559 9.4.2 Develop Four Web Service Methods 560 9.4.2.1 Develop Codes for the First Web Method SetSQLInsertSP 561 9.4.2.2 Develop Codes for User Defined Functions and Subroutine Procedures 563 9.4.2.3 Develop the Second Web Method GetSQLInsert 565 9.4.2.4 Develop the Third Web Method SQLInsertDataSet 568 9.4.2.5 Develop the Fourth Web Method GetSQLInsertCourse 572 9.4.3 Build Windows-based Web Service Clients to Consume the Web Services 578 9.4.3.1 Create a Windows-Based Consume Project and a Web Service Proxy Class 578 9.4.3.2 Develop the Graphic User Interface for the Client Project 579 9.4.3.3 Develop the Code to Consume the Web Service 581 9.4.4 Build Web-based Web Service Clients to Consume the Web Services 594 9.4.4.1 Create a New Web Site Project and Add an Existing Web Page 594 9.4.4.2 Add a Web Service Reference and Modify the Web Form Window 595 9.4.4.3 Modify the Codes for the Related Event Procedures 596 9.5 Build ASP.NET Web Service to Update and Delete Data for SQL Server Database 606 9.5.1 Modify the Default Namespace and Add Database Connection String 607 9.5.2 Create Our Customer-Built Base and Returned Classes 608 9.5.3 Create a Web Method to Call Stored Procedure to Update Student Records 609 9.5.4 Create a Web Method to Call Stored Procedure to Delete Student Records 611 9.5.5 Develop Two Stored Procedures WebUpdateStudentSP and WebDeleteStudentSP 613 9.5.5.1 Develop the Stored Procedure WebUpdateStudentSP 613 9.5.5.2 Develop the Stored Procedure WebDeleteStudentSP 616 9.6 Build Windows-Based Web Service Clients to Consume the Web Services 618 9.6.1 Modify the Student Form Window 618 9.6.2 Add a New Web Reference to Our Client Project 619 9.6.3 Build the Codes to the Update Button Click Event Procedure 620 9.6.4 Build the Codes to the Delete Button Click Event Procedure 621 9.7 Build Web-Based Web Service Clients to Consume the Web Services 624 9.7.1 Create a New Web Site Application Project and Add an Existing Web Page 625 9.7.2 Add a Web Service Reference and Modify the Web Form Window 625 9.7.3 Modify the Codes Inside the Back Button Click Event Procedure 626 9.7.4 Add the Codes to the Update Button Click Event Procedure 626 9.7.5 Develop Codes for the Delete Button Click Event Procedure 628 9.8 Chapter Summary 631 Homework 632 Appendix A: Install and Configure SQL Server 2017 Express Database 637 Appendix B: Download and Install DevExpress .NET UI Controls 649 Appendix C: Download & Install FrontPage Server Extension for Windows 10 651 Appendix D: How to Use Sample Database 655 Index 657

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Book SynopsisSubstrate-Integrated Millimeter-Wave Antennas for Next-Generation Communication and Radar Systems The first and only comprehensive text on substrate-integrated mmW antenna technology, state-of-the-art antenna design, and emerging wireless applications Substrate-Integrated Millimeter-Wave Antennas for Next-Generation Communication and Radar Systems elaborates the most important topics related to revolutionary millimeter-wave (mmW) technology. Following a clear description of fundamental concepts including substrate-integrated waveguides and loss analysis, the text treats key design methods, prototyping techniques, and experimental setup and testing. The authors also highlight applications of mmW antennas in 5G wireless communication and next-generation radar systems. Readers are prepared to put techniques into practice through practical discussions of how to set up testing for impedance matching, radiation patterns, gain from 24GHz up to 325 GHz, anTable of ContentsEditor biographies – to follow Contributors Preface to follow Chapter 1 Introduction to Millimeter Wave Antennas 1.1 Millimeter Waves 1.2 Propagation of Millimeter Waves 1.3 Millimeter Wave Technology 1.3.1 Important Features 1.3.2 Major Modern Applications 1.4 Unique Challenges of Millimeter Wave Antennas 1.5 Briefing of State-of-the-Art Millimeter Wave Antennas 1.6 Implementation Considerations of Substrate Integrated Millimeter Wave Antennas 1.6.1 Fabrication Processes and Materials of the Antennas 1.6.2 Commonly Used Transmission Line Systems for Antennas 1.7 Note on Losses in Microstrip-lines and Substrate Integrated Waveguides 1.8 Update of Millimeter Wave Technology in 5G and Beyond 1.9 Summary References Chapter 2 Measurement Methods and Setups of Antennas at 60-325-GHz Bands 2.1 Introduction 2.1.1 Far-field Antenna Measurement Setup 2.1.2 Near-field Antenna Measurement Setup 2.2 Sate-of-the-art mmW Measurement Systems 2.2.1 Commercially Available mmW Measurement Systems 2.2.2 Customized mmW Measurement Systems 2.3 Considerations for Measurement Setup Configuration 2.3.1 Near-field versus Compact Range 2.3.2 RF System 2.3.3 Interface Between the RF Instrument and AUT 2.3.4 On-Wafer Antenna Measurement 2.4 mmW Measurement Setup Examples 2.4.1 60-GHz Antenna Measurement Setup 2.4.2 140-GHz Antenna Measurement Setup 2.4.3 270-GHz Antenna Measurement Setup 2.5 Summary References Chapter 3 Substrate Integrated mmW Antennas in LTCC 3.1 Introduction 3.1.1 Unique Design Challenges and Promising Solutions 3.1.2 SIW Slot Antennas and Arrays in LTCC 3.2 High-gain mmW SIW Slot Antenna Arrays in LTCC 3.2.1 SIW Three-Dimensional Corporate Feed 3.2.2 Substrate Integrated Cavity Antenna Array at 60 GHz 3.2.3 Simplified Designs and High-order-mode Antenna Array at 140 GHz 3.2.4 Fully Substrate Integrated Antennas at 270 GHz 3.3 Summary References Chapter 4 Broadband Metamaterial-Mushroom Antenna Array at 60-GHz Bands 4.1 Introduction 4.2 Broadband Low-Profile CRLH-Mushroom Antenna 4.2.1 Working Principle 4.2.2 Impedance Matching 4.3 Broadband LTCC Metamaterial-Mushroom Antenna Array at 60 GHz 4.3.1 SIW Fed CRLH-Mushroom Antenna Element 4.3.2 Self-Decoupling Functionality 4.3.3 Self-Decoupled Metamaterial-Mushroom Subarray 4.3.4 Metamaterial-Mushroom Antenna Array 4.4 Summary References Chapter 5 Narrow-Wall-Fed Substrate Integrated Cavity Antenna at 60 GHz 5.1 Introduction 5.2 Broadband Techniques for Substrate Integrated Antennas 5.2.1 Enhancement of Impedance Matching for SIW Antennas 5.2.2 Multi-Mode Substrate Integrated Cavity Antennas 5.2.3 Substrate Integrated Cavity Backed Slot Antenna 5.2.4 Patch Loaded Substrate Integrated Cavity Antenna 5.2.5 Travelling-wave Elements Loaded Substrate Integrated Cavity Antenna 5.3 SIW Narrow Wall Fed SIC Antennas at Ka- and V-bands 5.3.1 SIW Narrow Wall Fed SIC Antenna 5.3.2 SIW Narrow Wall Fed SIC Antenna Array at 35 GHz 5.3.3 60-GHz SIW Narrow Wall Fed SIC Antenna Array 5.4 Summary References CHAPTER 6 Cavity-Backed SIW Slot Antennas at 60 GHz 6.1 Introduction 6.2 Operating Principle of the Cavity-backed Antenna 6.2.1 Configuration 6.2.2 Analysis of the Backing-cavity 6.2.3 Design of the Backing-cavity 6.3 Cavity-backed SIW Slot Antenna 6.3.1 Feeding techniques 6.3.2 The SIW Backing-cavity 6.3.3 Radiating Slot 6.4 Types of SIW CBSAs 6.4.1 Wideband CBSAs 6.4.2 Dual-band CBSAs 6.4.3 Dual-polarized and Circularly Polarized CBSAs 6.4.4 Miniaturized CBSAs 6.5 CBSA Design Examples at 60 GHz 6.5.1 SIW CBSA with Different Slot WLR 6.5.2 Array Examples with Different WLRs of Slot 6.6 Summary References Chapter 7 Circularly Polarized SIW Slot LTCC Antennas at 60 GHz 7.1 Introduction 7.2 Key Techniques of mmW CP Antenna Array 7.2.1 Antenna Element Selection 7.2.2 AR Bandwidth Enhancement Methods 7.3 Wideband CP LTCC SIW Antenna Array at 60 GHz 7.3.1 Wideband AR Element 7.3.2 Isolation Consideration 7.3.3 Experiment Results and Discussion 7.4 Summary References Chapter 8 Gain Enhancement of LTCC Microstrip Patch Antenna by Suppressing Surface Waves 8.1 Introduction 8.1.1 Surface waves in microstrip patch antennas 8.1.2 Surface waves effects on microstrip patch antenna 8.2 State-of-The-Art Methods for Suppressing Surface Waves in Microstrip Patch Antennas 8.3 Microstrip Patch Antennas with Partial Substrate Removal 8.3.1 Technique of partial substrate removal 8.3.2 60-GHz LTCC antenna with partial substrate removal 8.4 Summary References Chapter 9 Substrate Integrated Antennas for Millimeter Wave Automotive Radars 9.1 Introduction 9.1.1 Automotive Radar Classification 9.1.2 Frequency Bands for Automotive Radars 9.1.3 Comparison of 24-GHz and 77-GHz Bands 9.1.4 Antenna System Considerations for Automotive Radar Sensors 9.1.5 Fabrication and Packaging Considerations 9.2 Sate-of-the-Art Antennas for 24-GHz and 77-GHz Automotive Radars 9.2.1 Selected state-of-the-Art Antennas for 24-GHz Automotive Radars 9.2.2 Selected state-of-the-Art Antennas for 77-GHz Automotive Radars 9.3 Single-layer SIW Slot Antenna Array for 24-Ghz Automotive Radars 9.3.1 Antenna Configuration 9.3.2 Slot Array Design 9.3.3 Feeding Network Design 9.3.4 Experiment Results 9.4 Transmit-array Antenna for 77-Ghz Automotive Radars 9.4.1 Unit Cell 9.4.2 Four-beam Transmit-array 9.4.3 Results 9.5 Summary References Chapter 10 Sidelobe Reduction of Substrate Integrated Antenna Arrays at Ka-Band 10.1 Introduction 10.2 Feeding Networks for Substrate Integrated Antenna Array 10.2.1 Series Feeding Network 10.2.2 Parallel/Corporate Feeding Network 10.2.3 Flat Lens/Reflector-Based Quasi-Optics Feeding Network 10.2.4 Power Dividers 10.3 SIW Antenna Arrays with Sidelobe Reduction at Ka-Band 10.3.1 Double-layer 8×8 SIW Slot Array 10.3.2 16×16 Monopulse SIW Slot Array 10.4 Summary References Chapter 11 Substrate Edge Antennas 11.1 Introduction 11.2 State-of-the-Art 11.2.1 End-fire SEAs 11.2.2 Leaky-wave SEAs 11.3 Tapered Strips for Wideband Impedance Matching 11.3.1 Tapered Triangular Strips 11.3.2 Tapered Rectangular Strips 11.4 Embedded Planar Lens for Gain Enhancement 11.4.1 Embedded Metallic Lens 11.4.2Embedded Gap Lens 11.5 Prism Lens for Broadband Fixed-Beam Leaky-wave SEAs 11.6 Summary References

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Book SynopsisProvides a significant update to the definitive book on aircraft system design This book is written for anyone who wants to understand how industry develops the customer requirement for aircraft into a fully integrated, tested, and qualified product that is safe to fly and fit for purpose. The new edition of Design and Development of Aircraft Systems fully expands its already comprehensive coverage to include both conventional and unmanned systems. It also updates all chapters to bring them in line with current design practice and technologies taught in courses at Cranfield, Bristol, and Loughborough universities in the UK. Design and Development of Aircraft Systems, 3rd Edition begins with an introduction to the subject. It then introduces readers to the aircraft systems (airframe, vehicle, avionic, mission, and ground systems). Following that comes a chapter on the design and development process. Other chapters look at design drivers, Table of ContentsAbout the Authors xiii Series Preface xv Acknowledgements xvii Glossary of Terms xix 1 Introduction 1 1.1 General 1 1.2 Systems Development 3 1.3 Skills 8 1.4 Human Aspects 9 1.4.1 Introduction 9 1.4.2 Design Considerations 10 1.4.3 Legislation 12 1.4.4 Summary of Legal Threats 12 1.4.5 Conclusions 13 1.5 Overview 14 Exercises 17 References 17 Further Reading 17 2 The Aircraft Systems 19 2.1 Introduction 19 2.2 Definitions 19 2.3 Everyday Examples of Systems 21 2.4 Aircraft Systems of Interest 24 2.4.1 Airframe Systems 28 2.4.2 Vehicle Systems 28 2.4.3 Interface Characteristics of Vehicle Systems 30 2.4.4 Avionics Systems 31 2.4.5 Interface Characteristics of Vehicle and Avionics Systems 31 2.4.5.1 Vehicle Systems 32 2.4.5.2 Avionics Systems 32 2.4.6 Mission Systems 32 2.4.7 Interface Characteristics of Mission Systems 33 2.5 Ground Systems 33 2.6 Generic System Definition 34 Exercises 37 References 37 Further Reading 37 3 The Design and Development Process 39 3.1 Introduction 39 3.2 Definitions 39 3.3 The Product Lifecycle 41 3.4 Concept Phase 46 3.4.1 Engineering Process 48 3.4.2 Engineering Skills 48 3.5 Definition Phase 50 3.5.1 Engineering Process 52 3.5.2 Engineering Skills 53 3.6 Design Phase 56 3.6.1 Engineering Process 56 3.6.2 Engineering Skills 57 3.7 Build Phase 58 3.7.1 Engineering Process 59 3.7.2 Engineering Skills 59 3.8 Test Phase 60 3.8.1 Engineering Process 60 3.8.2 Engineering Skills 60 3.9 Operate Phase 61 3.9.1 Engineering Process 62 3.9.2 Engineering Skills 63 3.10 Disposal or Retirement Phase 63 3.10.1 Engineering Process 65 3.10.2 Engineering Skills 65 3.11 Refurbishment Phase 65 3.11.1 Engineering Process 66 3.11.2 Engineering Skills 66 3.12 Whole Lifecycle Tasks 66 3.13 Summary 67 Exercises 69 References 70 Further Reading 70 4 Design Drivers 73 4.1 Introduction 73 4.2 Design Drivers in the Business Environment 75 4.2.1 Customer 76 4.2.2 Market and Competition 76 4.2.3 Capacity 77 4.2.4 Financial Issues 77 4.2.5 Defence Policy 78 4.2.6 Leisure and Business Interests 78 4.2.7 Politics 79 4.2.8 Technology 79 4.2.9 Global Economy 80 4.3 Design Drivers in the Project Environment 80 4.3.1 Standards and Regulations 80 4.3.2 Availability 81 4.3.3 Cost 81 4.3.4 Programme 82 4.3.5 Performance 82 4.3.6 Skills and Resources 82 4.3.7 Health, Safety, and Environmental Issues 83 4.3.8 Risk 84 4.4 Design Drivers in the Product Environment 84 4.4.1 Functional Performance 84 4.4.2 Human–Machine Interface 85 4.4.3 Crew and Passengers 86 4.4.4 Stores and Cargo 86 4.4.5 Structure 87 4.4.6 Safety 87 4.4.7 Quality 87 4.4.8 Environmental Conditions 87 4.5 Design Drivers in the Product Operating Environment 88 4.5.1 Heat 88 4.5.2 Noise 89 4.5.3 RF Radiation 89 4.5.4 Solar Energy 90 4.5.5 Altitude 91 4.5.6 Temperature 91 4.5.7 Contaminants, and Destructive and Hazardous Substances 92 4.5.8 Lightning 92 4.5.9 Nuclear, Biological, and Chemical Contamination 92 4.5.10 Vibration 93 4.5.11 Shock 93 4.6 Interfaces with the Sub-system Environment 93 4.6.1 Physical Interfaces 94 4.6.2 Power Interfaces 94 4.6.3 Data Communication Interfaces 95 4.6.4 Input/Output Interfaces 95 4.6.5 Status/Discrete Data 95 4.7 Obsolescence 96 4.7.1 Introduction 96 4.7.2 The Threat of Obsolescence in the Product Lifecycle 97 4.7.2.1 Requirements Specification 98 4.7.2.2 People 99 4.7.2.3 Regulations 101 4.7.2.4 Design, Development, and Manufacture 101 4.7.2.5 The Supply Chain 103 4.7.3 Managing Obsolescence 103 4.8 Ageing Aircraft 106 4.8.1 Introduction 106 4.8.2 Some Examples 107 4.8.3 Systems Issues 108 4.8.4 Certification Issues 109 Exercises 109 References 110 Further Reading 110 5 System Architectures 113 5.1 Introduction 113 5.2 Definitions 114 5.3 System Architectures 115 5.3.1 Vehicle Systems 117 5.3.2 Avionic Systems 118 5.3.3 Mission Systems 118 5.3.4 Cabin Systems 119 5.3.5 Data Bus 119 5.4 Architecture Modelling and Trade-off 120 5.5 Example of a Developing Architecture 123 5.6 Evolution of Avionics Architectures 126 5.6.1 Distributed Analogue Architecture 127 5.6.2 Distributed Digital Architecture 128 5.6.3 Federated Digital Architecture 130 5.6.4 Integrated Modular Architecture 132 5.7 Example Architectures 135 5.7.1 Example 1: System Architecture 135 5.7.2 Example 2: Flight Control System 136 5.7.3 Example 3: Radar System 138 5.7.4 Example 4: Vehicle Systems Management 139 Exercises 149 References 149 Further Reading 149 6 System Integration 151 6.1 Introduction 151 6.2 Definitions 153 6.3 Examples of System Integration 153 6.3.1 Integration at the Component Level 153 6.3.2 Integration at the System Level 154 6.3.3 Integration at the Process Level 160 6.3.4 Integration at the Functional Level 163 6.3.5 Integration at the Information Level 166 6.3.6 Integration at the Prime Contractor Level 166 6.3.7 Integration Arising from Emergent Properties 167 6.3.8 Further Examples of Integrated Systems 169 6.3.8.1 The Airframe 169 6.3.8.2 Propulsion 171 6.3.8.3 Air Systems 171 6.4 System Integration Skills 172 6.5 Management of System Integration 175 6.5.1 Major Activities 175 6.5.2 Major Milestones 175 6.5.3 Decomposition and Definition Process 178 6.5.4 Integration and Verification Process 178 6.5.5 Component Engineering 178 6.6 Highly Integrated Systems 178 6.6.1 Integration of Primary Flight Control Systems 179 6.7 Discussion 182 Exercises 184 References 186 Further Reading 186 7 Verification of System Requirements 187 7.1 Introduction 187 7.2 Gathering Qualification Evidence in the Lifecycle 189 7.3 Test Methods 191 7.3.1 Inspection of Design 192 7.3.2 Calculation 192 7.3.3 Analogy 193 7.3.4 Modelling and Simulation 193 7.3.4.1 Modelling Techniques 197 7.3.5 Test Rigs 206 7.3.6 Environmental Testing 207 7.3.7 Integration Test Rigs 207 7.3.8 Aircraft Ground Testing 209 7.3.9 Flight Test 210 7.3.10 Trials 211 7.3.11 Operational Test 212 7.3.12 Demonstrations 212 7.4 An Example Using a Radar System 212 7.5 Summary 214 Exercises 215 References 215 Further Reading 216 8 Practical Considerations 217 8.1 Introduction 217 8.2 Stakeholders 218 8.2.1 Identification of Stakeholders 218 8.2.2 Classification of Stakeholders 219 8.3 Communications 220 8.3.1 The Nature of Communication 222 8.3.2 Examples of Organisation Communication Media 223 8.3.2.1 Mechanisms for Generating Information 225 8.3.2.2 Unauthorised Access 225 8.3.2.3 Data Storage and Access 226 8.3.2.4 Data Discipline 227 8.3.3 The Cost of Poor Communication 227 8.3.4 A Lesson Learned 228 8.4 Giving and Receiving Criticism 230 8.4.1 The Need for Criticism in the Design Process 230 8.4.2 The Nature of Criticism 230 8.4.3 Behaviours Associated with Criticism 231 8.4.4 Conclusions 232 8.5 Supplier Relationships 232 8.6 Engineering Judgement 234 8.7 Complexity 234 8.8 Emergent Properties 235 8.9 Aircraft Wiring and Connectors 236 8.9.1 Aircraft Wiring 236 8.9.2 Aircraft Breaks 237 8.9.3 Wiring Bundle Definition 238 8.9.4 Wiring Routing 239 8.9.5 Wiring Sizing 239 8.9.6 Aircraft Electrical Signal Types 241 8.9.7 Electrical Segregation 242 8.9.8 The Nature of Aircraft Wiring and Connectors 242 8.9.9 Use of Twisted Pairs and Quads 244 8.10 Bonding and Grounding 246 Exercise 248 References 248 Further Reading 248 9 Configuration Control 249 9.1 Introduction 249 9.2 Configuration Control Process 249 9.3 A Simple Portrayal of a System 250 9.4 Varying System Configurations 252 9.4.1 System Configuration A 252 9.4.2 System Configuration B 253 9.4.3 System Configuration C 254 9.5 Forwards and Backwards Compatibility 255 9.5.1 Forwards Compatibility 255 9.5.2 Backwards Compatibility 256 9.6 Factors Affecting Compatibility 256 9.6.1 Hardware 257 9.6.2 Software 257 9.6.3 Wiring 258 9.7 System Evolution 258 9.8 Configuration Control 259 9.8.1 Airbus A380 Example 261 9.9 Interface Control 264 9.9.1 Interface Control Document 264 9.9.2 Aircraft-level Data Bus Data 266 9.9.3 System Internal Data Bus Data 266 9.9.4 Internal System Input/Output Data 267 9.9.5 Fuel Component Interfaces 267 9.10 Control of Day-to-Day Documents 267 Exercise 268 10 Aircraft System Examples 269 10.1 Introduction 269 10.2 Design Considerations 269 10.3 Safety and Economic Considerations 271 10.4 Failure Severity Categorisation 272 10.5 Design Assurance Levels 272 10.6 Redundancy 273 10.6.1 Architecture Options 274 10.6.1.1 Simplex Architecture 274 10.6.1.2 Duplex Architecture 276 10.6.1.3 Dual/Dual Architecture 276 10.6.1.4 Triplex Architecture 276 10.6.1.5 Quadruplex Architecture 276 10.6.2 System Examples 277 10.6.2.1 Major Systems Event 277 10.6.2.2 Flight Critical Event 278 10.7 Integration of Aircraft Systems 280 10.7.1 Engine Control System 282 10.7.2 Flight Control System 283 10.7.3 Attitude Measurement System 284 10.7.4 Air Data System 284 10.7.5 Electrical Power System 285 10.7.6 Hydraulic Power System 286 10.8 Integration of Avionics Systems 287 References 290 11 Integration and Complexity: The Potential Impact on Flight Safety 291 11.1 Introduction 291 11.2 Integration 291 11.3 Complexity 294 11.4 Automation 298 11.5 Impact on Flight Safety Discussion 299 11.6 Single-pilot Operations 302 11.7 Postscript: Chaos Discussion 303 Exercises 307 References 307 Further Reading 308 12 Key Characteristics of Aircraft Systems 309 12.1 Introduction 309 12.2 Aircraft Systems 311 12.3 Avionics Systems 326 12.4 Mission Systems 336 12.5 Sizing and Scoping Systems 343 12.6 Analysis of the Fuel Penalties of Aircraft Systems 345 12.6.1 Introduction 345 12.6.2 Basic Formulation of Fuel Weight Penalties of Systems 346 12.6.3 Application of Fuel Weight Penalties Formulation for Multi-phase Flight 349 12.6.4 Analysis of Fuel Weight Penalties Formulation for Multi-phase Flight 350 12.6.5 Use of Fuel Weight Penalties to Compare Systems 350 12.6.6 Determining Input Data for Systems Weight Penalties Analysis 351 12.6.6.1 Lift/Drag Ratio 351 12.6.6.2 Specific Fuel Consumption 352 12.6.6.3 System Mass 352 12.6.6.4 System Drag Increase 352 12.6.6.5 Increase in sfc Due to Systems Power Off-takes 352 Nomenclature 354 References 354 13 Conclusions 357 13.1 What’s Next? 359 13.2 A Historical Footnote 361 References 362 Index 363

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Book SynopsisDescribes applications of time-domain EM reciprocity and the Cagniard-deHoop technique to achieve solutions to fundamental antenna radiation and scattering problems This book offers an account of applications of the time-domain electromagnetic (TD EM) reciprocity theorem for solving selected problems of antenna theory. It focuses on the development of both TD numerical schemes and analytical methodologies suitable for analyzing TD EM wave fields associated with fundamental antenna topologies. Time-Domain Electromagnetic Reciprocity in Antenna Modeling begins by applying the reciprocity theorem to formulate a fundamentally new TD integral equation technique the Cagniard-deHoop method of moments (CdH-MoM) regarding the pulsed EM scattering and radiation from a thin-wire antenna. Subsequent chapters explore the use of TD EM reciprocity to evaluate the impact of a scatterer and a lumped load on the performance of wire antennas and propose a straightforward Table of ContentsPreface xiii Acronyms xv 1 Introduction 1 1.1 Synopsis 2 1.2 Prerequisites 5 1.2.1 One-Sided Laplace Transformation 6 1.2.2 Lorentz’s Reciprocity Theorem 8 2 Cagniard-Dehoop Method of Moments for Thin-Wire Antennas 15 2.1 Problem Description 15 2.2 Problem Formulation 16 2.3 Problem Solution 18 2.4 Antenna Excitation 20 2.4.1 Plane-Wave Excitation 20 2.4.2 Delta-Gap Excitation 21 Illustrative Example 22 3 Pulsed EM Mutual Coupling Between Parallel Wire Antennas 25 3.1 Problem Description 25 3.2 Problem Formulation 26 3.3 Problem Solution 27 4 Incorporating Wire-Antenna Losses 29 4.1 Modification of the Impedance Matrix 30 5 Connecting a Lumped Element to The Wire Antenna 31 5.1 Modification of the Impedance Matrix 32 6 Pulsed EM Radiation from a Straight Wire Antenna 35 6.1 Problem Description 35 6.2 Source-Type Representations for the TD Radiated EM Fields 36 6.3 Far-Field TD Radiation Characteristics 38 7 EM Reciprocity Based Calculation of Td Radiation Characteristics 41 7.1 Problem Description 41 7.2 Problem Solution 42 Illustrative Numerical Example 43 8 Influence of a Wire Scatterer on a Transmitting Wire Antenna 47 8.1 Problem Description 47 8.2 Problem Solution 48 Illustrative Numerical Example 49 9 Influence of a Lumped Load on EM Scattering of a Receiving Wire Antenna 53 9.1 Problem Description 53 9.2 Problem Solution 54 Illustrative Numerical Example 55 10 Influence of a Wire Scatterer on a Receiving Wire Antenna 59 10.1 Problem Description 59 10.2 Problem Solution 59 Illustrative Numerical Example 61 11 EM-Field Coupling to Transmission Lines 65 11.1 Introduction 65 11.2 Problem Description 68 11.3 EM-Field-To-Line Interaction 68 11.4 Relation to Agrawal Coupling Model 71 11.5 Alternative Coupling Models Based on EM Reciprocity 73 11.5.1 EM Plane-Wave Incidence 73 11.5.2 Known EM Source Distribution 74 12 EM Plane-Wave Induced Thévenin’s Voltage on Transmission Lines 77 12.1 Transmission Line Above the Perfect Ground 77 12.1.1 Thévenin’s Voltage at x = x1 78 12.1.2 Thévenin’s Voltage at x = x2 81 12.2 Narrow Trace on a Grounded Slab 83 12.2.1 Thévenin’s Voltage at x = x1 85 12.2.2 Thévenin’s Voltage at x = x2 88 Illustrative Numerical Example 89 13 VED-Induced Thévenin’s Voltage on Transmission Lines 93 13.1 Transmission Line Above the Perfect Ground 93 13.1.1 Excitation EM Fields 94 13.1.2 Thévenin’s Voltage at x = x1 97 13.1.3 Thévenin’s Voltage at x = x2 98 13.2 Influence of Finite Ground Conductivity 98 13.2.1 Excitation EM Fields 98 13.2.2 Correction to Thévenin’s Voltage at x = x1 100 13.2.3 Correction to Thévenin’s Voltage at x = x2 101 Illustrative Numerical Example 101 14 Cagniard-Dehoop Method of Moments for Planar-Strip Antennas 103 14.1 Problem Description 105 14.2 Problem Formulation 106 14.3 Problem Solution 107 14.4 Antenna Excitation 109 14.4.1 Plane-Wave Excitation 110 14.4.2 Delta-Gap Excitation 111 14.5 Extension to a Wide-Strip Antenna 111 Illustrative Numerical Example 117 15 Incorporating Strip-Antenna Losses 121 15.1 Modification of the Impeditivity Matrix 122 15.1.1 Strip with Conductive Properties 123 15.1.2 Strip with Dielectric Properties 123 15.1.3 Strip with Conductive and Dielectric Properties 124 15.1.4 Strip with Drude-Type Dispersion 124 16 Connecting a Lumped Element to The Strip Antenna 125 16.1 Modification of the Impeditivity Matrix 126 17 Including a Pec Ground Plane 129 17.1 Problem Description 129 17.2 Problem Formulation 130 17.3 Problem Solution 131 17.4 Antenna Excitation 132 Illustrative Numerical Example 133 A Green’s Function Representation in an Unbounded, Homogeneous, and Isotropic Medium 137 B Time-Domain Response of an Infinite Cylindrical Antenna 141 B.1 Transform-Domain Solution 141 B.2 Time-Domain Solution 143 C Impedance Matrix 147 C.1 Generic Integral IA 147 C.2 Generic Integral IB 149 C.3 TD Impedance Matrix Elements 150 D Mutual-Impedance Matrix 151 D.1 Generic Integral JA 151 D.2 Generic Integral JB 153 D.3 TD Mutual-Impedance Matrix Elements 154 E Internal Impedance of a Solid Wire 157 F VED-Induced EM Coupling to Transmission Lines — Generic Integrals 159 F.1 Generic Integral I 159 F.2 Generic Integral J 163 F.3 Generic Integral K 165 G Impeditivity Matrix 169 G.1 Generic Integral J 169 G.1.1 Generic Integral JA 171 G.1.2 Generic Integral JB 175 H A Recursive Convolution Method and Its Implementation 177 H.1 Convolution-Integral Representation 177 H.2 Illustrative Example 179 H.3 Implementation of the Recursive Convolution Method 180 I Conductance and Capacitance of a Thin High-Contrast Layer 183 J Ground-Plane Impeditivity Matrix 187 J.1 Generic Integral I 187 J.1.1 Generic Integral IA 189 J.1.2 Generic Integral IB 193 K Implementation of CDH-Mom for Thin-Wire Antennas 195 K.1 Setting Space-time Input Parameters 195 K.2 Antenna Excitation 197 K.2.1 Plane-Wave Excitation 197 K.2.2 Delta-Gap Excitation 199 K.3 Impedance Matrix 200 K.4 Marching-on-in-Time Solution Procedure 202 K.5 Calculation of Far-Field TD Radiation Characteristics 203 L Implementation of VED-Induced Thévenin’s Voltages on a Transmission Line 205 L.1 Setting Space-Time Input Parameters 205 L.2 Setting Excitation Parameters 206 L.3 Calculating Thévenin’s Voltages 207 L.4 Incorporating Ground Losses 211 M Implementation of CDH-Mom for Narrow-Strip Antennas 215 M.1 Setting Space-Time Input Parameters 215 M.2 Delta-Gap Antenna Excitation 217 M.3 Impeditivity Matrix 217 M.4 Marching-on-in-Time Solution Procedure 200 References 223 Index 227

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Book SynopsisDC-DC Converter Topologies A comprehensive look at DC-DC converters and advanced power converter topologies for all skills levels As it can be rare for source voltage to meet the requirements of a Direct Current (DC) load, DC-DC converters are essential to access service. DC-DC power converters employ power semiconductor devices (like MOSFETs and IGBTs) as switches and passive elements such as capacitors, inductors, and transformers to alter the voltage provided by a DC source into the necessary DC voltage as is required by a DC load. This source can be a battery, solar panels, fuel cells, or a DC bus voltage fed by rectified AC utility voltage. As the many components of DC-DC converters can be differently arranged into circuit structures called topologies, there are as many possible circuit topologies as there are possible combinations of circuit elements. Focusing on DC-DC switch-mode power converters ranging from 50 W to 10kW, DC-DC Converter TopologiesTable of ContentsAbout the Author xv Preface xvi 1 Basic Concepts 1 1.1 Linear Voltage Regulators 1 1.2 Switch-Mode Power Supply Fundamentals 3 1.2.1 Buck Converter 3 1.2.2 Boost Converter 5 1.2.3 Buck–Boost Converter 6 1.3 PWM Converters with Voltage Step-Up and Step-Down Capabilities 8 1.3.1 Cuk Converter 8 1.3.2 Single-Ended Primary Inductance Converter (SEPIC) 9 1.3.3 Zeta Converter 10 1.3.4 Comparison Between Converters with Voltage Step-Up and Step-Down Capabilities 10 1.4 Interleaved Converters 12 1.5 Semiconductor Devices 14 1.5.1 Silicon Diodes 14 1.5.2 Silicon MOSFETs 15 1.5.3 Silicon IGBTs 17 1.5.4 Gate Drive Circuits 18 1.5.5 Wide Bandgap Devices 19 1.6 Snubbers 21 1.7 Conclusion 23 References 23 2 Non-isolated Zero-voltage Switching PWM Converters 25 2.1 Basic ZVS Principles for MOSFETS 26 2.2 ZVS-PWM Quasi-Square-Wave DC–DC Converters 28 2.3 ZVS-PWM DC–DC Converters with Auxiliary Circuits 30 2.3.1 Nonresonant Auxiliary Circuits 31 2.3.2 Resonant Auxiliary Circuits 37 2.3.3 Dual Auxiliary Circuits 40 2.4 Miscellaneous Considerations 42 2.4.1 Application-Specific ZVS-PWM Converters 42 2.4.2 ZVS-PWM Techniques in Converters with Wide Bandgap Devices 43 2.5 Conclusion 44 References 45 3 Non-isolated Zero-current Switching PWM Converters 46 3.1 ZCS-PWM Converters with Series-Resonant Auxiliary Circuits 47 3.1.1 ZCS-PWM Converter with Fully Resonant Auxiliary Circuit 48 3.1.2 ZCS-PWM Converter with Modified Resonant Auxiliary Circuit 51 3.1.3 Converter with Hard-Switching Auxiliary Circuit 51 3.2 ZCS-PWM Boost Converters with Conventional PWM Converter Main Switch Current Stress 52 3.2.1 ZCS-PWM Converter with Series Boost Diode 52 3.2.2 ZCS-PWM Converter with Output Resonance 54 3.2.3 ZCT-PWM Converters with Parallel Auxiliary Circuit 55 3.3 ZVSZCS-PWM Boost Converters 57 3.4 Conclusion 60 References 61 4 Basic Isolated Converters 63 4.1 Transformer Models 64 4.2 Flyback Converter 64 4.3 Forward Converter 67 4.4 Variations on the Forward Converter 69 4.4.1 Forward Converter with RCD Snubber 69 4.4.2 Forward Converter with LCDD Snubber 70 4.4.3 Forward Converter with Regenerative Energy Snubber 71 4.5 Basic Two-Switch Isolated Converters 72 4.5.1 Two-Switch Forward Converter 72 4.5.2 Push–Pull Converter 74 4.5.3 Half-Bridge Converter 76 4.6 Full-Bridge Converter 77 4.7 Conclusion 80 Reference 81 5 Secondary-side Implementations in Isolated DC–DC Converters 82 5.1 Synchronous Rectifiers 82 5.2 Current Doublers 90 5.3 Multi-Output Converters 94 5.4 Conclusion 98 References 99 6 Soft-switching Forward and Flyback Converters 102 6.1 Forward Converters with Resonant Reset 103 6.2 Active Clamp Converter 104 6.2.1 Modes of Operation 106 6.2.2 Design Considerations 110 6.2.3 Active Clamp Flyback Converter 114 6.3 Alternatives to the Active Clamp Converter 115 6.3.1 Forward Converters 115 6.3.2 Flyback Converters 117 6.3.3 Converters with Regenerative Energy Snubber 119 6.4 Conclusion 120 References 121 7 The ZVS-PWM Full-bridge Converter 123 7.1 DC–DC PWM Full-Bridge Converter with Basic PWM Control 124 7.2 ZVS-PWM Full-Bridge Converter with Phase-Shift PWM 125 7.3 Issues Related to the Operation of ZVS-PWM PWM Full-Bridge Converter 131 7.3.1 ZVS Operation 131 7.3.2 Duty-Cycle Loss 134 7.3.3 Voltage Ringing 136 7.4 ZVS-PWM PWM Full-Bridge Converter Design Considerations 137 7.5 Light Load Operation and Hybrid PWM 140 7.6 ZVS PWM Full-Bridge Converters with Wide Bandgap Devices 140 7.7 Conclusion 141 References 142 8 Variations on the Conventional Zero-voltage-Switching DC–DC PWM Full-bridge Converter 144 8.1 Modified ZVS-PWM DC–DC Full-Bridge Converter with Saturable Reactors 145 8.1.1 Modified ZVS-PWM-FB Converter with Primary-Side Saturable Reactor 145 8.1.2 Modified ZVS-PWM-FB Converters with Secondary-Side Saturable Reactors 146 8.2 Modified ZVS-PWM-FB Converters with Passive Series Auxiliary Circuits 149 8.3 ZVS-PWM-FB Converters with Passive Parallel Auxiliary Circuits 151 8.4 ZVS-PWM-FB Converters with Passive Parallel Auxiliary Circuits with a Transformer 153 8.4.1 ZVS-PWM-FB Converter with a Passive Auxiliary Series Auxiliary Circuit with a Transformer 153 8.4.2 ZVS-PWM-FB Converters with Passive Parallel Auxiliary Circuits and Reduced Output Current Ripple 156 8.5 ZVS-PWM-FB Converters with Active Auxiliary Circuits 157 8.6 ZVS-PWM-FB Converter with a Single Active Auxiliary Circuit 161 8.7 ZVS-PWM-FB Converters Based on Dual Half-Bridge Converters 164 8.8 ZVS-PWM-FB Converters with Modified Secondary-Side Circuits for ZVS Operation 167 8.9 Conclusion 170 References 172 9 Zero-voltage-zero-current-switching DC–DC Full-bridge PWM Converters 174 9.1 Fundamental ZVZCS-PWM DC–DC Full-Bridge Converter 175 9.2 ZVZCS-PWM DC–DC Full-Bridge Converters with Secondary Auxiliary Circuit 183 9.3 Variations of ZVZCS Converters for Full ZVS or Full ZCS Operation 193 9.3.1 ZVS Converters 193 9.3.2 ZCS Converters 194 9.3.3 ZVS-PWM Converters Based on ZVZCS-PWM Converters with Triangular Primary Current Waveform 195 9.4 Conclusion 198 References 199 10 Isolated Current-fed DC–DC PWM Converters 201 10.1 Basic Current-Fed Push–Pull Converter 203 10.2 Basic Two-Inductor Current-Fed Converter 204 10.3 Modified Two-Inductor Current-Fed Converter with Auxiliary Transformer 207 10.4 Basic Current-Fed Full-Bridge Topology 210 10.5 Current-Fed DC–DC Full-Bridge Converters with Blocking Diodes 212 10.6 Current-Fed DC–DC Full-Bridge Converters without Blocking Diodes 215 10.6.1 ZVS-PWM Active-Clamp Full-Bridge Converter 215 10.6.2 ZCS-PWM Full-Bridge Converter with Parallel Auxiliary Circuit 217 10.7 Conclusion 219 References 220 11 Resonant Converters Part I – Fundamentals 222 11.1 Resonant Power Conversion Fundamentals 223 11.2 Fundamental Resonant DC–DC Converters 228 11.2.1 Resonant Converter Analysis Using First Harmonic Approximation Method 231 11.2.2 Series-Resonant Converter vs Parallel-Resonant Converter 234 11.2.3 Series-Parallel-Resonant Converter 236 11.3 LLC Resonant Converter 238 11.4 Other Resonant DC–DC Converters 241 11.5 Conclusion 245 References 246 12 Resonant Converters Part II – PWM Controlled, Quasi-resonant, and Ultrahigh-frequency Converters 248 12.1 Fixed Frequency Resonant Converters 249 12.1.1 Full-Bridge Resonant Converters Operated with Phase-Shift PWM 249 12.1.2 Resonant Converters Operated with Asymmetrical PWM 252 12.1.3 Adding Variable Resonant Components 257 12.2 Quasi-Resonant Converters 258 12.2.1 Resonant Pulse Converters 264 12.2.2 Fixed-Frequency Quasi-Resonant Converters 265 12.3 Ultrahigh Frequency Converters 266 12.3.1 Multi-Resonant Converters 267 12.3.2 Ultrahigh Frequency Converters Based on Radio-Frequency Amplifier Circuits 268 12.3.3 Ultrahigh Frequency Converters with Air-Core Inductors 269 12.4 Conclusion 270 References 270 13 Three-level DC–DC Converters 273 13.1 Fundamental Three-Level DC–DC PWM Converters 274 13.1.1 Neutral-Point-Clamped Three-Level DC–DC Converter 274 13.1.2 Flying Capacitor Three-Level DC–DC Converter 280 13.1.3 Three-Level DC–DC Converter with Series Blocking Capacitor 286 13.1.4 Comparison of Fundamental Three-Level DC–DC Converter Topologies 291 13.2 Modified Three-Level DC–DC Converters 292 13.2.1 ZVS Three-Level Converters 292 13.2.2 ZVZCS Three-Level Converters 298 13.3 Stacked Converters 302 13.4 Three-Level DC–DC Converters in Applications with Low and Conventional DC Bus Voltage 306 13.5 Conclusion 307 References 308 14 High Gain Converters 311 14.1 Voltage Multiplier Circuits 312 14.1.1 Output Voltage Multiplier Circuits 312 14.1.2 Internal Voltage Multiplier Circuits 316 14.2 Switched Capacitor Converters 318 14.3 Voltage-Lift and Switched Inductor Converters 321 14.4 Cascaded and Quadratic Converters 326 14.5 Converters with Magnetic Coupling 328 14.5.1 Tapped Inductor Converters 328 14.5.2 Coupled Inductor Converters 329 14.5.3 Transformer-Coupled Converters 331 14.6 Multi-Level and Interleaved Converters 331 14.6.1 Multi-Level Converters 332 14.6.2 Interleaved Converters 335 14.7 Hybrid Converters and Converter Selection 336 14.8 Conclusion 340 References 340 15 Three-phase DC–DC Converters 343 15.1 Fundamental Voltage-Fed Three-Phase DC–DC PWM Converter 344 15.1.1 Basic Operating Principles with Symmetrical PWM 344 15.1.2 Operation with Asymmetrical PWM 346 15.1.3 Modified Output Section with Three Output Diodes 347 15.2 Resonant Converters 349 15.2.1 Parallel Resonant Converter Based on the Fundamental Converter 349 15.2.2 Three-Phase Series-Parallel Resonant Converters with Variable and Fixed Switching Frequency Operation 350 15.3 Three-Phase Current-Fed DC–DC PWM Converters 351 15.3.1 Three-Phase ZVS Active Clamp Converter 351 15.3.2 Three-Phase ZCS Converter 353 15.4 Higher-Power Three-Phase DC–DC Converters 355 15.4.1 High-Power Converter with Three Single-Phase PWM Full-Bridges 355 15.4.2 High-Power Converter with Three Single-Phase Resonant Full-Bridges 356 15.5 Three-Switch Three-Phase DC–DC PWM Converters 356 15.5.1 Three-Phase Push-Pull Converter 357 15.5.2 ZVS Active Clamp Converter 359 15.5.3 ZCS Converter with Secondary-Side Resonance 361 15.5.4 Converter with Mini-Flyback Snubber 362 15.6 Miscellaneous Three-Phase Converter Examples 363 15.6.1 Three-Phase DC–DC Multi-Level Converter 363 15.6.2 Three-Phase DC–DC High-Gain Converter 364 15.7 Three-Phase Transformer Implementations 365 15.8 Conclusion 367 References 367 16 Bidirectional and Dual Active Bridge Converters 369 16.1 Basic Non-Isolated Bidirectional Converters 370 16.2 ZVS Operation of the Fundamental Buck-Boost Bidirectional Converter 372 16.2.1 Bidirectional Quasi-Square Wave Converter 372 16.2.2 Four-Switch Buck-Boost Converter 373 16.2.3 Active Auxiliary Circuits 375 16.3 Bidirectional Converter Topologies with Transformer Isolation 377 16.4 Dual Active Bridge Converters 381 16.4.1 Dual Active Bridge Half-Bridge Converter 381 16.4.2 Dual Active Bridge PWM Full-Bridge Converters 383 16.5 Conclusion 387 References 388 17 Miscellaneous DC–DC Converters 391 17.1 Z-Source Converters 392 17.2 Low Voltage Gain Converters for Voltage Regulator Modules 396 17.3 T-Type Converters 401 17.4 Multi-Port Converters 405 17.4.1 Non-Isolated Multi-Input Converters 406 17.4.2 Isolated Multi-Port Converters 408 17.5 Conclusion 412 References 413 Appendix 415 Index 427

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Book SynopsisBy outlining how reliability engineering practices fit within a product development program, the reader will have a better understanding of how roles and goals align with the program and how this applies to their specific role.Reliability Culture: How Leaders Build Organizations that Create Reliable Products, will help readers develop a deep understanding of reliability, including what it really means for organizations, how to implement it in daily operations, and, most importantly, how to build a culture that is centered around reliability and can generate impressive profits. When senior leaders work toward reliability, product details often get lost in translation. This book will enable organizations to overcome this problem by showing leaders how their actions truly affect product development. They will be introduced to new methods that will immediately enable them to have carefully crafted product specifications translated into matching, highly reliable productsTable of ContentsSeries Editor’s Foreword by Dr. Andre Kleyner xi Acknowledgements xiii Introduction xv 1 The Product Development Challenge 1 Key Players 1 Follow the Carrot or Get Out of the Race 3 It’s Not That I’m Lazy, It’s That I Just Don’t Care 5 Product-specification Profiles 8 Product Drivers 9 Bounding Factors 10 Reliability Discipline 11 References 15 2 Balancing Business Goals and Reliability 17 Return on Investment 17 Program Accounting 18 Rule of 10s 20 Design for Reliability 21 Reliability Engineer’s Responsibility to Connect to the Business Case 23 Role of the Reliability Professional 26 Summary 28 References 29 3 Directed Product Development Culture 31 The Past, Present, and Future of Reliability Engineering 32 Influences 32 The Invention of “Inventing” 33 Quality and Inventing Are Behaviors 34 As Always, WWII Changed Everything 35 The Postwar Influence Diminishes 36 The Emergence of Japan 37 Reliability Is No Longer a Luxury 38 Understand the Intent 39 Levels of Awareness 40 Summary 41 References 42 4 Awakening 43 Stage 1 43 Stage 2 43 Stage 3 44 Stage 4 44 The Ownership Chart 44 Comparing Charts 45 Benefits of the Ownership Chart 45 Communicating Clearly 50 Behind the Words at Work 51 When You Want to Improve 53 My Personal Case 53 Getting the Message Across 54 The Importance of Time 54 When We Can’t Communicate at the Organizational Level 55 When Scheduling Trumps Testing 57 Summary 58 5 Goals and Intentions 61 Testing Intent 61 Testing to Improve 61 Quick Question 61 Ownership 62 Fear-driven Testing 62 Transferring Ownership 63 Leadership and Transference 64 Objectives and Transference 65 What Transferred Ownership Looks Like 67 The Benefits of Successful Transference 67 A Racing Bike Analogy 68 Guided by All the Goals All the Time 69 The Roadmap Conundrum 69 Why We Embrace Tunnel Vision 69 When No One Has a Plan 69 Summary 70 References 70 6 New Roles 71 Role of Change Agents 71 Reliability Czar 72 The Czar is a Link 73 Direct Input 74 Distilling Information 74 Who is the Czar? 74 How the Czar Works with the Team and Leadership 76 Tips for the Czar 77 Role of Facilitators 78 Facilitation Technique 78 Creating a Narrative 80 Role of Reliability Professionals 80 Stop Asking for Resources 81 Connect Reliability to the Market 81 Summary 83 7 Program Assessment 85 Measurements 85 What to Measure 86 Using Reliability Testing as Program Guidance 86 The Primary Wear-out Failure Mode 88 The Random Fail Rate During Use Life 90 Reliability Maturity Assessments 90 Steps for an Assessment 91 The Team 92 The Topics 93 The Scoring 94 Analyze: The Reliability Maturity Matrix 94 Review with the Team and Summarize 95 Recommend Actions 98 Assess Particular Areas in More Detail 98 Golden Nuggets 98 Summary 99 References 99 8 Reliability Culture Tools 101 Advancing Culture 101 Manipulative Managing 101 Manipulative Management in Action 102 An Alternative to Manipulation 102 Transfer Why 103 Reliability Bounding 103 Fire and Forget 103 Reliability Feedback 104 Strategy Bounding 104 Strategy Bounding Toolkit 104 Midprogram Feedback 105 The Bounding Number 105 Bounding ROI 106 Invest and Return Tables 107 Deciding by Bounding 110 Anchoring 110 Closed Loop Control 112 Open Loop Control 112 Intent Anchor 113 Delivery Anchor 114 The Value of Anchoring 115 Focus Rotation 115 The Focus Rotation Steps 115 Working in Freedom and with Ownership 116 The Gore Example 117 Why Don’t All Companies Do This? 118 Summary 118 9 Guiding the Program in Motion 119 Guidance Bounding 119 Guidance Bounding ROI 120 The Plan 120 The Issue 120 Technology Cascade 120 Timing is Everything 121 Our Choice 121 Using Bounding 121 The Results 122 Program Risk Effects Analysis 122 What Now? 123 Just Let It Go 123 Fully Access Risk 124 Program Freezes Don’t Work 124 The Chill Phase 125 PREA Tables and Calculations 126 Summary 130 10 Risk Analysis Guided Project Management 131 Failure Mode Effects Analysis Methodology 131 Design Failure Mode Effects Analysis 132 Have an Experienced Facilitator Who Is Only Facilitating 132 The Facilitator Should Not Be the Scribe or “Spreadsheet Master” 132 Don’t Let Conversations Go So Deep that 90% of the Room Is Just Listening Without Being Able to Contribute 133 Make a Scoring System that Is Meaningful, Not Standardized 133 The Scoring Is Comparative, Not Absolute 133 Reliability Design Risk Summary 134 The Objective of RDRS 134 Three Ranking Factors 135 Scoring and Evaluation 135 The Benefits of RDRS 136 Process Failure Mode Effects Analysis 136 Use Failure Mode Effects Analysis 136 Failure Reporting and Corrective Action System 137 Root Cause Analysis 138 Reaching a Wrong Conclusion 138 Reaching the Right Conclusion 138 The Stages of RCA 139 Brainstorming 140 Fundamentals of Brainstorming 140 Preparing for a Session 141 Select Participants 141 Draft a Background Memo 141 Create a List of Lead Questions 141 Three Simple Brainstorming Warm-ups 141 Setting Session Rules 142 Variations on Classic Brainstorming 142 Summary 143 References 144 11 The Reliability Program 145 Reliability Program Plan 145 Common Reliability Program Plan Pitfalls 146 The Plan Doesn’t Account for a Broad Audience 146 Not Including Return on Investment (ROI) 146 Too Much 147 Too Little 147 Major Elements of a Reliability Program Plan 149 Purpose 149 Scope 150 Acronyms and Definitions 150 Product Description 151 Design for Reliability (DfR) 151 Reliability Goals 152 Use Case, Environment, Uptime 153 Recommended Tools by Program Phase 154 Design Risk Analysis 155 Failure Mode Effects Analysis (FMEA) 155 Reliability Allocation Model 157 Testing 159 Summary 166 12 Sustained Culture 167 Lasting Change 167 The Seven-stage Process 167 Summary 168 Index 171

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Book SynopsisComprehensive and in-depth overview of the fundamental principles of virtual and augmented reality technologies and their key applications Virtual and Augmented Reality presents a treatment of the hot topics of VR and AR technologies, including the advances in 3D motion tracking and semantic scene understanding with artificial intelligence based on deep neural networks, to provide advanced coverage of the fundamental principles of these two subjects, as well as serve as a reference text for both academia and industry practitioners. The book includes necessary derivations, starting from the fundamentals, and builds up step-by-step to more advanced concepts, such as 3D graphics and computer vision techniques, all the way to overall systems architectures and applications. The book also covers historical perspectives, highlighting the seminal developments along the way, as well as look ahead to the future applications beyond the current state-of-the-art commercial deployments to depict the

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Book SynopsisExplore this indispensable guide covering the fundamentals of IOT and wearable devices from a leading voice in the field Fundamentals of IoT and Wearable Technology Design delivers a comprehensive exploration of the foundations of the Internet of Things (IoT) and wearable technology. Throughout the textbook, the focus is on IoT and wearable technology and their applications, including mobile health, environment, home automation, and smart living. Readers will learn about the most recent developments in the design and prototyping of these devices. This interdisciplinary work combines technical concepts from electrical, mechanical, biomedical, computer, and industrial engineering, all of which are used in the design and manufacture of IoT and wearable devices. Fundamentals of IoT and Wearable Technology Design thoroughly investigates the foundational characteristics, architectural aspects, and practical considerations, while offering readers detailed and systematic design and prototypTable of ContentsAbout the Author xv Preface xvii Acknowledgment xxi 1 Introduction and Historical Background 1 1.1 Introduction 1 1.1.1 IoT and Wearables Market Size 2 1.1.2 The World of IoT and Wearables 2 1.1.2.1 What Is an IoT Device? 3 1.1.2.2 Characteristics of IoT Systems 3 1.1.2.3 What Exactly Is a Wearable Device? 4 1.1.2.4 Characteristics of Wearable Devices 7 1.1.2.5 IoT vs. M2M 7 1.1.2.6 IoT vs. Wearables 8 1.1.3 IoT: Historical Background 10 1.1.4 Wearable Technology: Historical Background 12 1.1.4.1 The Wearables We Know Today 15 1.1.5 Challenges 19 1.1.5.1 Security 19 1.1.5.2 Privacy 20 1.1.5.3 Standards and Regulations 21 1.1.5.4 Energy and Power Issues 21 1.1.5.5 Connectivity 22 1.2 Conclusion 22 Problems 22 Interview Questions 23 Further Reading 24 2 Applications 27 2.1 Introduction 27 2.2 IoT and Wearable Technology Enabled Applications 27 2.2.1 Health care 27 2.2.2 Fitness and Well-being 29 2.2.3 Sports 30 2.2.4 Entertainment and Gaming 31 2.2.5 Pets 32 2.2.6 Military and Public Safety 33 2.2.7 Travel and Tourism 34 2.2.8 Aerospace 34 2.2.9 Education 35 2.2.10 Fashion 36 2.2.11 Business, Retail, and Logistics 36 2.2.12 Industry 37 2.2.12.1 The Industrial Internet of Things (IIoT) 37 2.2.13 Home Automation and Smart Living 38 2.2.14 Smart Grids 39 2.2.15 Environment and Agriculture 40 2.2.16 Novel and Unusual Applications 41 2.3 Smart Cities 42 2.4 Internet of Vehicles (IoV) 44 2.5 Conclusion 44 Problems 45 Interview Questions 46 Further Reading 46 3 Architectures 53 3.1 Introduction 53 3.2 IoT and Wearable Technology Architectures 54 3.2.1 Introduction 54 3.2.1.1 The Motivations Behind New Architectures 54 3.2.1.2 Edge Computing 56 3.2.1.3 Cloud, Fog, and Mist 57 3.2.2 IoT Architectures 59 3.2.2.1 The OSI Model 60 3.2.2.2 Why Does the OSI Model Matter? 60 3.2.2.3 Data Flow Across the OSI Model 62 3.2.2.4 Common IoT Architectures 62 3.2.2.5 Layer 1: Perception and Actuation (Sensors and Actuators) 67 3.2.2.6 Layer 2: Data Conditioning and Linking (Aggregation, Digitization, and Forwarding) 67 3.2.2.7 Layer 3: Network Transport (Preprocessing, Preliminary Analytics, and Routing) 68 3.2.2.8 Layer 4: Application (Analytics, Control, and Archiving) 69 3.2.3 Wearable Device Architecture 69 3.3 Conclusion 70 Problems 71 Technical Interview Questions 72 Further Reading 72 4 Hardware 77 4.1 Introduction 77 4.2 Hardware Components Inside IoT and Wearable Devices 77 4.2.1 Sensors 78 4.2.1.1 Sensor Properties 79 4.2.1.2 MEMS Sensors 80 4.2.1.3 Commonly Used Sensors in IoT and Wearable Devices 81 4.2.1.4 Wireless Sensors 83 4.2.1.5 Multisensor Modules 84 4.2.1.6 Signal Conditioning for Sensors 85 4.2.2 Actuators 85 4.2.3 Microcontrollers, Microprocessors, SoC, and Development Boards 86 4.2.3.1 Selecting the Right Processing Unit for Your IoT or Wearable Device 89 4.2.4 Wireless Connectivity Unit 90 4.2.5 Battery Technology 91 4.2.5.1 Power Management Circuits 94 4.2.6 Displays and Other User Interface Elements 95 4.2.7 Microphones and Speakers 95 4.3 Conclusion 95 Problems 96 Technical Interview Questions 97 Further Reading 97 5 Communication Protocols and Technologies 101 5.1 Introduction 101 5.2 Types of Networks 101 5.3 Network Topologies 103 5.3.1 Mesh 103 5.3.2 Star 104 5.3.3 Bus 104 5.3.4 Ring 104 5.3.5 Point to Point 104 5.4 Protocols 105 5.4.1 Application Layer Protocols 105 5.4.1.1 Constrained Application Protocol (CoAP) 106 5.4.1.2 Message Queuing Telemetry Transport (MQTT) 106 5.4.1.3 Extensible Messaging and Presence Protocol (XMPP) 106 5.4.1.4 Data Distribution Service (DDS) 106 5.4.1.5 AMQP (Advanced Message Queuing Protocol) 107 5.4.2 Transport Layer Protocols 107 5.4.2.1 Transmission Control Protocol (TCP) 107 5.4.2.2 User Datagram Protocol (UDP) 107 5.4.3 Network Layer Protocols 107 5.4.3.1 IPv4 and IPv6 107 5.4.3.2 6LoWPAN 107 5.4.3.3 RPL 108 5.4.3.4 Thread 108 5.4.3.5 LoRaWAN 108 5.4.4 Protocols and Technologies in Physical and Data Link Layers 108 5.4.4.1 Short Range 109 5.4.4.2 Medium Range 110 5.4.4.3 Long Range 110 5.5 Conclusion 112 Problems 112 Technical Interview Questions 113 Further Reading 114 6 Product Development and Design Considerations 119 6.1 Introduction 119 6.2 Product Development Process 119 6.2.1 Ideation and Research 120 6.2.2 Requirements/Specifications 120 6.2.3 Engineering Analysis 120 6.2.3.1 Hardware Design 120 6.2.3.2 Software Development 121 6.2.3.3 Mechanical Design 121 6.2.3.4 PCB Design 122 6.2.4 Prototyping 122 6.2.5 Testing and Validation 123 6.2.5.1 Review and Design Verification 123 6.2.5.2 Unit Testing 123 6.2.5.3 Integration Testing 123 6.2.5.4 Certification and Documentation 124 6.2.5.5 Production Review 124 6.2.6 Production 124 6.3 IoT and Wearable Product Requirements 124 6.3.1 Form Factor 125 6.3.2 Power Requirements 126 6.3.2.1 Energy Budget 126 6.3.3 Wireless Connectivity Requirements 127 6.3.3.1 RF Design and Antenna Matching 127 6.3.3.2 Link Budget 128 6.3.4 Cost Requirements 131 6.4 Design Considerations 131 6.4.1 Operational Factors 131 6.4.2 Durability and Longevity 131 6.4.3 Reliability 132 6.4.4 Usability and User Interface 132 6.4.5 Aesthetics 132 6.4.6 Compatibility 132 6.4.7 Comfort and Ergonomic Factors 133 6.4.8 Safety Factors 133 6.4.9 Washing Factors (Wash-ability) 133 6.4.10 Maintenance Factors 134 6.4.11 Packaging and Material Factors 134 6.4.12 Security Factors 134 6.4.13 Technology Obsolescence 135 6.5 Conclusion 135 Problems 135 Interview Questions 136 Further Reading 137 7 Cloud and Edge: Architectures, Topologies, and Platforms 139 7.1 Introduction 139 7.2 Cloud 140 7.2.1 Why Cloud? 140 7.2.2 Types of Cloud 140 7.2.2.1 Private Cloud 140 7.2.2.2 Public Cloud 141 7.2.2.3 Hybrid Cloud 141 7.2.2.4 Community Cloud 141 7.2.3 Cloud Services 141 7.2.3.1 Infrastructure as a Service (IaaS) 141 7.2.3.2 Software as a Service (SaaS) 142 7.2.3.3 Platform as a Service (PaaS) 142 7.2.3.4 Functions as a Service (FaaS) 142 7.2.4 OpenStack Architecture 142 7.2.4.1 Components of OpenStack 142 7.3 Edge and Fog 144 7.3.1 The OpenFog Reference Architecture 145 7.3.2 Fog Topologies 147 7.4 Platforms 148 7.4.1 Criteria for Choosing a Platform 150 7.5 Data Analytics and Machine Learning 151 7.6 Conclusion 151 Problems 152 Technical Interview Questions 152 References 153 Further Reading 154 8 Security 157 8.1 Introduction 157 8.2 Security Goals 158 8.3 Threats and Attacks 159 8.3.1 Threat Modeling 160 8.3.2 Common Attacks 161 8.4 Security Consideration 162 8.4.1 Blockchain 164 8.5 Conclusion 166 Problems 166 Technical Interview Questions 167 Further Reading 168 9 Concerns, Risks, and Regulations 171 9.1 Introduction 171 9.2 Privacy Concerns 171 9.3 Psychological and Social Concerns 173 9.3.1 Psychological Concerns 174 9.3.2 Social Concerns 176 9.4 Safety Concerns 177 9.5 Health Concerns 177 9.5.1 Electromagnetic Radiation and Specific Absorption Rate 177 9.5.2 Diseases and Effects 181 9.5.2.1 Cancer 181 9.5.2.2 Fertility 182 9.5.2.3 Vision and Sleep Disorders 182 9.5.2.4 Pain and Discomfort 182 9.5.2.5 Other Risks 183 9.5.3 Recommendations 183 9.6 Regulations 184 Further Reading 186 10 Detailed Product Design and Development: From Idea to Finished Product 189Scott Tattersall, Mustafa Kamoona, and Haider Raad 10.1 Introduction 189 10.2 Product I (IoT): Vineyard Monitor 189 10.2.1 Product Requirements and Design Considerations 190 10.2.2 Communication Network/Technology Selection 190 10.2.3 Hardware Selection and Breadboarding 191 10.2.3.1 Breadboarding Example 192 10.2.4 Prototyping 196 10.2.4.1 Fritzing 196 10.2.5 Power Consumption 197 10.2.6 Software, Cloud, Platforms, API, etc. 198 10.2.6.1 Sigfox Callback 198 10.2.6.2 RESTful Web Services 199 10.2.7 Microcontroller Coding 201 10.2.7.1 Sigfox Messages 203 10.2.7.2 Bit Packing 205 10.2.7.3 IFTTT Integration 207 10.2.8 From Breadboard to PCB 207 10.2.8.1 Hand Soldering the Surface Mount Components (SMCs) 209 10.2.9 Testing and Iteration 212 10.2.10 PCB to Finished Product 216 10.3 Product II (Wearable): Fall Detection Device 220 10.3.1 Product Requirements and Design Considerations 220 10.3.2 Design Block Diagram 220 10.3.3 Flowchart 222 10.3.4 Unified Modeling Language (UML) 223 10.3.5 Hardware Selection 223 10.3.6 Hardware Implementation and Connectivity 225 10.3.6.1 Hardware Modules and Interfaces Overview 229 10.3.7 Software Implementation 229 10.3.7.1 Fall Detection Algorithm 234 10.3.8 Smartphone iOS App 238 10.3.9 Cloud Solution 243 10.3.9.1 Cloud versus Edge Computing 244 10.3.10 Security 245 10.3.11 Power Consumption 245 10.3.12 Delivery 247 10.4 Conclusion 247 References 247 Further Reading 249 Index 251 Solution Manual 257

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Book SynopsisPresents a review of image denoising algorithms with practical MATLAB implementation guidance Digital Image Denoising in MATLAB provides a comprehensive treatment of digital image denoising, containing a variety of techniques with applications in high-quality photo enhancement as well as multi-dimensional signal processing problems such as array signal processing, radar signal estimation and detection, and more. Offering systematic guidance on image denoising in theories and in practice through MATLAB, this hands-on guide includes practical examples, chapter summaries, analytical and programming problems, computer simulations, and source codes for all algorithms discussed in the book. The book explains denoising algorithms including linear and nonlinear filtering, Wiener filtering, spatially adaptive and multi-channel processing, transform and wavelet domains processing, singular value decomposition, and various low variance optimization and low rank processing techniques. Throughout

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Book SynopsisThe classic text that covers practical image processing methods and theory for image texture analysis, updated second edition The revised second edition of Image Processing: Dealing with Textures updates the classic work on texture analysis theory and methods without abandoning the foundational essentials of this landmark work. Like the first, the new edition offers an analysis of texture in digital images that are essential to a diverse range of applications such as: robotics, defense, medicine and the geo-sciences. Designed to easily locate information on specific problems, the text is structured around a series of helpful questions and answers. Updated to include the most recent developments in the field, many chapters have been completely revised including: Fractals and Multifractals, Image Statistics, Texture Repair, Local Phase Features, Dual Tree Complex Wavelet Transform, Ridgelets and Curvelets and Deep Texture Features. The book takes a Table of ContentsPreface to the Second Edition vii Preface to the First Edition viii Acknowledgements ix About the Companion Website x 1 Introduction 1 2 Binary Textures 11 2.1 Shape Grammars 13 2.2 Boolean Models 21 2.3 Mathematical Morphology 51 3 Stationary Grey Texture Images 79 3.1 Image Binarisation 81 3.2 Grey Scale Mathematical Morphology 88 3.3 Fractals and Multifractals 104 3.4 Image Statistics 174 3.5 Texture Features from the Fourier Transform 227 3.6 Markov Random Fields 263 3.7 Gibbs Distributions 301 3.8 Texture Repair 348 4 Non-stationary Grey Texture Images 371 4.1 The Uncertainty Principle and its Implications in Signal and Image Processing 371 4.2 Gabor Functions 399 4.3 Prolate Spheroidal Sequence Functions 450 4.4 Local Phase Features 503 4.5 Wavelets 518 4.6 The Dual Tree Complex Wavelet Transform 594 4.7 Ridgelets and Curvelets 621 4.8 Where Image Processing and Pattern Recognition Meet 673 4.9 Laws’ Masks and the “What Looks Like Where” Space 697 4.10 Local Binary Patterns 727 4.11 The Wigner Distribution 735 4.12 Convolutional Neural Networks for Textures Feature Extraction 754 Bibliographical Notes 793 References 795 Index 801

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Book SynopsisA guide to two-phase heat transfer theory, practice, and applications Designed primarily as a practical resource for design and development engineers, Two-Phase Heat Transfer contains the theories and methods of two-phase heat transfer that are solution oriented. Written in a clear and concise manner, the book includes information on physical phenomena, experimental data, theoretical solutions, and empirical correlations. A very wide range of real-world applications and formulas/correlations for them are presented. The two-phase heat transfer systems covered in the book include boiling, condensation, gas-liquid mixtures, and gas-solid mixtures. The author?a noted expert in this field?also reviews the numerous applications of two-phase heat transfer such as heat exchangers in refrigeration and air conditioning, conventional and nuclear power generation, solar power plants, aeronautics, chemical processes, petroleum industry, and more. Special attention is giveTable of ContentsPreface xvii 1 Introduction 1 1.1 Scope and Objectives of the Book 1 1.2 Basic Definitions 1 1.3 Various Models 2 1.3.1 Homogeneous Model 2 1.3.2 Separated Flow Models 2 1.3.3 Flow Pattern-Based Models 3 1.4 Classification of Channels 3 1.4.1 Based on Physical Dimensions 3 1.4.2 Based on Condensation Studies 3 1.4.3 Based on Boiling Flow Studies 4 1.4.4 Based on Two-Component Flow 4 1.4.5 Discussion 5 1.4.6 Recommendation 5 1.5 Flow Patterns in Channels 5 1.5.1 Horizontal Channels 5 1.5.1.1 Description of Flow Patterns 5 1.5.1.2 Flow Pattern Maps 6 1.5.2 Vertical Channels 7 1.5.3 Inclined Channels 7 1.5.4 Annuli 8 1.5.5 Minichannels 8 1.5.6 Horizontal Tube Bundles with Crossflow 9 1.5.7 Vertical Tube Bundles 10 1.5.8 Effect of Low Gravity 10 1.5.9 Recommendations 12 1.6 Heat Transfer in Single-Phase Flow 12 1.6.1 Flow Inside Channels 12 1.6.2 Vertical Tube/Rod Bundles with Axial Flow 13 1.6.3 Various Geometries 14 1.6.4 Liquid Metals 14 1.7 Calculation of Pressure Drop 14 1.7.1 Single-Phase Pressure Drop in Pipes 14 1.7.2 Two-Phase Pressure Drop in Pipes 15 1.7.3 Annuli and Vertical Tube Bundles 17 1.7.4 Horizontal Tube Bundles 17 1.7.5 Recommendations 17 1.8 Calculation of Void Fraction 17 1.8.1 Flow Inside Pipes 17 1.8.2 Flow in Tube Bundles 18 1.8.3 Recommendations 18 1.9 CFD Simulation 18 1.10 General Information 19 Nomenclature 19 References 20 2 Heat Transfer During Condensation 25 2.1 Introduction 25 2.2 Condensation on Plates 25 2.2.1 Nusselt Equations 25 2.2.2 Modifications to the Nusselt Equations 26 2.2.3 Condensation with Turbulent Film 27 2.2.4 Condensation on Underside of a Plate 27 2.2.5 Recommendations 28 2.3 Condensation Inside Plain Channels 28 2.3.1 Laminar Condensation in Vertical Tubes 28 2.3.2 The Onset of Turbulence 28 2.3.3 Prediction of Heat Transfer in Turbulent Flow 29 2.3.3.1 Analytical Models 29 2.3.3.2 CFD Models 30 2.3.3.3 Empirical Correlations 30 2.3.3.4 Correlations Applicable to Both Macro and Minichannels 34 2.3.4 Recommendation 41 2.4 Condensation Outside Tubes 41 2.4.1 Single Tube 41 2.4.1.1 Stagnant Vapor 41 2.4.1.2 Moving Vapor 42 2.4.2 Bundles of Horizontal Tubes 42 2.4.2.1 Vapor Entry from Top 42 2.4.2.2 Vapor Entry from Side 44 2.4.3 Recommendations 44 2.5 Condensation with Enhanced Tubes 44 2.5.1 Condensation on Outside Surface 44 2.5.1.1 Single Tubes 44 2.5.1.2 Tube Bundles 46 2.5.2 Condensation Inside Enhanced Tubes 47 2.5.3 Recommendations 49 2.6 Condensation of Superheated Vapors 49 2.6.1 Stagnant Vapor on External Surfaces 49 2.6.2 Forced Flow on External Surfaces 49 2.6.3 Flow inside Tubes 50 2.6.4 Plate-Type Heat Exchangers 50 2.6.5 Recommendations 51 2.7 Miscellaneous Condensation Problems 51 2.7.1 Condensation on Stationary Cone 51 2.7.2 Condensation on a Rotating Disk 51 2.7.3 Condensation on Rotating Vertical Cone 52 2.7.4 Condensation on Rotating Tubes 52 2.7.5 Plate-Type Condensers 53 2.7.5.1 Recommendation 54 2.7.6 Effect of Oil in Refrigerants 54 2.7.6.1 Recommendation 55 2.7.7 Effect of Gravity 55 2.7.7.1 Some Formulas for Zero Gravity 55 2.7.7.2 Experimental Studies 55 2.7.7.3 Conclusion 55 2.7.8 Effect of Non-condensable Gases 56 2.7.8.1 Prediction Methods 56 2.7.8.2 Recommendation 57 2.7.9 Flooding in Upflow 57 2.7.10 Condensation in Thermosiphons 58 2.7.11 Condensation in Helical Coils 58 2.8 Condensation of Vapor Mixtures 59 2.8.1 Physical Phenomena 59 2.8.2 Prediction Methods 60 2.8.3 Recommendation 61 2.9 Liquid Metals 61 2.9.1 Stagnant Vapors 61 2.9.2 Interfacial Resistance 62 2.9.3 Moving Vapors 62 2.9.4 Recommendation 62 2.10 Dropwise Condensation 63 2.10.1 Prediction of Mode of Condensation 63 2.10.2 Theories of Dropwise Condensation 63 2.10.3 Methods to Get Dropwise Condensations 63 2.10.4 Some Experimental Studies 64 2.10.5 Prediction of Heat Transfer 64 2.10.6 Recommendations 66 Nomenclature 66 References 67 3 Pool Boiling 77 3.1 Introduction 77 3.2 Nucleate Boiling 77 3.2.1 Mechanisms of Nucleate Boiling 77 3.2.1.1 Bubble Agitation 77 3.2.1.2 Vapor–Liquid Exchange 77 3.2.1.3 Evaporative Mechanism 78 3.2.2 Bubble Nucleation 78 3.2.2.1 Inception of Boiling 78 3.2.2.2 Bubble Nucleation Cycle 79 3.2.2.3 Active Nucleation Site Density 81 3.2.2.4 Recommendations 81 3.2.3 Correlations for Heat Transfer 81 3.2.3.1 Conclusion and Recommendation 83 3.2.4 Multicomponent Mixtures 83 3.2.4.1 Physical Phenomena 83 3.2.4.2 Prediction of Heat Transfer 84 3.2.4.3 Recommendation 86 3.2.5 Liquid Metals 86 3.2.5.1 Physical Phenomena 86 3.2.5.2 Prediction of Heat Transfer 87 3.2.5.3 Recommendations 88 3.3 Critical Heat Flux 90 3.3.1 Models of Mechanisms 90 3.3.1.1 Bubble Interference Model 90 3.3.1.2 Hydrodynamic Instability Model 90 3.3.1.3 Macrolayer Dryout Model 91 3.3.1.4 Dry Spot Model 91 3.3.1.5 Interfacial Lift-off Model 92 3.3.2 Correlations for Inclined Surfaces 92 3.3.3 Various Correlations 93 3.3.4 Effect of Subcooling 93 3.3.5 Various Other Factors Affecting CHF 94 3.3.6 Evaluation of CHF Prediction Methods 94 3.3.7 Recommendations 94 3.3.8 Multicomponent Mixtures 95 3.3.8.1 Physical Phenomena and Prediction Methods 95 3.3.8.2 Recommendation 95 3.3.9 Liquid Metals 95 3.3.9.1 Physical Phenomena 97 3.3.9.2 Prediction of CHF 98 3.3.9.3 Recommendations 102 3.4 Transition Boiling 102 3.5 Minimum Film Boiling Temperature 104 3.5.1 Prediction Methods 104 3.5.1.1 Analytical Models 104 3.5.1.2 Empirical Correlations 105 3.5.2 Recommendations 106 3.6 Film Boiling 106 3.6.1 Methods for Predicting Heat Transfer 106 3.6.1.1 Vertical Plates 106 3.6.1.2 Horizontal Cylinders 107 3.6.1.3 Horizontal Plates 108 3.6.1.4 Inclined Plates 108 3.6.1.5 Spheres 109 3.6.2 Liquid Metals 109 3.6.3 Recommendations 110 3.7 Various Topics 110 3.7.1 Effect of Gravity 110 3.7.1.1 Scaling Method of Raj et al. 110 3.7.1.2 Scaling for Hydrogen 112 3.7.1.3 Some Other Studies 112 3.7.1.4 Recommendations 113 3.7.2 Effect of Oil in Refrigerants 113 3.7.2.1 Mechanisms 114 3.7.2.2 Correlations 114 3.7.2.3 Recommendation 115 3.7.3 Thermosiphons 115 3.7.4 Effect of Some Organic Additives 115 Nomenclature 115 References 116 4 Forced Convection Subcooled Boiling 123 4.1 Introduction 123 4.2 Inception of Boiling in Channels 123 4.2.1 Analytical Models and Correlations 123 4.2.2 Minichannels 125 4.2.3 Effect of Dissolved Gases 126 4.2.4 Recommendations 126 4.3 Prediction of Subcooled Boiling Regimes in Channels 126 4.3.1 Recommendation 127 4.4 Prediction of Void Fraction in Channels 127 4.4.1 Recommendations 129 4.5 Heat Transfer in Channels 129 4.5.1 Visual Observations and Mechanisms 129 4.5.2 Prediction of Heat Transfer 130 4.5.2.1 Some Dimensional Correlations 130 4.5.2.2 The Shah Correlation 130 4.5.2.3 Various Correlations 132 4.5.2.4 Recommendations 135 4.6 Single Cylinder with Crossflow 135 4.6.1 Experimental Studies 135 4.6.2 Prediction of Heat Transfer 135 4.6.2.1 Shah Correlation 135 4.6.2.2 Other Correlations 137 4.6.3 Recommendation 138 4.7 Various Geometries 138 4.7.1 Tube Bundles with Axial Flow 138 4.7.2 Tube Bundles with Crossflow 138 4.7.3 Flow Parallel to a Flat Plate 138 4.7.4 Helical Coils 138 4.7.5 Bends 139 4.7.6 Rotating Tube 139 4.7.7 Jets Impinging on Hot Surfaces 141 4.7.7.1 Experimental Studies and Correlations 142 4.7.7.2 Recommendations 145 4.7.8 Spray Cooling 145 Nomenclature 146 References 146 5 Saturated Boiling with Forced Flow 151 5.1 Introduction 151 5.2 Boiling in Channels 151 5.2.1 Effect of Various Parameters 151 5.2.2 Prediction of Heat Transfer 152 5.2.2.1 Correlations for Macro Channels 152 5.2.2.2 Correlations for Minichannels 158 5.2.2.3 Correlations for Both Minichannels and Macrochannels 159 5.2.2.4 Recommendations 162 5.3 Plate-Type Heat Exchangers 162 5.3.1 Herringbone Plate Type 162 5.3.1.1 Longo et al. Correlation 163 5.3.1.2 Almalfi et al. Correlation 163 5.3.1.3 Ayub et al. Correlation 164 5.3.1.4 Recommendation 164 5.3.2 Plane Plate Heat Exchangers 164 5.3.3 Serrated Fin Plate Heat Exchangers 164 5.3.4 Plate Fin Heat Exchangers 165 5.4 Boiling in Various Geometries 166 5.4.1 Helical Coils 166 5.4.1.1 Correlations for Heat Transfer 166 5.4.1.2 Evaluation of Correlations 167 5.4.1.3 Discussion 167 5.4.1.4 Recommendation 167 5.4.2 Rotating Disk 168 5.4.3 Cylinder Rotating in a Liquid Pool 169 5.4.3.1 Recommendation 169 5.4.4 Bends 170 5.4.5 Spiral Wound Heat Exchangers (SWHE) 170 5.4.6 Falling Thin Film on Vertical Surfaces 171 5.4.6.1 Various Studies and Correlations 171 5.4.6.2 Recommendation 171 5.4.7 Vertical Tube/Rod Bundles with Axial Flow 172 5.4.8 Spiral Plate Heat Exchangers 172 5.5 Horizontal Tube Bundles with Upward Crossflow 172 5.5.1 Physical Phenomena 172 5.5.2 Prediction Methods for Heat Transfer 173 5.5.2.1 Shah Correlation 175 5.5.3 Conclusion and Recommendation 176 5.6 Horizontal Tube Bundles with Falling Film Evaporation 177 5.6.1 Flow Patterns/Modes 177 5.6.2 Heat Transfer 178 5.6.3 Conclusion and Recommendation 180 5.7 Boiling of Multicomponent Mixtures 180 5.7.1 Boiling in Tubes 180 5.7.2 Boiling in Various Geometries 182 5.7.3 Conclusions and Recommendations 182 5.8 Liquid Metals 182 5.8.1 Inception of Boiling 182 5.8.2 Heat Transfer 184 5.8.2.1 Sodium 184 5.8.2.2 Potassium 184 5.8.2.3 Mercury 186 5.8.2.4 Cesium and Rubidium 186 5.8.2.5 Mixtures of Liquid Metals 187 5.8.3 Conclusions and Recommendations 187 5.9 Effect of Gravity 187 5.9.1 Experimental Studies 188 5.9.2 Conclusions and Recommendation 189 5.9.3 Effect of Oil in Refrigerants 189 5.9.3.1 Heat Transfer with Immiscible Oils 189 5.9.3.2 Heat Transfer with Miscible Oils 190 5.9.3.3 Conclusions and Recommendations 190 Nomenclature 191 References 192 6 Critical Heat Flux in Flow Boiling 201 6.1 Introduction 201 6.2 CHF in Tubes 201 6.2.1 Types of Boiling Crisis and Mechanisms 201 6.2.2 Prediction Methods 201 6.2.2.1 Analytical Models 201 6.2.2.2 Lookup Tables of CHF 202 6.2.2.3 Dimensional Correlations for Water 203 6.2.2.4 General Correlations 203 6.2.2.5 Fluid-to-Fluid Modeling 213 6.2.2.6 Non-uniform Heat Flux 214 6.2.3 Recommendations 216 6.3 CHF in Annuli 216 6.3.1 Vertical Annuli with Upflow 216 6.3.1.1 Dimensional Correlations for Water 216 6.3.1.2 General Correlations 217 6.3.1.3 Recommendations 220 6.3.2 Horizontal Annuli 221 6.3.3 Eccentric Annuli 221 6.4 CHF in Various Geometries 222 6.4.1 Single Cylinder with Crossflow 222 6.4.2 Horizontal Tube Bundles 224 6.4.2.1 Recommendation 226 6.4.3 Vertical Tube/Rod Bundles 227 6.4.3.1 Mixed Flow Analyses 227 6.4.3.2 Subchannel Analysis 228 6.4.3.3 Phenomenological Analyses 228 6.4.4 Falling Films on Vertical Surfaces 229 6.4.5 Flow Parallel to a Flat Plate 230 6.4.6 Helical Coils 230 6.4.6.1 Recommendation 232 6.4.7 Spiral Wound Heat Exchangers (SWHE) 232 6.4.8 Rotating Liquid Film 232 6.4.9 Bends 233 6.4.10 Jets Impinging on Hot Surfaces 234 6.4.10.1 Correlations for CHF in Free Stream Jets 234 6.4.10.2 Effect of Contact Angle 235 6.4.10.3 Multiple Jets 236 6.4.10.4 Effect of Heater Thickness 236 6.4.10.5 Confined Jets 236 6.4.10.6 Submerged Jets 236 6.4.10.7 Recommendations 236 6.4.11 Spray Cooling 236 6.4.12 Effect of Gravity 237 6.4.12.1 Terrestrial Studies 237 6.4.12.2 Experimental Studies at Low Gravities 238 6.4.12.3 CHF Prediction Methods 239 6.4.12.4 Recommendation 239 Nomenclature 239 References 240 7 Post-CHF Heat Transfer in Flow Boiling 247 7.1 Introduction 247 7.2 Film Boiling in Vertical Tubes 247 7.2.1 Physical Phenomena 247 7.2.2 Prediction of Dispersed Flow Film Boiling in Upflow 248 7.2.2.1 Empirical Correlations 248 7.2.2.2 Mechanistic Analyses 249 7.2.2.3 Phenomenological Correlations 249 7.2.2.4 Lookup Tables 254 7.2.2.5 Recommendations 256 7.2.3 Prediction of Inverted Annular Film Boiling in Upflow 256 7.2.3.1 Recommendations 257 7.2.4 Film Boiling in Downflow 257 7.3 Film Boiling in Horizontal Tubes 257 7.3.1 Prediction Methods 258 7.3.2 Recommendations 259 7.4 Film Boiling in Various Geometries 259 7.4.1 Annuli 259 7.4.2 Vertical Tube Bundles 260 7.4.3 Single Horizontal Cylinder 261 7.4.3.1 Recommendation 262 7.4.4 Spheres 262 7.4.5 Jets Impinging on Hot Surfaces 264 7.4.6 Bends 265 7.4.7 Helical Coils 265 7.4.8 Chilldown of Cryogenic Pipelines 266 7.4.9 Flow Parallel to a Plate 267 7.4.10 Spray Cooling 267 7.5 Minimum Film Boiling Temperature and Heat Flux 268 7.5.1 Flow in Channels 268 7.5.2 Jets Impinging on Hot Surfaces 268 7.5.3 Chilldown of Cryogenic Lines 269 7.5.4 Spheres 269 7.5.5 Spray Cooling 270 7.6 Transition Boiling 270 7.6.1 Flow in Channels 270 7.6.2 Jets on Hot Surfaces 271 7.6.3 Spheres 272 7.6.4 Spray Cooling 272 Nomenclature 273 References 274 8 Two-Component Gas–Liquid Heat Transfer 279 8.1 Introduction 279 8.2 Pre-mixed Mixtures in Channels 279 8.2.1 Flow Pattern-Based Prediction Methods 279 8.2.1.1 Bubbly Flow 279 8.2.1.2 Slug Flow 281 8.2.1.3 Annular Flow 282 8.2.1.4 Post-dryout Dispersed Flow 283 8.2.2 General Correlations 283 8.2.2.1 Horizontal Channels 283 8.2.2.2 Vertical Channels 286 8.2.2.3 Horizontal and Vertical Channels 288 8.2.2.4 Inclined Channels 289 8.2.3 Recommendations 289 8.3 Gas Flow through Channel Walls 290 8.3.1 Experimental Studies 290 8.3.2 Heat Transfer Prediction 292 8.3.3 Conclusions 292 8.4 Cooling by Air–Water Mist 292 8.4.1 Single Cylinders in Crossflow 292 8.4.2 Flow over Tube Banks 294 8.4.3 Flow Parallel to Plates 294 8.4.4 Wedges 295 8.4.5 Jets 295 8.4.6 Sphere 297 8.5 Evaporation from Water Pools 297 8.5.1 Introduction 297 8.5.2 Empirical Correlations 297 8.5.3 Analytical Models 298 8.5.3.1 Shah Model 298 8.5.3.2 Other Models 300 8.5.4 CFD Models 301 8.5.5 Occupied Swimming Pools 301 8.5.6 Conclusions and Recommendations 301 8.6 Various Topics 301 8.6.1 Jets Impinging on Hot Surfaces 301 8.6.2 Vertical Tube Bundle 302 8.6.3 Effect of Gravity 302 8.7 Liquid Metal–Gas in Channels 303 8.7.1 Mercury 303 8.7.2 Various Liquid Metals 304 8.7.3 Discussion 305 Nomenclature 305 References 306 9 Gas-Fluidized Beds 311 9.1 Introduction 311 9.2 Regimes of Fluidization 311 9.2.1 Regime Transition Velocities 312 9.2.1.1 Minimum Fluidization Velocity 312 9.2.1.2 Various Regime Transition Velocities 312 9.2.2 Void Fraction and Bed Expansion 313 9.3 Properties of Solid Particles 313 9.3.1 Density 313 9.3.2 Particle Diameter 313 9.3.3 Particle Shape Factor 314 9.3.4 Classification of Particles 314 9.4 Parameters Affecting Heat Transfer to Surfaces 315 9.4.1 Gas Velocity 315 9.4.2 Particle Size and Shape 315 9.4.3 Pressure and Temperature 316 9.4.4 Heat Transfer Surface Diameter 317 9.4.5 Properties of Gas and Solid 317 9.4.6 Gas Distribution 317 9.4.7 Length and Location of Tube 317 9.4.8 Bed Diameter and Height 318 9.4.9 Tube Inclination 318 9.5 Theories of Heat Transfer 318 9.5.1 Film Theory 318 9.5.2 Penetration Theory 318 9.5.2.1 Particle Theory 319 9.5.2.2 Packet Theory 319 9.6 Prediction Methods for Single Tubes and Spheres 319 9.6.1 Analytical Models 319 9.6.1.1 Particle Models 319 9.6.1.2 Packet Models 320 9.6.2 Empirical Correlations 321 9.6.2.1 Maximum Heat Transfer 321 9.6.2.2 Correlations for the Entire Range 324 9.6.3 Recommendations 325 9.7 Tube Bundles 326 9.7.1 Horizontal Tube Bundles 326 9.7.2 Vertical Tube Bundles 328 9.7.3 Recommendations 328 9.8 Radiation Heat Transfer 329 9.8.1 Radiation Heat Transfer Coefficient and Effective Emissivity 329 9.8.2 Temperature for Significant Radiation Contribution 329 9.8.3 Conclusions and Recommendations 330 9.9 Heat Transfer to Bed Walls 330 9.9.1 Prediction Methods 330 9.9.2 Conclusions and Recommendations 331 9.10 Heat Transfer in Freeboard Region 331 9.10.1 Experimental Studies and Prediction Methods 332 9.10.2 Recommendation 332 9.11 Heat Transfer Between Gas and Particles 332 9.12 Gas–Solid Flow in Pipes 333 9.12.1 Regimes of Gas–Solid Flow 333 9.12.2 Experimental Studies of Heat Transfer 334 9.12.3 Prediction of Heat Transfer 334 9.12.3.1 Various Methods 334 9.12.3.2 Shah Correlation 336 9.12.4 Recommendation 337 9.13 Solar Collectors with Particle Suspensions 337 Nomenclature 338 References 340 Appendix 347 Index 357

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Book SynopsisPresents strategies with reachability graph analysis for optimizing resource allocation systems Supervisory Control and Scheduling of Resource Allocation Systems offers an important guide to Petri net (PN) models and methods for supervisory control and system scheduling of resource allocation systems (RASs). Resource allocation systems are common in automated manufacturing systems, project management systems, cloud data centers, and software engineering systems. The authorstwo experts on the topicpresent a definition, techniques, models, and state-of-the art applications of supervisory control and scheduling problems. The book introduces the basic concepts and research background on resource allocation systems and Petri nets. The authors then focus on the deadlock-free supervisor synthesis for RASs using Petri nets. The book also investigates the heuristic scheduling of RASs based on timed Petri nets. Conclusions and open problems are provided in the lastTable of ContentsPreface xi Acknowledgments xvii Glossary xix Acronyms xxiii About the Authors xxv Part I Resource Allocation Systems and Petri Nets 1 1 Introduction 3 1.1 Resource Allocation Systems 3 1.2 Supervisory Control and Scheduling with Petri Nets 7 1.3 Summary 9 1.4 Bibliographical Notes 9 2 Preliminaries 11 2.1 Introduction 11 2.2 Petri Nets 12 2.2.1 Basic Concepts 12 2.2.2 Modeling Power of Petri Nets 16 2.2.2.1 Sequential Execution 16 2.2.2.2 Concurrency (Parallelism) 17 2.2.2.3 Synchronization 17 2.2.2.4 Conflict (choice) 17 2.2.2.5 Merging 17 2.2.2.6 Mutual Exclusion 18 2.2.3 Behavioral Properties of Petri Nets 18 2.2.3.1 Boundedness and Safeness 18 2.2.3.2 Liveness and Deadlock 19 2.2.3.3 Reversibility 19 2.2.3.4 Conservativeness 19 2.2.4 Subclasses of Petri Nets 20 2.2.4.1 Ordinary Nets and Generalized Nets 20 2.2.4.2 Pure Petri Nets 20 2.2.4.3 State Machines 21 2.2.4.4 Marked Graphs 22 2.2.4.5 Free-choice Nets 22 2.2.4.6 Extended Free-choice Nets 22 2.2.4.7 Asymmetric Choice Nets 22 2.2.5 Petri Nets for Resource Allocation Systems 22 2.2.5.1 PC2R 23 2.2.5.2 S*PR 24 2.2.5.3 S5PR 25 2.2.5.4 S4PR, S4R, S3 PGR2 and WS3 PSR 25 2.2.5.5 S3PR 26 2.2.5.6 ES3PR and S3PMR 26 2.2.5.7 LS3PR 27 2.2.5.8 ELS3PR 27 2.2.5.9 GLS3PR 28 2.2.6 Structural Analysis 28 2.2.7 Reachability Graph Analysis 30 2.2.7.1 Supervisory Control 30 2.2.7.2 System Scheduling 31 2.2.8 Petri Net Analysis Tools 32 2.3 Informed Heuristic Search 35 2.3.1 Basic Concepts of Heuristic A* Search 35 2.3.2 Properties of the A* Search 36 2.3.2.1 Completeness 36 2.3.2.2 Admissible Heuristics 36 2.3.2.3 Monotone (Consistent) Heuristics 36 2.3.2.4 More Informed Heuristics 36 2.4 Bibliographical Notes 37 Part II Supervisory Control 39 3 Behaviorally Maximal and Structurally Minimal Supervisor 41 3.1 Introduction 41 3.2 Petri Nets for Supervisory Synthesis 43 3.3 Optimal and Minimal Supervisory Synthesis 45 3.3.1 Reachability Graph Analysis 45 3.3.2 Supervisor Computation with Place Invariants 47 3.3.3 Optimal Supervisor Synthesis and Vector Covering Method 47 3.3.4 Optimal Supervisor with Fewest Monitors 49 3.3.5 Deadlock Prevention Policy 50 3.4 An Illustrative Example 52 3.5 Concluding Remarks 54 3.6 Bibliographical Notes 55 4 Supervisor Design with Fewer Places 57 4.1 Introduction 57 4.2 Critical and Free Activity Places 59 4.3 Properties of DP-Nets 62 4.4 Supervisor Design with Critical Activity Places 66 4.5 An Illustrative Example 70 4.6 Concluding Remarks 72 4.7 Bibliographical Notes 73 5 Redundant Constraint Elimination 75 5.1 Introduction 75 5.2 Minimal-Number-of-Monitors Problem 77 5.3 Elimination of Redundant Constraints 78 5.3.1 Redundant Reachability Constraints 78 5.3.2 Linear Program Method 79 5.3.3 Non-Linear Program Method 82 5.3.4 Supervisor Synthesis with Redundancy Elimination 84 5.4 Illustrative Examples 85 5.5 Concluding Remarks 91 5.6 Bibliographical Notes 91 6 Fast Iterative Supervisor Design 93 6.1 Introduction 93 6.2 Optimal Supervisor of a DP-net 94 6.3 Fast Synthesis of Optimal and Simple Supervisors 95 6.3.1 Multiobjective Supervisory Control 96 6.3.2 Design of an Optimal Control Place 97 6.3.3 Identification of Redundant Constraints 99 6.3.4 Iterative Deadlock Prevention 102 6.4 Illustrative Examples 107 6.5 Concluding Remarks 115 6.6 Bibliographical Notes 115 7 Supervisor Synthesis with Uncontrollable and Unobservable Transitions 117 7.1 Introduction 117 7.2 Supervisor Synthesis with Uncontrollability and Unobservability 119 7.2.1 DP-Nets with Uncontrollable and/or Unobservable Transitions 119 7.2.2 Admissible Markings and First-Met Inadmissible Markings 120 7.2.3 Design of an Admissible Monitor 123 7.2.4 Admissible and Structure-Minimal Supervisor Synthesis 125 7.3 Deadlock Prevention Policy 127 7.4 Illustrative Experiments 132 7.5 Concluding Remarks 136 7.6 Bibliographical Notes 136 Part III Heuristic Scheduling 137 8 Informed Heuristic Search in Reachability Graph 139 8.1 Introduction 139 8.2 System Scheduling with Place-Timed Petri Nets 140 8.2.1 Place-Timed Petri Nets 140 8.2.2 Conversion from an Untimed Petri Net 141 8.2.3 Synthesis of a Place-Timed Petri Net 143 8.2.3.1 Top-down Method 144 8.2.3.2 Bottom-up Method 145 8.3 State Evolution of Place-Timed Nets 145 8.4 A* Search on a Reachability Graph 152 8.5 A* Search with State Check 153 8.6 An Illustrative Example 155 8.7 Concluding Remarks 156 8.8 Bibliographical Notes 156 9 Controllable Heuristic Search 157 9.1 Introduction 157 9.2 Alternative Routes with Different Lengths 159 9.3 An Admissible Heuristic for SC-nets 160 9.4 A Controllable Heuristic Search 163 9.5 Randomly Generated Examples 166 9.6 Another Controllable Heuristic Search 168 9.6.1 A* Search and Depth-First Search 168 9.6.2 Controllable Hybrid Heuristic Search 171 9.7 Illustrative Results 176 9.8 Concluding Remarks 178 9.9 Bibliographical Notes 179 10 Hybrid Heuristic Search 181 10.1 Introduction 181 10.2 A*-BT Combinations 182 10.3 Illustrative Examples 187 10.4 Concluding Remarks 190 10.5 Bibliographical Notes 191 11 A* Search with More Informed Heuristics Functions 193 11.1 Introduction 193 11.2 More Informed Heuristics in A* Search 194 11.3 Combination of Admissible and Inadmissible Heuristics 195 11.4 Illustrative Examples 197 11.5 Concluding Remarks 203 11.6 Bibliographical Notes 204 12 Symbolic Heuristic Search 205 12.1 Introduction 205 12.2 Boolean Algebra and Binary Decision Diagram 206 12.3 Symbolic Evolution of Place-Timed Petri Nets 207 12.4 Symbolic Heuristic Search 213 12.5 Illustrative Examples 218 12.6 Concluding Remarks 224 12.7 Bibliographical Notes 226 13 Open Problems 227 13.1 Structural Analysis of Generalized Nets 227 13.2 Robust Supervisor Synthesis with Unreliable Resources 227 13.3 Alleviation of the State Explosion Problem 228 13.4 Optimization of Symbolic Variable Ordering 229 13.5 Multiobjective Scheduling 230 13.6 Anytime Heuristic Scheduling 230 13.7 Parallel Heuristic Search 231 13.8 Bidirectional Heuristic Search 232 13.9 Computing and Scheduling with GPUs 232 References 235 Index 253

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Book SynopsisThis book presents novel and efficient tools, techniques and approaches for reliability evaluation, reliability analysis, and design of reliable communication networks using graph theoretic concepts. In recent years, human beings have become largely dependent on communication networks, such as computer communication networks, telecommunication networks, mobile switching networks etc., for their day-to-day activities. In today''s world, humans and critical machines depend on these communication networks to work properly. Failure of these communication networks can result in situations where people may find themselves isolated, helpless and exposed to hazards. It is a fact that every component or system can fail and its failure probability increases with size and complexity. The main objective of this book is to devize approaches for reliability modeling and evaluation of such complex networks. Such evaluation helps to understand which network can give us better rTable of ContentsSeries Editor Preface ix Preface xiii 1 Introduction 1 1.1 Introduction 1 1.2 Network Reliability Measures 2 1.3 The Probabilistic Graph Model 4 1.4 Approaches for Network Reliability Evaluation 6 1.5 Motivation and Summary 7 2 Interconnection Networks 11 2.1 Interconnection Networks Classification 11 2.2 Multistage Interconnection Networks (MINs) 14 2.3 Research Issues in MIN Design 15 2.4 Some Existing MINs Implementations 19 2.5 Review of Topological Fault Tolerance 20 2.5.1 Redundant and Disjoint Paths 22 2.5.2 Backtracking 26 2.5.3 Dynamic Rerouting 27 2.6 MIN Topological Review on Disjoint Paths 27 2.6.1 Single-Disjoint Path Multistage Interconnection Networks 27 2.6.2 Two-Disjoint Paths Multistage Interconnection Networks 36 2.6.3 Three-Disjoint Paths Multistage Interconnection Networks 47 2.6.4 Four-Disjoint Paths Multistage Interconnection Networks 51 2.7 Hardware Cost Analysis 55 2.8 Observations 60 2.9 Summary 61 3 MIN Reliability Evaluation Techniques 63 3.1 Reliability Performance Criterion 63 3.1.1 Two Terminal or Terminal Pair Reliability (TPR) 64 3.1.2 Network or All Terminal Reliability (ATR) 64 3.1.3 Broadcast Reliability 65 3.2 Approaches for Reliability Evaluation 66 3.2.1 Continuous Time Markov Chains (CTMC) 67 3.2.2 Matrix Enumeration 67 3.2.3 Conditional Probability (CP) Method 67 3.2.4 Graph Models 69 3.2.5 Decomposition Method 70 3.2.6 Reliability Block Diagram (RBD) 71 3.2.7 Reliability Bounds 73 3.2.7.1 Lower Bound Reliability 75 3.2.7.2 Upper Bound Reliability 76 3.2.8 Monte Carlo Simulation 77 3.2.9 Path-Based or Cut-Based Approaches 78 3.3 Observations 81 4 Terminal Reliability Analysis of MIN Layouts 85 4.1 Chaturvedi and Misra Approach 87 4.1.1 Path Set Enumeration 88 4.1.2 Reliability Evaluation using MVI Techniques 96 4.1.3 Reliability Evaluation Techniques Comparison 99 4.1.3.1 Terminal Reliability of SEN, SEN+ and SEN+2 100 4.1.3.2 Broadcast Reliability of SEN, SEN +, and SEN+2 101 4.1.3.3 Comparison 102 4.2 Reliability Analysis of Multistage Interconnection Networks 104 4.3 Summary 113 5 Comprehensive MIN Reliability Paradigms Evaluation 115 5.1 Introduction 115 5.2 Reliability Evaluation Approach 119 5.2.1 Path Set Enumeration 120 5.2.1.1 Assumptions 120 5.2.1.2 Applied Approach 121 5.2.1.3 Path Tracing Algorithm (PTA) 122 5.2.1.4 Path Retrieval Algorithm (PRA) 123 5.3 Reliability Evaluation Using MVI Techniques 140 5.4 Summary 156 6 Dynamic Tolerant and Reliable Four Disjoint MIN Layouts 157 6.1 Topological Design Considerations 160 6.1.1 Topology 161 6.1.2 Switch Selection for Proposed 4DMIN 162 6.2 Proposed 4-Disjoint Multistage Interconnection Network (4DMIN) Layout 164 6.2.1 Switching Pattern 164 6.2.2 Redundant and Disjoint Paths 165 6.2.3 Routing and Dynamic Rerouting 166 6.2.4 Algorithm: Decision Making by Switches at Each Stage 168 6.2.5 Case Example 170 6.2.6 Disjoint and Dynamic Rerouting Approach in 4DMIN 172 6.2.7 Hardware Cost Analysis 172 6.3 Reliability Analysis and Comparison of MINs 174 6.4 Reliable Interconnection Network (RIN) Layout 181 6.4.1 Topology Design 185 6.4.2 Switching Pattern 187 6.4.3 Routing and Dynamic Rerouting 189 6.5 Reliability Analysis and Comparison of MINs 197 6.6 Summary 201 References 203 Index 213

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Book SynopsisElectrical Safety Engineering of Renewable Energy Systems A reference to designing and developing electrical systems connected to renewable energies Electrical Safety Engineering of Renewable Energy Systems is an authoritative text that offers an in-depth exploration to the safety challenges of renewable systems. The authorsnoted experts on the topiccover a wide-range of renewable systems including photovoltaic, wind, and cogeneration and propose a safety-by-design approach. The book clearly illustrates safe behavior in complex real-world renewable energy systems using practical approaches. The book contains a review of the foundational electrical engineering topics and highlights how safety engineering links to the renewable energies. Designed as an accessible resource, the text discusses the most relevant and current topics supported by rigorous analytical, theoretical and numerical analyses. The authors also provide guidelines for readers interested in practical applications. This iTable of Contents Preface ix Acknowledgments xi 1 Fundamental Concepts of Electrical Safety Engineering 1 1.1 Introduction 1 1.2 Electric Shock 2 1.2.1 Ventricular Fibrillation 3 1.2.2 The Heart-current Factor 5 1.3 The Electrical Impedance of the Human Body 6 1.3.1 The Internal Resistance of the Human Body 7 1.4 Thermal Shock 10 1.5 Heated Surfaces of Electrical Equipment and Contact Burn Injuries 12 1.6 Ground-Potential and Ground-Resistance 14 1.6.1 Area of Influence of a Ground-electrode 18 1.7 Hemispherical Electrodes in Parallel 18 1.8 Hemispherical Electrodes in Series 19 1.9 Person’s Body Resistance-to-ground and Touch Voltages 20 1.10 Identification of Extraneous-Conductive-Parts 24 1.11 Measuring Touch Voltages 26 2 Safety-by-Design Approach in AC/DC Systems 31 2.1 Introduction 31 2.2 Class I PV Equipment 33 2.3 Class II PV Equipment 35 2.4 Ground Faults and Ground Fault Protection 35 2.5 Functionally Grounded PV Systems 37 2.6 Non-Ground-Referenced PV Systems 40 2.7 Ground-Referenced PV Systems 42 2.8 Fire Hazard in Ground-Referenced PV Systems 44 2.9 Faults at Loads Downstream the PV Inverter in Ground-Referenced PV Systems 47 2.10 Non-Electrically Separated PV System 48 2.11 PV Systems Wiring Methods and Safety 50 2.12 d.c. Currents and Safety 52 2.13 Electrical Safety of PV Systems 55 2.14 Rapid-Shutdown of PV Arrays on Buildings 57 2.15 Hazard and Risk 58 3 Grounding and Bonding 63 3.1 Introduction 63 3.2 Basic Concepts of Grounding Systems: The Ground Rod 67 3.3 The Maxwell Method 77 3.4 Multiple Rods: Mutual Resistance 83 3.5 Ground Rings and Ground Grid 87 3.6 Complex Arrangements: Rings and Ground Grids Combined with Rods and Horizontal Electrodes 100 4 Lightning Protection Systems 107 4.1 Review of Natural Lightning Physics, Modeling and Protection 108 4.2 Lightning Protection of PV Systems 121 4.2.1 Ground-Mounted PV Systems 124 4.2.2 Rooftop Mounted PV Systems 126 4.2.3 Protection against Overvoltage 128 4.2.4 Surge Protective Devices (SPDs) 130 4.3 Lighting Protection of Wind Turbines 136 4.3.1 Lightning Protection System (LPS) 139 4.3.2 Step and Touch Voltages 143 4.3.3 Lightning Exposure Assessment 144 4.3.4 Assessment of the Average Annual Number of Dangerous Events NL Due to Flashes Directly to and near Service Cables 147 4.3.5 Lightning Protection Zones 149 4.4 High-Frequency Grounding Systems 151 4.4.1 Arrangement of Ground Electrodes 155 4.4.2 Effective Length of a Ground Electrode 157 4.4.3 Frequency-dependent Soil and Ionization 158 5 Renewable Energy System Protection and Coordination 169 5.1 Introduction 169 5.2 Power Collection Systems 170 5.3 Cable Connections 182 5.4 Offshore Wind Farm 188 5.5 Distributed Energy Resources: Battery Energy Storage Systems and Electric Vehicles 192 6 Soil Resistivity Measurements and Ground Resistance 205 6.1 Soil Resistivity Measurements 205 6.2 Wenner Method 208 6.3 Schlumberger Method 214 6.4 Multi-layer Soils 214 6.4.1 Ground Grid in Multi-layer Soil 217 6.4.2 Ground Rod in Multi-layer Soil 219 6.5 Fall-of-Potential Method for Ground Resistance Measurement 220 6.6 Slope Method for Grounding Resistance Measurement 223 6.7 Star-delta Method for Grounding Resistance Measurement 224 6.8 Four Potential Method for Grounding Resistance Measurement 225 6.9 Potentiometer Method for Grounding Resistance Measurement 226 Appendix 1: Performance of Grounding Systems in Transient Conditions 231 1 Grounding System Analysis 232 2 Mathematical Model 233 3 Computation of Impedances 235 4 Green’s Function 237 4.1 Static Formulation 237 4.1.1 One-Layer Ground 242 4.1.2 Two-Layer Ground 243 4.2 Dynamic Formulation 245 4.2.1 Equivalent Transmission Line Approach 249 5 Numerical Integration Aspects 252 5.1 Singular Term 252 5.2 Sommerfeld Integrals 254 Appendix 2: Cable Failures in Renewable Energy Systems 265 1 Cable Failures in Renewable Energy Systems: Introduction 266 2 Possible Solutions 267 2.1 Optimal Solutions 268 2.2 Termite Attacks Prevention 269 3 Non-destructive Methods for Cable Testing and Fault-locating 269 3.1 Insulation Resistance (IR) Test 271 3.1.1 IR Measurement of the Cable Insulation (XLPE) 271 3.1.2 IR Measurement of the Polyethylene (PE) Cable Jacket 272 3.2 High-Potential Test 272 3.3 LCR Test 273 3.3.1 Insulation Resistance (IR) 273 3.3.2 Dielectric Absorption Ratio (DAR) 273 3.3.3 Polarization Index (PI) 274 3.3.4 Quality Factor (Q) 274 3.3.5 Dissipation Factor (DF) 274 3.3.6 Time Domain Reflectometry (TDR) Test 275 3.3.7 Arc Reflection (ARC) Test 276 3.3.8 Bridge Methods 276 3.4 Cable Fault Analysis 279 3.4.1 Prelocation 279 3.4.2 Pinpointing 280 4 Sheath and Jacket Repairs 280 5 Termite Baiting Stations and Monitoring 281 6 Termite-proof Cables 283 Index 285

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Book SynopsisDiscover an innovative and fresh approach to teaching classical electromagnetics at a foundational level Introduction to Electromagnetic Waves with Maxwell''s Equations delivers an accessible and practical approach to teaching the well-known topics all electromagnetics instructors must include in their syllabus. Based on the author''s decades of experience teaching the subject, the book is carefully tuned to be relevant to an audience of engineering students who have already been exposed to the basic curricula of linear algebra and multivariate calculus. Forming the backbone of the book, Maxwell''s equations are developed step-by-step in consecutive chapters, while related electromagnetic phenomena are discussed simultaneously. The author presents accompanying mathematical tools alongside the material provided in the book to assist students with retention and comprehension. The book contains over 100 solved problems and examples with stepwise solutions ofTable of ContentsPreface 15 Mathematical Notation 23 List of Symbols 27 Special Functions 31 Frequently Used Identities 33 Tools to Understand Maxwell’s Equations 37 0 Preliminary 39 0.1 Scalar and Vector Fields 40 0.2 Cartesian Coordinate Systems 42 0.3 Basic Vector Operations 42 0.4 Orthogonal Coordinate Systems 43 0.4.1 Properties of a Cartesian Coordinate System 43 0.4.2 Cylindrical Coordinate System 44 0.4.3 Spherical Coordinate System 45 0.5 Electrostatics, Magnetostatics, and Electromagnetics 47 0.6 Time in Electromagnetics 49 0.7 Final Remarks 51 1 Gauss’ Law 53 1.1 Integral Form of Gauss’ Law 54 1.1.1 Differential Surface With Direction 55 1.1.2 Dot Product 56 1.1.3 Flux of Vector Fields 62 1.1.4 Meaning of Gauss’ Law and Its Application 66 1.1.5 Examples 67 1.2 Using the Integral Form of Gauss’ Law 69 1.2.1 Examples 71 1.3 Differential Form of Gauss’ Law 73 1.3.1 Electric Charge Density 73 1.3.2 Divergence of Vector Fields 75 1.3.3 Divergence Theorem and the Differential Form of Gauss’ Law 81 1.3.4 Examples 83 1.4 Using the Differential Form of Gauss’ Law 85 1.4.1 Examples 88 1.5 Boundary Conditions for Normal Electric Fields 89 1.6 Static Cases and Coulomb’s Law 92 1.6.1 Superposition Principle 93 1.6.2 Coulomb’s Law and Electric Force 99 1.6.3 Examples 101 1.7 Gauss’ Law and Dielectrics 106 1.7.1 Electric Dipole 112 1.7.2 Polarization 113 1.7.3 Equivalent Polarization Charges 115 1.7.4 Examples 120 1.8 Final Remarks 123 1.9 Exercises 124 1.10 Questions 127 2 Ampere’s Law 133 2.1 Integral Form of Ampere’s Law 134 2.1.1 Differential Length With Direction 135 2.1.2 Circulation of Vector Fields 137 2.1.3 Meaning of Ampere’s Law and Its Application 140 2.1.4 Examples 143 2.2 Using the Integral Form of Ampere’s Law 145 2.2.1 Examples 147 2.3 Differential Form of Ampere’s Law 151 2.3.1 Electric Current Density 152 2.3.2 Cross Product 154 2.3.3 Curl of Vector Fields 157 2.3.4 Stoke’s Theorem and the Differential Form of Ampere’s Law 164 2.3.5 Examples 165 2.4 Using the Differential Form of Ampere’s Law 169 2.4.1 Examples 172 2.5 Boundary Conditions for Tangential Magnetic Fields 173 2.6 Gauss’ Law and Ampere’s Law 176 2.7 Static Cases, Biot-Savart Law, and Ampere’s Force Law 179 2.7.1 Superposition Principle 180 2.7.2 Ampere’s Force Law and Magnetic Force 190 2.7.3 Examples 194 2.8 Ampere’s Law and Magnetic Materials 200 2.8.1 Magnetic Dipole 206 2.8.2 Magnetization 208 2.8.3 Equivalent Magnetization Currents 210 2.8.4 Examples 217 2.9 Final Remarks 218 2.10 Exercises 219 2.11 Questions 221 3 Faraday’s Law 225 3.1 Integral Form of Faraday’s Law 226 3.1.1 Meaning of Faraday’s Law and Its Application 227 3.1.2 Lorentz Force Law 229 3.2 Using the Integral Form of Faraday’s Law 231 3.2.1 Examples 236 3.3 Differential Form of Faraday’s Law 240 3.4 Boundary Conditions for Tangential Electric Fields 242 3.5 Combining Faraday’s Law with Gauss’ and Ampere’s Laws 244 3.6 Static Cases and Electric Scalar Potential 246 3.6.1 Gradient of Scalar Fields 248 3.6.2 Examples 252 3.6.3 Gradient Theorem 253 3.6.4 Gradient in Gauss’ Law, Ampere’s Law, and Faraday’s Law 254 3.6.5 Electric Potential Energy 257 3.6.5.1 Electric Potential Energy of Discrete Charge Distributions 261 3.6.5.2 Stored Electric Potential Energy by an Electric Dipole 263 3.6.5.3 Stored Electric Potential Energy in Charge Distributions 265 3.6.5.4 Electric Potential Energy and Electric Force 269 3.6.6 Examples 272 3.6.7 Poisson’s Equation and Laplace’s Equation 276 3.6.8 Examples 283 3.6.9 Finding Electric Scalar Potential From Electric Field Intensity 283 3.6.10 Examples 286 3.6.11 Electrostatic Boundary Value Problems 288 3.6.12 Examples 291 3.7 Final Remarks 294 3.8 Exercises 294 3.9 Questions 296 4 Gauss’ Law for Magnetic Fields 299 4.1 Integral and Differential Forms of Gauss’ law for Magnetic Fields 300 4.1.1 Meaning of Gauss’ law for Magnetic Fields 302 4.1.2 Examples 304 4.2 Boundary Conditions for Normal Magnetic Fields 306 4.2.1 Examples 307 4.3 Static Cases and Magnetic Vector Potential 308 4.3.1 Magnetic Vector Potential and Coulomb’s Gauge 309 4.3.2 Examples 318 4.3.3 Magnetic Potential Energy 321 4.3.3.1 Magnetic Potential Energy of Discrete Current Distributions 323 4.3.3.2 Stored Magnetic Potential Energy by a Magnetic Dipole 324 4.3.3.3 Stored Magnetic Potential Energy in Current Distributions 326 4.3.3.4 Magnetic Potential Energy and Magnetic Force 329 4.3.4 Examples 332 4.4 Combining All Maxwell’s Equations 334 4.4.1 Wave Equations 336 4.4.2 Wave Equations for Potentials 343 4.4.3 Time-Harmonic Sources and Helmholtz Equations 349 4.4.4 Examples 354 4.5 Final Remarks 359 4.6 Exercises 360 4.7 Questions 363 5 Basic Solutions of Maxwell’s Equations 365 5.1 Summary of Maxwell’s Equations, Wave Equations, and Helmholtz Equations 366 5.1.1 Examples 375 5.2 Electromagnetic Propagation and Radiation 377 5.2.1 Hertzian Dipole 382 5.2.2 Examples 385 5.3 Plane Waves 389 5.3.1 Examples 400 5.3.2 Polarization of Plane Waves 401 5.3.3 Examples 407 5.3.4 Power of Plane Waves 409 5.3.5 Reflection and Refraction of Plane Waves 412 5.3.6 General Case for Reflection and Refraction 416 5.3.6.1 Perpendicular Polarization 418 5.3.6.2 Parallel Polarization 421 5.3.7 Examples 423 5.3.8 Total Internal Reflection 427 5.3.9 Total Transmission 430 5.3.10 Examples 434 5.3.11 Reflection and Transmission for Two Parallel Interfaces 437 5.4 Final Remarks 440 5.5 Exercises 440 5.6 Questions 443 6 Analyses of Conducting Objects 447 6.1 Ohm’s Law 449 6.2 Joule’s Law 452 6.3 Relaxation Time 453 6.4 Boundary Conditions for Conducting Media 456 6.5 Analyses of Perfectly Conducting Objects 457 6.5.1 Electric Scalar Potential for PECs 458 6.5.2 Boundary Conditions for PECs 458 6.5.3 Basic Responses of PECs 460 6.5.4 Concerns in Geometric Representations of PECs 462 6.5.5 Electrostatics for PECs 464 6.5.6 Method of Images 466 6.5.7 Examples 470 6.6 Maxwell’s Equations in Conducting Media 474 6.6.1 Complex Permittivity 476 6.6.2 Power and Energy in Conducting Media 478 6.6.3 Plane Waves in Conducting Media 479 6.6.4 Power of Plane Waves in Conducting Media 483 6.6.5 Reflection from PECs 484 6.6.6 Examples 494 6.7 Capacitance 503 6.7.1 Capacitance and Electric Potential Energy 504 6.7.2 Parallel-Plate Capacitors 505 6.7.3 Spherical Capacitors 513 6.7.4 Cylindrical Capacitors 518 6.7.5 Examples 520 6.8 Resistance 528 6.8.1 Examples 535 6.9 Inductance 544 6.9.1 Examples 553 6.10 Final Remarks 559 6.11 Exercises 560 6.12 Questions 565 7 Transmission of Electromagnetic Waves 569 7.1 Antennas and Wireless Transmission 570 7.1.1 Basic Properties of Antennas 571 7.1.2 Antenna Design Parameters 582 7.1.3 Antenna Types 585 7.1.3.1 Antenna Arrays 588 7.1.4 Friis Transmission Equation 600 7.1.5 Examples 603 7.2 Waveguides 613 7.2.1 Transverse and Axial Fields 614 7.2.2 Rectangular Waveguides 617 7.2.2.1 Transverse Magnetic Modes 618 7.2.2.2 Transverse Electric Modes 620 7.2.2.3 Non-Existing Modes 623 7.2.2.4 Important Properties of Modes 624 7.2.3 Parallel-Plate Waveguides 628 7.2.4 Examples 630 7.3 Transmission Line Theory 635 7.3.1 Telegrapher’s Equations 637 7.3.1.1 Transmission Line With a Load 641 7.3.1.2 Special Cases 643 7.3.1.3 Common Cases 646 7.3.2 Voltage and Current Patterns 647 7.3.3 Examples 651 7.4 Concluding Remarks 658 7.5 Exercises 658 7.6 Questions 663 8 Concluding Chapter 669 8.1 Electromagnetic Spectrum 670 8.1.1 Radio Waves (3 Hz to 300 GHz) 671 8.1.2 Microwaves (300 MHz to 300 GHz) 672 8.1.3 Infrared Radiation (300 GHz to 400 THz) 673 8.1.4 Visible Range (400 THz to 800 THz) 674 8.1.5 Ultraviolet Radiation (800 THz to 30 PHz) 675 8.1.6 X-Rays (30 PHz to 30 EHz) 676 8.1.7 Gamma Rays (Above 30 EHz) 678 8.2 Brief History of Electromagnetism (Electricity, Magnetism, and a Little Optics) 679 8.3 Electromagnetism in Action 685 8.3.1 Snapshots From Nature 686 8.3.1.1 Blue Sky, Bright Sun, Red Sunset 686 8.3.1.2 Rainbow in Pocket 687 8.3.1.3 Green Leaf, Red Apple, Blue Sea 688 8.3.1.4 Electromagnetic Waves From Space 689 8.3.1.5 Magnetic Earth 690 8.3.2 Snapshots From Technology 691 8.3.2.1 Telegraph to Cellular Phones 691 8.3.2.2 Home: Where Electromagnetism Happens 693 8.3.2.3 Looking Inside Body 694 8.3.2.4 Seeing World with Sensors and Radars 696 8.3.2.5 Atoms Under Microscope 699 8.4 How to Solve Maxwell’s Equations 700 8.4.1 Full-Wave Methods 705 8.4.1.1 Differential-Equation Solvers 706 8.4.1.1.1 Finite-Difference Time-Domain Method (FDTD): 706 8.4.1.1.2 Finite Element Method (FEM): 707 8.4.1.2 Integral-Equation Solvers 708 8.4.1.2.1 Method of Moments (MoM): 709 8.4.1.2.2 Acceleration Algorithms: 709 8.4.1.2.3 FMM and MLFMA: 711 8.4.2 Asymptotic Techniques 711 8.4.2.0.1 Quasistatic Approximations: 712 8.4.2.0.2 Geometrical Optics: 713 8.4.2.0.3 Uniform Geometrical Theory of Diffraction: 713 8.4.2.0.4 Physical Optics: 714 Bibliography 717 Index 725

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Book SynopsisElectrical Processes in Organic Thin Film Devices A one-stop examination of fundamental electrical behaviour in organic electronic device architectures In Electrical Processes in Organic Thin Film Devices: From Bulk Materials to Nanoscale Architectures, distinguished researcher Michael C. Petty delivers an in-depth treatment of the electrical behaviour of organic electronic devices focused on first principles. The author describes the fundamental electrical behaviour of various device architectures and offers an introduction to the physical processes that play a role in the electrical conductivity of organic materials. Beginning with band theory, the text moves on to address the effects of thin film device architectures and nanostructures. The book discusses the applications to devices currently in the marketplace, like displays, as well as those under development (transistors, solar cells, and memories). Electrical Processes in Organic Thin Film DTable of ContentsChapter 1 – Electronic and Vibrational States in Organic Solids 1.1 Introduction 1.2 Band Theory for Inorganic Single Crystals 1.2.1 Schrödinger Wave Equation 1.2.2 Density of Electron States 1.2.3 Occupation of Energy States 1.2.4 Conductors, Semiconductors and Insulators 1.2.5 Electrons and Holes 1.2.6 Doping 1.3 Lattice Vibrations 1.4 Amorphous Inorganic Semiconductors 1.5 Organic Semiconductors 1.5.1 Electronic Orbitals and Bands in Important Organic Compounds 1.5.2 Molecular Crystals 1.5.3 Polymers 1.5.4 Charge-transfer Complexes 1.5.5 Graphene 1.5.6 Fullerenes and Carbon Nanotubes 1.5.7 Doping of Organic Semiconductors Problems References Further Reading Chapter 2 – Electrical Conductivity: Fundamental Principles 2.1 Introduction 2.2 Classical Model 2.3 Boltzmann Transport Equation 2.4 Ohm’s Law 2.5 Charge Carrier Mobility 2.6 Equilibrium Carrier Statistics 2.6.1 Intrinsic Conduction 2.6.2 Carrier Generation and Recombination 2.6.3 Extrinsic Conduction 2.6.4 Fermi Level Position 2.6.5 Meyer-Neldel Rule 2.7 Excess Carriers 2.7.1 Quasi-Fermi Level 2.7.2 Diffusion and Drift 2.7.3 Gradients in the Quasi-Fermi Levels 2.7.4 Carrier Lifetime 2.8 Superconductivity Problems References Further Reading Chapter 3 – Defects and Nanoscale Phenomena 3.1 Introduction 3.2 Material Purity 3.3 Point and Line Defects 3.4 Traps and Recombination Centres 3.4.1 Direct Recombination 3.4.2 Recombination via Traps 3.5 Grain Boundaries and Surfaces 3.5.1 Interface States 3.6 Polymer Defects 3.6.1 Solitons 3.6.2 Polarons and Bipolarons 3.7 Disordered Semiconductors 3.8 Electron Transport in Low Dimensional Systems 3.8.1 Two-dimensional Transport 3.8.2 One-dimensional Transport 3.8.3 Zero-dimensional Transport 3.9 Nanosystems 3.9.1 Scaling Laws 3.9.2 Interatomic Forces Problems References Further Reading Chapter 4 – Electrical Contacts: Ohmic and Rectifying Behaviour 4.1 Introduction 4.2 Practical Considerations 4.3 Neutral, Ohmic and Blocking Contacts 4.4 Schottky Barrier 4.4.1 Barrier Formation 4.4.2 Image Force 4.4.3 Current versus Voltage Behaviour 4.4.4 Effect of an Interfacial Layer 4.4.5 Organic Schottky Diodes 4.5 Molecular Devices 4.5.1 Metal/Molecule Contacts 4.5.2 Break Junctions 4.5.3 Molecular Rectifying Diodes 4.5.4 Molecular Resonant Tunnelling Devices Problems References Further Reading Chapter 5 – Metal/Insulator/Semiconductor Devices: The Field Effect 5.1 Introduction 5.2 Ideal MIS device 5.3 Departures from Ideality 5.3.1 Insulator Charge and Work Function Differences 5.3.2 Interface Traps 5.4 Organic MIS Devices 5.4.1 Inorganic Semiconductor/Organic Insulator Structures 5.4.2 Organic Semiconductor Structures Problems References Further Reading Chapter 6 – DC Conductivity 6.1 Introduction 6.2 Electronic versus Ionic Conductivity 6.3 Quantum Mechanical Tunnelling 6.4 Variable Range Hopping 6.5 Fluctuation-induced Tunnelling 6.6 Space Charge Injection 6.6.1 Effect of Traps 6.6.2 Two-carrier Injection 6.7 Schottky, Fowler-Nordheim and Poole-Frenkel Effects 6.8 Electrical Breakdown 6.8.1 Intrinsic Breakdown 6.8.2 Electromechanical Breakdown 6.8.3 Thermal Runaway 6.8.4 Contact Instability 6.8.5 Other Effects 6.9 Electromigration 6.10 Measurement of Trapping Parameters 6.10.1 Thermally Stimulated Conductivity 6.10.2 Capacitance Spectroscopy Problems References Further Reading Chapter 7 – Polarization and AC Conductivity 7.1 Introduction 7.2 Polarization 7.2.1 Dipole Creation 7.2.2 Permanent Polarization 7.2.3 Piezoelectricity, Pyroelectricity and Ferroelectricity 7.3 Conductivity at High Frequencies 7.3.1 Displacement Current 7.3.2 Frequency-dependent Permittivity 7.3.3 AC Conductivity 7.4 Impedance Spectroscopy 7.5 AC Electrical Measurements 7.5.1 Lock-in Amplifier 7.5.2 Scanning Microscopy 7.6 Electrical Noise Problems References Further Reading Chapter 8 – Organic Field Effect Transistors 8.1 Introduction 8.2 Physics of Operation 8.3 Transistor Fabrication 8.4 Practical Device Behaviour 8.4.1 Contact Resistance 8.4.2 Material Morphology and Traps 8.4.3 Short Channel Effects 8.4.4 Organic Semiconductors 8.4.5 Gate Dielectric 8.5 Organic Integrated Circuits 8.6 Nanotube and Graphene FETs 8.7 Single-electron Transistors 8.8 Transistor-based Chemical Sensors 8.8.1 Ion-sensitive FETs 8.8.2 Charge-flow Transistor Problems References Further Reading Chapter 9 – Electronic Memory 9.1 Introduction 9.2 Memory Types 9.3 Resistive Memory 9.4 Organic Flash Memory 9.5 Ferroelectric RAMs 9.6 Spintronics 9.7 Molecular Memories Problems References Further Reading Chapter 10 – Light-emitting Devices 10.1 Introduction 10.2 Light Emission Processes 10.3 Operating Principles 10.4 Colour Measurement 10.5 Photometric Units 10.6 OLED Efficiency 10.7 Device Architectures 10.7.1 Top- and Bottom-emitting OLEDs 10.7.2 Electrodes 10.7.3 Hole- and Electron-transport Layers 10.7.4 Triplet Management 10.7.5 Blended-layer and Molecularly-engineered Devices 10.8 Increasing the Light Output 10.8.1 Efficiency Losses 10.8.2 Microlenses and Shaped Substrates 10.8.3 Microcavities 10.8.4 Device Degradation 10.9 Full-colour Displays 10.10 Organic Semiconductor Lasers 10.11 OLED Lighting 10.12 Light-emitting Electrochemical Cells 10.13 Light-emitting Transistors Problems References Further Reading Chapter 11 – Photoconductive and Photovoltaic Devices 11.1 Introduction 11.2 Photoconductivity 11.2.1 Optical Absorption 11.2.2 Carrier Lifetime 11.2.3 Photosenstivity 11.3 Xerography 11.4 Photovoltaic Principles 11.4.1 Electrical Characteristics 11.4.2 Efficiency 11.5 Organic Solar Cells 11.5.1 Carrier Collection 11.5.2 Bulk Heterojunction Solar Cells 11.5.3 Electrodes and Device Architectures 11.5.4 Tandem Cells 11.5.5 Upconversion 11.5.6 Device Degradation 11.6 Dye-sensitized Solar Cells 11.7 Hybrid Solar Cells 11.7.1 Polymer-Metal Oxide Devices 11.7.2 Inorganic Semiconductor-Polymer Hole-transporter Cells 11.7.3 Perovskite Solar Cells 11.8 Luminescent Solar Concentrator 11.9 Organic Photodiodes and Phototransistors Problems References Further Reading Chapter 12 – Emerging Devices and Systems 12.1 Introduction 12.2 Molecular Logic Circuits 12.3 Inspiration from the Natural World 12.3.1 Amino Acids, Peptides and Proteins 12.3.2 Nucleotides, DNA and RNA 12.3.3 ATP, ADP 12.3.4 The Biological Membrane and Ion Transport 12.3.5 Electron Transport 12.3.6 Neurons 12.4 Computing Strategies 12.4.1 Von Neumann Computer 12.4.2 Biological Information Processing 12.4.3 Artificial Neural Networks 12.4.4 Organic Neuromorphic Devices 12.4.5 DNA and Microtubule Electronics 12.4.6 Quantum Computing 12.4.7 Evolvable Electronics 12.5 Fault Tolerance and Self Repair 12.6 Bacteriorhodopsin – A Light-driven Proton Pump 12.7 Photosynthesis and Artificial Molecular Architectures 12.8 Bio-chemical Sensors 12.8.1 Biocatalytic Sensors 12.8.2 Bioaffinity Sensors 12.9 Electronic Olfaction and Gustation Problems References Further Reading

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Book SynopsisA comprehensive survey of boundary conditions as applied in antenna and microwave engineering, material physics, optics, and general electromagnetics research. Boundary conditions are essential for determining electromagnetic problems. Working with engineering problems, they provide analytic assistance in mathematical handling of electromagnetic structures, and offer synthetic help for designing new electromagnetic structures.Boundary Conditions in Electromagneticsdescribes the most-general boundary conditions restricted by linearity and locality, and analyzes basic plane-wave reflection and matching problems associated to a planar boundary in a simple-isotropic medium. This comprehensive text first introduces known special cases of particular familiar forms of boundary conditions perfect electromagnetic conductor, impedance, and DB boundaries and then examines various general forms of boundary conditions. Subsequent chapters discuss sesquilinear boundary conditions and practicTable of ContentsPreface ix 1 Introduction 1 1.1 Basic Equations 1 1.2 Duality Transformation 3 1.3 Plane Waves 5 1.4 TE/TM Decomposition 8 1.5 Problems 10 2 Perfect Electromagnetic Conductor Boundary 11 2.1 PEMC Conditions 11 2.2 Eigenproblem of Dyadic Jt 12 2.3 Reflection from PEMC Boundary 14 2.4 Polarization Rotation 17 2.5 Point Source and PEMC Plane 18 2.6 Waveguide with PEMC Walls 20 2.7 Parallel-Plate PEMC Resonator 22 2.8 Modeling Small PEMC Particles 24 2.9 Problems 29 3 Impedance Boundary 33 3.1 Basic Conditions 33 3.2 Subclasses of Impedance Boundaries 36 3.3 Reflection from Impedance Boundary 38 3.4 Matched Waves 40 3.5 Simple-Isotropic Impedance Boundary 41 3.6 General Isotropic Boundary 48 3.7 Perfectly Anisotropic Boundary 52 3.8 Generalized Soft-and-Hard (GSH) Boundary 55 3.9 Duality Transformation of Impedance Boundaries 62 3.10 Realization of Impedance Boundaries 64 3.11 Problems 67 4 DB Boundary 71 4.1 Boundary Conditions Involving Normal Field Components 71 4.2 Reflection from DB Boundary 72 4.3 Realization of DB Boundary 75 4.4 Spherical DB Resonator 81 4.5 Circular DB Waveguide 84 4.6 D’B’ Boundary 92 4.7 Mixed-Impedance (DB/D’B’) Boundary 96 4.8 Problems 98 5 General Boundary Conditions 101 5.1 Electromagnetic Sheet as Boundary Surface 101 5.2 General Boundary Conditions (GBC) 102 5.3 Decomposition of Plane Waves 104 5.4 Reflection from GBC Boundary 106 5.5 Matched Waves 108 5.6 Eigenwaves 110 5.7 Duality Transformation 112 5.8 Soft-and-Hard/DB (SHDB) Boundary 113 5.9 Generalized Soft-and-Hard/DB (GSHDB) Boundary 122 5.10 GBC Boundaries with PEC/PMC Equivalence 127 5.11 Some Special GBC Boundaries 128 5.12 Summary of GBC Conditions 133 5.13 Reciprocity of GBC Boundaries 134 5.14 Realization of the GBC Boundary 139 5.15 Problems 140 6 Sesquilinear Boundary Conditions 143 6.1 Isotropic and Anisotropic SQL Boundaries 144 6.2 Reflection from Isotropic SQL Boundary 145 6.3 Eigenfields 149 6.4 Power Balance 151 6.5 Image theory 153 6.6 Problems 155 7 Scattering by Objects Defined by Boundary Conditions 157 7.1 Cross Sections and Efficiencies 157 7.2 PEC, PMC, and PEMC Objects 159 7.3 DB and D’B’-Boundary Objects 165 7.4 Impedance-Boundary Objects 169 7.5 Problems 176 A Electromagnetic Formulas 179 B Dyadics 183 C Four-Dimensional Formalism 189 D Solutions to Problems 197 References 247 Index 256

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Book SynopsisA comprehensive guide to the concepts and applications of queuing theory and traffic theory Network Traffic Engineering: Models and Applications provides an advanced level queuing theory guide for students with a strong mathematical background who are interested in analytic modeling and performance assessment of communication networks. The text begins with the basics of queueing theory before moving on to more advanced levels. The topics covered in the book are derived from the most cutting-edge research, project development, teaching activity, and discussions on the subject. They include applications of queuing and traffic theory in: LTE networks Wi-Fi networks Ad-hoc networks Automated vehicles Congestion control on the Internet The distinguished author seeks to show how insight into practical and real-world problems can be gained by means of quantitative modeling. Perfect for graduate studeTable of ContentsPreface xvii Acronyms xix Part I Models for Service Systems 1 1 Introduction 3 1.1 Network Traffic Engineering: What, Why, How 3 1.2 The Art of Modeling 8 1.3 An Example: Delay Equalization 13 1.3.1 Model Setting 14 1.3.2 Analysis by Equations 15 1.3.3 Analysis by Simulation 19 1.3.4 Takeaways 21 1.4 Outline of the Book 21 1.4.1 Plan 21 1.4.2 Use 25 1.4.3 Notation 27 1.5 Further Readings 29 Problems 30 2 Service Systems and Queues 33 2.1 Service System Structure 33 2.2 Arrival and Service Processes 35 2.3 The Queue as a Service System Model 38 2.4 Queues in Equilibrium 40 2.4.1 Queues and Stationary Processes 40 2.4.2 Little’s Law 45 2.5 Palm’s Distributions for a Queue 49 2.6 The Traffic Process 53 2.7 Performance Metrics 56 2.7.1 Throughput 56 2.7.2 Utilization 59 2.7.3 Loss 59 2.7.4 Delay 61 2.7.5 Age of Information 62 Summary and Takeaways 63 Problems 65 3 Stochastic Models for Network Traffic 71 3.1 Introduction 71 3.2 The Poisson Process 72 3.2.1 Light versus Heavy Tails 78 3.2.2 Inhomogeneous Poisson Process 79 3.2.3 Poisson Process in Multidimensional Spaces 84 3.2.3.1 Displacement 89 3.2.3.2 Mapping 89 3.2.3.3 Thinning 90 3.2.3.4 Distances 91 3.2.3.5 Sums and Products on Point Processes 92 3.2.3.6 Hard Core Processes 94 3.2.4 Testing for Poisson 96 3.3 The Markovian Arrival Process 100 3.4 Renewal Processes 103 3.4.1 Residual Inter-Event Time and Renewal Paradox 108 3.4.2 Superposition of Renewal Processes 110 3.4.3 Alternating Renewal Processes 111 3.4.4 Renewal Reward Processes 113 3.5 Birth-Death Processes 115 3.6 Branching Processes 121 Summary and Takeaways 125 Problems 126 Part II Queues 131 4 Single-Server Queues 133 4.1 Introduction and Notation 133 4.2 The Embedded Markov Chain Analysis of the M∕G∕1 Queue 134 4.2.1 Queue Length 136 4.2.2 Waiting Time 141 4.2.3 Busy Period and Idle Time 145 4.2.4 Remaining Service Time 148 4.2.5 Output Process 149 4.2.6 Evaluation of the Probabilities {ak}k∈ℤ 151 4.3 The M∕G∕1∕K Queue 152 4.3.1 Exact Solution 153 4.3.2 Asymptotic Approximation for Large K 157 4.4 Numerical Evaluation of the Queue Length PDF 166 4.5 A Special Case: the M∕M∕1 Queue 168 4.6 Optimization of a Single-Server Queue 170 4.6.1 Maximization of Net Profit 171 4.6.2 Minimization of Age of Information 174 4.6.2.1 General Expression of the Average Age of Information 175 4.6.2.2 Minimization of the Age of Information for an M∕M∕1 Model 177 4.7 The G∕M∕1 Queue 178 4.8 Matrix-Geometric Queues 185 4.8.1 Quasi Birth-Death (QBD) Processes 186 4.8.2 M∕G∕1 and G∕M∕1 Structured Processes 188 4.9 A General Result on Single-Server Queues 192 Summary and Takeaways 194 Problems 195 5 Multi-Server Queues 199 5.1 Introduction 199 5.2 The Erlang Loss System 201 5.2.1 Insensitivity Property of the Erlang Loss System 211 5.2.2 A Finite Population Model 213 5.2.3 Non-Poisson Input Traffic 214 5.2.3.1 Wilkinson’s Method 217 5.2.3.2 Fredericks’ Method 218 5.2.4 Multi-Class Erlang Loss System 221 5.3 Application of the Erlang Loss Model to Cellular Radio Access Network 224 5.3.1 Cell Dimensioning under Quality of Service Constraints 225 5.3.2 Number of Handoffs in a Connection Lifetime 230 5.3.3 Blocking in a Cell with User Mobility 232 5.3.4 Trade-off between Location Updating and Paging 234 5.3.5 Dimensioning of a Cell with Two Service Classes 236 5.4 The M∕M∕m Queue 238 5.4.1 Finite Queue Size Model 243 5.4.2 Resource Sharing versus Isolation 244 5.5 Infinite Server Queues 247 5.5.1 Analysis of Message Propagation in a Linear Network 252 Summary and Takeaways 257 Problems 258 6 Priorities and Scheduling 265 6.1 Introduction 265 6.2 Conservation Law 268 6.3 M∕G∕1 Priority Queueing 272 6.3.1 Non-FCFS Queueing Disciplines 273 6.3.2 Head-of-Line (HOL) Priorities 276 6.3.3 Preempt-Resume Priorities 283 6.3.4 Shortest Job First 284 6.3.5 Shortest Remaining Processing Time 286 6.3.6 The 𝜇C Rule 288 6.4 Processor Sharing 289 6.4.1 The M∕G∕1 Processor Sharing Model 290 6.4.2 Generalized Processor Sharing 293 6.4.3 Weighted Fair Queueing 298 6.4.4 Credit-Based Scheduling 302 6.4.5 Deficit Round Robin Scheduling 306 6.4.6 Least Attained Service Scheduling 308 6.5 Miscellaneous Scheduling 312 6.5.1 Scheduling on a Radio Link 312 6.5.1.1 Proportional Fairness 312 6.5.1.2 Multi-rate Orthogonal Multiplexing 313 6.5.2 Job Dispatching 318 6.6 Optimal Scheduling 324 6.6.1 Anticipative Systems 325 6.6.2 Server-Sharing, Nonanticipative Systems 325 6.6.3 Non-Server-Sharing, Nonanticipative Systems 326 Summary and Takeaways 327 Problems 327 7 Queueing Networks 331 7.1 Structure of a Queueing Network and Notation 331 7.2 Open Queueing Networks 332 7.2.1 Optimization of Network Capacities 345 7.2.2 Optimal Routing 347 7.2.3 Braess Paradox 350 7.3 Closed Queueing Networks 355 7.3.1 Arrivals See Time Averages (ASTA) 358 7.3.2 Buzen’s Algorithm for the Computation of the Normalization Constant 359 7.3.3 Mean Value Analysis 360 7.4 Loss Networks 369 7.4.1 Erlang Fixed-Point Approximation 373 7.4.2 Alternate Routing 378 7.5 Stability of Queueing Networks 381 7.5.1 Definition of Stability 385 7.5.2 Turning a Stochastic Discrete Queueing Network into a Deterministic Fluid Network 387 7.6 Further Readings 390 Appendix 391 Summary and Takeaways 394 Problems 394 8 Bounds and Approximations 399 8.1 Introduction 399 8.2 Bounds for the G∕G∕1 Queue 401 8.2.1 Mean Value Analysis 404 8.2.2 Output Process 406 8.2.3 Upper and Lower Bounds of the Mean Waiting Time 407 8.2.4 Upper Bound of the Waiting Time Probability Distribution 409 8.3 Bounds for the G∕G∕m Queue 412 8.4 Approximate Analysis of Isolated G∕G Queues 416 8.4.1 Approximations from Bounds 416 8.4.2 Approximation of the Arrival or Service Process 417 8.4.3 Reflected Brownian Motion Approximation 418 8.4.4 Heavy-traffic Approximation 423 8.5 Approximate Analysis of a Network of G∕G∕1 Queues 426 8.5.1 Superposition of Flows 427 8.5.2 Flow Through a Queue 428 8.5.3 Bernoulli Splitting of a Flow 428 8.5.4 Putting Pieces Together: The Decomposition Method 429 8.5.5 Bottleneck Approximation for Closed Queueing Networks 442 8.6 Fluid Models 443 8.6.1 Deterministic Fluid Model 444 8.6.2 From Fluid to Diffusion Model 452 8.6.3 Stochastic Fluid Model 456 8.6.4 Steady-State Analysis 459 8.6.4.1 Infinite Buffer Size (K = ∞) 462 8.6.4.2 Loss Probability 463 8.6.5 First Passage Times 466 8.6.6 Application of the Stochastic Fluid Model to a Multiplexer with ON-OFF Traffic Sources 468 Summary and Takeaways 471 Problems 472 Part III Networked Systems and Protocols 477 9 Multiple Access 479 9.1 Introduction 479 9.2 Slotted ALOHA 482 9.2.1 Analysis of the Naïve Slotted ALOHA 483 9.2.2 Finite Population Slotted ALOHA 487 9.2.3 Stabilized Slotted ALOHA 494 9.3 Pure ALOHA with Variable Packet Times 499 9.4 Carrier Sense Multiple Access (CSMA) 504 9.4.1 Features of the CSMA Protocol 505 9.4.1.1 Clear Channel Assessment 505 9.4.1.2 Persistence Policy 506 9.4.1.3 Retransmission Policy 507 9.4.2 Finite Population Model of CSMA 509 9.4.3 Multi-Packet Reception CSMA 513 9.4.3.1 Multi-Packet Reception 1-Persistent CSMA with Poisson Traffic 515 9.4.3.2 Multi-Packet Reception Nonpersistent CSMA with Poisson Traffic 519 9.4.4 Stability of CSMA 523 9.4.5 Delay Analysis of Stabilized CSMA 531 9.5 Analysis of the WiFi MAC Protocol 534 9.5.1 Outline of the IEEE 802.11 DCF Protocol 534 9.5.2 Model of CSMA/CA 538 9.5.2.1 The Back-off Process 540 9.5.2.2 Virtual Slot Time 543 9.5.2.3 Saturation Throughput 545 9.5.2.4 Service Times of IEEE 802.11 DCF 549 9.5.2.5 Correlation between Service Times 554 9.5.3 Optimization of Back-off Parameters 556 9.5.3.1 Maximization of Throughput 556 9.5.3.2 Minimization of Service Time Jitter 561 9.5.4 Fairness of CSMA/CA 565 9.6 Further Readings 570 Appendix 572 Summary and Takeaways 573 Problems 575 10 Congestion Control 579 10.1 Introduction 579 10.2 Congestion Control Architecture in the Internet 583 10.3 Evolution of Congestion Control in the Internet 587 10.3.1 TCP Reno 588 10.3.1.1 TCP Congestion Control Operations 589 10.3.1.2 NewReno 593 10.3.1.3 TCP Congestion Control with SACK 594 10.3.1.4 Congestion Window Validation 595 10.3.2 TCP CUBIC 596 10.3.3 TCP Vegas 598 10.3.4 Data Center TCP (DCTCP) 601 10.3.4.1 Marking at the Switch 602 10.3.4.2 ECN-Echo at the Receiver 603 10.3.4.3 Controller at the Sender 603 10.3.5 Bottleneck Bandwidth and RTT (BBR) 604 10.3.5.1 Delivery Rate Estimate 607 10.3.5.2 StartUp and Drain 608 10.3.5.3 ProbeBW 609 10.3.5.4 ProbeRTT 610 10.3.5.5 Pseudo-code of BBR Algorithm 610 10.4 Traffic Engineering with TCP 611 10.5 Fluid Model of a Single TCP Connection Congestion Control 614 10.5.1 Classic TCP with Fixed Capacity Bottleneck Link 615 10.5.2 Classic TCP with Variable Capacity Bottleneck Link 617 10.5.2.1 Discretization of the Evolution Equations 625 10.5.2.2 Accuracy of the Fluid Approximation of TCP 627 10.5.3 Application to Wireless Links 630 10.5.3.1 Random Capacity 630 10.5.3.2 TCP over Cellular Link 632 10.6 Fluid Model of Multiple TCP Connections Congestion Control 635 10.6.1 Negligible Buffering at the Bottleneck 635 10.6.2 Classic TCP with Drop Tail Buffer at the Bottleneck 637 10.6.3 Classic TCP with AQM at the Bottleneck 638 10.6.4 Data Center TCP with FIFO Buffer at the Bottleneck 639 10.7 Fairness and Congestion Control 642 10.8 Network Utility Maximization (NUM) 645 10.9 Challenges to TCP 652 10.9.1 Fat-Long Pipes 653 10.9.2 Wireless Channels 655 10.9.3 Bufferbloat 656 10.9.4 Interaction with Applications 658 Appendix 659 Summary and Takeaways 664 Problems 665 11 Quality-of-Service Guarantees 669 11.1 Introduction 669 11.2 Deterministic Service Guarantees 670 11.2.1 Arrival Curves 673 11.2.2 Service Curves 677 11.2.3 Performance Bounds 681 11.2.4 Regulators 683 11.2.5 Network Calculus 688 11.2.5.1 Single Node Analysis 689 11.2.5.2 End-to-End Analysis 692 11.3 Stochastic Service Guarantees 703 11.3.1 Multiplexing with Marginal Buffer Size 703 11.3.2 Multiplexing with Non-Negligible Buffer Size 711 11.3.3 Effective Bandwidth 714 11.3.3.1 Definition of the Effective Bandwidth 714 11.3.3.2 Properties of the Effective Bandwidth 715 11.3.3.3 Effective Bandwidth of a Markov Source 716 11.3.4 Network Analysis and Dimensioning 721 11.4 Further Readings 727 Appendix 728 Summary and Takeaways 732 Problems 733 A Refresher of Probability, Random Variables, and Stochastic Processes 735 A.1 Probability 735 A.2 Random Variables 737 A.3 Transforms of Probability Distribution Functions 739 A.4 Inequalities and Limit Theorems 744 A.4.1 Markov Inequality 744 A.4.2 Chebychev Inequality 745 A.4.3 Jensen Inequality 746 A.4.4 Chernov Bound 746 A.4.5 Union Bound 747 A.4.6 Central Limit Theorem (CLT) 747 A.5 Stochastic Processes 748 A.6 Markov Chains 749 A.6.1 Classification of States 750 A.6.2 Recurrence 751 A.6.3 Visits to a State 754 A.6.4 Asymptotic Behavior and Steady State 756 A.6.5 Absorbing Markov Chains 762 A.6.6 Continuous-Time Markov Processes 763 A.6.7 Sojourn Times in Process States 765 A.6.8 Reversibility 766 A.6.9 Uniformization 768 A.7 Wiener Process (Brownian Motion) 769 A.7.1 Wiener Process with an Absorbing Barrier 771 A.7.2 Wiener Process with a Reflecting Barrier 772 References 775 Index 789

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Book SynopsisA comprehensive guide to approaches to decoding, synthesizing and modeling pulse width modulation (PWM) converters Origin of Power Converters explores the original converter and provides a systematic examination of the development and modeling of power converters based on decoding and synthesizing approaches. The authorsnoted experts on the topicpresent an introduction to the origins of the converter and detail the fundamentals related to power the converter's evolution. They cover a range of converter synthesis approaches, synthesis of multi-stage/multi-level converters, extension of hard-switching converters to soft-switching ones, and determination of switch-voltage stresses in the converters. In later chapters, this comprehensive resource reviews conventional two-port network theory and the state-space averaged (SSA) modeling approach, from which systematic modeling approaches are based on the graft switch technique. In addition, the book reviews the Table of ContentsPreface xv Acknowledgments xvii About the Authors xviii Part I Decoding and Synthesizing 1 1 Introduction 3 1.1 Power Processing Systems 4 1.2 Non-PWM Converters Versus PWM Converters 7 1.2.1 Non-PWM Converters 7 1.2.2 PWM Power Converters 9 1.3 Well-Known PWM Converters 10 1.4 Approaches to Converter Development 17 1.5 Evolution 25 1.6 About the Text 26 1.6.1 Part I: Decoding and Synthesizing 26 1.6.2 Part II: Modeling and Applications 28 Further Reading 28 2 Discovery of Original Converter 31 2.1 Creation of Original Converter 31 2.1.1 Source–Load Approach 32 2.1.2 Proton–Neutron–Meson Analogy 32 2.1.3 Resonance Approach 33 2.2 Fundamental PWM Converters 34 2.2.1 Voltage Transfer Ratios 35 2.2.2 CCM Operation 36 2.2.3 DCM Operation 38 2.2.4 Inverse Operation 39 2.3 Duality 40 Further Reading 41 3 Fundamentals 43 3.1 DC Voltage and Current Offsetting 43 3.1.1 DC Voltage Offsetting 44 3.1.2 DC Current Offsetting 47 3.2 Capacitor and Inductor Splitting 49 3.3 DC-Voltage Blocking and Pulsating-Voltage Filtering 51 3.4 Magnetic Coupling 55 3.5 DC Transformer 58 3.6 Switch Grafting 62 3.7 Diode Grafting 67 3.8 Layer Scheme 72 Further Reading 74 4 Decoding Process 77 4.1 Transfer Ratios (Codes) 77 4.2 Transfer Code Configurations 82 4.2.1 Cascade Configuration 82 4.2.2 Feedback Configuration 82 4.2.3 Feedforward Configuration 83 4.2.4 Parallel Configuration 85 4.3 Decoding Approaches 86 4.3.1 Factorization 86 4.3.2 Long Division 88 4.3.3 Cross Multiplication 89 4.4 Decoding of Transfer Codes with Multivariables 91 4.5 Decoding with Component-Interconnected Expression 93 Further Reading 94 5 Synthesizing Process with Graft Scheme 95 5.1 Cell Approaches 95 5.1.1 P-Cell and N-Cell 96 5.1.2 Tee Canonical Cell and Pi Canonical Cell 97 5.1.3 Switched-Capacitor Cell and Switched-Inductor Cell 98 5.1.4 Inductor–Capacitor Component Cells 100 5.2 Converter Grafting Scheme 101 5.2.1 Synchronous Switch Operation 101 5.2.2 Grafting Active Switches 103 5.2.3 Grafting Passive Switches 108 5.3 Illustration of Grafting Converters 110 5.3.1 Grafting the Well-Known PWM Converters 110 5.3.1.1 Graft Boost on Buck 111 5.3.1.2 Graft Buck on Boost 112 5.3.1.3 Graft Buck on Buck–Boost 114 5.3.1.4 Graft Boost on Boost–Buck 116 5.3.1.5 Buck in Parallel with Buck–Boost 119 5.3.1.6 Grafting Buck on Buck to Achieve High Step-Down Voltage Conversion 119 5.3.1.7 Grafting Boost on Boost to Achieve High Step-up Voltage Conversion 120 5.3.1.8 Grafting Boost (CCM) on Buck (DCM) 121 5.3.1.9 Cascode Complementary Zeta with Buck 123 5.3.2 Grafting Various Types of Converters 124 5.3.2.1 Grafting Half-Bridge Resonant Inverter on Dither Boost Converter 124 5.3.2.2 Grafting Half-Bridge Resonant Inverter on Bidirectional Flyback Converter 124 5.3.2.3 Grafting Class-E Converter on Boost Converter 125 5.3.3 Integrating Converters with Active and Passive Grafted Switches 127 5.3.3.1 Grafting Buck on Boost with Grafted Diode 128 5.3.3.2 Grafting Half-Bridge Inverter on Interleaved Boost Converters in DCM 128 5.3.3.3 Grafting N-Converters with TGS 130 5.3.3.4 Grafting N-Converters with ΠGS 130 Further Reading 132 6 Synthesizing Process with Layer Scheme 133 6.1 Converter Layering Scheme 133 6.2 Illustration of Layering Converters 135 6.2.1 Buck Family 135 6.2.2 Boost Family 138 6.2.3 Other Converter Examples 142 6.3 Discussion 146 6.3.1 Deduction from Ćuk to Buck–Boost 146 6.3.2 Deduction from Sepic to Buck–Boost 148 6.3.3 Deduction from Zeta to Buck–Boost 149 6.3.4 Deduction from Sepic to Zeta 150 Further Reading 151 7 Converter Derivation with the Fundamentals 153 7.1 Derivation of Buck Converter 153 7.1.1 Synthesizing with Buck–Boost Converter 154 7.1.2 Synthesizing with Ćuk Converter 154 7.2 Derivation of z-Source Converters 154 7.2.1 Voltage-Fed z-Source Converters 155 7.2.1.1 Synthesizing with Sepic Converter 157 7.2.1.2 Synthesizing with Zeta Converter 160 7.2.2 Current-Fed z-Source Converters 161 7.2.2.1 Synthesizing with SEPIC Converter 162 7.2.2.2 Synthesizing with Zeta Converter 162 7.2.3 Quasi-z-Source Converter 162 7.2.3.1 Synthesizing with Sepic Converter 164 7.2.3.2 Synthesizing with Zeta Converter 165 7.3 Derivation of Converters with Switched Inductor or Switched Capacitor 166 7.3.1 Switched-Inductor Converters 167 7.3.1.1 High Step-Down Converter with Transfer Code D/(2 − D) 167 7.3.1.2 High Step-Down Converter with Transfer Code D/(2(1 − D)) 173 7.3.2 Switched-Capacitor Converters 178 7.3.2.1 High Step-Up Converter with Transfer Code (1 + D)/(1 − D) 178 7.3.2.2 High Step-Up Converter with Transfer Code 2D/(1 − D) 181 7.3.2.3 High Step-Up Converter with Transfer Code D/(1 − 2D) 184 7.4 Syntheses of Desired Transfer Codes 185 7.4.1 Synthesis of Transfer Code: D2/(D2 − 3D + 2) 186 7.4.1.1 Synthesizing with Buck–Boost Converter 187 7.4.1.2 Synthesizing with Zeta Converter 188 7.4.1.3 Synthesizing with Ćuk Converter 189 7.4.2 Synthesizing Converters with the Fundamentals 191 7.4.2.1 DC Voltage and DC Current Offsetting 191 7.4.2.2 Inductor and Capacitor Splitting 192 7.4.2.3 DC Voltage Blocking and Filtering 192 7.4.2.4 Magnetic Coupling 193 7.4.2.5 DC Transformer 194 7.4.2.6 Switch and Diode Grafting 195 7.4.2.7 Layer Technique 195 Further Reading 198 8 Synthesis of Multistage and Multilevel Converters 199 8.1 Review of the Original Converter and Its Variations of Transfer Code 199 8.2 Syntheses of Single-Phase Converters 201 8.3 Syntheses of Three-Phase Converters 203 8.4 Syntheses of Multilevel Converters 207 8.5 L–C Networks 210 Further Reading 212 9 Synthesis of Soft-Switching PWM Converters 215 9.1 Soft-Switching Cells 215 9.1.1 Passive Lossless Soft-Switching Cells 216 9.1.1.1 Near-Zero-Current Switching Mechanism 216 9.1.1.2 Near-Zero-Voltage Switching Mechanism 218 9.1.2 Active Lossless Soft-Switching Cells 220 9.1.2.1 Zero-Voltage Switching Mechanism 222 9.1.2.2 Zero-Current Switching Mechanism 226 9.2 Synthesis of Soft-Switching PWM Converters with Graft Scheme 230 9.2.1 Generation of Passive Soft-Switching PWM Converters 230 9.2.2 Generation of Active Soft-Switching PWM Converters 234 9.3 Synthesis of Soft-Switching PWM Converters with Layer Scheme 240 9.3.1 Generation of Passive Soft-Switching PWM Converters 240 9.3.2 Generation of Active Soft-Switching PWM Converters 245 9.4 Discussion 247 Further Reading 251 10 Determination of Switch-Voltage Stresses 255 10.1 Switch-Voltage Stress of the Original Converter 255 10.2 Switch-Voltage Stresses of the Fundamental Converters 257 10.2.1 The Six Well-Known PWM Converters 257 10.2.1.1 Boost Converter 257 10.2.1.2 Buck–Boost Converter 258 10.2.1.3 Ćuk, Sepic, and Zeta Converters 259 10.2.2 z-Source Converters 260 10.2.2.1 Voltage-Fed z-Source Converter 260 10.2.2.2 Current-Fed z-Source Converter 261 10.2.2.3 Quasi-z-Source Converter 262 10.3 Switch-Voltage Stresses of Non-Fundamental Converters 263 10.3.1 High Step-Down Switched-Inductor Converter 263 10.3.2 High Step-Down/Step-Up Switched-Inductor Converter 264 10.3.3 Compound Step-Down/Step-Up Switched-Capacitor Converter 265 10.3.4 High Step-Down Converter with Transfer Ratio of D2 267 10.3.5 High Step-Up Converter with Transfer Ratio of 1/(1 − D)2 268 Further Reading 270 11 Discussion and Conclusion 271 11.1 Will Identical Transfer Code Yield the Same Converter Topology? 271 11.2 Topological Duality Versus Circuital Duality 274 11.3 Graft and Layer Schemes for Synthesizing New Fundamental Converters 277 11.3.1 Synthesis of Buck–Boost Converter 278 11.3.2 Synthesis of Boost–Buck (Ćuk) Converter 279 11.3.3 Synthesis of Buck–Boost–Buck (Zeta) Converter 280 11.3.4 Synthesis of Boost–Buck–Boost (Sepic) Converter 282 11.3.5 Synthesis of Buck-Family Converters with Layer Scheme 284 11.3.6 Synthesis of Boost-Family Converters with Layer Scheme 286 11.4 Analogy of Power Converters to DNA 289 11.4.1 Replication 291 11.4.2 Mutation 291 11.5 Conclusions 295 Further Reading 296 Part II Modeling and Application 299 12 Modeling of PWM DC/DC Converters 301 12.1 Generic Modeling of the Original Converter 302 12.2 Series-Shunt and Shunt-Series Pairs 303 12.3 Two-Port Network 308 12.4 Small-Signal Modeling of the Converters Based on Layer Scheme 315 12.5 Quasi-Resonant Converters 323 Further Reading 326 13 Modeling of PWM DC/DC Converters Using the Graft Scheme 329 13.1 Cascade Family 330 13.2 Small-Signal Models of Buck-Boost and Ćuk Converters Operated in CCM 332 13.2.1 Buck-Boost Converter 336 13.2.2 Boost-Buck Converter 338 13.3 Small-Signal Models of Zeta and Sepic Operated in CCM 340 13.3.1 Zeta Converter 344 13.3.2 Sepic Converter 346 Further Reading 349 14 Modeling of Isolated Single-Stage Converters with High Power Factor and Fast Regulation 351 14.1 Generation of Single-Stage Converters with High Power Factor and Fast Regulation 352 14.2 Small-Signal Models of General Converter Forms Operated in CCM/DCM 355 14.3 An Illustration Example 361 Further Reading 365 15 Analysis and Design of an Isolated Single-Stage Converter Achieving Power Factor Correction and Fast Regulation 367 15.1 Derivation of the Single-Stage Converter 368 15.1.1 Selection of Individual Semi-Stages 369 15.1.2 Derivation of the Discussed Isolated Single-Stage Converter 369 15.2 Analysis of the Isolated Single-Stage Converter Operated in DCM + DCM 369 15.2.1 Buck-Boost Power Factor Corrector 370 15.2.2 Flyback Regulator 372 15.3 Design of a Peak Current Mode Controller for the ISSC 373 15.4 Practical Consideration and Design Procedure 377 15.4.1 Component Stress 377 15.4.2 Snubber Circuit 378 15.4.3 Design Procedure 379 15.5 Hardware Measurements 380 15.6 Design of an H∞ Robust Controller for the ISSC 382 15.6.1 H∞ Control 382 15.6.2 An Illustration Example of Robust Control and Hardware Measurements 386 Further Reading 392 Index 395

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Book SynopsisSystem Safety for the 21st Century Explore an authoritative and complete exploration of basic and advanced concepts in system safety engineering The Second Edition of System Safety for the 21st Century delivers an authoritative primer on the identification, evaluation, analysis, and control of hazards to people, components, sub-systems, systems, processes, and facilities. The book offers readers a complete discussion on techniques within system safety, the discipline on process safety, as well as a comprehensive treatment on professionalism within the safety industry. This new edition applies the concepts of system safety to medical disciplines and medical devices, offering readers the potential to have a significantly positive impact on the standing of American medical safety in the world. The latest edition also includes: A brand-new chapter on the risk management with current international and U.S. government standards Table of ContentsForeword xiii Preface xv Acknowledgments xvii About The Companion Website xix Part I Introduction to System Safety 1 1. The History of System Safety 3 The 1960s—Mil-Std-882, DoD, and Nasa 4 The 1970s—The Management Oversight and Risk Tree 4 The 1980s—Facility System Safety 5 The 1990s—Risk-Based Process System Safety 6 The 2000s—Quest for Intrinsic Safety 6 The 2010s—Risk Management Integration 7 The 2020s—Improvements and International Approach to Risk Maturing 7 Review Questions 8 Bibliography 8 2. Fundamentals of System Safety 9 Basic Definitions 9 Fundamental Safety Concepts 9 System Safety Fundamentals 13 System Safety Tenets 18 Review Questions 19 Bibliography 19 3. Current Approaches to System Safety 21 Department of Defense 21 Nasa 26 Facility System Safety 28 The Chemical Industry 31 Department of Energy 32 Review Questions 34 Bibliography 35 4. Problem Areas 37 Standardization 38 Risk Assessment Codes 39 Data 40 Communications 40 Life Cycle 41 Education and Training 41 Human Factors 41 Software 42 Review Questions 42 Bibliography 42 5. The Future of System Safety 43 More First-Time Safe Systems 43 Cost-Effective Management Tools 43 The Face of System Safety 44 Proactive or Reactive? 47 Review Questions 47 Bibliography 47 Part II System Safety Program Planning and Management 49 6. Establishing the Groundwork 51 Generic Model 51 Product Safety 51 Dual Programs 52 Planning and Development Methodology 52 Review Questions 53 7. Tasks 55 Hazard Identification 56 Hazard Analysis and Control 58 System Safety Support Tasks 60 Review Questions 61 8. System Safety Products 63 System Safety Program Plan 63 Preliminary Hazard List 64 Preliminary Hazard Analysis 66 Hazard Tracking Log 67 Subsystem Hazard Analysis 68 System Hazard Analysis 71 Operating Hazard Analysis 72 Change Analysis Report 73 Accident Analysis Report 74 Review Questions 75 9. Program Implementation 77 Steps 77 Review Questions 88 Table of Contents vii 10. Risk Management 89 Introduction 89 Types of Risk 89 Risk Management 90 Review Questions 96 Bibliography 96 Part Iii Analytical Aids 101 11. Analytical Trees 103 Purposes 104 Tree Construction 105 Fault Trees Versus Fault Tree Analysis 110 Review Questions 115 Bibliography 115 12. Risk Assessment and Risk Acceptance 117 Risk Management Concepts 117 Risk Assessment Shortcomings 123 Total Risk Exposure Codes 124 Review Questions 126 Bibliography 126 13. Human Factors 127 Human Reliability 127 Human Error Rates 129 Improving Human Reliability 130 Human Factors for Engineering Design 132 Review Questions 135 Bibliography 135 Part IV System Safety Analysis Techniques 137 14. Energy Trace and Barrier Analysis 139 Purpose of ETBA 139 Input Requirements 139 General Approach 140 Instructions 140 Review Questions 142 Bibliography 142 15. Failure Mode and Effects Analysis 143 Purpose of FMEA 144 Input Requirements 144 General Approach 144 Instructions 144 Appendix: Sample FMEA 147 Summary 147 Project Description 147 Methodology 149 Review Questions 152 Bibliography 152 16. Fault Tree Analysis 155 Purpose of FTA 155 Input Requirements 156 General Approach 156 Instructions 157 Appendix: Sample FTA 165 Summary 165 Project Description 166 Methodology 167 Review Questions 171 Bibliography 171 17. Project Evaluation Tree 173 Purpose of PET 174 Input Requirements 174 General Approach 174 Instructions 175 Appendix: PET User’s Guide 179 Review Questions 188 Bibliography 188 18. Change Analysis 189 Purpose 189 Input Requirements 190 General Approach 190 Instructions 190 Review Questions 193 Bibliography 193 19. Management Oversight and Risk Tree 195 Purpose of Mort and Mini-Mort 197 Input Requirements 198 General Approach 198 Instructions 205 Review Questions 221 Bibliography 221 20. Event and Causal Factors Charts 223 Purpose 223 Input Requirements 223 General Approach 224 Instructions 224 Review Questions 228 Bibliography 228 21. Other Analytical Techniques 229 Software Hazard Analysis 229 Common Cause Failure Analysis 229 Sneak Circuit Analysis 230 Extreme Value Projection 231 Time-Loss Analysis 235 Additional Techniques 237 Review Questions 238 Bibliography 238 Part V Process Safety 241 22. Process Safety Management 243 Introduction 243 Background 243 Future 248 Summary 249 Review Questions 249 Bibliography 249 Appendix: List of Highly Hazardous Chemicals, Toxics and Reactives 250 23. EPA’s Equivalent Process Safety Requirements—Risk Management Program (RMP) 255 Background 255 Overall Risk Management Program 255 Summary 259 Review Questions 260 Bibliography 260 Appendix: Substances Listed Under 40 CFR 68 261 24. Process Safety Implementation 263 Introduction 263 PSM Implementation 263 RMP Implementation 270 Implementation Lessons 271 Summary 272 Review Questions 272 Bibliography 273 25. Process Safety Reviews 275 Introduction 275 Mechanics of an Individual Audit 277 Lessons 279 Summary 281 Review Questions 281 Bibliography 281 Part VI System Safety Applied To The Medical Field 283 26. Medical Devices and Equipment 285 Introduction 285 Purpose 285 System Safety Review 285 System Safety Application to Medical Devices 286 System Safety Interface with Medical Devices 288 Considerations for Improvement 289 Conclusions 291 Review Questions 292 Bibliography 292 Appendix 293 27. Infection Control 295 Introduction 295 The Problem 296 What’s Being Done 296 System Safety Considerations 298 Further Improvements 298 System Safety Application 301 Cronavirus 303 Review Questions 304 Bibliography 305 28. Hospitals 307 Introduction 307 Challenges Faced 308 System Safety Application 312 Case Study Hypothetical System Safety Application to a Hospital 315 Anticipating the Future 318 Review Questions 319 Bibliography 319 29. Future Considerations 321 Introduction 321 Definitions 321 Health Care Future Discussion Areas 322 Research and Development 326 System Safety Application to Medical Care in the Future 327 Other Thoughts 329 Conclusions 330 Review Questions 331 Bibliography 331 Part VII Professionalism and Professional Development 333 30. Professionalism and Professional Development 335 Introduction 335 What is Professionalism? 335 Professional Development 337 Accreditation of Certifications 337 Why Become Certified? 339 Summary 341 Review Questions 342 Bibliography 342 Appendices 343 Appendix I: The Scope and Functions of the Professional Safety Position 343 Appendix II: International System Safety Society Fundamental Principles and Canons 347 Appendix III: Professional System Safety and Related Societies and Organizations 351 Glossary 357 Acronyms 365 Bibliography 369 Further Reading 373 About The Author 375 Book Contributor 377 Book Back Cover 379 Index 381

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Book SynopsisTrade ReviewReview of the fifth edition by Thomas B. Sheridan, Ford Professor Emeritus of Engineering and Applied Psychology, MassachusettsInstitute of TechnologyThe fifth edition of the Handbook of Human Factors and Ergonomics is the most authoritative and comprehensive reference work in the field.Review of the fourth edition and comment on the fifth edition by Donald A. Norman, Director and Co-Founder, University of California, San Diego Design Lab.I’m often asked for reading suggestions, especially for references to the literature on Human Factors and Ergonomics. In the past few months, I have been reading chapters of one book that has it all: Gavriel Salvendy’s massive tome, the Handbook of Human Factors and Ergonomics. The articles are all excellent. They all reflect up-to-date reviews of the areas they cover. They are wonderful self-study material, wonderful references, and would make excellent material in multiple courses. Consider it as essential piece of professional equipment. If you don’t know human factors, this is a great way to find the parts relevant to your work. And even if you are an expert, this book will be valuable because it is unlikely that you are expert at all the topics covered here, yet very likely you will need some of the ones you are not (yet) expert at. I follow my own advice. I consider myself an expert (I am a Fellow of the Human Factors Society), but I still learn each time I read from these pages. The 5th edition has new – and very important – chapters written by the authorities in each topic. It has kept up with the times and become even more valuable as both a text and a reference.From the Foreword to the second edition by John F. Smith, Jr., Chairman of the Board, Chief Executive Officer andPresident, General Motors CorporationThe publication of this second Handbook of Human Factors and Ergonomics is very timely. It is a comprehensive guide that contains practical knowledge and technical background on virtually all aspects of physical, cognitive, and social ergonomics. As such, it can be a valuable source of information for any individual or organization committed to providing competitive, high-quality products and safe, productive work environments.From the Foreword to the first edition by E. M. Estes, Retired President, General Motors CorporationRegardless of what phase of the economy a person is involved in, this handbook is a very useful tool. Every area of human factors from environmental conditions and motivation to the use of new communications systems, robotics, and business systems is well covered in the handbook by experts in every field.Table of ContentsAbout the Editors ix Contributors xi Foreword xxi Preface xxiii 1. Human Factors Function 1 1. The Discipline of Human Factors and Ergonomics 3Waldemar Karwowski and Wei Zhang 2. Human Systems Integration and Design 38Guy A. Boy 2. Human Factors Fundamentals 55 3. Sensation and Perception 57Robert W. Proctor and Janet D. Proctor 4. Selection and Control of Action 91Robert W. Proctor and Kim-Phuong L. Vu 5. Information Processing 114Christopher D. Wickens and C. Melody Carswell 6. Decision-Making Models, Decision Support, and Problem Solving 159Mark R. Lehto and Gaurav Nanda 7. Mental Workload 203G.M. Hancock, L. Longo, M.S. Young, and P.A. Hancock 8. Social and Organizational Foundation of Ergonomics: Multi-Level Systems Approaches 227Pascale Carayon 9. Emotional Design 236Feng Zhou, Yangjian Ji, and Roger Jianxin Jiao 10. Cross-Cultural Design 252Tom Plocher, Pei-Luen Patrick Rau, Yee-Yin Choong, and Zhi Guo 3. Design of Equipment, Tasks, Jobs, and Environments 281 11. Three-Dimensional (3D) Anthropometry and Its Applications in Product Design 283Liang Ma and Jianwei Niu 12. Basic Biomechanics and Workplace Design 303William S. Marras and Waldemar Karwowski 13. The Changing Nature of Task Analysis 358Erik Hollnagel 14. Workplace Design 368Nicolas Marmaras and Dimitris Nathanael 15. Job and Team Design 383Frederick P. Morgeson and Michael A. Campion 16. Design, Delivery, Evaluation, and Transfer of Effective Training Systems 414Tiffany M. Bisbey, Rebecca Grossman, Kareem Panton, Chris W. Coultas, and Eduardo Salas 17. Situation Awareness 434Mica R. Endsley 4. Design for Health, Safety, and Comfort 457 18. Sound and Noise: Measurement and Design Guidance 459John G. Casali 19. Vibration and Motion 494Neil J. Mansfield and Michael J. Griffin 20. Human Errors and Human Reliability 514Peng Liu, Renyou Zhang, Zijian Yin, and Zhizhong Li 21. Occupational Safety and Health Management 573Jeanne Mager Stellman, Sonalee Rau, and Pratik Thaker 22. Managing low-Back Disorder Risk in the Workplace 597William S. Marras and Waldemar Karwowski 23. Manual Materials Handling: Evaluation and Practical Considerations 630Fadi A. Fathallah and Ira Janowitz 24. Warnings and Hazard Communications 644Michael S. Wogalter, Christopher B. Mayhorn, and Kenneth R. Laughery, Sr. 25. Use of Personal Protective Equipment 668Grażyna Bartkowiak, Krzysztof Baszczyński, Anna Bogdan, Agnieszka Brochocka, Anna Dąbrowska, Rafał Hrynyk, Emilia Irzmańska, Danuta Koradecka, Emil Kozłowski, Katarzyna Majchrzycka, Krzysztof Makowski, Anna Marszałek, Magdalena Młynarczyk, Rafał Młyński, Grzegorz Owczarek, and Janżera 5. Human Performance Modeling 685 26. Mathematical Modeling in Human–Machine System Design and Evaluation 687Changxu Wu and Yili Liu 27. Modeling and Simulation of Human Systems 704Gunther E. Paul 28. Human Supervisory Control of Automation 736Thomas B. Sheridan 29. Digital Human Modeling in Design 761Vincent G. Duffy 30. Extended Reality (XR) Environments 782Kay M. Stanney, Hannah Nye, Sam Haddad, Kelly S. Hale, Christina K. Padron, and Joseph V. Cohn 31. Neuroergonomics 816Hasan Ayaz and Frédéric Dehais 6. System Evaluation 843 32. Accident and Incident Investigation 845Patrick G. Dempsey 33. Human Factors and Ergonomics Audits 853Colin G. Drury and Patrick G. Dempsey 34. Cost/Benefit Analysis for Human Systems Investments 880William B. Rouse and Dennis K. McBride 7. Human–Computer Interaction 893 35. Data Visualization 895Sumanta N. Pattanaik and R. Paul Wiegand 36. Representation Design 947John M. Flach, Kevin B. Bennett, Jonathan W. Butler, and Michael A. Heroux 37. Collecting and Analyzing User Insights 960Matthias Peissner, Kathrin Pollmann, and Nora Fronemann 38. Usability and User Experience: Design and Evaluation 972James R. Lewis and Jeff Sauro 39. Website Design and Evaluation 1016Kim-Phuong L. Vu, Robert W. Proctor, and Ya-Hsin Hung 40. Mobile Systems Design and Evaluation 1037June Wei and Siyi Dong 41. Human Factors in Ambient Intelligence Environments 1058Constantine Stephanidis, Margherita Antona, and Stavroula Ntoa 42. Human-Centered Design of Artificial Intelligence 1085George Margetis, Stavroula Ntoa, Margherita Antona, and Constantine Stephanidis 43. Cybersecurity, Privacy, and Trust 1107Abbas Moallem 44. Human–Robot Interaction 1121Jessie Y.C. Chen and Michael J. Barnes 45. Human Factors in Social Media 1143Qin Gao and Yue Chen 8. Design for Individual Differences 1187 46. Design for All in Digital Technologies 1189Constantine Stephanidis 47. Design for People Experiencing Functional Limitations 1216Gregg C. Vanderheiden, J. Bern Jordan, and Jonathan Lazar 48. Design for Aging 1249Jia Zhou and Qin Gao 49. Design of Digital Technologies for Children 1287Panos Markopoulos, Janet C. Read, and Michail Giannakos 9. Selected Applications 1305 50. Human Factors and Ergonomics Standards 1307Waldemar Karwowski, Redha Taiar, David Rodrick, Bohdana Sherehiy, and Robert R. Fox 51. Data Analytics in Human Factors 1351Matt Holman, Guy Walker, Melissa Bedinger, Annie Visser-Quinn, Kerri McClymont, Lindsay Beevers, and Terry Lansdown 52. Human Factors and Ergonomics in Design of A3: Automation, Autonomy, and Artificial Intelligence 1385Ben D. Sawyer, Dave B. Miller, Matthew Canham, and Waldemar Karwowski 53. Human Factors and Ergonomics in Health Care 1417Pascale Carayon, Kathryn Wust, Bat-Zion Hose, and Megan E. Salwei 54. Human Factors and Ergonomics in Digital Manufacturing 1438Dieter Spath and Martin Braun 55. Human Factors and Ergonomics in Aviation 1460Steven J. Landry 56. Human Side of Space Exploration and Habitation 1480Kevin R. Duda, Dava J. Newman, Joanna Zhang, Nicolas Meirhaeghe, and H. Larissa Zhou 57. Human Factors and Ergonomics for Sustainability 1512Klaus Fischer, Andrew Thatcher, and Klaus J. Zink Index 1529

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Book SynopsisMusculoskeletal Disorders Hands-on guidance and tools for the prevention of musculoskeletal injuries in the workplace In Musculoskeletal Disorders: The Fatigue Failure Mechanism, a team of accomplished occupational health experts delivers an essential and incisive discussion of how musculoskeletal disorders (MSDs) develop and progress, as well as how they can be prevented and controlled. Offering a novel, evidence-based approach to this costly problem, the book has broad implications for employers, insurers, and other stakeholders in workplace health and safety. The authors identify new risk assessment approaches based on the cumulative effects of exposure to highly variable loading conditions. These new approaches can also be applied to evaluate the efficacy of job rotation scenarios and to quantify exoskeleton efficacy. The complexities associated with fatigue failure in biological environments are also explored in addition to suggested models for underTable of ContentsPreface Acknowledgements Author the Editors 1. Introduction 2. Common Musculoskeletal Disorders 3. Structure and Function of The Musculoskeletal System 4. Structure and Function of the Nervous System, and Its Relation to Pain 5. Fundamental Biomechanics Concepts 6. Material Properties of Musculoskeletal and Peripheral Nerve Tissues 7. Fatigue Failure of Musculoskeletal Tissues 8. MSDs as a fatigue failure process 9. Fundamentals of Fatigue Failure Analysis 10. Fatigue failure in a biological environment 11. Injury and Self-Repair of Musculoskeletal Tissues 12. Personal Characteristics and MSD Risk 13. Using Fatigue Failure Principles to Assess MSD Risk 14. Implications for MSD Prevention 15. Optimizing Musculoskeletal Health 16. Status of knowledge and unanswered questions Index

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Book SynopsisDEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research Table of ContentsForeword xvi by Vipin Kumar, Regents Professor, University of Minnesota Acknowledgments xvii List of Contributors xviii List of Acronyms xxiv 1 Introduction 1 Gustau Camps-Valls, Xiao Xiang Zhu, Devis Tuia, and Markus Reichstein 1.1 A Taxonomy of Deep Learning Approaches 2 1.2 Deep Learning in Remote Sensing 3 1.3 Deep Learning in Geosciences and Climate 7 1.4 Book Structure and Roadmap 9 Part I Deep Learning to Extract Information from Remote Sensing Images 13 2 Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks 15 Jose E. Adsuara, Manuel Campos-Taberner, Javier García-Haro, Carlo Gatta, Adriana Romero, and Gustau Camps-Valls 2.1 Introduction 15 2.2 Sparse Unsupervised Convolutional Networks 17 2.2.1 Sparsity as the Guiding Criterion 17 2.2.2 The EPLS Algorithm 18 2.2.3 Remarks 18 2.3 Applications 19 2.3.1 Hyperspectral Image Classification 19 2.3.2 Multisensor Image Fusion 21 2.4 Conclusions 22 3 Generative Adversarial Networks in the Geosciences 24 Gonzalo Mateo-García, Valero Laparra, Christian Requena-Mesa, and Luis Gómez-Chova 3.1 Introduction 24 3.2 Generative Adversarial Networks 25 3.2.1 Unsupervised GANs 25 3.2.2 Conditional GANs 26 3.2.3 Cycle-consistent GANs 27 3.3 GANs in Remote Sensing and Geosciences 28 3.3.1 GANs in Earth Observation 28 3.3.2 Conditional GANs in Earth Observation 30 3.3.3 CycleGANs in Earth Observation 30 3.4 Applications of GANs in Earth Observation 31 3.4.1 Domain Adaptation Across Satellites 31 3.4.2 Learning to Emulate Earth Systems from Observations 33 3.5 Conclusions and Perspectives 36 4 Deep Self-taught Learning in Remote Sensing 37 Ribana Roscher 4.1 Introduction 37 4.2 Sparse Representation 38 4.2.1 Dictionary Learning 39 4.2.2 Self-taught Learning 40 4.3 Deep Self-taught Learning 40 4.3.1 Application Example 43 4.3.2 Relation to Deep Neural Networks 44 4.4 Conclusion 45 5 Deep Learning-based Semantic Segmentation in Remote Sensing 46 Devis Tuia, Diego Marcos, Konrad Schindler, and Bertrand Le Saux 5.1 Introduction 46 5.2 Literature Review 47 5.3 Basics on Deep Semantic Segmentation: Computer Vision Models 49 5.3.1 Architectures for Image Data 49 5.3.2 Architectures for Point-clouds 52 5.4 Selected Examples 55 5.4.1 Encoding Invariances to Train Smaller Models: The example of Rotation 55 5.4.2 Processing 3D Point Clouds as a Bundle of Images: SnapNet 59 5.4.3 Lake Ice Detection from Earth and from Space 62 5.5 Concluding Remarks 66 6 Object Detection in Remote Sensing 67 Jian Ding, Jinwang Wang, Wen Yang, and Gui-Song Xia 6.1 Introduction 67 6.1.1 Problem Description 67 6.1.2 Problem Settings of Object Detection 69 6.1.3 Object Representation in Remote Sensing 69 6.1.4 Evaluation Metrics 69 6.1.4.1 Precision-Recall Curve 70 6.1.4.2 Average Precision and Mean Average Precision 71 6.1.5 Applications 71 6.2 Preliminaries on Object Detection with Deep Models 72 6.2.1 Two-stage Algorithms 72 6.2.1.1 R-CNNs 72 6.2.1.2 R-fcn 73 6.2.2 One-stage Algorithms 73 6.2.2.1 Yolo 73 6.2.2.2 Ssd 73 6.3 Object Detection in Optical RS Images 75 6.3.1 Related Works 75 6.3.1.1 Scale Variance 75 6.3.1.2 Orientation Variance 75 6.3.1.3 Oriented Object Detection 75 6.3.1.4 Detecting in Large-size Images 76 6.3.2 Datasets and Benchmark 77 6.3.2.1 Dota 77 6.3.2.2 VisDrone 77 6.3.2.3 Dior 77 6.3.2.4 xView 77 6.3.3 Two Representative Object Detectors in Optical RS Images 78 6.3.3.1 Mask OBB 78 6.3.3.2 RoI Transformer 82 6.4 Object Detection in SAR Images 86 6.4.1 Challenges of Detection in SAR Images 86 6.4.2 Related Works 86 6.4.3 Datasets and Benchmarks 88 6.5 Conclusion 89 7 Deep Domain Adaptation in Earth Observation 90 Benjamin Kellenberger, Onur Tasar, Bharath Bhushan Damodaran, Nicolas Courty, and Devis Tuia 7.1 Introduction 90 7.2 Families of Methodologies 91 7.3 Selected Examples 93 7.3.1 Adapting the Inner Representation 93 7.3.2 Adapting the Inputs Distribution 97 7.3.3 Using (few, well chosen) Labels from the Target Domain 100 7.4 Concluding remarks 104 8 Recurrent Neural Networks and the Temporal Component 105 Marco Körner and Marc Rußwurm 8.1 Recurrent Neural Networks 106 8.1.1 Training RNNs 107 8.1.1.1 Exploding and Vanishing Gradients 107 8.1.1.2 Circumventing Exploding and Vanishing Gradients 109 8.2 Gated Variants of RNNs 111 8.2.1 Long Short-term Memory Networks 111 8.2.1.1 The Cell State c t and the Hidden State h t 112 8.2.1.2 The Forget Gate f t 112 8.2.1.3 The Modulation Gate V T and the Input Gate I T 112 8.2.1.4 The Output Gate o t 112 8.2.1.5 Training LSTM Networks 113 8.2.2 Other Gated Variants 113 8.3 Representative Capabilities of Recurrent Networks 114 8.3.1 Recurrent Neural Network Topologies 114 8.3.2 Experiments 115 8.4 Application in Earth Sciences 117 8.5 Conclusion 118 9 Deep Learning for Image Matching and Co-registration 120 Maria Vakalopoulou, Stergios Christodoulidis, Mihir Sahasrabudhe, and Nikos Paragios 9.1 Introduction 120 9.2 Literature Review 123 9.2.1 Classical Approaches 123 9.2.2 Deep Learning Techniques for Image Matching 124 9.2.3 Deep Learning Techniques for Image Registration 125 9.3 Image Registration with Deep Learning 126 9.3.1 2D Linear and Deformable Transformer 126 9.3.2 Network Architectures 127 9.3.3 Optimization Strategy 128 9.3.4 Dataset and Implementation Details 129 9.3.5 Experimental Results 129 9.4 Conclusion and Future Research 134 9.4.1 Challenges and Opportunities 134 9.4.1.1 Dataset with Annotations 134 9.4.1.2 Dimensionality of Data 135 9.4.1.3 Multitemporal Datasets 135 9.4.1.4 Robustness to Changed Areas 135 10 Multisource Remote Sensing Image Fusion 136 Wei He, Danfeng Hong, Giuseppe Scarpa, Tatsumi Uezato, and Naoto Yokoya 10.1 Introduction 136 10.2 Pansharpening 137 10.2.1 Survey of Pansharpening Methods Employing Deep Learning 137 10.2.2 Experimental Results 140 10.2.2.1 Experimental Design 140 10.2.2.2 Visual and Quantitative Comparison in Pansharpening 140 10.3 Multiband Image Fusion 143 10.3.1 Supervised Deep Learning-based Approaches 143 10.3.2 Unsupervised Deep Learning-based Approaches 145 10.3.3 Experimental Results 146 10.3.3.1 Comparison Methods and Evaluation Measures 146 10.3.3.2 Dataset and Experimental Setting 146 10.3.3.3 Quantitative Comparison and Visual Results 147 10.4 Conclusion and Outlook 148 11 Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives 150 Gencer Sumbul, Jian Kang, and Begüm Demir 11.1 Introduction 150 11.2 Deep Learning for RS CBIR 152 11.3 Scalable RS CBIR Based on Deep Hashing 156 11.4 Discussion and Conclusion 159 Acknowledgement 160 Part II Making a Difference in the Geosciences with Deep Learning 161 12 Deep Learning for Detecting Extreme Weather Patterns 163 Mayur Mudigonda, Prabhat Ram, Karthik Kashinath, Evan Racah, Ankur Mahesh, Yunjie Liu, Christopher Beckham, Jim Biard, Thorsten Kurth, Sookyung Kim, Samira Kahou, Tegan Maharaj, Burlen Loring, Christopher Pal, Travis O’Brien, Kenneth E. Kunkel, Michael F. Wehner, and William D. Collins 12.1 Scientific Motivation 163 12.2 Tropical Cyclone and Atmospheric River Classification 166 12.2.1 Methods 166 12.2.2 Network Architecture 167 12.2.3 Results 169 12.3 Detection of Fronts 170 12.3.1 Analytical Approach 170 12.3.2 Dataset 171 12.3.3 Results 172 12.3.4 Limitations 174 12.4 Semi-supervised Classification and Localization of Extreme Events 175 12.4.1 Applications of Semi-supervised Learning in Climate Modeling 175 12.4.1.1 Supervised Architecture 176 12.4.1.2 Semi-supervised Architecture 176 12.4.2 Results 176 12.4.2.1 Frame-wise Reconstruction 176 12.4.2.2 Results and Discussion 178 12.5 Detecting Atmospheric Rivers and Tropical Cyclones Through Segmentation Methods 179 12.5.1 Modeling Approach 179 12.5.1.1 Segmentation Architecture 180 12.5.1.2 Climate Dataset and Labels 181 12.5.2 Architecture Innovations: Weighted Loss and Modified Network 181 12.5.3 Results 183 12.6 Challenges and Implications for the Future 184 12.7 Conclusions 185 13 Spatio-temporal Autoencoders in Weather and Climate Research 186 Xavier-Andoni Tibau, Christian Reimers, Christian Requena-Mesa, and Jakob Runge 13.1 Introduction 186 13.2 Autoencoders 187 13.2.1 A Brief History of Autoencoders 188 13.2.2 Archetypes of Autoencoders 189 13.2.3 Variational Autoencoders (VAE) 191 13.2.4 Comparison Between Autoencoders and Classical Methods 192 13.3 Applications 193 13.3.1 Use of the Latent Space 193 13.3.1.1 Reduction of Dimensionality for the Understanding of the System Dynamics and its Interactions 195 13.3.1.2 Dimensionality Reduction for Feature Extraction and Prediction 199 13.3.2 Use of the Decoder 199 13.3.2.1 As a Random Sample Generator 201 13.3.2.2 Anomaly Detection 201 13.3.2.3 Use of a Denoising Autoencoder (DAE) Decoder 202 13.4 Conclusions and Outlook 203 14 Deep Learning to Improve Weather Predictions 204 Peter D. Dueben, Peter Bauer, and Samantha Adams 14.1 Numerical Weather Prediction 204 14.2 How Will Machine Learning Enhance Weather Predictions? 207 14.3 Machine Learning Across the Workflow of Weather Prediction 208 14.4 Challenges for the Application of ML in Weather Forecasts 213 14.5 The Way Forward 216 15 Deep Learning and the Weather Forecasting Problem: Precipitation Nowcasting 218 Zhihan Gao, Xingjian Shi, Hao Wang, Dit-Yan Yeung, Wang-chun Woo, and Wai-Kin Wong 15.1 Introduction 218 15.2 Formulation 220 15.3 Learning Strategies 221 15.4 Models 223 15.4.1 FNN-based Odels 223 15.4.2 RNN-based Models 225 15.4.3 Encoder-forecaster Structure 226 15.4.4 Convolutional LSTM 226 15.4.5 ConvLSTM with Star-shaped Bridge 227 15.4.6 Predictive RNN 228 15.4.7 Memory in Memory Network 229 15.4.8 Trajectory GRU 231 15.5 Benchmark 233 15.5.1 HKO-7 Dataset 234 15.5.2 Evaluation Methodology 234 15.5.3 Evaluated Algorithms 235 15.5.4 Evaluation Results 236 15.6 Discussion 236 Appendix 238 Acknowledgement 239 16 Deep Learning for High-dimensional Parameter Retrieval 240 David Malmgren-Hansen 16.1 Introduction 240 16.2 Deep Learning Parameter Retrieval Literature 242 16.2.1 Land 242 16.2.2 Ocean 243 16.2.3 Cryosphere 244 16.2.4 Global Weather Models 244 16.3 The Challenge of High-dimensional Problems 244 16.3.1 Computational Load of CNNs 247 16.3.2 Mean Square Error or Cross-entropy Optimization? 249 16.4 Applications and Examples 250 16.4.1 Utilizing High-Dimensional Spatio-spectral Information with CNNs 250 16.4.2 The Effect of Loss Functions in Retrieval of Sea Ice Concentrations 253 16.5 Conclusion 257 17 A Review of Deep Learning for Cryospheric Studies 258 Lin Liu 17.1 Introduction 258 17.2 Deep-learning-based Remote Sensing Studies of the Cryosphere 260 17.2.1 Glaciers 260 17.2.2 Ice Sheet 261 17.2.3 Snow 262 17.2.4 Permafrost 263 17.2.5 Sea Ice 264 17.2.6 River Ice 265 17.3 Deep-learning-based Modeling of the Cryosphere 265 17.4 Summary and Prospect 266 Appendix: List of Data and Codes 267 18 Emulating Ecological Memory with Recurrent Neural Networks 269 Basil Kraft, Simon Besnard, and Sujan Koirala 18.1 Ecological Memory Effects: Concepts and Relevance 269 18.2 Data-driven Approaches for Ecological memory Effects 270 18.2.1 A Brief Overview of Memory Effects 270 18.2.2 Data-driven Methods for Memory Effects 271 18.3 Case Study: Emulating a Physical Model Using Recurrent Neural Networks 272 18.3.1 Physical Model Simulation Data 272 18.3.2 Experimental Design 273 18.3.3 RNN Setup and Training 274 18.4 Results and Discussion 276 18.4.1 The Predictive Capability Across Scales 276 18.4.2 Prediction of Seasonal Dynamics 279 18.5 Conclusions 281 Part III Linking Physics and Deep Learning Models 283 19 Applications of Deep Learning in Hydrology 285 Chaopeng Shen and Kathryn Lawson 19.1 Introduction 285 19.2 Deep Learning Applications in Hydrology 286 19.2.1 Dynamical System Modeling 286 19.2.1.1 Large-scale Hydrologic Modeling with Big Data 286 19.2.1.2 Data-limited LSTM Applications 290 19.2.2 Physics-constrained Hydrologic Machine Learning 292 19.2.3 Information Retrieval for Hydrology 293 19.2.4 Physically-informed Machine Learning for Subsurface Flow and Reactive Transport Modeling 294 19.2.5 Additional Observations 296 19.3 Current Limitations and Outlook 296 20 Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models 298 Laure Zanna and Thomas Bolton 20.1 Introduction 298 20.2 The Parameterization Problem 299 20.3 Deep Learning Parameterizations of Subgrid Ocean Processes 300 20.3.1 Why DL for Subgrid Parameterizations? 300 20.3.2 Recent Advances in DL for Subgrid Parameterizations 300 20.4 Physics-aware Deep Learning 301 20.5 Further Challenges ahead for Deep Learning Parameterizations 303 21 Deep Learning for the Parametrization of Subgrid Processes in Climate Models 307 Pierre Gentine, Veronika Eyring, and Tom Beucler 21.1 Introduction 307 21.2 Deep Neural Networks for Moist Convection (Deep Clouds) Parametrization 309 21.3 Physical Constraints and Generalization 312 21.4 Future Challenges 314 22 Using Deep Learning to Correct Theoretically-derived Models 315 PeterA.G.Watson 22.1 Experiments with the Lorenz ’96 System 317 22.1.1 The Lorenz’96 Equations and Coarse-scale Models 318 22.1.1.1 Theoretically-derived Coarse-scale Model 318 22.1.1.2 Models with ANNs 319 22.1.2 Results 320 22.1.2.1 Single-timestep Tendency Prediction Errors 320 22.1.2.2 Forecast and Climate Prediction Skill 321 22.1.3 Testing Seamless Prediction 324 22.2 Discussion and Outlook 324 22.2.1 Towards Earth System Modeling 325 22.2.2 Application to Climate Change Studies 326 22.3 Conclusion 327 23 Outlook 328 Markus Reichstein, Gustau Camps-Valls, Devis Tuia, and Xiao Xiang Zhu Bibliography 331 Index 401

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Book SynopsisMANAGEMENT OF DATA CENTER NETWORKS Discover state-of-the-art developments in DCNs from leading international voices in the fieldIn Management of Data Center Networks, accomplished researcher and editor Dr. Nadjib Aitsaadi delivers a rigorous and insightful exploration of the network management challenges that present within intra- and inter-data center networks, including reliability, routing, and security. The book also discusses new architectures found in data center networks that aim to minimize the complexity of network management while maximizing Quality of Service, like Wireless/Wired DCNs, server-only DCNs, and more. As DCNs become increasingly popular with the spread of cloud computing and multimedia social networks employing new transmission technologies like 5G wireless and wireless fiber, the editor provides readers with chapters written by world-leading authors on topics like routing, the reliability of inter-data center networks, energy management, and security. The boTable of ContentsAbout the Editor xi Contributors xiii Acronyms xv Introduction xvii 1 Architectures of Data Center Networks: Overview 1Boutheina Dab, Ilhem Fajjari, Dallal Belabed, and Nadjib Aitsaadi 1.1 Taxonomy of DCN Architectures 1 1.1.1 Classification of DCN Architectures 2 1.1.2 Switch-Centric DCN Architectures Overview 3 1.1.2.1 Tree-Based DCN 3 1.1.2.2 Hierarchical DCN Architecture 4 1.1.2.3 Flat DCN Architecture 6 1.1.3 Server-Centric DCN Architectures Overview 7 1.1.4 Enhanced DCN Architectures Overview 10 1.1.4.1 Optical DCN Architecture 10 1.1.4.2 Wireless DCN Architecture 12 1.2 Comparison Between DCN Architectures 15 1.3 Proposed HDCN Architecture 15 1.3.1 HDCN Architecture Based on MSDC Model 19 1.3.1.1 ECMP Protocol 19 1.3.2 60GHz Technology in HDCN 20 1.3.3 Beamforming Technique in HDCN 21 1.4 Conclusion 23 References 23 2 Data Center Optimization Techniques 29Dallal Belabed 2.1 Ethernet Switching and Routing 29 2.2 Data Center Optimization Techniques 38 2.2.1 Virtual Network Embedding 38 2.2.2 Server Consolidation 40 2.2.3 Traffic Engineering 43 2.2.3.1 Link-State Traffic Engineering 44 2.2.3.2 MPLS Traffic Engineering 44 2.2.3.3 TCP Proportional Fairness Model 46 2.3 Conclusion 49 Bibliography 51 3 Resource Management in Hybrid (Wired/Wireless) Data Center Networks 57Boutheina Dab, Ilhem Fajjari, and Nadjib Aitsaadi 3.1 Routing and Wireless Channel Allocation Problematic in HDCN 58 3.1.1 Routing and Wireless Channel Assignment Challenges in HDCN 59 3.1.2 Routing and Wireless Channel Assignment Criteria in HDCN 61 3.2 Wireless Channel Allocation Strategies for One-Hop Communications in HDCN 62 3.2.1 Channel Allocation Problem in Wireless Networks 63 3.2.2 Omni-Directional Antennas Based Strategies 63 3.2.3 Beamforming-Based Strategies 67 3.3 Online Joint Routing and Wireless Channel Allocation Strategies in HDCN 69 3.3.1 Joint Routing and Channel Assignment in Mesh Networks 70 3.3.2 Online Joint Routing and Channel Assignment Strategies in HDCN 71 3.4 Joint Batch Routing and Channel Allocation Strategies in HDCN 75 3.5 Joint Batch Routing and Channel Allocation Strategies in HDCN 75 3.6 Summary 77 3.7 Conclusion 80 References 80 4 Inter-Data Center Networks: Routing and Reliability in Virtual Network Backbone 85Oussama Soualah, Ilhem Fajjari, and Nadjib Aitsaadi 4.1 Overview of Basic Virtual Network Embedding Without Reliability Constraint 85 4.1.1 Online Approaches 86 4.1.2 Batch Approaches 87 4.2 Overview of Virtual Network Embedding with Reliability Constraint 89 4.2.1 Distributed Approaches 89 4.2.2 Centralized Approaches 91 4.2.2.1 Substrate Router Failures 91 4.2.2.2 Substrate Link Failures 92 4.2.2.3 Substrate Router and Link Failures 94 4.2.2.4 Regional Failures 95 4.2.3 Summary 101 4.3 Conclusion 101 References 101 5 An Evaluation Method of Optimal Cost Saving in a Data Center with Proactive Management 105Ruben Milocco, Pascale Minet, Éric Renault, and Selma Boumerdassi 5.1 Introduction 106 5.2 RelatedWork 108 5.3 Framework for DC Modeling 111 5.3.1 Notations and Assumptions 111 5.3.2 Energy Computation 111 5.3.2.1 Single-Resource Case 111 5.3.2.2 Extension to the Multi-resource Case 114 5.4 Cost Formulation 114 5.4.1 Example 115 5.4.2 Methodology 116 5.4.3 Relative Energy Cost Saving 116 5.4.4 Upper Bound Computation 118 5.5 Application to a Real DC 118 5.5.1 Generalities 119 5.5.1.1 Selection of the Sampling Interval 119 5.5.1.2 Selection of Possible Values for the Costs 119 5.5.1.3 Dynamic Capacity Provisioning Based on Energy Prediction 119 5.5.2 Application to a Google Dataset 120 5.5.2.1 Energy Computation 120 5.5.2.2 Evaluation of the Upper Bound 122 5.5.2.3 Computation of the Relative Energy Cost Saving 123 5.5.2.4 Discussion of Results 124 5.6 Conclusion 124 References 125 Index 129

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Book SynopsisMove your career forward with AWS certification! Prepare for the AWS Certified Data Analytics Specialty Exam with this thorough study guide This comprehensive study guide will help assess your technical skills and prepare for the updated AWS Certified Data Analytics exam. Earning this AWS certification will confirm your expertise in designing and implementing AWS services to derive value from data. The AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is designed for business analysts and IT professionals who perform complex Big Data analyses. This AWS Specialty Exam guide gets you ready for certification testing with expert content, real-world knowledge, key exam concepts, and topic reviews. Gain confidence by studying the subject areas and working through the practice questions. Big data concepts covered in the guide include: Collection Storage Processing Analysis Visualization DTable of ContentsIntroduction xxi Assessment Test xxx Chapter 1 History of Analytics and Big Data 1 Evolution of Analytics Architecture Over the Years 3 The New World Order 5 Analytics Pipeline 6 Data Sources 7 Collection 8 Storage 8 Processing and Analysis 9 Visualization, Predictive and Prescriptive Analytics 9 The Big Data Reference Architecture 10 Data Characteristics: Hot, Warm, and Cold 11 Collection/Ingest 12 Storage 13 Process/Analyze 14 Consumption 15 Data Lakes and Their Relevance in Analytics 16 What is a Data Lake? 16 Building a Data Lake on AWS 19 Step 1: Choosing the Right Storage – Amazon S3 Is the Base 19 Step 2: Data Ingestion – Moving the Data into the Data Lake 21 Step 3: Cleanse, Prep, and Catalog the Data 22 Step 4: Secure the Data and Metadata 23 Step 5: Make Data Available for Analytics 23 Using Lake Formation to Build a Data Lake on AWS 23 Exam Objectives 24 Objective Map 25 Assessment Test 27 References 29 Chapter 2 Data Collection 31 Exam Objectives 32 AWS IoT 33 Common Use Cases for AWS IoT 35 How AWS IoT Works 36 Amazon Kinesis 38 Amazon Kinesis Introduction 40 Amazon Kinesis Data Streams 40 Amazon Kinesis Data Analytics 54 Amazon Kinesis Video Streams 61 AWS Glue 64 Glue Data Catalog 66 Glue Crawlers 68 Authoring ETL Jobs 69 Executing ETL Jobs 71 Change Data Capture with Glue Bookmarks 71 Use Cases for AWS Glue 72 Amazon SQS 72 Amazon Data Migration Service 74 What is AWS DMS Anyway? 74 What Does AWS DMS Support? 75 AWS Data Pipeline 77 Pipeline Definition 77 Pipeline Schedules 78 Task Runner 79 Large-Scale Data Transfer Solutions 81 AWS Snowcone 81 AWS Snowball 82 AWS Snowmobile 85 AWS Direct Connect 86 Summary 87 Review Questions 88 References 90 Exercises & Workshops 91 Chapter 3 Data Storage 93 Introduction 94 Amazon S3 95 Amazon S3 Data Consistency Model 96 Data Lake and S3 97 Data Replication in Amazon S3 100 Server Access Logging in Amazon S3 101 Partitioning, Compression, and File Formats on S3 101 Amazon S3 Glacier 103 Vault 103 Archive 104 Amazon DynamoDB 104 Amazon DynamoDB Data Types 105 Amazon DynamoDB Core Concepts 108 Read/Write Capacity Mode in DynamoDB 108 DynamoDB Auto Scaling and Reserved Capacity 111 Read Consistency and Global Tables 111 Amazon DynamoDB: Indexing and Partitioning 113 Amazon DynamoDB Accelerator 114 Amazon DynamoDB Streams 115 Amazon DynamoDB Streams – Kinesis Adapter 116 Amazon DocumentDB 117 Why a Document Database? 117 Amazon DocumentDB Overview 119 Amazon Document DB Architecture 120 Amazon DocumentDB Interfaces 120 Graph Databases and Amazon Neptune 121 Amazon Neptune Overview 122 Amazon Neptune Use Cases 123 Storage Gateway 123 Hybrid Storage Requirements 123 AWS Storage Gateway 125 Amazon EFS 127 Amazon EFS Use Cases 130 Interacting with Amazon EFS 132 Amazon EFS Security Model 132 Backing Up Amazon EFS 132 Amazon FSx for Lustre 133 Key Benefits of Amazon FSx for Lustre 134 Use Cases for Lustre 135 AWS Transfer for SFTP 135 Summary 136 Exercises 137 Review Questions 140 Further Reading 142 References 142 Chapter 4 Data Processing and Analysis 143 Introduction 144 Types of Analytical Workloads 144 Amazon Athena 146 Apache Presto 147 Apache Hive 148 Amazon Athena Use Cases and Workloads 149 Amazon Athena DDL, DML, and DCL 150 Amazon Athena Workgroups 151 Amazon Athena Federated Query 153 Amazon Athena Custom UDFs 154 Using Machine Learning with Amazon Athena 154 Amazon EMR 155 Apache Hadoop Overview 156 Amazon EMR Overview 157 Apache Hadoop on Amazon EMR 158 EMRFS 166 Bootstrap Actions and Custom AMI 167 Security on EMR 167 EMR Notebooks 168 Apache Hive and Apache Pig on Amazon EMR 169 Apache Spark on Amazon EMR 174 Apache HBase on Amazon EMR 182 Apache Flink, Apache Mahout, and Apache MXNet 184 Choosing the Right Analytics Tool 186 Amazon Elasticsearch Service 188 When to Use Elasticsearch 188 Elasticsearch Core Concepts (the ELK Stack) 189 Amazon Elasticsearch Service 191 Amazon Redshift 192 What is Data Warehousing? 192 What is Redshift? 193 Redshift Architecture 195 Redshift AQUA 198 Redshift Scalability 199 Data Modeling in Redshift 205 Data Loading and Unloading 213 Query Optimization in Redshift 217 Security in Redshift 221 Kinesis Data Analytics 225 How Does It Work? 226 What is Kinesis Data Analytics for Java? 228 Comparing Batch Processing Services 229 Comparing Orchestration Options on AWS 230 AWS Step Functions 230 Comparing Different ETL Orchestration Options 230 Summary 231 Exam Essentials 232 Exercises 232 Review Questions 235 References 237 Recommended Workshops 237 Amazon Athena Blogs 238 Amazon Redshift Blogs 240 Amazon EMR Blogs 241 Amazon Elasticsearch Blog 241 Amazon Redshift References and Further Reading 242 Chapter 5 Data Visualization 243 Introduction 244 Data Consumers 245 Data Visualization Options 246 Amazon QuickSight 247 Getting Started 248 Working with Data 250 Data Preparation 255 Data Analysis 256 Data Visualization 258 Machine Learning Insights 261 Building Dashboards 262 Embedding QuickSight Objects into Other Applications 264 Administration 265 Security 266 Other Visualization Options 267 Predictive Analytics 270 What is Predictive Analytics? 270 The AWS ML Stack 271 Summary 273 Exam Essentials 273 Exercises 274 Review Questions 275 References 276 Additional Reading Material 276 Chapter 6 Data Security 279 Introduction 280 Shared Responsibility Model 280 Security Services on AWS 282 AWS IAM Overview 285 IAM User 285 IAM Groups 286 IAM Roles 287 Amazon EMR Security 289 Public Subnet 290 Private Subnet 291 Security Configurations 293 Block Public Access 298 VPC Subnets 298 Security Options during Cluster Creation 299 EMR Security Summary 300 Amazon S3 Security 301 Managing Access to Data in Amazon S3 301 Data Protection in Amazon S3 305 Logging and Monitoring with Amazon S3 306 Best Practices for Security on Amazon S3 308 Amazon Athena Security 308 Managing Access to Amazon Athena 309 Data Protection in Amazon Athena 310 Data Encryption in Amazon Athena 311 Amazon Athena and AWS Lake Formation 312 Amazon Redshift Security 312 Levels of Security within Amazon Redshift 313 Data Protection in Amazon Redshift 315 Redshift Auditing 316 Redshift Logging 317 Amazon Elasticsearch Security 317 Elasticsearch Network Configuration 318 VPC Access 318 Accessing Amazon Elasticsearch and Kibana 319 Data Protection in Amazon Elasticsearch 322 Amazon Kinesis Security 325 Managing Access to Amazon Kinesis 325 Data Protection in Amazon Kinesis 326 Amazon Kinesis Best Practices 326 Amazon QuickSight Security 327 Managing Data Access with Amazon QuickSight 327 Data Protection 328 Logging and Monitoring 329 Security Best Practices 329 Amazon DynamoDB Security 329 Access Management in DynamoDB 329 IAM Policy with Fine-Grained Access Control 330 Identity Federation 331 How to Access Amazon DynamoDB 332 Data Protection with DynamoDB 332 Monitoring and Logging with DynamoDB 333 Summary 334 Exam Essentials 334 Exercises/Workshops 334 Review Questions 336 References and Further Reading 337 Appendix Answers to Review Questions 339 Chapter 1: History of Analytics and Big Data 340 Chapter 2: Data Collection 342 Chapter 3: Data Storage 343 Chapter 4: Data Processing and Analysis 344 Chapter 5: Data Visualization 346 Chapter 6: Data Security 346 Index 349

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Book SynopsisLearn how radio access network (RAN) slicing allows 5G networks to adapt to a wide range of environments in this masterful resource Radio Access Network Slicing and Virtualization for 5G Vertical Industriesprovides readers with a comprehensive and authoritative examination of crucial topics in the field of radio access network (RAN) slicing. Learn from renowned experts as they detail how this technology supports and applies to various industrial sectors, including manufacturing, entertainment, public safety, public transport, healthcare, financial services, automotive, and energy utilities. Radio Access Network Slicing and Virtualization for 5G Vertical Industries explains how future wireless communication systems must be built to handle high degrees of heterogeneity, including different types of applications, device classes, physical environments, mobility levels, and carrier frequencies. The authors describe how RAN slicing can be utilized to adapt 5G technologies to such wide-ranTable of ContentsAbout the Editors xiii Preface xvii List of Contributors xxiii List of Abbreviations xxvii Part I Waveforms and Mixed-Numerology 1 1 ICI Cancellation Techniques Based on Data Repetition for OFDM Systems 3Miaowen Wen, Jun Li, Xilin Cheng and Xiang Cheng 1.1 OFDM History 3 1.2 OFDM Principle 4 1.2.1 Subcarrier Orthogonality 4 1.2.2 Discrete Implementation 5 1.2.3 OFDM in Multipath Channel 6 1.3 Carrier Frequency Offset Effect 8 1.3.1 Properties of ICI Coefficients 9 1.3.2 Carrier-to-Interference Power Ratio 9 1.4 ICI Cancellation Techniques 11 1.4.1 One-Path Cancellation with Mirror Mapping 11 1.4.1.1 MSR Scheme 12 1.4.1.2 MCSR Scheme 13 1.4.2 Two-Path Cancellation with Mirror Mapping 14 1.4.2.1 MCVT Scheme 15 1.4.2.2 MCJT Scheme 15 1.4.3 CIR Comparison 16 1.5 Experiment on Sea 17 1.5.1 Experiment Settings 18 1.5.2 Experiment Results 21 1.6 Summary 22 References 23 2 Filtered OFDM: An Insight into Intrinsic In-Band Interference 25Juquan Mao, Lei Zhang and Pei Xiao 2.1 Introduction 25 2.1.1 Notations 26 2.2 System Model for f-OFDM SISO System 26 2.3 In-Band Interference Analysis and Discussion 30 2.3.1 Channel Diagonalization and In-Band Interference-Free Systems 30 2.3.2 In-Band Interference Power 31 2.3.3 In-Band Interference Mitigation: A Practical Approach for Choosing CR Length 32 2.3.4 An Alternative for In-Band Interference Mitigation: Frequency Domain Equalization (FDE) 33 2.3.4.1 Linear Equalizers 33 2.3.4.2 Nonlinear Equalizers 34 2.4 Numerical Results 34 2.4.1 Numerical Results for In-Band Interference 35 2.5 Conclusion 38 1.2 Appendix 38 1.2.1 Derivation of zk 38 2.3 Appendix 39 2.3.1 Proof of 𝚯preBeing a Strict Upper Triangle 39 3.4 Appendix 39 3.4.1 Proof of Property 2.A.2 39 References 40 3 Windowed OFDM for Mixed-Numerology 5G and Beyond Systems 43Bowen Yang, Xiaoying Zhang, Lei Zhang, Arman Farhang, Pei Xiao and Muhammad Ali Imran 3.1 Introduction 43 3.2 W-OFDM System Model 45 3.2.1 Single Numerology System Model 46 3.2.2 System Model for Mixed Numerologies 48 3.3 Inter-numerology Interference Analysis 50 3.3.1 Inter-numerology Interference Analysis for Numerology 1 50 3.3.2 Inter-numerology Interference Analysis for Numerology 2 52 3.4 Numerical Results and Discussion 54 3.5 Conclusions 57 3.6 Derivation of (3.9) 57 3.7 Derivations of (3.28) 58 3.8 Derivations of (3.30) 59 References 59 4 Generalized Frequency Division Multiplexing: Unified Multicarrier Framework 63Ahmad Nimr, Zhongju Li, Marwa Chafii and Gerhard Fettweis 4.1 Overview of MulticarrierWaveforms 63 4.1.1 Time–Frequency Representation 64 4.1.1.1 Discrete-Time Representation 65 4.1.1.2 Relation to Gabor Theory 66 4.1.2 GFDM As a FlexibleWaveform 66 4.1.2.1 GFDM with Multiple Prototype Pulses 67 4.1.3 Generalized Block-Based Multicarrier 68 4.1.3.1 Transmitter 69 4.1.3.2 Receiver 69 4.2 GFDM As a Flexible Framework 70 4.2.1 GFDM Representations 71 4.2.1.1 Filter Bank Representation 71 4.2.1.2 Vector Representation 71 4.2.1.3 2D-Block Representation 72 4.2.1.4 GFDM Matrix Structure 73 4.2.2 Architecture and Extended Flexibility 74 4.2.2.1 Alternative Interpretation of GFDM 75 4.2.2.2 Extended Flexibility 76 4.2.2.3 Flexible Hardware Architecture 76 4.3 GFDM for OFDM Enhancement 78 4.3.1 Transmitter 78 4.3.2 Receiver 79 4.3.2.1 LMMSE GFDM-Based Receiver 79 4.4 Conclusions 80 References 80 5 Filter Bank Multicarrier Modulation 83Behrouz Farhang-Boroujeny 5.1 Introduction 83 5.1.1 Notations: 83 5.2 FBMC Methods 84 5.3 Theory 84 5.3.1 CMT 85 5.3.2 SMT 88 5.4 Prototype Filter Design 92 5.4.1 Prototype Filters for Time-Invariant Channels 92 5.4.2 Prototype Filters for Time-Varying Channels 93 5.5 Synchronization and Tracking Methods 94 5.5.1 Preamble Design 95 5.5.2 Channel Tracking 96 5.5.3 Timing Tracking 97 5.6 Equalization 97 5.7 Computational Complexity 98 5.8 Applications 98 References 99 6 Orthogonal Time–Frequency Space Modulation: Principles and Implementation 103Arman Farhang and Behrouz Farhang-Boroujeny 6.1 Introduction 103 6.2 OTFS Principles 105 6.3 OFDM-Based OTFS 107 6.4 Channel Impact 108 6.5 Simplified Modem Structure 110 6.6 Complexity Analysis 113 6.7 Recent Results and Potential Research Directions 114 References 117 Part II RAN Slicing and 5G Vertical Industries 121 7 Multi-Numerology Waveform Parameter Assignment in 5G 123Ahmet Yazar and Hüseyin Arslan 7.1 Introduction 123 7.1.1 Problem Definitions 125 7.1.2 Literature Review 126 7.2 Waveform Parameter Options 128 7.3 Waveform Parameter Assignment 130 7.4 Conclusion 132 References 132 8 Network Slicing with Spectrum Sharing 137Yue Liu, Xu Yang and Laurie Cuthbert 8.1 The Need for Spectrum Sharing 137 8.2 Historical Approaches to Spectrum Sharing 139 8.2.1 Classifications of Spectrum Sharing 140 8.2.1.1 Orthogonality 140 8.2.1.2 Sharing Rights 141 8.2.1.3 Allocation of Resources 142 8.3 Network Slicing in the RAN 144 8.4 Radio Resource Allocation that Considers Spectrum Sharing 146 8.4.1 Example Radio Resource Allocation for Sharing Through Network Slicing 147 8.4.2 Other Considerations 153 8.5 Isolation 156 8.5.1 Example Isolation Results Using CAC 157 8.5.1.1 Type A: Baseline – CACWithout Network Isolation and Without Protection for Existing Users 158 8.5.1.2 Type B: Optimum Types – B1 and B2 158 8.5.1.3 Type C: Without Compensation – C1 and C2 159 8.6 Conclusions 162 Acknowledgments 163 References 163 9 Access Control and Handoff Policy Design for RAN Slicing 167Yao Sun, Lei Zhang, Gang Feng and Muhammad Ali Imran 9.1 A Framework of User Access Control for RAN Slicing 167 9.1.1 System Model for RAN Slicing 168 9.1.2 UE Association Problem Description 170 9.1.3 Admission Control Mechanisms Design for RAN Slicing 170 9.1.3.1 Optimal QoS AC Mechanism 171 9.1.3.2 Num-AC Mechanism 176 9.1.4 Experiments, Results, and Discussions 177 9.2 Smart Handoff Policy Design for RAN Slicing 179 9.2.1 RAN Slice Based Mobile Network Model 179 9.2.2 Multi-Agent Reinforcement Learning Based Handoff Framework 181 9.2.3 LESS Algorithm for Target BS and NS Selection 181 9.2.3.1 q-Value Update Policy 182 9.2.3.2 Optimal Action Policy 183 9.2.4 Experiment, Results, and Discussions 184 9.3 Summary 186 References 186 10 Robust RAN Slicing 189Ruihan Wen and Gang Feng 10.1 Introduction 189 10.2 Network Model 190 10.2.1 Slice Failure Detection Process 190 10.2.2 System Model 191 10.3 Robust RAN Slicing 193 10.3.1 Failure Recovery Problem Formulation 193 10.3.2 Robust RAN Slicing Problem Formulation 195 10.3.3 Variable Neighborhood Search Based Heuristic for Robust RAN Slicing 196 10.4 Numerical Results 199 10.4.1 Performance Metrics 199 10.4.2 Simulation Scenarios and Settings 200 10.4.3 Results 201 10.5 Conclusions and Future Work 206 References 206 11 Flexible Function Split Over Ethernet Enabling RAN Slicing 209Ghizlane Mountaser and Toktam Mahmoodi 11.1 Flexible Functional Split Toward RAN Slicing 209 11.1.1 Full Centralization and CPRI 209 11.1.2 RAN Functional Split 210 11.1.3 Flexible Functional Split as RAN Slicing Enabler 213 11.2 Fronthaul Reliability and Slicing by Deploying Multipath at the Fronthaul 213 11.2.1 Packet-Based Fronthaul 213 11.2.2 Multipath Packet-Based Fronthaul for Enhancing Reliability 213 11.2.3 Slicing Within Multipath Fronthaul 214 11.3 Experimentation Results Evaluation of Flexible Functional Split for RAN Slicing 214 11.3.1 Experimental Setup 214 11.3.2 Evaluation and Discussion of the Results 215 11.4 Simulation Results Analysis of Multipath Packet-Based Fronthaul for RAN Slicing 217 11.4.1 Simulation System Model 217 References 219 12 Service-Oriented RAN Support of Network Slicing 221Wei Tan, Feng Han, Yinghao Jin and Chenchen Yang 12.1 Introduction 221 12.2 General Concept and Principles 222 12.2.1 Network Slicing Concepts 223 12.2.2 Overall RAN Subsystem 224 12.2.3 Key Principles of Network Slicing in RAN 225 12.3 RAN Subsystem Deployment Scenarios 227 12.4 Key Technologies to Enable Service-Oriented RAN Slicing 229 12.4.1 Device Awareness of RAN Part of Network Slice 230 12.4.2 Slice-Specific RAN Part of Network Slice 232 12.4.3 Mission-Driven Resource Utilization, Sharing, and Aggregation 234 12.4.4 Slice-Aware Connected UE Mobility 235 12.4.5 Slice-Level Handlings for Idle/Inactive UEs 237 12.5 Summary 237 References 238 13 5G Network Slicing for V2X Communications: Technologies and Enablers 239Claudia Campolo, Antonella Molinaro and Vincenzo Sciancalepore 13.1 Introduction 239 13.2 Vehicular Applications 240 13.3 V2X Communication Technologies 241 13.3.1 The C-V2X Technology 242 13.3.1.1 The PC5 Radio Interface 242 13.3.1.2 The LTE-Uu Interface 242 13.3.1.3 Core Network 243 13.3.2 C-V2X Toward 5G 243 13.3.2.1 Radio Interface 243 13.3.2.2 Core Network 244 13.4 Cloudification in V2X Environments 245 13.4.1 The Role of MEC 245 13.4.2 ETSI MEC-Based Programmable Interfaces 246 13.4.3 MEC-Based Support for V2X Applications 247 13.5 Transport and Tunneling Protocol for V2X 248 13.5.1 GTP-U Encapsulation 248 13.5.2 Segment Routing v6 248 13.5.3 Scalability and Flexibility in SRv6 250 13.6 Network Slicing for V2X 251 13.6.1 3GPP Specifications 251 13.6.2 Literature Overview 252 13.7 Lessons Learnt and Guidelines 255 13.7.1 Slice Mapping and Identification 255 13.7.2 Multi-tenancy Management 255 13.7.3 Massive Communications 255 13.7.4 Transparent Mobility 256 13.7.5 Isolation 256 13.8 Conclusions 256 References 256 14 Optimizing Resource Allocation in URLLC for Real-Time Wireless Control Systems 259Bo Chang, Liying Li and Guodong Zhao 14.1 Introduction 259 14.2 System Model with Latency and Reliability Constraints 261 14.2.1 Wireless Control Model 262 14.2.2 Wireless Communication Model 266 14.3 Communication-Control Co-Design 267 14.3.1 Communication Constraint 267 14.3.2 Control Constraint 268 14.3.3 Problem Formulation 269 14.4 Optimal Resource Allocation for The Proposed Co-Design 270 14.4.1 Relationship Between Control and Communication 270 14.4.2 Optimal Resource Allocation 271 14.4.2.1 Problem Conversion 271 14.4.2.2 Problem Solution 272 14.4.3 Optimal Control Convergence Rate 273 14.5 Simulations Results 273 14.5.1 Control Performance 274 14.5.2 Communication Performance 276 14.6 Conclusions 279 References 279 Index 283

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Book SynopsisThe latest developments and recent progress on the key technologies enabling next-generation 6G mobile networks Toward 6G: A New Era of Convergence offers an up-to-date guide to the emerging 6G vision by describing new human-centric services made possible by combinations of mobile robots, avatars, and smartphones, which will be increasingly replaced with wearable displays and haptic interfaces that provide immersive extended reality (XR) experiences. The authorsnoted experts on the topicinclude a review of their work and information on the recent progress on the Tactile Internet and multi-sensory haptic communications. The book highlights decentralized edge computing in particular via Ethereum blockchain technologies, most notably the so-called decentralized autonomous organization (DAO) for crowdsourcing of human skills to solve problems that machines (such as autonomous artificial intelligence agents and robots) alone cannot solve well. The book also coTable of ContentsAuthor Biographies xi Foreword xiii Preface xv Acknowledgments xvii Acronyms xix 1 The 6G Vision 1 1.1 Introduction 1 1.2 Evolution of Mobile Networks and Internet 3 1.3 6G Network Architectures and Key Enabling Technologies 6 1.3.1 Four-Tier Networks: Space-Air-Ground-Underwater 6 1.3.2 Key Enabling Technologies 7 1.3.2.1 Millimeter-Wave and Terahertz Communications 7 1.3.2.2 Reconfigurable Intelligent Surfaces 8 1.3.2.3 From Network Softwarization to Network Intelligentization 9 1.4 Toward 6G: A New Era of Convergence 11 1.5 Scope and Outline of Book 13 1.5.1 Scope 13 1.5.2 Outline 14 2 Immersive Tactile Internet Experiences via Edge Intelligence 19 2.1 Introduction 19 2.2 The Tactile Internet: Automation or Augmentation of the Human? 26 2.3 Haptic Traffic Characterization 32 2.3.1 Teleoperation Experiments 33 2.3.1.1 6-DoF Teleoperation without Deadband Coding 33 2.3.1.2 1-DoF Teleoperation with Deadband Coding 33 2.3.1.3 Packetization 33 2.3.2 Packet Interarrival Times 34 2.3.3 Sample Autocorrelation 39 2.4 FiWi Access Networks: Revisited for Clouds and Cloudlets 41 2.4.1 FiWi: EPON and WLAN 42 2.4.2 C-RAN: Cloud vs. Cloudlet 45 2.4.3 Low-Latency FiWi Enhanced LTE-A HetNets 45 2.5 Delay Analysis 48 2.5.1 Assumptions 48 2.5.2 Local Teleoperation 48 2.5.3 Nonlocal Teleoperation 53 2.6 Edge Sample Forecast 54 2.7 Results 58 2.8 Conclusions 63 3 Context- and Self-Awareness for Human-Agent-Robot Task Coordination 65 3.1 Introduction 65 3.2 System Model 67 3.2.1 Network Architecture 67 3.2.2 Energy and Motion Models of Mobile Robots 69 3.3 Context-Aware Multirobot Task Coordination 71 3.3.1 Illustrative Case Study 71 3.3.2 Problem Formulation 72 3.3.3 The Proposed Algorithm 76 3.4 Self-Aware Optimal Motion Planning 77 3.5 Delay and Reliability Analysis 81 3.5.1 Delay Analysis 81 3.5.1.1 Transmission Delay from MU to OLT 83 3.5.1.2 Transmission Delay from OLT to MR 84 3.5.1.3 End-to-End Delay from MR to MU 84 3.5.2 Reliability Analysis 84 3.6 Results 86 3.7 Conclusion 93 4 Delay-Constrained Teleoperation Task Scheduling and Assignment 95 4.1 Introduction 95 4.2 System Model and Network Architecture 97 4.3 Problem Statement 99 4.3.1 Problem Formulation 99 4.3.2 Model Scalability 102 4.4 Algorithmic Solution 103 4.4.1 Illustrative Case Study 103 4.4.2 Proposed Task Coordination Algorithm 104 4.4.3 Complexity Analysis 106 4.5 Delay Analysis 106 4.5.1 Local Teleoperation 108 4.5.2 Nonlocal Teleoperation 109 4.6 Results 109 4.7 Discussion 118 4.8 Conclusion 118 5 Cooperative Computation Offloading in FiWi-Enhanced Mobile Networks 121 5.1 Introduction 121 5.2 System Model 124 5.3 Energy-Delay Analysis of the Proposed Cooperative Offloading 126 5.3.1 Average Response Time 127 5.3.1.1 Delay Analysis of WiFi Users 130 5.3.1.2 Delay Analysis of 4G LTE-A Users 130 5.3.1.3 Delay Analysis of Backhaul EPON 131 5.3.2 Average Energy Consumption per Task 132 5.4 Energy-Delay Trade-off via Self-Organization 134 5.5 Results 137 5.6 Conclusions 145 6 Decentralization via Blockchain 147 6.1 Introduction 147 6.2 Blockchain Technologies 150 6.2.1 Ethereum vs. Bitcoin Blockchains 150 6.2.2 Ethereum: The DAO 154 6.3 Blockchain IoT and Edge Computing 155 6.3.1 Blockchain IoT (BIoT): Recent Progress and Related Work 155 6.3.2 Blockchain Enabled Edge Computing 157 6.4 Decentralizing the Tactile Internet 158 6.4.1 AI-enhanced MEC 159 6.4.2 Crowdsourcing 160 6.5 Nudging: From Judge Contract to Nudge Contract 162 6.5.1 Cognitive Assistance: From AI to Intelligence Amplification (IA) 162 6.5.2 HITL Hybrid-Augmented Intelligence 162 6.5.3 Decentralized Self-Organizing Cooperative (DSOC) 163 6.5.4 Nudge Contract: Nudging via Smart Contract 163 6.6 Conclusions 165 7 XR in the 6G Post-Smartphone Era 167 7.1 Introduction 167 7.2 6G Vision: Putting (Internet of No) Things in Perspective 169 7.3 Extended Reality (XR): Unleashing Its Full Potential 170 7.3.1 The Reality–Virtuality Continuum 170 7.3.2 The Multiverse: An Architecture of Advanced XR Experiences 171 7.4 Internet of No Things: Invisible-to-Visible (I2V) Technologies 173 7.4.1 Extrasensory Perception Network (ESPN) 175 7.4.2 Nonlocal Awareness of Space and Time: Mimicking the Quantum Realm 176 7.4.2.1 Precognition 178 7.4.2.2 Eternalism 178 7.5 Results 180 7.6 Conclusions 181 Appendix A Proof of Lemmas 183 A.1 Proof of Lemma 3.1 183 A.2 Proof of Lemma 3.2 184 A.3 Proof of Lemma 3.3 185 A.4 Proof of Lemma 5.1 186 Bibliography 191 Index 203

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Book SynopsisA playbook on product-led strategy for software product teams There''s a common strategy used by the fastest growing and most successful businesses of our time. These companies are building their entire customer experience around their digital products, delivering software that is simple, intuitive and delightful, and that anticipates and exceeds the evolving needs of users. Product-led organizations make their products the vehicle for acquiring and retaining customers, driving growth, and influencing organizational priorities. They represent the future of business in a digital-first world. This book is meant to help you transform your company into a product-led organization, helping to drive growth for your business and advance your own career. It provides: A holistic view of the quantitative and qualitative insights teams need to make better decisions and shape better product experiences. A guide to setting goals for product success and mTable of ContentsPreface ix Introducing Product-led Strategy xv Section One Leveraging Data to Create a Great Product Chapter 1 Start with the End in Mind 3 Chapter 2 You Are What You Measure 27 Chapter 3 Turning Customer Data into Insights 45 Chapter 4 How to Measure Feelings 63 Section Two Product is the Center of the Customer Experience Chapter 5 Marketing in a Product-led World 81 Chapter 6 Converting Users into Customers 95 Chapter 7 Getting Customers Off to a Fast Start Through Onboarding 99 Chapter 8 Delivering Value 121 Chapter 9 Customer Self-Service 133 Chapter 10 Renew and Expand: Creating Customers for Life 145 Section Three A New Way of Delivering Product Chapter 11 Product-led Design 157 Chapter 12 Launching and Driving Adoption 161 Chapter 13 The Art of Letting Go 175 Chapter 14 What Users Want 183 Chapter 15 Dynamic Roadmapping 195 Chapter 16 Building Modern Product Teams 207 Conclusion: A Call to Action 217 Acknowledgments 219 About the Author 221 Index 223

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Book SynopsisBattery technology is constantly changing, and the concepts and applications of these changes are rapidly becoming increasingly more important as more and more industries and individuals continue to make greener choices in their energy sources. As global dependence on fossil fuels slowly wanes, there is a heavier and heavier importance placed on cleaner power sources and methods for storing and transporting that power. Battery technology is a huge part of this global energy revolution. Rechargeable battery technologies have been a milestone for moving toward a fossil-fuel-free society. They include groundbreaking changes in energy storage, transportation, and electronics. Improvements in battery electrodes and electrolytes have been a remarkable development, and, in the last few years, rechargeable batteries have attracted significant interest from scientists as they are a boon for electric vehicles, laptops and computers, mobile phones, portable electronics, and grid-level electric

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Book SynopsisBattery technology is constantly changing, and the concepts and applications of these changes are rapidly becoming increasingly more important as more and more industries and individuals continue to make greener choices in their energy sources. As global dependence on fossil fuels slowly wanes, there is a heavier and heavier importance placed on cleaner power sources and methods for storing and transporting that power. Battery technology is a huge part of this global energy revolution. Zinc batteries are an advantageous choice over lithium-based batteries, which have dominated the market for years in multiple areas, most specifically in electric vehicles and other battery-powered devices. Zinc is the fourth most abundant metal in the world, which is influential in its lower cost, making it a very attractive material for use in batteries. Zinc-based batteries have been around since the 1930s, but only now are they taking center stage in the energy, automotive, and other industTable of ContentsPreface xiii 1 Carbon Nanomaterials for Zn-Ion Batteries 1Prasun Banerjee, Adolfo Franco Jr, Rajender Boddula, K. Chandra Babu Naidu and Ramyakrishna Pothu 1.1 Introduction 2 1.2 Co4N (CN) - Carbon Fibers Network (CFN) -Carbon Cloth (CC) 2 1.3 N-Doping of Carbon Nanofibers 2 1.4 NiCo2S4 on Nitrogen-Doped Carbon Nanotubes 4 1.5 3D Phosphorous and Sulfur Co-Doped C3N4 Sponge With C Nanocrystal 5 1.6 2D Carbon Nanosheets 6 1.7 N-Doped Graphene Oxide With NiCo2O4 6 1.8 Conclusions 7 Acknowledgements 8 References 8 2 Construction, Working, and Applications of Different Zn-Based Batteries 11G. Ranjith Kumar, K. Chandra Babu Naidu, D. Baba Basha, D. Prakash Babu, M.S.S.R.K.N. Sarma, Ramyakrishna Pothu, and Rajender Boddula 2.1 Introduction 12 2.2 History 13 2.3 Types of Batteries 14 2.3.1 Primary Battery 14 2.3.2 Secondary Battery 14 2.4 Zinc-Carbon Batteries 18 2.5 Zinc-Cerium Batteries 19 2.6 Zinc-Bromine Flow Batteries 20 References 21 3 Nickel and Cobalt Materials for Zn Batteries 25Sonal Singh, Rishabh Sharma and Manika Khanuja 3.1 Introduction 26 3.2 Zinc Batteries 27 3.3 Nickel-Zinc Battery 27 3.3.1 History 27 3.3.2 Basics 28 3.3.3 Materials and Cost 30 3.3.4 Reliability 30 3.3.5 Voltage Drop 30 3.3.6 Performance 31 3.4 Advantages 31 3.5 Challenges 32 3.6 Effect of Metallic Additives, Cobalt and Zinc, on Nickel Electrode 32 3.7 Conclusion 33 References 34 4 Manganese-Based Materials for Zn Batteries 37S. Ramesh, K. Chandrababu Naidu, K. Venkata Ratnam, H. Manjunatha, D. Baba Basha and A. Mallikarjauna 4.1 Introduction 37 4.2 History of the Zinc and Zinc Batteries 38 4.3 Characteristics of Batteries 41 4.3.1 Capacity 41 4.3.2 Current 41 4.3.3 Power Density 41 4.4 MN-Based Zn Batteries 42 4.5 Conclusion 44 References 47 5 Electrolytes for Zn-Ion Batteries 51Praveen Kumar Yadav, Sapna Raghav, Jyoti Raghav and S. S. Swarupa Tripathy 5.1 Introduction 52 5.2 Electrolytes for Rechargeable Zinc Ion Batteries (RZIBs) 53 5.2.1 Aqueous Electrolytes (AqEs) 54 5.2.1.1 Pros and Cons of AEs 55 5.2.1.2 Neutral or Mildly Acidic Electrolytes 58 5.2.2 Non-Aqueous Electrolytes 59 5.2.2.1 Solid Polymer Electrolytes 60 5.2.2.2 Hydrogel or Gel Electrolytes 61 5.2.2.3 Gel Polymer Electrolytes 63 5.2.3 Ionic Liquid Electrolytes 63 5.2.4 Bio-Electrolyte 65 5.3 Summary 65 Abbreviation Table 66 Acknowledgments 66 References 67 6 Anode Materials for Zinc-Ion Batteries 73Muhammad Mudassir Hassan, Muhammad Inam Khan, Abdur Rahim and Nawshad Muhammad 6.1 Introduction 73 6.2 Storage Mechanism 75 6.3 Zinc-Ion Battery Anodes 77 6.4 Future Prospects 81 6.5 Conclusion 81 References 82 7 Cathode Materials for Zinc-Air Batteries 85Seyedeh Maryam Mousavi and Mohammad Reza Rahimpour 7.1 Introduction 85 7.1.1 Cathode Definition 86 7.2 Zinc Cathode Structure 87 7.3 Non-Valuable Materials for Cathode Electrocatalytic 89 7.4 Electrochemical Specifications of Activated Carbon as a Cathode 92 7.4.1 Electrochemical Evaluation of Cathode Substances La1−XCaxCoO3 Zinc Batteries 92 7.5 Extremely Durable and Inexpensive Cathode Air Catalyst 93 7.5.1 Co3O4/Mno2 NPs Dual Oxygen Catalyst as Cathode for Zn-Air Rechargeable Battery 94 7.5.2 Carbon Nanotubes (CNT) Employing Nitrogen as Catalyst in the Zinc/Air Battery System 94 7.5.3 Magnesium Oxide NPs Modified Catalyst for the Use of Air Electrodes in Zn/Air Batteries 94 7.5.4 Silver-Magnesium Oxide Nanocatalysts as Cathode for Zn-Air Batteries 95 7.5.5 One-Step Preparation of C-N Ni/Co-Doped Nanotube Hybrid as Outstanding Cathode Catalysts for Zinc-Air Batteries 95 7.6 Hierarchical Co3O4 Nano-Micro Array With Superior Working Characteristics Using Cathode Ray on Pliable and Rechargeable Battery 96 7.7 Dual Function Oxygen Catalyst Upon Active Iron-Based Zn-Air Rechargeable Batteries 97 7.7.1 Co4N and NC Fiber Coupling Connected to a Free-Acting Binary Cathode for Strong, Efficient, and Pliable Air Batteries 98 7.8 Conclusion 98 Nomenclature 99 References 99 8 Anode Materials for Zinc-Air Batteries 103Abbas Ghareghashi and Ali Mohebbi 8.1 Introduction 104 8.2 Zinc Anodes 105 8.2.1 Downsizing of Zn Anodes 106 8.2.2 Design of Membrane Separators 107 8.2.3 The Use of ZnO Instead of Zn 108 8.2.4 Increase of Surface Area in Zn Anode Structure 110 8.2.5 Coating of Zn Anode 111 8.2.5.1 Bismuth Oxide-Based Glasses 112 8.2.5.2 Silica 114 8.2.5.3 Carbon Nanotubes 115 8.2.5.4 ZnO@C 116 8.2.5.5 Zn-Al LDHs 116 8.2.5.6 ZnO@C-ZnAl LDHs 118 8.2.5.7 Tapioca 119 8.2.5.8 TiO2 122 8.3 Conclusions 123 References 124 9 Safety and Environmental Impacts of Zn Batteries 131Saurabh Sharma, Abhishek Anand, Amritanshu Shukla and Atul Sharma 9.1 Introduction 131 9.2 Working Principle of Zinc-Based Batteries 132 9.2.1 Zinc-Air Batteries Basic Principle and Advances 133 9.2.2 Zinc Organic Polymer Batteries 135 9.2.3 Zinc-Ion Batteries 137 9.2.3.1 Zinc-Silver Batteries 137 9.2.3.2 Zinc-Nickel Batteries 138 9.2.3.3 Zinc-Manganese Battery 140 9.3 Batteries: Environment Impact, Solution, and Safety 141 9.3.1 Disposal of Batteries and Environmental Impact 143 9.3.2 Recycling of Zinc-Based Batteries 143 9.4 Conclusion 146 Acknowledgement 147 References 147 10 Basics and Developments of Zinc-Air Batteries 151Seyedeh Maryam Mousavi and Mohammad Reza Rahimpour 10.1 Introduction 151 10.1.1 Public Specifications 151 10.2 Zinc-Air Electrode Chemical Reaction 153 10.3 Zinc/Air Battery Construction 154 10.4 Primary Zn/Air Batteries 157 10.5 Principles of Configuration and Operation 159 10.6 Developments in Electrical Fuel Zn/Air Batteries 161 10.6.1 Zn/Air Versus Metal/Air Systems 161 10.7 Conclusion 162 References 164 11 History and Development of Zinc Batteries 167Pallavi Jain, Sapna Raghav, Ankita Dhillon and Dinesh Kumar 11.1 Introduction 167 11.2 Basic Concept 169 11.2.1 Components of Batteries 169 11.2.2 Classification of Batteries 171 11.2.2.1 Primary Batteries 171 11.2.2.2 Secondary or Rechargeable Batteries (RBs) 171 11.3 Cell Operation 172 11.3.1 Process of Discharge 172 11.3.2 Process of Charge 172 11.4 History 173 11.5 Different Types of Zinc Batteries 174 11.5.1 Zinc-Carbon Batteries 174 11.5.2 Zinc/Manganese Oxide Batteries (Alkaline Batteries) 174 11.5.3 Zinc/Silver Oxide Battery 174 11.5.4 Zn-Air (Zn-O2) Batteries 176 11.5.4.1 Mechanically Rechargeable Batteries (Zn-O2 Batteries) 177 11.5.4.2 Electrically Rechargeable Batteries (Zn-O2 Batteries) 178 11.5.5 Hybrid Zn-O2 Batteries 178 11.5.5.1 Hybrid Zn-Ni/O2 Batteries 178 11.5.5.2 Hybrid Zn-Co/O2 Batteries 179 11.5.6 Aqueous Zinc-Ion Rechargeable Batteries 180 11.5.6.1 Zn2+ Insertion/Extraction Mechanism 180 11.5.6.2 Chemical Conversion Mechanism 180 11.5.6.3 H+ and Zn2+ Insertion/Extraction Mechanism 181 11.6 Future Perspectives 181 11.7 Conclusion 182 Abbreviations 182 Acknowledgement 183 References 183 12 Electrolytes for Zinc-Air Batteries 187Zahra Farmani, Mohammad Amin Sedghamiz, and Mohammad Reza Rahimpour 12.1 Introduction 187 12.2 Aqueous Electrolytes 188 12.2.1 Alkaline Electrolytes 189 12.2.1.1 Dissolution of Zinc in Alkaline Systems 189 12.2.1.2 Insoluble Carbonates Precipitation 192 12.2.1.3 Effect of Water 193 12.2.1.4 Hydrogen Evolution 194 12.2.2 Neutral Electrolytes 195 12.2.3 Acidic Electrolytes 196 12.3 Electrolytes of Non-Aqueous 197 12.3.1 Non-Aqueous Electrolytes 199 12.3 Summary 203 References 206 13 Security, Storage, Handling, Influences and Disposal/Recycling of Zinc Batteries 215Manju Yadav and Dinesh Kumar 13.1 Introduction 215 13.2 Security of Zinc Battery 217 13.2.1 Modifications for Improving Performance 218 13.2.1.1 High Surface Area 218 13.2.1.2 Carbon-Based Electrode Additives 221 13.2.1.3 Discharge-Capturing Electrode Additives 221 13.2.1.4 Electrode Coatings 222 13.2.1.5 Electrolyte Additives 222 13.2.1.6 Heavy-Metals Electrode Additive 222 13.2.1.7 Polymeric Binders 223 13.2.2 Storage and Handling 224 13.3 Influence of Zinc Battery 224 13.3.1 Consumption of Natural Resources 225 13.3.2 Toxicity of Batteries to Humans 226 13.3.3 Toxicity of Batteries to the Aquatic Environment 226 13.4 Disposal/Recycling Options 227 Acknowledgement 228 References 228 14 Materials for Ni-Zn Batteries 235Vaishali Tomar and Dinesh Kumar 14.1 Introduction 235 14.1.1 Functioning Principles of Nickel-Zinc Battery 237 14.1.2 Ni-Zn Battery Design 238 14.2 Expansion of Ni-Zn Battery 239 14.2.1 Active Materials for the Battery 240 14.3 Application 241 14.4 Conclusion 242 Acknowledgement 243 References 243 Index 249

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