Electronics and communications engineering Books

Book SynopsisThis book provides a comprehensive guide to the benefits and developments of wind energy, including energy storage and conversion methods, making it a must-read for those interested in sustainable energy. By going through this book, one can learn more about the usefulness of adopting renewable energies, particularly in light of the widespread use of wind-based devices. Here, we present an in-depth presentation of several developments in wind technological systems, focusing on applications and operational approaches. With the depletion of fossil fuel-based energy resources, the development of alternative sources of energy is becoming extremely crucial. Meanwhile, the planet is on the brink of an energy disaster due to the rapidly rising global need for energy. Additionally, the widespread usage of fossil fuel-based energy resources is aggravating global warming and harming the environment. However, there are reliable and eco-friendly substitutes to fossil fuels, for example wind and man

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Book SynopsisClear and concise guide to MBPLE, with industrial case studies Written in a to-the-point style, Model-Based Product Line Engineering (MBPLE) is the only theoretical and practical foundational book on MBPLE that brings together the topics of model-based systems engineering (MBSE) and feature-based product line engineering (PLE). It examines how PLE can benefit from a model-based and model-centric approach and, in turn, how MBSE combined with holistic PLE can boost model reuse and improve the MBSE business case. The book combines both management and engineering aspects to deliver comprehensive coverage of the subject. The book covers real-life challenges and implementations of MBPLE, discussing adoption obstacles faced by engineering organizations and how to overcome them to ensure a successful MBPLE deployment. Dozens of SysML v2 views, SysML v1 diagrams, SysML v2 code snippets and illustrations are included throughout to elucidate key concepts. Additional supplementary learning materia

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Book SynopsisRELIABILITY PREDICTION FOR MICROELECTRONICS Wiley Series in Quality & Reliability Engineering REVOLUTIONIZE YOUR APPROACH TO RELIABILITY ASSESSMENT WITH THIS GROUNDBREAKING BOOK Reliability evaluation is a critical aspect of engineering, without which safe performance within desired parameters over the lifespan of machines cannot be guaranteed. With microelectronics in particular, the challenges to evaluating reliability are considerable, and statistical methods for creating microelectronic reliability standards are complex. With nano-scale microelectronic devices increasingly prominent in modern life, it has never been more important to understand the tools available to evaluate reliability. Reliability Prediction for Microelectronics meets this need with a cluster of tools built around principles of reliability physics and the concept of remaining useful life (RUL). It takes as its core subject the physics of failure', combining a thorough understanding of conventional approaches to

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Book SynopsisBrings a powerful toolkit to bear on engineering and scientific endeavors. This book describes the fundamental principles of systems science so engineers and other scholars can put them into practical use at work and in their personal lives. Systems science aims to determine systemic similarities among different disciplines and to develop applicable solutions in many fields of inquiry. Systems Science for Engineers and Scholars readers will discover: Ten systems science principles that open engineers' and scholars' horizons to practical insights related to their areas of interest A methodology for designing holistic systems that exhibit resilient behavior to overcome systems' context uncertainties The most critical current dilemma of humankindthe global environment and energy crises, as well as a systemic, no-nonsense action plan to deal with these issues Independent articles describing how engineers and scholars can utilize systems science creatively in (1) engineering and systemicTable of ContentsPREFACE 10 ACKNOWLEDGMENTS 12 PART 1 - FACETS OF SYSTEMS SCIENCE AND ENGINEERING 14 CHAPTER 1: INTRODUCTION TO SYSTEMS SCIENCE 15 1.1 FOREWORD 15 1.2 CRITICAL HUMANITY CHALLENGE 19 1.3 SYSTEMS SCIENCE IN BRIEF 21 1.4 EARLY SYSTEMS PIONEERS 28 1.5 RECOMMENDED BOOKS ON SYSTEMS SCIENCE 30 1.6 CRITICISM OF SYSTEMS SCIENCE 31 1.7 BIBLIOGRAPHY 34 CHAPTER 2: PRINCIPLES OF SYSTEMS SCIENCE (PART I) 36 2.1 INTRODUCTION 36 2.2 UNIVERSAL CONTEXT 36 2.3 SYSTEMS BOUNDARY 41 2.4 SYSTEMS HIERARCHY 45 2.5 SYSTEMS INTERACTIONS 49 2.6 SYSTEMS CHANGE 54 2.7 BIBLIOGRAPHY 63 CHAPTER 3: PRINCIPLES OF SYSTEMS SCIENCE (PART II) 65 3.1 INTRODUCTION 65 3.2 SYSTEMS INPUT/OUTPUT 65 3.3 SYSTEMS COMPLEXITY 70 3.4 SYSTEMS CONTROL 83 3.5 SYSTEMS EVOLUTION 86 3.6 SYSTEMS EMERGENCE 95 3.7 BIBLIOGRAPHY 99 CHAPTER 4: SYSTEMS THINKING 101 4.1 INTRODUCTION 101 4.2 HISTORY OF SYSTEMS THINKING 101 4.3 FUNDAMENTAL CONCEPTS OF SYSTEMS THINKING 102 4.4 THE ICEBERG MODEL OF SYSTEMS THINKING 104 4.5 EXPLORING SYSTEMS THINKING AS A SYSTEM 105 4.6 BARRIERS TO SYSTEMS THINKING 107 4.7 BIBLIOGRAPHY 109 CHAPTER 5: SYSTEMS ENGINEERING 110 5.1 INTRODUCTION 110 5.2 PHILOSOPHY OF ENGINEERING 110 5.3 BASIC SYSTEMS ENGINEERING CONCEPTS 119 5.4 SYSTEMS ENGINEERING DEFICIENCIES 124 5.5 BIBLIOGRAPHY 135 CHAPTER 6: COMPARATIVE ANALYSIS – TWO DOMAINS 136 6.1 INTRODUCTION 136 6.2 A CASE FOR COMPARISON 136 6.3 STRUCTURE AND FUNCTION OF A COMPUTER HARD DRIVE (CHD) 137 6.4 FUNCTIONAL CORRELATIONS BETWEEN CHD AND THE DHD 139 6.5 CONCLUSIONS 144 6.6 ACKNOWLEDGMENT 145 6.7 BIBLIOGRAPHY 145 PART 2 - HOLISTIC SYSTEMS DESIGN 146 CHAPTER 7: HOLISTIC SYSTEMS CONTEXT 147 7.1 INTRODUCTION 147 7.2 RETHINKING THE CONTEXT OF THE SYSTEM 147 7.3 COMPONENTS OF SYSTEMS' CONTEXT 148 7.4 BIBLIOGRAPHY 152 CHAPTER 8: EXAMPLE - UAV SYSTEM OF INTEREST (SOI) 154 8.1 INTRODUCTION 154 8.2 EXAMPLE - UAV SYSTEM 154 8.3 BIBLIOGRAPHY 163 CHAPTER 9: EXAMPLE - UAV CONTEXT (PART I) 164 9.1 INTRODUCTION 164 9.2 UAV CONTEXT - NATURAL SYSTEMS 164 9.3 UAV CONTEXT - SOCIAL SYSTEMS 167 9.4 UAV CONTEXT - RESEARCHAPTER SYSTEMS 168 9.5 UAV CONTEXT - FORMATION SYSTEMS 173 9.6 UAV CONTEXT - SUSTAINMENT SYSTEMS 176 9.7 UAV CONTEXT - BUSINESS SYSTEMS 178 9.8 UAV CONTEXT - COMMERCIAL SYSTEMS 180 9.9 BIBLIOGRAPHY 186 CHAPTER 10: EXAMPLE - UAV CONTEXT (PART II) 188 10.1 INTRODUCTION 188 10.2 UAV CONTEXT - FINANCIAL SYSTEMS 188 10.3 UAV CONTEXT - POLITICAL SYSTEMS 191 10.4 UAV CONTEXT - LEGAL SYSTEMS 194 10.5 UAV CONTEXT - CULTURAL SYSTEMS 196 10.6 UAV CONTEXT - BIOSPHERE SYSTEMS 202 10.7 BIBLIOGRAPHY 203 PART 3 - GLOBAL ENVIRONMENT AND ENERGY - CRISIS AND ACTION PLAN 205 CHAPTER 11: GLOBAL ENVIRONMENT CRISES 206 11.1 INTRODUCTION 206 11.2 CLIMATE CHANGE 208 11.3 BIODIVERSITY LOSS 216 11.4 BIBLIOGRAPHY 227 CHAPTER 12: SYSTEMIC ENVIRONMENT ACTION PLAN 229 12.1 INTRODUCTION 229 12.2 SUSTAINING THE EARTH'S ENVIRONMENT 229 12.3 SUSTAINING HUMAN SOCIETY 238 12.4 BIBLIOGRAPHY 247 CHAPTER 13: GLOBAL ENERGY CRISIS 248 13.1 INTRODUCTION 248 13.2 CURRENT GLOBAL ENERGY STATUS 248 13.3 ENERGY RETURN ON INVESTMENT (EROI) 250 13.4 RENEWABLE ENERGY 253 13.5 FOSSIL FUELS ENERGY 258 13.6 CONVENTIONAL FISSION REACTION ENERGY 259 13.7 BIBLIOGRAPHY 261 CHAPTER 14: SYSTEMIC ENERGY ACTION PLAN 262 14.1 THE GLOBAL ENERGY DILEMMA 262 14.2 RENEWABLE ENERGY – ACTION PLAN 262 14.3 FOSSIL FUELS ENERGY – ACTION PLAN 263 14.4 CARS AND TRUCKS ACTION PLAN 264 14.5 FISSION REACTION ENERGY – ACTION PLAN 264 14.6 SMALL MODULAR REACTORS (SMRS) ACTION PLAN 265 14.7 FUSION NUCLEAR ENERGY ACTION PLAN 269 14.8 BIBLIOGRAPHY 273 PART 4 - MORE SYSTEMS SCIENCE FOR ENGINEERS AND SCHOLARS 274 CHAPTER 15: ENGINEERING AND SYSTEMIC PSYCHOLOGY 275 15.1 INTRODUCTION 275 15.2 SCHEMA THEORY 275 15.3 COGNITIVE BIASES 276 15.4 SYSTEMS FAILURES 279 15.5 COGNITIVE DEBIASING 285 15.6 BIBLIOGRAPHY 288 CHAPTER 16: DELIVERING VALUE AND RESOLVING CONFLICTS 289 16.1 INTRODUCTION 289 16.2 DELIVERING SYSTEMS VALUE 289 16.3 CONFLICT ANALYSIS AND RESOLUTION 294 16.4 BIBLIOGRAPHY 299 CHAPTER 17: MULTI-OBJECTIVE MULTI-AGENT DECISION MAKING 300 17.1 INTRODUCTION 300 17.2 UTILITY-BASED REWARDS 300 17.3 REPRESENTATION OF THE DECISION PROCESS 301 17.4 KEY TYPES OF DECISION PROCESSES 302 17.5 EXAMPLE-1 - WOLVES AND SHEEP PREDATION 305 17.6 EXAMPLE-2 - COOPERATIVE TARGET OBSERVATION 308 17.7 EXAMPLE-3 - SEAPORT LOGISTICS 310 17.8 BIBLIOGRAPHY 313 CHAPTER 18: SYSTEMS ENGINEERING USING CATEGORY THEORY 315 18.1 INTRODUCTION 315 18.2 THE PROBLEM OF MULTIDISCIPLINARY, COLLABORATIVE DESIGN 315 18.3 BRIEF BACKGROUND ON CATEGORY THEORY AND SYSTEMS ENGINEERING 316 18.4 EXAMPLE - DESIGNING AN ELECTRIC VEHICLE 317 18.5 CATEGORY THEORY (CT) AS A SYSTEM SPECIFICATION LANGUAGE 322 18.6 CATEGORICAL MULTIDISCIPLINARY COLLABORATIVE DESIGN (C-MCD) 329 18.7 THE C-MCD CATEGORIES 331 18.8 THE CATEGORICAL DESIGN PROCESS 339 18.9 CONCLUSION 340 18.10 ACKNOWLEDGMENT 340 18.11 BIBLIOGRAPHY 340 CHAPTER 19: HOLISTIC RISK MANAGEMENT USING SOSF METHODOLOGY 342 19.1 INTRODUCTION 342 19.2 LIMITATIONS OF CURRENT RISK MANAGEMENT PRACTICES 342 19.3 FEATURES OF SYSTEMS OF SYSTEMS FAILURES (SOSF) 343 19.4 EXAMPLE-1 - HOLISTIC RISK MANAGEMENT AND FAILURE CLASSES 347 19.5 EXAMPLE-2 – SYNTHETIC SOSF RISK MANAGEMENT 354 19.6 CONCLUSION 358 19.7 ACKNOWLEDGMENT 358 19.8 BIBLIOGRAPHY 358 CHAPTER 20: SYSTEMIC ACCIDENTS AND MISHAPS ANALYSES 360 20.1 INTRODUCTION TO ACCIDENT CAUSATION MODELS 360 20.2 BASIC ACCIDENTS AND MISHAPS CONCEPTS 360 20.3 CLASSIFICATION OF INCIDENT CAUSATION MODELS 361 20.4 SYSTEMS THEORETIC ACCIDENT MODEL AND PROCESS (STAMP) 362 20.5 CAUSAL ANALYSIS SYSTEM THEORY (CAST) 365 20.6 CAST PROCEDURE 366 20.7 CAST EXAMPLE - CH-53 HELICOPTERS MID-AIR COLLISION 367 20.8 BIBLIOGRAPHY 374 APPENDIX-A: DISTINGUISHED SYSTEMS SCIENCE RESEARCHERS 376 APPENDIX-B: DISTINGUISHED SYSTEMS THINKING RESEARCHERS 378 APPENDIX-C: PERMISSIONS TO USE THIRD-PARTY COPYRIGHT MATERIAL 380 APPENDIX-D: LIST OF ACRONYMS 392 INDEX 398

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Book SynopsisLearn the basicsand moreof nanoscale computation and communication in this emerging and interdisciplinary field The field of nanoscale computation and communications systems is a thriving and interdisciplinary research area which has made enormous strides in recent years. A working knowledge of nanonetworks, their conceptual foundations, and their applications is an essential tool for the next generation of scientists and network engineers. Nanonetworks: The Future of Communication and Computation offers a thorough, accessible overview of this subject rooted in extensive research and teaching experience. Offering a concise and intelligible introduction to the key paradigms of nanoscale computation and communications, it promises to become a cornerstone of education in these fast-growing areas. Readers will also find: Detailed treatment of topics including network paradigms, machine learning, safety and securityCoverage of the history, applications, and important theories of nanonetwor

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Book SynopsisDiscover the essential guide to harnessing the power of cutting-edge smart sensors in Industry 4.0, offering deep insights into fundamentals, fabrication techniques, and real-world IIoT applications, equipping you with the knowledge to revolutionize your industrial processes and stay ahead in the digital era. Over the last decade, technologies like the Internet of Things (IoT), big data, cloud computing, blockchain, artificial intelligence (AI), machine learning, device automation, smart sensors, etc., have become highly developed fundamental supports of Industry 4.0, replacing the conventional production systems with advanced methods, and thereby endorsing the smart industry vision. Industry 4.0 is more flexible and agile in dealing with several risk factors, further enabling improved productivity and efficiency, distribution, increased profitability, data integrity, and enhancing customer experience in the current commercial environment. For understanding and analyzin

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Book SynopsisMachine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and oTable of ContentsForeword xiii Acknowledgments xv General Introduction xvii 1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning 1 1.1 Learning Styles for Machine Learning 2 1.1.1 Supervised Learning 2 1.1.1.1 Overfitting and Underfitting 3 1.1.1.2 K-Folds Cross-Validation 4 1.1.1.3 Train/Test Split 4 1.1.1.4 Confusion Matrix 5 1.1.1.5 Loss Functions 7 1.1.2 Unsupervised Learning 9 1.1.3 Semi-Supervised Learning 9 1.1.4 Reinforcement Learning 9 1.2 Essential Python Tools for Machine Learning 9 1.2.1 Data Manipulation with Python 10 1.2.2 Python Machine Learning Libraries 10 1.2.2.1 Scikit-learn 10 1.2.2.2 TensorFlow 10 1.2.2.3 Keras 12 1.2.2.4 PyTorch 12 1.2.3 Jupyter Notebook and JupyterLab 13 1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs 13 1.3.1 Installation 13 1.3.2 HephAIstos Function 15 1.4 Where to Find the Datasets and Code Examples 32 Further Reading 33 2 Feature Engineering Techniques in Machine Learning 35 2.1 Feature Rescaling: Structured Continuous Numeric Data 36 2.1.1 Data Transformation 37 2.1.1.1 StandardScaler 37 2.1.1.2 MinMaxScaler 39 2.1.1.3 MaxAbsScaler 40 2.1.1.4 RobustScaler 40 2.1.1.5 Normalizer: Unit Vector Normalization 42 2.1.1.6 Other Options 43 2.1.1.7 Transformation to Improve Normal Distribution 44 2.1.1.8 Quantile Transformation 48 2.1.2 Example: Rescaling Applied to an SVM Model 50 2.2 Strategies to Work with Categorical (Discrete) Data 57 2.2.1 Ordinal Encoding 59 2.2.2 One-Hot Encoding 61 2.2.3 Label Encoding 62 2.2.4 Helmert Encoding 63 2.2.5 Binary Encoding 64 2.2.6 Frequency Encoding 65 2.2.7 Mean Encoding 66 2.2.8 Sum Encoding 68 2.2.9 Weight of Evidence Encoding 68 2.2.10 Probability Ratio Encoding 70 2.2.11 Hashing Encoding 71 2.2.12 Backward Difference Encoding 72 2.2.13 Leave-One-Out Encoding 73 2.2.14 James-Stein Encoding 74 2.2.15 M-Estimator Encoding 76 2.2.16 Using HephAIstos to Encode Categorical Data 77 2.3 Time-Related Features Engineering 77 2.3.1 Date-Related Features 79 2.3.2 Lag Variables 79 2.3.3 Rolling Window Feature 82 2.3.4 Expending Window Feature 84 2.3.5 Understanding Time Series Data in Context 85 2.4 Handling Missing Values in Machine Learning 88 2.4.1 Row or Column Removal 89 2.4.2 Statistical Imputation: Mean, Median, and Mode 90 2.4.3 Linear Interpolation 91 2.4.4 Multivariate Imputation by Chained Equation Imputation 92 2.4.5 KNN Imputation 93 2.5 Feature Extraction and Selection 97 2.5.1 Feature Extraction 97 2.5.1.1 Principal Component Analysis 98 2.5.1.2 Independent Component Analysis 102 2.5.1.3 Linear Discriminant Analysis 110 2.5.1.4 Locally Linear Embedding 115 2.5.1.5 The t-Distributed Stochastic Neighbor Embedding Technique 123 2.5.1.6 More Manifold Learning Techniques 125 2.5.1.7 Feature Extraction with HephAIstos 130 2.5.2 Feature Selection 131 2.5.2.1 Filter Methods 132 2.5.2.2 Wrapper Methods 146 2.5.2.3 Embedded Methods 154 2.5.2.4 Feature Importance Using Graphics Processing Units (GPUs) 167 2.5.2.5 Feature Selection Using HephAIstos 168 Further Reading 170 3 Machine Learning Algorithms 175 3.1 Linear Regression 176 3.1.1 The Math 176 3.1.2 Gradient Descent to Optimize the Cost Function 177 3.1.3 Implementation of Linear Regression 182 3.1.3.1 Univariate Linear Regression 182 3.1.3.2 Multiple Linear Regression: Predicting Water Temperature 185 3.2 Logistic Regression 202 3.2.1 Binary Logistic Regression 202 3.2.1.1 Cost Function 203 3.2.1.2 Gradient Descent 204 3.2.2 Multinomial Logistic Regression 204 3.2.3 Multinomial Logistic Regression Applied to Fashion MNIST 204 3.2.3.1 Logistic Regression with scikit-learn 205 3.2.3.2 Logistic Regression with Keras on TensorFlow 208 3.2.4 Binary Logistic Regression with Keras on TensorFlow 210 3.3 Support Vector Machine 211 3.3.1 Linearly Separable Data 212 3.3.2 Not Fully Linearly Separable Data 214 3.3.3 Nonlinear SVMs 216 3.3.4 SVMs for Regression 217 3.3.5 Application of SVMs 219 3.3.5.1 SVM Using scikit-learn for Classification 220 3.3.5.2 SVM Using scikit-learn for Regression 222 3.4 Artificial Neural Networks 223 3.4.1 Multilayer Perceptron 224 3.4.2 Estimation of the Parameters 225 3.4.2.1 Loss Functions 225 3.4.2.2 Backpropagation: Binary Classification 226 3.4.2.3 Backpropagation: Multi-class Classification 227 3.4.3 Convolutional Neural Networks 230 3.4.4 Recurrent Neural Network 232 3.4.5 Application of MLP Neural Networks 233 3.4.6 Application of RNNs: LST Memory 242 3.4.7 Building a CNN 246 3.5 Many More Algorithms to Explore 249 3.6 Unsupervised Machine Learning Algorithms 251 3.6.1 Clustering 251 3.6.1.1 K-means 253 3.6.1.2 Mini-batch K-means 255 3.6.1.3 Mean Shift 257 3.6.1.4 Affinity Propagation 259 3.6.1.5 Density-based Spatial Clustering of Applications with Noise 262 3.7 Machine Learning Algorithms with HephAIstos 264 References 270 Further Reading 270 4 Natural Language Processing 273 4.1 Classifying Messages as Spam or Ham 274 4.2 Sentiment Analysis 281 4.3 Bidirectional Encoder Representations from Transformers 286 4.4 BERT’s Functionality 287 4.5 Installing and Training BERT for Binary Text Classification Using TensorFlow 288 4.6 Utilizing BERT for Text Summarization 294 4.7 Utilizing BERT for Question Answering 296 Further Reading 297 5 Machine Learning Algorithms in Quantum Computing 299 5.1 Quantum Machine Learning 303 5.2 Quantum Kernel Machine Learning 306 5.3 Quantum Kernel Training 328 5.4 Pegasos QSVC: Binary Classification 333 5.5 Quantum Neural Networks 337 5.5.1 Binary Classification with EstimatorQNN 338 5.5.2 Classification with a SamplerQNN 343 5.5.3 Classification with Variational Quantum Classifier 348 5.5.4 Regression 351 5.6 Quantum Generative Adversarial Network 352 5.7 Quantum Algorithms with HephAIstos 368 References 372 Further Reading 373 6 Machine Learning in Production 375 6.1 Why Use Docker Containers for Machine Learning? 375 6.1.1 First Things First: The Microservices 375 6.1.2 Containerization 376 6.1.3 Docker and Machine Learning: Resolving the “It Works in My Machine” Problem 376 6.1.4 Quick Install and First Use of Docker 377 6.1.4.1 Install Docker 377 6.1.4.2 Using Docker from the Command Line 378 6.1.5 Dockerfile 380 6.1.6 Build and Run a Docker Container for Your Machine Learning Model 381 6.2 Machine Learning Prediction in Real Time Using Docker and Python REST APIs with Flask 389 6.2.1 Flask-RESTful APIs 390 6.2.2 Machine Learning Models 392 6.2.3 Docker Image for the Online Inference 393 6.2.4 Running Docker Online Inference 394 6.3 From DevOps to MLOPS: Integrate Machine Learning Models Using Jenkins and Docker 396 6.3.1 Jenkins Installation 397 6.3.2 Scenario Implementation 399 6.4 Machine Learning with Docker and Kubernetes: Install a Cluster from Scratch 405 6.4.1 Kubernetes Vocabulary 405 6.4.2 Kubernetes Quick Install 406 6.4.3 Install a Kubernetes Cluster 407 6.4.4 Kubernetes: Initialization and Internal Network 410 6.5 Machine Learning with Docker and Kubernetes: Training Models 415 6.5.1 Kubernetes Jobs: Model Training and Batch Inference 415 6.5.2 Create and Prepare the Virtual Machines 415 6.5.3 Kubeadm Installation 415 6.5.4 Create a Kubernetes Cluster 416 6.5.5 Containerize our Python Application that Trains Models 418 6.5.6 Create Configuration Files for Kubernetes 422 6.5.7 Commands to Delete the Cluster 424 6.6 Machine Learning with Docker and Kubernetes: Batch Inference 424 6.6.1 Create Configuration Files for Kubernetes 427 6.7 Machine Learning Prediction in Real Time Using Docker, Python Rest APIs with Flask, and Kubernetes: Online Inference 428 6.7.1 Flask-RESTful APIs 428 6.7.2 Machine Learning Models 431 6.7.3 Docker Image for Online Inference 432 6.7.4 Running Docker Online Inference 433 6.7.5 Create and Prepare the Virtual Machines 434 6.7.6 Kubeadm Installation 434 6.7.7 Create a Kubernetes Cluster 435 6.7.8 Deploying the Containerized Machine Learning Model to Kubernetes 437 6.8 A Machine Learning Application that Deploys to the IBM Cloud Kubernetes Service: Python, Docker, Kubernetes 440 6.8.1 Create Kubernetes Service on IBM Cloud 440 6.8.2 Containerization of a Machine Learning Application 443 6.8.3 Push the Image to the IBM Cloud Registry 446 6.8.4 Deploy the Application to Kubernetes 448 6.9 Red Hat OpenShift to Develop and Deploy Enterprise ML/DL Applications 452 6.9.1 What is OpenShift? 453 6.9.2 What Is the Difference Between OpenShift and Kubernetes? 453 6.9.3 Why Red Hat OpenShift for ML/DL? To Build a Production-Ready ML/DL Environment 454 6.10 Deploying a Machine Learning Model as an API on the Red Hat OpenShift Container Platform: From Source Code in a GitHub Repository with Flask, Scikit-Learn, and Docker 454 6.10.1 Create an OpenShift Cluster Instance 455 6.10.1.1 Deploying an Application from Source Code in a GitHub Repository 457 Further Reading 463 Conclusion: The Future of Computing for Data Science? 465 Index 477

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Book SynopsisComprehensive guide to hybrid communication systems using visible light communication, radio over fiber, and auto channel switching technologies Hybrid Communication Systems for Future 6G and Beyond explores the future of wireless communication and discusses how we can create more efficient and reliable ways to communicate by unlocking the potential of three specific technologies: visible light communication (VLC), radio over fiber (RoF) technology, and auto channel switching. This book begins by exploring the potential of VLC technology, which is currently considered the best alternative to wireless communication. It then moves on to describe how RoF technology can provide a powerful backhaul solution for VLC. Later chapters cover auto channel switching and how it can facilitate data traffic sharing between WiFi and LiFi technologies. Case studies of successful hybrid communication system implementations are included throughout the text to showcase real-world

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Book SynopsisAn introduction to a cutting-edge, environmentally friendly insulation material The installation and maintenance of high-voltage cables is an infrastructure problem with potentially major environmental impacts. In recent years, polypropylene has emerged as an environmentally friendly material for insulating high-voltage cables, particularly HVDC power cables and HVAC power cables. Polypropylene Cable Insulation begins with an introduction to high-voltage cables and the development of polypropylene insulation before describing the dielectric properties and applications of this insulation in both HVDC and HVAC contexts. The result is a thorough, accessible guide to an essential part of any environmentally friendly power grid. Readers will also find: Detailed explorations of the relationship between space charge behaviors and trap characteristics Discussion of topics including polarization and dielectric relaxation, electrical treeing degradation, partial discharge, and moreGraphs and ta

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Book SynopsisA comprehensive text that investigates a systematic approach to handling the design of resilient systems Resilient systems are an essential bulwark to enable the capability of a system against unprecedented adversities. Systems may include transportation, such as aircraft and rail, power systems, and urban infrastructure. Resilience may enable the preservation of physical assets and human lives. It can also require architectural restructuring of the system itself or simple measures, such an increase in design margin. Architecting Resilient Systems creates a comprehensive list of design principles for creating systems where resilience is essential. With a detailed approach to both these general principles and their practical applications, it permits the creation and management of resilient systems in virtually any key area or industry. Richly supported with case evidence and fully updated to reflect the latest research and best practice, it''s a critical t

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Book SynopsisA masterful guide to the interplay between cybersecurity and its societal, economic, and political impacts, equipping students with the critical thinking needed to navigate and influence security for our digital world. JOSIAH DYKSTRA, Trail of Bits A comprehensive, multidisciplinary introduction to the technology and policy of cybersecurity. Start here if you are looking for an entry point to cyber. BRUCE SCHNEIER, author of A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back The first-ever introduction to the full range of cybersecurity challenges Cybersecurity is crucial for preserving freedom in a connected world. Securing customer and business data, preventing election interference and the spread of disinformation, and understanding the vulnerabilities of key infrastructural systems are just a few of the areas in which cybersecurity professionals are indispensable. This textbook provides a comprehensive, student-oriented introduction to this capacious,

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