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• 1. Block codes for error correction
• 2. Finite fields
• 3. Communication channels and error probability
• 4. Reed-Solomon codes and their decoding
• 5. Cyclic codes
• 6. Frames
• 7. Maximum likelihood decoding and convolutional codes
• 8. Combinations of several codes
• 9. Decoding Reed-Solomon and BCH codes
• 10. Iterative decoding
• 11. Algebraic geometry codes.

"This book, updated and enlarged for the second edition, is written as a text for a course aimed at 3rd or 4th year students. Only some familiarity with elementary linear algebra and probability is directly assumed, but some maturity is required. The students may specialize in discrete mathematics, computer science, or communication engineering. The book is also a suitable introduction to coding theory for researchers from related fields or for professionals who want to supplement their theoretical basis. The book gives the coding basics for working on projects in any of the above areas, but material specific to one of these fields has not been included. The chapters cover the codes and decoding methods that are currently of most interest in research, development, and application. They give a relatively brief presentation of the essential results, emphasizing the interrelations between different methods and proofs of all important results. A sequence of problems at the end of each chapter serves to review the results and give the student an appreciation of the concepts. In addition, some problems and suggestions for projects indicate direction for further work. The presentation encourages the use of programming tools for studying codes, implementing decoding methods, and simulating performance. Specific examples of programming exercises are provided on the book's home page." --Back cover.

• List of Figures ix
• List of Tables xi
• Preface xiii
• Acknowledgments xv
• Acronyms xvii
• Chapter 1 Introduction 1
• 1.1 Motivation 1
• 1.2 Assumptions 3
• 1.3 For Whom is This Book? 4
• 1.4 Book Structure 4
• Chapter 2 Evolution of IT Architectures and Paradigms 7
• 2.1 Evolution of IT Architectures 7
• 2.1.1 Monolith 7
• 2.1.2 Service Oriented Architecture 9
• 2.1.3 Microservices 12
• 2.2 Actors and Agents 15
• 2.2.1 Actors 15
• 2.2.2 Agents 17
• 2.3 From ACID to BASE, CAP, and NoSQL - The Database (R)evolution 22
• 2.4 The Cloud 24
• 2.5 From Distributed Sensor Networks to the Internet of Things and Cyber-Physical Systems 27
• 2.6 The Rise of Big Data 28
• Chapter 3 Sources of Data 31
• 3.1 The Internet 32
• 3.1.1 The Semantic Web 32
• 3.1.2 Linked Data 35
• 3.1.3 Knowledge Graphs 36
• 3.1.4 Social Media 38
• 3.1.5 Web Mining 38
• 3.2 Scientific Data 40
• 3.2.1 Biomedical Data 40
• 3.2.2 Physics and Astrophysics Data 41
• 3.2.3 Environmental Sciences 44
• 3.3 Industrial Data 45
• 3.3.1 Smart Factories 45
• 3.3.2 SmartGrid 47
• 3.3.3 Aviation 47
• 3.4 Internet of Things 48
• Chapter 4 Big Data Tasks 51
• 4.1 Recommender Systems 51
• 4.2 Search 52
• 4.3 Ad-tech and RTB Algorithms 55
• 4.4 Cross-Device Graph Generation 57
• 4.5 Forecasting and Prediction Systems 58
• 4.6 Social Media Big Data 59
• 4.7 Anomaly and Fraud Detection 61
• 4.8 New Drug Discovery 63
• 4.9 Smart Grid Control and Monitoring 64
• 4.10 IoT and Big Data Applications 65
• Chapter 5 Cloud Computing 67
• 5.1 Cloud Enabled Architectures 67
• 5.1.1 Cloud Management Platforms 67
• 5.1.2 Efficient Cloud Computing 73
• 5.1.3 Distributed Storage Systems 75
• 5.2 Agents and the Cloud 82
• 5.2.1 Multi-agent Versus Cloud Paradigms 83
• 5.2.2 Agents in the Cloud 83
• Chapter 6 Big Data Architectures 87
• 6.1 Big Data Computation Models 87
• 6.1.1 MapReduce 87
• 6.1.2 Directed Acyclic Graph Models 89
• 6.1.3 All-Pairs 92
• 6.1.4 Very Large Bitmap Operations 93
• 6.1.5 Message Passing Interface 94
• 6.1.6 Graphical Processing Unit Computing 95
• 6.2 Publish-Subscribe Systems 97
• 6.3 Stream Processing 99
• 6.3.1 Information Flow Processing Concepts 99
• 6.3.2 Stream Processing Systems 101
• 6.4 Higer Level Big Data Architectures 110
• 6.4.1 Spark 110
• 6.4.2 Lambda 112
• 6.4.3 Multi-Agent View of the Lambda Architecture 113
• 6.4.4 Questioning the Lambda 115
• 6.5 Industry and Other Approaches 116
• 6.6 Actor and Agent-Based Big Data Architectures 118
• Chapter 7 Big Data Analytics, Mining, and Machine Learning 121
• 7.1 To SQL or Not to SQL 122
• 7.1.1 SQL Hadoop Interfaces 123
• 7.1.2 From Shark to SparkSQL 125
• 7.2 Big Data Mining and Machine Learning 128
• 7.2.1 Graph Mining 133
• 7.2.2 Agent Based Machine Learning and Data Mining 134
• Chapter 8 Physically Distributed Systems - Mobile Cloud, Internet of Things, Edge Computing 137
• 8.1 Mobile Cloud 138
• 8.2 Edge and Fog Computing 145
• 8.2.1 Business Case: Mobile Context Aware Recommender System 147
• 8.3 Internet of Things 148
• 8.3.1 IoT Fundamentals 148
• 8.3.2 IoT and the Cloud 151
• 8.3.3 MAS in IoT 156
• Chapter 9 Summary 159
• Bibliography 161
• Index 179.
• (source: Nielsen Book Data)

Provides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence-enabling next generation systems to be built by incorporating the best aspects of the field. This book: Illustrates how data sets are produced and how they can be utilized in various areas of industry and science Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks Discusses current and emerging Big Data applications of Artificial Intelligence Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning. .
(source: Nielsen Book Data)

• 1. Artificial Intelligence (AI) for IoHT - an Introduction.
• 2. Role of Internet of Things and Cloud Computing Technologies in the Healthcare Sector.
• 3. An Extensive Overview of Wearable Technologies in Healthcare Sector.
• 4. IoHT and Cloud-Based Disease Diagnosis Model Using Particle Swarm Optimization with Artificial Neural Networks.
• 5. IoHT-Based Improved Grey Optimization with Support Vector Machine for Gastrointestinal Hemorrhage Detection and Diagnosis Model.
• 6. An Effective-Based Personalized Medicine Recommendation System Using Ensemble of Extreme Learning Machine Model.
• 7. A Novel Map Reduce-Based Hybrid Decision Tree with TFIDF Algorithm for Public Sentiment Mining of Diabetes Mellitus.
• 8. IoHT with Artificial Intelligence-Based Breast Cancer Diagnosis Model.
• 9. Artificial Intelligence with Cloud-Based Medical Image Retrieval System Using Deep Neural Network.
• 10. IoHT with Cloud-Based Brain Tumor Detection Using Particle Swarm Optimization with Support Vector Machine.
• 11. Artificial Intelligence-Based Hough Transform with an Adaptive Neuro-Fuzzy Inference System for a Diabetic Retinopathy Classification Model.
• 12. An IoHT-Based Intelligent Skin Lesion Detection and Classification Model in Dermoscopic Images.
• 13. An IoHT-Based Image Compression Model Using Modified Cuckoo Search Algorithm with Vector Quantization.
• 14. An Effective Secure Medical Image Transmission Using Improved Particle Swarm Optimization and Wavelet Transform.
• 15. IoHT with Wearable Devices-Based Feature Extraction and Deep Neural Networks Classification Model for Heart Disease Diagnosis.
• (source: Nielsen Book Data)

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.
(source: Nielsen Book Data)

• Introduction to ARM Cortex Processors
• Developing with Cortex M Processors
• Cortex-M Architecture
• Debugging with CoreSight
• Cortex Microcontroller Interface Standard
• Advanced Architecture Features
• Developing with an RTOS
• Practical DSP for Cortex-M4
• Future Trends.
• (source: Nielsen Book Data)

The Designer's Guide to the Cortex-M Family is a tutorial-based book giving the key concepts required to develop programs in C with a Cortex M- based processor. The book begins with an overview of the Cortex- M family, giving architectural descriptions supported with practical examples, enabling the engineer to easily develop basic C programs to run on the Cortex- M0/M0+/M3 and M4. It then examines the more advanced features of the Cortex architecture such as memory protection, operating modes and dual stack operation. Once a firm grounding in the Cortex M processor has been established the book introduces the use of a small footprint RTOS and the CMSIS DSP library. With this book you will learn: The key differences between the Cortex M0/M0+/M3 and M4 How to write C programs to run on Cortex-M based processors How to make best use of the Coresight debug system How to do RTOS development The Cortex-M operating modes and memory protection Advanced software techniques that can be used on Cortex-M microcontrollers How to optimise DSP code for the cortex M4 and how to build real time DSP systems.
(source: Nielsen Book Data)

• Preface xi 1. Mathematical Foundations 1 1.1 Sets and Logic
• Naively 1 1.2 Relations and Functions 40 1.3 Big and Small Infinite Sets
• Diagonalization 52 1.4 Induction from a User's Perspective 61 1.5 Why Induction Ticks 68 1.6 Inductively Defined Sets 1.7 Recursive Definitions of Functions 1.8 Additional Exercises 85 2. Algorithms, Computable Functions and Computations 91 2.1 A Theory of Computability 91 2.2 A programming Formalism for the Primitive Recursive Functions Function Class 147 2.3 URM Computations and their Arithmetization 141 2.4 A double-recursion that leads outside the Primitive Recursive Function Class 2.5 Semi-computable Relations: Unsolvability 2.6 The Iteration Theorem of Kleene 172 2.7 Diagonalization Revisited
• Unsolvability via Reductions 175 2.8 Productive and Creative Sets 209 2.9 The Recursion Theorem 214 2. 10 Completeness 217 2.11 Unprovability from Unsolvability 221 2.12 Additional Exercises 234 3. A Subset of the URM Language
• FA and NFA 241 3.1 Deterministic Finite Automata and their Languages 243 3.2 Nondeterministic Finite Automata 3.3 Regular Expressions 266 3.4 Regular Grammars and Languages 277 3.5 Additional Exercises 287 4. Adding a stack of a NFA: Pushdown Automata 4.1 The PDA 294 4.2 PDA Computations 294 4.3 The PDA-acceptable Languages are the Context Free Languages 305 4.4 Non-Context Free Languages
• Another Pumping Lemma 312 4.5 Additional Exercise 322 5. Computational Complexity 325 5.1 Adding a second stack
• Turning Machines 325 5.2 Axt, loop program, and Grzegorczyk hierarchies 5.3 Additional Exercised.
• (source: Nielsen Book Data)

Learn the skills and acquire the intuition to assess the theoretical limitations of computer programming Offering an accessible approach to the topic, Theory of Computation focuses on the metatheory of computing and the theoretical boundaries between what various computational models can do and not do--from the most general model, the URM (Unbounded Register Machines), to the finite automaton. A wealth of programming-like examples and easy-to-follow explanations build the general theory gradually, which guides readers through the modeling and mathematical analysis of computational phenomena and provides insights on what makes things tick and also what restrains the ability of computational processes. Recognizing the importance of acquired practical experience, the book begins with the metatheory of general purpose computer programs, using URMs as a straightforward, technology-independent model of modern high-level programming languages while also exploring the restrictions of the URM language. Once readers gain an understanding of computability theory--including the primitive recursive functions--the author presents automata and languages, covering the regular and context-free languages as well as the machines that recognize these languages. Several advanced topics such as reducibilities, the recursion theorem, complexity theory, and Cook's theorem are also discussed. Features of the book include: A review of basic discrete mathematics, covering logic and induction while omitting specialized combinatorial topics A thorough development of the modeling and mathematical analysis of computational phenomena, providing a solid foundation of un-computability The connection between un-computability and un-provability: Godel's first incompleteness theorem The book provides numerous examples of specific URMs as well as other programming languages including Loop Programs, FA (Deterministic Finite Automata), NFA (Nondeterministic Finite Automata), and PDA (Pushdown Automata). Exercises at the end of each chapter allow readers to test their comprehension of the presented material, and an extensive bibliography suggests resources for further study. Assuming only a basic understanding of general computer programming and discrete mathematics, Theory of Computation serves as a valuable book for courses on theory of computation at the upper-undergraduate level. The book also serves as an excellent resource for programmers and computing professionals wishing to understand the theoretical limitations of their craft.
(source: Nielsen Book Data)

• 1. Introduction.
• 2. Image Examination.
• 3. Image Thresholding.
• 4. Image Segmentation.
• 5. Medical Image Processing with Hybrid Image Processing Method.
• 6. Deep Learning for Medical Image Processing.
• 7. Conclusion.
• (source: Nielsen Book Data)

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing.
(source: Nielsen Book Data)

• Foreword List of Contributors List of Acronyms
• 1 Introduction to M2M 1.1 What is M2M? 1.2 The Business of M2M 1.3 Accelerating M2M Maturity 1.3.1 High-Level M2M Frameworks 1.3.2 Policy and Government Incentives 1.4 M2M Standards 1.4.1 Which Standards for M2M? 1.5 Roadmap of the Book References Part I M2M CURRENT LANDSCAPE
• 2 The Business of M2M 2.1 The M2M Market 2.1.1 Healthcare 2.1.2 Transportation 2.1.3 Energy 2.2 The M2M Market Adoption: Drivers and Barriers 2.3 The M2M Value Chain 2.4 Market Size Projections 2.5 Business Models 2.5.1 Network Operator- or CSP-Led Model 2.5.2 MVNO-Led Model 2.5.3 Corporate Customer-Led Model 2.6 M2M Business Metrics 2.7 Market Evolution Reference
• 3 Lessons Learned from Early M2M Deployments 3.1 Introduction 3.2 Early M2M Operational Deployments 3.2.1 Introduction 3.2.2 Early M2M Operational Deployment Examples 3.2.3 Common Questions in Early M2M Deployments 3.2.4 Possible Optimization of M2M Deployments 3.3 Chapter Conclusion Reference Part II M2M ARCHITECTURE AND PROTOCOLS
• 4 M2M Requirements and High-Level Architectural Principles 4.1 Introduction 4.2 Use-Case-Driven Approach to M2M Requirements 4.2.1 What is a Use Case? 4.2.2 ETSI M2M Work on Use Cases 4.2.3 Methodology for Developing Use Cases 4.3 Smart Metering Approach in ETSI M2M 4.3.1 Introduction 4.3.2 Typical Smart Metering Deployment Scenario 4.4 eHealth Approach in ETSI M2M 4.4.1 Introduction 4.5 ETSI M2M Service Requirements: High-Level Summary and Applicability to Different Market Segments 4.6 Traffic Models-/Characteristics-Approach to M2M Requirements and Considerations for Network Architecture Design 4.6.1 Why Focus on Wireless Networks? 4.7 Description of M2M Market Segments/Applications 4.7.1 Automotive 4.7.2 Smart Telemetry 4.7.3 Surveillance and Security 4.7.4 Point of Sale (PoS) 4.7.5 Vending Machines 4.7.6 eHealth 4.7.7 Live Video 4.7.8 Building Automation 4.7.9 M2M Industrial Automation 4.8 M2M Traffic Characterization 4.8.1 Detailed Traffic Characterization for Smart Metering 4.8.2 Global Traffic Characterization 4.9 High-Level Architecture Principles for M2M Communications 4.10 Chapter Conclusions References
• 5 ETSI M2M Services Architecture 5.1 Introduction 5.2 High-Level System Architecture 5.3 ETSI TC M2M Service Capabilities Framework 5.4 ETSI TC M2M Release 1 Scenarios 5.5 ETSI M2M Service Capabilities 5.5.1 Reachability, Addressing, and Repository Capability (xRAR) 5.5.2 Remote Entity Management Capability (x REM) 5.5.3 Security Capability (xSEC) 5.6 Introducing REST Architectural Style for M2M 5.6.1 Introduction to REST 5.6.2 Why REST for M2M? 5.6.3 REST Basics 5.6.4 Applying REST to M2M 5.6.5 Additional Functionalities 5.7 ETSI TC M2M Resource-Based M2M Communication and Procedures 5.7.1 Introduction 5.7.2 Definitions Used in this Section 5.7.3 Resource Structure 5.7.4 Interface Procedures 5.8 Chapter Conclusion References
• 6 M2M Optimizations in Public Mobile Networks 6.1 Chapter Overview 6.2 M2M over a Telecommunications Network 6.2.1 Introduction 6.2.2 M2M Communication Scenarios 6.2.3 Mobile or Fixed Networks 6.2.4 Data Connections for M2M Applications 6.3 Network Optimizations for M2M 6.3.1 Introduction 6.3.2 3GPP Standardization of Network Improvements for Machine Type Communications 6.3.3 Cost Reduction 6.3.4 M2M Value-Added Services 6.3.5 Numbering, Identifiers, and Addressing 6.3.6 Triggering Optimizations 6.3.7 Overload and Congestion Control References
• 7 The Role of IP in M2M 7.1 Introduction 7.1.1 IPv6 in Brief 7.1.2 Neighbor Discovery Protocol 7.2 IPv6 for M2M 7.3 6LoWPAN 7.3.1 Framework 7.3.2 Header Compression 7.3.3 Neighbor Discovery 7.4 Routing Protocol for Low-Power and Lossy Networks (RPL) 7.4.1 RPL Topology 7.5 CoRE 7.5.1 Message Formats 7.5.2 Transport Protocol 7.5.3 REST Architecture References
• 8 M2M Security 8.1 Introduction 8.1.1 Security Characteristics of Cellular M2M 8.2 Trust Relationships in the M2M Ecosystem 8.3 Security Requirements 8.3.1 Customer/M2M Device User 8.3.2 Access Network Provider 8.3.3 M2M Service Provider 8.3.4 Application Provider 8.3.5 Bootstrapping Requirements 8.4 Which Types of Solutions are Suitable? 8.4.1 Approaches Against Hijacking 8.4.2 Public Key Solutions 8.4.3 Smart Card-Based Solutions 8.4.4 Methods Based on Pre-Provisioned Symmetric Keys 8.4.5 Protocol for Automated Bootstrapping Based on Identity-Based Encryption 8.4.6 Security for Groups of M2M Devices 8.5 Standardization Efforts on Securing M2M and MTC Communications 8.5.1 ETSI M2M Security 8.5.2 3GPP Security Related to Network Improvements for Machine Type Communications References
• 9 M2M Terminals and Modules 9.1 M2M Module Categorization 9.1.1 Access Technology 9.1.2 Physical Form Factors 9.2 Hardware Interfaces 9.2.1 Power Interface 9.2.2 USB (Universal Serial Bus) Interface 9.2.3 UART (Universal Asynchronous Receiver/ Transmitter) Interface 9.2.4 Antenna Interface 9.2.5 UICC (Universal Integrated Circuit Card) Interface 9.2.6 GPIO (General-Purpose Input/Output Port) Interface 9.2.7 SPI (Serial Peripheral Interface) Interface 9.2.8 I2C (Inter-Integrated Circuit Bus) Interface 9.2.9 ADC (Analog-to-Digital Converter) Interface 9.2.10 PCM (Pulse Code Modulation) Interface 9.2.11 PWM (Pulse Width Modulation) Interface 9.2.12 Analog Audio Interface 9.3 Temperature and Durability 9.4 Services 9.4.1 Application Execution Environment 9.4.2 Connectivity Services 9.4.3 Management Services 9.4.4 Application Services 9.5 Software Interface 9.5.1 AT Commands 9.5.2 SDK Interface 9.6 Cellular Certification 9.6.1 Telecom Industry Certification 9.6.2 MNO Certification
• 10 Smart Cards in M2M Communication 10.1 Introduction 10.2 Security and Privacy Issues in M2M Communication 10.3 The Grounds for Hardware-Based Security Solutions 10.4 Independent Secure Elements and Trusted Environments 10.4.1 Trusted Environments in M2M Devices 10.4.2 Trusting Unknown Devices: The Need for Security Certification 10.4.3 Advantages of the Smart Card Model 10.5 Specific Smart Card Properties for M2M Environments 10.5.1 Removable Smart Cards versus Embedded Secure Elements 10.5.2 UICC Resistance to Environmental Constraints 10.5.3 Adapting the Card Application Toolkit to Unattended Devices 10.5.4 Reaching UICC Peripheral Devices with Toolkit Commands 10.5.5 Confidential Remote Management of Third-Party Applications 10.6 Smart Card Future Evolutions in M2M Environments 10.6.1 UICC-Based M2M Service Identity Module Application 10.6.2 Internet Protocol Integration of the UICC 10.7 Remote Administration of M2M Secure Elements 10.7.1 Overview 10.7.2 Late Personalization of Subscription 10.7.3 Remote Management of Subscriptions on the Field References Part III BOOK CONCLUSIONS AND FUTURE VISION
• 11 Conclusions Index.
• (source: Nielsen Book Data)

A comprehensive introduction to M2M Standards and systems architecture, from concept to implementation Focusing on the latest technological developments, M2M Communications: A Systems Approach is an advanced introduction to this important and rapidly evolving topic. It provides a systems perspective on machine-to-machine services and the major telecommunications relevant technologies. It provides a focus on the latest standards currently in progress by ETSI and 3GPP, the leading standards entities in telecommunication networks and solutions. The structure of the book is inspired by ongoing standards developments and uses a systems-based approach for describing the problems which may be encountered when considering M2M, as well as offering proposed solutions from the latest developments in industry and standardization. The authors provide comprehensive technical information on M2M architecture, protocols and applications, especially examining M2M service architecture, access and core network optimizations, and M2M area networks technologies. It also considers dominant M2M application domains such as Smart Metering, Smart Grid, and eHealth. Aimed as an advanced introduction to this complex technical field, the book will provide an essential end-to-end overview of M2M for professionals working in the industry and advanced students. Key features: * First technical book emerging from a standards perspective to respond to this highly specific technology/business segment * Covers the main challenges facing the M2M industry today, and proposes early roll-out scenarios and potential optimization solutions * Examines the system level architecture and clearly defines the methodology and interfaces to be considered * Includes important information presented in a logical manner essential for any engineer or business manager involved in the field of M2M and Internet of Things * Provides a cross-over between vertical and horizontal M2M concepts and a possible evolution path between the two * Written by experts involved at the cutting edge of M2M developments.
(source: Nielsen Book Data)

• Preface. Acknowledgements. Editors. Contributors.
• Chapter 1 Another Set of Eyes in Anesthesiology.
• Chapter 2 Dermatological Machine Learning Clinical Decision Support System.
• Chapter 3 Vision and AI.
• Chapter 4 Thermal Dose Modeling for Thermal Ablative Cancer Treatments by Cellular Neural Networks.
• Chapter 5 Ensembles of Convolutional Neural Networks with Different Activation Functions for Small to Medium-Sized Biomedical Datasets.
• Chapter 6 Analysis of Structural MRI Data for Epilepsy Diagnosis Using Machine Learning Techniques.
• Chapter 7 Artificial Intelligence-Powered Ultrasound for Diagnosis and Improving Clinical Workflow.
• Chapter 8 Machine Learning for E/MEG-Based Identification of Alzheimer's Disease.
• Chapter 9 Some Practical Challenges with Possible Solutions for Machine Learning in Medical Imaging.
• Chapter 10 Detection of Abnormal Activities Stemming from Cognitive Decline Using Deep Learning.
• Chapter 11 Classification of Left Ventricular Hypertrophy and NAFLD through Decision Tree Algorithm.
• Chapter 12 The Cutting Edge of Surgical Practice: Applications of Machine Learning to Neurosurgery.
• Chapter 13 A Novel MRA-Based Framework for the Detection of Cerebrovascular Changes and Correlation to Blood Pressure.
• Chapter 14 Early Classification of Renal Rejection Types: A Deep Learning Approach. Index.
• (source: Nielsen Book Data)

Provides a comprehensive overview of machine learning and deep learning techniques for biomedical imaging Includes thoracic imaging, abdominal imaging, brain imaging, and retinal imaging Covers new and emerging methods in machine learning Features contributions from leading experts Presents tools to improve computer aided diagnosis.
(source: Nielsen Book Data)

• Foreword Gregory Chaitin Part I Mechanisms in Programs and Nature 1. Hyperbolic Cellular Automata Maurice Margenstern 2. A Lyapunov View on the Stability of Cellular Automata Jan M. Baetens & Bernard De Baets 3. On the Necessity of Complexity Joost J. Joosten 4. Computational Technosphere and Cellular Engineering Mark Burgin
• Part II The World of Numbers & Simple Programs 5. Cellular Automata: Models of the Physical World Herbert W. Franke 6. Symmetry and Complexity of Cellular Automata: Towards an Analytical Theory of Dynamical System Klaus Mainzer 7. A New Kind of Science: Ten Years Later David H. Bailey
• Part III Everyday Systems 8. A New Kind of Finance Philip Z. Maymin 9. The Relevance and Importance of Computation Universality in Economics Kumaraswamy Velupillai 10. Exploring the Sources of and Nature of Computational Irreducibility Brian Beckage, Stuart Kauffman, Louis Gross, Asim Zia, Gabor Vattay and Chris Koliba
• Part IV Fundamental Physics 11. The Principle of a Finite Density of Information Gilles Dowek and Pablo Arrighi 12. Artificial Cosmogenesis: A New Kind of Cosmology Clement Vidal 13. Do Particles Evolve? Tommaso Bolognesi
• Part V The Behavior of Systems & the Notion of Computation 14. An Incompleteness Theorem for the Natural World Rudy Rucker 15. Pervasiveness of Universalities of Cellular Automata: Fascinating Life-like Behaviours Emmanuel Sapin 16. Wolfram's Classification and Computation in Cellular Automata Classes III and IV Genaro J. Martinez, Juan Carlos Seck Tuoh Mora and Hector Zenil
• Part VI Irreducibility & Computational Equivalence 17. Exploring the Computational Limits of Haugeland's Game as a Two-Dimensional Cellular Automaton Drew Reisinger, Taylor Martin, Mason Blankenship, Christopher Harrison, Jesse Squires and Anthony Beavers 18. Irreducibility and Computational Equivalence Herve Zwrin and Jean-Paul Delahaye 19. Computational Equivalence and Classical Recursion Theory Klaus Sutner
• Part VII Deliberations and Philosophical Implications 20. Wolfram and the Computing Nature Gordana Dodig-Crnkovic 21. A New Kind of Philosophy. Manifesto for a Digital Ontology Jacopo Tagliabue 22. Free Will For Us, not For Robots Selmer Bringsjord
• Afterword Cristian Calude.
• (source: Nielsen Book Data)

It is clear that computation is playing an increasingly prominent role in the development of mathematics, as well as in the natural and social sciences. The work of Stephen Wolfram over the last several decades has been a salient part in this phenomenon helping founding the field of Complex Systems, with many of his constructs and ideas incorporated in his book A New Kind of Science (ANKS) becoming part of the scientific discourse and general academic knowledge--from the now established Elementary Cellular Automata to the unconventional concept of mining the Computational Universe, from today's widespread Wolfram's Behavioural Classification to his principles of Irreducibility and Computational Equivalence. This volume, with a Foreword by Gregory Chaitin and an Afterword by Cris Calude, covers these and other topics related to or motivated by Wolfram's seminal ideas, reporting on research undertaken in the decade following the publication of Wolfram's NKS book. Featuring 39 authors, its 23 contributions are organized into seven parts: Mechanisms in Programs & Nature Systems Based on Numbers & Simple Programs Social and Biological Systems & Technology Fundamental Physics The Behavior of Systems & the Notion of Computation Irreducibility & Computational Equivalence Reflections and Philosophical Implications.
(source: Nielsen Book Data)

• 1.Introduction
• 2. The Management Team
• 3. Maintaining the Network
• 4. The Network Providers
• 5. The Data Network
• 6. The Voice Network
• 7. New Technologies
• 8. Support for the Network Manager
• 9. Education Support for the Network Manager.
• (source: Nielsen Book Data)

This essential handbook for the data communications/network manager and planner covers a variety of data communication and IS topics. The Network Manager's Handbook addresses technical issues associated with local and wide area networking, purchasing communications services, supporting the network's users, understanding the telecommunications regulatory environment, personnel issues, and more.
(source: Nielsen Book Data)

• Part I-Background:
• 1. Overview of Internet of Things. 2 Smart applications.
• 3. IoT challenges. Part II- Blockchain overview. 4 Python Basics.
• 5. Cryptography primitives.
• 6. Blockchain technology and technical foundations.
• 7. Verification and validation methods used by Blockchain.
• 8. Data structures for Blockchain. Part III-Probabilistic data structures: An overview.
• 9. Introduction to probabilistic data structures.
• 10. Membership Query Probabilistic Data Structures.
• 11. Cardinality Estimation Probabilistic Data Structures.
• 12. Frequency Count Query Probabilistic Data Structures.
• 13. Approximate Similarity Search Query Probabilistic Data Structures. Part IV-Integration of Probabilistic Data Structures with Blockchain.
• 14. Applicability of membership query PDS with Blockchain.
• 15. Applicability of cardinality estimation PDS with Blockchain.
• 16. Applicability of frequency estimation PDS with Blockchain.
• 17. Applicability of approximate similarity search PDS with Blockchain.
• (source: Nielsen Book Data)

This book covers theory and practical knowledge of Probabilistic data structures (PDS) and Blockchain (BC) concepts. It introduces the applicability of PDS in BC to technology practitioners and explains each PDS through code snippets and illustrative examples. Further, it provides references for the applications of PDS to BC along with implementation codes in python language for various PDS so that the readers can gain confidence using hands on experience. Organized into five sections, the book covers IoT technology, fundamental concepts of BC, PDS and algorithms used to estimate membership query, cardinality, similarity and frequency, usage of PDS in BC based IoT and so forth.
(source: Nielsen Book Data)

• Preface ix Acknowledgments xiii Introduction xv 1 Principles and Concepts of Cloud Computing 1 1.1 Kinds of Modern Software Architectures 1 1.2 Characteristic Features of Modern Software 3 1.3 Basic Concepts of Modern Software Architecture 4 1.4 Service-Oriented Architecture (SOA) 6 1.5 Software as A Service (SaaS) 8 1.6 Key Ideas and Principles of Cloud Computing 8 1.7 Components of Cloud Platforms and Kinds of Cloud Servicing 11 1.8 Layers of the Cloud Architecture 14 1.9 Scheme of Architecture of the Cloud 15 1.10 Roles of People in Cloud Computing 16 1.11 Standards of Cloud Computing 17 1.12 How the Clouds Come True: Organization of Datacenters and Cloud Hardware 20 1.13 Specifics and Components of Software for Cloud Computing 22 1.14 Cloud Computing-Related Trends Activities and Resources 25 Exercises to
• Chapter 1 29 2 Platforms of Cloud Computing 33 2.1 A Variety of Cloud Platforms: The First Impression 33 2.2 Amazon AWS Cloud Platform A Pioneer of Cloud Computing 36 2.3 IBM Cloud 49 2.4 Oracle Cloud 58 2.5 Google Cloud Platform 64 2.6 HP Helion Cloud Platform 70 2.7 Salesforce Cloud Platform 79 Exercises to
• Chapter 2 88 3 Principles and Pillars of Trustworthy Computing 91 3.1 Vital Issues of Trustworthy Computing 91 3.2 The Trustworthy Computing Initiative by Microsoft 93 3.3 The Security Pillar 94 3.4 The Reliability Pillar 99 3.5 The Privacy Pillar 101 3.6 The Business Integrity Pillar 103 3.7 Tools and Software Lifecycle Models to Support Trustworthy Computing 106 Exercises to
• Chapter 3 110 4 Making Cloud Computing Trustworthy 113 4.1 Psychological Barriers Between the Customers and the Cloud and the Ways to Overcome Them 113 4.2 User Interface for Cloud Computing Its Convenience Usability and Functionality for Trustworthy Cloud Computing 116 4.3 Threats and Attacks to Clouds 120 4.4 Trustworthy Cloud Computing from Hardware Side: Datacenter Architecture Servers Clusters Hypervisors 124 4.5 Trustworthy Cloud Computing from Operating System Side: Desirable OS Features to Implement Clouds and Datacenters 126 4.6 Using Aspect-Oriented Programming for Refactoring Cloud Services and Making Them Trustworthy: The Contribution of St. Petersburg University 129 Exercises to
• Chapter 4 142 5 Example of a Trustworthy Cloud Computing Platform in Detail: Microsoft Azure 147 5.1 Overview of Microsoft Azure Architecture and its Evolution 147 5.2 User Interface and the Management Portal of Microsoft Azure 152 5.3 The Compute Component: Managing and Operating Cloud Services 161 5.4 The Storage Component: Managing and Operating Cloud Storage 178 5.5 The SQL Azure Component: The Cloud Database 187 5.6 Networking in the Azure Cloud: Network-as-a-Service (NaaS) Content Delivery Network (CDN) Virtual Network Traffic Manager 196 5.7 Active Directory in the Cloud: A Way of Structuring User Accounts 202 5.8 Development of Microsoft Azure Cloud Services with Microsoft Visual Studio 206 5.9 Visual Studio Online and its Relation to Microsoft Azure 215 5.10 Developing Mobile Services and Connected Mobile Applications for Microsoft Azure 220 5.11 Media Services 234 5.12 The .NET Platform The Basis of Azure Implementation 237 5.13 Azure Tools 252 5.14 Machine Learning in the Cloud: Azure Machine Learning Studio 257 5.15 Parallel Processing of Big Data in the Cloud: Using Apache Hadoop in Microsoft Azure 261 5.16 Perspectives of Microsoft Azure 265 Exercises to
• Chapter 5 266 6 Conclusions: Perspectives of Trustworthy Cloud Computing 271 6.1 Integration of Clouds. The Intercloud IEEE Standard 271 6.2 The TCLOUDS Project by the European Union 280 6.3 Further Developments and Trends of Trustworthy Cloud Computing 291 Exercises to Conclusions 296 Appendix A Example of Microsoft Azure Cloud Service: Filemanager 299 References 309 Index 317.
• (source: Nielsen Book Data)

Introduces the topic of cloud computing with an emphasis on the trustworthiness of cloud computing systems and services This book describes the scientific basis of cloud computing, explaining the ideas, principles, and architectures of cloud computing as well the different types of clouds and the services they provide. The text reviews several cloud computing platforms, including Microsoft Azure, Amazon, Oracle, Google, HP, IBM, Salesforce, and Kaavo. The author addresses the problem of trustworthiness in cloud computing and provides methods to improve the security and privacy of cloud applications. The end-of-chapter exercises and supplementary material on the book's companion website will allow readers to grasp the introductory and advanced level concepts of cloud computing. * Examines cloud computing platforms such as Microsoft Azure, Amazon, Oracle, Google, HP, IBM, Salesforce, and Kaavo * Analyzes the use of aspect-oriented programming (AOP) for refactoring cloud services and improving the security and privacy of cloud applications * Contains practical examples of cloud computing, test questions, and end-of-chapter exercises * Includes presentations, examples of cloud projects and other teaching resources at the author s website (http://www.vladimirsafonov.org/cloud) Trustworthy Cloud Computing is written for advanced undergraduate and graduate students in computer science, data science, and computer engineering as well as software engineers, system architects, system managers, and software developers new to cloud computing.
(source: Nielsen Book Data)

• About the Authors ix Acknowledgments xi
• 1 Introduction 1 References 6
• 2 The Business of Cloud Computing 7 2.1 IT Industry Transformation through Virtualization and Cloud 7 2.2 The Business Model Around Cloud 13 2.2.1 Cloud Providers 14 2.2.2 Software and Service Vendors 15 2.3 Taking Cloud to the Network Operators 15 References 18
• 3 CPU Virtualization 19 3.1 Motivation and History 20 3.2 A Computer Architecture Primer 21 3.2.1 CPU, Memory, and I/O 21 3.2.2 How the CPU Works 23 3.2.3 In-program Control Transfer: Jumps and Procedure Calls 25 3.2.4 Interrupts and Exceptions the CPU Loop Refined 28 3.2.5 Multi-processing and its Requirements The Need for an Operating System 34 3.2.6 Virtual Memory Segmentation and Paging 38 3.2.7 Options in Handling Privileged Instructions and the Final Approximation of the CPU Loop 42 3.2.8 More on Operating Systems 44 3.3 Virtualization and Hypervisors 48 3.3.1 Model, Requirements, and Issues 49 3.3.2 The x86 Processor and Virtualization 52 3.3.3 Dealing with a Non-virtualizable CPU 55 3.3.4 I/O Virtualization 57 3.3.5 Hypervisor Examples 60 3.3.6 Security 65 References 69
• 4 Data Networks The Nervous System of the Cloud 71 4.1 The OSI Reference Model 74 4.1.1 Host-to-Host Communications 74 4.1.2 Interlayer Communications 76 4.1.3 Functional Description of Layers 79 4.2 The Internet Protocol Suite 85 4.2.1 IP The Glue of the Internet 87 4.2.2 The Internet Hourglass 98 4.3 Quality of Service in IP Networks 102 4.3.1 Packet Scheduling Disciplines and Traffic Specification Models 103 4.3.2 Integrated Services 105 4.3.3 Differentiated Services 109 4.3.4 Multiprotocol Label Switching (MPLS) 112 4.4 WAN Virtualization Technologies 117 4.5 Software-Defined Network 120 4.6 Security of IP 125 References 129
• 5 Networking Appliances 131 5.1 Domain Name System 131 5.1.1 Architecture and Protocol 134 5.1.2 DNS Operation 140 5.1.3 Top-Level Domain Labels 142 5.1.4 DNS Security 145 5.2 Firewalls 149 5.2.1 Network Perimeter Control 153 5.2.2 Stateless Firewalls 155 5.2.3 Stateful Firewalls 158 5.2.4 Application-Layer Firewalls 161 5.3 NAT Boxes 163 5.3.1 Allocation of Private IP Addresses 165 5.3.2 Architecture and Operation of the NAT Boxes 168 5.3.3 Living with NAT 172 5.3.4 Carrier-Grade NAT 180 5.4 Load Balancers 184 5.4.1 Load Balancing in a Server Farm 185 5.4.2 A Practical Example: A Load-Balanced Web Service 187 5.4.3 Using DNS for Load Balancing 188 References 191
• 6 Cloud Storage and the Structure of a Modern Data Center 193 6.1 Data Center Basics 195 6.1.1 Compute 196 6.1.2 Storage 196 6.1.3 Networking 198 6.2 Storage-Related Matters 198 6.2.1 Direct-Attached Storage 200 6.2.2 Network-Attached Storage 208 6.2.3 Storage Area Network 215 6.2.4 Convergence of SAN and Ethernet 221 6.2.5 Object Storage 230 6.2.6 Storage Virtualization 233 6.2.7 Solid-State Storage 236 References 242
• 7 Operations, Management, and Orchestration in the Cloud 245 7.1 Orchestration in the Enterprise 247 7.1.1 The Service-Oriented Architecture 253 7.1.2 Workflows 255 7.2 Network and Operations Management 259 7.2.1 The OSI Network Management Framework and Model 261 7.2.2 Policy-Based Management 264 7.3 Orchestration and Management in the Cloud 267 7.3.1 The Life Cycle of a Service 268 7.3.2 Orchestration and Management in OpenStack 274 7.4 Identity and Access Management 287 7.4.1 Implications of Cloud Computing 289 7.4.2 Authentication 291 7.4.3 Access Control 295 7.4.4 Dynamic Delegation 299 7.4.5 Identity Federation 302 7.4.6 OpenStack Keystone (A Case Study) 303 References 309 Appendix: Selected Topics 313 A.1 The IETF Operations and Management Standards 313 A.1.1 SNMP 313 A.1.2 COPS 316 A.1.3 Network Configuration (NETCONF) Model and Protocol 319 A.2 Orchestration with TOSCA 324 A.3 The REST Architectural Style 329 A.3.1 The Origins and Development of Hypermedia 329 A.3.2 Highlights of the World Wide Web Architecture 332 A.3.3 The Principles of REST 334 A.4 Identity and Access Management Mechanisms 336 A.4.1 Password Management 336 A.4.2 Kerberos 338 A.4.3 Access Control Lists 341 A.4.4 Capability Lists 342 A.4.5 The Bell LaPadula Model 343 A.4.6 Security Assertion Markup Language 345 A.4.7 OAuth 2.0 347 A.4.8 OpenID Connect 349 A.4.9 Access Control Markup Language 351 References 353 Index 355.
• (source: Nielsen Book Data)

Cloud Computing: Business Trends and Technologies provides a broad introduction to Cloud computing technologies and their applications to IT and telecommunications businesses (i.e., the network function virtualization, NFV). To this end, the book is expected to serve as a textbook in a graduate course on Cloud computing. The book examines the business cases and then concentrates on the technologies necessary for supporting them. In the process, the book addresses the principles of as well as the known problems with the underlying technologies, such as virtualization, data communications, network and operations management, security and identity management. It introduces, through open-source case studies (based on OpenStack), an extensive illustration of lifecycle management. The book also looks at the existing and emerging standards, demonstrating their respective relation to each topic. Overall, this is an authoritative textbook on this emerging and still-developing discipline, which Guides the reader through basic concepts, to current practices, to state-of-the-art applications. Considers technical standards bodies involved in Cloud computing standardization. Is written by innovation experts in operating systems and data communications, each with over 20 years experience in business, research, and teaching.
(source: Nielsen Book Data)

This book presents original results on the leading edge of computer science research. The first chapter begins by presenting several supervised machine learning methods and their applications in analysing microarray and genotyping data to classify and predict sample phenotypes. Chapter two presents the design and implementation of the EstiNet cloud, and evaluates its performance. Chapter three proposes a simple high switching power supply designed especially for large battery charging. Chapter four studies the constant control of temperature, with the microcontroller PIC, by iterative adjustments of the temperature on a thermal sensor from the heat source. Chapter five proposes two types of pinning control protocols to ensure group consensus regardless of the magnitude of the coupling strengths among the agents. Chapter six describes how point clouds are constructed, and provides a brief discussion on the currently available software that is used for the processing of point clouds. The final chapter reviews the metamodels used to approximate computationally expensive simulations.
(source: Nielsen Book Data)

• Introduction xi Acknowledgments xv Abbreviations xvii
• 1. Basics 1 1.1 Cloud Computing Fundamentals 1 1.2 Roles in Cloud Computing 6 1.3 Applications 9 1.3.1 Application Service Quality 11 1.4 Demand, Supply, Capacity, and Fungibility 13 1.5 Demand Variability 16 1.6 Chapter Review 18
• 2. Rethinking Capacity Management 19 2.1 Capacity Management 19 2.2 Demand Management 21 2.3 Performance Management 21 2.4 Canonical Capacity Management 23 2.4.1 Traditional Capacity Management 24 2.4.2 ITIL Capacity Management 27 2.4.3 eTOM Capacity Management 28 2.4.4 Discussion 30 2.5 Three Cloud Capacity Management Problems 30 2.5.1 Physical Resource Capacity Management 31 2.5.2 Virtual Resource Capacity Management 32 2.5.3 Application Capacity Management 33 2.6 Cloud Capacity Management as a Value Chain 36 2.7 Chapter Review 39
• 3. Lean Thinking on Cloud Capacity Management 41 3.1 Lean Thinking Overview 41 3.2 Goal 42 3.3 Seeing Waste (Nonvalue-Adding Activities) 43 3.3.1 Reserve Capacity 45 3.3.2 Excess Application Capacity 46 3.3.3 Excess Online Infrastructure Capacity 46 3.3.4 Excess Physical Infrastructure Capacity 46 3.3.5 Inadequate Capacity 47 3.3.6 Infrastructure Overhead 48 3.3.7 Capacity Management Overhead 48 3.3.8 Resource Overhead 49 3.3.9 Power Management Overhead 50 3.3.10 Workload Migration 50 3.3.11 Complexity Overhead 51 3.3.12 Resource Allocation Failure 51 3.3.13 Leaking and Lost Resources 53 3.3.14 Waste Heat 53 3.3.15 Carbon Footprint 54 3.4 Key Principles 54 3.4.1 Move toward Flow 55 3.4.2 Pull versus Push 55 3.4.3 Level the Workload 55 3.4.4 Stop and Fix Problems 55 3.4.5 Master Practices 56 3.4.6 Visual Management 57 3.4.7 Use Well-Tested Technology 57 3.4.8 Take a Long-Term Perspective 58 3.4.9 Grow, Learn, and Teach Others 58 3.4.10 Develop Exceptional People 58 3.4.11 Partners Help Each Other Improve 58 3.4.12 Go See 59 3.4.13 Implement Rapidly 59 3.4.14 Become a Learning Organization 59 3.5 Pillar: Respect 59 3.6 Pillar: Continuous Improvement 61 3.7 Foundation 62 3.8 Cadence 62 3.9 Lean Capacity Management Philosophy 63 3.10 Chapter Review 64
• 4. Lean Cloud Capacity Management Strategy 67 4.1 Lean Application Service Provider Strategy 68 4.1.1 User Workload Placement 71 4.1.2 Application Performance Management 73 4.2 Lean Infrastructure Service Provider Strategies 73 4.2.1 Physical Resource Capacity Management 76 4.3 Full Stream Optimization 77 4.4 Chapter Review 79
• 5. Electric Power Generation as Cloud Infrastructure Analog 81 5.1 Power Generation as a Cloud Infrastructure Analog 81 5.2 Business Context 83 5.3 Business Structure 86 5.4 Technical Similarities 88 5.5 Impedance and Fungibility 91 5.6 Capacity Ratings 94 5.7 Bottled Capacity 95 5.8 Location of Production Considerations 95 5.9 Demand Management 97 5.10 Demand and Reserves 98 5.11 Service Curtailment 99 5.12 Balance and Grid Operations 100 5.13 Chapter Review 103
• 6. Application Capacity Management as an Inventory Management Problem 105 6.1 The Application Capacity Management Service Delivery Chain 105 6.2 Traditional Application Service Production Chain 107 6.3 Elasticity and Demand-Driven Capacity Management 108 6.4 Application Service as Retail Analog 110 6.4.1 Locational Consideration 112 6.4.2 Inventory and Capacity 112 6.4.3 Service Level 113 6.4.4 Inventory Carrying Costs 114 6.4.5 Inventory Decision, Planning, and Ordering 115 6.4.6 Agility 118 6.4.7 Changing Consumption Patterns 118 6.5 Chapter Review 118
• 7. Lean Demand Management 119 7.1 Infrastructure Demand Management Techniques 120 7.1.1 Resource Scheduling 121 7.1.2 Resource Curtailment 121 7.1.3 Mandatory Demand Shaping 122 7.1.4 Voluntary Demand Shaping 123 7.1.5 Scheduling Maintenance Actions 123 7.1.6 Resource Pricing 123 7.2 Application Demand Management Techniques 124 7.2.1 Queues and Buffers 124 7.2.2 Load Balancers 124 7.2.3 Overload Controls 125 7.2.4 Explicit Demand Management Actions 125 7.2.5 Scheduling Maintenance Actions 125 7.2.6 User Pricing Strategies 126 7.3 Full Stream Analysis Methodology 126 7.3.1 Analyze Applications' Natural Demand Patterns 127 7.3.2 Analyze Applications' Tolerances 128 7.3.3 Create Attractive Infrastructure Pricing Models 129 7.3.4 Deploy Optimal Infrastructure Demand Management Models 130 7.4 Chapter Review 131
• 8. Lean Reserves 133 8.1 What Is Reserve Capacity? 133 8.2 Uses of Reserve Capacity 135 8.2.1 Random Demand Peaks 135 8.2.2 Component or Resource Failure 136 8.2.3 Infrastructure Element Failure 136 8.2.4 Infrastructure Resource Curtailment or Demand Management Action 137 8.2.5 Demand Exceeding Forecast 137 8.2.6 Lead Time Demand 137 8.2.7 Catastrophic Failures and Force Majeure Events 139 8.3 Reserve Capacity as a Feature 139 8.4 Types of Reserve Capacity 140 8.4.1 Automatic Infrastructure Power Management Controls 140 8.4.2 Utilize Application Reserve Capacity 141 8.4.3 Place/Migrate Demand into Underutilized Capacity 141 8.4.4 Grow Online Capacity 141 8.4.5 Service Curtailment/Degradation 141 8.4.6 Mandatory Demand Shaping 141 8.4.7 Voluntary Demand Shaping 142 8.4.8 Emergency Reserves 142 8.5 Limits of Reserve Capacity 144 8.6 Ideal Reserve 144 8.6.1 Normal (Co-located) Reserve 144 8.6.2 Emergency (Geographically Distributed) Reserve 146 8.7 Chapter Review 147
• 9. Lean Infrastructure Commitment 149 9.1 Unit Commitment and Infrastructure Commitment 150 9.2 Framing the Unit Commitment Problem 151 9.3 Framing the Infrastructure Commitment Problem 153 9.4 Understanding Element Startup Time 155 9.5 Understanding Element Shutdown Time 157 9.6 Pulling It All Together 160 9.7 Chapter Review 166
• 10. Lean Cloud Capacity Management Performance Indicators 167 10.1 Perfect Capacity Metrics 168 10.2 Capacity Management Metrics 172 10.3 Infrastructure Commitment Metrics 173 10.4 Waste Metrics 174 10.4.1 Reserve Capacity Waste Metrics 174 10.4.2 Excess Application Capacity Metrics 175 10.4.3 Excess Online Infrastructure Capacity Metrics 175 10.4.4 Excess Physical Infrastructure Capacity Metrics 175 10.4.5 Inadequate Capacity Metrics 175 10.4.6 Infrastructure Overhead Waste Metrics 176 10.4.7 Capacity Management Overhead Waste Metrics 176 10.4.8 Resource Overhead Waste Metrics 176 10.4.9 Power Management Overhead Waste Metrics 177 10.4.10 Workload Migration Metrics 177 10.4.11 Complexity Overhead Metrics 178 10.4.12 Resource Allocation Failure Metrics 178 10.4.13 Leaking and Lost Resources 179 10.4.14 Waste Heat Metrics 179 10.4.15 Carbon Footprint Metrics 180 10.5 Key Principle Indicators 180 10.6 Cost of Poor Quality 181 10.7 Metrics and Service Boundaries 182 10.8 Measurements and Maturity 183 10.9 Chapter Review 185
• 11. Summary 187 11.1 Cloud Computing as a Service Delivery Chain 187 11.2 Lean Cloud Computing 190 11.3 Reimagining Cloud Capacity 192 11.4 Lean Demand Management 195 11.5 Lean Reserves 197 11.6 Lean Infrastructure Service Provider Considerations 198 11.7 Lean Application Service Provider Considerations 198 11.8 Lean Infrastructure Commitment 199 11.9 Visualizing Perfect Capacity 201 11.10 Lean Cloud Computing Metrics 203 11.11 Concluding Remarks 204 References 207 About the Author 211 Index 213.
• (source: Nielsen Book Data)

Applies lean manufacturing principles across the cloud service delivery chain to enable application and infrastructure service providers to sustainably achieve the shortest lead time, best quality, and value * Applies lean thinking across the cloud service delivery chain to recognize and minimize waste * Leverages lessons learned from electric power industry operations to operations of cloud infrastructure * Applies insights from just-in-time inventory management to operation of cloud based applications * Explains how traditional, Information Technology Infrastructure Library (ITIL) and Enhanced Telecom Operation Map (eTOM) capacity management evolves to lean computing for the cloud.
(source: Nielsen Book Data)

This book provides an overview of technologies to maximize the quality of user experience for mobile, data-centric applications. Chapters cover techniques mobile network operators can use to maximize the effectiveness of congested networks, techniques that mobile application developers can use to minimize the impact of congested networks on user experience, and techniques that websites and data center operators can use to support a growing number of mobile users. With no technical jargon and a practical approach, this book offers a useful resource for a wide range of mobile network developers

• Part 1. Traceability of Information and Knowledge Management 1. Intelligent Traceability of Equipment. 2. A Knowledge-orientedâ ¨ Maintenance Platform. 3. Intelligent Traceability Application.
• Part 2. Post-prognostic Decision 4. Position of Decision within the PHM Context. 5. Towards a Policy ofâ ¨ Predictive Maintenance. 6. Maintenance in Operational Conditions.
• (source: Nielsen Book Data)

This book is the second volume in a set of books dealing with the evolution of technology, IT and organizational approaches and what this means for industrial equipment. The authors address this increasing complexity in two parts, focusing specifically on the field of Prognostics and Health Management (PHM). Having tackled the PHM cycle in the first volume, the purpose of this book is to tackle the other phases of PHM, including the traceability of data, information and knowledge, and the ability to make decisions accordingly. The book concludes with a summary analysis and perspectives regarding this emerging domain, since without traceability, knowledge and decision, any prediction of the health state of a system cannot be exploited.
(source: Nielsen Book Data)

• Preface
• Trajectory Planning
• Mobile Technology: Advances, Applications & Challenges
• Accessibility Models of Cloud Services Based on a Distributed Cloud Architecture
• Parallel Dynamic Programming for Large-Scale Data Applications
• Ensemble of Both Texture & Color Features for Reliable Person Re-Identification
• Detecting Movement within Indoor Environments Using Passive & Active Tracking Technologies
• Quadratic Programming Formulation for Controlling the Emissions of Air Pollution Point Sources
• Index.
• (source: Nielsen Book Data)

This book presents original results on the leading edge of computer science research. Chapter One discusses motion planning for robots with rigid bodies. Chapter Two provides an overview of handheld/mobile computing devices, pathology applications, social media in the context of pathology, and novel mobile-driven technologies like wearables and mixed reality tech, and discusses their potential impact in pathology. Chapter Three focuses on reliability and accessibility issues of a distributed cloud service based on Semi-Markov modeling. Chapter Four examines parallel dynamic programming for large-scale date applications. Chapter Five presents the ensemble of both texture and color features for reliable person re-identification. Chapter Six provides an overview of common techniques and commercial products used in tracking people and objects within indoor environments. Chapter Seven analyses quadratic programming formulation for controlling the emissions of air pollution point sources.
(source: Nielsen Book Data)

• Introduction -Leslie Willcocks, Chris Sauer and Mary Lacity.- PART I. CRITICAL RESEARCH.- Chapter 1: Doolin, B. (1998), "Information technology as disciplinary technology: being critical in interpretive research on information systems, " JIT, Vol. 13, pp. 301-311.- Chapter 2: Brooke, C. (2002), "What does it mean to be 'critical' in IS research?" JIT, Vol. 17, pp. 49-57.- Chapter 3: Brook, C. (2002), "Critical perspectives on information systems-- an impression of the research landscape, " JIT, Vol. 17, pp. 271-283.- Chapter 4: Doolin, B. and Lowe, A. (2002), "To reveal is to critique: actor-network theory and critical information systems research, " JIT, Vol. 17, 69-78-. Chapter 5: Cecez-Kecmanovic, D., Janson, M., and Brown, A. (2002), "The rationality framework for a critical study of information systems, JIT, Vol. 17, pp. 215-227.- PART II. GROUNDED THEORY APPROACHES.- Chapter 6: Urquhart, C. and Fernandez, W. (2013), "Using grounded theory method in information systems: the researcher as blank slate and other myths, " JIT, Vol. 28, pp. 224-236.- Chapter 7: Seidel, S., and Urquhart, C. (2013), "On emergence and forcing in information systems grounded theory studies: the case of Strauss and Corbin, " JIT, Vol. 28, pp. 237-260.- PART III. HISTORICAL APPROACHES.- Chapter 8: Land, F. (2010), "The use of history in IS research: an opportunity missed?" JIT, Vol. 25, pp. 385-394.- Chapter 9: Mitev, N., de Vaujany, F.X. (2012), "Seizing the opportunity: towards a historiography of information systems, " JIT, Vol. 27, pp. 110-124-. Chapter 10: Bonner, W. (2013), "History and IS - Broadening our view and understanding: Actor-Network Theory as a methodology, " JIT, Vol. 28, pp. 111-123.
• .
• (source: Nielsen Book Data)

This edited three volume edition brings together significant papers previously published in the Journal of information Technology (JIT) over its 30 year publication history. The three volumes of Enacting Research Methods in Information Systems celebrate the methodological pluralism used to advance our understanding of information technology's role in the world today. In addition to quantitative methods from the positivist tradition, JIT also values methodological articles from critical research perspectives, interpretive traditions, historical perspectives, grounded theory, and action research and design science approaches. Volume 1 covers Critical Research, Grounded Theory, and Historical Approaches. Volume 2 deals with Interpretive Approaches and also explores Action Research. Volume 3 focuses on Design Science Approaches and discusses Alternative Approaches including Semiotics Research, Complexity Theory and Gender in IS Research. The Journal of Information Technology (JIT) was started in 1986 by Professors Frank Land and Igor Aleksander with the aim of bringing technology and management together and bridging the 'great divide' between the two disciplines. The Journal was created with the vision of making the impact of complex interactions and developments in technology more accessible to a wider audience. Retaining this initial focus, the JIT has gone on to extend into new and innovative areas of research such as the launch of JITTC in 2010. A high impact journal, JIT shall continue to publish leading trends based on significant research in the field.
(source: Nielsen Book Data)