Tabla de Contenidos: “…7.1.1 Installing extra dependencies -- 7.1.2 Developing a machine learning model -- 7.1.3 Developing locally with external systems -- 7.2 Moving data from between systems -- 7.2.1 Implementing a PostgresToS3Operator -- 7.2.2 Outsourcing the heavy work -- Summary -- 8 Building custom components -- 8.1 Starting with a PythonOperator -- 8.1.1 Simulating a movie rating API -- 8.1.2 Fetching ratings from the API -- 8.1.3 Building the actual DAG -- 8.2 Building a custom hook -- 8.2.1 Designing a custom hook -- 8.2.2 Building our DAG with the MovielensHook -- 8.3 Building a custom operator -- 8.3.1 Defining a custom operator -- 8.3.2 Building an operator for fetching ratings -- 8.4 Building custom sensors -- 8.5 Packaging your components -- 8.5.1 Bootstrapping a Python package -- 8.5.2 Installing your package -- Summary -- 9 Testing -- 9.1 Getting started with testing -- 9.1.1 Integrity testing all DAGs -- 9.1.2 Setting up a CI/CD pipeline -- 9.1.3 Writing unit tests -- 9.1.4 Pytest project structure -- 9.1.5 Testing with files on disk -- 9.2 Working with DAGs and task context in tests -- 9.2.1 Working with external systems -- 9.3 Using tests for development -- 9.3.1 Testing complete DAGs -- 9.4 Emulate production environments with Whirl -- 9.5 Create DTAP environments -- Summary -- 10 Running tasks in containers -- 10.1 Challenges of many different operators -- 10.1.1 Operator interfaces and implementations -- 10.1.2 Complex and conflicting dependencies -- 10.1.3 Moving toward a generic operator -- 10.2 Introducing containers -- 10.2.1 What are containers? …”

Tabla de Contenidos: “…8.5 Ergonomic error handling for libraries -- 8.5.1 Issue: Unable to return multiple error types -- 8.5.2 Wrapping downstream errors by defining our own error type -- 8.5.3 Cheating with unwrap() and expect() -- 8.6 MAC addresses -- 8.6.1 Generating MAC addresses -- 8.7 Implementing state machines with Rust's enums -- 8.8 Raw TCP -- 8.9 Creating a virtual networking device -- 8.10 "Raw" HTTP -- Summary -- 9 Time and timekeeping -- 9.1 Background -- 9.2 Sources of time -- 9.3 Definitions -- 9.4 Encoding time -- 9.4.1 Representing time zones -- 9.5 clock v0.1.0: Teaching an application how to tell the time -- 9.6 clock v0.1.1: Formatting timestamps to comply with ISO 8601 and email standards -- 9.6.1 Refactoring the clock v0.1.0 code to support a wider architecture -- 9.6.2 Formatting the time -- 9.6.3 Providing a full command-line interface -- 9.6.4 clock v0.1.1: Full project -- 9.7 clock v0.1.2: Setting the time -- 9.7.1 Common behavior -- 9.7.2 Setting the time for operating systems that use libc -- 9.7.3 Setting the time on MS Windows -- 9.7.4 clock v0.1.2: The full code listing -- 9.8 Improving error handling -- 9.9 clock v0.1.3: Resolving differences between clocks with the Network Time Protocol (NTP) -- 9.9.1 Sending NTP requests and interpreting responses -- 9.9.2 Adjusting the local time as a result of the server's response -- 9.9.3 Converting between time representations that use different precisions and epochs -- 9.9.4 clock v0.1.3: The full code listing -- Summary -- 10 Processes, threads, and containers -- 10.1 Anonymous functions -- 10.2 Spawning threads -- 10.2.1 Introduction to closures -- 10.2.2 Spawning a thread -- 10.2.3 Effect of spawning a few threads -- 10.2.4 Effect of spawning many threads -- 10.2.5 Reproducing the results -- 10.2.6 Shared variables -- 10.3 Differences between closures and functions…”

Tabla de Contenidos: “…4.4.3 Load Balancing: Link-Path Formulation -- 4.4.4 Minimum Average Delay: Link-Path Formulation -- 4.4.5 How Many Nonzero Flows at Optimality? -- 4.5 Multicommodity Network Flow Problem: Non-Splittable Flow -- 4.6 Node-Link Formulation -- 4.6.1 Minimum Cost Single-Commodity Network Flow Problem -- 4.6.2 Minimum Cost Multicommodity Network Flow Problem -- 4.6.3 Load Balancing Multicommodity Network Flow Problem -- 4.6.4 Shortest Path Routing -- 4.6.5 Shortest Path Tree -- 4.7 Generating Traf c Matrix -- 4.8 Summary -- Further Lookup -- Exercises -- Part 2 Internet Routing -- 5 IP Routing and Distance Vector Protocol Family -- 5.1 Routers, Networks, and Routing Information: Some Basics -- 5.1.1 Routing Table -- 5.1.2 Communication of Routing Information -- 5.2 Static Routes -- 5.3 Routing Information Protocol, Version 1 (RIPv1) -- 5.3.1 Communication and Message Format -- 5.3.2 General Operation -- 5.3.3 Is RIPv1 Good to Use? …”

Tabla de Contenidos: “…Front Cover -- Multicore and GPU Programming: An Integrated Approach -- Copyright -- Dedication -- Contents -- List of Tables -- Preface -- What Is in This Book -- Using This Book as a Textbook -- Software and Hardware Requirements -- Sample Code -- Chapter 1: Introduction -- 1.1 The era of multicore machines -- 1.2 A taxonomy of parallel machines -- 1.3 A glimpse of contemporary computing machines -- 1.3.1 The cell BE processor -- 1.3.2 Nvidia's Kepler -- 1.3.3 AMD's APUs -- 1.3.4 Multicore to many-core: tilera's TILE-Gx8072 and intel's xeon phi -- 1.4 Performance metrics -- 1.5 Predicting and measuring parallel program performance -- 1.5.1 Amdahl's law -- 1.5.2 Gustafson-barsis's rebuttal -- Exercises -- Chapter 2: Multicore and parallel program design -- 2.1 Introduction -- 2.2 The PCAM methodology -- 2.3 Decomposition patterns -- 2.3.1 Task parallelism -- 2.3.2 Divide-and-conquer decomposition -- 2.3.3 Geometric decomposition -- 2.3.4 Recursive data decomposition -- 2.3.5 Pipeline decomposition -- 2.3.6 Event-based coordination decomposition -- 2.4 Program structure patterns -- 2.4.1 Single-program, multiple-data -- 2.4.2 Multiple-program, multiple-data -- 2.4.3 Master-worker -- 2.4.4 Map-reduce -- 2.4.5 Fork/join -- 2.4.6 Loop parallelism -- 2.5 Matching decomposition patterns with program structure patterns -- Exercises -- Chapter 3: Shared-memory programming: threads -- 3.1 Introduction -- 3.2 Threads -- 3.2.1 What is a thread? …”

Tabla de Contenidos: “…Maheswari, Mohankumar, and Banuroopa -- 2.1 5G - An Introduction -- 2.1.1 Industry Applications -- 2.1.2 Healthcare -- 2.1.3 Retail -- 2.1.4 Agriculture -- 2.1.5 Manufacturing -- 2.1.6 Logistics -- 2.1.7 Sustainability of 5G Networks -- 2.1.8 Implementation of 5G -- 2.1.9 Architecture of 5G Technology -- 2.2 5G and AI -- 2.2.1 Gaming and Virtual Reality -- 2.3 AI and 5G -- 2.3.1 Continuous Learning AI Model -- 2.4 Challenges and Roadmap -- 2.4.1 Technical Issues -- 2.4.2 Technology Roadmap -- 2.4.3 Deployment Roadmap -- 2.5 Mathematical Models -- 2.5.1 The Insights of Mathematical Modeling in 5G Networks -- 2.6 Conclusion -- References -- 3 Sustainable Paradigm for Computing the Security of Wireless Internet of Things: Blockchain Technology Sana Zeba, Mohammed Amjad, and Danish Raza Rizvi -- 3.1 Introduction -- 3.2 Research Background -- 3.2.1 The Internet of Things -- 3.2.1.1 Security Requirements in Wireless IoT -- 3.2.1.2 Layered Architecture of Wireless IoT -- 3.2.2 Blockchain Technology -- 3.2.2.1 Types of Blockchain -- 3.2.2.2 Integration of Blockchain with Wireless Internet of Things -- 3.3 Related Work -- 3.3.1 Security Issues in Wireless IoT System -- 3.3.2 Solutions of Wireless IoT Security Problem -- 3.4 Research Methodology -- 3.5 Comparison of Various Existing Solutions -- 3.6 Discussion of Research Questions -- 3.7 Future Scope of Blockchain in IoT -- 3.8 Conclusion -- References -- 4 Cognitive IoT Based Health Monitoring Scheme Using Non-Orthogonal MultipleAccess Ashiqur Rahman Rahul, Saifur Rahman Sabuj, Majumder Fazle Haider, andShakil Ahmed -- 4.1 Introduction -- 4.2 Related Work -- 4.3 System Model and Implementation -- 4.3.1 Network Description -- 4.3.2 Sensing and Transmission Analysis -- 4.3.3 Pathloss Model -- 4.3.4 Mathematical Model Evaluation -- 4.3.4.1 Effectual Throughput -- 4.3.4.2 Interference Throughput -- 4.3.4.3 Energy Efficiency -- 4.3.4.4 Optimum Power -- 4.3.4.4.1 Optimum Power Derivation for HRC -- 4.2.3.4.2 Optimum Power Derivation for MRC -- 4.4 Simulation Results -- 4.5 Conclusion -- 4.A Appendices -- 4.A.1 Proof of Optimum Power Transmission for HRC Device at EffectualState (z = 0) -- 4.A.2 Proof of Optimum Power Transmission for HRC Device inInterference State (z = 1) -- 4.A.3 Proof of Optimum Power Transmission for MRC Device at EffectualState (z = 0) -- 4.A.4 Proof of Optimum Power Transmission for MRC Device inInterference State (z = 1) -- References -- 5 Overview of Resource Management for Wireless Adhoc Network Mehajabeen Fatima and Afreen Khueaheed -- 5.1 Introduction -- 5.1.1 Wired and Wireless Network Design Approach -- 5.1.2 History -- 5.1.3 Spectrum of Wireless Adhoc Network -- 5.1.4 Enabling and Networking Technologies -- 5.1.5 Taxonomy of Wireless Adhoc Network (WANET) -- 5.2 Mobile Adhoc Network (MANET) -- 5.2.1 Introduction to MANET -- 5.2.2 Common Characteristics of MANET -- 5.2.3 Advantages and Disadvantages -- 5.2.4 Applications of MANET -- 5.2.5 Major Issues of MANET -- 5.3 Vehicular Adhoc Network (VANET) -- 5.3.1 Introduction of VANET -- 5.3.2 Common Features of VANET -- 5.3.3 Pros, Cons, Applications -- 5.4 Wireless Mesh Network (WMN) -- 5.4.1 Preface of WMN -- 5.4.2 Common Traits of WMN -- 5.4.3 WMN Has Many Open Issues and Research Challenges -- 5.4.4 Performance Metrics -- 5.4.5 Advantages and Disadvantages -- 5.4.6 Prominent Areas and Challenges of WMN -- 5.5 Wireless Sensor Network (WSN) -- 5.5.1 Overview of WSN -- 5.5.2 Common Properties of WSN -- 5.5.3 Benefits, Harms, and Usage of WSN -- 5.6 Intelligent Management in WANET -- 5.6.1 Major Issues of WANET -- 5.6.2 Challenges of MAC Protocols -- 5.6.3 Routing Protocols -- 5.6.3.1 Challenges of Routing Protocols -- 5.6.3.1.1 Scalability -- 5.6.3.1.2 Quality of Service -- 5.6.3.1.3 Security -- 5.6.4 Energy and Battery Management -- 5.7 Future Research Directions -- 5.8 Conclusion -- References -- 6 Survey: Brain Tumor Detection Using MRI Image with Deep Learning Techniques Chalapathiraju Kanumuri and C.H. …”

Tabla de Contenidos: “…Preinstallation planning -- 3.1 Hardware and software prerequisites -- 3.1.1 Managing Server hardware -- 3.1.2 Managing Server software -- 3.1.3 Data Collector hardware -- 3.1.4 Data Collector software -- 3.2 Managing Server configuration options -- 3.2.1 How many? -- 3.2.2 Managing Server topologies -- 3.3 Data Collector considerations -- 3.4 Network and protocol considerations -- 3.5 Firewalls -- 3.6 Repository database sizing -- 3.7 DB2 drivers -- 3.8 Established systems for composite request monitoring -- 3.8.1 CICS -- 3.8.2 IMS -- 3.9 Middleware for composite request monitoring -- 3.9.1 CICS Transaction Gateway -- 3.9.2 IMS Connect -- 3.9.3 WebSphere MQ…”

Tabla de Contenidos: “…Chapter 4 Machine Learning Integration in Agriculture Domain: Concepts and Applications -- 4.1 Introduction -- 4.2 Fog Computing in Agriculture -- 4.2.1 Smart Farming -- 4.3 Methodology -- 4.3.1 Data Source -- 4.3.2 Data Analysis and Pre-Processing -- 4.3.3 Feature Extraction -- 4.3.4 Model Selection -- 4.3.5 Hyper-Parameter Tuning -- 4.3.6 Train-Test Split -- 4.4 Results and Discussion -- 4.4.1 Modeling Algorithms -- 4.5 Conclusion -- 4.6 Future Scope -- References -- Chapter 5 Role of Intelligent IoT Applications in Fog Computing -- 5.1 Introduction -- 5.1.1 PaaS/SaaS Platforms Have Various Benefits That are Crucial to the Success of Many Small IoT Startup Businesses -- 5.2 Cloud Service Model's Drawbacks -- 5.3 Fog Computation -- 5.3.1 Standardization -- 5.3.2 Growing Use Cases for Fog Computing -- 5.3.3 IoT Applications with Intelligence -- 5.3.4 Graphics Processing Units -- 5.4 Recompenses of FoG -- 5.5 Limitation of Fog Computing -- 5.6 Fog Computing with IoT -- 5.6.1 Benefits of Fog Computing with IoT -- 5.6.2 Challenges of Fog Computing with IoT -- 5.7 Edge AI Embedded -- 5.7.1 Key Software Characteristics in Fog Computing -- 5.7.2 Fog Cluster Management -- 5.7.3 Technology for Computing in the Fog -- 5.7.4 Concentrating Intelligence -- 5.7.5 Device-Driven Intelligence -- 5.8 Network Intelligence Objectives -- 5.9 Farming with Fog Computation (Case Study) -- 5.10 Conclusion -- References -- Chapter 6 SaaS-Based Data Visualization Platform-A Study in COVID-19 Perspective -- 6.1 Introduction -- 6.1.1 Motivation and the Problem of Interest -- 6.2 Summary of Objectives -- 6.3 What is a Pandemic? …”

Tabla de Contenidos: “…Front Cover -- Networks-on-Chip: From Implementations to Programming Paradigms -- Copyright -- Contents in Brief -- Contents -- Preface -- About the Editor-in-Chief and Authors -- Editor-in-Chief -- Authors -- Part I: Prologue -- Chapter 1: Introduction -- 1.1 The dawn of the many-core era -- 1.2 Communication-centric cross-layer optimizations -- 1.3 A baseline design space exploration of NoCs -- 1.3.1 Topology -- 1.3.2 Routing algorithm -- 1.3.3 Flow control -- 1.3.4 Router microarchitecture -- 1.3.5 Performance metric -- 1.4 Review of NoC research -- 1.4.1 Research on topologies -- 1.4.2 Research on unicast routing -- 1.4.3 Research on supporting collective communications -- 1.4.4 Research on flow control -- 1.4.5 Research on router microarchitecture -- 1.5 Trends of real processors -- 1.5.1 The MIT Raw processor -- 1.5.2 The Tilera TILE64 processor -- 1.5.3 The Sony/Toshiba/IBM Cell processor -- 1.5.4 The U.T. …”

Tabla de Contenidos: “…7.1.2 Management of Conflict in Agile RM Framework -- 7.1.2.1 A Holistic View of Technology -- 7.1.3 Ratings and Context Information -- 7.1.4 Dynamic Traffic Steering -- 7.1.4.1 Reduced-Overhead Interference Mitigation -- 7.1.5 Reducing Overhead Interference Mitigation -- 7.1.5.1 RIM and 5G NR -- 7.1.5.2 Contributions and Differences -- 7.1.6 High-Level 5NR - RIM Framework -- 7.1.7 OS RIM-RS -- 7.1.7.1 OS RIM-RS -- 7.1.8 Radio-Access Network -- 7.1.9 Improve RAN Coverage -- 7.1.9.1 Improving RAN Coverage Can Take Many Forms -- 7.1.10 Related Work -- 7.1.11 Conclusion -- References -- Chapter 8 Interference Techniques Based on Deep Learning in Wireless Networks -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Suppression Techniques -- 8.3.1 Deep Learning Based Approach -- 8.3.2 Adjacent Channel Interference (ACI) -- 8.3.3 Co-Channel Interference -- 8.3.4 Self-Interference -- 8.3.5 Homogeneous Technology Interference -- 8.3.6 Heterogeneous Technology Interference -- 8.4 Classification of Interference Suppression Techniques -- 8.5 A Blind Approach of Interference Cancellation Using Neural Networks -- 8.6 Interference Estimation -- 8.6.1 Feature Extraction -- 8.6.2 Channel Usage Ratio -- 8.7 Machine Learning Model-Decision Tree -- 8.8 Lookup Table -- 8.9 Conclusion -- References -- Chapter 9 Implementing SDN Process-Based MRMC Wireless Networks -- 9.1 Multi-Radio Multi Channel Network in Wireless Network -- 9.1.1 The Concept of QoS inWN -- 9.2 QoS Challenges in MRMC Based Wireless Networks -- 9.2.1 Bandwidth Limitation -- 9.2.2 Removal of Redundancy -- 9.2.3 Energy and Delay Trade-Off -- 9.2.4 Buffer Size Limitation -- 9.3 Opportunistic Routing -- 9.4 The Need for Software-Defined Network in WSN for Enhancing QoS -- 9.4.1 QoS Management -- 9.5 ILP Problem Design -- 9.5.1 Network Model -- 9.5.2 Interference Model -- 9.5.3 VL Request Design…”

“…Metrology, the science of measurement, is crucial for many sciences and technological developments. Since metrology helps to improve many other sciences, the book reflects in general metrology and some special metrological approaches at different fields such as radiation and frequency measurements in detail. …”

“…Too many developing countries are not on track to reach the Millennium Development Goals (MDGs) by 2015 and need to pursue better and more sustained ways of reducing poverty. …”