“…The Ruby on Rails 2.3 Tutorial teaches web development with Ruby on Rails, an open-source web framework that powers many of the web's top sites, including Twitter, Hulu, and the Yellow Pages. …”

Tabla de Contenidos: “…-- 6.5.4 No Impermissible Gambles -- 6.5.5 Forcing Uncertainty into the Model of a Single Decision-.Maker -- 6.6 Modeling a Learning Health System -- 6.6.1 Reasonable Diversity of Conscientious and Informed Experts -- 6.6.2 Reconciling Social Value, Concern for Welfare, Equal Concern, and No Impermissible Gambles -- 6.6.3 More General Relevance of the Result -- 6.6.4 The Limit of Reasonable Diversity -- 6.7 The Integrative Approach versus Alternatives -- 6.7.1 Criteria for Evaluating Alternative Frameworks for Risk Assessment and Management -- 6.7.2 Other Appeals to Uncertainty and Component Analysis -- 6.7.3 The Belmont Approach -- 6.8 Conclusion -- 7. …”

“…Part 2, “Scenarios” identifies several potential technical scenarios for coexistence via point-to-point integration between applications deployed in the IBM WebSphere Application Server and applications deployed in the Microsoft .NET Framework. This part provides in-depth technical details on how to implement certain scenarios using today’s existing technologies. …”

Tabla de Contenidos: “…Component software installation -- 8.1 Installing Perl -- 8.2 Installing WebSphere Application Server Network Deployment -- 8.3 Installing WebSphere Application Server ND Fix Pack -- 8.4 Installing and configuring ITSM server and clients -- 8.4.1 Introduction -- 8.4.2 Prerequisites -- 8.4.3 ITSM product installation on the WIP server -- 8.4.4 ITSM server initialization and customization -- 8.4.5 ITSM installation on the DB2 server -- 8.4.6 ITSM installation on the blade servers -- 8.5 Installing the Tivoli monitoring solution -- 8.5.1 Installing Tivoli Management Framework V4.1 -- 8.5.2 Installing Tivoli Desktop for Windows -- 8.5.3 Installing IBM Tivoli Monitoring (ITM) -- 8.5.4 Installing ITM Component Services -- 8.5.5 Installing ITM for Databases: DB2 component -- 8.5.6 Installing ITM for Web Infrastructure: WebSphere Application Server component -- 8.5.7 Installing ITM for Web Infrastructure: the Apache HTTP Server Component -- 8.5.8 ITM Web Health Console -- Chapter 9. …”

Tabla de Contenidos: “…Wave RATs 8 -- 1.5.6 Redesigning Backhaul Links 9 -- 1.5.7 Energy Efficiency 9 -- 1.5.8 Allocation of New Spectrum for 5G 10 -- 1.5.9 Spectrum Sharing 10 -- 1.5.10 RAN Virtualisation 10 -- 1.6 5G in Europe 11 -- 1.6.1 Horizon 2020 Framework Programme 11 -- 1.6.2 5G Infrastructure PPP 12 -- 1.6.3 METIS Project 13 -- 1.6.4 5G Innovation Centre 14 -- 1.6.5 Visions of Companies 14 -- 1.7 5G in North America 15 -- 1.7.1 Academy Research 15 -- 1.7.2 Company R&D 15 -- 1.8 5G in Asia 16 -- 1.8.1 5G in China 16 -- 1.8.2 5G in South Korea 19 -- 1.8.3 5G in Japan 21 -- 1.9 5G Architecture 23 -- 1.10 Conclusion 24 -- Acknowledgements 25 -- References 25 -- 2 The 5G Internet 29 -- 2.1 Introduction 29 -- 2.2 Internet of Things and Context?]…”

Tabla de Contenidos: “…Introduction to TensorBoard: the TensorFlow visualization framework -- 7.2.3. Wrapping up -- 7.3. Getting the most out of your models -- 7.3.1. …”

Tabla de Contenidos: “…3.2.4 Production Possibilities Schedule -- 3.2.5 Static and Dynamic Gains from Tradere-Examined -- 3.3 Factor Proportions Theory -- 3.3.1 Heckscher-Ohlin Theorem -- 3.3.2 Factor Price Equalisation -- 3.3.3 Leontief Paradox -- 3.3.4 Distribution of Income -- 3.4 Neo-Factor Proportions Theories -- 3.5 Country Similarity Theory -- 3.6 Intra-Industry Trade -- 3.6.1 Nature of Intra-Industry Trade -- 3.6.2 Measures of Intra-Industry Trade -- 3.6.3 Gains from Intra-Industry Trade -- 3.7 Trade in Intermediate Products and Services: Outsourcing -- 3.7.1 Outsourcing and Off-Shoring -- 3.7.2 Bases of Outsourcing -- 3.7.3 Gains from Outsourcing -- 3.7.4 Outsourcing of Services -- 3.8 the National Competitive Advantage -- 3.9 Terms of Trade -- Summary -- Review Questions -- Objective Type Questions -- Case Study -- Study Topic -- References -- Chapter 4: Theoretical Framework of Foreign Direct Investment -- 4.1 Theories of FDI -- 4.1.1 Macdougall-Kemp Hypothesis -- 4.1.2 Industrial Organisation Theory -- 4.1.3 Location-Specific Theory -- 4.1.4 Product Cycle Theory -- 4.1.5 Internalisation Approach -- 4.1.6 Eclectic Paradigm -- 4.1.7 Currency Based Approaches -- 4.1.8 Politico-Economic Theories -- 4.1.9 Modified Theories for Third World Firms -- 4.2 Benefits and Costs of FDI -- 4.2.1 Benefits to the Host Country -- 4.2.2 Benefits for Home Country -- 4.2.3 Cost to the Host Country -- 4.2.4 Cost to Home Country -- 4.3 M&as and the Value of the Firm -- 4.4 Control of MNCs -- 4.4.1 Conflicts with Home Country and Measures of Control -- 4.4.2 Conflict with the Host Government and the Measures of Control -- 4.4.3 Code for Control At the International Level -- Summary -- Review Questions -- Objective Type Questions -- Case Study 1 -- Case Study 2 -- References -- Select Further Readings -- Chapter 5: Balance of Payments -- 5.1 Structure of Balance of Payments…”

“…In TensorFlow in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. …”

Tabla de Contenidos: “…Chapter 4: Regime-switching Methods for Forecasting -- 4.1 REGIME-SWITCHING AUTOREGRESSIVE MODEL -- 4.1.1 Physically Motivated Regime Definition -- 4.1.2 Data-driven Regime Determination -- 4.1.3 Smooth Transition between Regimes -- 4.1.4 Markov Switching between Regimes -- 4.2 REGIME-SWITCHING SPACE-TIME MODEL -- 4.3 CALIBRATION IN REGIME SWITCHING METHOD -- 4.3.1 Observed Regime Changes -- 4.3.2 Unobserved Regime Changes -- 4.3.3 Framework of Calibrated Regime-switching -- 4.3.4 Implementation Procedure -- 4.4 CASE STUDY -- 4.4.1 Modeling Choices and Practical Considerations -- 4.4.2 Forecasting Results -- Part II: Wind Turbine Performance Analysis -- Chapter 5: Power Curve Modeling and Analysis -- 5.1 IEC BINNING: SINGLE-DIMENSIONAL POWER CURVE -- 5.2 KERNEL-BASED MULTI-DIMENSIONAL POWER CURVE -- 5.2.1 Need for Nonparametric Modeling Approach -- 5.2.2 Kernel Regression and Kernel Density Estimation -- 5.2.3 Additive Multiplicative Kernel Model -- 5.2.4 Bandwidth Selection -- 5.3 OTHER DATA SCIENCE METHODS -- 5.3.1 k-Nearest Neighborhood Regression -- 5.3.2 Tree-based Regression -- 5.3.3 Spline-based Regression -- 5.4 CASE STUDY -- 5.4.1 Model Parameter Estimation -- 5.4.2 Important Environmental Factors Affecting Power Output -- 5.4.3 Estimation Accuracy of Different Models -- Chapter 6: Production Efficiency Analysis and Power Curve -- 6.1 THREE EFFICIENCY METRICS -- 6.1.1 Availability -- 6.1.2 Power Generation Ratio -- 6.1.3 Power Coefficient -- 6.2 COMPARISON OF EFFICIENCY METRICS -- 6.2.1 Distributions -- 6.2.2 Pairwise Differences -- 6.2.3 Correlations and Linear Relationships -- 6.2.4 Overall Insight -- 6.3 A SHAPE-CONSTRAINED POWER CURVE MODEL -- 6.3.1 Background of Production Economics -- 6.3.2 Average Performance Curve -- 6.3.3 Production Frontier Function and Effi ciency Metric -- 6.4 CASE STUDY…”

Tabla de Contenidos: “…11.2 Various Existing Proposed Anti-Poaching Systems -- 11.3 System Framework and Construction -- 11.4 Results and Discussions -- 11.5 Conclusion and Future Scope -- References -- Chapter 12 Tumor Detection Using Morphological Image Segmentation with DSP Processor TMS320C6748 -- 12.1 Introduction -- 12.2 Image Processing -- 12.2.1 Image Acquisition -- 12.2.2 Image Segmentation Method -- 12.3 TMS320C6748 DSP Processor -- 12.4 Code Composer Studio -- 12.5 Morphological Image Segmentation -- 12.5.1 Optimization -- 12.6 Results and Discussions -- 12.7 Conclusions -- References -- Chapter 13 Design Challenges for Machine/Deep Learning Algorithms -- 13.1 Introduction -- 13.2 Design Challenges of Machine Learning -- 13.2.1 Data of Low Quality -- 13.2.2 Training Data Underfitting -- 13.2.3 Training Data Overfitting -- 13.2.4 Insufficient Training Data -- 13.2.5 Uncommon Training Data -- 13.2.6 Machine Learning Is a Time-Consuming Process -- 13.2.7 Unwanted Features -- 13.2.8 Implementation is Taking Longer Than Expected -- 13.2.9 Flaws When Data Grows -- 13.2.10 The Model's Offline Learning and Deployment -- 13.2.11 Bad Recommendations -- 13.2.12 Abuse of Talent -- 13.2.13 Implementation -- 13.2.14 Assumption are Made in the Wrong Way -- 13.2.15 Infrastructure Deficiency -- 13.2.16 When Data Grows, Algorithms Become Obsolete -- 13.2.17 Skilled Resources are Not Available -- 13.2.18 Separation of Customers -- 13.2.19 Complexity -- 13.2.20 Results Take Time -- 13.2.21 Maintenance -- 13.2.22 Drift in Ideas -- 13.2.23 Bias in Data -- 13.2.24 Error Probability -- 13.2.25 Inability to Explain -- 13.3 Commonly Used Algorithms in Machine Learning -- 13.3.1 Algorithms for Supervised Learning -- 13.3.2 Algorithms for Unsupervised Learning -- 13.3.3 Algorithm for Reinforcement Learning -- 13.4 Applications of Machine Learning -- 13.4.1 Image Recognition…”

Tabla de Contenidos: “…2.6 Proposed Network Structure -- 3 Experiment -- 3.1 Training Process -- 3.2 Results -- 3.3 Discussion -- 4 Conclusions -- References -- AI4U: Modular Framework for AI Application Design -- 1 Motivation -- 2 Related Work -- 3 Proposed Approach -- 4 The Application Area -- 4.1 Watchman Scenario -- 4.2 Parkmonitor Scenario -- 4.3 Tracker Scenario -- 4.4 Mapping the Environment Scenario -- 4.5 Vehicle Counting -- 4.6 Space Surveyer -- 4.7 Mission to Mars -- 4.8 First Conclusions -- 5 Conclusions and Further Work -- References -- A Competent Deep Learning Model to Detect COVID-19 Using Chest CT Images -- 1 Introduction -- 2 Literature Review -- 3 Our Proposed Methodology -- 3.1 Dataset Description and Preprocessing -- 3.2 Methodology -- 4 Results -- 5 Future Work and Conclusion -- References -- AI in Prostate MRI Analysis: A Short, Subjective Review of Potential, Status, Urgent Challenges, and Future Directions -- 1 Introduction -- 2 The Potential of Artificial Intelligence in MpMRI Analysis -- 2.1 Prostate Segmentation -- 2.2 Prostate Lesion Detection and Characterization -- 3 Urgent Challenges -- 3.1 Datasets -- 3.2 Defining Ground Truth -- 3.3 Different Evaluation Criteria -- 3.4 Limited Multireader Studies and Prospective Evaluation -- 4 Future Directions -- 5 Conclusions -- References -- Performance of Deep CNN and Radiologists in Prostate Cancer Classification: A Comparative Pilot Study -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Radiological Assessment Study -- 2.3 Deep CNN Model -- 2.4 Probability Mapping -- 2.5 Statistical Analysis -- 3 Results -- 3.1 Results of Raw CNN Predictions -- 3.2 CNN Performance Compared to Human Raters -- 3.3 Diagnostic Accuracy of Combined Assessment -- 4 Discussion and Conclusion -- References -- Assessing GAN-Based Generative Modeling on Skin Lesions Images -- 1 Introduction -- 2 Materials and Methods…”

Tabla de Contenidos: “…3.3 Methodology of Designing Quantum Multiplexer (QMUX) -- 3.3.1 QMUX Using CSWAP Gates -- 3.3.1.1 Generalization -- 3.3.2 QMUX Using Controlled-R Gates -- 3.4 Analysis and Synthesis of Proposed Methodology -- 3.5 Complexity and Cost of Quantum Circuits -- 3.6 Conclusion -- References -- Chapter 4 Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain -- 4.1 Introduction -- 4.1.1 Quantum Computing Convolutional Neural Network -- 4.2 Literature Survey -- 4.3 Quantum Algorithms Characteristics Used in Machine Learning Problems -- 4.3.1 Minimizing Quantum Algorithm -- 4.3.2 K-NN Algorithm -- 4.3.3 K-Means Algorithm -- 4.4 Tree Tensor Networking -- 4.5 TNN Implementation on IBM Quantum Processor -- 4.6 Neurotomography -- 4.7 Conclusion and Future Scope -- References -- Chapter 5 Building a Virtual Reality-Based Framework for the Education of Autistic Kids -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Proposed Work -- 5.3.1 Methodology -- 5.3.2 Work Flow of Neural Style Transfer -- 5.3.3 A-Frame -- 5.3.3.1 Setting Up the Virtual World and Adding Components -- 5.3.3.2 Adding Interactivity Through Raycasting -- 5.3.3.3 Animating the Components -- 5.3.4 Neural Style Transfer -- 5.3.4.1 Choosing the Content and Styling Image -- 5.3.4.2 Image Preprocessing and Generation of a Random Image -- 5.3.4.3 Model Design and Extraction of Content and Style -- 5.3.4.4 Loss Calculation -- 5.3.4.5 Model Optimization -- 5.4 Evaluation Metrics -- 5.5 Results -- 5.5.1 A-Frame -- 5.5.2 Neural Style Transfer -- 5.6 Conclusion -- References -- Chapter 6 Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Proposed Model -- 6.3.1 URL Feature Extractor -- 6.3.2 Dataset -- 6.3.3 Methodologies -- 6.3.3.1 AdaBoost Classifier…”

Tabla de Contenidos: “…7.1.3 Domain description assumption 3 -- 7.1.4 Domain description assumption 4 -- 7.2 The evaluation inference -- 7.2.1 Evaluation assumption 1 -- 7.2.2 Evaluation assumption 2 -- 7.2.3 Evaluation assumption 3 -- 7.2.4 Evaluation assumption 4 -- 7.3 The generalization inference -- 7.3.1 Generalization assumption 1 -- 7.3.2 Generalization assumption 2 -- 7.3.3 Generalization assumption 3 -- 7.3.4 Generalization assumption 4 -- 7.4 The explanation inference -- 7.4.1 Explanation assumption 1 -- 7.4.2 Explanation assumption 2 -- 7.4.3 Explanation assumption 3 -- 7.4.4 Explanation assumption 4 -- 7.4.5 Explanation assumption 5 -- 7.5 The extrapolation inference -- 7.5.1 Extrapolation assumption 1 -- 7.5.2 Extrapolation assumption 2 -- 7.5.3 Extrapolation assumption 3 -- 7.6 Considerations outside the validity framework -- 7.6.1 CMC and practicality -- 7.6.2 Stakeholder take-up and assessment literacy -- 7.6.3 Building a universal model of IC -- 7.6.4 Application of the IC construct and rating scale -- 7.6.5 The parameters of the IC tasks -- Chapter 8 Conclusions -- 8.1 Significance of this book -- 8.2 Outstanding issues, limitations, and future research -- References -- Appendix I: S-H interview protocol -- Appendix II: Norming questionnaire -- English translation -- Chinese version -- Appendix III: The IC test -- Appendix IV: The IC rating scale -- English version -- Chinese version -- Appendix V: The self-assessment questionnaire -- English version -- Chinese version -- Appendix VI: The peer-assessment questionnaire -- Author Information -- Series Index…”

Tabla de Contenidos: “…-- 4 Aims of this Book -- 5 Structure of the Book -- Part 1 International Humanitarian Relief in Non-international Armed Conflicts -- Chapter 1 Actors Involved in Relief Actions -- 1 Affected State -- 2 Non-state Armed Groups -- 2.1 Characteristics -- 2.2 Definition -- 2.3 With and without Territorial Control -- 2.4 Responsible Organ or Functionary -- 3 Humanitarian Actors -- 3.1 In General -- 3.2 Types of Humanitarian Actors -- 3.2.1 Third States -- 3.2.2 International Relief Organisations -- 3.2.3 Non-governmental Relief Organisations -- 3.2.4 The International Committee of the Red Cross (ICRC) -- 4 Non-belligerent States -- 5 Civilians -- Chapter 2 Legal Framework -- 1 Overview -- 2 Relevant Laws and Principles -- 2.1 The Principles of State Sovereignty and Non-interference -- 2.2 International Humanitarian Law (IHL) -- 2.2.1 Geneva Conventions and Additional Protocols -- 2.2.2 Customary Law -- 2.2.3 Fundamental Principles of IHL -- 2.3 Humanitarian Principles -- 2.4 International Human Rights Law -- 2.4.1 Application of ihrl and ihl during Armed Conflicts -- 2.4.2 Relevant IHRL Provisions -- 2.5 International Criminal Law -- Chapter 3 The Concept of Humanitarian Relief -- 1 Historical Perspective -- 2 Defining Humanitarian Relief -- 2.1 Characteristics of Humanitarian Relief -- 2.2 Distinction from Other Forms of Aid during Armed Conflicts -- 2.2.1 Development Assistance -- 2.2.2 Peace-Keeping Missions -- 3 Implementation of International Humanitarian Relief -- 3.1 Negotiating Humanitarian Access -- 3.2 Crossline and Cross-Border Provision…”

Tabla de Contenidos: “…Arrays Class -- 4.4 String -- 4.4.1 Array of Strings -- 4.4.2 String Class Methods -- 4.4.3 StringBuffer Class -- 4.4.4 StringBuilder Class -- 4.5 Collection Framework -- 4.5.1 Vector Class -- 4.5.2 Vector Methods -- 4.6 Key Points -- Exercise Questions -- Objective Questions -- Short-answer Questions -- Long-answer Questions -- Assignment Questions…”

Tabla de Contenidos: “…8.4.1 Internet Infrastructure -- 8.4.2 High Hardware and Software Cost -- 8.4.3 Less Qualified Workforce -- 8.5 Conclusion and Discussion -- References -- Chapter 9 Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids -- 9.1 Introduction -- 9.2 Applications of Synchrophasor Data -- 9.2.1 Voltage Stability Analysis -- 9.2.2 Transient Stability -- 9.2.3 Out of Step Splitting Protection -- 9.2.4 Multiple Event Detection -- 9.2.5 State Estimation -- 9.2.6 Fault Detection -- 9.2.7 Loss of Main (LOM) Detection -- 9.2.8 Topology Update Detection -- 9.2.9 Oscillation Detection -- 9.3 Utility Big Data Issues Related to PMU-Driven Applications -- 9.3.1 Heterogeneous Measurement Integration -- 9.3.2 Variety and Interoperability -- 9.3.3 Volume and Velocity -- 9.3.4 Data Quality and Security -- 9.3.5 Utilization and Analytics -- 9.3.6 Visualization of Data -- 9.4 Big Data Analytics Platforms for PMU Data Processing -- 9.4.1 Hadoop -- 9.4.2 Apache Spark -- 9.4.3 Apache HBase -- 9.4.4 Apache Storm -- 9.4.5 Cloud-Based Platforms -- 9.5 Conclusions -- References -- Chapter 10 Intelligent Enterprise-Level Big Data Analytics for Modeling and Management in Smart Internet of Roads -- 10.1 Introduction -- 10.2 Fully Convolutional Deep Neural Network for Autonomous Vehicle Identification -- 10.2.1 Detection of the Bounding Box of the License Plate -- 10.2.2 Segmentation Objective -- 10.2.3 Spatial Invariances -- 10.2.4 Model Framework -- 10.2.4.1 Increasing the Layer of Transformation -- 10.2.4.2 Data Format of Sample Images -- 10.2.4.3 Applying Batch Normalization -- 10.2.4.4 Network Architecture -- 10.2.5 Role of Data -- 10.2.6 Synthesizing Samples -- 10.2.7 Invariances -- 10.2.8 Reducing Number of Features -- 10.2.9 Choosing Number of Classes -- 10.3 Experimental Setup and Results -- 10.3.1 Sparse Softmax Loss…”

Tabla de Contenidos: “…-- 1.6 Market drivers for SSI -- 1.6.1 E-commerce -- 1.6.2 Banking and finance -- 1.6.3 Healthcare -- 1.6.4 Travel -- 1.7 Major challenges to SSI adoption -- 1.7.1 Building out the new SSI ecosystem -- 1.7.2 Decentralized key management -- 1.7.3 Offline access -- References -- 2 The basic building blocks of SSI -- 2.1 Verifiable credentials -- 2.2 Issuers, holders, and verifiers -- 2.3 Digital wallets -- 2.4 Digital agents -- 2.5 Decentralized identifiers (DIDs) -- 2.6 Blockchains and other verifiable data registries -- 2.7 Governance frameworks -- 2.8 Summarizing the building blocks -- References -- 3 Example scenarios showing how SSI works -- 3.1 A simple notation for SSI scenario diagrams -- 3.2 Scenario 1: Bob meets Alice at a conference -- 3.3 Scenario 2: Bob meets Alice through her online blog -- 3.4 Scenario 3: Bob logs in to Alice's blog to leave a comment -- 3.5 Scenario 4: Bob meets Alice through an online dating site -- 3.6 Scenario 5: Alice applies for a new bank account -- 3.7 Scenario 6: Alice buys a car -- 3.8 Scenario 7: Alice sells the car to Bob -- 3.9 Scenario summary -- Reference…”

Tabla de Contenidos: “…2.6.1 IT Support -- 2.7 I4.0 in Functional Field -- 2.7.1 I4.0 Logistics -- 2.7.2 Resource Planning -- 2.7.3 Systems for Warehouse Management -- 2.7.4 Transportation Management Systems -- 2.7.5 Transportation Systems with Intelligence -- 2.7.6 Information Security -- 2.8 Existing Technology in I4.0 -- 2.8.1 Applications of I4.0 in Existing Industries -- 2.8.2 Additive Manufacturing (AM) -- 2.8.3 Intelligent Machines -- 2.8.4 Robots that are Self-Aware -- 2.8.5 Materials that are Smart -- 2.8.6 IoT -- 2.8.7 The Internet of Things in Industry (IIoT) -- 2.8.8 Sensors that are Smart -- 2.8.9 System Using a Smart Programmable Logic Controller (PLC) -- 2.8.10 Software -- 2.8.11 Augmented Reality (AR)/Virtual Reality (VR) -- 2.8.12 Gateway for the Internet of Things -- 2.8.13 Cloud -- 2.8.14 Applications of Additive Manufacturing in I4.0 -- 2.8.15 Artificial Intelligence (AI) -- 2.9 Applications in Current Industries -- 2.9.1 I4.0 in Logistics -- 2.9.2 I4.0 in Manufacturing Operation -- 2.10 Future Scope of Research -- 2.10.1 Theoretical Framework of I4.0 -- 2.11 Discussion and Implications -- 2.11.1 Hosting: Microsoft -- 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens -- 2.11.3 A Systematic Computational Analysis -- 2.11.4 Festo Proximity Sensor -- 2.11.5 Connectivity Hardware: HMS -- 2.11.6 IT Security: Claroty -- 2.11.7 Accenture Is a Systems Integrator -- 2.11.8 Additive Manufacturing: General Electric -- 2.11.9 Augmented and Virtual Reality: Upskill -- 2.11.10 ABB Collaborative Robots -- 2.11.11 Connected Vision System: Cognex -- 2.11.12 Drones/UAVs: PINC -- 2.11.13 Self-Driving in Vehicles: Clear Path Robotics -- 2.12 Conclusion -- References -- Chapter 3 Interaction of Internet of Things and Sensors for Machining -- 3.1 Introduction -- 3.2 Various Sensors Involved in Machining Process -- 3.2.1 Direct Method Sensors…”

Tabla de Contenidos: “…How to build it - Reference implementation for Java edition -- 8.1 Prerequisites -- 8.2 Key concepts -- 8.3 Implement the information architecture -- 8.3.1 Create the site framework -- 8.3.2 Create the taxonomy -- 8.4 Implement workflows -- 8.4.1 What is a workflow -- 8.4.2 Workflow stages -- 8.4.3 Create workflows -- 8.5 Create content templates -- 8.6 Involve the authors -- 8.7 Security -- 8.8 Implement components -- 8.8.1 Navigators -- 8.8.2 Menus -- 8.8.3 Page design -- 8.8.4 The relationship between page designs and content templates -- 8.8.5 More examples -- 8.9 Search -- 8.9.1 Implementing search capability -- 8.9.2 UserName component -- 8.9.3 Customization -- 8.9.4 Style sheets -- 8.10 Summary -- Chapter 9. …”

Tabla de Contenidos: “…2.6.1 IT Support -- 2.7 I4.0 in Functional Field -- 2.7.1 I4.0 Logistics -- 2.7.2 Resource Planning -- 2.7.3 Systems for Warehouse Management -- 2.7.4 Transportation Management Systems -- 2.7.5 Transportation Systems with Intelligence -- 2.7.6 Information Security -- 2.8 Existing Technology in I4.0 -- 2.8.1 Applications of I4.0 in Existing Industries -- 2.8.2 Additive Manufacturing (AM) -- 2.8.3 Intelligent Machines -- 2.8.4 Robots that are Self-Aware -- 2.8.5 Materials that are Smart -- 2.8.6 IoT -- 2.8.7 The Internet of Things in Industry (IIoT) -- 2.8.8 Sensors that are Smart -- 2.8.9 System Using a Smart Programmable Logic Controller (PLC) -- 2.8.10 Software -- 2.8.11 Augmented Reality (AR)/Virtual Reality (VR) -- 2.8.12 Gateway for the Internet of Things -- 2.8.13 Cloud -- 2.8.14 Applications of Additive Manufacturing in I4.0 -- 2.8.15 Artificial Intelligence (AI) -- 2.9 Applications in Current Industries -- 2.9.1 I4.0 in Logistics -- 2.9.2 I4.0 in Manufacturing Operation -- 2.10 Future Scope of Research -- 2.10.1 Theoretical Framework of I4.0 -- 2.11 Discussion and Implications -- 2.11.1 Hosting: Microsoft -- 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens -- 2.11.3 A Systematic Computational Analysis -- 2.11.4 Festo Proximity Sensor -- 2.11.5 Connectivity Hardware: HMS -- 2.11.6 IT Security: Claroty -- 2.11.7 Accenture Is a Systems Integrator -- 2.11.8 Additive Manufacturing: General Electric -- 2.11.9 Augmented and Virtual Reality: Upskill -- 2.11.10 ABB Collaborative Robots -- 2.11.11 Connected Vision System: Cognex -- 2.11.12 Drones/UAVs: PINC -- 2.11.13 Self-Driving in Vehicles: Clear Path Robotics -- 2.12 Conclusion -- References -- Chapter 3 Interaction of Internet of Things and Sensors for Machining -- 3.1 Introduction -- 3.2 Various Sensors Involved in Machining Process -- 3.2.1 Direct Method Sensors…”