Tabla de Contenidos: “…9.3 Artificial Intelligence: Transport System and Healthcare -- 9.4 Artificial Intelligence Algorithms -- 9.5 AI Workflow -- 9.6 AI for ITS and e-Healthcare Tasks -- 9.7 Intelligent Transportation, Healthcare, and IoT -- 9.8 AI Techniques Used in ITS and e-Healthcare -- 9.9 Challenges of AI and ML in ITS and e-Healthcare -- 9.10 Conclusions -- References -- Chapter 10 Classification of Dementia Using Statistical First-Order and Second-Order Features -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Dataset -- 10.2.2 Image Pre-Processing -- 10.3 Proposed Framework -- 10.3.1 Discrete Wavelet Transform -- 10.3.1.1 Statistical Features -- 10.3.2 Classification -- 10.3.2.1 K-Nearest Neighbor -- 10.3.2.2 Linear Discriminant Analysis -- 10.3.2.3 Support Vector Machine -- 10.3.3 Performance Measure -- 10.4 Experimental Results and Discussion -- 10.5 Conclusion -- References -- Chapter 11 Pulmonary Embolism Detection Using Machine and Deep Learning Techniques -- 11.1 Introduction -- 11.2 The State-of-the-Art of PE Detection Models -- 11.3 Literature Survey -- 11.4 Publications Analysis -- 11.5 Conclusion -- References -- Chapter 12 Computer Vision Techniques for Smart Healthcare Infrastructure -- 12.1 Introduction -- 12.2 Literature Survey -- 12.2.1 Computer Vision -- 12.2.1.1 Computer Vision Techniques for Safety and Driver Assistance -- 12.2.1.2 Types of Optical Character Recognition Systems -- 12.2.1.3 Phases of Optical Character Recognition -- 12.2.1.4 Threshold Segmentation -- 12.2.1.5 Edge Detection Operator -- 12.2.1.6 Use Cases of OCR -- 12.2.1.7 List of Research Papers -- 12.2.2 How is IoT Changing the Face of Information Science? …”

Tabla de Contenidos: “…25.7 TESTING DEVICE FOR THE PRACTICAL ASSESSMENT OF THERMOFORMABILITY* -- Chapter 26 Analysis of Completely Formed, Filled, and Sealed Containers -- 26.1 BURST PRESSURE AND IMPERMEABILITY -- 26.1.1 Vacuum -- 26.1.2 Air Pressure -- 26.2 RHODAMINE TEST -- 26.3 GENERAL REMARK ON IMPERMEABILITY TESTS -- 26.4 OPENING FORCE -- Chapter 27 Automated Packaging -- 27.1 CUTTING SUBSEQUENT TO FORMING -- 27.2 CHARACTERISTICS OF THERMOFORMING PACKAGING LINES -- 27.2.1 Indexed Production -- 27.2.2 Continuous Production -- 27.2.3 Quasi-Continuous Production -- 27.3 CHARACTERISTICS OF PACKAGING MACHINE CONTROL -- 27.3.1 Indexed Production -- 27.3.2 Continuous Production -- 27.3.3 Quasi-Continuous Production -- 27.4 FORMING, FILLING, AND SEALING (FFS) -- 27.4.1 The Picker Line -- 27.4.2 Speed Lines -- 27.4.3 Lines with Automatic Handling -- 27.5 FORMING, PUNCHING, FILLING, AND SEALING (FPF) -- 27.6 FORMING, CROSS-CUTTING, FILLING, SEALING/PUNCHING (FCFS) -- 27.7 CONCLUSION -- Chapter 28 Production of Flowerpots -- 28.1 THE THERMOFORMING MACHINE -- 28.2 HEATING SYSTEM -- 28.3 THE FORMING STATION -- 28.3.1 Plug Materials -- 28.4 FORMING AIR -- 28.5 CHALK AS A FILLER -- 28.5.1 Calcium Carbonate in Thermoforming Applications -- 28.5.2 Natural Calcium Carbonate -- 28.5.3 Advantages of Calcium Carbonate -- 28.5.4 Summary -- Chapter 29 Perforated Fruit and Vegetable Trays Made Easy -- Chapter 30 Steel Rule Die Punching -- 30.1 BASICS -- 30.2 SHAPES OF CUTTING EDGES -- 30.3 CREATION OF ANGEL HAIR DURING STEEL RULE DIE CUTTING -- 30.3.1 Development of Angel Hair -- 30.3.2 Influence of Steel Rule Temperature on the Creation of Angel Hair -- 30.3.3 Influence of Film Material on Creation of Angel Hair -- Chapter 31 Production of Meat Trays -- 31.1 VACUUM PACKAGING -- 31.2 ACTIVE PACKAGING -- 31.3 MAP -- 31.4 PRODUCTION PROCESS FOR THE PP TRAY -- 31.5 NEW DEVELOPMENTS…”

“…Numerous types of graphs exist in the literature, depending on the edge set of G.A simple graph has no self-loop or edges. …”

Tabla de Contenidos: “…Data sources for Tivoli Web Site Analyzer -- 4.1 Web server log files -- 4.1.1 Log file formats -- 4.1.2 IBM HTTP Server for Windows NT -- 4.1.3 Using IBM HTTP Server V5R3M0 for OS/390® log files -- 4.1.4 IBM WebSphere Edge Server log files -- 4.1.5 IBM Tivoli Access Manager for e-business log files -- 4.1.6 WebSphere Portal Server -- 4.1.7 Microsoft IIS Server log files…”

Tabla de Contenidos: “…7.2.3 Controlling the position of UI elements -- 7.3 Programming interactivity in the UI -- 7.3.1 Programming an invisible UIController -- 7.3.2 Creating a pop-up window -- 7.3.3 Setting values using sliders and input fields -- 7.4 Updating the game by responding to events -- 7.4.1 Integrating an event system -- 7.4.2 Broadcasting and listening for events from the scene -- 7.4.3 Broadcasting and listening for events from the HUD -- Summary -- 8 Creating a third-person 3D game: Player movement and animation -- 8.1 Adjusting the camera view for third-person -- 8.1.1 Importing a character to look at -- 8.1.2 Adding shadows to the scene -- 8.1.3 Orbiting the camera around the player character -- 8.2 Programming camera-relative movement controls -- 8.2.1 Rotating the character to face movement direction -- 8.2.2 Moving forward in that direction -- 8.3 Implementing the jump action -- 8.3.1 Applying vertical speed and acceleration -- 8.3.2 Modifying the ground detection to handle edges and slopes -- 8.4 Setting up animations on the player character -- 8.4.1 Defining animation clips in the imported model -- 8.4.2 Creating the animator controller for these animations -- 8.4.3 Writing code that operates the animator -- Summary -- 9 Adding interactive devices and items within the game -- 9.1 Creating doors and other devices -- 9.1.1 Doors that open and close on a keypress -- 9.1.2 Checking distance and facing before opening the door -- 9.1.3 Operating a color-changing monitor -- 9.2 Interacting with objects by bumping into them -- 9.2.1 Colliding with physics-enabled obstacles -- 9.2.2 Operating the door with a trigger object -- 9.2.3 Collecting items scattered around the level -- 9.3 Managing inventory data and game state -- 9.3.1 Setting up player and inventory managers -- 9.3.2 Programming the game managers…”

Tabla de Contenidos: “…6.7.4 Canonical Correlation Forest -- 6.7.5 Extreme Gradient Boosting -- 6.7.6 Light Gradient Boosting Machines -- 6.7.7 Gradient Boosting Machines -- 6.7.8 Categorical Boosting -- 6.7.9 Natural Gradient Boosting -- 6.8 Concluding Remarks -- References -- Chapter 7 Deep Learning -- 7.1 Fundamentals -- 7.1.1 Stochastic Gradient Descent -- 7.1.2 Backpropagation -- 7.1.3 Regularization -- 7.1.3.1 Weight Decay -- 7.1.3.2 Dropout -- 7.1.3.3 Data Augmentation -- 7.1.3.4 Early Stopping -- 7.1.4 Activation Functions -- 7.1.5 Loss Functions -- 7.2 Neural Network Architectures -- 7.2.1 Multilayer Perceptron -- 7.2.2 Convolutional Neural Networks -- 7.2.2.1 Convolutional Layers -- 7.2.2.2 Pooling Layers -- 7.2.2.3 Fully Connected Layers -- 7.2.2.4 Receptive Field and Feature Map -- 7.2.2.5 Training CNNs -- 7.2.2.6 Data Structures in CNNs -- 7.2.2.7 Evolving Trends in CNN Design -- 7.2.3 Recurrent Neural Networks -- 7.2.3.1 Long- and Short-Term Memory -- 7.2.3.2 Gated Recurrent Unit -- 7.2.4 Vision Transformers -- 7.2.5 Deep Multilayer Perceptron -- 7.2.6 Generative Adversarial Networks -- 7.2.7 Deep Autoencoders -- 7.2.7.1 Undercomplete Autoencoders -- 7.2.7.2 Regularized Autoencoders -- 7.2.7.3 Sparse Autoencoders -- 7.2.7.4 Denoising Autoencoders -- 7.2.7.5 Variational Autoencoders -- 7.3 Learning Paradigms -- 7.3.1 Transfer Learning -- 7.3.2 Semi-Supervised Learning -- 7.3.3 Reinforcement Learning -- 7.3.4 Active Learning -- 7.3.5 Multitask Learning -- 7.4 Application of DL in Remote Sensing -- 7.4.1 Semantic Segmentation -- 7.4.2 Object Detection -- 7.4.3 Scene Classification -- 7.4.4 Change Detection -- 7.5 Concluding Remarks -- References -- Chapter 8 Object-B ased Image Analysis -- 8.1 Clustering-Based Segmentation -- 8.1.1 Mean-Shift Algorithm -- 8.1.2 Superpixel Segmentation -- 8.2 Thresholding-Based Segmentation -- 8.3 Edge-Based Segmentation…”

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: “…4.4 Wires and Registers -- 4.5 Defining the Module Behavior -- 4.6 Parameters -- 4.7 Variables -- 4.8 Data Types -- 4.9 Decision Making -- 4.10 Loops -- 4.11 Summary -- Chapter 5: Design Automation of FPGAs -- 5.1 Introduction -- 5.2 Simulation -- 5.2.1 Simulators -- 5.2.2 Test Benches -- 5.2.3 Test Bench Goals -- 5.2.4 Simple Test Bench: Instantiating Components -- 5.2.5 Adding Stimuli -- 5.2.6 Assertions -- 5.3 Libraries -- 5.3.1 Introduction -- 5.3.2 Using Libraries -- 5.3.3 Std_logic Libraries -- 5.4 std_logic Type Definition -- 5.5 Synthesis -- 5.5.1 Design Flow for Synthesis -- 5.5.2 Synthesis Issues -- 5.6 RTL Design Flow -- 5.7 Physical Design Flow -- 5.8 Place and Route -- 5.8.1 Recursive Cut -- 5.8.2 Simulated Annealing -- 5.9 Timing Analysis -- 5.10 Design Pitfalls -- 5.10.1 Initialization -- 5.10.2 Floating Point Numbers and Operations -- 5.11 Summary -- Chapter 6: Synthesis -- 6.1 Introduction -- 6.1.1 HDL Supported in RTL Synthesis -- Initial conditions -- Concurrent edges -- 6.2 Numeric Types -- 6.3 Wait Statements -- 6.4 Assertions -- 6.5 Loops -- 6.6 Some Interesting Cases Where Synthesis May Fail -- 6.7 What Is Being Synthesized? …”

Tabla de Contenidos: “…Review of Medical Drones in Healthcare Applications -- 4.1 Introduction -- 4.1.1 IoD-Internet of Drones -- 4.1.2 Drones in Health Care -- 4.2 Internet of Medical Drone (IoMD) in Health Care -- 4.3 Architecture of Healthcare IoMD -- 4.3.1 Data Collection Phase -- 4.3.2 Data Reporting Phase -- 4.3.3 Data Processing Phase -- 4.3.4 Tools and Technologies of IoMD -- 4.4 Applications of IoMD Healthcare System -- 4.4.1 Search and Rescue -- 4.4.2 Transport and Delivery -- 4.4.3 Medical Care -- 4.5 Usage of IoMD for Health Care -- 4.5.1 Drone Functions to Combat COVID-19 in India -- 4.5.1.1 Surveillance and Lockdown Enforcement -- 4.5.1.2 Public Broadcast -- 4.5.1.3 Monitoring Body Temperatures -- 4.5.1.4 Medical and Emergency Food Supplies Delivery -- 4.5.1.5 Surveying and Mapping -- 4.5.1.6 Spraying Disinfectants -- 4.6 Recent Technologies in Healthcare IoMD -- 4.6.1 IoMDs -- 4.6.2 Cloud/Edge Computing -- 4.6.3 Internet Protocols -- 4.6.4 Artificial Intelligence -- 4.6.5 Blockchain -- 4.7 Challenges and Open Issues for Future Research -- 4.7.1 Challenges in IoMD for Healthcare Services -- 4.7.1.1 Adoption of IoMD…”

Tabla de Contenidos: “…(Comprehensive Office Building, Zhujiajiao Administration Center, Quingpu, Shangai, China, 2004-06), p. 334 ; PAULO MENDES DA ROCHA (Our Lady of the Conception Chapel, Recife, Pernambuco, Brazil, 2004-06), p. 340 ; CORINNA MENN (Conn Viewinf Platform, Flims, Switzerland, 2005-06), p. 346 ; MERKX + GIROD ARCHITECTEN (Selexyz Dominicanen Bookstore, Maastricht, The Netherlands, 2005-07), p. 352 ; JOSÉ RAFAEL MONEO (Prado Museum Extension, Madrid, Spain, 2001-07), p. 358 ; MORPHOSIS (Hypo Alpe-Adria Bank Headquarters, Udine, Italy, 2004-06), p. 364 ; MVRDV (GYRE Building, Shibuya-ku, Tokyo, Japan, 2006-07), p. 372 ; MANFREDI NICOLETTI (New Arezzo Hall of Justice, Arezzo, Italy, 2001-07), p. 378 ; VALERIO OLGIATI (Atelier Bardill, Scharans, Switzerland, 2006-07), p. 382 ; STUDIO PEI – ZHU (Digital Beijing, Control Center for the 2008 Olympics, Beijing, China, 2005-08), p. 386 ; RENZO PIANO (The New York Times Building, New York, New York, USA, 2005-07), p. 394 ; PTW ARCHITECTS (The Watercube, National Swimming Center, Beijing, China, 2003-08), p. 404 ; STUDIO ARNE QUINZE (Uchronia Message out of the Future, Burning Man Festival, Black Rock City, Black Rock Desert, Nevada, USA, 2006), p. 412 ; RCR ARQUITECTES (The Edge, Bussines Bay, Dubai, United Arab Emirates, 2008-), p. 418 ; ROJKIND ARQUITECTOS (Nestlé Chocolate Museum (Phase I), Toluca, Mexico City, Mexico, 2007), p. 424 ; MARC ROLINET (Chapel of the Deaconesses of Reuilly, Versailles, France, 2004-07), p. 430 ; HANS-JORG RUCH (Chesa Madalena, Zuoz, Switzerland, 2001-02), p. 438 ; SANAA/SEJIMA + NISHIZAWA (De Kunstlinie, Theater and Cultural Center, Almere, The Netherlands, 2004-07 / New Museum of Contemporary Art, New York, New York, USA, 2005-07), p. 442 ; THOMAS SCHÜTTE (Model for a Hotel, Fourth Plinth, Trafalgar Square, London, UK, 2007-08), p. 456 ; SCOPE CLEAVER (Princeton University Gallery of the Arts, Princeton University (120, 204, 24), “Second Life”, 2007), p. 460 ; ÁLVARO SIZA VIEIRA (Adega Mayor Winery, Argamassas Estate – Campo Maior, Portugal, 2005-06 / Iberê Camargo Foundation, Porto Alegre, Rio Grande do Sul, Brazil, 1998-2008), p. 466 ; SNØHETTA (Oslo Opera House, Oslo, Norway, 2003-08), p. 484 ; TONKIN LIU (Singing Ringing Tree, Crown Point, Burnley, UK, 2005-06), p. 492 ; BERNARD TSCHUMI (Zénith Concert Hall, Limoges, France, 2005-07), p. 496 ; UNSTUDIO (Hotel Castell, Zuoz, Switzerland, 2001-04 / Theater Agora, Lelystad, The Netherlands, 2004-07), p. 502 ; URBANIS (Dafen Art Museum, Dafen Village, Shenzhen, China, 2006-08), p. 516 ; PEKKA VAPAAVUORI (KUMUU, Main Building of the Art Museum of Estonia, Tallinn, Estonia, 2003-06), p. 524 ; VARIOUS ARCHITECTS (Make It Right/Pink Project, New Orleans, Louisiana, USA, 2007-), p. 530 ; WANDEL HOEFER LORCH (Ohel Jakob Synagogue, Jewish Center in Munich, St.…”

Tabla de Contenidos: “…Front Cover -- 5G NR -- Copyright Page -- Contents -- Acknowledgments -- List of Abbreviations -- Introduction and Background -- 1 Introduction to 5G -- 2 Use Cases and Deployment Scenarios -- 2.1 Use Cases -- 2.2 Deployment Scenarios -- 3 Key Performance Indicators, Architectural, System, and Service Requirements -- 3.1 Definition of the Performance Metrics -- 3.2 Test Environments -- 3.3 High-Level Architectural Requirements -- 3.4 System Performance Requirements -- 3.5 Service Requirements -- 4 ITU-R IMT-2020 Standardization Activities -- 5 3GPP 5G Standardization Activities -- 6 Spectrum and Regulations -- 7 Future Outlook -- References -- ITU-R Specifications -- 3GPP Specifications -- Articles, Books, White Papers, and Application Notes -- 1 5G Network Architecture -- 1.1 Design Principles and Prominent Network Topologies -- 1.1.1 Network and Service Requirements -- 1.1.2 Virtualization of Network Functions -- 1.1.2.1 Architectural Aspects -- 1.1.2.2 Functional Aspects -- 1.1.2.3 Operational Aspects -- 1.1.2.4 Legacy Support and Interworking Aspects -- 1.1.3 Separation of Control and User Planes (Software-Defined Networks) -- 1.1.3.1 Architectural Aspects -- 1.1.4 Network Slicing -- 1.1.5 Heterogeneous and Ultra-dense Networks -- 1.1.6 Cloud-RAN and Virtual-RAN -- 1.1.6.1 Architectural Aspects -- 1.1.6.2 Fronthaul Transport and Functional Split Options -- 1.1.6.3 Backhaul Transport Options -- 1.1.7 Mobile Edge Computing -- 1.1.7.1 Service and Deployment Scenarios -- 1.1.7.2 Architectural Aspects -- 1.1.8 Network Sharing -- 1.2 Reference Architectures -- 1.2.1 Access Network -- 1.2.1.1 Reference Architecture: Network Entities and Interfaces -- 1.2.1.1.1 Xn Control-Plane/User-Plane Functions and Procedures -- 1.2.1.1.2 F1 Control-Plane/User-Plane Functions and Procedures -- 1.2.1.1.3 E1 Control-Plane Functions and Procedures…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Foreword -- Preface -- Acknowledgment -- Chapter 1 Introduction -- 1.1 Introduction -- 1.2 5G Targets -- 1.3 5G Technology Components -- 1.4 5G Spectrum -- 1.5 5G Capabilities -- 1.6 5G Capacity Boost -- 1.7 5G Standardization and Schedule -- 1.8 5G Use Cases -- 1.9 Evolution Path from LTE to 5G -- 1.10 5G‐Advanced -- 1.11 Summary -- Chapter 2 5G Targets and Standardization -- 2.1 Introduction -- 2.2 ITU -- 2.2.1 IMT Vision for 2020 and Beyond -- 2.2.2 Standardization of IMT‐2020 Radio Interface Technologies -- 2.3 NGMN -- 2.3.1 NGMN 5G Use Cases -- 2.3.2 NGMN 5G Requirements -- 2.3.3 NGMN 5G Architecture Design Principles -- 2.3.4 Spectrum, Intellectual Property Rights (IPR), and Further Recommendations by NGMN -- 2.4 3GPP Schedule and Phasing -- 2.5 Evolution Towards 5G‐Advanced and 6G -- References -- Chapter 3 Technology Components -- 3.1 Introduction -- 3.2 Spectrum Utilization -- 3.2.1 Frequency Bands -- 3.2.2 Bandwidth Options -- 3.2.3 Spectrum Occupancy -- 3.2.4 Control Channel Flexibility -- 3.2.5 Dynamic Spectrum Sharing -- 3.3 Beamforming -- 3.4 Flexible Physical Layer and Protocols -- 3.4.1 Flexible Numerology -- 3.4.2 Short Transmission Time and Mini‐slot -- 3.4.3 Self‐Contained Subframe -- 3.4.4 Asynchronous HARQ -- 3.4.5 Lean Carrier -- 3.4.6 Adaptive Reference Signals -- 3.4.7 Adaptive UE Specific Bandwidth -- 3.4.8 Distributed MIMO -- 3.4.9 Waveforms -- 3.4.10 Channel Coding -- 3.4.11 Pipeline Processing and Front‐Loaded Reference Signals -- 3.4.12 Connected Inactive State -- 3.4.13 Grant‐Free Access -- 3.4.14 Cell Radius of 300 km -- 3.5 Network Slicing -- 3.6 Dual Connectivity with LTE -- 3.7 Radio Cloud and Edge Computing -- 3.8 Summary -- Reference -- Chapter 4 Spectrum -- 4.1 Introduction -- 4.2 Millimeter Wave Spectrum Above 20 GHz…”

“…It explains how to protect your organization’s values, reputation, and profitability by focusing not only on culture, but organizational character; seeking solutions to challenges from all levels of personnel; and skillfully blending a “hard-edged” demand for results with a “soft-edged” spirit of collaboration. …”

“…It can be obtained by considering Euclidean polygons and identifying their edges via translations. The vertices of the polygons form singularities if the translation structure can not be extended to them. …”

“…Linguistics falls in the gap between arts and science, on the edges of which the most fascinating discoveries and the most important problems are found. …”
Sumario

Tabla de Contenidos: “…based Radio Access Network 42 -- 2.3.1.6 Mobile Edge and Fog Computing 42 -- 2.3.2 5G Mobile Core Network 44 -- 2.3.2.1 Software Defined Networking 44 -- 2.3.2.2 Network Function Virtualization 44 -- 2.3.2.3 Cloud Computing 46 -- 2.3.3 G End?]…”