Tabla de Contenidos: “…3.1.2 Es gilt deutsches Recht -- 3.1.3 Verhältnis zur Plattform / Investitionsschutz -- 3.1.4 Nutzungsbedingungen -- 3.2 Account-Name -- 3.2.1 Wahl des Account-Namens -- 3.2.2 ID-Grabbing -- 3.3 Impressum in Social Media -- 3.3.1 Impressumspflicht -- 3.3.2 Platzierung des Impressums -- 3.4 Gewinnspiele -- 3.4.1 Gewinnspiele und Glücksspiele -- 3.4.2 Rechtsrahmen von Gewinnspielen -- 3.4.3 Teilnahmebedingungen -- 3.4.4 Datenschutz -- 3.4.5 Besonderheiten bei Facebook -- 3.4.6 Besonderheiten in anderen sozialen Netzwerken -- 3.5 Schleichwerbung in Social Media -- 3.5.1 Falsche Fans -- 3.5.2 Gekaufte Bewertungen -- 3.5.3 Verkaufte Posts -- 3.5.4 Einsatz von Testimonials -- 3.6 Preisangaben in Social Media -- 3.7 User Generated Content in sozialen Netzwerken -- 3.7.1 Umgang mit User-Kommentaren -- 3.7.2 Urheberverletzungen durch Nutzer-Content -- 3.7.3 Community-Regeln -- 3.7.4 Besonderheiten bei Unternehmens-Blogs -- 3.8 Direktnachrichten -- 3.9 Werbeanzeigen auf Social-Media-Plattformen -- 3.10 Like-Button und Social-Plug-ins auf der Unternehmenswebsite -- 3.11 Social Media Guidelines -- 3.12 Betreuung von Social-Media-Kanälen durch Agenturen -- 3.13 Social Media Monitoring und CRM-Systeme -- 3.13.1 Datenschutzrechtliche Themen -- 3.13.2 Urheberrechtliche Probleme -- 3.13.3 Verbindung mit CRM-Tools -- 3.14 Verkauf von Blogs und Social-Media-Präsenzen -- 3.14.1 Verkauf eines Blogs -- 3.14.2 Übertragung von Profilen auf Facebook und Twitter -- Kapitel 4: Website-Inhalte und Content Marketing -- 4.1 Texte -- 4.2 Fotos -- 4.2.1 Urheberschutz an Fotos -- 4.2.2 Verwendung von Fotos auf der Unternehmenswebsite -- 4.2.3 Abgebildete Personen -- 4.2.4 Fotos von Firmenevents -- 4.3 Zeichnungen, Grafiken und Stadtpläne -- 4.4 Musik, Podcasts und Videos -- 4.5 Games -- 4.6 Links und Frames -- 4.6.1 Links - Verweisungen auf andere Seiten…”

Tabla de Contenidos: “…3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry…”

Tabla de Contenidos: “…3.2.1 When authorization occurs -- 3.2.2 Access intent -- 3.3 Database Recovery Control and the RECON -- 3.4 Lock management -- 3.4.1 Lock properties -- 3.5 Full function locking overview -- 3.5.1 Database record locks -- 3.5.2 Block locks -- 3.5.3 Busy locks -- 3.5.4 Extend locks -- 3.5.5 Data set reference locks -- 3.5.6 Command locks -- 3.6 Fast Path DEDB locking overview -- 3.6.1 Control interval locks -- 3.6.2 Unit of work locks -- 3.6.3 AREA locks -- 3.6.4 AREA notify locks -- 3.6.5 Fast Path command lock -- 3.6.6 Fast Path buffer overflow locks -- 3.6.7 Summary of Fast Path locking -- 3.7 Specific use of locks and their effect in data sharing -- 3.8 IRLM Version 2 Release 1 -- 3.8.1 Deadlock control -- 3.8.2 Storing lock information -- 3.8.3 The lock structure in the Coupling Facility -- 3.8.4 Following a lock request -- 3.9 The buffer invalidation process -- 3.9.1 OSAM and VSAM buffer invalidation using XES services -- 3.9.2 Buffer invalidation and Fast Path DEDBs -- 3.10 The IMS interface to the notification process -- 3.10.1 Global database commands -- 3.10.2 Database write errors -- 3.10.3 Data set extensions -- 3.10.4 The Coupling Facility and notifications -- 3.11 Components involved in preserving database integrity -- Chapter 4. …”

Tabla de Contenidos: “…3.2.1 Absolute rechtwinklige Koordinaten -- 3.2.2 Relative rechtwinklige Koordinaten -- 3.3 Polarkoordinaten -- 3.3.1 Relative Polarkoordinaten -- 3.3.2 Absolute Polarkoordinaten -- 3.3.3 Zusammenfassung der Koordinateneingaben -- 3.3.4 Beispiel mit verschiedenen Koordinatenarten -- 3.4 Koordinateneingabe im ORTHO-Modus -- 3.4.1 Befehlsoptionen in der dynamischen Eingabe -- 3.5 Koordinaten-Übung -- 3.6 Polarer Spurfang -- 3.7 Objektfang -- 3.7.1 Temporärer Objektfang -- 3.7.2 Permanenter Objektfang -- 3.7.3 Übungen -- 3.8 Komplexer Objektfang -- 3.8.1 Objektfangspur -- 3.8.2 Temporärer Spurpunkt -- 3.8.3 Von Punkt -- 3.8.4 Hilfslinie -- 3.8.5 Parallele -- 3.8.6 Objektfang »Punktfilter« -- 3.8.7 Objektfang »Mitte zwischen 2 Punkten« -- 3.9 KREIS -- 3.9.1 Optionen des Befehls KREIS -- 3.10 Was gibt's noch? …”

Tabla de Contenidos: “…3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry…”

Tabla de Contenidos: “…Le secteur des soins de santé -- Encadré 39. Les principes de la Loi canadienne sur la santé -- Encadré 40. …”

Tabla de Contenidos: “…3.6.1 Workload characteristics -- 3.6.2 Data placement in the DS6000 -- 3.6.3 Open systems LVM striping -- 3.7 .Performance and sizing considerations for z/OS -- 3.7.1 Performance potential in z/OS environments -- 3.8 Logical disks - number and size -- 3.9 Logical disk sizes - general considerations -- 3.9.1 Future requirements -- 3.9.2 Maximum number of devices -- 3.10 Configuring I/O ports -- 3.10.1 Multiple host attachment -- Chapter 4. …”

Tabla de Contenidos: “…3.2.1 Configure SP Ethernet admin LAN adapter -- 3.2.2 Set routes -- 3.2.3 Host name resolution -- 3.2.4 DNS -- 3.3 Networks -- 3.3.1 The SP Ethernet admin LAN -- 3.3.2 Frame and node cabling -- 3.3.3 SP LAN topologies -- 3.3.4 Additional LANs -- 3.3.5 Switch network -- 3.3.6 Subnetting considerations -- 3.4 Routing considerations -- 3.5 Using NIS in a Cluster 1600 configuration -- 3.6 Using AFS® in a Cluster 1600 configuration -- 3.7 Related documentation -- 3.8 Sample questions -- 3.9 Exercises -- Chapter 4. I/O devices and file systems -- 4.1 Key concepts you should study -- 4.2 I/O devices -- 4.2.1 External disk storage -- 4.2.2 Internal I/O adapters -- 4.3 Multiple rootvg support -- 4.3.1 The Volume_Group class -- 4.3.2 Volume group management commands -- 4.3.3 How to declare a new rootvg -- 4.3.4 Booting from external disks -- 4.4 Global file systems -- 4.4.1 Network File System (NFS) -- 4.4.2 The DFS and AFS file systems -- 4.5 Related documentation -- 4.6 Sample questions -- 4.7 Exercises -- Chapter 5. …”

Tabla de Contenidos: “…3.4.13 Unix System Services programs considerations -- 3.5 The primary interface for Debug Tool -- 3.5.1 Review of screen areas -- 3.5.2 Descriptions of frequently used commands -- 3.5.3 Descriptions of some interesting commands -- 3.5.4 Finishing a Debug Tool session -- 3.5.5 Recording how many times each source line runs -- 3.6 New features of Debug Tool -- 3.6.1 Playback support (COBOL) -- 3.6.2 Automonitor support -- 3.6.3 Support for optimized code (COBOL) -- 3.6.4 Enhancements for TSO allocation commands -- 3.6.5 Assembler support -- 3.7 Dynamic Debug and separate debug file -- 3.7.1 Dynamic Debug -- 3.7.2 Separate debug file -- 3.7.3 Advantages -- 3.7.4 How this helps application programmers -- 3.8 Making debugging more comfortable -- 3.8.1 INSPSAFE -- 3.8.2 INSPPREF -- 3.8.3 INSPCMD -- 3.8.4 INSPLOG -- 3.8.5 Copy into the command line -- 3.8.6 What if I forgot the correct command syntax -- 3.9 Hints and tips -- 3.9.1 I only have 12 PF keys -- 3.9.2 Pitfalls -- 3.9.3 Systems programmer notes -- 3.9.4 Customer concerns -- 3.9.5 How to point to a debug file or listing -- Chapter 4. …”

Tabla de Contenidos: “…3.2.5 VST3 and the GUID -- 3.2.6 VST3 Plugin Class Factory -- 3.3 Description: Plugin Description Strings -- 3.4 Description: Plugin Options/Features -- 3.4.1 Side Chain Input -- 3.4.2 Latency -- 3.4.3 Tail Time -- 3.4.4 Custom GUI -- 3.4.5 Factory Presets and State Save/Load -- 3.4.6 VST3 Support for 64-bit Audio -- 3.5 Initialization: Defining Plugin Parameters -- 3.5.1 Thread-Safe Parameter Access -- 3.5.2 Initialization: Defining Plugin Channel I/O Support -- 3.5.3 Initialization: Channel Counts and Sample Rate Information -- 3.6 The Buffer Process Cycle -- 3.6.1 Processing: Updating Plugin Parameters From GUI Controls -- 3.6.2 Processing: Resetting the Algorithm and Preparing for Streaming -- 3.6.3 Processing: Accessing the Audio Buffers -- 3.6.4 Processing: Writing Output Parameters -- 3.6.5 Processing: VST3 Soft Bypass -- 3.7 Destruction/Termination -- 3.8 Retrieving VST3 Host Information -- 3.9 Validating Your Plugin -- 3.10 Using ASPiK to Create VST3 Plugins -- 3.11 Bibliography -- Chapter 4: AU Programming Guide -- 4.1 Setting Up the AU SDK -- 4.1.1 AU Sample Projects -- 4.1.2 AU Documentation -- 4.2 AU Architecture and Anatomy -- 4.2.1 AU Base Classes -- 4.2.2 MacOS Bundle ID -- 4.2.3 AU Programming Notes -- 4.3 Description: Plugin Description Strings -- 4.4 Description: Plugin Options/Features -- 4.4.1 Side Chain Input -- 4.4.2 Latency -- 4.4.3 Tail Time -- 4.4.4 Custom GUI -- 4.4.5 Factory Presets and State Save/Load -- 4.5 Initialization: Defining Plugin Parameters -- 4.5.1 Thread-Safe Parameter Access -- 4.5.2 Initialization: Defining Plugin Channel I/O Support -- 4.5.3 Initialization: Channel Counts and Sample Rate Information -- 4.6 The Buffer Process Cycle -- 4.6.1 Processing: Updating Plugin Parameters From GUI Controls -- 4.6.2 Processing: Resetting the Algorithm and Preparing for Streaming…”

Tabla de Contenidos: “…Self-excited d.c. machine -- 3.9. Toy motors -- 3.10. Review questions -- Chapter 4: D.C. motor drives -- 4.1. …”

Tabla de Contenidos: “…3.8 Tensor metadata: Size, offset, and stride -- 3.8.1 Views of another tensor's storage -- 3.8.2 Transposing without copying -- 3.8.3 Transposing in higher dimensions -- 3.8.4 Contiguous tensors -- 3.9 Moving tensors to the GPU -- 3.9.1 Managing a tensor's device attribute -- 3.10 NumPy interoperability -- 3.11 Generalized tensors are tensors, too -- 3.12 Serializing tensors -- 3.12.1 Serializing to HDF5 with h5py -- 3.13 Conclusion -- 3.14 Exercises -- 3.15 Summary -- 4 Real-world data representation using tensors -- 4.1 Working with images -- 4.1.1 Adding color channels -- 4.1.2 Loading an image file -- 4.1.3 Changing the layout -- 4.1.4 Normalizing the data -- 4.2 3D images: Volumetric data -- 4.2.1 Loading a specialized format -- 4.3 Representing tabular data -- 4.3.1 Using a real-world dataset -- 4.3.2 Loading a wine data tensor -- 4.3.3 Representing scores -- 4.3.4 One-hot encoding -- 4.3.5 When to categorize -- 4.3.6 Finding thresholds -- 4.4 Working with time series -- 4.4.1 Adding a time dimension -- 4.4.2 Shaping the data by time period -- 4.4.3 Ready for training -- 4.5 Representing text -- 4.5.1 Converting text to numbers -- 4.5.2 One-hot-encoding characters -- 4.5.3 One-hot encoding whole words -- 4.5.4 Text embeddings -- 4.5.5 Text embeddings as a blueprint -- 4.6 Conclusion -- 4.7 Exercises -- 4.8 Summary -- 5 The mechanics of learning -- 5.1 A timeless lesson in modeling -- 5.2 Learning is just parameter estimation -- 5.2.1 A hot problem -- 5.2.2 Gathering some data -- 5.2.3 Visualizing the data -- 5.2.4 Choosing a linear model as a first try -- 5.3 Less loss is what we want -- 5.3.1 From problem back to PyTorch -- 5.4 Down along the gradient -- 5.4.1 Decreasing loss -- 5.4.2 Getting analytical -- 5.4.3 Iterating to fit the model -- 5.4.4 Normalizing inputs -- 5.4.5 Visualizing (again)…”

Tabla de Contenidos:

Tabla de Contenidos: “…3.7 Doc. 7 Striding statue of Djed-her (Cairo, Egyptian Museum, JE 37357) -- 3.8 Doc. 8 Funerary papyri of Djed-her -- 3.9 Doc. 9 The canopic chest of Djed-her (EA 8537) -- 3.10 Doc. 10 Book of the Dead papyrus (Turin, Museo Egizio di Torino, cat. no. 1830) -- 3.11 Doc. 11 Funerary papyri of Pa-kher-Khonsu -- 3.12 Doc. 12 Book of the Dead Papyrus of Pa-kher-Khonsu (Turin, Museo Egizio di Torino, cat. no. 1832) -- 3.13 Doc. 13 Cuboid statue of Pa-kher-Khonsu (Cairo, Egyptian Museum, JE 38592) -- 3.14 Doc. 14 Mummy bandages of Ta-sherit-Min (Paris, Louvre, AF 11954 (X. 25) +11956 (X. 24) + E 18865 + 27459) -- Doc. 14a Mummy bandage AF 11954 (X. 25) -- Doc. 14b Mummy bandage AF 11956 (X. 24) -- Doc. 14c Fragment of a mummy bandage E 18865 -- Doc. 14d Fragment of a mummy bandage E 27459 -- Chapter 4 -- Primary sources for the family of Kalutj/Nes-Khonsu (G108 + G137) from TT 414 -- 4.1 Doc. 15 Coffin assemblage of Hor (Reg. …”

Tabla de Contenidos: “…Cover -- Title page -- Copyright page -- Contents -- Preface -- Chapter 1 Properties of Fluids 17 -- 1.1 Introduction 17 -- 1.1.1 Nomenclature 19 -- 1.1.2 Dimensions and Units 20 -- 1.1.3 Basic Concepts of Fluid Flow 26 -- 1.2 Density 27 -- 1.3 Compressibility 32 -- 1.4 Ideal Gases 36 -- 1.4.1 Equation of State 36 -- 1.4.2 Mixtures of Ideal Gases 37 -- 1.4.3 Thermodynamic Properties 39 -- 1.4.4 Speed of Sound in an Ideal Gas 44 -- 1.5 Standard Atmosphere 44 -- 1.6 Viscosity 46 -- 1.6.1 Newtonian Fluids 46 -- 1.6.2 Non-Newtonian Fluids 53 -- 1.7 Surface Tension 55 -- 1.8 Vapor Pressure 61 -- 1.8.1 Evaporation, Transpiration, and Relative Humidity 63 -- 1.8.2 Cavitation and Boiling 64 -- 1.9 Thermodynamic Properties of Liquids 67 -- 1.9.1 Specific Heat 67 -- 1.9.2 Latent Heat 68 -- 1.10 Summary of Properties of Water and Air 69 -- Key Equations in Properties of Fluids 70 -- Problems 72 -- Chapter 2 Fluid Statics 87 -- 2.1 Introduction 87 -- 2.2 Pressure Distribution in Static Fluids 88 -- 2.2.1 Characteristics of Pressure 88 -- 2.2.2 Spatial Variation in Pressure 89 -- 2.2.3 Practical Applications 92 -- 2.3 Pressure Measurements 101 -- 2.3.1 Barometer 101 -- 2.3.2 Bourdon Gauge 103 -- 2.3.3 Pressure Transducer 104 -- 2.3.4 Manometer 105 -- 2.4 Forces on Plane Surfaces 110 -- 2.5 Forces on Curved Surfaces 120 -- 2.6 Buoyancy 127 -- 2.6.1 Fully Submerged Bodies 127 -- 2.6.2 Partially Submerged Bodies 132 -- 2.6.3 Buoyancy Effects Within Fluids 138 -- 2.7 Rigid-Body Motion of Fluids 139 -- 2.7.1 Liquid with Constant Acceleration 141 -- 2.7.2 Liquid in a Rotating Container 145 -- Key Equations in Fluid Statics 148 -- Problems 150 -- Chapter 3 Kinematics and Streamline Dynamics 177 -- 3.1 Introduction 177 -- 3.2 Kinematics 178 -- 3.2.1 Tracking the Movement of Fluid Particles 181 -- 3.2.2 The Material Derivative 188 -- 3.2.3 Flow Rates 190.…”

Tabla de Contenidos: “…3.3.1 Evolution of Energy Sector Regulation -- 3.3.2 Energy Pipeline Regulations -- 3.3.3 The Energy ISAC -- 3.4 Legacy Fuels -- 3.4.1 Coal -- 3.4.2 The Rise of Oil and the Automobile -- 3.4.3 Natural Gas Middlemen -- 3.4.4 Nuclear Fuel -- 3.5 Legacy Energy Infrastructure -- 3.5.1 Oil Refineries -- 3.5.2 Oil Transmission and Distribution -- 3.5.3 Oil Storage -- 3.5.4 The Natural Gas Supply Chain -- 3.5.5 The Critical Gulf of Mexico Cluster -- 3.5.6 Critical Refineries -- 3.5.7 Critical Transmission Pipelines -- 3.6 Renewables -- 3.7 Solar - Photovoltaic (PV) -- 3.7.1 Wind -- 3.7.2 The Hydrogen Circle -- 3.7.3 Others -- 3.8 Batteries and Reservoirs -- 3.8.1 Modern Batteries -- 3.8.2 Grid Scale Storage - LDES -- 3.9 Discussion -- References -- Chapter 4 The Vulnerable Powergrid -- 4.1 What Is the Grid? …”

Tabla de Contenidos: “…2.7 Role of 5G for Transit Operations -- 2.8 Role of 5G in Advanced Driver Assistance Systems (ADAS) -- 2.9 Role of 5G in Logistics Operations -- 2.10 Summary -- References -- Chapter 3 Network Management in Smart Cities -- 3.1 Introduction -- 3.2 Forensics -- 3.2.1 Digital Device Forensics -- 3.2.2 Other Digital Forensics -- 3.2.3 The Need for IoT Forensics -- 3.3 Challenges in IoT Forensics -- 3.3.1 General Issues -- 3.3.2 Evidence Identification, Collection, and Preservation -- 3.3.3 Evidence Analysis and Correlation -- 3.3.4 Presentation -- 3.4 Opportunities of IoT Forensics -- 3.5 Cloud Computing Security -- 3.5.1 Effectively Manage Identities -- 3.5.2 Key Concerns about Cloud Computing -- 3.5.3 Trends in Big Data as an Enabling Technology -- 3.6 Smart Cities -- 3.6.1 Smart City Concept -- 3.6.2 Cloud Computing Benefits in the Context of Smart City -- 3.7 Smarter Grid -- 3.8 Smart Home -- 3.9 Smart City Data Plan Challenges -- 3.9.1 Compatibility between Smart City Devices -- 3.9.2 Simplicity -- 3.9.3 Mobility and Geographic Control -- 3.10 Software-Defined Network-Based Smart City Network Management -- 3.10.1 Centralized Control -- 3.10.2 Simplicity and Inerrability -- 3.10.3 Virtualization -- 3.10.4 Compatibility -- 3.10.5 Challenges of SDN in Smart City Applications -- 3.11 Software-Defined Things Framework -- 3.11.1 Reactive Smart City Device Management -- 3.11.2 Smart Mobility and Smart Traffic Management -- 3.11.3 Smart Environment -- 3.11.4 Security -- 3.11.5 Advanced Optical Network Architecture for Next-Generation Internet Access -- 3.12 Conclusion and Future Work -- References -- Chapter 4 Energy-Efficient Reinforcement Learning in Wireless Sensor Networks Using 5G for Smart Cities -- 4.1 Introduction -- 4.1.1 Problem Statement -- 4.1.2 Objectives -- 4.2 Literature Review -- 4.2.1 Wireless Sensor Network…”

Tabla de Contenidos: “…Front Cover -- Cyber-Physical Systems: AI and COVID-19 -- Copyright Page -- Contents -- List of contributors -- 1 AI-based implementation of decisive technology for prevention and fight with COVID-19 -- 1.1 Introduction -- 1.2 Related work -- 1.3 Proposed work -- 1.3.1 Face mask detection -- 1.3.2 Detection of COVID from CT images -- 1.4 Results and analysis -- 1.4.1 Face mask detection -- 1.4.2 CT scan image-based COVID-19 patient identification -- 1.5 Conclusion -- References -- 2 Internet of Things-based smart helmet to detect possible COVID-19 infections -- 2.1 Introduction -- 2.1.1 Epidemiology -- 2.1.2 Treatment -- 2.1.3 Prevention -- 2.1.4 Symptoms -- 2.1.5 Stages of COVID-19 -- 2.1.6 Key merits of IoT for COVID-19 pandemic -- 2.1.7 Internet of Things process required for COVID-19 -- 2.1.8 IoT applications for COVID-19 -- 2.2 Related work -- 2.3 IoT-based smart helmet to detect the infection of COVID-19 -- 2.3.1 Objective -- 2.3.2 Methodology -- 2.3.2.1 Efficiency of smart helmet -- 2.3.2.2 Components of smart helmet -- 2.3.2.2.1 Thermal camera -- 2.3.2.2.2 Optical camera -- 2.3.2.2.3 Arduino Integrated Development Environment (IDE) -- 2.3.2.2.4 Proteus software -- 2.3.2.2.5 Google Location History -- 2.4 Conclusion -- References -- 3 Role of mobile health in the situation of COVID-19 pandemics: pros and cons -- 3.1 Introduction -- 3.2 Implementation of a training module for the mHealth care worker -- 3.3 Government policies for the scale-up of the mHealth services -- 3.4 Popular models of mHealth serving for pandemic COVID-19 -- 3.5 Ethical consideration -- 3.6 Superiority of mHealth services over other available services -- 3.7 Probability of conflict of interest between user and service provider -- 3.8 Legal consideration -- 3.9 Protection of privacy of end-users -- 3.10 Conclusion -- 3.11 Future prospects -- References…”

Tabla de Contenidos: “…3.6.4 Key frame selection and ranking -- 3.7 Time-complexity analysis -- 3.7.1 Complexity analysis of an exocentric video summarization algorithm -- 3.7.2 Complexity analysis of the scalable exocentric video summarization algorithm -- 3.8 Experimental test bed -- 3.8.1 Dataset(s) -- 3.8.2 Performance measures -- 3.8.2.1 Objective measures -- 3.8.2.2 Subjective measures -- 3.9 Results of Delaunay graph based exocentric video summarization methods -- 3.9.1 Results of constrained Delaunay graph clustering based summary -- 3.9.1.1 Performance analysis with information theoretic presampling -- 3.9.1.2 Performance analysis with deviation ratio constraint -- 3.9.1.3 Performance comparison with state-of-the-art methods -- 3.9.1.4 Performance comparison with K-means clustering -- 3.9.1.5 Clustering performance analysis -- 3.9.1.6 Tuning of the parameters -- 3.9.1.7 Key frame visualization -- 3.9.2 Results of Delaunay graph based summary with user customization -- 3.9.3 Results of Delaunay graph based summary in enhanced feature space -- 3.9.3.1 Performance analysis with semantic features -- 3.9.3.2 Performance analysis with CCA -- 3.10 Results of graph modularity based solution -- 3.11 Results of scalable exocentric video summarization -- 3.11.1 Objective evaluations -- 3.11.2 Subjective evaluations -- 3.11.3 Comparison of execution times -- 3.12 Summary -- 3.12.1 Summary of exocentric video summarization -- 3.12.2 Summary of scalable exocentric video summarization -- References -- 4 Multi-view exocentric video summarization -- 4.1 Motivation -- 4.2 Chapter organization -- 4.3 Related work -- 4.4 Proposed method -- 4.4.1 Video preprocessing -- 4.4.1.1 Shot detection and representation -- 4.4.1.2 Feature extraction -- 4.4.2 Unimportant shot elimination using Gaussian entropy -- 4.4.3 Multi-view correlation using bipartite matching…”

Tabla de Contenidos: “…2.7.2 Face Detection and Identification in Real-World Situations -- 2.8 Future Direction in Object Detection and Tracking -- 2.8.1 Future Plans for Object Tracking and Detection -- 2.8.1.1 Multiobject Tracking -- 2.8.2 3D Object Tracking and Detection -- 2.8.3 Real-Time Performance -- 2.9 Conclusion -- References -- Chapter 3 Printing Organs with 3D Technology -- 3.1 Introduction -- 3.2 Bioprinting in Three Dimensions (3D) -- 3.3 3D Printing Types -- 3.3.1 Inkjet Bioprinting -- 3.3.2 Microextrusion Bioprinting -- 3.3.3 Laser-Assisted Printing -- 3.3.4 Stereolithography -- 3.3.5 3D Bioprinting Materials and Cells -- 3.4 Applications for 3D Printing in Cells -- 3.4.1 Blood Vessels -- 3.4.2 Liver -- 3.4.3 Cartilage -- 3.4.4 Muscle -- 3.4.5 Bone -- 3.4.6 Skin -- 3.4.7 Neutralization of Neurons -- 3.4.8 Pancreas -- 3.5 New Developments -- 3.6 Progress in India -- 3.7 Limitation -- 3.8 A Future Point of View -- 3.9 Conclusion -- References -- Chapter 4 Comparative Evaluation of Machine Learning Algorithms for Bank Fraud Detection -- 4.1 Introduction -- 4.2 Proposed Framework -- 4.3 Results -- 4.4 Concluding Remarks and Future Scope -- References -- Chapter 5 An Overview of Computational-Based Strategies for Drug Repositioning -- 5.1 Introduction -- 5.2 Drug Repositioning -- 5.2.1 Computational Strategies for Drug Repositioning -- 5.2.1.1 IoT in Drug Repositioning -- 5.2.1.2 AI and ML in Drug Repositioning -- 5.2.1.3 Digital Twin in Drug Repurposing -- 5.2.1.4 Cloud Computing in Drug Repositioning -- 5.2.1.5 Big Data in Drug Repositioning -- 5.3 Challenges and Opportunities for Drug Repurposing -- 5.4 Conclusion -- References -- Chapter 6 Improving Performance With Feature Selection, Extraction, and Learning -- 6.1 Introduction -- 6.2 Feature Selection -- 6.2.1 Filter Methods -- 6.2.1.1 Procedure -- 6.2.1.2 Advantages -- 6.2.1.3 Disadvantages…”