Tabla de Contenidos: “…4 Learning Convolutional Neural Networks for Object Detection with Very Little Training Data -- 4.1 Introduction -- 4.2 Fundamentals -- 4.2.1 Types of Learning -- 4.2.2 Convolutional Neural Networks -- 4.2.2.1 Arti cial neuron -- 4.2.2.2 Arti cial neural network -- 4.2.2.3 Training -- 4.2.2.4 Convolutional neural networks -- 4.2.3 Random Forests -- 4.2.3.1 Decision tree -- 4.2.3.2 Random forest -- 4.3 Related Work -- 4.4 Traf c Sign Detection -- 4.4.1 Feature Learning -- 4.4.2 Random Forest Classi cation -- 4.4.3 RF to NN Mapping -- 4.4.4 Fully Convolutional Network -- 4.4.5 Bounding Box Prediction -- 4.5 Localization -- 4.6 Clustering -- 4.7 Dataset -- 4.7.1 Data Capturing -- 4.7.2 Filtering -- 4.8 Experiments -- 4.8.1 Training and Test Data -- 4.8.2 Classi cation -- 4.8.3 Object Detection -- 4.8.4 Computation Time -- 4.8.5 Precision of Localizations -- 4.9 Conclusion -- Acknowledgment -- References -- 5 Multimodal Fusion Architectures for Pedestrian Detection -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Visible Pedestrian Detection -- 5.2.2 Infrared Pedestrian Detection -- 5.2.3 Multimodal Pedestrian Detection -- 5.3 Proposed Method -- 5.3.1 Multimodal Feature Learning/Fusion -- 5.3.2 Multimodal Pedestrian Detection -- 5.3.2.1 Baseline DNN model -- 5.3.2.2 Scene-aware DNN model -- 5.3.3 Multimodal Segmentation Supervision -- 5.4 Experimental Results and Discussion -- 5.4.1 Dataset and Evaluation Metric -- 5.4.2 Implementation Details -- 5.4.3 Evaluation of Multimodal Feature Fusion -- 5.4.4 Evaluation of Multimodal Pedestrian Detection Networks -- 5.4.5 Evaluation of Multimodal Segmentation Supervision Networks -- 5.4.6 Comparison with State-of-the-Art Multimodal Pedestrian Detection Methods -- 5.5 Conclusion -- Acknowledgment -- References -- 6 Multispectral Person Re-Identi cation Using GAN for Color-to-Thermal Image Translation…”

Tabla de Contenidos: “…Drives without current control -- 4.4. Chopper-fed d.c. motor drives -- 4.4.1. Performance of chopper-fed d.c. motor drives -- 4.4.2. …”

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: “…3.9 The with Statement -- 3.10 The return Statement -- 3.11 The throw and try Statements -- 3.12 The mixin Statement -- 3.13 The scope Statement -- 3.14 The synchronized Statement -- 3.15 The asm Statement -- 3.16 Summary and Quick Reference -- 4 Arrays, Associative Arrays, and Strings -- 4.1 Dynamic Arrays -- 4.1.1 Length -- 4.1.2 Bounds Checking -- 4.1.3 Slicing -- 4.1.4 Copying -- 4.1.5 Comparing for Equality -- 4.1.6 Concatenating -- 4.1.7 Array-wise Expressions -- 4.1.8 Shrinking -- 4.1.9 Expanding -- 4.1.10 Assigning to .length -- 4.2 Fixed-Size Arrays -- 4.2.1 Length -- 4.2.2 Bounds Checking -- 4.2.3 Slicing -- 4.2.4 Copying and Implicit Conversion -- 4.2.5 Comparing for Equality -- 4.2.6 Concatenating -- 4.2.7 Array-wise Operations -- 4.3 Multidimensional Arrays -- 4.4 Associative Arrays -- 4.4.1 Length -- 4.4.2 Reading and Writing Slots -- 4.4.3 Copying -- 4.4.4 Comparing for Equality -- 4.4.5 Removing Elements -- 4.4.6 Iterating -- 4.4.7 User-Defined Types as Keys -- 4.5 Strings -- 4.5.1 Code Points -- 4.5.2 Encodings -- 4.5.3 Character Types -- 4.5.4 Arrays of Characters + Benefits = Strings -- 4.6 Arrays' Maverick Cousin: The Pointer -- 4.7 Summary and Quick Reference -- 5 Data and Functions. …”

Tabla de Contenidos: “…Case study -- 4.1 Business case -- 4.1.1 Current building permit process -- 4.2 Redbrook County legacy system -- 4.3 Business problem -- 4.3.1 User requirements -- 4.4 Proposed solution -- 4.4.1 System architecture -- 4.4.2 Web-based application modules -- 4.4.3 Content Manager data model -- 4.4.4 OnDemand system setup -- Part 2 Developing CM Web applications with Information Integration for Content -- Chapter 5. …”

Tabla de Contenidos: “…4.2 IBM Tivoli Storage Manager for OS/400 PASE prerequisite software -- 4.2.1 OS/400 PASE -- 4.2.2 Program temporary fix requirements -- 4.3 Installing the IBM Tivoli Storage Manager server code -- 4.4 Downloading IBM Tivoli Storage Manager for OS/400 PASE fixes -- 4.4.1 IBM FTP server via Web browser -- 4.4.2 IBM FTP server to OS/400 -- 4.4.3 IBM Tivoli Storage Manager support Web page -- 4.5 Loading and applying IBM Tivoli Storage Manager PTFs -- Chapter 5. …”

Tabla de Contenidos: “…3.5.3 Proposed Distributed Ledger-Based IoT Cloud IAM -- 3.6 Conclusion -- References -- 4 Automated TSR Using DNN Approach for Intelligent Vehicles -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Neural Network (NN) -- 4.4 Methodology -- 4.4.1 System Architecture -- 4.4.2 Database -- 4.5 Experiments and Results -- 4.5.1 FFNN -- 4.5.2 RNN -- 4.5.3 CNN -- 4.5.4 CNN -- 4.6 Discussion -- 4.7 Conclusion -- References -- 5 Honeypot: A Trap for Attackers -- 5.1 Introduction -- 5.1.1 Research Honeypots -- 5.1.2 Production Honeypots -- 5.2 Method -- 5.2.1 Low-Interaction Honeypots -- 5.2.2 Medium-Interaction Honeypots -- 5.2.3 High-Interaction Honeypots -- 5.3 Cryptanalysis -- 5.3.1 System Architecture -- 5.3.2 Possible Attacks on Honeypot -- 5.3.3 Advantages of Honeypots -- 5.3.4 Disadvantages of Honeypots -- 5.4 Conclusions -- References -- 6 Examining Security Aspects in Industrial-Based Internet of Things -- 6.1 Introduction -- 6.2 Process Frame of IoT Before Security -- 6.2.1 Cyber Attack -- 6.2.2 Security Assessment in IoT -- 6.2.2.1 Security in Perception and Network Frame -- 6.3 Attacks and Security Assessments in IIoT -- 6.3.1 IoT Security Techniques Analysis Based on its Merits -- 6.4 Conclusion -- References -- 7 A Cooperative Navigation for Multi-Robots in Unknown Environments Using Hybrid Jaya-DE Algorithm -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Problem Formulation -- 7.4 Multi-Robot Navigation Employing Hybrid Jaya-DE Algorithm -- 7.4.1 Basic Jaya Algorithm -- 7.5 Hybrid Jaya-DE -- 7.5.1 Mutation -- 7.5.2 Crossover -- 7.5.3 Selection -- 7.6 Simulation Analysis and Performance Evaluation of Jaya-DE Algorithm -- 7.7 Total Navigation Path Deviation (TNPD) -- 7.8 Average Unexplored Goal Distance (AUGD) -- 7.9 Conclusion -- References -- 8 Categorization Model for Parkinson's Disease Occurrence and Severity Prediction -- 8.1 Introduction…”

Tabla de Contenidos: “…4.3.4 Installing Microsoft Internet Explorer -- 4.3.5 Installing Netscape Navigator -- 4.3.6 Completing the installation of prerequisite software -- 4.4 Installing IBM DB2 Universal Database -- 4.4.1 Checking database installation prerequisites -- 4.4.2 Installing DB2 UDB Express -- 4.5 Installing WebSphere Business Integration Server Express -- Chapter 5. …”

Tabla de Contenidos: “…4.2.1 Grundlagen - so arbeiten Proxys -- 4.2.2 Einen Proxy-Server nutzen -- 4.2.3 Öffentliche Proxys in der Praxis -- 4.2.4 Vor- und Nachteile von Proxy-Servern -- 4.2.5 Proxy-Verwaltung mit FoxyProxy -- 4.3 VPN, SSH und Socks - so bleiben Black Hats anonym -- 4.3.1 Virtual Private Networks (VPN) -- 4.3.2 SSH-Tunnel -- 4.3.3 SOCKS-Proxy -- 4.3.4 Kaskadierung für höchste Anonymität und Vertraulichkeit -- 4.3.5 Proxifier - Für unwillige Programme -- 4.4 Deep Web und Darknet - im Untergrund unterwegs -- 4.4.1 Wo geht es bitte zum Untergrund? …”

Tabla de Contenidos: “…4.3 Improved Dynamic Butterfly Optimization Algorithm (IDBOA)-Based Task Scheduling (IDBOATS) -- 4.3.1 Problem Definition -- 4.3.2 Delay Constraint -- 4.3.3 Green Energy Model -- 4.3.4 Energy Consumption Model -- 4.3.5 Constraint-Imposed Optimization Problem -- 4.3.6 Primitives of Dynamic Butterfly Optimization Algorithm (DBOA) -- 4.3.7 Classical Butterfly Optimization Algorithm -- 4.3.8 Transformation of BOA into DBOA using Mutation-Based Local Searching Strategy (MLSS) -- 4.4 Results and Discussion -- 4.5 Conclusion -- References -- Chapter 5 Wireless Power Transfer for IoT Applications-A Review -- 5.1 Introduction -- 5.2 Sensors -- 5.3 Actuators -- 5.4 Energy Requirement in Wireless Sensor Networks (WSNs) -- 5.5 Wireless Sensor Network and Green IoT (G-IoT) -- 5.6 Purpose of G-IoT -- 5.7 Motivation -- 5.8 Contribution -- 5.9 Need of the Work -- 5.10 Energy Transferring Schemes in WSAN -- 5.11 Electromagnetic Induction -- 5.11.1 Electrodynamic and Electrostatic -- 5.11.2 Electrostatic Field -- 5.11.3 Electrostatic Force -- 5.11.4 Electromagnetic -- 5.11.5 Electromagnetic Field -- 5.12 Inductive Coupling -- 5.13 Resonance Inductive Coupling -- 5.14 Wireless Power Transmission Using Microwaves -- 5.15 Electromagnetic Radiations -- 5.16 Conclusion -- References -- Chapter 6 Adaptive Energy Intelligence Using AI/ML Techniques -- 6.1 Introduction -- 6.2 Evolution of Cyber Physical System -- 6.3 Relationship With Internet of Things -- 6.4 Challenges in Design and Integration of Cyber Physical Systems -- 6.5 Future Challenges and Promises -- 6.6 Machine Learning Models -- 6.7 Estimation of Building Energy Consumption -- 6.8 Development of Artificial Intelligence -- 6.9 Usage of AI/ML in Adaptive Energy Management -- 6.10 Use of Hybrid/Ensemble Machine Learning Algorithm for Better Prediction -- 6.11 Conclusion -- References…”

Tabla de Contenidos: “…4.3 Mid‐Band Spectrum at 3.3-5.0 GHz and at 2.6 GHz -- 4.4 Low‐Band Spectrum Below 3 GHz -- 4.5 Unlicensed Band -- 4.6 Shared Band -- 4.7 3GPP Frequency Variants -- 4.8 Summary -- References -- Chapter 5 5G Architecture -- 5.1 Introduction -- 5.2 5G Architecture Options -- 5.3 5G Core Network Architecture -- 5.3.1 Access and Mobility Management Function -- 5.3.2 Session Management Function -- 5.3.3 User Plane Function -- 5.3.4 Data Storage Architecture -- 5.3.5 Policy Control Function -- 5.3.6 Network Exposure Function -- 5.3.7 Network Repository Function -- 5.3.8 Network Slice Selection -- 5.3.9 Non‐3GPP Interworking Function -- 5.3.10 Auxiliary 5G Core Functions -- 5.4 5G RAN Architecture -- 5.4.1 NG‐Interface -- 5.4.2 Xn‐Interface -- 5.4.3 E1‐Interface -- 5.4.4 F1‐Interface -- 5.5 Network Slicing -- 5.5.1 Interworking with LTE -- 5.6 Summary -- References -- Chapter 6 5G Physical Layer -- 6.1 Introduction -- 6.2 5G Multiple Access Principle -- 6.3 Physical Channels and Signals -- 6.4 Basic Structures for 5G Frame Structure -- 6.5 5G Channel Structures and Beamforming Basics -- 6.6 Random Access -- 6.7 Downlink User Data Transmission -- 6.8 Uplink User Data Transmission -- 6.9 Uplink Signaling Transmission -- 6.10 Downlink Signaling Transmission -- 6.11 Physical Layer Procedures -- 6.11.1 HARQ Procedure -- 6.11.2 Uplink Power Control -- 6.11.3 Timing Advance -- 6.12 5G MIMO and Beamforming Operation -- 6.12.1 Downlink MIMO Transmission Schemes -- 6.12.2 Beam Management Framework -- 6.12.2.1 Initial Beam Acquisition -- 6.12.2.2 Beam Measurement and Reporting -- 6.12.2.3 Beam Indication: QCL and Transmission Configuration Indicator (TCI) -- 6.12.2.4 Beam Recovery -- 6.12.3 CSI Framework -- 6.12.3.1 Reporting Settings -- 6.12.3.2 Resource Settings -- 6.12.3.3 Reporting Configurations -- 6.12.3.4 Report Quantity Configurations -- 6.12.4 CSI Components…”

Tabla de Contenidos: “…Archivo Fotográfico Cerro de las Cabezas, No Inv. CC_01413 -- Figura 44. A la izquierda, excavación de la tríada betílica (1995) -- a la derecha, restauración y consolidación (1998). © Ayto. …”

Tabla de Contenidos: “…Plan of the excavated buildings, B1, B5 and B12-B15 (scale 1:200). -- Figure 4.4. Building B13, plan (scale 1:100). -- Figure 4.5. …”

Tabla de Contenidos: “…3.6.4 Opening Scene Builder and Creating the File Welcome.fxml 142 -- 3.6.5 Adding an Image to the Folder Containing Welcome.fxml 144 -- 3.6.6 Creating a VBox Layout Container 144 -- 3.6.7 Configuring the VBox 144 -- 3.6.8 Adding and Configuring a Label 144 -- 3.6.9 Adding and Configuring an ImageView 146 -- 3.6.10 Previewing the Welcome GUI 147 -- 3.7 Wrap-Up 148 -- 4 Control Statements: Part 1 -- Assignment, ++and -- Operators 156 -- 4.1 Introduction 157 -- 4.2 Algorithms 157 -- 4.3 Pseudocode 158 -- 4.4 Control Structures 158 -- 4.4.1 Sequence Structure in Java 159 -- 4.4.2 Selection Statements in Java 160 -- 4.4.3 Iteration Statements in Java 160 -- 4.4.4 Summary of Control Statements in Java 160 -- 4.5 if Single-Selection Statement 161 -- 4.6 if...else Double-Selection Statement 162 -- 4.6.1 Nested if...else Statements 163 -- 4.6.2 Dangling-else Problem 164 -- 4.6.3 Blocks 164 -- 4.6.4 Conditional Operator (?…”

Tabla de Contenidos: “…Kapitel 4: Eingänge und Ausgänge -- 4.1 Digitale Eingänge -- 4.1.1 Pin als Eingang setzen -- 4.1.2 Digitalen Eingang lesen -- 4.1.3 Digitalen Eingang entprellen -- 4.1.4 Hohe Eingangssignale -- 4.2 Digitale Ausgänge -- 4.2.1 Ausgang setzen und ausgeben -- 4.2.2 Praxis-Tipp: Status eines Ausgangs lesen -- 4.3 Analoge Welt -- 4.3.1 Analoge Signale einlesen -- 4.3.2 Analoge Signale ausgeben (PWM) -- 4.3.3 Analoge Signale ausgeben (DAC), (nur für Arduino UNO R4) -- 4.4 Serielle Kommunikation -- 4.4.1 Serielle Schnittstelle - Anschluss (nur Arduino UNO R4) -- 4.4.2 Serielle Schnittstelle - Software -- 4.4.3 Schnittstellenerweiterung -- 4.4.4 I2C/2-Wire (Two-Wire) -- 4.5 Drahtlose Kommunikation -- 4.5.1 433-MHz-Kommunikation (nur Arduino UNO R3) -- 4.5.2 Daten übertragen mit RFM12B Transceiver (nur Arduino UNO R3) -- 4.6 Keyboard und Maus mit USB HID (nur Arduino UNO R4) -- 4.6.1 Minikeyboard mit Funktionen -- 4.6.2 Maus-Funktionen -- 4.7 Projekt: Würfel -- Kapitel 5: Sensoren -- 5.1 Sensoren -- 5.1.1 LDR (Fotowiderstand) -- 5.1.2 NTC/PTC -- 5.1.3 Integrierte Temperatursensoren -- 5.1.4 Feuchtesensoren -- 5.1.5 Kombinierte Umweltsensoren -- 5.1.6 Schaltersensoren -- 5.1.7 Abstandssensoren -- 5.1.8 Beschleunigungssensor -- 5.1.9 Kompass -- 5.1.10 Hall-Sensor -- 5.2 Projekt Kompass mit Richtungsanzeige -- 5.3 Projekt Gefrierschrankwächter (nur Arduino UNO R3) -- 5.4 Kontaktloses Fieberthermometer -- Kapitel 6: Aktoren -- 6.1 Relais -- 6.2 Servos -- 6.2.1 Analoge Temperaturanzeige -- 6.2.2 Servos als Motoren für Miniroboter -- 6.3 Motoren -- 6.4 Lasten schalten -- 6.5 Projekt: Roboter mit Wii-Steuerung -- Kapitel 7: Anzeigen -- 7.1 Leuchtdiode (LED) -- 7.1.1 Konstantstromquelle mit Transistor -- 7.1.2 Konstantstromquelle mit Spannungsregler -- 7.1.3 Helligkeit steuern -- 7.1.4 LED als Berührungssensor (nur Arduino UNO R3) -- 7.1.5 Jetzt wird es hell…”

Tabla de Contenidos: “…4.2.2 Artificial Intelligence -- 4.2.3 Deep Reinforcement Learning -- 4.2.4 Energy Efficiency in WSN -- 4.2.5 Reinforcement Learning -- 4.2.6 5G (5th Generation) -- 4.2.7 Smart Cities -- 4.3 Methodology -- 4.3.1 Clustering -- 4.3.2 Grouping of Nodes -- 4.3.3 Q-Learning -- 4.3.4 Reinforcement Learning -- 4.3.5 K-Means Clustering -- 4.4 Design Aspects -- 4.4.1 Design Considerations -- 4.4.1.1 Fault Tolerance -- 4.4.1.2 Lifetime -- 4.4.1.3 Scalability -- 4.4.1.4 Data Aggregation -- 4.4.1.5 Cost -- 4.4.1.6 Environment -- 4.4.1.7 Heterogeneity Support -- 4.4.1.8 Autonomous Operations -- 4.4.1.9 Limited Memory and Processing Capability -- 4.4.2 Node Creation -- 4.4.3 Distance Computation -- 4.4.4 Energy Parameters -- 4.4.5 WSN Environment -- 4.4.6 Q-Learning Agent -- 4.4.7 Classification of Different Nodes -- 4.4.8 Data Transfer Directly from Primary Node to Base Station -- 4.4.9 Selection of Base Nodes -- 4.5 Results -- 4.6 Conclusion -- References -- Chapter 5 The Role of 5G Networks in Healthcare Applications -- 5.1 Introduction -- 5.2 5G Networks -- 5.3 Applications of 5G Networks -- 5.3.1 Smart Healthcare Applications -- 5.3.2 Internet of Things (IoT) Devices in Healthcare Applications -- 5.3.3 5G Networks in Healthcare Applications -- 5.4 Challenges in the Deployment of 5G Networks in Healthcare Applications -- 5.4.1 Security and Data Privacy Issues -- 5.4.2 Ethical Issues -- 5.5 Recommendations for Leadership -- 5.6 Conclusion -- Reference list -- Chapter 6 Energy Consumption in Smart City Projects in the Era of 5G: An Analysis of User-Generated Content -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Research Development and Findings -- 6.4 Discussion and Implications -- 6.4.1 Discussion -- 6.4.2 Theoretical Implications -- 6.4.3 Managerial Implication -- 6.5 Conclusion -- References -- Chapter 7 The Role of 5G in Railway Applications…”

Tabla de Contenidos: “…4.2.4.9 The Need For Prescriptive Analytics in Maintenance: A Case Study -- 4.3 Big Data Analytics Methods -- 4.3.1 Defining Big Data Analytics -- 4.3.2 Defining Big Data Via the Three Vs -- 4.3.2.1 Data Volume as a Defining Attribute of Big Data -- 4.3.2.2 Data Type Variety as a Defining Attribute of Big Data -- 4.3.2.3 Data Feed Velocity as a Defining Attribute of Big Data -- 4.3.3 Text Analytics -- 4.3.4 Audio Analytics -- 4.3.5 Video Analytics -- 4.3.6 Social Media Analytics -- 4.4 Maintenance Strategies with Big Data Analytics -- 4.5 Data-Driven and Model-Driven Approaches -- 4.5.1 Data Mining and Knowledge Discovery -- 4.6 Maintenance Descriptive Analytics -- 4.7 Maintenance Diagnostic Analytics -- 4.8 Maintenance Predictive Analytics -- 4.9 Maintenance Prescriptive Analytics -- 4.10 Big Data Analytics Methods -- 4.10.1 Text Analytics -- 4.10.2 Audio Analytics -- 4.10.3 Video Analytics -- 4.10.4 Social Media Analytics -- 4.11 Big Data Management and Governance -- 4.12 Big Data Access and Analysis -- 4.13 Big Data Visualisation -- 4.14 Big Data Ingestion -- 4.15 Big Data Cluster Management -- 4.16 Big Data Distributions -- 4.17 Data Governance -- 4.18 Data Access -- 4.19 Data Analysis -- 4.20 Bid Data File System -- 4.20.1 Quantcast File System -- 4.20.2 Hadoop Distributed File System -- 4.20.3 Cassandra File System (CFS) -- 4.20.4 GlusterFS -- 4.20.5 Lustre -- 4.20.6 Parallel Virtual File System -- 4.20.7 Orange File System (OrangeFS) -- 4.20.8 BeeGFS -- 4.20.9 MapR-FS -- 4.20.9.1 Kudu -- References -- Chapter 5 Data-Driven Decision-Making -- 5.1 Data for Decision-Making -- 5.1.1 Data-Driven Decision-Making -- 5.1.2 The Process of Data-Driven Decision-Making -- 5.1.3 The Context of Data-Driven Decision-Making -- 5.1.4 The Importance of Data-Driven Decision-Making -- 5.1.5 Common Challenges of Data-Driven Decision-Making…”

Tabla de Contenidos: “…3.2.2 Differential privacy -- 3.3 DP guaranteed algorithms -- 3.3.1 Sample-level DP -- 3.3.1.1 Algorithms and discussion -- 3.3.2 Client-level DP -- 3.3.2.1 Clipping strategies for client-level DP -- 3.3.2.2 Algorithms and discussion -- 3.4 Performance of clip-enabled DP-FedAvg -- 3.4.1 Main results -- 3.4.1.1 Convergence theorem -- 3.4.1.2 DP guarantee -- 3.4.2 Experimental evaluation -- 3.5 Conclusion and future work -- References -- 4 Assessing vulnerabilities and securing federated learning -- 4.1 Introduction -- 4.2 Background and vulnerability analysis -- 4.2.1 Definitions and notation -- 4.2.1.1 Horizontal federated learning -- 4.2.1.2 Vertical federated learning -- 4.2.2 Vulnerability analysis -- 4.2.2.1 Clients' updates -- 4.2.2.2 Repeated interaction -- 4.3 Attacks on federated learning -- 4.3.1 Training-time attacks -- 4.3.1.1 Byzantine attacks -- 4.3.1.2 Backdoor attacks -- 4.3.2 Inference-time attacks -- 4.4 Defenses -- 4.4.1 Protecting against training-time attacks -- 4.4.1.1 In Situ defenses -- 4.4.1.2 Post Facto defenses -- 4.4.2 Protecting against inference-time attacks -- 4.5 Takeaways and future work -- References -- 5 Adversarial robustness in federated learning -- 5.1 Introduction -- 5.2 Attack in federated learning -- 5.2.1 Targeted data poisoning attack -- 5.2.1.1 Label flipping -- 5.2.1.2 Backdoor -- 5.2.1.2.1 Trigger-based backdoor -- 5.2.1.2.2 Semantic backdoor -- 5.2.2 Untargeted model poisoning attack -- 5.3 Defense in federated learning -- 5.3.1 Vector-wise defense -- 5.3.2 Dimension-wise defense -- 5.3.3 Certification -- 5.3.4 Personalization -- 5.3.5 Differential privacy -- 5.3.6 The gap between distributed training and federated learning -- 5.3.7 Open problems and further work -- 5.4 Conclusion -- References -- 6 Evaluating gradient inversion attacks and defenses -- 6.1 Introduction -- 6.2 Gradient inversion attacks…”

Tabla de Contenidos: “…4 Combating COVID-19 using object detection techniques for next-generation autonomous systems -- 4.1 Introduction -- 4.2 Need for object detection -- 4.3 Object detection techniques -- 4.3.1 R-CNN family -- 4.3.1.1 R-CNN -- 4.3.1.1.1 Network architecture -- 4.3.1.1.2 Advantages -- 4.3.1.1.3 Disadvantages -- 4.3.1.2 Fast R-CNN -- 4.3.1.2.1 Network architecture -- 4.3.1.2.2 The RoI pooling layer -- 4.3.1.2.3 Advantages -- 4.3.1.2.4 Disadvantages -- 4.3.1.3 Faster R-CNN -- 4.3.1.3.1 Network architecture -- 4.3.1.3.2 Advantages -- 4.3.1.3.3 Disadvantages -- 4.3.2 YOLO family -- 4.3.2.1 YOLOv1 -- 4.3.2.1.1 Network architecture -- 4.3.2.1.2 Advantages -- 4.3.2.1.3 Disadvantages -- 4.3.2.2 YOLOv2 -- 4.3.2.2.1 Improvements made over YOLOv1 -- 4.3.2.2.2 Network architecture -- 4.3.2.2.3 Advantages -- 4.3.2.2.4 Disadvantages -- 4.3.2.3 YOLOv3 -- 4.3.2.3.1 Improvements made over YOLOv2 -- 4.3.2.3.2 Network architecture -- 4.3.2.3.3 Advantages -- 4.3.2.3.4 Disadvantages -- 4.4 Applications of objection detection during COVID-19 crisis -- 4.4.1 Module for autonomous systems (pothole detection) -- 4.4.1.1 Architecture -- 4.4.1.2 Results -- 4.4.2 Social distancing detector -- 4.4.2.1 Results -- 4.4.3 COVID-19 detector based on X-rays -- 4.4.3.1 Architecture -- 4.4.3.1.1 Results -- 4.4.4 Face mask detector -- 4.4.4.1 Architecture -- 4.4.4.1.1 Results -- 4.5 Conclusion -- References -- 5 Non-contact measurement system for COVID-19 vital signs to aid mass screening-An alternate approach -- 5.1 Introduction -- 5.2 COVID-19 global scenarios -- 5.2.1 Infections, recovery and mortality rate -- 5.2.2 Economy and environmental impacts -- 5.3 Measurement and testing protocols of COVID-19 -- 5.3.1 Measurement methods -- 5.3.1.1 Pathophysiological tools -- 5.3.1.1.1 Nucleic acid amplification tests -- 5.3.1.1.2 Serological testing -- 5.3.1.2 Physiological assessment tools…”

Tabla de Contenidos: “…3.7.3.3 Data transmission through 5G terminal by ZigBee network -- 3.8 Conclusions -- References -- 4 An overview of low power hardware architecture for edge computing devices -- 4.1 Introduction -- 4.2 Basic concepts of cloud, fog and edge computing infrastructure -- 4.2.1 Role of edge computing in Internet of Things -- 4.2.2 Edge intelligence and 5G in Internet of Things based smart healthcare system -- 4.3 Low power hardware architecture for edge computing devices -- 4.3.1 Objectives of hardware development in edge computing -- 4.3.2 System architecture -- 4.3.3 Central processing unit architecture -- 4.3.4 Input-output architecture -- 4.3.5 Power consumption -- 4.3.6 Data processing and algorithmic optimization -- 4.4 Examples of edge computing devices -- 4.5 Edge computing for intelligent healthcare applications -- 4.5.1 Edge computing for healthcare applications -- 4.5.2 Advantages of edge computing for healthcare applications -- 4.5.3 Implementation challenges of edge computing in healthcare systems -- 4.5.4 Applications of edge computing based healthcare system -- 4.5.5 Patient data security in edge computing -- 4.6 Impact of edge computing, Internet of Things and 5G on smart healthcare systems -- 4.7 Conclusion and future scope of research -- References -- 5 Convergent network architecture of 5G and MEC -- 5.1 Introduction -- 5.2 Technical overview on 5G network with MEC -- 5.2.1 5G with multi-access edge computing (MEC): a technology enabler -- 5.2.2 Application splitting in MEC -- 5.2.3 Layered service oriented architecture for 5G MEC -- 5.3 Convergent network architecture for 5G with MEC -- 5.4 Current research in 5G with MEC -- 5.5 Challenges and issues in implementation of MEC -- 5.5.1 Communication and computation perspective -- 5.5.1.1 MEC service orchestration and programmability…”