Tabla de Contenidos: “…Chapter 4 Long Term Evolution (LTE) and LTE-Advanced Pro -- 4.1 Introduction and Overview -- 4.2 Network Architecture and Interfaces -- 4.2.1 LTE Mobile Devices and the LTE Uu Interface -- 4.2.2 The eNode-B and the S1 and X2 Interfaces -- 4.2.3 The Mobility Management Entity (MME) -- 4.2.4 The Serving Gateway (S-GW) -- 4.2.5 The PDN-Gateway -- 4.2.6 The Home Subscriber Server (HSS) -- 4.2.7 Billing, Prepaid and Quality of Service -- 4.3 FDD Air Interface and Radio Network -- 4.3.1 OFDMA for Downlink Transmission -- 4.3.2 SC-FDMA for Uplink Transmission -- 4.3.3 Quadrature Amplitude Modulation for Subchannels -- 4.3.4 Reference and Synchronization Signals -- 4.3.5 The LTE Channel Model in the Downlink Direction -- 4.3.6 Downlink Management Channels -- 4.3.7 System Information Messages -- 4.3.8 The LTE Channel Model in the Uplink Direction -- 4.3.9 MIMO Transmission -- 4.3.10 HARQ and Other Retransmission Mechanisms -- 4.3.11 PDCP Compression and Ciphering -- 4.3.12 Protocol Layer Overview -- 4.4 TD-LTE Air Interface -- 4.5 Scheduling -- 4.5.1 Downlink Scheduling -- 4.5.2 Uplink Scheduling -- 4.6 Basic Procedures -- 4.6.1 Cell Search -- 4.6.2 Attach and Default Bearer Activation -- 4.6.3 Handover Scenarios -- 4.6.4 Default and Dedicated Bearers -- 4.7 Mobility Management and Power Optimization -- 4.7.1 Mobility Management in RRC Connected State -- 4.7.2 Mobility Management in RRC Idle State -- 4.7.3 Mobility Management and State Changes in Practice -- 4.8 LTE Security Architecture -- 4.9 Interconnection with UMTS and GSM -- 4.9.1 Cell Reselection between LTE and GSM/UMTS -- 4.9.2 RRC Connection Release with Redirect between LTE and GSM/UMTS -- 4.9.3 Handover from LTE to UMTS -- 4.10 Interworking with CDMA2000 Networks -- 4.10.1 Cell Reselection between LTE and CDMA2000 Networks -- 4.10.2 RRC Connection Release with Redirect between LTE and CDMA2000…”

Tabla de Contenidos: “…Chapter 4 Geodesy -- 4.1 International Terrestrial Reference Frame -- 4.1.1 Polar Motion -- 4.1.2 Tectonic Plate Motion -- 4.1.3 Solid Earth Tides -- 4.1.4 Ocean Loading -- 4.1.5 Relating of Nearly Aligned Frames -- 4.1.6 ITRF and NAD83 -- 4.2 International Celestial Reference System -- 4.2.1 Transforming Terrestrial and Celestial Frames -- 4.2.2 Time Systems -- 4.3 Datum -- 4.3.1 Geoid -- 4.3.2 Ellipsoid of Rotation -- 4.3.3 Geoid Undulations and Deflections of the Vertical -- 4.3.4 Reductions to the Ellipsoid -- 4.4 3D Geodetic Model -- 4.4.1 Partial Derivatives -- 4.4.2 Reparameterization -- 4.4.3 Implementation Considerations -- 4.4.4 GPS Vector Networks -- 4.4.5 Transforming Terrestrial and Vector Networks -- 4.4.6 GPS Network Examples -- 4.4.6.1 Montgomery County Geodetic Network -- 4.4.6.2 SLC Engineering Survey -- 4.4.6.3 Orange County Densification -- 4.5 Ellipsoidal Model -- 4.5.1 Reduction of Observations -- 4.5.1.1 Angular Reduction to Geodesic -- 4.5.1.2 Distance Reduction to Geodesic -- 4.5.2 Direct and Inverse Solutions on the Ellipsoid -- 4.5.3 Network Adjustment on the Ellipsoid -- 4.6 Conformal Mapping Model -- 4.6.1 Reduction of Observations -- 4.6.2 Angular Excess -- 4.6.3 Direct and Inverse Solutions on the Map -- 4.6.4 Network Adjustment on the Map -- 4.6.5 Similarity Revisited -- 4.7 Summary -- Chapter 5 Satellite Systems -- 5.1 Motion of Satellites -- 5.1.1 Kepler Elements -- 5.1.2 Normal Orbital Theory -- 5.1.3 Satellite Visibility and Topocentric Motion -- 5.1.4 Perturbed Satellite Motion -- 5.1.4.1 Gravitational Field of the Earth -- 5.1.4.2 Acceleration due to the Sun and the Moon -- 5.1.4.3 Solar Radiation Pressure -- 5.1.4.4 Eclipse Transits and Yaw Maneuvers -- 5.2 Global Positioning System -- 5.2.1 General Description -- 5.2.2 Satellite Transmissions at 2014 -- 5.2.2.1 Signal Structure -- 5.2.2.2 Navigation Message…”

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

Tabla de Contenidos: “…4.2 Object Detection -- 4.3 Two-Stage Object Detectors -- 4.3.1 R-CNN -- 4.3.2 SPPNet -- 4.3.3 Fast RCNN -- 4.3.4 Faster RCNN -- 4.3.5 R-FCN -- 4.3.6 FPN -- 4.3.7 Mask RCNN -- 4.3.8 G-RCNN -- 4.4 One-Stage Object Detectors -- 4.4.1 YOLO -- 4.4.2 CenterNet -- 4.4.3 SSD -- 4.4.4 RetinaNet -- 4.4.5 EfficientDet -- 4.4.6 YOLOR -- 4.5 Discussion On Model Performance -- 4.5.1 Future Trends -- 4.6 Conclusion -- References -- Chapter 5 Deep Learning Algorithms for Computer Vision: A Deep Insight Into Principles and Applications -- 5.1 Introduction -- 5.2 Preliminary Concepts of Deep Learning -- 5.2.1 Artificial Neural Network -- 5.2.2 Convolution Neural Network (CNNs) -- 5.3 Recurrent Neural Network (RNNs) -- 5.4 Overview of Applied Deep Learning in Computer Vision -- 5.6 Industrial Applications of Computer Vision -- 5.7 Future Scope in Computer Vision -- 5.8 Conclusion -- References -- Chapter 6 Handwritten Equation Solver Using Convolutional Neural Network -- 6.1 Introduction -- 6.2 State-Of-The-Art -- 6.3 Convolutional Neural Network -- 6.3.1 Convolution Layer -- 6.3.2 Pooling Layer -- 6.3.3 Fully Connected Layer -- 6.3.4 Activation Function -- 6.4 Handwritten Equation Recognition -- 6.4.1 Dataset Preparation -- 6.4.2 Proposed Methodology -- 6.4.2.1 Dataset Acquisition -- 6.4.2.2 Preprocessing -- 6.4.2.3 Recognition Through CNN Model -- 6.4.2.4 Processing Inside CNN Model -- 6.4.3 Solution Approach -- 6.5 Results and Discussion -- 6.6 Conclusion and Future Scope -- References -- Chapter 7 Agriware: Crop Suggester System By Estimating the Soil Nutrient Indicators -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Proposed Methodology -- 7.4 Experimental Results and Discussion -- 7.5 Conclusion and Future Work -- References -- Chapter 8 A Machine Learning Based Expeditious Covid-19 Prediction Model Through Clinical Blood Investigations -- 8.1 Introduction…”

Tabla de Contenidos: “…3.5.1 Conditional Probability Function -- 3.5.2 Conditional Probability Density Function -- 3.6 Expectations -- 3.6.1 Moments -- 3.6.2 Expected Value of Function of Random Variable -- 3.6.3 Covariance -- 3.7 Conditional Expectations -- 3.8 Convergence of Random Variables -- 3.9 Random Processes -- 3.10 Markov Processes -- 3.11 Hidden Markov Models -- 3.12 Gaussian Processes -- Exercises -- Part II Optimization -- Chapter 4 Optimization Theory -- 4.1 Basic Concepts and Terminology -- 4.1.1 Optimization Problems -- 4.1.2 Equivalent Problems -- 4.2 Convex Sets -- 4.2.1 Convexity‐Preserving Operations -- 4.2.1.1 Intersection -- 4.2.1.2 Affine Transformation -- 4.2.1.3 Perspective Transformation -- 4.2.2 Examples of Convex Sets -- 4.2.2.1 Hyperplanes and Halfspaces -- 4.2.2.2 Polyhedral Sets -- 4.2.2.3 Norm Balls and Ellipsoids -- 4.2.2.4 Convex Cones -- 4.2.3 Generalized Inequalities -- 4.3 Convex Functions -- 4.3.1 First‐ and Second‐Order Conditions for Convexity -- 4.3.2 Convexity‐Preserving Operations -- 4.3.2.1 Scaling, Sums, and Integrals -- 4.3.2.2 Pointwise Maximum and Supremum -- 4.3.2.3 Affine Transformation -- 4.3.2.4 Perspective Transformation -- 4.3.2.5 Partial Infimum -- 4.3.2.6 Square of Nonnegative Convex Functions -- 4.3.3 Examples of Convex Functions -- 4.3.3.1 Norms -- 4.3.3.2 Indicator and Support Functions -- 4.3.4 Conjugation -- 4.3.5 Dual Norms -- 4.4 Subdifferentiability -- 4.4.1 Subdifferential Calculus -- 4.4.1.1 Nonnegative Scaling -- 4.4.1.2 Summation -- 4.4.1.3 Affine Transformation -- 4.4.1.4 Pointwise Maximum -- 4.4.1.5 Subgradients of Conjugate Functions -- 4.5 Convex Optimization Problems -- 4.5.1 Optimality Condition -- 4.5.2 Equality Constrained Convex Problems -- 4.6 Duality -- 4.6.1 Lagrangian Duality -- 4.6.2 Lagrange Dual Problem -- 4.6.3 Fenchel Duality -- 4.7 Optimality Conditions…”

Tabla de Contenidos: “…Chapter 4 Robust Zero Watermarking Algorithm for Medical Images Based on BRISK and DCT -- 4.1 Introduction -- 4.2 Fundamental Theory -- 4.2.1 BRISK Feature Extraction Algorithm -- 4.2.2 Discrete Cosine Transform (DCT) -- 4.2.3 Logistic Mapping -- 4.3 Proposed Algorithm -- 4.3.1 Medical Image Feature Extraction -- 4.3.2 Watermark Encryption -- 4.3.3 Embed Watermark -- 4.3.4 Watermark Extraction and Decryption -- 4.4 Experiments and Results -- 4.4.1 Test Different Images -- 4.4.2 Conventional Attacks -- 4.4.3 Geometric Attacks -- 4.4.4 Compare with Other Algorithms -- 4.5 Conclusion -- References -- Chapter 5 Robust Color Images Zero-Watermarking Algorithm Based on Stationary Wavelet Transform and Daisy Descriptor -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Material and Techniques -- 5.3.1 Daisy Descriptor -- 5.3.2 Stationary Wavelet Transform -- 5.3.3 Tent Chaotic Map -- 5.3.4 Proposed Algorithm -- 5.4 Experiment and Results -- 5.4.1 Evaluation Parameter -- 5.4.2 Feasibility Analysis -- 5.4.3 Results and Analysis -- 5.5 Conclusion -- References -- Chapter 6 Robust Multi-watermarking Algorithm Based on DarkNet53 -- 6.1 Introduction -- 6.2 Basic Theory -- 6.2.1 DarkNet53 -- 6.2.2 Discrete Cosine Transform -- 6.2.3 Logistic Map -- 6.3 Proposed Algorithm -- 6.3.1 Improvement of DarkNet53 Network Model -- 6.3.2 Encryption of Watermark -- 6.3.3 Watermark Embedding -- 6.3.4 Extraction of a Watermark -- 6.3.5 Decryption of a Watermark -- 6.4 Experimental Results and Analysis -- 6.4.1 Performance -- 6.4.2 Reliability Analysis -- 6.4.3 Traditional Attack -- 6.4.4 Geometric Attack -- 6.5 Conclusion -- References -- Chapter 7 Robust Multi-watermark Algorithm for Medical Images Based on SqueezeNet Transfer Learning -- 7.1 Introduction -- 7.2 Fundamental Theory -- 7.2.1 SqueezeNet Neural Network -- 7.2.2 Transfer Learning…”

Tabla de Contenidos: “…SELECCIÓ DE MATERIALS -- 4.1 QÜESTIONARI SOBRE ELS MATERIALS -- 4.2 SELECCIÓ QUALITATIVA -- 4.3 SELECCIÓ QUANTITATIVA -- BIBLIOGRAFIA…”
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Tabla de Contenidos: “….) -- Com. in quatuor evangelia 2-4 (3 v.) -- Com. in evangelium Mat. (2 v.) -- Com. in S. …”

Tabla de Contenidos: “…Vita Brevis; 2Virtuous Poverty of Reason: The Bucolic Heinsius (1603-4); 3. Dwelling on the Pagan-Christian Borders: Heinsius and Cunaeus on Nonnus (1610); 4. …”
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Tabla de Contenidos: “…The long-term impact of the COVID-19 crisis on firms' employment -- 4.3. The impact of the COVID-19 crisis on firms' digitalisation -- 5. …”

Tabla de Contenidos: “…COVER; CONTENTS; BION SEMINARS AT THE TAVISTOCK CLINIC; SEMINAR ONE 28 June 1976; SEMINAR TWO 4 July 1977; SEMINAR THREE 5 July 1977; SEMINAR FOUR 3 July 1978; SEMINAR FIVE 4 July 1978; SEMINAR SIX 5 July 1978; SEMINAR SEVEN 27 March 1979; SEMINAR EIGHT 28 March 1979; APPENDIX A Extract from Péguy's Basic Verities; APPENDIX B Interview by Anthony G. …”

Tabla de Contenidos: “…Evidence for policy formulation -- 4.1 Evaluation of paediatric pharmacokinetics data -- 4.2 Evidence for the safety of delamanid in the treatment of MDR-TB -- 4.3 Evidence for the efficacy of delamanid in the treatment of MDR-TB -- 5. …”

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Tabla de Contenidos: “…. -- disc 2, lectures 3-4. 3. Exploring the nature and dynamism of hope. 4. …”

Tabla de Contenidos: “…EL LENGUAJE DRAMÁTICO -- 4.3. PERSONAJES DRAMÁTICOS -- 4.3.1. CLASIFICACIÓN DE PERSONAJES SEGÚN SU PAPEL -- 4.3.2. …”
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Tabla de Contenidos: “…4.2.2 - Classification of Voltage Stability4.2.3 - Introduction of Voltage Stability Terms; 4.2.4 - Influential Factors of Voltage Stability; 4.3 - Evaluation indicator L of voltage stability in the power grid; 4.3.1 - Overview of Voltage Stability Assessment Indicator L; 4.3.2 - Derivation of Voltage Indicator L in Multinode System; 4.4 - The model for indicator calculation; 4.4.1 - Combination Point of the PSO and the BP network; 4.4.2 - Combination Procedure of the PSO and BP Network and Its Parameter Setting; 4.5 - Example analysis; 4.5.1 - Five-Node Circuit Diagram and Known Parameters…”

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Tabla de Contenidos: “…3.2 Criminal Investigation in Germany -- 3.2.1 The Notion and Outline of Criminal Investigation -- 3.2.2 Prosecutorial Discretion -- 3.2.3 Prosecutorial Objectivity -- 3.3 Criminal Investigation in Poland -- 3.3.1 The Notion and Outline of Criminal Investigation -- 3.3.2 Prosecutorial Discretion -- 3.3.3 Prosecutorial Objectivity -- 3.4 Criminal Investigation in Italy -- 3.4.1 The Notion and Outline of Criminal Investigation -- 3.4.2 Prosecutorial Discretion -- 3.4.3 Prosecutorial Objectivity -- 3.5 Criminal Investigation in the United States of America -- 3.5.1 The Notion and Outline of Criminal Investigation -- 3.5.2 Prosecutorial Discretion -- 3.5.3 Prosecutorial Objectivity -- 3.6 Summary -- References -- Chapter 4 Powers of the Prosecutor Over Initiation of Investigation -- 4.1 General Considerations -- 4.2 Initiating Investigation in Germany -- 4.2.1 Threshold For Initiating Investigation -- 4.2.2 Procedure For Initiating Investigation -- 4.2.3 Authority Responsible For Initiating Investigation -- 4.3 Initiating Investigation in Poland -- 4.3.1 Threshold For Initiating Investigation -- 4.3.2 Procedure For Initiating Investigation -- 4.3.3 Authority Responsible For Initiating Investigation -- 4.4 Initiating Investigation in Italy -- 4.4.1 Threshold For Initiating Investigation -- 4.4.2 Procedure For Initiating Investigation -- 4.4.3 Authority Responsible For Initiating Investigation -- 4.5 Initiating Investigation in the United States of America -- 4.5.1 Threshold For Initiating Investigation -- 4.5.2 Procedure For Initiating Investigation -- 4.5.3 Authority Responsible For Initiating Investigation -- 4.6 Summary -- References -- Chapter 5 Powers of the Prosecutor Over the Conduct of Investigation -- 5.1 General Considerations -- 5.2 Conduct of Investigation in Germany -- 5.2.1 The Relationship Between Prosecutor and Police…”

Tabla de Contenidos: “…Adaptación simplificada 11.4.3. Adaptación Libre 11.4.4. Adaptación desconocida 11.5. …”