Tabla de Contenidos: “…ELECTRICALLY SMALL ANTENNAS: CANONICAL TYPES 59 -- 3.1 Introduction / 59 -- 3.2 Dipole Basic Characteristics / 59 -- 3.2.1 Dipole Impedance and Bandwidth / 59 -- 3.2.2 Resistive and Reactive Loading / 67 -- 3.2.3 Other Loading Configurations / 76 -- 3.2.4 Short Flat Resonant Dipoles / 78 -- 3.2.5 Spherical Helix Antennas / 82 -- 3.2.6 Multiple Resonance Antennas / 84 -- 3.2.6.1 Spherical Dipole; Arc Antennas / 84 -- 3.2.6.2 Multiple Mode Antennas / 86 -- 3.2.6.3 Q Comparisons / 87 -- 3.2.7 Evaluation of Moment Method Codes for Electrically Small Antennas / 88 -- 3.3 Partial Sleeve, PIFA, and Patch / 93 -- 3.3.1 Partial Sleeve / 93 -- 3.3.2 PIFA Designs / 94 -- 3.3.3 Patch with Permeable Substrate / 98 -- 3.4 Loops / 101 -- 3.4.1 Air Core Loops, Single and Multiple Turns / 101 -- 3.4.2 Permeable Core Loops / 107 -- 3.4.3 Receiving Loops / 114 -- 3.4.4 Vector Sensor / 116 -- 3.5 Dielectric Resonator Antennas / 120 -- References / 127 -- 4. …”

Tabla de Contenidos: “…The requirements for physical mobility -- Wireless communication -- Mobility -- Portability -- Overview of IPv4 and IPv6 -- IPv4 -- IPv6 -- Mobile IP -- Goals of mobile IP -- Applicability -- Mobility support in IPv4 -- Mobility support in IPv6 -- Cellular IP -- The cellular IP access network -- Routing and paging cache -- TCP for mobility -- Indirect TCP -- Snooping TCP -- Mobile TCP -- Mobile databases -- Design issues -- Problems in mobile databases -- Commercially available systems -- The CODA file system-A case study -- Cache manager Venus -- Venus states -- Design criteria -- Summary -- Problems -- Multiple-choice questions -- Further reading -- Mobile Ad Hoc Networks -- MANET characteristics -- Classification of MANETs -- Technologies for ad hoc networks -- Routing in MANETs -- Traditional routing protocols -- Requirements for routing protocols -- Classification of routing protocols -- Proactive routing protocols-The DSDV protocol -- Example of DSDV operation -- Reactive routing protocols -- Dynamic source routing -- Route discovery in DSR -- Route maintenance in DSR -- Route cache in DSR -- Adaptive on-demand distance vector protocol -- Route discovery in AODV -- Route maintenance in AODV -- Comparison between DSR and AODV -- Summary -- Problems -- Multiple-choice questions -- Further reading -- Wireless Sensor Networks -- Applications of wireless sensor networks -- Differences from mobile ad hoc networks -- Design issues -- WSN architecture -- Sensor hardware components -- WSN communications architecture -- Routing protocols for WSN -- Data-centric protocols -- Flooding and gossiping -- Sensor protocols for information via negotiation -- Hierarchical protocols -- Low-energy adaptive clustering hierarchy -- PEGASIS -- TEEN and APTEEN -- Location-based protocols -- Case study -- The MICA mote -- TinyOS -- Development work in WSN -- Summary…”

Tabla de Contenidos: “…-- Predicting heart disease using logistic regression -- Summary -- Chapter 6: Naive Bayes and Sentiment Analysis -- Conditional probability -- Bayes theorem -- Naïve bayes algorithm -- Advantages of naïve bayes -- Disadvantages of naïve bayes -- Sentimental analysis -- Concepts for sentimental analysis -- Tokenization -- Stop words removal -- Stemming -- N-grams -- Term presence and Term Frequency -- TF-IDF -- Bag of words -- Dataset -- Data exploration of text data -- Sentimental analysis on this dataset -- SVM or Support Vector Machine -- Summary -- Chapter 7: Decision Trees -- What is a decision tree? …”

Tabla de Contenidos: “…Moxibustion for Demons: Oral Transmission on Corpse-Vector Disease -- BUDDHISM AND THE MEDICAL TRADITIONS -- 52. …”

Tabla de Contenidos: “…OVERVIEW -- 1 Upgrading Traditional Biotechnological Processes -- TRADITIONAL FOOD FERMENTATION -- DECENTRALIZED SMALL-SCALE PROCESSES -- BASIC PROCESSING OPERATIONS -- NUTRITIVE VALUE -- STABILIZATION OF NATURAL FERMENTATIONS BY INOCULUM ENRICHMENT -- MULTISTRAIN DEHYDRATED STARTER -- ENZYME PRODUCTION BY KOJI TECHNIQUE -- DRY MATTER BALANCE -- IMPLEMENTATION -- REFERENCES -- 2 Genetic Improvement of Microbial Starter Cultures -- TRADITIONAL GENETIC IMPROVEMENT STRATEGIES -- Mutation and Selection -- Natural Gene Transfer Methods -- Transduction -- Conjugation -- Transformation -- Electroporation -- GENETIC ENGINEERING -- Prerequisites -- Metabolism And Biochemistry Of The Host -- Transformable Hosts -- Vector Systems -- Efficient Gene Transfer Systems -- Expression Systems -- Properties of Interest -- Limitations -- 3 Sudan's Fermented Food Heritage -- FERMENTED FOODS -- Sorghum-Based Foods -- Dairy Products -- Fish Products -- Meat Products -- Vegetable Products -- Alcoholic Products -- FERMENTED FOODS AND SURVIVAL STRATEGIES -- BIOTECHNOLOGY AND FERMENTED FOODS -- REFERENCES -- 4 Lesser-Known Fermented Plant Foods -- REGIONAL PERSPECTIVES -- PRODUCTS FROM CEREAL GRAINS -- Ahai -- Ting -- Maasa -- PRODUCTS FROM ROOT TUBERS -- Farinha Puba -- Kokonte -- Masato (masata) -- Chuno -- PRODUCTS FROM LEGUMES, PULSES, AND OTHER SEEDS -- PRODUCTS FROM FRUITS AND VEGETABLES -- COMMERCIALIZATION -- REFERENCES -- 5 Lactic Acid Fermentations -- SAUERKRAUT -- KOREAN KIMCHI -- PICKLED VEGETABLES -- INDIAN IDLI AND DOSA -- PHILIPPINE PUTO…”

Tabla de Contenidos: “…Approximation of the vector field -- 3.2.2. The algorithm -- 4. Pathwise construction of affine processes with time-change -- Bibliography -- Part II. …”

Tabla de Contenidos: “…1.13.6 Applying Cognition to Develop Health and Wellness -- 1.13.7 Welltok -- 1.13.8 CaféWell Concierge in Action -- 1.14 Advantages of Cognitive Computing -- 1.15 Features of Cognitive Computing -- 1.16 Limitations of Cognitive Computing -- 1.17 Conclusion -- References -- 2 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges -- 2.1 Introduction -- 2.2 Cyber-Physical System Architecture -- 2.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS) -- 2.4 Machine Learning Applications in CPS -- 2.4.1 K-Nearest Neighbors (K-NN) in CPS -- 2.4.2 Support Vector Machine (SVM) in CPS -- 2.4.3 Random Forest (RF) in CPS -- 2.4.4 Decision Trees (DT) in CPS -- 2.4.5 Linear Regression (LR) in CPS -- 2.4.6 Multi-Layer Perceptron (MLP) in CPS -- 2.4.7 Naive Bayes (NB) in CPS -- 2.5 Use of IoT in CPS -- 2.6 Use of Big Data in CPS -- 2.7 Critical Analysis -- 2.8 Conclusion -- References -- 3 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Research Objectives -- 3.1.3 Research Approach -- 3.1.4 Limitations -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Methodological Approach -- 3.3.2 Methods of Analysis -- 3.4 Results -- 3.4.1 Impact of Project Outcomes -- 3.4.2 Results Obtained During the Methodology -- 3.5 Discussion -- 3.6 Originality and Innovativeness of the Research -- 3.6.1 Validation and Quality Control of Methods -- 3.6.2 Cost-Effectiveness of the Research -- 3.7 Conclusion -- References -- 4 Advanced Cognitive Models and Algorithms -- 4.1 Introduction -- 4.2 Microsoft Azure Cognitive Model -- 4.2.1 AI Services Broaden in Microsoft Azure -- 4.3 IBM Watson Cognitive Analytics -- 4.3.1 Cognitive Computing -- 4.3.2 Defining Cognitive Computing via IBM Watson Interface -- 4.3.3 IBM Watson Analytics -- 4.4 Natural Language Modeling…”

Tabla de Contenidos: “…4.4.2 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 4.4.3 Conclusion -- 4.5 Artificial Neural Networks -- 4.5.1 How It Works -- Step 1: Determine the Topology and Activation Function -- Step 2: Initiation -- Step 3: Calculating Error -- Step 4: Weight Adjustment -- 4.5.2 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 4.5.3 Conclusion -- 4.6 Support Vector Machines -- Concept and Terminology -- 4.6.1 How It Works -- 4.6.2 How to Implement -- Implementation 1: Linearly Separable Dataset -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Process Execution and Interpretation -- Example 2: Linearly Non-Separable Dataset -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Execution and Interpretation -- Parameter Settings -- 4.6.3 Conclusion -- 4.7 Ensemble Learners -- Wisdom of the Crowd -- 4.7.1 How It Works -- Achieving the Conditions for Ensemble Modeling -- 4.7.2 How to Implement -- Ensemble by Voting -- Bootstrap Aggregating or Bagging -- Implementation -- Boosting -- AdaBoost -- Implementation -- Random Forest -- Implementation -- 4.7.3 Conclusion -- References -- 5 Regression Methods -- 5.1 Linear Regression -- 5.1.1 How it Works -- 5.1.2 How to Implement -- Step 1: Data Preparation -- Step 2: Model Building -- Step 3: Execution and Interpretation -- Step 4: Application to Unseen Test Data -- 5.1.3 Checkpoints -- 5.2 Logistic Regression -- 5.2.1 How It Works -- How Does Logistic Regression Find the Sigmoid Curve? …”

Tabla de Contenidos: “…Special IP Addresses -- Chapter Review -- Questions -- Answers -- Chapter 8 The Wonderful World of Routing -- Historical/Conceptual -- How Routers Work -- Test Specific -- Routing Tables -- Freedom from Layer 2 -- Network Address Translation -- Dynamic Routing -- Routing Metrics -- Distance Vector -- Link State -- EIGRP-the Lone Hybrid -- Dynamic Routing Makes the Internet -- Working with Routers -- Connecting to Routers -- Basic Router Configuration -- Router Problems -- Chapter Review -- Questions -- Answers -- Chapter 9 TCP/IP Applications -- Historical/Conceptual -- Transport Layer and Network Layer Protocols -- How People Communicate -- Test Specific -- TCP -- UDP -- ICMP -- IGMP -- The Power of Port Numbers -- Registered Ports -- Connection Status -- Rules for Determining Good vs. …”

Tabla de Contenidos: “…Chapter 10 Research Design Machine Maintenance Management Software Module for Garment Industry -- 10.1 Introduction -- 10.2 Building a Maintenance Process for Garment Industry Machine -- 10.2.1 Maintenance Process for Machinery -- 10.2.2 Information in the Maintenance Management Machine Records -- 10.3 Designing a "Machine Maintenance Management" Software Module -- 10.3.1 Database Design -- 10.3.2 Designing a "Machine Maintenance Management" Software Module -- 10.4 Conclusion -- References -- Part 3: Adoption of ICT for Digitalization, Artificial Intelligence, and Machine Learning -- Chapter 11 Performance Comparison of Prediction of a Hydraulic Jump Depth in a Channel Using Various Machine Learning Models -- Nomenclature -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Materials and Methods -- 11.3.1 Equation of the Hydraulic Jump -- 11.3.2 Data Used in the Study -- 11.4 Machine Learning Models -- 11.4.1 Features of Machine Learning Models -- 11.4.2 Support Vector Machine (SVM) -- 11.4.3 Decision Tree (DT) -- 11.4.4 Random Forest (RF) -- 11.4.5 Artificial Neural Network (ANN) -- 11.5 Results and Discussion -- 11.6 Conclusions -- References -- Chapter 12 Creating a Video from Facial Image Using Conditional Generative Adversarial Network -- 12.1 Introduction -- 12.2 Related Works -- 12.3 Methodology -- 12.3.1 The Proposed Model -- 12.3.2 Conditional Generative Adversarial Network (cGAN) -- 12.3.3 Hidden Affine Transformation -- 12.4 Experiments -- 12.4.1 Dataset -- 12.4.2 Dlib -- 12.4.3 Evaluation -- 12.4.4 Result -- 12.5 Conclusion -- References -- Chapter 13 Deep Learning Framework for Detecting, Classifying, and Recognizing Invoice Metadata -- 13.1 Introduction -- 13.2 Related Works -- 13.3 Invoice Data Analysis -- 13.4 Proposed Method -- 13.5 Experiments -- 13.6 Conclusion and Perspectives -- References…”

Tabla de Contenidos: “…7.2.4 Facial Recognition -- 7.2.5 Thumb Impression Recognition -- 7.3 Proposed Method -- 7.3.1 Mathematical Model: Pascal's Triangle Graded Mean Approach -- 7.3.2 Proposed Fuzzy Approximation Method (FAM) -- 7.3.3 Application of FAM in Facial Recognition -- 7.3.4 Application of FAM in Thumb Recognition -- 7.3.5 Proposed Algorithm and Coding -- 7.4 Experimental Analysis -- 7.5 Proposed Solution -- 7.6 Application Over Facial Recognition -- 7.7 Application of Thumb Impression Recognition -- 7.8 Advantages of the Proposed Method -- 7.9 Conclusion -- References -- Chapter 8 A Deep Learning System for Deep Surveillance -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Method and Approach -- 8.3.1 Dataset Used -- 8.3.2 Mathematical Modelling -- 8.3.3 Frames Extraction and Object Detection -- 8.3.4 Image Pre-Processing -- 8.4 Model Implementations -- 8.4.1 SoftMax Regression -- 8.4.2 Support Vector Machine (SVM) -- 8.4.3 MatConvNet -- 8.4.4 CNN -- 8.4.5 Spatially-Sparse CNN -- 8.4.6 Implementation -- 8.5 Results and Comparative Analysis -- 8.6 Conclusions and Future Research Direction -- References -- Chapter 9 Study of Traditional, Artificial Intelligence and Machine Learning Based Approaches for Moving Object Detection -- 9.1 Introduction -- 9.2 Literature Review -- 9.3 Approaches for MOD -- 9.3.1 Traditional Approaches for MOD -- 9.3.1.1 Background Subtraction Methods -- 9.3.1.2 Optical Flow-Based Techniques -- 9.3.1.3 Frame Differencing and Morphological Operations -- 9.3.1.4 Challenges and Limitations -- 9.3.2 ML Approaches for MOD -- 9.3.2.1 Supervised Learning for Object Detection -- 9.3.2.2 Unsupervised Learning Approaches for Anomaly Detection -- 9.3.2.3 Transfer Learning and Domain Adaptation -- 9.3.2.4 Evaluation Metrics for ML-Based MOD -- 9.3.3 AI Approaches in MOD -- 9.3.3.1 AI-Powered Object Tracking -- 9.3.3.2 Reinforcement Learning for MOD…”

Tabla de Contenidos: “…Graph Representations -- 4.4.2. Vector Clocks -- 4.4.3. Event Orderings -- 5. Model Checking -- 6. …”

Tabla de Contenidos: “…Front Cover -- Wheeled Mobile Robotics: From Fundamentals Towards Autonomous Systems -- Copyright -- Contents -- Preface -- Acknowledgements -- Chapter 1: Introduction to Mobile Robotics -- 1.1 Introduction -- 1.1.1 Robots -- 1.1.2 Mobile -- 1.1.3 Wheels -- 1.1.4 Autonomous Mobile Systems -- 1.2 History -- 1.3 About the Book -- References -- Chapter 2: Motion Modeling for Mobile Robots -- 2.1 Introduction -- 2.2 Kinematics of Wheeled Mobile Robots -- 2.2.1 Differential Drive -- 2.2.2 Bicycle Drive -- 2.2.3 Tricycle Drive -- 2.2.4 Tricycle With a Trailer -- 2.2.5 Car (Ackermann) Drive -- 2.2.6 Synchronous Drive -- 2.2.7 Omnidirectional Drive -- 2.2.8 Tracked Drive -- 2.3 Motion Constraints -- 2.3.1 Holonomic Constraints -- 2.3.2 Nonholonomic Constraints -- 2.3.3 Integrability of Constraints -- 2.3.4 Vector Fields, Distribution, Lie Bracket -- 2.3.5 Controllability of Wheeled Mobile Robots -- 2.4 Dynamic Model of a Mobile System With Constraints -- 2.4.1 State-Space Representation of the Dynamic Model of a Mobile System With Constraints -- 2.4.2 Kinematic and Dynamic Model of Differential Drive Robot -- Kinematic Model and Constraints -- Dynamic Model -- References -- Chapter 3: Control of Wheeled Mobile Systems -- 3.1 Introduction -- 3.2 Control to Reference Pose -- 3.2.1 Orientation Control -- 3.2.2 Forward-Motion Control -- 3.2.3 Basic Approaches -- Control to Reference Position -- Control to Reference Pose Using an Intermediate Point -- Control to Reference Pose Using an Intermediate Direction -- Control on a Segmented Continuous Path Determined by a Line and a Circle Arc -- From Reference Pose Control to Reference Path Control -- 3.3 Trajectory Tracking Control -- 3.3.1 Trajectory Tracking Control Using Basic Approaches -- 3.3.2 Decomposing Control to Feedforward and Feedback Action -- 3.3.3 Feedback Linearization…”

Tabla de Contenidos: “…20.4 Frequency-Modulated Continuous-Wave Radar Link Budget -- 20.5 Frequency-Modulated Continuous-Wave Implementation Considerations -- 20.6 Frequency-Modulated Continuous-Wave Interference -- 20.7 Frequency-Modulated Continuous-Wave Beamforming -- 20.8 Frequency-Modulated Continuous-Wave Range-Doppler Processing -- 20.9 Frequency-Modulated Continuous-Wave Radar Front-End Processing -- 20.10 Frequency-Modulated Continuous-Wave Pulse-Doppler Processing -- 20.11 Frequency-Modulated Continuous-Wave Radar Back-End Processing -- 20.12 Noncoherent Antenna Magnitude Summation -- 20.13 Cell Averaging-Constant False Alarm Rate -- 20.14 Ordered Sort-Constant False Alarm Rate -- 20.15 Angle of Arrival Estimation -- 21 - Space Time Adaptive Processing (STAP) Radar -- 21.1 Space Time Adaptive Processing Radar Concept -- 21.2 Steering Vector -- 21.3 Interference Covariance Matrix -- 21.4 Space Time Adaptive Processing Optimal Filter -- 21.5 Space Time Adaptive Processing Radar Computational Requirements -- 22 - Synthetic Array Radar -- 22.1 Introduction -- 22.2 Synthetic Array Radar Resolution -- 22.3 Pulse Compression -- 22.4 Azimuth Resolution -- 22.5 Synthetic Array Radar Processing -- 22.6 Synthetic Array Radar Doppler Processing -- 22.7 Synthetic Array Radar Impairments -- 23 - Introduction to Video Processing -- 23.1 Color Spaces -- 23.2 Interlacing -- 23.3 Deinterlacing -- 23.4 Image Resolution and Bandwidth -- 23.5 Chroma Scaling -- 23.6 Image Scaling and Cropping -- 23.7 Alpha Blending and Compositing -- 23.8 Video Compression -- 23.9 Digital Video Interfaces -- 23.10 Legacy Analog Video Interfaces -- 24 - DCT, Entropy, Predictive Coding, and Quantization -- 24.1 Discrete Cosine Transform -- 24.2 Entropy -- 24.3 Huffman Coding -- 24.4 Markov Source -- 24.5 Predictive Coding -- 24.6 Differential Encoding -- 24.7 Lossless Compression -- 24.8 Quantization…”

Tabla de Contenidos: “…Front Cover -- Estimation and Control of Large-Scale Networked Systems -- Copyright -- Contents -- Preface -- Acknowledgments -- Notation and Symbols -- 1 Introduction -- 1.1 A General View on Control System Design -- 1.2 Communication and Control -- 1.3 Book Contents -- 1.3.1 Controllability and Observability of a Control System -- 1.3.2 Centralized and Distributed State Estimations -- 1.3.3 State Estimations and Control With Imperfect Communications -- 1.3.4 Veri cation of Stability and Robust Stability -- 1.3.5 Distributed Controller Design for an LSS -- 1.3.6 Structure Identi cation for an LSS -- 1.3.7 Attack Estimation/Identi cation and Other Issues -- 1.4 Bibliographic Notes -- References -- 2 Background Mathematical Results -- 2.1 Linear Space and Linear Algebra -- 2.1.1 Vector and Matrix Norms -- 2.1.2 Hamiltonian Matrices and Distance Among Positive De nite Matrices -- 2.2 Generalized Inverse of a Matrix -- 2.3 Some Useful Transformations -- 2.4 Set Function and Submodularity -- 2.5 Probability and Random Process -- 2.6 Markov Process and Semi-Markov Process -- 2.7 Bibliographic Notes -- References -- 3 Controllability and Observability of an LSS -- 3.1 Introduction -- 3.2 Controllability and Observability of an LTI System -- 3.2.1 Minimal Number of Inputs/Outputs Guaranteeing Controllability/Observability -- 3.2.2 A Parameterization of Desirable Input/Output Matrices -- 3.2.3 Some Nitpicking -- 3.3 A General Model for an LSS -- 3.4 Controllability and Observability for an LSS -- 3.4.1 Subsystem Transmission Zeros and Observability of an LSS -- 3.4.2 Observability Veri cation -- 3.4.3 A Condition for Controllability and Its Veri cation -- 3.4.4 In/Out-degree and Controllability/Observability of a Networked System -- 3.5 Construction of Controllable/Observable Networked Systems -- 3.6 Bibliographic Notes -- Appendix 3.A…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Preface -- Acknowledgments -- Chapter 1: Introduction -- 1.1 WIND ENERGY BACKGROUND -- 1.2 ORGANIZATION OF THIS BOOK -- 1.2.1 Who Should Use This Book -- 1.2.2 Note for Instructors -- 1.2.3 Datasets Used in the Book -- Part I: Wind Field Analysis -- Chapter 2: A Single Time Series Model -- 2.1 TIME SCALE IN SHORT- TERM FORECASTING -- 2.2 SIMPLE FORECASTING MODELS -- 2.2.1 Forecasting Based on Persistence Model -- 2.2.2 Weibull Distribution -- 2.2.3 Estimation of Parameters in Weibull Distribution -- 2.2.4 Goodness of Fit -- 2.2.5 Forecasting Based on Weibull Distribution -- 2.3 DATA TRANSFORMATION AND STANDARDIZATION -- 2.4 AUTOREGRESSIVE MOVING AVERAGE MODELS -- 2.4.1 Parameter Estimation -- 2.4.2 Decide Model Order -- 2.4.3 Model Diagnostics -- 2.4.4 Forecasting Based on ARMA Model -- 2.5 OTHER METHODS -- 2.5.1 Kalman Filter -- 2.5.2 Support Vector Machine -- 2.5.3 Artificial Neural Network -- 2.6 PERFORMANCE METRICS -- 2.7 COMPARING WIND FORECASTING METHODS -- Chapter 3: Spatio temporal Models -- 3.1 COVARIANCE FUNCTIONS AND KRIGING -- 3.1.1 Properties of Covariance Functions -- 3.1.2 Power Exponential Covariance Function -- 3.1.3 Kriging -- 3.2 SPATIO-TEMPORAL AUTOREGRESSIVE MODELS -- 3.2.1 Gaussian Spatio-temporal Autoregressive Model -- 3.2.2 Informative Neighborhood -- 3.2.3 Forecasting and Comparison -- 3.3 SPATIO-TEMPORAL ASYMMETRY AND SEPARABILITY -- 3.3.1 Definition and Quantification -- 3.3.2 Asymmetry of Local Wind Field -- 3.3.3 Asymmetry Quantification -- 3.3.4 Asymmetry and Wake Effect -- 3.4 ASYMMETRIC SPATIO-TEMPORAL MODELS -- 3.4.1 Asymmetric Non-separable Spatio-temporal Model -- 3.4.2 Separable Spatio-temporal Models -- 3.4.3 Forecasting Using Spatio-temporal Model -- 3.4.4 Hybrid of Asymmetric Model and SVM -- 3.5 CASE STUDY…”

Tabla de Contenidos: “…More details about differential privacy -- A.1 The formal definition of differential privacy -- A.2 Other differential privacy mechanisms -- A.2.1 Geometric mechanism -- A.2.2 Gaussian mechanism -- A.2.3 Staircase mechanism -- A.2.4 Vector mechanism -- A.2.5 Wishart mechanism -- A.3 Formal definitions of composition properties of DP -- A.3.1 The formal definition of sequential composition DP -- A.3.2 The formal definition of parallel composition DP -- references -- Appendix -- index -- inside back cover…”

Tabla de Contenidos: “…Mathematical Concepts in Kinematics -- 2.1 Introduction -- 2.2 Complex Numbers and Operations -- 2.2.1 Complex Number Forms -- 2.2.2 Complex Number Addition -- 2.2.3 Complex Number Multiplication and Differentiation -- 2.3 Vector and Point Representation -- 2.4 Linear Simultaneous Equations, Matrices, and Matrix Operations -- 2.4.1 Linear Simultaneous Equation Systems and Matrices -- 2.4.2 Matrix Transpose, Addition, Subtraction, and Multiplication -- 2.4.3 The Identity Matrix and Matrix Inversion -- 2.5 Intermediate and Total Spatial Motion -- 2.6 General Transformation Matrix -- 2.7 Summary -- References -- Additional Reading -- Problems -- 3. …”

Tabla de Contenidos: “…11.2 Challenges in recognition of hand gestures -- 11.3 Approaches' types -- 11.4 Literature review on vision-based gesture recognition -- 11.5 Data acquisition -- 11.6 Pre-processing of acquired data -- 11.7 Segmentation -- 11.7.1 Segmentation of skin color -- 11.7.2 Other methods for segmentation -- 11.7.3 Tracking -- 11.8 Feature extraction -- 11.8.1 Principal component analysis (PCA) -- 11.8.2 Linear discriminant analysis (LDA) -- 11.8.3 Shift-invariant feature transform (SIFT) -- 11.9 Classification -- 11.9.1 Artificial neural network (ANN) -- 11.9.2 Support vector machine (SVM) -- 11.9.3 Euclidean distance classifier -- 11.9.4 K-nearest neighbor classifier -- 11.10 Literature review on sensor-based gesture recognition -- 11.10.1 Data glove -- 11.10.2 Electromyography (EMG) -- 11.10.3 Radar and WiFi -- 11.11 Discussion -- 11.11.1 Critical review on the previous survey -- 11.11.2 Reviews on considered algorithms and methodologies -- 11.12 Conclusion and future work -- References -- Index…”

Tabla de Contenidos: “…6.3.3.2 Gradient Boosting Classifier -- 6.3.3.3 K-Nearest Neighbors -- 6.3.3.4 Logistic Regression -- 6.3.3.5 Artificial Neural Networks -- 6.3.3.6 Support Vector Machines (SVM) -- 6.3.3.7 Naïve Bayes Classifier -- 6.4 Results -- 6.5 Conclusions -- References -- Chapter 7 Detection of Malicious Emails and URLs Using Text Mining -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Dataset Description -- 7.4 Proposed Architecture -- 7.5 Methodology -- 7.5.1 Methodology for the URL Dataset -- 7.5.2 Methodology for the Email Dataset -- 7.5.2.1 Overcoming the Overfitting Problem -- 7.5.2.2 Tokenization -- 7.5.2.3 Applying Machine Learning Algorithms -- 7.5.3 Detecting Presence of Malicious URLs in Otherwise Non-Malicious Emails -- 7.5.3.1 Preparation of Dataset -- 7.5.3.2 Creation of Features -- 7.5.3.3 Applying Machine Learning Algorithms -- 7.6 Results -- 7.6.1 URL Dataset -- 7.6.2 Email Dataset -- 7.6.3 Final Dataset -- 7.7 Conclusion -- References -- Chapter 8 Quantum Data Traffic Analysis for Intrusion Detection System -- 8.1 Introduction -- 8.2 Literature Overview -- 8.3 Methodology -- 8.3.1 Autoviz -- 8.3.2 Dataset -- 8.3.3 Proposed Models -- 8.3.3.1 Decision Tree -- 8.3.3.2 Random Forest Classifier Algorithm -- 8.3.3.3 AdaBoost Classifier -- 8.3.3.4 Ridge Classifier -- 8.3.3.5 Logistic Regression -- 8.3.3.6 SVM-Linear Kernel -- 8.3.3.7 Naive Bayes -- 8.3.3.8 Quadratic Discriminant Analysis -- 8.4 Results -- 8.5 Conclusion -- References -- Chapter 9 Quantum Computing in Netnomy: A Networking Paradigm in e-Pharmaceutical Setting -- 9.1 Introduction -- 9.2 Discussion -- 9.2.1 Exploring Market Functioning via Quantum Network Economy -- 9.2.1.1 Internal Networking Marketing -- 9.2.1.2 Layered Marketing -- 9.2.1.3 Role of Marketing in Pharma Network Organizations -- 9.2.1.4 Role of Marketing in Vertical Networking Organizations…”