Tabla de Contenidos: “…-- Preface xiii -- Acknowledgments xv -- 1 IP Technology Disrupts Voice Telephony 1 -- 1.1 Introduction to the Public Switched Telephone Network 1 -- 1.2 The Digital PSTN 2 -- 1.3 The Packet Revolution in Telephony 8 -- 1.3.1 Summary of Packet Switching 9 -- 1.3.2 Link Capacity: TDM versus Packets 11 -- 1.3.3 VoIP and “The Cloud” 13 -- IN SHORT: Reading Network Drawings 14 -- 2 Traditional Telephones Still Set Expectations 17 -- 2.1 Availability: How the Bell System Ensured Service 18 -- 2.2 Call Completion 19 -- 2.3 Sound Quality: Encoding for Recognizable Voices 20 -- 2.4 Low Latency 23 -- 2.5 Call Setup Delays 24 -- 2.6 Impairments Controlled: Echo, Singing, Distortion, Noise 25 -- 3 From Circuits to Packets 27 -- 3.1 Data and Signaling Preceded Voice 27 -- 3.1.1 X.25 Packet Data Service 27 -- 3.1.2 SS7: PSTN Signaling on Packets 28 -- 3.1.3 ISDN 29 -- 3.2 Putting Voice into Packets 30 -- 3.2.1 Voice Encoding 31 -- 3.2.2 Dicing and Splicing Voice Streams 32 -- 3.2.3 The Latency Budget 33 -- 4 Packet Transmission and Switching 37 -- 4.1 The Physical Layer: Transmission 39 -- IN SHORT: The Endian Wars 40 -- 4.2 Data Link Protocols 41 -- 4.3 IP, the Network Protocol 43 -- 4.4 Layer 4 Transport Protocols 47 -- 4.4.1 Transmission Control Protocol 47 -- 4.4.2 User Datagram Protocol 50 -- 4.4.3 Stream Control Transmission Protocol 51 -- 4.5 Higher Layer Processes 54 -- 4.5.1 RTP 54 -- 4.5.2 RTCP 57 -- 4.5.3 Multiplexing RTP and RTCP on One UDP Port 58 -- 4.5.4 RTP Mixers and Translators 59 -- 4.5.5 Layered Encoding 60 -- 4.5.6 Profiles for Audio and Video Conferences 60 -- 4.5.7 Security via Encryption 61 -- IN SHORT: Public Key Infrastructure (PKI) 62 -- 4.6 Saving Bandwidth 64 -- 4.6.1 Voice Compression 64 -- 4.6.2 Header Compression 66 -- 4.6.3 Silence Suppression, VAD 67 -- 4.6.4 Sub-Packet Multiplexing 69 -- 4.6.5 Protocol and Codec Selection 70 -- 4.7 Differences: Circuit versus Packet Switched 71 -- 4.7.1 Power to the Desktop Phone 71 -- 4.7.2 Phone as Computer and Computer as Phone 72.…”

Tabla de Contenidos: “…4.3 The Abuse of Information and Knowledge -- 4.3.1 Projected Use of Machines to Block Human Abuse -- 4.3.2 Abuse of Machines Against Humans -- 4.4 Indulgence of Societies in Negative Social Settings -- 4.5 Deception and Cowardice in Humans -- 4.6 Negative Social Change: Deterioration and Decay -- 4.7 Social Decay of Nations and Cultures -- Conclusions -- References -- 5 Role of Devices, Computers and Networks -- 5.1 Introduction -- 5.2 Devices, Machines, Humans, and Social Realities -- 5.2.1 Beneficial and Constructive Role -- 5.2.2 Detrimental and Exploitive Role -- 5.3 Reality and Oscillation of Social Norms -- 5.3.1 Social Energy and Ensuing Shifts -- 5.3.2 Change in the Four (K, C, W, and E) Spaces -- 5.3.3 Noise in the Social Setting -- 5.4 Typical Global Shifts in Societies and Nations -- 5.5 Oscillation of Social Norms -- 5.5.1 Social Lais Sez-Faire -- 5.5.2 Social Supervision -- Conclusions -- References -- II. …”

Tabla de Contenidos: “…5.2.3 Kinematics of Rotating Frames -- Rotational Kinematics Expressed by Quaternions -- 5.2.4 Projective Geometry -- Multiview Geometry -- Singular Cases -- 3D Reconstruction -- 5.3 Pose Measurement Methods -- 5.3.1 Dead Reckoning -- Odometry -- Inertial Navigation -- 5.3.2 Heading Measurement -- 5.3.3 Active Markers and Global Position Measurement -- Global Navigation Satellite System -- 5.3.4 Navigation Using Environmental Features -- Straight-Line Features -- Split-and-Merge Algorithm -- Evolving Straight-Line Clustering -- Hough Transform -- Color Features -- Artificial Pattern Markers -- Natural Local Image Features -- 5.3.5 Matching of Environment Models: Maps -- 5.4 Sensors -- 5.4.1 Sensor Characteristics -- 5.4.2 Sensor Classifications -- References -- Chapter 6: Nondeterministic Events in Mobile Systems -- 6.1 Introduction -- 6.2 Basics of Probability -- 6.2.1 Discrete Random Variable -- 6.2.2 Continuous Random Variable -- 6.2.3 Bayes' Rule -- 6.3 State Estimation -- 6.3.1 Disturbances and Noise -- 6.3.2 Estimate Convergence and Bias -- 6.3.3 Observability -- 6.4 Bayesian Filter -- 6.4.1 Markov Chains -- 6.4.2 State Estimation From Observations -- 6.4.3 State Estimation From Observations and Actions -- 6.4.4 Localization Example -- 6.4.5 Environment Sensing -- 6.4.6 Motion in the Environment -- 6.4.7 Localization in the Environment -- 6.5 Kalman Filter -- 6.5.1 Kalman Filter in Matrix Form -- 6.5.2 Extended Kalman Filter -- 6.5.3 Kalman Filter Derivatives -- 6.6 Particle Filter -- References -- Chapter 7: Autonomous Guided Vehicles -- 7.1 Introduction -- 7.2 Autonomous Transportation Vehicles -- 7.2.1 About -- 7.2.2 Setup -- 7.2.3 Sensors -- 7.2.4 Localization and Mapping -- 7.2.5 Control -- 7.2.6 Path Planning -- 7.2.7 Decision Making -- 7.3 Wheeled Mobile Robots in Agriculture -- 7.3.1 Introduction (About) -- 7.3.2 Service Unit Setup…”

Tabla de Contenidos: “…6.1.3 Diatoms and Their Growth -- 6.1.4 Growth Data Analysis and Growth Parameter Estimation -- 6.2 Simulation Trials -- 6.2.1 Methodology for the Simulation Trials -- 6.2.2 Candidate Methods for Estimating the Specific Growth Rate -- 6.2.3 Simulation Trials Results -- 6.2.3.1 Results with Only the Noise Challenge -- 6.2.3.2 Results when Crashing Occurs -- 6.2.3.3 Results when Censoring Occurs -- 6.2.3.4 Overall Results and Ranking of the Methods -- 6.3 Empirical Example -- 6.4 Conclusions and Recommendations -- References -- Chapter 7 Integrating Metabolic Modeling and High-Throughput Data to Characterize Diatoms Metabolism -- 7.1 Introduction -- 7.2 Characterization of Diatom Genomes -- 7.2.1 Available Genomics Data -- 7.2.2 Computational Tools to Allocate Gene Functions by Subcellular Localization -- 7.3 Metabolic Modeling of Diatoms: Data and Outcomes -- 7.3.1 Using Genomic Information to Build Genome-Scale Metabolic Models -- 7.3.2 Comprehensive Diatom Omic Datasets Are Useful to Constrain Metabolic Models -- 7.3.3 Unraveling New Knowledge About Central Carbon Metabolism of Diatoms -- 7.3.4 Light-Driven Metabolism that Enables Acclimation to High Light Intensities -- 7.4 Modeling Applications to Study Bioproduction and Genome Changes in Diatoms -- 7.4.1 Predicting Diatom-Heterotroph Interactions and Horizontal Gene Transfer Using Community Metabolic Models -- 7.4.2 Optimization and Scale-Up of the Production of Valuable Metabolites -- 7.4.3 Potential for the Study of Proteome Allocation in Diatoms -- 7.5 Conclusions -- References -- Part III: Diatom Motility -- Chapter 8 Modeling the Synchronization of the Movement of Bacillaria paxillifer by a Kuramoto Model with Time Delay -- 8.1 Introduction -- 8.2 Materials and Methods -- 8.3 Time Dependence of the Relative Motion of Adjacent Diatoms…”

Tabla de Contenidos: “…Front Cover -- Digital Signal Processing 101 -- Digital Signal Processing 101: Everything you Need to Know to Get Started -- Copyright -- Contents -- Acknowledgments -- Introduction -- 1 - Numerical Representation -- 1.1 Integer Fixed Point Representation -- 1.2 Fractional Fixed Point Representation -- 1.3 Floating Point Representation -- 2 - Complex Numbers and Exponentials -- 2.1 Complex Addition and Subtraction -- 2.2 Complex Multiplication -- 2.3 Polar Representation -- 2.4 Complex Multiplication Using Polar Representation -- 2.5 Complex Conjugate -- 2.6 The Complex Exponential -- 2.7 Measuring Angles in Radians -- 3 - Sampling, Aliasing, and Quantization -- 3.1 Sampling Effects -- 3.2 Nyquist Sampling Rule -- 3.3 Quantization -- 3.4 Signal to Noise Ratio -- 4 - Frequency Response -- 4.1 Frequency Response and the Complex Exponential -- 4.2 Normalizing Frequency Response -- 4.3 Sweeping Across the Frequency Response -- 4.4 Example Frequency Responses -- 4.5 Linear Phase Response -- 4.6 Normalized Frequency Response Plots -- 5 - Finite Impulse Response (FIR) Filters -- 5.1 Finite Impulse Response Filter Construction -- 5.2 Computing Frequency Response -- 5.3 Computing Filter Coefficients -- 5.4 Effect of Number of Taps on Filter Response -- 6 - Windowing -- 6.1 Truncation of Coefficients -- 6.2 Tapering of Coefficients -- 6.3 Sample Coefficient Windows -- 7 - Decimation and Interpolation -- 7.1 Decimation -- 7.2 Interpolation -- 7.3 Resampling by Noninteger Value -- 8 - Infinite Impulse Response (IIR) Filters -- 8.1 Infinite Impulse Response and Finite Impulse Response Filter Characteristic Comparison -- 8.2 Bilinear Transform -- 8.3 Frequency Prewarping -- 9 - Complex Modulation and Demodulation -- 9.1 Modulation Constellations -- 9.2 Modulated Signal Bandwidth -- 9.3 Pulse-Shaping Filter -- 9.4 Raised Cosine Filter…”

Tabla de Contenidos: “…4.8 Two-Element Uniform Array -- 4.9 Uniform Linear Arrays -- 4.10 Field Strength of a Uniform Linear Array -- 4.11 First Side Lobe Ratio (SLR) -- 4.12 Broadside and End-fire Arrays -- 4.13 Patterns of Array of Non-isotropic Radiators -- 4.14 Multiplication of Patterns -- 4.15 Generalised Expression of Principle of Pattern Multiplication -- 4.16 Radiation Pattern Characteristics -- 4.17 Binomial Arrays -- 4.18 Effect of Earth on Vertical Patterns -- 4.19 Effect of Earth on Radiation Resistance -- 4.20 Methods of Excitation of Antennas -- 4.21 Impedance Matching Techniques -- 4.22 Transmission Loss between Transmitting and Receiving Antennas (Friis Formula) -- 4.23 Antenna Temperature and Signal-to-noise Ratio -- Points to Remember -- Solved Problems -- Objective Questions -- Exercise Problems -- Chapter 5: Array Synthesis -- 5.1 Introduction -- 5.2 Synthesis Methods -- 5.3 Schelkunoff Polynomial Method -- 5.4 Fourier Transform Method -- 5.5 Line Source Design by Fourier Transform Method -- 5.6 Design of Linear Array by Fourier Transform Method -- 5.7 Linear Array Design by Woodward-Lawson Method -- 5.8 Dolph-Chebychev Method (Tschebyscheff Distribution) -- 5.9 Determination of Dolph-Chebychev Amplitude Distribution -- 5.10 Advantages of Dolph-Tschebyscheff Method -- 5.11 Taylor's Method -- 5.12 Laplace Transform Method -- 5.13 Standard Amplitude Distributions -- Points to Remember -- Solved Problems -- Objective Questions -- Exercise Problems -- Chapter 6: HF, VHF and UHF Antennas -- 6.1 Introduction -- 6.2 Isotropic Radiators -- 6.3 Directional Antennas -- 6.4 Omni-directional Antenna -- 6.5 Resonant Antennas -- 6.6 Non-resonant Antennas (Travelling Wave Antennas) -- 6.7 LF Antennas -- 6.8 Antennas for HF, VHF, UHF -- 6.9 Dipole Arrays -- 6.10 Broadside Array -- 6.11 End-fire Array -- 6.12 Folded Dipole -- 6.13 V-Antenna -- 6.14 Inverted V-Antenna…”

Tabla de Contenidos: “…Quality Control -- 1.4.4. Channel Noise -- 1.4.5. System Simulation -- 1.5. Elementary Set Theory -- 1.5.1. …”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Bioinspired Algorithms: Opportunities and Challenges -- 1.1 Introduction -- 1.1.1 Definition and Significance of Bioinspired Algorithms -- 1.1.2 Overview of the Chapter -- 1.2 Bioinspired Principles and Algorithms -- 1.2.1 Evolutionary Algorithms -- 1.2.2 Swarm Intelligence Algorithms -- 1.2.3 Artificial Neural Networks -- 1.2.4 Other Bioinspired Algorithms -- 1.3 Opportunities of Bioinspired Algorithms -- 1.3.1 Solving Complex Optimization Problems -- 1.3.2 Robustness in Dealing With Uncertainty and Noise -- 1.3.3 Parallel and Distributed Computing -- 1.3.4 Application Areas and Success Stories -- 1.4 Challenges of Bioinspired Algorithms -- 1.4.1 Parameter Tuning and Algorithm Configuration -- 1.4.2 Lack of Theoretical Analysis and Understanding -- 1.4.3 Risk of Premature Convergence -- 1.4.4 Computational Cost for Large-Scale Problems -- 1.4.5 Ethical Considerations and Limitations -- 1.5 Prominent Bioinspired Algorithms -- 1.5.1 Genetic Algorithms -- 1.5.2 Particle Swarm Optimization -- 1.5.3 Ant Colony Optimization -- 1.5.4 Artificial Neural Networks -- 1.6 Applications of Bioinspired Algorithms -- 1.6.1 Optimization Problems -- 1.6.2 Pattern Recognition and Machine Learning -- 1.6.3 Swarm Robotics -- 1.6.4 Other Domains -- 1.7 Future Research Directions -- 1.7.1 Improving Efficiency and Scalability -- 1.7.2 Enhancing Interpretability and Explainability -- 1.7.3 Integration With Other Computational Techniques -- 1.7.4 Addressing Ethical Concerns -- 1.8 Conclusion -- 1.8.1 Summary of Key Points -- 1.8.2 Implications and Future Prospects of Bioinspired Algorithms -- References -- Chapter 2 Evaluation of Phytochemical Screening and In Vitro Antiurolithiatic Activity of Myristica fragrans by Titrimetry Method Using Machine Learning -- 2.1 Introduction…”

Tabla de Contenidos: “…2.5 Mobility Management, Handover, and UE Measurements -- 2.5.1 Network-Controlled Mobility -- 2.5.1.1 Control-Plane Handover Procedures -- 2.5.1.2 User-Plane Handover Procedures -- 2.5.2 UE-Based Mobility -- 2.5.3 Paging -- 2.5.4 Measurements -- 2.6 UE and Network Identifiers -- 2.7 Random-Access Procedure (L2/L3 Aspects) -- 2.8 Multi-radio Dual Connectivity (L2/L3 Aspects) -- 2.9 Carrier Aggregation (L2/L3 Aspects) -- References -- 3GPP Specifications -- IETF Specifications -- Articles, Books, White Papers, and Application Notes -- 3 New Radio Access Physical Layer Aspects (Part 1) -- 3.1 Channel Models and Propagation Characteristics -- 3.1.1 Characteristics of Wireless Channels -- 3.1.1.1 Path Loss Models -- 3.1.1.2 Delay Spread -- 3.1.1.3 Doppler Spread -- 3.1.1.4 Angular Spread -- 3.1.1.5 Blockage -- 3.1.1.6 Oxygen Absorption -- 3.1.1.7 LoS Path Loss Probability -- 3.1.2 Two- and Three-Dimensional Channel Models -- 3.2 Waveforms -- 3.2.1 OFDM Basics and Transmission Characteristics -- 3.2.1.1 Cyclic Prefix -- 3.2.1.2 Pre- and Post-processing Signal-to-Noise Ratio -- 3.2.1.3 Peak-to-Average Power Ratio -- 3.2.1.4 Error Vector Magnitude -- 3.2.1.5 Carrier Frequency Offset -- 3.2.1.6 Phase Noise -- 3.2.2 DFT-S-OFDM Basics and Transmission Characteristics -- 3.2.3 Other Waveform Candidates -- 3.2.3.1 Filtered-OFDM -- 3.2.3.2 Filter Bank Multicarrier -- 3.2.3.3 Universal Filtered Multicarrier -- 3.2.3.4 Generalized Frequency Division Multiplexing -- 3.2.3.5 Faster Than Nyquist Signal Processing -- 3.2.3.6 Comparison of the Candidate Waveforms -- 3.3 Multiple-Access Schemes -- 3.3.1 Orthogonal Frequency Division Multiple Access -- 3.3.2 Single-Carrier Frequency Division Multiple Access -- 3.3.3 Non-orthogonal Multiple-Access Schemes -- 3.3.3.1 Sparse Code Multiple Access -- 3.3.3.2 Power-Domain Non-orthogonal Multiple Access…”

Tabla de Contenidos: “…-- 7.9.2 Data -- CHAPTER 8: PROCEDURAL CONTENT GENERATION -- 8.1 Pseudorandom Numbers -- 8.1.1 Numeric Mixing and Game Seeds -- 8.1.2 Halton Sequence -- 8.1.3 Phyllotaxic Angles -- 8.1.4 Poisson Disk -- 8.2 Lindenmayer Systems -- 8.2.1 Simple L-systems -- 8.2.2 Adding Randomness to L-systems -- 8.2.3 Stage-Specific Rules -- 8.3 Landscape Generation -- 8.3.1 Modifiers and Height-Maps -- 8.3.2 Noise -- 8.3.3 Perlin Noise -- 8.3.4 Faults -- 8.3.5 Thermal Erosion -- 8.3.6 Hydraulic Erosion -- 8.3.7 Altitude Filtering -- 8.4 Dungeons and Maze Generation -- 8.4.1 Mazes by Depth First Backtracking…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface to the Second Edition -- Acknowledgments -- Acronyms -- Chapter 1 Basics of Fourier Analysis -- 1.1 Forward and Inverse Fourier Transform -- 1.1.1 Brief History of FT -- 1.1.2 Forward FT Operation -- 1.1.3 IFT -- 1.2 FT Rules and Pairs -- 1.2.1 Linearity -- 1.2.2 Time Shifting -- 1.2.3 Frequency Shifting -- 1.2.4 Scaling -- 1.2.5 Duality -- 1.2.6 Time Reversal -- 1.2.7 Conjugation -- 1.2.8 Multiplication -- 1.2.9 Convolution -- 1.2.10 Modulation -- 1.2.11 Derivation and Integration -- 1.2.12 Parseval's Relationship -- 1.3 Time-Frequency Representation of a Signal -- 1.3.1 Signal in the Time Domain -- 1.3.2 Signal in the Frequency Domain -- 1.3.3 Signal in the Joint Time-Frequency (JTF) Plane -- 1.4 Convolution and Multiplication Using FT -- 1.5 Filtering/Windowing -- 1.6 Data Sampling -- 1.7 DFT and FFT -- 1.7.1 DFT -- 1.7.2 FFT -- 1.7.3 Bandwidth and Resolutions -- 1.8 Aliasing -- 1.9 Importance of FT in Radar Imaging -- 1.10 Effect of Aliasing in Radar Imaging -- 1.11 Matlab Codes -- References -- Chapter 2 Radar Fundamentals -- 2.1 Electromagnetic Scattering -- 2.2 Scattering from PECs -- 2.3 Radar Cross Section -- 2.3.1 Definition of RCS -- 2.3.2 RCS of Simple-Shaped Objects -- 2.3.3 RCS of Complex-Shaped Objects -- 2.4 Radar Range Equation -- 2.4.1 Bistatic Case -- 2.4.2 Monostatic Case -- 2.5 Range of Radar Detection -- 2.5.1 Signal-to-Noise Ratio -- 2.6 Radar Waveforms -- 2.6.1 Continuous Wave -- 2.6.2 Frequency-Modulated Continuous Wave -- 2.6.3 Stepped-Frequency Continuous Wave -- 2.6.4 Short Pulse -- 2.6.5 Chirp (LFM) Pulse -- 2.7 Pulsed Radar -- 2.7.1 Pulse Repetition Frequency -- 2.7.2 Maximum Range and Range Ambiguity -- 2.7.3 Doppler Frequency -- 2.8 Matlab Codes -- References -- Chapter 3 Synthetic Aperture Radar -- 3.1 SAR Modes -- 3.2 SAR System Design…”

Tabla de Contenidos: “…2.6.1 Use of Texture and Context -- 2.6.2 Using Ancillary Multisource Data -- 2.7 Epilogue -- References -- Chapter 3 Dimensionality Reduction: Feature Extraction and Selection -- 3.1 Feature Extraction -- 3.1.1 Principal Component Analysis -- 3.1.2 Minimum/Maximum Autocorrelation Factors -- 3.1.3 Maximum Noise Fraction (MNF) Transformation -- 3.1.4 Independent Component Analysis -- 3.1.5 Projection Pursuit -- 3.2 Feature Selection -- 3.2.1 Greedy Search Methods -- 3.2.2 Simulated Annealing -- 3.2.3 Separability Indices -- 3.2.4 Filter-Based Methods -- 3.2.4.1 Correlation-Based Feature Selection -- 3.2.4.2 Information Gain -- 3.2.4.3 Gini Impurity Index -- 3.2.4.4 Minimum Redundancy-Maximum Relevance -- 3.2.4.5 Chi-Square Test -- 3.2.4.6 Relief-F -- 3.2.4.7 Symmetric Uncertainty -- 3.2.4.8 Fisher's Test -- 3.2.4.9 OneR -- 3.2.5 Wrappers -- 3.2.5.1 Genetic Algorithm -- 3.2.5.2 Particle Swarm Optimization -- 3.2.5.3 Feature Selection with SVMs -- 3.2.6 Embedded Methods -- 3.2.6.1 K-Nearest Neighbor-Based Feature Selection -- 3.2.6.2 Feature Selection with Ensemble Learners -- 3.2.6.3 Hilbert-Schmidt Independence Criterion with Lasso -- 3.3 Concluding Remarks -- References -- Chapter 4 Multisource Image Fusion and Classification -- 4.1 Image Fusion -- 4.1.1 Image Fusion Methods -- 4.1.1.1 PCA-Based Image Fusion -- 4.1.1.2 IHS-Based Image Fusion -- 4.1.1.3 Brovey Transform -- 4.1.1.4 Gram-Schmidt Transform -- 4.1.1.5 Wavelet Transform -- 4.1.1.6 Deep Learning for Image Fusion -- 4.1.2 Assessment of Fused Image Quality -- 4.1.3 Performance Evaluation of Fusion Methods -- 4.2 Multisource Classification Using the Stacked-Vector Method -- 4.3 The Extension of Bayesian Classification Theory -- 4.3.1 An Overview -- 4.3.1.1 Feature Extraction -- 4.3.1.2 Probability or Evidence Generation -- 4.3.1.3 Multisource Consensus…”

Tabla de Contenidos: “…-- 11.4 Models -- 11.5 Uses of Clustering -- 11.5.1 In Character of Most Tumor Cells -- 11.5.2 In Web Crawlers Like Google -- 11.5.3 Shopper Segmentation -- 11.5.4 In Biology -- 11.5.5 In Land Use -- 11.6 Bunching Algorithms or Clustering Algorithms -- 11.6.1 K-Means Clustering -- 11.6.2 Mean-Shift Clustering -- 11.6.3 Thickness or Density-Based Spatial Clustering of Application with Noise (DBSCAN) -- 11.6.4 Assumption Maximization Clustering Utilizing Gaussian Combination Models -- 11.6.5 Agglomerative Hierarchical Clustering -- 11.7 Instances of Clustering Algorithms -- 11.7.1 Library Setup -- 11.7.2 Grouping or Clustering Dataset -- 11.7.3 Fondness or Affinity Propagation -- 11.7.4 Agglomerative Clustering -- 11.7.5 BIRCH -- 11.7.6 DBSCAN -- 11.7.7 K-Means -- 11.7.8 Mini-Batch K-Means -- 11.7.9 Mean Shift -- 11.7.10 OPTICS -- 11.7.11 Unearthly or Spectral Clustering -- 11.7.12 Gaussian Mixture Model -- 11.8 Python Implementation of K-Means -- 11.8.1 Stacking the Data -- 11.8.2 Plotting the Information -- 11.8.3 Choosing the Component -- 11.8.4 Clustering -- 11.8.5 Clustering Results -- 11.8.6 WCSS and Elbow Technique -- 11.8.7 Uses of K-Mean Bunching -- 11.8.8 Benefits of K-Means -- 11.8.9 Bad Marks of K-MEAN -- References -- Chapter 12 Reinforcement Learning -- 12.1 Model -- 12.2 Terms Utilized in Reinforcement Learning -- 12.3 Key Elements of Reinforcement Learning -- 12.4 Instances of Reinforcement Learning -- 12.5 Advantages of Reinforcement Learning -- 12.6 Challenges with Reinforcement Learning -- 12.7 Sorts of Reinforcement -- 12.7.1 Positive -- 12.7.2 Negative…”

Tabla de Contenidos: “…Signal Propagation and Channel Model 95 -- 5.1 Introduction 95 -- 5.2 Wireless Channel Propagation 96 -- 5.2.1 Path Loss and Shadowing 96 -- 5.2.2 Multipath Fading 97 -- 5.2.3 Multipath Channel Parameters 98 -- 5.2.4 MIMO Channel 104 -- 5.3 Front-End Electronics Effects 105 -- 5.3.1 Carrier Frequency Offset 105 -- 5.3.2 Sampling Clock Offset 106 -- 5.3.3 Phase Noise 106 -- 5.3.4 IQ Imbalance and DC Offset 107 -- 5.3.5 Power Amplifier Nonlinearity 110 -- 5.4 Channel Model 111 -- 5.4.1 Model for Front-End Impairments 112 -- 5.4.2 Multipath Rayleigh Fader Model 113 -- 5.4.3 Channel Models Used in Standards 116 -- Summary 122 -- References 123 -- PART TWO: MIMO-OFDM RECEIVER PROCESSING -- 6. …”

Tabla de Contenidos: “…8.6 Conclusions -- Acknowledgments -- References -- 9 Computational lung sound classification: a review -- 9.1 Introduction -- 9.2 Data processing -- 9.2.1 Audio signal preprocessing -- 9.2.1.1 Signal splitting -- 9.2.1.2 Noise filtering -- 9.2.1.3 Resampling -- 9.2.1.4 Amplitude scaling -- 9.2.1.5 Segment splitting -- 9.2.1.6 Padding -- 9.2.2 Feature extraction -- 9.2.2.1 Features for conventional classifiers -- 9.2.2.2 Time-frequency representations for deep learning -- 9.2.3 Data augmentation -- 9.2.3.1 Time domain -- 9.2.3.2 Time-frequency domain -- 9.3 Data modeling -- 9.3.1 Machine learning -- 9.3.1.1 Conventional classifiers -- 9.3.1.2 Deep learning architectures -- 9.3.1.2.1 Convolutional neural networks -- 9.3.1.2.2 Recurrent networks -- 9.3.1.2.3 Hybrid systems -- 9.3.2 Learning paradigm -- 9.3.2.1 Transfer learning -- 9.3.2.2 Postprocessing -- 9.4 Recent public lung sound datasets -- 9.4.1 ICBHI 2017 dataset -- 9.4.2 The Abdullah University Hospital 2020 dataset -- 9.4.3 HF_Lung_V1 dataset -- 9.5 Conclusion -- References -- 10 Clinical applications of machine learning in heart failure -- 10.1 Introduction -- 10.2 Diagnosis -- 10.2.1 Automatic diagnosis, classification, and phenotyping of heart failure -- 10.2.2 Detection of heart failure-associated arrhythmia -- 10.3 Management -- 10.3.1 Prognostic prediction -- 10.3.2 Development of therapy -- 10.3.3 Optimal patient selection for specific therapies or recommendation of optimal therapy -- 10.4 Prevention -- 10.5 Conclusion -- References -- 11 Role of artificial intelligence and radiomics in diagnosing renal tumors: a survey -- 11.1 Introduction -- 11.2 Basic background -- 11.2.1 Deep learning -- 11.2.2 Machine learning -- 11.2.3 Radiomics -- 11.3 Steps of artificial intelligence-based diagnostic systems -- 11.3.1 Image acquisition -- 11.3.2 Image segmentation…”

Tabla de Contenidos: “…Design Problem 7.5: Instantaneous Operation Wideband RF-Spectrum Analyzer -- Design Problem 7.6: Efficient Free-Space to Single-Mode Fiber (SMF) Light Coupling -- Design Problem 7.7: Fiber-Optic Variable Optical Attenuator (VOA) Using Single AOD -- Design Problem 7.8: High-Dynamic-Range Fiber-Optic Variable Optical Attenuator (VOA) Using an AOD -- Design Problem 7.9: High-Dynamic-Range Fiber-Optic Variable Optical Attenuator (VOA) Using AODs -- Design Problem 7.10: Bulk AOTF-Based Multi-Wavelength Variable Optical Attenuator (MVOA) -- Design Problem 7.11: Analog RF Transversal Filter Design Using AOTF -- Problems -- Additional Reading -- 8 Photonic Systems Using Liquid Crystal and Liquid Devices -- Design Problem 8.1: Liquid Crystal Analog Fiber-Optic Variable Optical Attenuator (VOA) -- Design Problem 8.2: Liquid-Crystal-Based Polarization Dependent-Loss (PDL) Compensator -- Design Problem 8.3: Liquid Crystal Deflector Fiber-Optic VOA -- Design Problem 8.4: Wavelength-Tunable Fiber-Optic VOA Using Liquid Crystal Controls -- Design Problem 8.5: Multi-Wavelength Wavelength Fiber-Optic VOA Using Liquid Crystal Controls -- Design Problem 8.6: Liquid Crystal Freespace 1 × 2 Switch Using Cube PBS -- Design Problem 8.7: Higher-Performance LC Freespace 1 × 2 Switch Using Cube PBS -- Design Problem 8.8: Low-Noise LC 1-Bit Freespace TDU Using PBSs -- Design Problem 8.9: SMF Optical TDU Using LC Polarization Control -- Design Problem 8.10: 2 × 2 Fiber-Optic Switch Using LC Devices and Cube PBSs -- Design Problem 8.11: Fast Reset-Speed Variable Focal-Length Lens Using LCs -- Design Problem 8.12: Zero Propagation-Loss Variable-Link Distance Optical Wireless Communications -- Design Problem 8.13: Fast 3D Optical Sensing and Imaging -- Design Problem 8.14: Fast 1D Optical Sensing and Imaging Using Multiple Wavelengths…”

Tabla de Contenidos: “…-- TITLES -- DESCRIPTIONS -- TAGS -- CONTENT DISTRIBUTION -- LINKS -- LIKING: MICRO ACTS OF APPRECIATION YIELD MACRO IMPACTS -- NOTES -- Chapter 11: The New Media University 801 -- ESTABLISHING A SYNDICATION NETWORK -- SYNDICATING SOCIAL OBJECTS: AN ILLUSTRATION -- CHANNELING ILLUSTRATION: AN ACTIVITY STREAM -- AGGREGATION: ASSEMBLING THE PIECES -- EXAMPLE OF ACTIVITY STREAM -- IN-NETWORK AGGREGATION -- SYNDICATION: WEBCASTING SOCIAL OBJECTS -- AUTOPOSTS AND SYNDICATION -- DON'T CROSS THE STREAMS -- DESTINATION UNKNOWN: DEFINING THE JOURNEY THROUGH YOUR EXPERIENCE -- NOTES -- Chapter 12: The New Media University 901 -- ESTABLISHING AN ONLINE PRESENCE AND DEFINING THE BRAND PERSONA -- ONLINE PROFILES SPEAK VOLUMES ABOUT YOU AND YOUR BRAND -- MULTIPLE PERSONALITY DISORDER -- MULTIPLE PERSONALITY ORDER -- DISCOVERY AND ACTUALIZATION -- SHAPING THE BRAND PERSONA -- THE CENTER OF GRAVITY: CORE VALUES -- BRAND PILLARS -- BRAND CHARACTERISTICS -- PROMISE -- BRAND ASPIRATIONS -- OPPORTUNITIES -- CULTURE -- PERSONALITY -- NOTES -- Chapter 13: The New Media University 1001 -- FROM INTROVERSION TO EXTROVERSION -- THE NOW WEB -- THE RISE OF THE STATUSPHERE -- NEWS NO LONGER BREAKS, IT TWEETS -- THE ATTENTION RUBICON -- CHANNELING OUR FOCUS: THE ATTENTION DASHBOARD -- THE SOCIAL EFFECT: THE FUTURE OF BRANDING AND WORD OF MOUTH MARKETING -- NOTES -- Chapter 14: The New Media University 1101 -- IMPROVING THE SIGNAL-TO-NOISE RATIO -- Chapter 15: Fusing the Me in Social Media and the We in the Social Web -- CASTING A DIGITAL SHADOW: YOUR REPUTATION PRECEDES YOU -- DEFINING YOUR ONLINE PERSONA -- YOUR BRAND VERSUS THE BRANDS YOU REPRESENT…”

Tabla de Contenidos: “…Sostak / An Algorithm for Modelling the Impact of the Judicial Conflict-Resolution Process on Construction Investment, Reprinted from: Sustainability 2018, 10, 182, doi: 10.3390/su10010182 -- Agnieszka Le ́sniak and Krzysztof Zima / Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method, Reprinted from: Sustainability 2018, 10, 1608, doi: 10.3390/su10051608 -- Murat Gunduz and Ayman Fahmi Naser / Cost Based Value Stream Mapping as a Sustainable Construction Tool for Underground Pipeline Construction Projects, Reprinted from: Sustainability 2017, 9, 2184, doi: 10.3390/su9122184 -- Radoslaw Trojanek, Justyna Tanas, Saulius Raslanas and Audrius Banaitis / The Impact of Aircraft Noise on Housing Prices in Poznan, Reprinted from: Sustainability 2017, 9, 2088, doi: 10.3390/su9112088 -- Hassan Hashemi, Seyed Meysam Mousavi, Edmundas Kazimieras Zavadskas, Alireza Chalekaee and Zenonas Turskis / A New Group Decision Model Based on Grey-Intuitionistic Fuzzy-ELECTRE and VIKOR for Contractor Assessment Problem, Reprinted from: Sustainability 2018, 10, 1635, doi: 10.3390/su10051635 -- Patrick Dallasega and Erwin Rauch / Sustainable Construction Supply Chains through Synchronized Production Planning and Control in Engineer-to-Order Enterprises, Reprinted from: Sustainability 2017, 9, 1888, doi: 10.3390/su9101888 -- Audrius Vaitkus, Viktoras Vorobjovas, Faustina Tuminien?…”

Tabla de Contenidos: “…. -- Using fully procedural noise patterns -- Getting ready -- How to do it... -- How it works... -- See also -- Adding detail with Detail Texturing -- Getting ready -- How to do it... -- How it works... -- See also -- Chapter 8: Going Beyond Traditional Materials -- Technical requirements -- Playing a video on an in-game TV -- Getting ready -- How to do it... -- How it works... -- See also -- Capturing the scene through a CCTV camera -- Getting ready…”

Tabla de Contenidos: “…-- The Ideal Dataset -- Datasets and Domain Expertise -- Data, Ethics, and Responsible AI -- Minimizing Unknowns -- Ensuring Domain Expertise -- Data-Centric Machine Learning -- Estimating Data Requirements -- A Practical Workflow for Estimating Data Requirements -- Getting Your Hands on Data -- The Unique Challenges of Capturing Data at the Edge -- Storing and Retrieving Data -- Getting Data into Stores -- Collecting Metadata -- Ensuring Data Quality -- Ensuring Representative Datasets -- Reviewing Data by Sampling -- Label Noise -- Common Data Errors -- Drift and Shift -- The Uneven Distribution of Errors -- Preparing Data -- Labeling -- Formatting -- Data Cleaning -- Feature Engineering -- Splitting Your Data…”