Tabla de Contenidos: “…Improving exploration with Dirichlet noise -- 14.5. Modern techniques for deeper neural networks -- 14.5.1. …”

Tabla de Contenidos: “…Eine verhaltenstheoretische Begründung: Der Noise Trader- Ansatz. -- 4.5. Ursachenanalyse der amerikanischen Vermögenspreisblase ... 4.5.1. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Foreword -- Preface -- Chapter 1 Graphene Chemical Derivatives Synthesis and Applications: State‐of‐the‐Art and Perspectives -- 1.1 Introduction -- 1.2 Graphene Oxide: Synthesis Methods and Chemistry Alteration -- 1.3 Graphene Oxide Reduction and Functionalization -- 1.4 Applications of CMGs -- 1.5 Concluding Remarks -- Acknowledgments -- References -- Chapter 2 2D/2D Graphene Oxide‐Layered Double Hydroxide Nanocomposite for the Immobilization of Different Radionuclides -- 2.1 Introduction -- 2.2 Synthesis of GO/LDH Composite -- 2.2.1 Co‐precipitation -- 2.2.2 Hydrothermal Preparation -- 2.2.3 Self‐Assembly of LDH Nanosheets with GO Nanosheets -- 2.3 Removal of Radionuclides -- 2.3.1 U(VI) Removal -- 2.3.2 Sorption of Eu(III) with the Presence of GO on LDH -- 2.3.3 Co‐remediation Anionic SeO42− and Cationic Sr2+ -- 2.4 Conclusion -- References -- Chapter 3 2D Nanomaterials for Biomedical Applications -- 3.1 Introduction -- 3.1.1 Photothermal and Photodynamic Therapy -- 3.1.2 Bioimaging and Drug/Gene Delivery -- 3.1.3 Biosensors -- 3.1.4 Antibacterial Activity -- 3.1.5 Tissue Engineering and Regenerative Medicine -- 3.2 Conclusions -- References -- Chapter 4 Novel Two‐Dimensional Nanomaterials for Next‐Generation Photodetectors -- 4.1 Introduction -- 4.2 2D Materials for PDs -- 4.2.1 Graphene -- 4.2.2 TMDs (Transition Metal Dichalcogenides) -- 4.2.3 MXenes (2D Transition Metal Carbides/Nitrides) -- 4.2.4 Xenes (Monoelemental 2D Materials) -- 4.3 The Physical Mechanism Enabling Photodetection -- 4.4 Characterization Parameters for Photodetectors -- 4.4.1 Responsivity -- 4.4.2 Detectivity -- 4.4.3 External Quantum Efficiency -- 4.4.4 Gain -- 4.4.5 Response Time -- 4.4.6 Noise Equivalent Power -- 4.5 Synthesis Methods for 2D Materials -- 4.5.1 Mechanical Exfoliation -- 4.5.2 Liquid Exfoliation…”

Tabla de Contenidos: “…6.3.3.2 Short Period -- 6.3.3.3 Third Oscillatory Mode -- 6.3.3.4 Roll Subsidence -- 6.3.3.5 Dutch Roll -- 6.3.3.6 Spiral -- 6.4 Aircraft Lift and Drag Estimation -- 6.4.1 Fuselage Lift and Moment Coefficients -- 6.4.2 Wing-Tail Interference Effects -- 6.4.3 Estimating the Wing's Maximum Lift Coefficient -- 6.4.4 Drag Estimation -- 6.5 Estimating the Longitudinal Aerodynamic Derivatives -- 6.6 Estimating the Lateral Aerodynamic Derivatives -- 6.7 Perturbation Analysis of Trimmed Flight -- 6.7.1 Perturbation Analysis of Longitudinal Trimmed Flight -- 6.7.2 Perturbation Analysis of Lateral Trimmed Flight -- 6.7.2.1 Control Settings for Steady Sideslip -- 6.7.2.2 Control Settings for Turn Coordination and Banking -- 6.7.3 Perturbations of Coupled Trimmed Flight -- 6.7.4 Simplified Analysis of Complex Manoeuvres: The Sidestep Manoeuvre -- Chapter Highlights -- Exercises -- Answers to Selected Exercises -- Note -- References -- Chapter 7 Aircraft Dynamic Response: Numerical Simulation and Non-Linear Phenomenon -- 7.1 Introduction -- 7.2 Longitudinal and Lateral Modal Equations -- 7.3 Methods of Computing Aircraft Dynamic Response -- 7.3.1 Laplace Transform Method -- 7.3.2 Aircraft Response Transfer Functions -- 7.3.3 Direct Numerical Integration -- 7.4 System Block Diagram Representation -- 7.4.1 Numerical Simulation of Flight Using MATLAB[sup(®)]/Simulink[sup(®)] -- 7.5 Atmospheric Disturbance: Deterministic Disturbances -- 7.6 Principles of Random Atmospheric Disturbance Modelling -- 7.6.1 White Noise: Power Spectrum and Autocorrelation -- 7.6.2 Linear Time-Invariant System with Stochastic Process Input -- 7.7 Application to Atmospheric Turbulence Modelling -- 7.8 Aircraft Non-Linear Dynamic Response Phenomenon -- 7.8.1 Aircraft Dynamic Non-Linearities and Their Analysis -- 7.8.2 High-Angle-of-Attack Dynamics and Its Consequences…”

Tabla de Contenidos: “…3.2.4 Binary Logistic Regression with Keras on TensorFlow -- 3.3 Support Vector Machine -- 3.3.1 Linearly Separable Data -- 3.3.2 Not Fully Linearly Separable Data -- 3.3.3 Nonlinear SVMs -- 3.3.4 SVMs for Regression -- 3.3.5 Application of SVMs -- 3.3.5.1 SVM Using scikit-learn for Classification -- 3.3.5.2 SVM Using scikit-learn for Regression -- 3.4 Artificial Neural Networks -- 3.4.1 Multilayer Perceptron -- 3.4.2 Estimation of the Parameters -- 3.4.2.1 Loss Functions -- 3.4.2.2 Backpropagation: Binary Classification -- 3.4.2.3 Backpropagation: Multi-class Classification -- 3.4.3 Convolutional Neural Networks -- 3.4.4 Recurrent Neural Network -- 3.4.5 Application of MLP Neural Networks -- 3.4.6 Application of RNNs: LST Memory -- 3.4.7 Building a CNN -- 3.5 Many More Algorithms to Explore -- 3.6 Unsupervised Machine Learning Algorithms -- 3.6.1 Clustering -- 3.6.1.1 K-means -- 3.6.1.2 Mini-batch K-means -- 3.6.1.3 Mean Shift -- 3.6.1.4 Affinity Propagation -- 3.6.1.5 Density-based Spatial Clustering of Applications with Noise -- 3.7 Machine Learning Algorithms with HephAIstos -- References -- Further Reading -- Chapter 4 Natural Language Processing -- 4.1 Classifying Messages as Spam or Ham -- 4.2 Sentiment Analysis -- 4.3 Bidirectional Encoder Representations from Transformers -- 4.4 BERT's Functionality -- 4.5 Installing and Training BERT for Binary Text Classification Using TensorFlow -- 4.6 Utilizing BERT for Text Summarization -- 4.7 Utilizing BERT for Question Answering -- Further Reading -- Chapter 5 Machine Learning Algorithms in Quantum Computing -- 5.1 Quantum Machine Learning -- 5.2 Quantum Kernel Machine Learning -- 5.3 Quantum Kernel Training -- 5.4 Pegasos QSVC: Binary Classification -- 5.5 Quantum Neural Networks -- 5.5.1 Binary Classification with EstimatorQNN -- 5.5.2 Classification with a SamplerQNN…”

Tabla de Contenidos: “…3.2 Deep learning approaches for digital signal processing -- 3.3 Optical IM/DD systems based on deep learning -- 3.3.1 ANN receiver -- 3.3.1.1 PAM transmission -- 3.3.1.2 Sliding window FFNN processing -- 3.3.2 Auto-encoders -- 3.3.2.1 Auto-encoder design based on a feed-forward neural network -- 3.3.2.2 Auto-encoder design based on a recurrent neural network -- 3.3.3 Performance -- 3.3.4 Distance-agnostic transceiver -- 3.4 Implementation on a transmission link -- 3.4.1 Conventional PAM transmission with ANN-based receiver -- 3.4.2 Auto-encoder implementation -- 3.5 Outlook -- References -- 4 Machine learning techniques for passive optical networks -- 4.1 Background -- 4.2 The validation of NN effectiveness -- 4.3 NN for nonlinear equalization -- 4.4 End to end deep learning for optimal equalization -- 4.5 FPGA implementation of NN equalizer -- 4.6 Conclusions and perspectives -- References -- 5 End-to-end learning for fiber-optic communication systems -- 5.1 Introduction -- 5.2 End-to-end learning -- 5.3 End-to-end learning for fiber-optic communication systems -- 5.3.1 Direct detection -- 5.3.2 Coherent systems -- 5.3.2.1 Nonlinear phase noise channel -- 5.3.2.2 Perturbation models (NLIN and GN) -- 5.3.2.3 Split-step Fourier method (SSFM) -- 5.4 Gradient-free end-to-end learning -- 5.5 Conclusion -- Acknowledgments -- References -- 6 Deep learning techniques for optical monitoring -- 6.1 Introduction -- 6.2 Building blocks of deep learning-based optical monitors -- 6.2.1 Digital coherent reception as a data-acquisition method -- 6.2.2 Deep learning and representation learning -- 6.2.3 Combination of digital coherent reception and deep learning -- 6.3 Deep learning-based optical monitors -- 6.3.1 Training mode of DL-based optical monitors -- 6.3.2 Advanced topics for the training mode of DL-based optical monitors…”

“…Intrigued by ""texted"" sonorities-the rhythms, musics, ordinary noises, and sounds of language in narratives-Julie Huntington examines the soundscapes in contemporary Francophone novels such as Ousmane Sembene's God's Bits of Wood (Senegal), and Patrick Chamoiseau's Solibo Magnificent (Martinique). …”

“…Practical coverage includes Up-to-date microwave simulation design examples based on ADS and easily adaptable to any simulator Detailed, step-by-step derivations of key design parameters related to procedures, devices, and performance Relevant, hands-on problem sets in every chapter Clear discussions of microwave IC categorization and roles; passive device impedances and equivalent circuits; coaxial and microstrip transmission lines; active devices (FET, BJT, DC Bias); and impedance matching A complete, step-by-step introduction to circuit simulation using the ADS toolset and window framework Low noise amplifier (LNA) design: gains, stability, conjugate matching, and noise circles Power amplifier (PA) design: optimum load impedances, classification, linearity, and composite PAs Microwave oscillator design: oscillation conditions, phase noise, basic circuits, and dielectric resonators Phase lock loops (PLL) design: configuration, operation, components, and loop filters Mixer design: specifications, Schottky diodes, qualitative analysis of mixers (SEM, SBM, DBM), and quantitative analysis of single-ended mixer (SEM) Microwave Circuit Design brings together all the practical skills graduate students and professionals need to successfully design today’s active microwave circuits. …”

Tabla de Contenidos: “…-- 3.4 Managing Biases -- 3.5 Summary -- Chapter 4: Rule #2: Know the Domain -- 4.1 Cautionary Tale #1: ``Discovering'' Random Noise -- 4.2 Cautionary Tale #2: Jumping at Shadows -- 4.3 Cautionary Tale #3: It Pays to Ask -- 4.4 Summary -- Chapter 5: Rule #3: Suspect Your Data -- 5.1 Controlling Data Collection -- 5.2 Problems with Controlled Data Collection -- 5.3 Rinse (and Prune) Before Use -- 5.3.1 Row Pruning -- 5.3.2 Column Pruning -- 5.4 On the Value of Pruning -- 5.5 Summary -- Chapter 6: Rule #4: Data Science Is Cyclic -- 6.1 The Knowledge Discovery Cycle -- 6.2 Evolving Cyclic Development -- 6.2.1 Scouting -- 6.2.2 Surveying -- 6.2.3 Building -- 6.2.4 Effort -- 6.3 Summary -- Part II: Data Mining: A Technical Tutorial -- Chapter 7: Data Mining and SE -- 7.1 Some Definitions -- 7.2 Some Application Areas…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Editors -- List of Contributors -- Chapter 1 A Review Approach On Deep Learning Algorithms in Computer Vision -- 1.1 Introduction -- 1.2 Deep Learning Algorithms -- 1.2.1 Convolutional Neural Networks -- 1.2.2 Restricted Boltzmann Machines -- 1.2.3 Deep Boltzmann Machines -- 1.2.4 Deep Belief Networks -- 1.2.5 Stacked (de-Noising) Auto-Encoders -- 1.2.5.1 Auto-Encoders -- 1.2.5.2 Denoising Auto Encoders -- 1.3 Comparison of the Deep Learning Algorithms -- 1.4 Challenges in Deep Learning Algorithms -- 1.5 Conclusion and Future Scope -- References -- Chapter 2 Object Extraction From Real Time Color Images Using Edge Based Approach -- 2.1 Introduction -- 2.2 Applications of Object Extraction -- 2.3 Edge Detection Techniques -- 2.3.1 Roberts Edge Detection -- 2.3.2 Sobel Edge Detection -- 2.3.3 Prewitt's Operator -- 2.3.4 Laplacian Edge Detection -- 2.4 Related Work -- 2.5 Proposed Model -- 2.6 Results and Discussion -- 2.7 Conclusion -- References -- Chapter 3 Deep Learning Techniques for Image Captioning -- 3.1 Introduction to Image Captioning -- 3.1.1 How Does Image Recognition Work? …”

Tabla de Contenidos: “…8.3 Simplified Scripts for Frequently Used Analyses -- 8.4 Example: Difference of Biases -- 8.5 Sampling from the Prior Distribution in JAGS -- 8.6 Probability Distributions Available in JAGS -- 8.6.1 Defining new likelihood functions -- 8.7 Faster Sampling with Parallel Processing in RunJAGS -- 8.8 Tips for Expanding JAGS Models -- 8.9 Exercises -- Chapter 9: Hierarchical Models -- 9.1 A Single Coin from a Single Mint -- 9.1.1 Posterior via grid approximation -- 9.2 Multiple Coins from a Single Mint -- 9.2.1 Posterior via grid approximation -- 9.2.2 A realistic model with MCMC -- 9.2.3 Doing it with JAGS -- 9.2.4 Example: Therapeutic touch -- 9.3 Shrinkage in Hierarchical Models -- 9.4 Speeding up JAGS -- 9.5 Extending the Hierarchy: Subjects Within Categories -- 9.5.1 Example: Baseball batting abilities by position -- 9.6 Exercises -- Chapter 10: Model Comparison and Hierarchical Modeling -- 10.1 General Formula and the Bayes Factor -- 10.2 Example: Two Factories of Coins -- 10.2.1 Solution by formal analysis -- 10.2.2 Solution by grid approximation -- 10.3 Solution by MCMC -- 10.3.1 Nonhierarchical MCMC computation of each model'smarginal likelihood -- 10.3.1.1 Implementation with JAGS -- 10.3.2 Hierarchical MCMC computation of relative model probability -- 10.3.2.1 Using pseudo-priors to reduce autocorrelation -- 10.3.3 Models with different "noise" distributions in JAGS -- 10.4 Prediction: Model Averaging -- 10.5 Model Complexity Naturally Accounted for -- 10.5.1 Caveats regarding nested model comparison -- 10.6 Extreme Sensitivity to Prior Distribution -- 10.6.1 Priors of different models should be equally informed -- 10.7 Exercises -- Chapter 11: Null Hypothesis Significance Testing -- 11.1 Paved with Good Intentions -- 11.1.1 Definition of p value -- 11.1.2 With intention to fix N -- 11.1.3 With intention to fix z.…”

“…They help you quickly make sense of the noises that filter in from outside, but they can also limit your ability to see the true picture. …”

Tabla de Contenidos: “…Alton and Alan Manning -- 6.1 Introduction 104 -- 6.2 Previous Studies of Decorative and Indicative Effects 106 -- 6.3 Experiments and Results 111 -- 6.3.1 Study One: Restaurant Menu Design 112 -- 6.3.2 Study Two: Graph Design and Recall Accuracy 114 -- 6.3.3 Study Three: Diagram Design and Recall Accuracy 116 -- 6.4 Practical Implications for Engineers and Technical Communicators 117 -- 6.5 Conclusion 119 -- References 121 -- PRACTICAL INSIGHTS FROM APPLIED GLOBAL TECHNOLOGIES 123 -- 7 COST OF INFORMATION OVERLOAD IN END-USER DOCUMENTATION 125 -- Prasanna Bidkar -- 7.1 Introduction 126 -- 7.2 Information Overload 126 -- 7.3 Causes of Information Overload 128 -- 7.4 Sources of Noise in User Documentation 129 -- 7.4.1 Information Content 129 -- 7.4.2 Channel 130 -- 7.4.3 Receiver 131 -- 7.5 Effects of Information Overload on Users 132 -- 7.6 The Current Study 133 -- 7.6.1 The Survey 133 -- 7.6.2 Results and Observations 133 -- 7.7 Cost of Information Overload 135 -- 7.7.1 Cost Framework 135 -- 7.7.2 Scenario 1: Ideal Scenario 136 -- 7.7.3 Scenario 2 136.…”

“…Many difficulties in image generation, reconstruction, de-noising skills, artifact removal, segmentation, detection, and control tasks are being overcome with the help of advanced artificial intelligence approaches. …”

Tabla de Contenidos: “…Syed and Ghazanfar Ali Safdar -- 6.1 Fundamentals of Signal Processing 151 -- 6.1.1 Channel Model 151 -- 6.1.1.1 Additive Gaussian Noise Channel 151 -- 6.1.1.2 Linear Filter Channel 152 -- 6.1.1.3 Band Limited Channel 153 -- 6.1.2 Modulation Technique 153 -- 6.1.3 Error Probability 154 -- 6.2 Spectrum Sensing Techniques 155 -- 6.2.1 Primary Transmitter Detection 155 -- 6.2.1.1 Energy Detector 156 -- 6.2.1.2 Matched Filter Detection 158 -- 6.2.1.3 Cyclostationary Feature Detection 159 -- 6.2.1.4 Waveform Detection 160 -- 6.2.1.5 Wavelet Detection 161 -- 6.2.1.6 Hybrid Sensing 162 -- 6.2.1.7 Multi?]…”

“…It describes howsignal-to-noise ratio measures the positive quality contribution from controllable or design factors versus the negative quality contribution from uncontrollable or noise factors. …”

“…The book contains detailed information on fusion inertial measurements for orientation stabilization and its validation in flight tests, also proposing substantial theoretical and practical validation for improving the dropped or noised signals. In addition, the book contains different strategies to control and navigate aerial systems. …”

“…Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. …”

“…The purpose of this workshop is to bring together researchers from academia, industry, and government to create a forum for discussing recent advances in large-scale graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards addressing domain specific challenges and handling noise in real-world graphs…”