Tabla de Contenidos: “…Systematic Search Plans Defined by Dynamical Systems and Analysis of Linear Motions on Tori and Billiard Strategies in Rectangular Domains -- 4. …”

Tabla de Contenidos: “…Velocity Planning via Model-Based Reinforcement Learning: Demonstrating Results on PILCO for One-Dimensional Linear Motion with Bounded Acceleration -- By Hsuan-Cheng Liao, Han-Jung Chou and Jing-Sin Liu 27 -- 6. …”

Tabla de Contenidos: “…Acoustical Metal Pan Ceilings. Linear Metal Ceilings. Security Ceiling Systems. …”

Tabla de Contenidos: “…. -- Chemoprevention by isothiocyanates : molecular basis of apoptosis induction / Nakamura, Y. -- Ginger-derived phenolic substances with cancer preventive and therapeutic potential / Kundu, J.K., Na, H.…”

Tabla de Contenidos: “…DATOS OBTENIDOS PARA GENERAR EL MODELO DEL VIDEO (...) -- TABLA 10. ANÁLISIS DE VARIANZA PARA LOS DATOS OBTENIDOS DEL VIDEO (...) -- TABLA 11. …”

Tabla de Contenidos: “…Front Cover -- Handbook of Computational Economics -- Copyright -- Contents -- Contributors -- Introduction to the Series -- Introduction to the Handbook of Computational Economics, Volume 4, Heterogeneous Agent Modeling -- 1 Introduction -- 2 Macroeconomics -- 3 Finance -- 4 Experiments -- 5 Networks -- 6 Other -- Acknowledgments -- References -- Part 1 Macroeconomics -- 1 Heterogeneous Expectations and Micro-Foundations in Macroeconomics -- 1 Introduction -- 2 Expectations Operators and Bounded Rationality -- 2.1 Expectations Operators -- 2.2 The Economic Environment -- 2.3 Bounded Optimality -- 2.3.1 Rational Expectations -- 2.3.2 The Shadow-Price Approach -- 2.3.3 The Shadow-Price Approach in the Linearized Model -- 2.3.4 The Euler Equation Approach in the Linearized Model -- 2.3.5 The Finite Horizon Approach in the Linearized Model -- 2.3.6 The In nite Horizon Approach in the Linearized Model -- 2.3.7 A Nod to Value Functions -- 2.3.8 A Defense of Anticipated Utility -- 2.4 Aggregating Household Decision Rules -- 3 Equilibria with Heterogeneous Expectations -- 3.1 Extrinsic Heterogeneity -- 3.2 Rationally Heterogeneous Expectations -- 3.2.1 Predictor Selection -- 3.2.2 Heterogeneous Beliefs and Economic Dynamics: Stability Reversal -- 3.3 Intrinsic Heterogeneity -- 4 Asset-Pricing Applications -- 4.1 Regime-Switching Returns -- 4.2 Bubbles with Rationally Heterogeneous Expectations -- 4.3 Restricted Perceptions and Endogenous Fluctuations -- 4.4 Related Literature -- 5 Monetary Applications -- 5.1 A Monetary Search Model with Heterogeneous Expectations -- 5.2 Heterogeneous Beliefs and Bargaining -- 5.3 Equilibrium with Heterogeneous Beliefs -- 5.4 Uncertainty and Welfare -- 5.5 Related Literature -- 6 DSGE Applications -- 6.1 Rationally Heterogeneous Expectations and Monetary Policy Rules…”

Tabla de Contenidos: “…Preface xiii Table of Engineering Applications xvii Part 1 Introduction 1 Mathematica Environment and Basic Syntax 3 1.1 Introduction 3 1.2 Selecting Notebook Characteristics 4 1.3 Notebook Cells 8 1.4 Delimiters 12 1.5 Basic Syntax 12 1.5.1 Introduction 12 1.5.2 Templates: Greek Symbols and Mathematical Notation 15 1.5.3 Variable Names and Global Variables 18 1.6 Mathematical Constants 19 1.7 Complex Numbers 21 1.8 Elementary, Trigonometric, Hyperbolic, and a Few Special Functions 22 1.9 Strings 25 1.9.1 String Creation: StringJoin[] and ToString[] 25 1.9.2 Labeled Output: Print[], NumberForm[], EngineeringForm[], and TraditionalForm[] 26 1.10 Conversions, Relational Operators, and Transformation Rule 28 1.11 Engineering Units and Unit Conversions: Quantity[] and UnitConvert[] 30 1.12 Creation of CDF Documents and Documents in Other Formats 33 1.13 Functions Introduced in Chapter 1 34 Exercises 35 2 List Creation and Manipulation: Vectors and Matrices 39 2.1 Introduction 39 2.2 Creating Lists and Vectors 39 2.2.1 Introduction 39 2.2.2 Creating a List with Table[] 45 2.2.3 Summing Elements of a List: Total[] 46 2.2.4 Selecting Elements of a List 47 2.2.5 Identifying List Elements Matching a Pattern: Position[] 49 2.3 Creating Matrices 51 2.3.1 Introduction 51 2.3.2 Matrix Generation Using Table[] 54 2.3.3 Accessing Elements of Arrays 55 2.4 Matrix Operations on Vectors and Arrays 56 2.4.1 Introduction 56 2.4.2 Matrix Inverse and Determinant: Inverse[] and Det[] 57 2.5 Solution of a Linear System of Equations: LinearSolve[] 58 2.6 Eigenvalues and Eigenvectors: EigenSystem[] 59 2.7 Functions Introduced in Chapter 2 61 References 61 Exercises 61 3 User-Created Functions, Repetitive Operations, and Conditionals 69 3.1 Introduction 69 3.2 Expressions and Procedures as Functions 69 3.2.1 Introduction 69 3.2.2 Pure Function: Function[] 74 3.2.3 Module[] 78 3.3 Find Elements of a List that Meet a Criterion: Select[] 80 3.4 Conditionals 82 3.4.1 If[] 82 3.4.2 Which[] 83 3.5 Repetitive Operations 83 3.5.1 Do[] 83 3.5.2 While[] 83 3.5.3 Nest[] 84 3.5.4 Map[] 84 3.6 Examples of Repetitive Operations and Conditionals 85 3.7 Functions Introduced in Chapter 3 92 Exercises 92 4 Symbolic Operations 95 4.1 Introduction 95 4.2 Assumption Options 101 4.3 Solutions of Equations: Solve[] 101 4.4 Limits: Limit[] 105 4.5 Power Series: Series[], Coefficient[], and CoefficientList[] 108 4.6 Optimization: Maximize[]/Minimize[] 112 4.7 Differentiation: D[] 114 4.8 Integration: Integrate[] 120 4.9 Solutions of Ordinary Differential Equations: DSolve[] 126 4.10 Solutions of Partial Differential Equations: DSolve[] 136 4.11 Laplace Transform: LaplaceTransform[] and InverseLaplaceTransform[] 138 4.12 Functions Introduced in Chapter 4 145 References 145 Exercises 146 5 Numerical Evaluations of Equations 151 5.1 Introduction 151 5.2 Numerical Integration: NIntegrate[] 151 5.3 Numerical Solutions of Differential Equations: NDSolveValue[] and ParametricNDSolveValue[] 154 5.4 Numerical Solutions of Equations: NSolve[] 178 5.5 Roots of Transcendental Equations: FindRoot[] 180 5.6 Minimum and Maximum: FindMinimum[] and FindMaximum[] 182 5.7 Fitting of Data: Interpolation[] and FindFit[] 186 5.8 Discrete Fourier Transforms and Correlation: Fourier[], InverseFourier[], and ListCorrelate[] 189 5.9 Functions Introduced in Chapter 5 194 References 195 Exercises 196 6 Graphics 209 6.1 Introduction 209 6.2 2D Graphics 209 6.2.1 Basic Plotting 209 6.2.2 Basic Graph Enhancements 213 6.2.3 Common 2D Shapes: Graphics[] 217 6.2.4 Additional Graph Enhancements 222 6.2.5 Combining Figures: Show[] and GraphicsGrid[] 238 6.2.6 Tooltip[] 241 6.2.7 Exporting Graphics 244 6.3 3D Graphics 244 6.4 Summary of Functions Introduced in Chapter 6 253 References 254 Exercises 254 7 Interactive Graphics 263 7.1 Interactive Graphics: Manipulate[] 263 References 287 Exercises 287 Part 2 Engineering Applications 8 Vibrations of Spring Mass Systems and Thin Beams 293 8.1 Introduction 293 8.2 Single Degree-of-Freedom Systems 294 8.2.1 Periodic Force on a Single Degree-of-Freedom System 294 8.2.2 Squeeze Film Damping and Viscous Fluid Damping 298 8.2.3 Electrostatic Attraction 302 8.2.4 Single Degree-of-Freedom System Energy Harvester 304 8.3 Two Degrees-of-Freedom Systems 307 8.3.1 Governing Equations 307 8.3.2 Response to Harmonic Excitation: Amplitude Response Functions 307 8.3.3 Enhanced Energy Harvester 310 8.4 Thin Beams 315 8.4.1 Natural Frequencies and Mode Shapes of a Cantilever Beam with In-Span Attachments 315 8.4.2 Effects of Electrostatic Force on the Natural Frequency and Stability of a Beam 318 8.4.3 Response of a Cantilever Beam with an In-Span Attachment to an Impulse Force 323 References 326 9 Statistics 327 9.1 Descriptive Statistics 327 9.1.1 Introduction 327 9.1.2 Location Statistics: Mean[], StandardDeviation[], and Quartile[] 327 9.1.3 Continuous Distribution Functions: PDF[] and CDF[] 329 9.1.4 Histograms and Probability Plots: Histogram[] and ProbabilityScalePlot [] 331 9.1.5 Whisker Plot: BoxWhiskerChart[] 332 9.1.6 Creating Data with Specified Distributions: RandomVariate[] 334 9.2 Probability of Continuous Random Variables 334 9.2.1 Probability for Different Distributions: NProbability[] 334 9.2.2 Inverse Cumulative Distribution Function: InverseCDF[] 337 9.2.3 Distribution Parameter Estimation: EstimatedDistribution[] and FindDistributionParameters[] 337 9.2.4 Confidence Intervals: CI[] 340 9.2.5 Hypothesis Testing: LocationTest[] and VarianceTest[] 342 9.3 Regression Analysis: LinearModelFit[] 343 9.3.1 Simple Linear Regression 343 9.3.2 Multiple Linear Regression 347 9.4 Nonlinear Regression Analysis: NonLinearModelFit[] 351 9.5 Analysis of Variance (ANOVA) and Factorial Designs: ANOVA[] 354 9.6 Functions Introduced in Chapter 9 358 10 Control Systems and Signal Processing 359 10.1 Introduction 359 10.2 Model Generation: State-Space and Transfer Function Representation 359 10.2.1 Introduction 359 10.2.2 State-Space Models: StateSpaceModel[] 360 10.2.3 Transfer Function Models: TransferFunctionModel[] 362 10.3 Model Connections Closed-Loop Systems and System Response: SystemsModelFeedbackConnect[] and SystemsModelSeriesConnect[] 363 10.4 Design Methods 369 10.4.1 Root Locus: RootLocusPlot[] 369 10.4.2 Bode Plot: BodePlot[] 371 10.4.3 Nichols Plot: NicholsPlot[] 372 10.5 Signal Processing 374 10.5.1 Filter Models: ButterworthFilterModel[], EllipticFilterModel[], ... 374 10.5.2 Windows: HammingWindow[], HannWindow[], ... 381 10.5.3 Spectrum Averaging 385 10.6 Aliasing 388 10.7 Functions Introduced in Chapter 10 390 Reference 391 11 Heat Transfer and Fluid Mechanics 393 11.1 Introduction 393 11.2 Conduction Heat Transfer 394 11.2.1 One-Dimensional Transient Heat Diffusion in Solids 394 11.2.2 Heat Transfer in Concentric Spheres: Ablation of a Tumor 398 11.2.3 Heat Flow Through Fins 401 11.3 Natural Convection Along Heated Plates 405 11.4 View Factor Between Two Parallel Rectangular Surfaces 408 11.5 Internal Viscous Flow 411 11.5.1 Laminar Flow in Horizontal Cylindrical Pipes 411 11.5.2 Flow in Three Reservoirs 412 11.6 External Flow 416 11.6.1 Pressure Coefficient of a Joukowski Airfoil 416 11.6.2 Surface Profile in Nonuniform Flow in Open Channels 419 References 423 Index 425.…”

Tabla de Contenidos: “…Parameter estimation best practices; Avoidance of linear dependence; Minimization of parameter errors; Scaled sensitivity coefficients; 2.3.2. …”

Tabla de Contenidos: “…Intro -- Table of Contents -- Title Page -- Copyright -- Chapter 1: Amplification in Linear Mode -- 1.1. Principles of microwave amplification -- 1.2. …”

Tabla de Contenidos: “…Implementing a lives counterCreating a modular coin counter; Creating a symbolic lives counter; Implementing a linear health bar; Implementing a radial health bar; Creating a health bar with armor; Using multiple bars to make a multibar; Developing a kingdom hearts health bar style; Chapter 3: Implementing Timers; Introduction; Implementing a numeric timer; Creating a linear timer; Implementing a radial timer; Creating a mixed timer; Creating a well-formatted timer; Developing a well-formatted countdown that changes; Chapter 4: Creating Panels for Menus; Introduction…”

Tabla de Contenidos: “…References -- Summary -- Chapter 5: Bayesian Regression Models -- Generalized linear regression -- The arm package -- The Energy efficiency dataset -- Regression of energy efficiency with building parameters -- Ordinary regression -- Bayesian regression -- Simulation of the posterior distribution -- Exercises -- References -- Summary -- Chapter 6: Bayesian Classification Models -- Performance metrics for classification -- The Naïve Bayes classifier -- Text processing using the tm package -- Model training and prediction -- The Bayesian logistic regression model -- The BayesLogit R package -- The dataset -- Preparation of the training and testing datasets -- Using the Bayesian logistic model -- Exercises -- References -- Summary -- Chapter 7: Bayesian Models for Unsupervised Learning -- Bayesian mixture models -- The bgmm package for Bayesian mixture models -- Topic modeling using Bayesian inference -- Latent Dirichlet allocation -- R packages for LDA -- The topicmodels package -- The lda package -- Exercises -- References -- Summary -- Chapter 8: Bayesian Neural Networks -- Two-layer neural networks -- Bayesian treatment of neural networks -- The brnn R package -- Deep belief networks and deep learning -- Restricted Boltzmann machines -- Deep belief networks -- The darch R package -- Other deep learning packages in R -- Exercises -- References -- Summary -- Chapter 9: Bayesian Modeling at Big Data Scale -- Distributed computing using Hadoop -- RHadoop for using Hadoop from R -- Spark - in-memory distributed computing -- SparkR -- Linear regression using SparkR -- Computing clusters on the cloud -- Amazon Web Services -- Creating and running computing instances on AWS -- Installing R and RStudio -- Running Spark on EC2 -- Microsoft Azure -- IBM Bluemix -- Other R packages for large scale machine learning -- The parallel R package…”

Tabla de Contenidos: “…Preface -- Contributors -- Chapter 1 Complex-Valued Adaptive Signal Processing -- 1.1 Introduction -- -- 1.2 Preliminaries -- 1.3 Optimization in the Complex Domain -- 1.4 Widely Linear Adaptive Filtering -- 1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons -- 1.6 Complex Independent Component Analysis -- 1.7 Summary -- 1.8 Acknowledgment -- 1.9 Problems -- References -- Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors -- 2.1 Introduction -- 2.2 Statistical Characterization of Complex Random Vectors -- 2.3 Complex Elliptically Symmetric (CES) Distributions -- 2.4 Tools to Compare Estimators -- 2.5 Scatter and Pseudo-Scatter Matrices -- 2.6 Array Processing Examples -- 2.7 MVDR Beamformers Based on M-Estimators -- 2.8 Robust ICA -- 2.9 Conclusion -- 2.10 Problems -- References -- Chapter 3 Turbo Equalization -- 3.1 Introduction -- 3.2 Context -- 3.3 Communication Chain -- 3.4 Turbo Decoder: Overview -- 3.5 Forward-Backward Algorithm -- 3.6 Simplified Algorithm: Interference Canceler -- 3.7 Capacity Analysis -- 3.8 Blind Turbo Equalization -- 3.9 Convergence -- 3.10 Multichannel and Multiuser Settings -- 3.11 Concluding Remarks -- 3.12 Problems -- References -- Chapter 4 Subspace Tracking for Signal Processing -- 4.1 Introduction -- 4.2 Linear Algebra Review -- 4.3 Observation Model and Problem Statement -- 4.4 Preliminary Example: Oja's Neuron -- 4.5 Subspace Tracking -- 4.6 Eigenvectors Tracking -- 4.7 Convergence and Performance Analysis Issues -- 4.8 Illustrative Examples -- 4.9 Concluding Remarks -- 4.10 Problems -- References -- Chapter 5 Particle Filtering -- 5.1 Introduction -- 5.2 Motivation for Use of Particle Filtering -- 5.3 The Basic Idea -- 5.4 The Choice of Proposal Distribution and Resampling -- 5.5 Some Particle Filtering Methods -- 5.6 Handling Constant Parameters -- 5.7 Rao-Blackwellization -- 5.8 Prediction -- 5.9 Smoothing -- 5.10 Convergence Issues -- 5.11 Computational Issues and Hardware Implementation -- 5.12 Acknowledgments…”

Tabla de Contenidos:

Tabla de Contenidos: “…Cover; Title Page; Copyright Page; Contents; Preface; A short history of audio technology; Part One: Principles of Digital Signal Processing; 1 Introduction; 2 Principles of sampling; The Nyquist theorem; Sampling frequency; Sample-hold circuits; Aperture control; Characteristics and terminology of sample-hold circuits; 3 Principles of quantization; Quantization error; Calculation of theoretical signal-to-noise ratio; Masking of quantization noise; Conversion codes; 4 Overview of A/D conversion systems; Linear (or uniform) quantization; Companding systems; Floating-point conversion…”

Tabla de Contenidos: “…Hash Search and Other Non-SearchesLookup Searches; Ransack Search; Linear Search; Binary Search in a List; Proportional Search; Binary Search in a Tree; Should You Use a List or a Tree for Binary Searching?…”

Tabla de Contenidos: “…Channel Coding and Decoding: Part 1-Block Coding and Decoding; 6.1 Simple Block Coding; 6.2 Linear block codes; 6.3 Performance of the Block Coders; 6.4 Benefits and Costs of Block Coders; 6.5 Conclusion; Chapter 7. …”

Tabla de Contenidos: “…Position of the problem; 4.2. Linear estimation; 4.3. Best estimate - conditional expectation; 4.4. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Concepts of Machine Learning -- Chapter 1: Introduction to Machine Learning with Qlik -- Introduction to Qlik tools -- Insight Advisor -- Qlik AutoML -- Advanced Analytics Integration -- Basic statistical concepts with Qlik solutions -- Types of data -- Mean, median, and mode -- Variance -- Standard deviation -- Standardization -- Correlation -- Probability -- Defining a proper sample size and population -- Defining a sample size -- Training and test data in machine learning -- Concepts to analyze model performance and reliability -- Regression model scoring -- Multiclass classification scoring and binary classification scoring -- Feature importance -- Summary -- Chapter 2: Machine Learning Algorithms and Models with Qlik -- Regression models -- Linear regression -- Logistic regression -- Lasso regression -- Clustering algorithms, decision trees, and random forests -- K-means clustering -- ID3 decision tree -- Boosting algorithms and Naive Bayes -- XGBoost -- Gaussian Naive Bayes -- Neural networks, deep learning, and natural-language models -- Summary -- Chapter 3: Data Literacy in a Machine Learning Context -- What is data literacy? …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Become an Adaptive Thinker -- Technical requirements -- How to be an adaptive thinker -- Addressing real-life issues before coding a solution -- Step 1 - MDP in natural language -- Step 2 - the mathematical representation of the Bellman equation and MDP -- From MDP to the Bellman equation -- Step 3 - implementing the solution in Python -- The lessons of reinforcement learning -- How to use the outputs -- Machine learning versus traditional applications -- Summary -- Questions -- Further reading -- Chapter 2: Think like a Machine -- Technical requirements -- Designing datasets - where the dream stops and the hard work begins -- Designing datasets in natural language meetings -- Using the McCulloch-Pitts neuron -- The McCulloch-Pitts neuron -- The architecture of Python TensorFlow -- Logistic activation functions and classifiers -- Overall architecture -- Logistic classifier -- Logistic function -- Softmax -- Summary -- Questions -- Further reading -- Chapter 3: Apply Machine Thinking to a Human Problem -- Technical requirements -- Determining what and how to measure -- Convergence -- Implicit convergence -- Numerical - controlled convergence -- Applying machine thinking to a human problem -- Evaluating a position in a chess game -- Applying the evaluation and convergence process to a business problem -- Using supervised learning to evaluate result quality -- Summary -- Questions -- Further reading -- Chapter 4: Become an Unconventional Innovator -- Technical requirements -- The XOR limit of the original perceptron -- XOR and linearly separable models -- Linearly separable models -- The XOR limit of a linear model, such as the original perceptron -- Building a feedforward neural network from scratch…”

“…La integración de herramientas de inteligencia artificial en la creación de Neutralia ha demostrado cómo estas tecnologías pueden aumentar tanto la eficiencia como la creatividad. La capacidad de generar múltiples opciones de diseño rápidamente y de personalizar modelos digitales ha permitido una mayor flexibilidad y adaptabilidad en el proceso de diseño. …”
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