Tabla de Contenidos: “…; Using Volumetric Shaders; Product Placement with Alpha Channels; Animating Materials with Noise Shaders; Optimizing a CINEMA 4D Scene; Chapter 7: Working with Cameras in CINEMA 4D and CINEWARE; Cameras in CINEMA 4D and CINEWARE; Using Camera Layers in After Effects; Attaching a Target Camera to a Spline; Controlling Multiple Cameras in CINEWARE; Adding a Stage Object to Change Cameras; Chapter 8: Multi-Pass Rendering with CINEWARE; Rendering in CINEMA 4D Lite; Creating Shadows with Ambient Occlusion…”

Tabla de Contenidos: “…Image Adjustments:Beyond the Basics; Black & white; Devignette; Noise reduction; Chromatic aberration; Sharpening; Versions; Brushes; Quick brushes; Quick brush descriptions; Using brushes with regular adjustments; Adjustment presets; Lift and stamp; External editor; Plug-ins; Conclusion; Chapter 6. …”

Tabla de Contenidos: “…Non-Destructive Adjustments -- Image Corrections -- Color, Contrast, and Brightness Adjustments -- Noise Reduction -- Image Sharpening -- Scaling and Resizing an Image -- Contact Sheets -- Saving Files -- Index…”

Tabla de Contenidos: “…Sanchez -- Kelly Cline -- Anna Bryukhanova -- Chapter 7 Setting Up Your Digital Darkroom -- Hardware Choices -- Ocean Fury -- Software Choices -- Hardy Waterlily -- Choosing A Color Space -- Assignment -- Chapter 8 Digital Editing Basics -- Focus On Quality -- Setting Defaults And Creating Presets -- Two Generations -- Adjusting White Balance -- Making Exposure Adjustments -- Sharpening For Stock -- Young People In A Movie Theater -- Dealing With Noise -- Assignment -- Chapter 9 Avoiding Rejection -- Digital House Cleaning -- Healthy Eating -- Avoid Overprocessing -- Wintery Decorations -- Assignment -- Chapter 10 Seeing Like An Inspector -- Being Your Worst Critic…”

Tabla de Contenidos: “…Positivity and Extrapolation -- Nonlinearities in Linear Regression -- Linearizing the Treatment -- Nonlinear FWL and Debiasing -- Regression for Dummies -- Conditionally Random Experiments -- Dummy Variables -- Saturated Regression Model -- Regression as Variance Weighted Average -- De-Meaning and Fixed Effects -- Omitted Variable Bias: Confounding Through the Lens of Regression -- Neutral Controls -- Noise Inducing Control -- Feature Selection: A Bias-Variance Trade-Off -- Key Ideas -- Chapter 5. …”

Tabla de Contenidos: “…Conflict resolution should be formally organized to make clear how to do it well: Ground rules -- Decision audits -- Notes -- Chapter 6 Cognitive readiness - risk-solution designers -- Notes -- Chapter 7 The human element -- Human element in the workplace -- Cognitive map: John Malone, the consummate deal-maker -- Decoding the miscalculation by Jeff Zucker at CNN -- Notes -- Chapter 8 Cognitive risk governance: Advanced ERM and cybersecurity -- Cognitive governance -- Simple example -- Intentional control design -- A fundamental approach to reduce risk -- Achieving resiliency -- The science of cognitive control -- Cybersecurity and enterprise risk management - asymmetric risk -- Human factors and socio-technical risk -- Cognitive risk mitigation - bias and noise: the fifth pillar -- Notes -- Additional References…”

Tabla de Contenidos: “…-- Spoken Q-signals -- Contesting or Radiosport -- Antenna Varieties -- Feed Lines -- Antenna Tuners -- Repeater Operating -- Grid Squares -- Interference and Noise -- Connector Parts -- Solar and Geomagnetic Activity -- Chapter 18 Technical Fundamentals -- Electrical Units and Symbols -- Ohm's Law -- Power -- Decibels -- Attenuation, Loss, and Gain -- Bandwidth -- Filters -- Antenna Patterns -- Standing Wave Ratio (SWR) -- Battery Characteristics -- Satellite Tracking -- Chapter 19 Tips for Masters -- Listening to Everything -- Learning How It Works -- Following the Protocol -- Keeping Your Axe Sharp -- Practice to Make Perfect -- Paying Attention to Detail -- Knowing What You Don't Know -- Maintaining Radio Discipline -- Make Small Improvements Continuously…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Foreword (Historical Perspective) -- Preface -- Introduction -- Chapter 1 Signal Digitizing - Sampling and Coding -- 1.1 Fourier Analysis -- 1.1.1 Fourier Series Expansion of a Periodic Function -- 1.1.2 Fourier Transform of a Function -- 1.2 Distributions -- 1.2.1 Definition -- 1.2.2 Differentiation of Distributions -- 1.2.2.1 The Fourier Transform of a Distribution -- 1.3 Some Commonly Studied Signals -- 1.3.1 Deterministic Signals -- 1.3.2 Random Signals -- 1.3.3 Gaussian Signals -- 1.3.3.1 Peak Factor of a Random Signal -- 1.4 The Norms of a Function -- 1.5 Sampling -- 1.6 Frequency Sampling -- 1.7 The Sampling Theorem -- 1.8 Sampling of Sinusoidal and Random Signals -- 1.8.1 Sinusoidal Signals -- 1.8.2 Discrete Random Signals -- 1.8.3 Discrete Noise Generation -- 1.9 Quantization -- 1.10 The Coding Dynamic Range -- 1.11 Nonlinear Coding with the 13‐segment A‐law -- 1.12 Optimal Coding -- 1.13 Quantity of Information and Channel Capacity -- 1.14 Binary Representations -- Exercises -- References -- Chapter 2 The Discrete Fourier Transform -- 2.1 Definition and Properties of the Discrete Fourier Transform -- 2.2 Fast Fourier Transform (FFT) -- 2.2.1 Decimation‐in‐time Fast Fourier Transform -- 2.2.2 Decimation‐in‐frequency Fast Fourier Transform -- 2.2.3 Radix‐4 FFT Algorithm -- 2.2.4 Split‐radix FFT Algorithm -- 2.3 Degradation Arising from Wordlength Limitation Effects -- 2.4 Calculation of a Spectrum Using the DFT -- 2.4.1 The Filtering Function of the DFT -- 2.4.2 Spectral Resolution -- 2.5 Fast Convolution -- 2.6 Calculations of a DFT Using Convolution -- 2.7 Implementation -- Exercises -- References -- Chapter 3 Other Fast Algorithms for the FFT -- 3.1 Kronecker Product of Matrices -- 3.2 Factorizing the Matrix of a Decimation‐in‐Frequency Algorithm -- 3.3 Partial Transforms…”

Tabla de Contenidos: “…Machine generated contents note: Part I - Basic correlation structures Chapter 0 - R basics 0.1 Getting started 0.2 Special R conventions 0.3 Common structures 0.4 Common functions 0.5 Time series functions 0.6 Importing data Chapter 1 - Review of regression and more about R 1.1 Goals of this chapter 1.2 The simple(st) regression model 1.3 Simulating the data from a model and estimating the model parameters in R 1.4 Basic inference for the model 1.5 Residuals analysis - What can go wrong... 1.6 Matrix manipulation in R Chapter 2 - The modeling approach taken in this book and some examples of typical serially correlated data 2.1 Signal and noise 2.2 Time series data 2.3 Simple regression in the framework 2.4 Real data and simulated data 2.5 The diversity of time series data 2.6 Getting data into R Chapter 3 - Some comments on assumptions 3.1 Introduction 3.2 The normality assumption 3.3 Equal variance 3.4 Independence 3.5 Power of logarithmic transformations illustrated 3.6 Summary Chapter 4 - The autocorrelation function and AR(1), AR(2) models 4.1 Standard models - What are the alternatives to white noise? …”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright and Credits -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1 - A Whirlwind of Stable Diffusion -- Chapter 1: Introducing Stable Diffusion -- Evolution of the Diffusion model -- Before Transformer and Attention -- Transformer transforms machine learning -- CLIP from OpenAI makes a big difference -- Generate images -- DALL-E 2 and Stable Diffusion -- Why Stable Diffusion -- Which Stable Diffusion to use -- Why this book -- References -- Chapter 2: Setting Up the Environment for Stable Diffusion -- Hardware requirements to run Stable Diffusion -- GPU -- System memory -- Storage -- Software requirements -- CUDA installation -- Installing Python for Windows, Linux, and macOS -- Installing PyTorch -- Running a Stable Diffusion pipeline -- Using Google Colaboratory -- Using Google Colab to run a Stable Diffusion pipeline -- Summary -- References -- Chapter 3: Generating Images Using Stable Diffusion -- Logging in to Hugging Face -- Generating an image -- Generation seed -- Sampling scheduler -- Changing a model -- Guidance scale -- Summary -- References -- Chapter 4: Understanding the Theory Behind Diffusion Models -- Understanding the image-to-noise process -- A more efficient forward diffusion process -- The noise-to-image training process -- The noise-to-image sampling process -- Understanding Classifier Guidance denoising -- Summary -- References -- Chapter 5: Understanding How Stable Diffusion Works -- Stable Diffusion in latent space -- Generating latent vectors using diffusers -- Generating text embeddings using CLIP -- Initializing time step embeddings -- Initializing the Stable Diffusion UNet -- Implementing a text-to-image Stable Diffusion inference pipeline -- Implementing a text-guided image-to-image Stable Diffusion inference pipeline -- Summary -- References…”

“…What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. …”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- Acknowledgments -- Introduction -- Chapter 1: Hiring -- The Job Application Form -- The Screening Call -- Panel Interviews -- Asynchronous Technical Test -- How to Schedule Interviews -- What to Do When Technology Fails -- A New Set of Biases -- Bias #1: Internet Quality -- Bias #2: Video Call Backgrounds -- Bias #3: Noise -- Winning People Over When They Have Multiple Offers -- Takeaways from This Chapter -- Chapter 2: Onboarding -- The Week Before -- Welcome Email -- Onboarding Buddies -- Putting It All Together -- The First Week -- Creating a Welcoming Environment -- Setting and Managing Expectations -- Working on Building Social Connections -- Familiarizing Them with the Way of Working -- Putting It All Together -- Beyond Week One -- One-on-Ones -- Simple Tickets -- Deploying Code -- Pull Requests -- The Importance of Feedback -- Takeaways from This Chapter -- Chapter 3: Meetings -- How to Schedule a Great Meeting -- Name/Description -- Attendees -- Date/Time -- Length -- Team Retrospectives/Brainstorming -- Days 1-2: Asynchronous -- Days 3-4: Asynchronous -- Day 5: Synchronous -- Team Building -- Games -- Escape Rooms -- Live Creative Experiences -- Watch-n-Learn -- Good Old-Fashioned Social Hour -- The "Stand-Up" -- Takeaways from This Chapter -- Chapter 4: One-on-Ones -- An Introduction to One-on-Ones -- The Benefits of One-on-Ones -- Remote One-on-Ones -- Why One-on-Ones Are Better When Remote -- How to Structure Your One-on-Ones -- The Importance of a Shared Meeting Agenda -- Types of One-on-Ones -- The First One-on-One -- The Feedback One-on-One -- The Social One-on-One -- The Peer One-on-One -- The Manager One-on-One -- Troubleshooting and Mistakes -- When There's "Nothing to Talk About" -- "We'll Talk As Needed" -- The Emotional Overload -- Takeaways from This Chapter…”

“…These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective…”

“…Hands-on techniques show readers how to expose and shoot for raw image capture as well as use features such as the Adjustment Brush and the Graduated Filter and the new and improved Noise Reduction, Perspective Crop, and automatic Lens Correction. …”

Tabla de Contenidos: “…Chapter 2: Building Materials and Shaders -- Technical requirements -- Introducing materials -- Creating materials -- Assigning materials -- Discovering shaders -- Summary -- Further reading -- Chapter 3: Adding and Creating Textures -- Technical requirements -- Understanding UVs and texture coordinates -- Using the UV Editor -- Importing and applying a texture -- Creating textures procedurally -- Noise Texture -- Bump -- Emission -- ColorRamp -- Principled BSDF -- Mix Shader -- Exporting your textures -- Changing the rendering engine -- Baking a texture File -- Summary -- Further reading…”

Tabla de Contenidos: “…6.2.1 Quadrature Amplitude Modulation Simulator 339 -- 6.2.2 Filtered Noise Method 339 -- 6.2.3 Sum of Sinusoids Method (Jakes Method) 341 -- 6.2.4 Frequency Domain Method 343 -- 6.2.5 Reverberation Chambers (or Mode-Stirred Chambers) 344 -- 6.3 Wideband Stochastic Channel Simulator 346 -- 6.3.1 Time Domain Channel Simulators 346 -- 6.3.2 Frequency Domain Simulators 348 -- 6.4 Frequency Domain Implementation Using Fast Convolution 349 -- 6.5 Channel Block Realization from Measured Data 351 -- 6.6 Theoretical Prediction of System Performance in Additive White Gaussian Noise 353 -- 6.6.1 Matched Filter and Correlation Detector 354 -- 6.6.2 Bit Error Rate of the Matched Filter Detector in AWGN 356 -- 6.6.3 Bit Error Rate with Noncoherent Detectors 357 -- 6.6.4 Comparison of BER of Coherent and Noncoherent Detectors 358 -- 6.6.5 Higher Order Modulation 358 -- 6.7 Prediction of System Performance in Fading Channels 361 -- 6.7.1 Narrowband Signals 361 -- 6.7.2 Wideband Signals 363 -- 6.8 Bit Error Rate Prediction for Wireless Standards 364 -- 6.8.1 IEEE 802.16-d Standard 365 -- 6.8.2 IEEE 802.11-a Standard 371 -- 6.8.3 Third Generation WCDMA Standard 372 -- 6.9 Enhancement of Performance Using Diversity Gain 376 -- 6.9.1 Diversity Combining Methods 377 -- 6.9.2 Diversity Gain Prediction of Rayleigh Fading Channels from Measurements in a Reverberation Chamber 382 -- References 383 -- Appendix 1 385 -- A.1 Probability Distribution Functions 385 -- A.2 The Gaussian (Normal) Distribution 385 -- A.3 The Rayleigh Distribution 387 -- A.4 The Rician Distribution 388 -- A.5 The Nakagami m-Distribution 389 -- A.6 The Weibull Distribution 390 -- A.7 The Log-Normal Distribution 390 -- A.8 The Suzuki Distribution 391 -- A.9 The Chi-Square Distribution 391 -- References 391 -- Appendix 2 393 -- Index 395.…”

Tabla de Contenidos: “…Z·olzer 2.1 Signal Quantization 25 2.1.1 Classical Quantization Model 25 2.1.2 Quantization Theorem 29 2.1.3 Statistics of Quantization Error 34 2.2 Dither 41 2.2.1 Basics 41 2.2.2 Implementation 44 2.2.3 Examples 45 2.3 Spectrum Shaping of Quantization -- Noise Shaping 48 2.4 Number Representation 53 2.4.1 Fixed-point Number Representation 54 2.4.2 Floating-point Number Representation 58 2.4.3 Effects on Format Conversion and Algorithms 62 2.5 JS Applet -- Quantization, Dither, and Noise Shaping 64 2.6 Exercises 65 References 67 3 Sampling Rate Conversion 69; U.…”

Tabla de Contenidos: “…Intro -- Foreword -- Preface -- Pioneering Space-Borne SAR Interferometry -- Organising Airborne Polarimetric SAR Campaigns and Scientific Studies -- Dialoguing with POLinSAR Scientists and Training the Next Generation -- Pioneering Space-Borne SAR Polarimetric Interferometry -- Future Missions -- From Science to Applications -- Outlook -- In Memoriam -- Contents -- Symbols -- 1: Basic Principles of SAR Polarimetry -- 1.1 Theory of Radar Polarimetry -- 1.1.1 Wave Polarimetry -- 1.1.1.1 Electromagnetic Waves and Wave Polarization Descriptors -- 1.1.1.2 Totally and Partially Polarized Waves -- 1.1.1.3 Change of Polarization Basis -- 1.1.2 Scattering Polarimetry -- 1.1.2.1 The Scattering Matrix -- 1.1.2.2 Scattering Polarimetry Descriptors -- 1.1.2.3 Partial Scattering Polarimetry -- 1.1.2.4 Change of Polarization Basis -- 1.1.2.5 Scatterers Characterization by Single, Dual, Compact and Full Polarimetry -- 1.2 SAR Data Statistical Description and Speckle Noise Filtering -- 1.2.1 One-Dimensional Gaussian Distribution -- 1.2.2 Multidimensional Gaussian Distribution -- 1.2.3 The Wishart Distribution -- 1.2.4 The Polarimetric Covariance and Coherency Matrix -- 1.2.5 The Polarimetric Coherence -- 1.2.6 Polarimetric Speckle Noise Filtering -- 1.2.6.1 PolSAR Speckle Noise Filtering Principles -- 1.2.6.2 PolSAR Speckle Noise Filtering Alternatives -- 1.3 Polarimetric Scattering Decomposition Theorems -- 1.3.1 Coherent Scattering Decomposition Techniques -- 1.3.1.1 The Pauli Decomposition -- 1.3.2 Incoherent Scattering Decompositions Techniques -- 1.3.2.1 Three-Component Freeman Decomposition -- 1.3.2.2 Four-Component Yamaguchi Decomposition -- 1.3.2.3 Non-negative Eigenvalue Decomposition -- 1.3.2.4 Eigenvector-Eigenvalue-Based Decomposition -- 1.3.2.5 The Touzi Target Scattering Decompositions -- 1.4 Polarimetric SAR Interferometry…”

Tabla de Contenidos: “…The Audio-for-Visual and Media Production Environment -- The Audio-for-Gaming Production Environment -- The Project Studio -- Primary Factors Governing Studio and Control Room Acoustics -- Acoustic Isolation -- Walls -- Floors -- Risers -- Ceilings -- Windows and Doors -- ISO-Rooms and ISO-Booths -- Acoustic Partitions -- Noise Isolation Within the Control Room -- Symmetry in Control-Room Design -- Frequency Balance -- Reflections -- Absorption -- High-Frequency Absorption -- Low-Frequency Absorption -- Flexible Surfaces -- The Practical Side of Acoustics -- Symmetry -- 25/25/50 -- Speaker Placement -- Room Reflections and Acoustic Reverberation -- Acoustic Echo Chambers -- Chapter 4 Microphones: Design and Application -- Microphone Design -- The Dynamic Microphone -- The Ribbon Microphone -- Further Developments in Ribbon Technology -- The Condenser Microphone -- Powering a Condenser Mic -- External Power Supply -- Phantom Power -- The Electret-Condenser Microphone -- Microphone Characteristics -- Directional Response -- Frequency Response -- Transient Response -- Output Characteristics -- Sensitivity Rating -- Equivalent Noise Rating -- Overload Characteristics -- Microphone Impedance -- Balanced/Unbalanced Lines -- Soldering, Baby! …”

Tabla de Contenidos: “…Uncertainty Historical Perspective Info-gap Uncertainty, Probability and Fuzziness Some Info-gap Models Uncertainty and Convexity Axioms of Info-gap Uncertainty Problems Robustness and Opportunity Robustness and Opportunity Immunity Functions Generic Decision Algorithms Multi-criterion Reward Three Components of Info-gap Decision Models Preferences Trade-offs Production Volume With Uncertain Costs Engineering Design: Cantilever Structural Reliability Sequential Decisions Project Management Manufacturing Process Control Portfolio Investment Search and Evasion Robustness and Opportunity General Robustness and Opportunity Functions Value Judgments Normalization Analogical Reasoning Calibration by Consequence Severity Robustness Function for Environmental Management Calibration by Consequence Severity Rationality and Preference Antagonistic and Sympathetic Immunities Immunity Functions Reward-coherent Action Vibrating Mechanical Contact Multi-tasking of Computer Jobs Formulation Deriving Robustness and Opportunity Functions Results Gambling and Risk Sensitivity Risk Sensitivity and the Robustness Curve Risk Sensitivity and Two Robustness Curves Initial Commitment and Uncertain Future Uniformly Bounded Uncertainty Bounded Fourier Uncertainty Risk Sensitivity, Robustness and Opportunity Risk-neutral Line Pure Competition With Uncertain Cost Risk Assessment in Project Management More on the Robustness Premium Robustness Premium and Resource Commitment Value of Information Informativeness of an Info-gap Model Demand Value of Information Uncertain Loads on a Cantilever Cantilever: Disjoint Info-gap Models Gathering Information in Project Management Windfall Cost of Information Formulation Initial Commitment and Uncertain Future: Revisited Allais 'Paradox' Formulation Probability Uncertainty Utility Uncertainty Ellsberg 'Paradox' Expected-utility Risk Aversion Learning Learning and Deciding Info-gap Supervision of a Classifier Robustness of a Classifier Asymptotic Robustness Robust-optimal Classifier A Proof Robust Severe Tests of Truth Updating Info-gap Models Plantar Pressures in Metatarsal Pathology Acoustic Noise Empirical Robustness Updating the Acoustic Uncertainty Model Coherent Uncertainties and Consensus Preference Preservation Under Altered Information Examples of Coherent Uncertainties Principal-agent Contract Bidding Proofs Retrospective Essay: Risk Assessment in Project Management Info-gap Uncertainty: What Is It? …”