Tabla de Contenidos: “…. -- Getting Started with Data Mining -- Scikit-learn tutorials -- Extending the Jupyter Notebook -- More datasets -- Other Evaluation Metrics -- More application ideas -- Classifying with scikit-learn Estimators -- Scalability with the nearest neighbor -- More complex pipelines -- Comparing classifiers -- Automated Learning -- Predicting Sports Winners with Decision Trees -- More complex features -- Dask -- Research -- Recommending Movies Using Affinity Analysis -- New datasets -- The Eclat algorithm -- Collaborative Filtering -- Extracting Features with Transformers -- Adding noise -- Vowpal Wabbit -- word2vec -- Social Media Insight Using Naive Bayes -- Spam detection -- Natural language processing and part-of-speech tagging -- Discovering Accounts to Follow Using Graph Mining -- More complex algorithms -- NetworkX -- Beating CAPTCHAs with Neural Networks -- Better (worse?) …”

“…What You Will Learn Find out how to clean and make datasets ready so you can acquire actual insights by removing noise and outliers Develop the skills to use modern machine learning techniques to retrieve information and transform data to knowledge. retrieve information from large amount of data in text format. …”

Tabla de Contenidos: “…2.4.2 Filtering and aliasing2.4.3 Quantisation; 2.4.4 Relationship between sample resolution and sound quality; 2.4.5 Use of dither; 2.4.6 Types of dither; 2.4.7 Oversampling in A/D conversion; 2.4.8 Noise shaping in A/D conversion; 2.5 D/A conversion; 2.5.1 A basic D/A convertor; 2.5.2 Oversampling in D/A conversion; 2.6 Sound quality versus sample rates and resolutions; 2.6.1 Psychoacoustic limitations; 2.6.2 Sampling rate; 2.6.3 Quantising resolution; 2.7 Direct Stream Digital (DSD); 2.8 Changing the resolution of an audio signal (requantisation); 2.9 Dynamic range enhancement…”

Tabla de Contenidos: “…1.6.1 Supply Voltage1.6.2 Logic Levels; 1.6.3 Noise Margins; 1.6.4 DC Transfer Characteristics; 1.6.5 The Static Discipline; 1.7 CMOS Transistors*; 1.7.1 Semiconductors; 1.7.2 Diodes; 1.7.3 Capacitors; 1.7.4 nMOS and pMOS Transistors; 1.7.5 CMOS NOT Gate; 1.7.6 Other CMOS Logic Gates; 1.7.7 Transmission Gates; 1.7.8 Pseudo-nMOS Logic; 1.8 Power Consumption*; 1.9 Summary and a Look Ahead; Exercises; Interview Questions; 2 Combinational Logic Design; 2.1 Introduction; 2.2 Boolean Equations; 2.2.1 Terminology; 2.2.2 Sum-of-Products Form; 2.2.3 Product-of-Sums Form; 2.3 Boolean Algebra…”

Tabla de Contenidos: “…Philip Crosby -- Crosby's 14 Steps11 -- Kaoru Ishikawa -- Genichi Taguchi -- Design of Experiments -- Taguchi's Approach -- Advantage of Doe Using Taguchi's Approach -- A Review of Taguchi's Method -- Quality Characteristic -- Factors and Levels -- Interaction Between Factors -- Noise Factors and Outer Arrays -- Order of Running Experiments -- Orthogonal Arrays -- MSD and S/N Ratios -- Taguchi's Quality Loss Function -- Dr Shingo Shigeo -- Shingo's Key Teachings -- The Shingo Prize for Operational Excellence -- Masaaki Imai -- Summary -- Key Terms -- Case Study -- Deming's Red Bead Experiment at Technosystems -- Components of the Red Bead Experiment -- For Discussion -- Short-answer Questions -- Match the Following -- Discussion Questions -- Projects -- End Notes -- Chapter 3: The Philosophy of Total Quality Management -- Introduction -- Definitions of TQM -- The Total Quality System -- The Evolution of TQM-from Inspection to Total Quality Management2 -- The Four Stages of TQM -- Seven Phases in the Development of TQM3 -- Principles of Total Quality Management4 -- The Key Elements of TQM5 -- Core Concepts of TQM6 -- Customer Satisfaction -- Internal Customer Satisfaction -- All Work is Process -- Measurement -- Synergy in Teamwork -- People Make Quality -- Continuous Improvement Cycle -- Prevention -- Total Quality Management Excellence Model (TQMEX)7 -- The Japanese 5-S Practice (5 S) -- Business Process Management (BPM) -- Quality Control Circles (QCC) -- The Seven Quality Control Tools -- Quality Management Systems -- Total Productive Maintenance -- Traditional Organization Versus TQM Organization8 -- Common Barriers to the Implementation and Development of a TQM Programme -- Prerequisites for TQM -- TQM Implementation9 -- Benefits of TQM10 -- Summary -- Key Terms -- Case Study -- Total Quality Management at Sundaram-Clayton Ltd -- The Company…”

Tabla de Contenidos: “…Installing and using NLTK -- Extending the vectorizer with NLTK's stemmer -- Stop words on steroids -- Our achievements and goals -- Clustering -- K-means -- Getting test data to evaluate our ideas -- Clustering posts -- Solving our initial challenge -- Another look at noise -- Tweaking the parameters -- Summary -- Chapter 7: Recommendations -- Rating predictions and recommendations -- Splitting into training and testing -- Normalizing the training data -- A neighborhood approach to recommendations -- A regression approach to recommendations -- Combining multiple methods -- Basket analysis -- Obtaining useful predictions -- Analyzing supermarket shopping baskets -- Association rule mining -- More advanced basket analysis -- Summary -- Chapter 8: Artificial Neural Networks and Deep Learning -- Using TensorFlow -- TensorFlow API -- Graphs -- Sessions -- Useful operations -- Saving and restoring neural networks -- Training neural networks -- Convolutional neural networks -- Recurrent neural networks -- LSTM for predicting text -- LSTM for image processing -- Summary -- Chapter 9: Classification II - Sentiment Analysis -- Sketching our roadmap -- Fetching the Twitter data -- Introducing the Naïve Bayes classifier -- Getting to know the Bayes theorem -- Being naïve -- Using Naïve Bayes to classify -- Accounting for unseen words and other oddities -- Accounting for arithmetic underflows -- Creating our first classifier and tuning it -- Solving an easy problem first -- Using all classes -- Tuning the classifier's parameters -- Cleaning tweets -- Taking the word types into account -- Determining the word types -- Successfully cheating using SentiWordNet -- Our first estimator -- Putting everything together -- Summary -- Chapter 10: Topic Modeling -- Latent Dirichlet allocation -- Building a topic model -- Comparing documents by topic…”

Tabla de Contenidos: “…ESD: Analog Circuits and Design; Copyright; Contents; About the Author; Preface; Acknowledgments; Chapter 1 Analog, ESD, and EOS; 1.1 ESD in Analog Design; 1.2 Analog Design Discipline and ESD Circuit Techniques; 1.2.1 Analog Design: Local Matching; 1.2.2 Analog Design: Global Matching; 1.2.3 Symmetry; 1.2.3.1 Layout Symmetry; 1.2.3.2 Thermal Symmetry; 1.2.4 Analog Design: Across Chip Linewidth Variation; 1.3 Design Symmetry and ESD; 1.4 ESD Design Synthesis and Architecture Flow; 1.5 ESD Design and Noise; 1.6 ESD Design Concepts: Adjacency; 1.7 Electrical Overstress…”

“…It has the potential to improve performance in the presence of severe and changing modeling uncertainties and noise. An important advantage of the SVSF is the availability of a set of secondary performance indicators that pertain to each estimate. …”

Tabla de Contenidos: “…Foundations of Interpersonal Communication -- The Benefits of Studying InterpersonalCommunication -- Personal Benefits -- Professional Benefits -- Personal and Professional Choices -- The Elements of Interpersonal Communication -- Source-Receiver -- Messages -- Channel -- Noise -- Context -- Effects -- Ethics -- The Principles of Interpersonal Communication -- Interpersonal Communication Varies in Mindfulness -- Interpersonal Communication Exists on a Continuum -- Interpersonal Communication Is Transactional -- Interpersonal Communication Is Purposeful -- Interpersonal Communication Is Ambiguous -- Interpersonal Communication Refers to Content and Relationship -- Interpersonal Communication Is a Series of Punctuated Events -- Interpersonal Communication Is Inevitable, Irreversible, and Unrepeatable -- Summary of Concepts -- Summary of Skills -- Key Terms -- Chapter 2. …”

Tabla de Contenidos: “…Gates -- Controls -- Threshold -- Hysteresis -- Range -- Attack and release -- Hold -- Look-ahead -- Side-chain filters -- Key input -- Output level -- Stereo link -- Meters -- Applications -- Removing noise -- Removing spill -- Reshaping dynamic envelopes -- In practice -- The problem with gates -- Gate alternatives: Manual gating -- Gate alternatives: De-noisers -- Tricks -- Manual look-ahead -- Kick-clicker -- Adding sub-bass to a kick -- Tighten bass to a kick -- 20. …”

Tabla de Contenidos: “…Using Monte Carlo methods for stock price prediction -- Exploring the Amazon stock price trend -- Handling the stock price trend as a time series -- Introducing the Black-Scholes model -- Applying the Monte Carlo simulation -- Studying risk models for portfolio management -- Using variance as a risk measure -- Introducing the Value-at-Risk metric -- Estimating VaR for some NASDAQ assets -- Summary -- Chapter 10: Simulating Physical Phenomena Using Neural Networks -- Technical requirements -- Introducing the basics of neural networks -- Understanding biological neural networks -- Exploring ANNs -- Understanding feedforward neural networks -- Exploring neural network training -- Simulating airfoil self-noise using ANNs -- Importing data using pandas -- Scaling the data using sklearn -- Viewing the data using Matplotlib -- Splitting the data -- Explaining multiple linear regression -- Understanding a multilayer perceptron regressor model -- Approaching deep neural networks -- Getting familiar with convolutional neural networks -- Examining recurrent neural networks -- Analyzing long short-term memory networks -- Exploring GNNs -- Introducing graph theory -- Adjacency matrix -- GNNs -- Simulation modeling using neural network techniques -- Concrete quality prediction model -- Summary -- Chapter 11: Modeling and Simulation for Project Management -- Technical requirements -- Introducing project management -- Understanding what-if analysis -- Managing a tiny forest problem -- Summarizing the Markov decision process -- Exploring the optimization process -- Introducing MDPtoolbox -- Defining the tiny forest management example -- Addressing management problems using MDPtoolbox -- Changing the probability of a fire starting -- Scheduling project time using the Monte Carlo simulation -- Defining the scheduling grid -- Estimating the task's time…”

Tabla de Contenidos: “…Privacy-enhancing technologies -- Differential privacy -- Federated learning -- Secure multi-party computation (SMC) -- Homomorphic encryption -- Anonymization -- De-identification -- Differential privacy -- Summary -- Chapter 4: Overview of Differential Privacy Algorithms and Applications of Differential Privacy -- Differential privacy algorithms -- Laplace distribution -- Gaussian distribution -- Comparison of noise-adding algorithms to apply differential privacy -- Generating aggregates using differential privacy -- Sensitivity -- Queries that use differential privacy -- Clipping -- Overview of real-life applications of differential privacy -- Differential privacy usage at Uber -- Differential privacy usage at Apple -- Differential privacy usage in the US Census -- Differential privacy at Google -- Summary -- Chapter 5: Developing Applications with Differential Privacy Using Open Source Frameworks -- Open source frameworks to implement differential privacy -- Introduction to the PyDP framework and its key features -- Examples and demonstrations of PyDP in action -- Developing a sample banking application with PyDP to showcase differential privacy techniques -- Protecting against membership inference attacks -- Applying differential privacy to large datasets -- Use case - generating differentially private aggregates on a large dataset -- PipelineDP high-level architecture -- Tumult Analytics -- Machine learning using differential privacy -- Synthetic Dataset Generation: Introducing Fraudulent Transactions -- Develop a classification model using scikit-learn -- High-level implementation of the SGD algorithm -- Applying differential privacy options using machine learning -- Generating gradients using differential privacy -- Clustering using differential privacy -- Deep learning using differential privacy -- Fraud detection model using PyTorch…”

Tabla de Contenidos: “…Strategies to Improve Our Ability to Engage in Civil Listening -- Stages and Types of Listening -- Noise -- Multitasking -- Selective Listening -- Stage 2: Understanding and Comprehensive Listening -- Stage 3: Remembering -- Stage 4: Interpreting and Empathic Listening -- Stage 5: Evaluating and Critical Listening -- Stage 6: Responding and Active Listening -- Improving Our Ability to Engage in Civil and Effective Listening -- Stage 1: Improving the Ability to Hear Messages and Engage in Appreciative Listening -- Stage 2: Improving the Ability to Understand Messages and Engage in Comprehensive Listening -- Stage 3: Improving the Ability to Remember Messages -- Stage 4: Improving the Ability to Interpret Messages and Engage in Empathic Listening -- Stage 5: Improving the Ability to Evaluate Messages and Engage in Critical Listening -- Stage 6: Improving the Ability to Respond to Messages and Engage in Active Listening -- Confirming Listening Response Styles -- Prompting and Questioning -- Reassuring and Expressing Concern -- Paraphrasing Thoughts and Feelings -- Paraphrasing for Content -- Paraphrasing for Feelings -- Listening, Confirming Responses, and Contexts -- Listening, Confirming Responses, and Culture -- Listening, Confirming Responses, and Gender -- Listening, Confirming Responses, and the Workplace -- Chapter Summary -- Chapter Review Questions -- 6. …”

Tabla de Contenidos: “…Industrial Papers (Invited) -- Data Mining as an Automated Service -- Trends and Challenges in the Industrial Applications of KDD -- Stream Mining I -- Finding Event-Oriented Patterns in Long Temporal Sequences -- Mining Frequent Episodes for Relating Financial Events and Stock Trends -- Graph Mining -- An Efficient Algorithm of Frequent Connected Subgraph Extraction -- Classifier Construction by Graph-Based Induction for Graph-Structured Data -- Clustering I -- Comparison of the Performance of Center-Based Clustering Algorithms -- Automatic Extraction of Clusters from Hierarchical Clustering Representations -- Text Mining -- Large Scale Unstructured Document Classification Using Unlabeled Data and Syntactic Information -- Extracting Shared Topics of Multiple Documents -- An Empirical Study on Dimensionality Optimization in Text Mining for Linguistic Knowledge Acquisition -- A Semi-supervised Algorithm for Pattern Discovery in Information Extraction from Textual Data -- Bio Mining -- Mining Patterns of Dyspepsia Symptoms Across Time Points Using Constraint Association Rules -- Predicting Protein Structural Class from Closed Protein Sequences -- Learning Rules to Extract Protein Interactions from Biomedical Text -- Predicting Protein Interactions in Human by Homologous Interactions in Yeast -- Web Mining -- Mining the Customer’s Up-To-Moment Preferences for E-commerce Recommendation -- A Graph-Based Optimization Algorithm for Website Topology Using Interesting Association Rules -- A Markovian Approach for Web User Profiling and Clustering -- Extracting User Interests from Bookmarks on the Web -- Stream Mining II -- Mining Frequent Instances on Workflows -- Real Time Video Data Mining for Surveillance Video Streams -- Distinguishing Causal and Acausal Temporal Relations -- Bayesian Networks -- Online Bayes Point Machines -- Exploiting Hierarchical Domain Values for Bayesian Learning -- A New Restricted Bayesian Network Classifier -- Clustering II -- AGRID: An Efficient Algorithm for Clustering Large High-Dimensional Datasets -- Multi-level Clustering and Reasoning about Its Clusters Using Region Connection Calculus -- An Efficient Cell-Based Clustering Method for Handling Large, High-Dimensional Data -- Association Rules I -- Enhancing SWF for Incremental Association Mining by Itemset Maintenance -- Reducing Rule Covers with Deterministic Error Bounds -- Evolutionary Approach for Mining Association Rules on Dynamic Databases -- Semi-structured Data Mining -- Position Coded Pre-order Linked WAP-Tree for Web Log Sequential Pattern Mining -- An Integrated System of Mining HTML Texts and Filtering Structured Documents -- A New Sequential Mining Approach to XML Document Similarity Computation -- Classification I -- Optimization of Fuzzy Rules for Classification Using Genetic Algorithm -- Fast Pattern Selection for Support Vector Classifiers -- Averaged Boosting: A Noise-Robust Ensemble Method -- Improving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule -- Data Analysis -- HOT: Hypergraph-Based Outlier Test for Categorical Data -- A Method for Aggregating Partitions, Applications in K.D.D. -- Efficiently Computing Iceberg Cubes with Complex Constraints through Bounding -- Extraction of Tag Tree Patterns with Contractible Variables from Irregular Semistructured Data -- Association Rules II -- Step-by-Step Regression: A More Efficient Alternative for Polynomial Multiple Linear Regression in Stream Cube -- Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining -- Mining Open Source Software (OSS) Data Using Association Rules Network -- Parallel FP-Growth on PC Cluster -- Feature Selection -- Active Feature Selection Using Classes -- Electricity Based External Similarity of Categorical Attributes -- Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers -- Dealing with Relative Similarity in Clustering: An Indiscernibility Based Approach -- Stream Mining III -- Considering Correlation between Variables to Improve Spatiotemporal Forecasting -- Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach -- When to Update the Sequential Patterns of Stream Data? …”

Tabla de Contenidos: “…1 - Second General Artificial Intelligence Workshop (GAIW 2007) -- Towards Tractable Local Closed World Reasoning for the Semantic Web -- Optimal Brain Surgeon for General Dynamic Neural Networks -- Answer-Set Programming Based Dynamic User Modelling for Recommender Systems -- Application of Logic Wrappers to Hierarchical Data Extraction from HTML -- Relaxing Feature Selection in Spam Filtering by Using Case-Based Reasoning Systems -- Gödel and Computability -- Prospective Logic Agents -- An Iterative Process for Building Learning Curves and Predicting Relative Performance of Classifiers -- Modelling Morality with Prospective Logic -- Change Detection in Learning Histograms from Data Streams -- Real-Time Intelligent Decision Support System for Bridges Structures Behavior Prediction -- Semi-fuzzy Splitting in Online Divisive-Agglomerative Clustering -- On the Use of Rough Sets for User Authentication Via Keystroke Dynamics -- The Halt Condition in Genetic Programming -- Two Puzzles Concerning Measures of Uncertainty and the Positive Boolean Connectives -- 2 - First Workshop on AI Applications for Sustainable Transportation Systems (AIASTS 2007) -- Nonlinear Models for Determining Mode Choice -- Adaptation in Games with Many Co-evolving Agents -- 3 - Third Workshop on Artificial Life and Evolutionary Algorithms (ALEA 2007) -- Symmetry at the Genotypic Level and the Simple Inversion Operator -- A Genetic Programming Approach to the Generation of Hyper-Heuristics for the Incapacitated Examination Timetabling Problem -- Asynchronous Stochastic Dynamics and the Spatial Prisoner’s Dilemma Game -- Improving Evolutionary Algorithms with Scouting -- Stochastic Barycenters and Beta Distribution for Gaussian Particle Swarms -- Exploiting Second Order Information in Computational Multi-objective Evolutionary Optimization -- 4 - First Workshop on Ambient Intelligence Technologies and Applications (AMITA 2007) -- Ambient Intelligence – A State of the Art from Artificial Intelligence Perspective -- Ubiquitous Ambient Intelligence in a Flight Decision Assistance System -- Argumentation-Based Decision Making in Ambient Intelligence Environments -- Intelligent Mixed Reality for the Creation of Ambient Assisted Living -- Medical Imaging Environment – A Multi-Agent System for a Computer Clustering Based Multi-display -- 5 - Second Workshop on Building and Applying Ontologies for the Semantic Web (BAOSW 2007) -- Partial and Dynamic Ontology Mapping Model in Dialogs of Agents -- Using Ontologies for Software Development Knowledge Reuse -- 6 - First Workshop on Business Intelligence (BI 2007) -- Analysis of the Day-of-the-Week Anomaly for the Case of Emerging Stock Market -- A Metamorphosis Algorithm for the Optimization of a Multi-node OLAP System -- Experiments for the Number of Clusters in K-Means -- A Network Algorithm to Discover Sequential Patterns -- Adaptive Decision Support for Intensive Care -- A Tool for Interactive Subgroup Discovery Using Distribution Rules -- Quantitative Evaluation of Clustering’s for Marketing Applications: A Web Portal Case Study -- Resource-Bounded Fraud Detection -- 7 - First Workshop on Computational Methods in Bioinformatics and Systems Biology (CMBSB 2007) -- System Stability Via Stepping Optimal Control: Theory and Applications -- Evaluating Simulated Annealing Algorithms in the Optimization of Bacterial Strains -- Feature Extraction from Tumor Gene Expression Profiles Using DCT and DFT -- 8 - Second Workshop on Intelligent Robotics (IROBOT 2007) -- An Omnidirectional Vision System for Soccer Robots -- Generalization and Transfer Learning in Noise-Affected Robot Navigation Tasks -- Heuristic Q-Learning Soccer Players: A New Reinforcement Learning Approach to RoboCup Simulation -- Human Robot Interaction Based on Bayesian Analysis of Human Movements -- Understanding Dynamic Agent’s Reasoning -- 9 - Fourth Workshop on Multi-agent Systems: Theory and Applications (MASTA 2007) -- Convergence of Independent Adaptive Learners -- Multi-agent Learning: How to Interact to Improve Collective Results -- A Basis for an Exchange Value-Based Operational Notion of Morality for Multiagent Systems -- Intelligent Farmer Agent for Multi-agent Ecological Simulations Optimization -- Tax Compliance Through MABS: The Case of Indirect Taxes -- 10 - First Workshop on Search Techniques for Constraint Satisfaction (STCS 2007) -- Efficient and Tight Upper Bounds for Haplotype Inference by Pure Parsimony Using Delayed Haplotype Selection -- GRASPER -- 11 - Second Workshop on Text Mining and Applications (TEMA 2007) -- Text Segmentation Using Context Overlap -- Automatic Extraction of Definitions in Portuguese: A Rule-Based Approach -- N-Grams and Morphological Normalization in Text Classification: A Comparison on a Croatian-English Parallel Corpus -- Detection of Strange and Wrong Automatic Part-of-Speech Tagging -- New Techniques for Relevant Word Ranking and Extraction…”

Tabla de Contenidos: “…9.5 Expanders and gates9.6 Multiband compressor; 9.7 Reverberation and echo; 9.8 Filters; 9.9 Restoration; 9.10 Special effects; 9.11 Time/pitch; 9.12 Spatial effects; 9.13 Basic effects; 9.14 Multitrack effects; 9.15 Real-time controls and effects; 10 Reviewing material; 10.1 Check there are no missed edits; 10.2 Assessing levels; 10.3 Listening on full quality speakers; 11 Mastering; 11.1 Line-up and transmission formats; 11.2 Generate silence; 11.3 Generate noise; 11.4 Generate tones; 11.5 Changeovers; 11.6 Mastering process; 11.7 CD labelling; 11.8 Email and the World Wide Web…”

Tabla de Contenidos: “…2.7 Formal Verification2.8 Embedded Systems and Real-Time Operating Systems; 2.9 Electronic System-Level Design; 2.10 Creating a Design Specification; 2.11 Unified Modeling Language; 2.12 Reading a Component Data Sheet; 2.13 Digital Input/Output; 2.13.1 Introduction; 2.13.2 Logic-Level Definitions; 2.13.3 Noise Margin; 2.13.4 Interfacing Logic Families; 2.14 Parallel and Serial Interfacing; 2.14.1 Introduction; 2.14.2 Parallel I/O; 2.14.3 Serial I/O; 2.15 System Reset; 2.16 System Clock; 2.17 Power Supplies; 2.18 Power Management; 2.19 Printed Circuit Boards and Multichip Modules…”

Tabla de Contenidos: “…-- 3.1.2 - Environmental cues that entrain circadian rhythms -- 3.1.3 - Health problems associated with circadian rhythms and shift work -- 3.1.4 - Jet lag and daylight savings time -- 3.1.5 - Circadian rhythms in the infants and the elderly -- 3.2 - Interaction between sleepiness and circadian rhythms -- 3.2.1 - Alertness and vigilance: interaction between circadian rhythms and sleep pressure -- 3.2.2 - Shift work -- 3.2.3 - Melatonin -- 3.2.4 - Sleep inertia -- References -- Chapter 4 - Sleep hygiene recommendations -- 4.1 - Make sleep a priority -- 4.2 - Light -- 4.3 - Consistent bedtime -- 4.4 - Bedtime routine -- 4.5 - Noise -- 4.6 - Temperature -- 4.7 - Stimulants -- 4.7.1 - Caffeine…”

Tabla de Contenidos: “…-- Improving the Perspective Warp -- Adding Layer Styles -- Using the Free Transform, Warp, or Puppet Warp Option After Applying Perspective Warp -- Perspective Warp for Type and Shape Layers -- Project: Touch Up Your Images After Using the Perspective Warp with the Content-Aware Move Tool -- Option 1: Use the Original Photo -- Option 2: Content-Aware Fill Workspace -- Option 3: Content-Aware Fill Dialog Box -- Option 4: Content-Aware Move Tool (J) -- Content-Aware Move Tool Options -- Content-Aware Scale -- Content-Aware Scale Options -- Project: Sky Replacement -- Summary -- Chapter 8: Basic Filters and Smart Filters -- Basic Filters -- Project: Working with Various Backgrounds and Filters -- Working in the Filter Gallery -- Artistic Folder -- Brush Strokes Folder -- Distort Folder -- Sketch Folder -- Stylize Folder -- Texture Folder -- Adjusting Textures -- Project: Loading a Custom Texture to Create Another Distortion -- Correcting Textures -- Combining Gallery Filters -- Adjusting Filter Masks -- Isolating a Selection on a Smart Filter Mask -- Smart Filter Blending Modes -- Basic Filters Continued -- Blur (Radial Blur and Shape Blur) -- Radial Blur -- Shape Blur -- Blur Gallery Workspace (Spin Blur, Path Blur) -- Spin Blur -- Effects Panel -- Noise Panel -- Motion Effects Panel -- Path Blur -- Spin and Path Blur Options…”

Tabla de Contenidos: “…1.4.5 Binary Addition1.4.6 Signed Binary Numbers; 1.5 LOGIC GATES; 1.5.1 NOT Gate; 1.5.2 Buffer; 1.5.3 AND Gate; 1.5.4 OR Gate; 1.5.5 Other Two-Input Gates; 1.5.6 Multiple-Input Gates; 1.6 BENEATH THE DIGITAL ABSTRACTION; 1.6.1 Supply Voltage; 1.6.2 Logic Levels; 1.6.3 Noise Margins; 1.6.4 DC Transfer Characteristics; 1.6.5 The Static Discipline; 1.7 CMOS TRANSISTORS; 1.7.1 Semiconductors; 1.7.2 Diodes; 1.7.3 Capacitors; 1.7.4 nMOS and pMOS Transistors; 1.7.5 CMOS NOT Gate; 1.7.6 Other CMOS Logic Gates; 1.7.7 Transmission Gates; 1.7.8 Pseudo-nMOS Logic; 1.8 POWER CONSUMPTION…”