Tabla de Contenidos: “…Creating and funding an account with MetaMask -- Using MetaMask and Remix IDE to deploy a smart contract -- Adding a custom network to MetaMask and connecting it with Remix IDE -- Importing accounts into MetaMask using keystore files -- Deploying a contract with MetaMask -- Interacting with a contract through MetaMask using Remix IDE -- Summary -- Chapter 11 : Tools, Languages, and Frameworks for Ethereum Developers -- Languages -- The Solidity compiler -- Installing solc -- Experimenting with solc -- Tools, libraries, and frameworks -- Node.js -- Ganache -- ganache-cli -- Ganache UI -- Truffle -- Drizzle -- Other tools -- Contract development and deployment -- Writing smart contracts -- Testing smart contracts -- Deploying smart contracts -- The Solidity language -- Functions -- Variables -- Local variables -- Global variables -- State variables -- Data types -- Value types -- Reference types -- Control structures -- Events -- Inheritance -- Libraries -- Error handling -- Summary -- Chapter 12 : Web3 Development Using Ethereum -- Interacting with contracts using Web3 and Geth -- Deploying contracts -- Using solc to generate ABI and code -- Querying contracts with Geth -- Interacting with Geth using POST requests -- Interacting with contracts via frontends -- Installing the web3.js JavaScript library -- Creating a web3 object -- Creating an app.js JavaScript file -- Creating a frontend webpage -- Calling contract functions -- Creating a frontend webpage -- Deploying and interacting with contracts using Truffle -- Installing and initializing Truffle -- Compiling, testing, and migrating using Truffle -- Interacting with the contract -- Using Truffle to test and deploy smart contracts -- Deployment on decentralized storage using IPFS -- Summary -- Chapter 13 : The Merge and Beyond -- Introduction -- Ethereum after The Merge -- The Beacon Chain…”

Tabla de Contenidos: “…Dent and Co. . 441 -- 4.3.4.1 Dents Reihenpublikationen . 444 -- 4.3.4.2 Die Everyman's Library . 459 -- 4.3.4.3 Reputation des Verlages J. …”

Tabla de Contenidos: “…Subpart G-Sources for Brachytherapy -- Subpart H-Sealed Sources for Diagnosis -- Subpart I-Teletherapy -- Subpart J-Training and Experience Requirements -- Subpart K-Enforcement -- PART 36-LICENSES AND RADIATION SAFETY REQUIREMENTS FOR IRRADIATORS -- Subpart A-General Provisions -- Subpart B-Specific Licensing Requirements -- Subpart C-Design and Performance Requirements for Irradiators -- Subpart D-Operation of Irradiators -- Subpart E-Records -- Subpart F-Enforcement -- E Nuclear Regulatory Commission Agreement and Non-agreement States -- AGREEMENT STATES -- NON-AGREEMENT STATES -- F Regulatory Chronology -- G History of Radiation Regulation in Medicine -- PROFESSIONAL SELF-REGULATION -- GOVERNMENT REGULATION -- NUCLEAR REGULATORY COMMISSION -- Atomic Energy Act -- Other Federal Agencies -- Federal Radiation Council -- Other Executive Agencies -- PHS Radiological Health Program -- Environmental Protection Agency -- STATE REGULATION -- REFERENCES -- H Public Meeting Documents -- ORGANIZATIONS FORMALLY CONTACTED FOR PUBLIC MEETING PARTICIPATION -- COMMITTEE FOR REVIEW AND EVALUATION OF THE MEDICAL USE PROGRAM OF THE NUCLEAR REGULATORY COMMISSION -- PUBLIC MEETING ANNOUNCEMENT AND REQUEST FOR WRITTEN TESTIMONY -- Background -- Providing Testimony -- Guide to Preparing Testimony -- TOPICS -- Regulation -- Data/Risk -- Quality Management/Quality Assurance -- Education -- Other -- PUBLIC HEARING OCTOBER 12, 1994 ORGANIZATIONS AND INDIVIDUALS SUBMITTING TESTIMONY -- QUESTION SUMMARIES -- I Quality Management Technical Panel -- LIST OF PARTICIPANTS, TUESDAY, OCTOBER 11, 1994 -- Speakers -- Observers -- Subcommitee Members -- J Commissioned Papers -- K The Linear, No-Threshold Model -- ADOPTION OF THE LINEAR, NO-THRESHOLD MODEL -- Divided Scientific Opinion, 1958-1966 -- Joint Committee on Atomic Energy Hearings, 1957-1960s…”

Tabla de Contenidos: “…Las que tratan del modo de librar los caudales a quienes y recoger las datas (h . 23 v.-26 r.) . 6 . …”

Tabla de Contenidos: “…Adding UARTs to the RPi -- Summary -- Chapter 10 Interacting with the Physical Environment -- Interfacing to Actuators -- DC Motors -- Stepper Motors -- Relays -- Interfacing to Analog Sensors -- Linear Analog Sensors -- Nonlinear Analog Sensors -- Analog Sensor Signal Conditioning -- Interfacing to an Analog Accelerometer -- Interfacing to Local Displays -- MAX7219 Display Modules -- Character LCD Modules -- OLED Dot-Matrix Display -- Building C/C++ Libraries -- Makefiles -- CMake -- Summary -- Chapter 11 Real-Time Interfacing Using the Arduino -- The Arduino -- An Arduino Serial Slave -- A UART Echo Test Example -- UART Command Control of an Arduino -- An Arduino I2C Slave -- An I2C Test Circuit -- I2C Register Echo Example -- I2C Temperature Sensor Example -- I2C Temperature Sensor with a Warning LED -- Arduino Slave Communication Using C/C++ -- An I2C Ultrasonic Sensor Application -- An Arduino SPI Slave -- Programming the Arduino from the RPi Command Line -- Summary -- Part III Advanced Interfacing and Interaction -- Chapter 12 The Internet of Things -- The Internet of Things (IoT) -- The RPi as an IoT Sensor -- The RPi as a Sensor Web Server -- Nginx -- GNU Cgicc Applications (Advanced) -- A C/C++ Web Client -- Network Communications Primer -- A C/C++ Web Client -- Secure Communication Using OpenSSL -- The RPi as a "Thing" -- ThingSpeak -- The Linux Cron Scheduler -- Sending E-mail from the RPi -- If This Then That (IFTTT) -- Large-Scale IoT Frameworks -- MQ Telemetry Transport (MQTT) -- IBM Bluemix Internet of Things -- An IBM IoT MQTT Node.js Publish Example -- An IBM IoT MQTT C++ Publish Example -- Visualize Data Using IBM Quickstart -- The C++ Client/Server -- IoT Device Management -- Remote Monitoring of the RPi -- RPi Watchdog Timers -- Static IP Addresses -- Power over Ethernet (PoE) -- Summary…”

Tabla de Contenidos: “…P 500 Returns -- 6.4.1.2 Linear Regression for Predicting S&amp -- P 500 Returns -- 6.4.2 Prediction of 10‐Year Bond Returns -- 6.4.2.1 10‐Year Bond Returns -- 6.4.2.2 Linear Regression for Predicting 10‐Year Bond Returns -- Part II Risk Management -- Chapter 7 Volatility Prediction -- 7.1 Applications of Volatility Prediction -- 7.1.1 Variance and Volatility Trading -- 7.1.2 Covariance Trading -- 7.1.3 Quantile Estimation -- 7.1.4 Portfolio Selection -- 7.1.5 Option Pricing -- 7.2 Performance Measures for Volatility Predictors -- 7.3 Conditional Heteroskedasticity Models -- 7.3.1 GARCH Predictor -- 7.3.1.1 Predicting the Squared Returns -- 7.3.1.2 Predicting the Realized Volatility -- 7.3.1.3 S&amp -- P 500 Volatility Prediction with GARCH(1,1) -- 7.3.2 ARCH Predictor -- 7.3.2.1 Predicting the Squared Returns -- 7.3.2.2 S&amp -- P 500 Volatility Prediction with ARCH(p) -- 7.4 Moving Average Methods -- 7.4.1 Sequential Sample Variance -- 7.4.2 Exponentially Weighted Moving Average -- 7.4.2.1 Asymmetric Exponentially Weighted Moving Average -- 7.5 State Space Predictors -- 7.5.1 Linear Regression Predictor -- 7.5.1.1 Prediction with Volatility and Mean -- 7.5.1.2 Prediction with Past Squared Returns -- 7.5.2 Kernel Regression Predictor -- Chapter 8 Quantiles and Value‐at‐Risk -- 8.1 Definitions of Quantiles -- 8.2 Applications of Quantiles -- 8.2.1 Reserve Capital -- 8.2.1.1 Value‐at‐Risk of a Portfolio -- 8.2.1.2 Decomposition of the Loss of a Portfolio -- 8.2.1.3 Losses over Several Periods -- 8.2.2 Margin Requirements -- 8.2.3 Quantiles as a Risk Measure -- 8.3 Performance Measures for Quantile Estimators -- 8.3.1 Measuring the Probability of Exceedances -- 8.3.1.1 Cross‐Validation -- 8.3.1.2 Probability Differences -- 8.3.1.3 Confidence of the Performance Measure -- 8.3.1.4 Probability Differences Over All Time Intervals…”

Tabla de Contenidos: “…2.8.1 Machine Learning Projects -- 2.8.2 Spam Detection Using Machine Learning -- 2.8.3 Spam Detection for YouTube Comments Using Naïve Bayes Classifier -- 2.8.4 Fake News Detection -- 2.8.5 House Price Prediction -- 2.8.6 Gold Price Prediction -- Bibliography -- Chapter 3 Multilayer Perceptron (in Neural Networks) -- 3.1 Multilayer Perceptron for Digit Classification -- 3.1.1 Implementation of MLP using TensorFlow for Classifying Image Data -- 3.2 Training Multilayer Perceptron -- 3.3 Backpropagation -- References -- Chapter 4 Kernel Machines -- 4.1 Different Kernels and Their Applications -- 4.2 Some Other Kernel Functions -- 4.2.1 Gaussian Radial Basis Function (RBF) -- 4.2.2 Laplace RBF Kernel -- 4.2.3 Hyperbolic Tangent Kernel -- 4.2.4 Bessel Function of the First-Kind Kernel -- 4.2.5 ANOVA Radial Basis Kernel -- 4.2.6 Linear Splines Kernel in One Dimension -- 4.2.7 Exponential Kernel -- 4.2.8 Kernels in Support Vector Machine -- References -- Chapter 5 Linear and Rule-Based Models -- 5.1 Least Squares Methods -- 5.2 The Perceptron -- 5.2.1 Bias -- 5.2.2 Perceptron Weighted Sum -- 5.2.3 Activation Function -- 5.2.3.1 Types of Activation Functions -- 5.2.4 Perceptron Training -- 5.2.5 Online Learning -- 5.2.6 Perceptron Training Error -- 5.3 Support Vector Machines -- 5.4 Linearity with Kernel Methods -- References -- Chapter 6 Distance-Based Models -- 6.1 Introduction -- 6.1.1 Distance-Based Clustering -- 6.2 K-Means Algorithm -- 6.2.1 K-Means Algorithm Working Process -- 6.3 Elbow Method -- 6.4 K-Median -- 6.4.1 Algorithm -- 6.5 K-Medoids, PAM (Partitioning Around Medoids) -- 6.5.1 Advantages -- 6.5.2 Drawbacks -- 6.5.3 Algorithm -- 6.6 CLARA (Clustering Large Applications) -- 6.6.1 Advantages -- 6.6.2 Disadvantages -- 6.7 CLARANS (Clustering Large Applications Based on Randomized Search) -- 6.7.1 Advantages -- 6.7.2 Disadvantages…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Spark for Machine Learning -- Spark overview and Spark advantages -- Spark overview -- Spark advantages -- Spark computing for machine learning -- Machine learning algorithms -- MLlib -- Other ML libraries -- Spark RDD and dataframes -- Spark RDD -- Spark dataframes -- Dataframes API for R -- ML frameworks, RM4Es and Spark computing -- ML frameworks -- RM4Es -- The Spark computing framework -- ML workflows and Spark pipelines -- ML as a step-by-step workflow -- ML workflow examples -- Spark notebooks -- Notebook approach for ML -- Step 1: Getting the software ready -- Step 2: Installing the Knitr package -- Step 3: Creating a simple report -- Spark notebooks -- Summary -- Chapter 2: Data Preparation for Spark ML -- Accessing and loading datasets -- Accessing publicly available datasets -- Loading datasets into Spark -- Exploring and visualizing datasets -- Data cleaning -- Dealing with data incompleteness -- Data cleaning in Spark -- Data cleaning made easy -- Identity matching -- Identity issues -- Identity matching on Spark -- Entity resolution -- Short string comparison -- Long string comparison -- Record deduplication -- Identity matching made better -- Crowdsourced deduplication -- Configuring the crowd -- Using the crowd -- Dataset reorganizing -- Dataset reorganizing tasks -- Dataset reorganizing with Spark SQL -- Dataset reorganizing with R on Spark -- Dataset joining -- Dataset joining and its tool - the Spark SQL -- Dataset joining in Spark -- Dataset joining with the R data table package -- Feature extraction -- Feature development challenges -- Feature development with Spark MLlib -- Feature development with R -- Repeatability and automation -- Dataset preprocessing workflows…”

Tabla de Contenidos: “…Supervised Learning and Binary Classification -- 3.2.3. Generalized Linear Model -- 3.2.4. Adding Regularization With Lasso -- 3.2.5. …”

Tabla de Contenidos: “…-- Applications of POS tagging -- Training a POS tagger -- Training a sentiment classifier for movie reviews -- Training a bag-of-words classifier -- Summary -- Chapter 3: Deep Learning and TensorFlow -- Deep learning -- Perceptron -- Activation functions -- Sigmoid -- Hyperbolic tangent -- Rectified linear unit -- Neural network -- One-hot encoding -- Softmax -- Cross-entropy -- Training neural networks -- Backpropagation -- Gradient descent -- Stochastic gradient descent -- Regularization techniques -- Dropout -- Batch normalization -- L1 and L2 normalization -- Convolutional Neural Network -- Kernel -- Max pooling -- Recurrent neural network -- Long-Short Term Memory -- TensorFlow -- General Purpose - Graphics Processing Unit -- CUDA -- cuDNN -- Installation -- Hello world! …”

Tabla de Contenidos: “…-- Architecture of CNNs -- Types of layers in a CNN -- Building a perceptron-based linear regressor -- Building an image classifier using a single layer neural network -- Building an image classifier using a Convolutional Neural Network -- Summary -- Index…”

Tabla de Contenidos: “…11.5.2 Wide-Sense Ergodic Processes 333 -- 11.6 Gaussian Processes 336 -- 11.7 Poisson Processes 339 -- 11.8 Summary 341 -- Problems 341 -- 12 Analysis and Processing of Random Processes 345 -- 12.1 Stochastic Continuity, Differentiation, and Integration 345 -- 12.1.1 Mean-Square Continuity 345 -- 12.1.2 Mean-Square Derivatives 346 -- 12.1.3 Mean-Square Integrals 347 -- 12.2 Power Spectral Density 347 -- 12.3 Noise 353 -- 12.3.1 White Noise 353 -- 12.4 Sampling of Random Signals 355 -- 12.5 Optimum Linear Systems 357 -- 12.5.1 Systems Maximizing Signal-to-Noise Ratio 357 -- 12.5.2 Systems Minimizing Mean-Square Error 359 -- 12.6 Summary 362 -- Problems 362 -- Bibliography 365 -- Books 365 -- Internet Websites 368 -- Answers 369 -- Index 387.…”

Tabla de Contenidos: “…Machine generated contents note: Chapter 1: Introduction to High Performance Drives 1.1 Preliminary Remarks 1.2 General Overview of High Performance Drives 1.3 Challenges and Requirements for Electric Drives for Industrial Applications 1.4 Organization of the Book References Chapter 2: Mathematical and Simulation Models of AC Machines 2.1 Preliminary Remarks 2.2 DC Motors 2.2.1 Separately Excited DC Motor Control 2.2.2 Series DC Motor Control 2.3 Squirrel Cage Induction Motor 2.3.1 Space vector representation 2.3.2 Per unit model of Induction Motor 2.3.3 Double Fed Induction Generator (DFIG) 2.4 Mathematical Model of Permanent Magnet Synchronous Motor Problems References Chapter 3: Pulse Width Modulation of Power Electronic DC-AC Converter 3.1 Preliminary Remarks 3.2 Classification of Pulse Width Modulation Schemes for Voltage source inverter 3.3 Pulse Width Modulated Inverters 3.3.1 Single Phase Half Bridge Inverters 3.3.1.1 Matlab/Simulink Model of Half Bridge Inverter 3.3.2 Single Phase Full Bridge Inverters 3.3.2.1 Matlab/Simulink Model of Single-phase Full-Bridge Inverter 3.4 Three-phase PWM voltage source inverter 3.4.1 Carrier based Sinusoidal PWM 3.4.2 Third Harmonic Injection Carrier-based PWM 3.4.3 Carrier-based PWM With Offset Addition 3.4.4 Space Vector PWM 3.4.5 Discontinuous Space Vector PWM 3.4.6 Matlab/Simulink Model for space vector PWM 3.4.7 Space Vector PWM in Over-modulation Region 3.4.8 Artificial Neural Network Based PWM 3.5 Relationship Between Carrier-based PWM and Space Vector PWM 3.6 Multi-level Inverters 3.6.1 Diode Clamped Multi-level Inverters 3.6.2 Flying Capacitor Type Multi-level Inverter 3.6.3 Cascaded H-Bridge Multi-level Inverter 3.7 Impedance Source or Z-Source Inverter 3.7.1 Circuit Analysis 3.7.2 Carrier-based Simple Boost PWM control of a Z-source Inverter 3.7.3 Carrier-based Maximum Boost PWM control of a Z-source Inverter 3.7.4 Matlab/Simulink model of Z-source inverter 3.8 Quasi Impedance Source or qZSI Inverter 3.8.1 Matlab/Simulink model of qZ-source inverter 3.9 Dead Time Effect in a Multi-phase Inverter 3.10 Summary References Chapter 4: Field Oriented Control of AC Machines 4.1 Introduction 4.2 Induction Machines Control 4.2.1 Control of Induction Motor using V/f method 4.2.2 Vector Control of Induction Motor [4.1-4.16] 4.2.3 Direct and Indirect Field Oriented Control 4.2.4 Rotor and stator flux computation 4.2.5 Adaptive flux observer 4.2.6 Stator Flux Orientation 4.2.7 Field Weakening Control 4.3 Vector Control of Double Fed Induction Generator (DFIG) 4.3.1 Introduction 4.3.2 Vector Control of DFIG connected with the Grid (abModel) 4.3.3 Simulation Results 4.4 Control of Permanent Magnet Synchronous Machine 4.4.1 Introduction 4.4.2 Vector Control of PMSM in dq axis 4.4.3 Vector Control of PMSM in a-baxis using PI controller 4.4.4 Scalar Control of PMSM Exercises Additional tasks Possible tasks for DFIG References Chapter 5: XXXXXXXXXX Chapter 6: Nonlinear Control of Electrical Machines Using Nonlinear Feedback 6.1 Introduction 6.2 dynamic system linearization using non-linear feedback 6.3 Nonlinear Control of Separately Excited DC Motor 6.3.1 Matlab/Simulink Nonlinear Control Model 6.3.2 Nonlinear Control Systems 6.3.3 Speed Controller 6.3.4 Control for variable m 6.3.5 Field Current Controller 6.3.6 Simulation Results 6.4 Multiscalar model (MM) of induction motor 6.4.1 Multiscalar variables 6.4.2 Nonlinear linearization of induction motor fed by voltage controlled VSI 6.4.3 Design of system control 6.4.4 Nonlinear linearization of induction motor fed by current controlled VSI 6.4.5 Stator oriented nonlinear control system (based on Ys,is) 6.4.6 Rotor-Stator Fluxes based Model 6.4.7 Stator Oriented Multiscalar Model 6.4.8 Multiscalar Control of Induction Motor 6.4.9 Induction Motor Model 6.4.10 State Transformation 6.4.11 Decoupled IM Model 6.5 MM of double fed induction machine (DFIM) 6.6 Nonlinear Control of Permanent Magnet Synchronous Machine 6.6.1 Nonlinear Control of PMSM for a dq motor model 6.6.2 Nonlinear Vector Control of PMSM in a-baxis 6.6.3 PMSM in a-b(x-y) axis 6.6.4 Transformation 6.6.5 Control System 6.6.6 Simulation Results Problems References Chapter 7: Five-Phase Induction Motor Drive System 7.1 Preliminary remarks 7.2 Advantages and Applications of Multi-phase drives 7.3 Modelling and Simulation of a Five-phase Induction motor drive 7.3.1 Five-phase Induction motor model 7.3.1.1 Phase variable model 7.3.1.2 Model transformation 7.3.1.3 Machine model in an arbitrary common reference frame 7.3.1.4 Matlab/Simulink model of main fed five-phase induction motor drive 7.3.2 Five-phase Two-level Voltage source Inverter Model 7.3.2.A Ten-Step Mode of Operation 7.3.2.A.1 Fourier analysis of the five-phase inverter output voltages 7.3.2.A.2 Matlab/Simulink Modelling for Ten-step Mode 7.3.2.A.3 Prototype of a five-phase VSI for ten-step operation 7.3.2.A.4 Experimental Results for Ten-Step mode 7.3.2.A.5 PWM mode of operation of five-phase VSI 7.3.3 PWM Schemes of a Five-phase VSI 7.3.3.1 Carrier-based Sinusoidal PWM scheme 7.3.3.2 Matlab/Simulink simulation of Carrier-based sinusoidal PWM 7.3.3.3 5th Harmonic Injection Based pulse width modulation scheme 7.3.3.4 Matlab/Simulink simulation of 5th harmonic injection PWM 7.3.3.5 Offset addition based pulse width modulation scheme 7.3.3.6 Space Vector Pulse Width Modulation Scheme 7.3.3.7 Matlab/Simulink model of SVPWM 7.4 Indirect Rotor Field Oriented Control of Five-phase induction motor 7.4.1 Matlab/Simulink Model of Field oriented control of five-phase Induction machine 7.5 Field Oriented Control of Five-phase induction motor with current control in the Synchronous reference frame 7.6 Model Predictive Control (MPC) 7.6.1 MPC Applied to a Five-phase Two-Level VSI 7.6.2 Matlab/Simulink of MPC for Five-phase VSI 7.7 Summary 7.8 Bibliography Chapter 8: Sensorless Speed Control of AC Machines 8.1 Preliminary Remarks 8.2 Sensorless Control of Induction Motor 8.2.1 Observer 1 8.2.2 Observer 2 8.2.3 Observer 3 8.2.4 MRAS- (closed loop) speed estimator 8.2.5 The use of power measurements 8.3 Sensorless Control of PMSM 8.3.1 Control system of PMSM 8.3.2 Adaptive backstepping observer 8.3.3 Model Reference Adaptive System for PMSM 8.3.4 Simulation Results 8.4 MRAS-Based Sensorless Control of Five-Phase Induction Motor Drive 8.4.1 MRAS-BASED SPEED ESTIMATOR 8.4.2 Simulation Results References Chapter 9: Selected Problems of Induction Motor Drives with Voltage Inverter and Inverter Output Filters 9.1 Drives and filters - overview 9.2 Three phase to two phase transformations 9.3 Voltage and current common mode component 9.3.1 Matlab/Simulink model of induction motor drive with PWM inverter and common mode voltage 9.4 Induction motor common mode circuit 9.5 Bearing current types and reduction methods 9.5.1 Common mode choke 9.5.2 Common mode transformers 9.5.3 Common mode voltage reduction by PWM modifications 9.6 Inverter output filters 9.6.1 Selected structures of inverter output filters 9.6.2 Inverter output filters design 9.6.3 Motor choke 9.6.4 Matlab/Simulink model of induction motor drive with PWM inverter and differential mode (normal mode) LC filter 9.7 Estimation problems in the drive with filters 9.7.1 Introduction 9.7.2 Speed observer with disturbances model 9.7.3 Simple observer based on motor stator models 9.8 Motor control problems in the drive with filters 9.8.1 Introduction 9.8.2 Field oriented control 9.8.3 Nonlinear field oriented control 9.8.4 Nonlinear multiscalar control 9.9 Predictive current control in the drive system with output filter 9.9.1 Control system 9.9.2 Predictive current controller 9.9.3 EMF estimation technique 9.10 Problems 9.11 Questions 9.12 References Index…”

Tabla de Contenidos: “…Distortion, Saturation, and Clipping -- 10.1 Introduction: Linear and Nonlinear Processing…”

Tabla de Contenidos: “…10 - Discrete and Fast Fourier Transforms (DFT, FFT) -- 10.1 Discrete Fourier Transform and Inverse Discrete Fourier Transform Equations -- 10.2 First Discrete Fourier Transform Example -- 10.3 Second Discrete Fourier Transform Example -- 10.4 Third Discrete Fourier Transform Example -- 10.5 Fourth Discrete Fourier Transform Example -- 10.6 Fast Fourier Transform -- 10.7 Filtering Using the Fast Fourier Transform and Inverse Fast Fourier Transform -- 10.8 Bit Growth in Fast Fourier Transforms -- 10.9 Bit Reversal Addressing -- 11 - Digital Upconversion and Downconversion -- 11.1 Digital Upconversion -- 11.2 Digital Downconversion -- 11.3 Intermediate Frequency Subsampling -- 12 - Error-Correction Coding -- 12.1 Linear Block Encoding -- 12.2 Linear Block Decoding -- 12.3 Minimum Coding Distance -- 12.4 Convolutional Encoding -- 12.5 Viterbi Decoding -- 12.6 Soft Decision Decoding -- 12.7 Cyclic Redundancy Check -- 12.8 Shannon Capacity and Limit Theorems -- 13 - Matrix Inversion -- 13.1 Matrix Basics -- 13.2 Cholesky Decomposition -- 13.3 4×4 Cholesky Example -- 13.4 QR Decomposition -- 13.5 Gram-Schmidt Method -- 13.6 QR Decomposition Restructuring for Parallel Implementation -- 14 - Field-Oriented Motor Control -- 14.1 Magnetism Basics -- 14.2 AC Motor Basics -- 14.3 DC Motor Basics -- 14.4 Electronic Commutation -- 14.5 AC Induction Motor -- 14.6 Motor Control -- 14.7 Park and Clark Transforms -- 15 - Analog and Time Division Multiple Access Wireless Communications -- 15.1 Early Digital Innovations -- 15.2 Frequency Modulation -- 15.3 Digital Signal Processor -- 15.4 Digital Voice Phone Systems -- 15.5 Time Division Multiple Access Modulation and Demodulation -- 16 - CDMA Wireless Communications -- 16.1 Spread Spectrum Technology -- 16.2 Direct Sequence Spread Spectrum -- 16.3 Walsh Codes -- 16.4 Concept of Code Division Multiple Access…”

Tabla de Contenidos: “…3.2.2 Parabolic Trough Collector -- 3.2.3 Compound Parabolic -- 3.3 Point Focus and Other Concentrating Solar Collectors -- 3.3.1 Central Receiver System -- 3.3.2 Solar Dish -- 3.3.3 Fresnel Lens -- 3.4 Improving the Thermal Performance of Solar Concentrating Collectors -- 3.5 Industrial Adoption and Costs of Solar Concentrating Collectors -- 3.6 Conclusions and Recommendations -- References -- 4 Nanotechnology for Heat Transfer -- 4.1 Introduction -- 4.2 Classification of Nanomaterials -- 4.2.1 Zero-dimensional (0D) -- 4.2.2 One-dimensional (1D) -- 4.2.3 Two-dimensional (2D) -- 4.2.4 Three-dimensional (3D) -- 4.3 Heat Transfer Characteristics and Applications of Nanotechnology on the Heat Transfer Enhancement -- 4.3.1 Convective Heat Transfer -- 4.3.2 Boiling Heat Transfer -- 4.3.3 Thermal Conductivity -- 4.3.4 Viscosity -- 4.4 A Review of Studies and Recent Advances Using Nanomaterials in Energy Conversion, Energy Storage, and Heat Transfer Developm -- 4.5 Recent Advances -- 4.6 Challenges and Future Scope -- 4.7 Conclusion -- References -- 5 Nanofluids in Linear Fresnel Reflector -- 5.1 Introduction and Recent Advances of Linear Fresnel Reflectors -- 5.2 The Idea of Using Nanofluids in Solar Collectors -- 5.3 A Review of Studies with Nanofluid-based Linear Fresnel Reflector -- 5.4 Remarks and Future Scope -- 5.4.1 Advantages of LFR -- 5.4.2 Disadvantages of LFR -- 5.5 Conclusions -- References -- 6 Thermal Management and Performance Enhancement of Parabolic Trough Concentrators Using Nanofluids -- 6.1 Introduction -- 6.2 Recent Advances of Parabolic Trough Collectors -- 6.3 Application of Nanofluids in PTCs -- 6.4 State-of-Art Studies on Using Nanofluids in Parabolic Trough Collectors -- 6.5 Conclusions and Future Scope -- References -- 7 Developing Innovations in Parabolic Trough Collectors (PTCs) Based on Numerical Studies -- 7.1 Introduction…”

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“…En concreto, algunos de los hábitos extendidos desde los años 90 han ido quebrándose con tanta fuerza que bien puede hablar se de ́revolución seriada» para referidos a dicha transformación. …”
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“…Recuperar la aportación pedagógica de Célestin Freinet y compartirla con el lector es lo que pretende este libro. Hablar, conversar y pensar sobre Freinet a partir de /Las invariantes pedagógicas/ que el autor considera uno de sus libros más importantes. …”
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Tabla de Contenidos: “…5.10 TRABAJANDO LAS AUTOFORMAS CON LAS MACROS -- 5.11 ATAJOS EN MACROS -- 5.12 UTILIZACIÓN CONJUNTA DE FUNCIONES Y MACROS -- 5.13 HACIENDO HABLAR A EXCEL -- 5.14 GOBERNANDO LAS TABLAS DINÁMICAS CON MACROS -- 6 CONCLUSIONES E INFORMACIÓN AUXILIAR -- 6.1 RELACIÓN DE FUNCIONES INCORPORADAS EN EXCEL -- ÍNDICE ALFABÉTICO…”