Tabla de Contenidos: “…Instaurer une stratégie nationale -- 6.2. Cibler le point d'intervention -- 6.2.1. Catégories de bâtiments -- Graphique 18. …”

Tabla de Contenidos: “…Impact des redevances d'émission et de terminaison d'appels sur l'incitation à cibler certains utilisateurs finaux -- Encadré 6. …”

Tabla de Contenidos: “…6.9.6 Elementeinstellungen mit Pipette und Spritze -- 6.9.7 Tastaturkürzel -- 6.9.8 Symbolleiste »Elemente bearbeiten« -- 6.9.9 Symbolleiste »Elemente anordnen« -- 6.10 Drag&amp -- Drop -- 6.11 Übungsfragen -- Kapitel 7: Treppen -- 7.1 Standardtreppen -- 7.2 Individuelle Treppen -- 7.3 Benutzerdefinierte Treppe -- 7.3.1 Deckendurchbruch -- 7.4 Übungsfragen -- Kapitel 8: Fassaden -- 8.1 Das Fassaden-Werkzeug -- 8.2 Fassaden mit Polylinienkontur -- 8.3 Fassaden bearbeiten -- 8.4 Symbolleiste Fassade -- 8.5 Übungsfragen -- Kapitel 9: Morph-Elemente -- 9.1 Das Morph-Werkzeug -- 9.2 Morph-Bearbeitung -- 9.2.1 Die Morph-Symbolleiste -- 9.2.2 Glätten -- 9.2.3 Arbeiten mit der Pet-Palette -- 9.3 Übungsfragen -- Kapitel 10: Bemaßung und Text -- 10.1 Bemaßungseinstellungen -- 10.2 Linear bemaßen -- 10.2.1 Bemaßungsvorgang -- 10.2.2 Geometriemethoden -- 10.3 Automatisch bemaßen -- 10.3.1 Außenbemaßung -- 10.3.2 Innenbemaßung -- 10.3.3 Änderungen an der Bemaßung -- 10.4 Das Text-Werkzeug -- 10.4.1 Einstellungen und Darstellung -- 10.4.2 Texterstellung -- 10.4.3 Texte bearbeiten -- 10.4.4 Etiketten -- 10.4.5 Text ersetzen und Rechtschreibung prüfen -- 10.5 Änderungsmarken und Änderungsmanager -- 10.6 Übungsfragen -- Kapitel 11: Raumstempel, Listen und Auswertungen -- 11.1 Raumstempel -- 11.1.1 Feineinstellungen -- 11.1.2 Anzeige von Raumstempeln und Raum-Kategorien -- 11.1.3 Räume anpassen -- 11.1.4 Raum nach Dachlinien erzeugen -- 11.1.5 Eigene Raumkategorien -- 11.2 Listen -- 11.2.1 Elementlisten -- 11.2.2 Listen zur Dokumentation -- 11.3 Übungsfragen -- Kapitel 12: Schnitte, Ansichten, Innenansichten, Arbeitsblätter, Details und 3D-Dokumente -- 12.1 Schnitte -- 12.2 Ansichten -- 12.3 Innenansichten -- 12.4 Arbeitsblätter -- 12.5 Details -- 12.6 Die grafischen Überschreibungen -- 12.7 Das 3D-Dokument -- 12.8 3D-Schnitte -- 12.8.1 3D-Dokument erstellen…”

Tabla de Contenidos: “…7.5.3.5 Laser beam cutting of opaque material at partially submerged condition -- 7.5.3.6 Laser beam cutting of transparent material at partially submerged condition -- 7.5.3.7 Hybrid waterjet laser cutting -- 7.6 Pulsed IR laser ablation of Inconel 625 superalloy at submerged condition: A case study -- 7.6.1 Experimental setup -- 7.6.2 Development of mathematical model -- 7.6.3 ANOVA analysis -- 7.6.4 Effects of different process parameters on machining responses -- 7.6.4.1 Effect of different process parameters on kerf width -- 7.6.4.2 Effect of different process parameters on depth of cut -- 7.6.4.3 Effect of different process parameters on HAZ width -- Conclusion -- Acknowledgment -- References -- 8 Glass molding process for microstructures -- 8.1 Application of microstructures -- 8.1.1 Optical imaging in an optical system -- 8.1.1.1 Refraction -- 8.1.1.2 Diffraction -- 8.1.2 Positioning sensor in machine tools and measurement equipment -- 8.1.2.1 Linear grating -- 8.1.2.2 Face grating -- 8.1.3 Micro fluid control in a biomedical field -- 8.2 Fundamental of glass molding technique -- 8.2.1 Introduction -- 8.2.2 Materials suited for optical microstructures molding -- 8.2.2.1 Polymethyl methacrylate -- 8.2.2.2 Low-melting optical glass -- 8.2.2.3 Infrared Materials -- 8.2.3 Mold material -- 8.2.3.1 Commonly used mold material -- 8.2.3.2 Mold machining method -- 8.2.3.3 New mold plating material -- 8.3 Modeling and simulation of microstructure molding -- 8.3.1 Modeling of viscoelastic constitutive -- 8.3.1.1 The Maxwell model -- Creep -- Relaxation -- Recovery -- 8.3.1.2 The Kelvin model -- Creep -- Relaxation -- Recovery -- 8.3.1.3 The Burger model -- 8.3.2 Simulation of microstructure molding process -- 8.3.2.1 2D modeling -- 8.3.2.2 3D modeling -- 8.3.3 GMP simulation coupling heat transfer and viscous deformation…”

Tabla de Contenidos: “…3.3 Computing the Best Local Alignment -- 3.3.1 StripedAlignment -- 3.3.2 ChunkedAlignment1 -- 3.3.3 ChunkedAlignment2 -- 3.3.4 Memory requirements -- 3.4 Experimental Results -- StripedScore -- StripedAlignment -- ChunkedAlignment1 -- 4 Alignment of three sequences -- 4.1 Three-Sequence Alignment Algorithm -- 4.2 Computing the Score of the Best Alignment -- 4.2.1 Layered algorithm -- GPU computational strategy -- Analysis -- 4.2.2 Sloped algorithm -- GPU computational strategy -- Analysis -- 4.3 Computing the Best Alignment -- 4.3.1 Layered-BT1 -- 4.3.2 Layered-BT2 -- 4.3.3 Layered-BT3 -- 4.4 Experimental Results -- 4.4.1 Computing the score of the best alignment -- 4.4.2 Computing the alignment -- 5 Conclusion -- References -- Chapter 9: Augmented block cimmino distributed algorithm for solving tridiagonal systems on GPU -- 1 Introduction -- 2 ABCD Solver for tridiagonal systems -- 3 GPU implementation and optimization -- 3.1 QR Method and Givens Rotation -- 3.2 Sparse Storage Format -- 3.3 Coalesced Memory Access -- 3.4 Boundary Padding -- 3.4.1 Padding of the augmented matrix -- 3.4.2 Padding for Givens rotation -- 4 Performance evaluation -- 4.1 Comparison With CPU Implementation -- 4.2 Speedup by Memory Coalescing -- 4.3 Boundary Padding -- 5 Conclusion and future work -- References -- Chapter 10: GPU computing applied to linear and mixed-integer programming -- 1 Introduction -- 2 Operations Research in Practice -- 3 Exact Optimization Algorithms -- 3.1 The Simplex Method -- 3.2 Dynamic Programming -- 3.2.1 Knapsack problems -- 3.2.2 Multiple-choice knapsack problem -- 3.3 Branch-and-Bound -- 3.3.1 Knapsack problem -- 3.3.2 Flow-shop scheduling problem -- 3.3.3 Traveling salesman problem -- 4 Metaheuristics -- 4.1 Genetic Algorithms -- 4.1.1 The traveling salesman problem -- 4.1.2 Scheduling problems -- 4.1.3 Knapsack problems…”

Tabla de Contenidos: “…Creating a "namespace", or a named library, with an IIFE -- 6.8. The methods of Function.prototype -- 6.8.1. …”

Tabla de Contenidos: “…13.5 Wait State Generation -- Questions -- Part II: Assembly Language Programs -- Chapter 14: Simple Assembly Language Programs -- 14.1 Exchange 10 Bytes -- 14.2 Add two Multi-Byte Numbers -- 14.3 Add two Multi-Byte BCD Numbers -- 14.4 Block Movement without Overlap -- 14.5 Block Movement with Overlap -- 14.6 Add N Numbers of Size 8 Bits -- 14.7 Check the Fourth Bit of a Byte -- 14.8 Subtract two Multi-Byte Numbers -- 14.9 Multiply two numbers of Size 8 Bits -- 14.10 Divide a 16-Bit Number by an 8-Bit Number -- Questions -- Chapter 15: Use of PC in Writing and Executing 8085 Programs -- 15.1 Steps Needed to Run an Assembly Language Program -- 15.2 Creation of .ASM File using a Text Editor -- 15.3 Generation of .OBJ File using a Cross-Assembler -- 15.4 Generation of .HEX File using a Linker -- 15.5 Downloading the Machine Code to the Kit -- 15.6 Running the Downloaded Program on the Kit -- 15.7 Running the Program using the PC as a Terminal -- Questions -- Chapter 16: Additional Assembly Language Programs -- 16.1 Search for a Number using Linear Search -- 16.2 Find the Smallest Number -- 16.3 Compute the HCF of Two 8-Bit Numbers -- 16.4 Check for '2 out of 5' Code -- 16.5 Convert ASCII to Binary -- 16.6 Convert Binary to ASCII -- 16.7 Convert BCD to Binary -- 16.8 Convert Binary to BCD -- 16.9 Check for Palindrome -- 16.10 Compute the LCM of Two 8-Bit Numbers -- 16.11 Sort Numbers using Bubble Sort -- 16.12 Sort Numbers using Selection Sort -- 16.13 Simulate Decimal up Counter -- 16.14 Simulate Decimal down Counter -- 16.15 Display Alternately 00 and FF in the Data Field -- 16.16 Simulate a Real-Time Clock -- Questions -- Chapter 17: More Complex Assembly Language Programs -- 17.1 Subtract Multi-Byte BCD Numbers -- 17.2 Convert 16-Bit Binary to BCD -- 17.3 Do an operation on Two Numbers Based on the Value of X.…”

Tabla de Contenidos: “…Text Mining Software and Libraries -- A.3. Text Mining Frameworks and Packages -- A.3.1. …”

Tabla de Contenidos: “…., safe failure fraction) -- (c) Predict the random hardware failures -- (d) Software (referred to as "program") -- (i) Requirements -- (ii) Software library modules -- (iii) Software design specification -- (iv) Code -- (v) Programming support tools…”

Tabla de Contenidos: “…Objects and Classes -- 9.1 Introduction -- 9.2 Defining Classes for Objects -- 9.3 UML Class Diagrams -- 9.4 Using Classes from the Python Library: the datetime Class -- 9.5 Immutable Objects vs. …”

Tabla de Contenidos: “…1.15.5 Superconductors -- 1.15.6 3D printing -- 1.15.7 Autonomous vehicle -- 1.16 Conclusion -- References -- Chapter 2: Advances of deep learning and related applications -- 2.1 Introduction -- 2.2 Deep learning techniques -- 2.3 Multilayer perceptron -- 2.4 Convolutional neural network -- 2.5 Recurrent neural network -- 2.6 Long-term short-term memory -- 2.7 GRU -- 2.8 Autoencoders -- 2.9 Attention mechanism -- 2.10 Deep generative models -- 2.11 Restricted Boltzmann machine -- 2.12 Deep belief network -- 2.13 Modern deep learning platforms -- 2.13.1 PyTorch -- 2.13.2 TensorFlow -- 2.13.3 Keras -- 2.13.4 Caffe (Convolutional architecture for fast feature embedding) and Caffe2 -- 2.13.5 Deeplearning4j -- 2.13.6 Theano -- 2.13.7 Microsoft cognitive toolkit (CNTK) -- 2.14 Challenges of deep learning -- 2.15 Applications of deep learning -- 2.16 Conclusion -- References -- Chapter 3: Machine learning for big data and neural networks -- 3.1 Introduction -- 3.2 Machine learning and fundamentals -- 3.2.1 Supervised learning -- 3.2.2 Decision trees -- 3.2.3 Linear regression -- 3.2.4 Naive Bayes -- 3.2.5 Logistic regression -- 3.3 Unsupervised learning -- 3.3.1 K-Means algorithm -- 3.3.2 Principal component analysis -- 3.4 Reinforcement learning -- 3.5 Machine learning in large-scale data -- 3.6 Data analysis in big data -- 3.7 Predictive modelling -- 3.7.1 Understanding customer behavior and preferences -- 3.7.2 The role of supply chain and performance management in organizational success -- 3.7.3 Management of quality and enhancement -- 3.7.4 Risk mitigation and detection of fraud -- 3.8 Neural networks -- 3.8.1 Artificial neural network -- 3.8.2 RNN -- 3.8.3 CNN -- 3.8.4 Deep learning using convolutional neural networks to find building defects -- 3.9 Data generation and manipulation -- 3.9.1 Generative Adversarial Networks…”

Tabla de Contenidos: “…Guidance for using this redbook -- 3.1 IBM WebFacing Tool scenario in this redbook -- 3.2 Sample 5250 application -- 3.3 Converting the sample 5250 application -- 3.4 Comparing the WebFacing application with the 5250 application -- 3.5 Improving the WebFacing application -- 3.5.1 Refining the generated user interface -- 3.5.2 Integrating with other Web applications -- Part 2 Moving to a WebFacing application -- Chapter 4. 5250 sample application -- 4.1 Setting up the environment -- 4.1.1 Restoring the FLGHT400 library -- 4.1.2 Setting up the user profile -- 4.2 Application overview -- 4.2.1 The Flight Reservation System -- 4.2.2 Application files -- 4.2.3 The database files -- 4.3 Using the Flight Reservation System application -- 4.3.1 Starting the application -- 4.3.2 Creating a new reservation -- 4.3.3 Generating reports…”

Tabla de Contenidos: “…11.3 Converting column or index values to datetimes -- 11.4 Using the DatetimeProperties object -- 11.5 Adding and subtracting durations of time -- 11.6 Date offsets -- 11.7 The Timedelta object -- 11.8 Coding challenge -- 11.8.1 Problems -- 11.8.2 Solutions -- Summary -- 12 Imports and exports -- 12.1 Reading from and writing to JSON files -- 12.1.1 Loading a JSON file Into a DataFrame -- 12.1.2 Exporting a DataFrame to a JSON file -- 12.2 Reading from and writing to CSV files -- 12.3 Reading from and writing to Excel workbooks -- 12.3.1 Installing the xlrd and openpyxl libraries in an Anaconda environment -- 12.3.2 Importing Excel workbooks -- 12.3.3 Exporting Excel workbooks -- 12.4 Coding challenge -- 12.4.1 Problems -- 12.4.2 Solutions -- Summary -- 13 Configuring pandas -- 13.1 Getting and setting pandas options -- 13.2 Precision -- 13.3 Maximum column width -- 13.4 Chop threshold -- 13.5 Option context -- Summary -- 14 Visualization -- 14.1 Installing matplotlib -- 14.2 Line charts -- 14.3 Bar graphs -- 14.4 Pie charts -- Summary -- Appendix A. …”

Tabla de Contenidos: “…8.1.3 Service-level tests: Testing a microservice from outside its process -- 8.1.4 Unit-level tests: Testing endpoints from within the process -- 8.2 Testing libraries: Microsoft.AspNetCore.TestHost and xUnit -- 8.2.1 Meet Microsoft.AspNetCore.TestHost -- 8.2.2 Meet xUnit -- 8.2.3 xUnit and Microsoft.AspNetCore.TestHost working together -- 8.3 Writing unit tests using Microsoft.AspNetCore.TestHost -- 8.3.1 Setting up a unit-test project -- 8.3.2 Using the TestServer and HttpClient to unit-test endpoints -- 8.3.3 Injecting mocks into endpoints -- 8.4 Writing service-level tests -- 8.4.1 Creating a service-level test project -- 8.4.2 Creating mocked endpoints -- 8.4.3 Executing the test scenario against the microservice under test -- Summary -- Part 3 Handling cross-cutting concerns: Building a reusable microservice platform -- 9 Cross-cutting concerns: Monitoring and logging -- 9.1 Monitoring needs in microservices -- 9.2 Logging needs in microservices -- 9.2.1 Tracing requests across microservices -- 9.2.2 Structured logging with Serilog -- 9.3 Implementing the monitoring endpoints -- 9.3.1 Implementing the /health/live monitoring endpoint -- 9.3.2 Implementing the /health/startup monitoring endpoint -- 9.4 Implementing structured logging -- 9.4.1 Adding a trace ID to all log messages -- 9.4.2 Trace ID is included in outgoing HTTP requests -- 9.4.3 Logging unhandled exceptions -- 9.5 Implementing monitoring and logging in Kubernetes -- 9.5.1 Configure monitoring in Kubernetes -- Summary -- 10 Securing microservice-to-microservice communication -- 10.1 Microservice security concerns -- 10.1.1 Authenticating users at the edge -- 10.1.2 Authorizing users in microservices -- 10.1.3 How much should microservices trust each other? …”

Tabla de Contenidos: “…8.2 Methodology -- 8.3 AI-Based Predictive Modeling -- 8.3.1 Linear Regression -- 8.3.2 Random Forests -- 8.3.3 XGBoost -- 8.3.4 SVM -- 8.4 Performance Indices -- 8.4.1 Root Mean Squared Error (RMSE) -- 8.4.2 Mean Squared Error (MSE) -- 8.4.3 R2 (R-Squared) -- 8.5 Results and Discussion -- 8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase -- 8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase -- 8.5.3 K Cross Fold Validation -- 8.6 Conclusions -- References -- Chapter 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Research Methodology -- 9.3.1 Dataset and Metrics -- 9.4 Result and Discussion -- 9.5 Conclusion -- References -- Chapter 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA -- 10.1 Introduction -- 10.2 System Model -- 10.3 User Clustering -- 10.4 Optimal Power Allocation for EE-SE Tradeoff -- 10.4.1 Multiobjective Optimization Problem -- 10.4.2 Multiobjective PSO -- 10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA -- 10.5 Numerical Results -- 10.6 Conclusion -- References -- Chapter 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews -- 11.1 Introduction -- 11.1.1 Related Work -- 11.2 Materials and Methods -- 11.2.1 Data Cleaning and Pre-Processing -- 11.2.2 Feature Extraction -- 11.2.3 Classifiers -- 11.3 Results and Experiments -- 11.4 Conclusion -- References -- Chapter 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm -- 12.1 Introduction -- 12.2 Genetic Algorithm GA: An Evolutionary Computational Technique -- 12.3 Design of Multiobjective Optimization Problem…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Smart Technologies in Manufacturing -- Chapter 1 Smart Manufacturing Systems for Industry 4.0 -- Abbreviations -- 1.1 Introduction -- 1.2 Research Methodology -- 1.3 Pillars of Smart Manufacturing -- 1.3.1 Manufacturing Technology and Processes -- 1.3.2 Materials -- 1.3.3 Data -- 1.3.4 Sustainability -- 1.3.5 Resource Sharing and Networking -- 1.3.6 Predictive Engineering -- 1.3.7 Stakeholders -- 1.3.8 Standardization -- 1.4 Enablers and Their Applications -- 1.4.1 Smart Design -- 1.4.2 Smart Machining -- 1.4.3 Smart Monitoring -- 1.4.4 Smart Control -- 1.4.5 Smart Scheduling -- 1.5 Assessment of Smart Manufacturing Systems -- 1.6 Challenges in Implementation of Smart Manufacturing Systems -- 1.6.1 Technological Issue -- 1.6.2 Methodological Issue -- 1.7 Implications of the Study for Academicians and Practitioners -- 1.8 Conclusion -- References -- Chapter 2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities -- Abbreviations -- 2.1 Introduction to Smart Manufacturing -- 2.1.1 Background of SM -- 2.1.2 Traditional Manufacturing versus Smart Manufacturing -- 2.1.3 Concept and Evolution of Industry 4.0 -- 2.1.4 Motivations for Research in Smart Manufacturing -- 2.1.5 Objectives and Need of Industry 4.0 -- 2.1.6 Research Methodology -- 2.1.7 Principles of I4.0 -- 2.1.8 Benefits/Advantages of Industry 4.0 -- 2.2 Technology Pillars of Industry 4.0 -- 2.2.1 Automation in Industry 4.0 -- 2.2.1.1 Need of Automation -- 2.2.1.2 Components of Automation -- 2.2.1.3 Applications of Automation -- 2.2.2 Robots in Industry 4.0 -- 2.2.2.1 Need of Robots -- 2.2.2.2 Advantages of Robots -- 2.2.2.3 Applications of Robots -- 2.2.2.4 Advances Robotics -- 2.2.3 Additive Manufacturing (AM) -- 2.2.3.1 Additive Manufacturing's Potential Applications…”

Tabla de Contenidos: “…7.1 Introduction -- 7.2 Related Work -- 7.3 Materials and Methods -- 7.3.1 Methodology for the Current Work -- 7.3.1.1 Data Collection for Wheat Crop -- 7.3.1.2 Data Pre-Processing -- 7.3.1.3 Implementation of the Proposed Hybrid Model -- 7.3.2 Techniques Used for Feature Selection -- 7.3.2.1 ReliefF Algorithm -- 7.3.2.2 Genetic Algorithm -- 7.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction -- 7.3.3.1 K-Nearest Neighbor -- 7.3.3.2 Artificial Neural Network -- 7.3.3.3 Logistic Regression -- 7.3.3.4 Naïve Bayes -- 7.3.3.5 Support Vector Machine -- 7.3.3.6 Linear Discriminant Analysis -- 7.4 Experimental Result and Analysis -- 7.5 Conclusion -- Acknowledgment -- References -- Chapter 8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences -- 8.1 Introduction -- 8.2 Types of Wireless Sensor for Smart Agriculture -- 8.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture -- 8.4 ML and WSN-Based Techniques for Smart Agriculture -- 8.5 Future Scope in Smart Agriculture -- 8.6 Conclusion -- References -- Part III: Smart City and Villages -- Chapter 9 Impact of Data Pre-Processing in Information Retrieval for Data Analytics -- 9.1 Introduction -- 9.1.1 Tasks Involved in Data Pre-Processing -- 9.2 Related Work -- 9.3 Experimental Setup and Methodology -- 9.3.1 Methodology -- 9.3.2 Application of Various Data Pre-Processing Tasks on Datasets -- 9.3.3 Applied Techniques -- 9.3.3.1 Decision Tree -- 9.3.3.2 Naive Bayes -- 9.3.3.3 Artificial Neural Network -- 9.3.4 Proposed Work -- 9.3.4.1 PIMA Diabetes Dataset (PID) -- 9.3.5 Cleveland Heart Disease Dataset -- 9.3.6 Framingham Heart Study -- 9.3.7 Diabetic Dataset -- 9.4 Experimental Result and Discussion -- 9.5 Conclusion and Future Work -- References…”

Tabla de Contenidos: “…Cover -- Contents -- Foreword -- Preface to the Second Edition -- Preface to the First Edition -- Chapter 1: Introduction to Computers and Computing -- 1.1 Hardware -- Operating Systems -- 1.2 Evolution of Programming Languages -- 1.3 Brief History of C++ -- 1.4 C++ as a Superset of C Programming Language -- 1.5 To Run a Program -- 1.6 An Informal Introduction to C++ Program -- Summary -- Chapter 2: Moving from C to C++I -- 2.1 Fundamentals -- 2.1.1 Comments -- 2.1.2 Character set -- 2.1.3 Identifiers and keywords -- 2.2 Data Types -- 2.2.1 Simple data types -- 2.2.2 Aggregate data types -- 2.2.3 Pointer data type -- 2.2.4 Enumerated data type -- 2.2.5 Type reference -- 2.2.6 Type void -- 2.3 Constants and Variables Declarations -- 2.4 Operators and Expressions -- 2.5 Library Functions -- 2.6 Statements -- 2.7 Pre-processor Directives -- 2.8 C++ is a Block-Structured Language -- 2.9 Typedef Facility -- 2.10 Simple Input-Output -- 2.10.1 Input-output with cin and cout -- 2.10.2 Console input-output -- 2.11 Control Statements -- 2.11.1 if statement -- 2.11.2 Switch statement -- 2.12 Iteration Statements -- 2.12.1 for statement -- 2.12.2 while statement -- 2.12.3 do-while statement -- 2.12.4 break and continue statements -- 2.12.5 goto statement -- 2.12.6 Comparison of all the three constructs -- 2.13 End of Chapter Programs -- 2.13.1 Sum of series -- 2.13.2 Accuracy of type float is limited! …”

Tabla de Contenidos: “…9.3.4 Analog-to-digital converter to field-programmable gate array interface -- 9.4 Algorithms used in evaluation of reconfigurable ultrasonic smart sensor platform -- 9.4.1 Coherent averaging -- 9.4.2 Split-spectrum processing -- 9.4.3 Chirplet signal decomposition -- 9.5 Hardware realization of ultrasonic imaging algorithms using reconfigurable ultrasonic smart sensor platform -- 9.5.1 Averaging implementation -- 9.5.2 Split-spectrum processing implementation -- 9.5.3 Chirplet signal decomposition implementation -- 9.5.4 Resource usage and timing constraints -- 9.6 Future trends -- 9.7 Conclusion -- 9.8 Sources of further information and advice -- References -- 10 - Advanced optical incremental sensors: encoders and interferometers -- 10.1 Introduction -- 10.2 Displacement interferometers -- 10.2.1 Basics of displacement interferometry -- 10.2.1.1 Homodyne interferometers (detection) -- 10.2.1.2 Heterodyne interferometers (detection) -- 10.2.1.3 Signals -- 10.2.2 Interferometer concepts -- 10.2.2.1 Linear interferometer -- 10.2.2.2 Plane mirror interferometer -- 10.2.3 Phase detection and interpolation -- 10.3 Sources of error and compensation methods -- 10.3.1 Setup dependent error sources -- 10.3.1.1 Cosine error -- 10.3.1.2 Abbe error -- 10.3.1.3 Dead path error -- 10.3.1.4 Target uniformity -- 10.3.1.5 Mechanical stability -- 10.3.2 Instrument dependent error sources -- 10.3.2.1 (Split) frequency -- 10.3.2.2 Beam walk-off -- 10.3.2.3 Electronics and data age -- 10.3.2.4 Periodic deviation -- 10.3.3 Environment dependent error sources -- 10.3.3.1 Thermal effects on the interferometer -- 10.3.3.2 Refractive index of air -- 10.4 Optical encoders -- 10.4.1 Imaging incremental encoder -- 10.4.2 Interferential encoders -- 10.4.2.1 Diffraction physics -- 10.4.2.2 Sensitivities -- 10.4.2.3 Schematic setups -- 10.4.2.4 Phase detection…”

Tabla de Contenidos: “…Definition of Mass -- 4.3 Conservation of Linear Momentum -- 4.4 Invariance of Momentum Conservation Under Galilean Transformation -- 4.5 Illustrative Examples of Momentum Conservation -- 4.5.1 Example 1: Velocity of a Large Block After Being Hit by Bullets -- 4.5.2 Example 2: Recoil Velocity of a Cannon -- 4.6 Propulsion of a Rocket -- 4.7 Worked Out Examples. …”