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: “…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: “…Intro -- Table des matières -- Statistiques de base de la République tchèque (2012) -- Résumé -- Principales conclusions -- Principales recommandations -- Soutenir la reprise et la croissance potentielle -- Favoriser la concurrence -- Renforcer l'utilisation des compétences et faciliter le passage de l'école à la vie active -- Évaluation et recommandations -- Une reprise inégale se dessine après une récession prolongée -- Graphique 1. …”

Tabla de Contenidos: “…-- 8.2 A Few Practical Concerns -- 8.3 Binary versus Multiclass -- 8.4 Example Script -- 8.5 Specific Classifiers -- 8.6 Evaluating Classifiers -- 8.7 Selecting Classification Cutoffs -- 8.8 Further Reading -- 8.9 Glossary -- Chapter 9 Technical Communication and Documentation -- 9.1 Several Guiding Principles -- 9.2 Slide Decks -- 9.3 Written Reports -- 9.4 Speaking: What Has Worked for Me -- 9.5 Code Documentation -- 9.6 Further Reading -- 9.7 Glossary -- Part II Stuff You Still Need to Know -- Chapter 10 Unsupervised Learning: Clustering and Dimensionality Reduction -- 10.1 The Curse of Dimensionality -- 10.2 Example: Eigenfaces for Dimensionality Reduction -- 10.3 Principal Component Analysis and Factor Analysis -- 10.4 Skree Plots and Understanding Dimensionality -- 10.5 Factor Analysis -- 10.6 Limitations of PCA -- 10.7 Clustering -- 10.8 Further Reading -- 10.9 Glossary -- Chapter 11 Regression -- 11.1 Example: Predicting Diabetes Progression -- 11.2 Least Squares -- 11.3 Fitting Nonlinear Curves -- 11.4 Goodness of Fit: R2 and Correlation -- 11.5 Correlation of Residuals -- 11.6 Linear Regression -- 11.7 LASSO Regression and Feature Selection -- 11.8 Further Reading -- 11.9 Glossary -- Chapter 12 Data Encodings and File Formats -- 12.1 Typical File Format Categories -- 12.2 CSV Files -- 12.3 JSON Files -- 12.4 XML Files…”

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: “…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: “…7.3.3 Methodology Flow Chart -- 7.3.4 Data Pre-Processing -- 7.3.5 Models Deployed -- 7.3.6 Training and Testing -- 7.4 Analysis -- 7.4.1 Datasets -- 7.4.2 Feature Extraction -- 7.4.3 Splitting of Data into Samples -- 7.4.4 Algorithms Used -- 7.4.4.1 Multinomial Logistic Regression -- 7.4.4.2 K-Nearest Neighbors -- 7.4.4.3 Decision Tree -- 7.4.4.4 Support Vector Machine (SVM) -- 7.4.4.5 Random Forest -- 7.5 Results and Discussion -- 7.5.1 Importance of Classification Reports -- 7.5.2 Importance of Confusion Matrices -- 7.5.3 Decision Tree -- 7.5.4 Random Forest -- 7.5.5 K-Nearest Neighbors -- 7.5.6 Logistic Regression -- 7.5.7 Support Vector Machine -- 7.5.8 Comparison of the Algorithms -- 7.5.8.1 Accuracies -- 7.5.8.2 Precision and Recall -- 7.6 Conclusions -- 7.7 Scope of Future Work -- References -- Chapter 8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment -- 8.1 Introduction -- 8.1.1 Color Image Processing -- 8.1.2 Motivation -- 8.1.3 Objectives -- 8.2 Literature Review -- 8.2.1 Gas Turbine Power Plants -- 8.2.2 Artificial Intelligent Methods -- 8.3 Materials and Methods -- 8.3.1 Feature Extraction -- 8.3.2 Classification -- 8.4 Results and Discussion -- 8.4.1 Fisher's Linear Discriminant Function (FLDA) and Curvelet -- 8.5 Conclusion -- 8.5.1 Future Scope of Work -- References -- Chapter 9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate -- Abbreviations -- 9.1 Introduction -- 9.2 Numerical Experimentation Program -- 9.3 Discussion of the Results -- 9.4 Conclusion -- Acknowledgements -- References -- Part II: Integration of Digital Technologies to Operations -- Chapter 10 Edge Computing-Based Conditional Monitoring -- 10.1 Introduction -- 10.1.1 Problem Statement -- 10.2 Literature Review -- 10.3 Edge Computing -- 10.4 Methodology…”

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: “…-- Les écoles d'hier et d'aujourd'hui -- Attitudes vis-à-vis des enseignants -- Attitudes à l'égard du changement -- Opinions des parents -- Satisfaction du système et des services éducatifs locaux -- Influence souhaitée et perçue, et demande de choix -- Raisons du choix -- Possibilités inégales d'exercer un choix ou une influence -- Attitudes à l'égard du secteur privé et de la cohésion -- Les parents : conser vateurs ou radicaux -- Les médias et autres influences formant les opinions sur les écoles -- Consensus et conflits du point de vue politique -- Écoles, vie professionnelle et marché du travail -- Aspects transgénérationnels et attitudes du contri buable face à l'école…”

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. …”

Tabla de Contenidos: “…Preface xv -- Acknowledgments xxi -- Contributors xxiii -- PART I CDN AND MEDIA STREAMING BASICS 1 -- 1 CLOUD-BASED CONTENT DELIVERY AND STREAMING 3 /Mukaddim Pathan -- 1.1 Introduction 3 -- 1.2 CDN Overview 5 -- 1.3 Workings of a CDN 10 -- 1.4 CDN Trends 21 -- 1.5 Research Issues 28 -- 1.6 Conclusion 29 -- References 29 -- 2 LIVE STREAMING ECOSYSTEMS 33 /Dom Robinson -- 2.1 Introduction 33 -- 2.2 Live Streaming Pre-Evolution 34 -- 2.3 Live, Linear, Nonlinear 35 -- 2.4 Media Streaming 37 -- 2.5 Related Network Models 38 -- 2.6 Streaming Protocol Success 43 -- 2.7 Platform Divergence and Codec Convergence 44 -- 2.8 Adaptive Bitrate (ABR) Streaming 45 -- 2.9 Internet Radio and HTTP 48 -- 2.10 Conclusion 48 -- References 49 -- 3 PRACTICAL SYSTEMS FOR LIVE STREAMING 51 /Dom Robinson -- 3.1 Introduction 51 -- 3.2 Common Concepts in Live Streaming 52 -- 3.3 The Practicals 56 -- 3.4 Conclusion 69 -- References 70 -- 4 EFFICIENCY OF CACHING AND CONTENT DELIVERY IN BROADBAND ACCESS NETWORKS 71 /Gerhard Haslinger -- 4.1 Introduction 71 -- 4.2 Options and Properties for Web Caching 73 -- 4.3 Zipf Laws for Requests to Popular Content 75 -- 4.4 Efficiency and Performance Modeling for Caches 76 -- 4.5 Effect of Replacement Strategies on Cache Hit Rates 78 -- 4.6 Replacement Methods Based on Request Statistics 81 -- 4.7 Global CDN and P2P Overlays for Content Delivery 84 -- 4.8 Summary and Conclusion 86 -- Acknowledgments 87 -- References 87 -- 5 ANYCAST REQUEST ROUTING FOR CONTENT DELIVERY NETWORKS 91 /Hussein A. …”

Tabla de Contenidos: “…Bemaßungen, Höhenkoten, Texte und Beschriftungen -- 4.1 Die Bemaßungsbefehle -- 4.2 Die ausgerichtete Bemaßung -- 4.2.1 Beispiel für ausgerichtete Bemaßung -- 4.2.2 EQ-Bedingung -- 4.2.3 Fensterbreiten und Wandlängen gleichsetzen -- 4.2.4 Bemaßungsstil -- 4.2.5 Maßkette bearbeiten -- 4.2.6 Weitere Maßketten -- 4.2.7 Bemaßung mit Referenzlinie -- 4.3 Die lineare Bemaßung -- 4.3.1 Maßtexte ergänzen -- 4.4 Winkelbemaßung -- 4.5 Radius- und Durchmesserbemaßungen -- 4.6 Bogenlängenbemaßung…”

Tabla de Contenidos: “…-- 1.1 Die Geschichte‌ von COBOL -- 1.2 Fest definierter Sprachumfang -- 1.3 Prozedurale Programmierung -- 1.4 Linearer Programmablauf -- 1.5 Datenfelder mit fester Länge -- 1.6 Module statt Instanzen -- Kapitel 2: Programmstruktur und grundlegende Sprachelemente -- 2.1 COBOL-Programmstruktur‌ -- 2.1.1 Die Bedeutung der Programmteile (DIVISIONs) -- 2.1.2 Die Hierarchie in einem COBOL-Programm -- 2.1.3 Das COBOL-Programm im Überblick -- 2.2 COBOL-Sprachelemente‌ -- 2.2.1 Reservierte Wörter‌‌‌ -- 2.2.2 Programmiererwörter‌ -- 2.2.3 Literale‌ -- 2.2.4 Figurative Konstanten‌ -- 2.2.5 Trennsymbole -- 2.2.6 Operatoren -- 2.2.7 Sonderregister‌ -- 2.3 COBOL-Zeichensatz‌ -- 2.4 Interpretation der COBOL-Klausel- und -Anweisungsformate -- 2.5 Das Codierformat‌ -- 2.5.1 Fixed-form reference format‌ -- 2.5.2 Free-form reference format‌ -- Kapitel 3: Bedeutung der 4 DIVISIONs -- 3.1 IDENTIFICATION DIVISION‌ -- 3.2 ENVIRONMENT DIVISION‌ -- 3.2.1 CONFIGURATION SECTION‌ -- 3.2.2 INPUT-OUTPUT SECTION‌ -- 3.2.3 FILE-CONTROL‌ -- 3.2.4 I-O-CONTROL‌ -- 3.3 DATA DIVISION‌ -- 3.3.1 FILE SECTION‌ -- 3.3.2 WORKING-STORAGE SECTION‌ -- 3.3.3 LOCAL-STORAGE SECTION‌ -- 3.3.4 LINKAGE SECTION‌ -- 3.4 PROCEDURE DIVISION‌ -- 3.4.1 USING‌-Zusatz -- 3.4.2 RAISING‌-Zusatz -- 3.4.3 DECLARATIVES‌ (Sondervereinbarungen) -- 3.4.4 END PROGRAM‌ -- 3.4.5 Aufbau der PROCEDURE DIVISION -- Kapitel 4: Definitionen von Datenfeldern -- 4.1 Stufennummer 77‌‌ -- 4.2 PICTURE‌‌-Klausel -- 4.2.1 Alphabetische Datenfelder‌‌‌ -- 4.2.2 Alphanumerische Datenfelder‌‌‌ -- 4.2.3 Numerische Datenfelder‌‌‌ -- 4.2.4 Boolesche Datenfelder‌‌‌ -- 4.2.5 Alphanumerische druckaufbereitete Datenfelder‌‌‌ -- 4.2.6 Numerische druckaufbereitete Datenfelder‌‌‌…”

Tabla de Contenidos: “…Various machine learning algorithms -- 4.1 Linear regression -- 4.2 SVM -- 4.3 Naive Bayes -- 4.4 Logistic regression -- 4.5 k-Nearest neighbors -- 4.6 Decision trees -- 4.7 RF algorithm -- 4.8 Boosted gradient decision trees -- 4.9 Clustering with k-means -- 4.10 Analysis by principal components -- 5. …”

Tabla de Contenidos: “…Solar energy as renewable energy -- 5.1 Solar photovoltaic -- 5.1.1 Working of a solar photovoltaic system -- 5.1.2 Classifications of solar panels -- 5.1.3 Applications of a photovoltaic system -- 5.1.4 Efficiency calculation of a solar cell -- 5.1.5 Disadvantages of a PV system -- 5.2 Concentrated solar power -- 5.2.1 Working of a concentrated solar power -- 5.2.2 Types of concentrated solar power -- 5.2.3 Parabolic trough system -- 5.2.4 Linear Fresnel reflectors -- 5.2.5 Solar dish -- 5.2.5.1 SAIC/STM solar dish system -- 5.2.5.2 ARUN solar dish system -- 5.2.6 Power tower or central receiver system -- 5.3 Thermal storage -- 5.4 Power block -- 6. …”

Tabla de Contenidos: “…Kovasznay flow -- 5.4 Implementation -- 5.4.1 Data structures and performance implications -- 5.4.2 Matrix and matrix-free implicit solvers -- 5.4.3 Unstructured grid filtering for LES -- 5.4.3.1 Face-based filtering -- 5.4.3.2 Derivative-based filter -- 5.4.3.3 Generalized top-hat filter -- 5.4.3.4 Function based filter -- 5.5 Scale resolving turbulence simulations -- References -- 6 Finite difference methods for turbulence simulations -- 6.1 Introduction -- 6.2 Grid topologies -- 6.2.1 Navier-Stokes equations in curvilinear coordinates -- 6.2.1.1 Conservation laws on curvilinear grids -- 6.2.1.2 The geometric conservation constraint -- 6.2.2 Cartesian octree topologies -- 6.2.3 Numerical considerations for abrupt grid changes -- 6.3 Grid staggering and flux evaluations -- 6.3.1 Primitive variable placement -- 6.3.2 Staggered flux evaluations in collocated variable formulation -- 6.4 Robustness of inviscid flux discretization -- 6.4.1 Linear schemes -- 6.4.1.1 Kinetic energy preservation and entropy consistency -- 6.4.2 Flows with discontinuities -- 6.4.2.1 Nonlinear schemes: WENO interpolation and WCNS -- 6.4.2.2 Artificial dissipation -- 6.5 Finite difference schemes for LES: dispersion/dissipation errors -- 6.5.1 Are low-dispersion error schemes relevant for LES in turbulence-dominated flows? …”

Tabla de Contenidos: “…4.3 Nonconcentrator collectors -- 4.3.1 Flat plate collector -- Applications -- 4.3.2 Evacuated tube collectors -- Application -- 4.4 Concentrator collectors -- 4.4.1 Compound parabolic and Fresnel lens collectors -- Application -- 4.4.2 Parabolic trough collector -- Parameter calculation -- More equations for the model -- Application -- 4.4.3 Parabolic dish reflector -- Application -- 4.4.4 Central receiver or heliostat field reflector -- Applications -- References -- 5 - Solar photovoltaic thermal systems -- 5.1 Introduction -- 5.2 Photovoltaic thermal technology -- 5.3 Solar cell or PV cell -- 5.3.1 Crystalline solar cell -- 5.3.2 Thin-film solar cell -- 5.3.3 Amorphous solar cell -- 5.3.4 Organic and polymer solar cell -- 5.3.5 Dye-sensitized solar cell -- 5.3.6 Hybrid solar cell -- 5.3.7 PV cell electrical parameters -- p-n junction -- Short-circuit current (Isc) -- Open-circuit voltage (Voc) -- Fill factor -- Solar cell efficiency -- Detailed balance -- Boundary conditions -- 5.3.8 Performance of PV cell -- Effect of temperature -- Solar to electricity system -- Solar to fuel system -- Solar electricity to fuel system -- 5.4 Energy conversion in different types of PVT systems -- 5.4.1 Energy conversion in PVT/water system -- Evaluation criterion of the PV/T system -- 5.4.2 Energy conversion in glazed PVT/water system -- 5.4.3 Energy conversion in unglazed PVT/water and PVT-PCM systems -- 5.4.4 Energy conversion in PVT/air system -- References -- Further reading -- 6 - Solar thermal power plant -- 6.1 Introduction -- 6.2 Basic concept of solar thermal power plant -- 6.3 Solar thermal power generation technologies -- 6.3.1 Solar tower power plant -- 6.3.2 Parabolic through solar power plant -- 6.3.3 Parabolic dish solar power plant -- 6.3.4 Linear Fresnel reflector solar power plant -- 6.3.5 Solar chimney power plant…”