Tabla de Contenidos: “…Using data augmentation to label regression data -- Using k-means clustering to label regression data -- Summary -- Part 2: Labeling Image Data -- Chapter 4: Exploring Image Data -- Technical requirements -- Visualizing image data using Matplotlib in Python -- Loading the data -- Checking the dimensions -- Visualizing the data -- Checking for outliers -- Performing data preprocessing -- Checking for class imbalance -- Identifying patterns and relationships -- Evaluating the impact of preprocessing -- Practice example of visualizing data -- Practice example for adding annotations to an image -- Practice example of image segmentation -- Practice example for feature extraction -- Analyzing image size and aspect ratio -- Impact of aspect ratios on model performance -- Image resizing -- Image normalization -- Performing transformations on images - image augmentation -- Summary -- Chapter 5: Labeling Image Data Using Rules -- Technical requirements -- Labeling rules based on image visualization -- Image labeling using rules with Snorkel -- Weak supervision -- Rules based on the manual visualization of an image's object color -- Real-world applications -- A practical example of plant disease detection -- Labeling images using rules based on properties -- Bounding boxes -- Example 1 - image classification - a bicycle with and without a person -- Example 2 - image classification - dog and cat images -- Labeling images using transfer learning -- Example - digit classification using a pre-trained classifier -- Example - person image detection using the YOLO V3 pre-trained classifier -- Example - bicycle image detection using the YOLO V3 pre-trained classifier -- Labeling images using transformations -- Summary -- Chapter 6: Labeling Image Data Using Data Augmentation -- Technical requirements -- Training support vector machines with augmented image data…”

Tabla de Contenidos: “…-- The importance of threat modeling in the software development lifecycle -- Why should we perform threat modeling? -- Threat modeling techniques…”

Tabla de Contenidos: “…Step 2: Read the Release Notes -- Step 3: Perform a Pre-Upgrade Test -- Step 4: Prepare Your Esx Host -- Installation/Upgrade Steps -- Step 1: Read the Release Notes -- Step 2: Read All Relevant Documentation -- Step 3: Is Support Available for the Hardware Configuration? …”

Tabla de Contenidos: “…Implementing instance segmentation using Mask R-CNN -- Predicting multiple instances of multiple classes -- Summary -- Questions -- Chapter 10: Applications of Object Detection and Segmentation -- Multi-object instance segmentation -- Fetching and preparing data -- Training the model for instance segmentation -- Making inferences on a new image -- Human pose detection -- Crowd counting -- Implementing crowd counting -- Image colorization -- 3D object detection with point clouds -- Theory -- Input encoding -- Output encoding -- Training the YOLO model for 3D object detection -- Data format -- Data inspection -- Training -- Testing -- Action recognition from video -- Identifying an action in a given video -- Training a recognizer on a custom dataset -- Summary -- Questions -- Section 3: Image Manipulation -- Chapter 11: Autoencoders and Image Manipulation -- Understanding autoencoders -- How autoencoders work -- Implementing vanilla autoencoders -- Implementing convolutional autoencoders -- Grouping similar images using t-SNE -- Understanding variational autoencoders -- The need for VAEs -- How VAEs work -- KL divergence -- Building a VAE -- Performing an adversarial attack on images -- Understanding neural style transfer -- How neural style transfer works -- Performing neural style transfer -- Understanding deepfakes -- How deepfakes work -- Generating a deepfake -- Summary -- Questions -- Chapter 12: Image Generation Using GANs -- Introducing GANs -- Using GANs to generate handwritten digits -- Using DCGANs to generate face images -- Implementing conditional GANs -- Summary -- Questions -- Chapter 13: Advanced GANs to Manipulate Images -- Leveraging the Pix2Pix GAN -- Leveraging CycleGAN -- How CycleGAN works -- Implementing CycleGAN -- Leveraging StyleGAN on custom images -- The evolution of StyleGAN -- Implementing StyleGAN -- Introducing SRGAN…”

Tabla de Contenidos: “…Fine-Tuning Proactive Caching -- ROLAP Dimensions -- Linking -- Writeback -- Cube and Dimension Processing Options -- Summary -- Chapter 10: Introduction to MDX -- The Importance of MDX -- Writing Your First MDX Queries -- MDX Object Names -- Other Elements of MDX Syntax -- MDX Core Functions -- Filtering MDX Result Sets -- Calculated Members and Named Sets -- Creating Objects by Using Scripts -- The TopCount Function -- Rank Function and Combinations -- Head and Tail Functions -- Hierarchical Functions in MDX -- Date Functions -- Using Aggregation with Date Functions -- About Query Optimization -- Summary -- Chapter 11: Advanced MDX -- Querying Dimension Properties -- Looking at Date Dimensions and MDX Seasonality -- Creating Permanent Calculated Members -- Creating Permanent Calculated Members in BIDS -- Creating Calculated Members Using MDX Scripts -- Using IIf -- About Named Sets -- About Scripts -- Understanding SOLVE_ORDER -- Creating Key Performance Indicators -- Creating KPIs Programmatically -- Additional Tips on KPIs -- Using MDX with SSrS and PerformancePoint Server -- Using MDX with SSRS 2008 -- Using MDX with PerformancePoint Server 2007 -- Summary -- Chapter 12: Understanding Data Mining Structures -- Reviewing Business Scenarios -- Categories of Data Mining Algorithms -- Working in the BiDS Data Mining Interface -- Understanding Data Types and Content Types -- Setting Advanced Data Properties -- Choosing a Data Mining Model -- Picking the Best Mining Model Viewer -- Mining Accuracy Charts and Prediction -- Data Mining Algorithms -- Microsoft Naïve Bayes -- Microsoft Decision Trees Algorithm -- Microsoft Linear Regression Algorithm -- Microsoft Time Series Algorithm -- Microsoft Clustering Algorithm -- Microsoft Sequence Clustering -- Microsoft Association Algorithm -- Microsoft Neural Network Algorithm -- Microsoft Logistic Regression…”

Tabla de Contenidos: “…. -- Running KNN with cross-validation in place of a validation partition -- Using a convenience function to run KNN -- Using a convenience function to run KNN for multiple k values -- See also -- Performing linear regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- Forcing lm to use a specific factor level as the reference -- Using other options in the formula expression for linear models -- See also -- Performing variable selection in linear regression -- Getting ready -- How to do it... -- How it works... -- See also -- Building regression trees -- Getting ready -- How to do it... -- How it works... -- There's more... -- Generating regression trees for data with categorical predictors -- Generating regression trees using the ensemble method - Bagging and Boosting -- See also -- Building random forest models for regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- Controlling forest generation -- See also -- Using neural networks for regression -- Getting ready -- How to do it... -- How it works... -- See also -- Performing k-fold cross-validation -- Getting ready -- How to do it... -- How it works... -- See also -- Performing leave-one-out cross-validation to limit overfitting -- How to do it…”

Tabla de Contenidos: “…Experimental Setup -- 6.1.1. Performance Analysis -- 6.1.2. Power Analysis -- 6.1.3. …”

Tabla de Contenidos: “…Preface -- About the Authors -- 1 Introduction and Overview -- 1.1 Switching and Transmission -- 1.1.1 Roles of Switching and Transmission -- 1.1.2 Telephone Network Switching and Transmission Hierarchy -- 1.2 Multiplexing and Concentration -- 1.3 Timescales of Information Transfer -- 1.3.1 Sessions and Circuits -- 1.3.2 Messages -- 1.3.3 Packets and Cells -- 1.4 Broadband Integrated Services Network -- Problems -- 2 Circuit Switch Design Principles -- 2.1 Space-Domain Circuit Switching -- 2.1.1 Nonblocking Properties -- 2.1.2 Complexity of Nonblocking Switches -- 2.1.3 Clos Switching Network -- 2.1.4 Benes Switching Network -- 2.1.5 Baseline and Reverse Baseline Networks -- 2.1.6 Cantor Switching Network -- 2.2 Time-Domain and Time-Space-Time Circuit Switching -- 2.2.1 Time-Domain Switching -- 2.2.2 Time-Space-Time Switching -- Problems -- 3 Fundamental Principles of Packet Switch Design -- 3.1 Packet Contention in Switches -- 3.2 Fundamental Properties of Interconnection Networks -- 3.2.1 Definition of Banyan Networks -- 3.2.2 Simple Switches Based on Banyan Networks -- 3.2.3 Combinatoric Properties of Banyan Networks -- 3.2.4 Nonblocking Conditions for the Banyan Network -- 3.3 Sorting Networks -- 3.3.1 Basic Concepts of Comparison Networks -- 3.3.2 Sorting Networks Based on Bitonic Sort -- 3.3.3 The Odd-Even Sorting Network -- 3.3.4 Switching and Contention Resolution in Sort-Banyan Network -- 3.4 Nonblocking and Self-Routing Properties of Clos Networks -- 3.4.1 Nonblocking Route Assignment -- 3.4.2 Recursiveness Property -- 3.4.3 Basic Properties of Half-Clos Networks -- 3.4.4 Sort-Clos Principle -- Problems -- 4 Switch Performance Analysis and Design Improvements -- 4.1 Performance of Simple Switch Designs -- 4.1.1 Throughput of an Internally Nonblocking Loss System -- 4.1.2 Throughput of an Input-Buffered Switch -- 4.1.3 Delay of an Input-Buffered Switch -- 4.1.4 Delay of an Output-Buffered Switch -- 4.2 Design Improvements for Input Queueing Switches -- 4.2.1 Look-Ahead Contention Resolution…”

Tabla de Contenidos: “…Methods of communication -- Meetings -- Telephone -- Conference calls -- E-mail -- Social media -- Post -- Allocating the right people -- Who is capable of performing the task? -- Who is ideal for the task? -- Who is available? …”

Tabla de Contenidos: “…Chapter 8: The Process Inventory Accountability Model -- Driving Employee Engagement at Spotify: Balancing Autonomy and Accountability -- Establishing a Culture of Accountability -- Process Inventory Framework Operating Model -- Process Center of Excellence (COE) -- Organizational Structure -- Process COE Process Inventory -- Lead Process Delivery -- Accountabilities -- Processes -- Required Competencies -- Manage Process Demand -- Accountabilities -- Processes -- Required Competencies -- Deliver Process Inventory and Deliver Process Models -- Accountabilities -- Processes -- Required Competencies -- Control Tower -- Accountabilities -- Processes -- Required Competencies -- Business/Functional Areas -- Process Owner -- Risk Owner -- Product Manager -- Subject Matter Experts -- Data Stewards -- Technology -- IT Asset Management -- Enterprise Architecture -- Customer Experience (CX) -- Risk -- Second Line of Defense -- Key Takeaways -- Chapter 9: Process Methods and Modeling Rules -- Unilever's Digital Transformation of Supply Chain Through Modeling Processes -- The Importance of Models -- Guiding Principles for Success -- Process Inventory -- Construct Process Inventory -- Engage Business Leaders to Plan Effort -- Best Practices -- Perform Prep Work -- Best Practices -- Standards -- Conduct Interview Sessions -- Best Practices -- Align Metadata and Perform Reviews -- Best Practices -- Conduct Readout with Leadership -- Best Practices -- Process Models -- Construct Process Models -- Identify Scope via Process Inventory -- Best Practices -- Conduct Process Modeling Workshops -- Best Practices -- Standards -- Validate and Publish -- Best Practice -- Key Takeaways -- Chapter 10: Modeling Platform and Data Infrastructure -- FedEx Dataworks: Leveraging Operational Data for Business Performance -- Business Process Analysis Platform Market…”

Tabla de Contenidos: “…Understanding coordinate conversion -- Understanding reprojection -- Understanding coordinate format conversion -- Calculating the area of a polygon -- Using ChatGPT to measure a polygon perimeter -- Summary -- Questions -- Further reading -- Chapter 6: Creating and Editing GIS Data -- Technical requirements -- Editing shapefiles -- Accessing the shapefile -- Changing a shapefile -- Adding fields -- Merging shapefiles -- Splitting shapefiles -- Performing selections -- Aggregating geometry -- Extracting geometry -- Connecting polygon faces to the nearest line point -- Creating images for visualization -- Dot density calculations -- Choropleth maps -- Using spreadsheets -- Creating heat maps -- Using GPS data -- Turning addresses into points with geocoding -- Performing GIS analysis faster with multiprocessing -- Summary -- Questions -- Further reading -- Chapter 7: Python and Remote Sensing -- Technical requirements -- Examining raster data properties -- Swapping image bands -- Creating image histograms -- Performing a histogram stretch -- Clipping images -- Classifying images -- Extracting features from images -- Understanding change detection -- Extracting image footprints using ChatGPT -- Summary -- Questions -- Further reading -- Chapter 8: Python and Elevation Data -- Technical requirements -- Accessing ASCII Grid files -- Reading grids -- Writing grids -- Creating a shaded relief -- Creating elevation contours -- Working with LiDAR data -- Creating a grid from the LiDAR data -- Using PIL to visualize LiDAR data -- Creating a triangulated irregular network -- Colorizing LiDAR with aerial images -- Classifying LiDAR -- Working with bathymetry -- Summary -- Questions -- Further reading -- Part 3: Practical Geospatial Processing Techniques -- Chapter 9: Advanced Geospatial Modeling -- Technical requirements…”

Tabla de Contenidos: “…Établir des outils appropriés au dialogue de performance…”

Tabla de Contenidos: “…12.5 Discussion and Future Work -- 12.6 Conclusions -- References -- Chapter 13 Empirical Analysis of Machine Learning Techniques Under Class Imbalance and Incomplete Datasets -- 13.1 Introduction -- 13.2 Related Work -- 13.2.1 Class Imbalance -- 13.2.2 Missing Values -- 13.2.3 Missing Value in Class Imbalance Datasets -- 13.3 Methodology -- 13.4 Results -- 13.4.1 Overall Performance -- 13.4.2 Effect of Class Imbalance and Missing Values -- 13.5 Conclusion -- References -- Chapter 14 Gabor Filter as Feature Extractor in Anomaly Detection From Radiology Images -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Research Methodology -- 14.3.1 Data Set -- 14.3.2 Gabor Filter -- 14.4 Results -- 14.5 Discussion -- 14.6 Conclusion -- References -- Chapter 15 Discriminative Features Selection From Zernike Moments for Shape Based Image Retrieval System -- 15.1 Introduction -- 15.2 Zernike Moments Descriptor (ZMD) -- 15.2.1 Zernike Moments (ZMs) -- 15.2.2 Orthogonality -- 15.2.3 Rotation Invariance -- 15.2.4 Features Selection -- 15.3 Discriminative Features Selection -- 15.4 Similarity Measure -- 15.5 Experimental Study -- 15.5.1 Experiment Setup -- 15.5.2 Performance Measurement -- 15.5.3 Experiment Results -- 15.6 Discussions and Conclusions -- References -- Chapter 16 Corrected Components of Zernike Moments for Improved Content Based Image Retrieval: A Comprehensive Study -- 16.1 Introduction -- 16.2 Proposed Descriptors -- 16.2.1 Invariant Region Based Descriptor Using Corrected ZMs Features -- 16.2.2 Selection of Appropriate Features -- 16.2.3 Invariant Contour Based Descriptor Using HT -- 16.3 Similarity Metrics -- 16.4 Experimental Study and Performance Evaluation -- 16.4.1 Measurement of Retrieval Accuracy -- 16.4.2 Performance Comparison and Experiment Results -- 16.5 Discussion and Conclusion -- References…”

Tabla de Contenidos: “…Types of Sources and Contents of the Articles -- 3.2. Approaches to Performing Quality Assurance for Mobile Applications -- 3.2.1. …”

Tabla de Contenidos: “…-- 10.2 Operations Systems -- 10.3 TMN Conceptual Model -- 10.4 TMN Standards -- 10.5 TMN Architecture -- 10.5.1 Functional Architecture -- 10.5.2 Physical Architecture -- 10.5.3 Information Architecture -- 10.6 TMN Management Service Architecture -- 10.7 TMN Integrated View -- 10.8 TMN Implementation -- 10.8.1 OMNI Point -- 10.8.2 eTOM -- Summary -- Exercises -- Chapter 11: Network Management Applications -- 11.1 Configuration Management -- 11.1.1 Network Provisioning -- 11.1.2 Inventory Management -- 11.1.3 Network Topology -- 11.2 Fault Management -- 11.2.1 Fault Detection -- 11.2.2 Fault Location and Isolation Techniques -- 11.3 Performance Management -- 11.3.1 Performance Metrics -- 11.3.2 Data Monitoring -- 11.3.3 Problem Isolation -- 11.3.4 Performance Statistics -- 11.4 Event Correlation Techniques -- 11.4.1 Rule-Based Reasoning -- 11.4.2 Model-Based Reasoning -- 11.4.3 Case-Based Reasoning -- 11.4.4 Codebook Correlation Model -- 11.4.5 State Transition Graph Model -- 11.4.6 Finite State Machine Model -- 11.5 Security Management -- 11.5.1 Policies and Procedures -- 11.5.2 Resources to Prevent Security Breaches -- 11.5.3 Firewalls -- 11.5.4 Cryptography -- 11.5.5 Authentication and Authorization -- 11.5.6 Client-Server Authentication Systems -- 11.5.7 Message Transfer Security -- 11.5.8 Network Protection from Virus Attacks -- 11.6 Accounting Management -- 11.7 Report Management -- 11.8 Policy-Based Management -- 11.9 Service Level Management -- Summary -- Exercises -- Part IV: Broadband Network Management -- Chapter 12: Broadband Network Management: WAN -- 12.1 Broadband Network and Services -- 12.2 ATM Technology -- 12.2.1 Virtual Path-Virtual Circuit -- 12.2.2 ATM Packet Size -- 12.2.3 Integrated Service -- 12.2.4 WAN/SONET -- 12.2.5 ATM LAN Emulation…”

Tabla de Contenidos: “…Chapter 4: Regime-switching Methods for Forecasting -- 4.1 REGIME-SWITCHING AUTOREGRESSIVE MODEL -- 4.1.1 Physically Motivated Regime Definition -- 4.1.2 Data-driven Regime Determination -- 4.1.3 Smooth Transition between Regimes -- 4.1.4 Markov Switching between Regimes -- 4.2 REGIME-SWITCHING SPACE-TIME MODEL -- 4.3 CALIBRATION IN REGIME SWITCHING METHOD -- 4.3.1 Observed Regime Changes -- 4.3.2 Unobserved Regime Changes -- 4.3.3 Framework of Calibrated Regime-switching -- 4.3.4 Implementation Procedure -- 4.4 CASE STUDY -- 4.4.1 Modeling Choices and Practical Considerations -- 4.4.2 Forecasting Results -- Part II: Wind Turbine Performance Analysis -- Chapter 5: Power Curve Modeling and Analysis -- 5.1 IEC BINNING: SINGLE-DIMENSIONAL POWER CURVE -- 5.2 KERNEL-BASED MULTI-DIMENSIONAL POWER CURVE -- 5.2.1 Need for Nonparametric Modeling Approach -- 5.2.2 Kernel Regression and Kernel Density Estimation -- 5.2.3 Additive Multiplicative Kernel Model -- 5.2.4 Bandwidth Selection -- 5.3 OTHER DATA SCIENCE METHODS -- 5.3.1 k-Nearest Neighborhood Regression -- 5.3.2 Tree-based Regression -- 5.3.3 Spline-based Regression -- 5.4 CASE STUDY -- 5.4.1 Model Parameter Estimation -- 5.4.2 Important Environmental Factors Affecting Power Output -- 5.4.3 Estimation Accuracy of Different Models -- Chapter 6: Production Efficiency Analysis and Power Curve -- 6.1 THREE EFFICIENCY METRICS -- 6.1.1 Availability -- 6.1.2 Power Generation Ratio -- 6.1.3 Power Coefficient -- 6.2 COMPARISON OF EFFICIENCY METRICS -- 6.2.1 Distributions -- 6.2.2 Pairwise Differences -- 6.2.3 Correlations and Linear Relationships -- 6.2.4 Overall Insight -- 6.3 A SHAPE-CONSTRAINED POWER CURVE MODEL -- 6.3.1 Background of Production Economics -- 6.3.2 Average Performance Curve -- 6.3.3 Production Frontier Function and Effi ciency Metric -- 6.4 CASE STUDY…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Dedication -- Preface -- Author Biography -- Chapter 1 Concepts and Foundations Automation and Emerging Technologies -- 1.1 Introduction -- 1.2 Structure and Framework of Automation and Key Performance Indexes (KPIs) -- 1.3 Advanced Image Processing Techniques -- 1.4 Fuzzy and Its Recent Advances -- 1.5 Automatic Detection and Its Applications in Infrastructure -- 1.6 Feature Extraction and Fragmentation Methods -- 1.7 Feature Prioritization and Selection Methods -- 1.8 Classification Methods and Its Applications in Infrastructure Management -- 1.9 Models of Performance Measures and Quantification in Automation -- 1.10 Nature-Inspired Optimization Algorithms (NIOAS) -- 1.11 Summary and Conclusion -- 1.12 Questions and Exercise -- Chapter 2 The Structure and Framework of Automation and Key Performance Indices (KPIs) -- 2.1 Introduction -- 2.2 Macro Plan and Architecture of Automation -- 2.2.1 Infrastructure Automation -- 2.2.2 Importance of Infrastructure Automation Evaluation -- 2.3 A General Framework and Design of Automation -- 2.4 Infrastructure Condition Index and Its Relationship with Cracking -- 2.4.1 Road Condition Index -- 2.4.2 Bridge Condition Index -- 2.4.3 Tunnel Condition Index -- 2.5 Automation, Emerging Technologies, and Futures Studies -- 2.6 Summary and Conclusion -- 2.7 Questions -- Further Reading -- Chapter 3 Advanced Images Processing Techniques -- Introduction -- 3.1 Preprocessing (PPS) -- 3.1.1 Edge Preservation Index (EPI) -- 3.1.2 Edge-Strength Similarity-Based Image Quality Metric (ESSIM) -- 3.1.3 QILV Index -- 3.1.4 Structural Content Index (SCI) -- 3.1.5 Signal-To-Noise Ratio Index (PSNR) -- 3.1.6 Computational time index (CTI) -- 3.2 Preprocessing Using Single-Level Methods -- 3.2.1 Single-Level Methods -- 3.2.2 Linear Location Filter (LLF) -- 3.2.3 Median Filter…”

Tabla de Contenidos: “…5.6.2 Future Work -- 5.7 Conclusion -- References -- Chapter 6 Performance Analysis of Terahertz Microstrip Antenna Designs: A Review -- 6.1 Introduction -- 6.2 Microstrip Antenna Design -- 6.2.1 Feeding Methods -- 6.3 Challenges of Terahertz Antenna Development -- 6.4 Antenna Performance Attributes -- 6.5 Comparative Analysis of Microstrip THZ Antennas -- 6.6 Conclusion -- References -- Chapter 7 Smart Antenna for Home Automation Systems -- 7.1 Introduction -- 7.2 Home Automation Antenna Geometry and Robotics Process Automation -- 7.3 Results for Home Automation Smart Antenna -- 7.4 Conclusion -- References -- Chapter 8 Special Military Application Antenna for Robotics Process Automation -- 8.1 Introduction -- 8.2 Special Military Application Antenna for Robotics Process Automation -- 8.3 Results for Special Military Application Antenna -- 8.4 Conclusion -- References -- Chapter 9 Blockchain Based Humans-Agents Interactions/Human-Robot Interactions: A Systematic Literature Review and Research Agenda -- 9.1 Introduction -- 9.2 Conceptual Foundation -- 9.2.1 Blockchains -- 9.2.2 Cryptocurrencies -- 9.2.3 Smart Contracts -- 9.2.4 Decentralized Identity -- 9.3 Motivation -- 9.4 Blockchain and Robotics Overview -- 9.5 Human-Robot Interaction -- 9.5.1 Games -- 9.5.2 Commitment Strategies and Conditioning -- 9.6 Applications of HRI -- 9.6.1 HRI Application in Industry -- 9.6.2 HRI in Rehabilitation and Medical -- 9.6.3 HRI in Agriculture -- 9.6.4 Robochains -- 9.7 Transactions between Robots and Human Beings -- 9.7.1 A Ledger that is Decentralized, Immutable, and Publicly Verifiable -- 9.7.2 Contracts -- 9.8 Escrow Services -- 9.9 Challenges for HRI -- 9.9.1 Task Dynamic Analysis -- 9.9.2 Teaching a Robot and Avoiding Unintended Consequences -- 9.9.3 Connecting Mutual "Mental" Models to Prevent Working in Conflict -- 9.9.4 Role of Robots in Education…”

Tabla de Contenidos: “…241 -- 8.2 Quality-Driven Cross-Layer Framework 242 -- 8.3 Application Layer 244 -- 8.4 Rate Control at the Transport Layer 244 -- 8.4.1 Background 244 -- 8.4.2 System Model 246 -- 8.4.3 Network Setting 246 -- 8.4.4 Problem Formulation 248 -- 8.4.5 Problem Solution 248 -- 8.4.6 Performance Evaluation 249 -- 8.5 Routing at the Network Layer 252 -- 8.5.1 Background 252 -- 8.5.2 System Model 254 -- 8.5.3 Routing Metric 255 -- 8.5.4 Problem Formulation 257 -- 8.5.5 Problem Solution 258 -- 8.5.6 Implementation Considerations 262 -- 8.5.7 Performance Evaluation 263 -- 8.6 Content-Aware Real-Time Video Streaming 265 -- 8.6.1 Background 265.…”

Tabla de Contenidos: “…Structure de la production et indicateurs de performance -- Tableau J. Indicateurs du marché du travail -- Tableau K. …”