http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
"Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience. With this book readers will learn the basics of adaptation and learning in networks, the essentials of detection, estimation and filtering, Bayesian inference in networks, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the perspective of flow of information, and how to apply signal processing methods in distributed settings. Presents the first book on cooperative signal processing and graph signal processing Provides a range of applications and application areas that are thoroughly coveredIncludes an editor in chief and associate editor from the IEEE Transactions on Signal Processing and Information Processing over Networks who have recruited top contributors for the book"--
Front Cover; Cooperative and Graph Signal Processing: Principles and Applications; Copyright; Contents; Contributors; Preface; Part 1: Basics of Inference Over Networks; Chapter 1: Asynchronous Adaptive Networks; 1.1 Introduction; 1.1.1 Asynchronous Behavior; 1.1.2 Organization of the Chapter; 1.2 Single-Agent Adaptation and Learning; 1.2.1 Risk and Loss Functions; 1.2.2 Conditions on Cost Function; 1.2.3 Stochastic-Gradient Approximation; 1.2.4 Conditions on Gradient Noise Process; 1.2.5 Random Updates; 1.2.6 Mean-Square-Error Stability; 1.2.7 Mean-Square-Error Performance.
1.3 Centralized Adaptation and Learning1.3.1 Noncooperative MSE Processing; 1.3.2 Centralized MSE Processing; 1.3.3 Stochastic-Gradient Centralized Solution; 1.3.4 Performance of Centralized Solution; 1.3.5 Comparison With Noncooperative Processing; 1.4 Synchronous Multiagent Adaptation and Learning; 1.4.1 Strongly Connected Networks; 1.4.2 Distributed Optimization; 1.4.3 Synchronous Consensus Strategy; 1.4.4 Synchronous Diffusion Strategies; 1.5 Asynchronous Multiagent Adaptation and Learning; 1.5.1 Asynchronous Model; 1.5.2 Mean Graph; 1.5.3 Random Combination Policy.
1.5.4 Perron Vectors1.6 Asynchronous Network Performance; 1.6.1 MSD Performance; 1.7 Network Stability and Performance; 1.7.1 MSE Networks; 1.7.2 Diffusion Networks; 1.8 Concluding Remarks; References; Chapter 2: Estimation and Detection Over Adaptive Networks; 2.1 Introduction; 2.2 Inference Over Networks; 2.2.1 Canonical Inference Problems; 2.2.2 Distributed Inference Problem; Architectures with fusion center; Fully flat architectures; 2.2.3 Inference Over Adaptive Networks; 2.3 Diffusion Implementations; 2.4 Distributed Adaptive Estimation (DAE).
2.4.1 Constructing the Distributed Adaptive Estimation Algorithm2.4.2 Mean-Square-Error Performance; 2.4.3 Useful Comparisons; 2.4.4 DAE at Work; 2.5 Distributed Adaptive Detection (DAD); 2.5.1 Constructing the Distributed Adaptive Detection Algorithm; 2.5.2 Detection Performance; 2.5.3 Weak Law of Small Step-Sizes; 2.5.4 Asymptotic Normality; 2.5.5 Large Deviations; 2.5.6 Refined Large Deviations Analysis: Exact Asymptotics; 2.5.7 DAD at Work; 2.6 Universal Scaling Laws: Estimation Versus Detection; Appendix; A.1 Procedure to Evaluate Eq. (2.69); References.
Chapter 3: Multitask Learning Over Adaptive Networks With Grouping Strategies3.1 Introduction; 3.2 Network Model and Diffusion LMS; 3.2.1 Network Model; 3.2.2 A Brief Review of Diffusion LMS; 3.3 Group Diffusion LMS; 3.3.1 Motivation; 3.3.2 Group Diffusion LMS Algorithm; 3.3.3 Network Behavior; Mean weight behavior analysis; Mean-square error behavior analysis; 3.4 Grouping Strategies; 3.4.1 Fixed Grouping Strategy; 3.4.2 Adaptive Grouping Strategy; 3.4.3 Adaptive Combination Strategy; 3.5 Simulations; 3.5.1 Model Validation; 3.5.2 Performance of the Adaptive Grouping Strategy.
Includes bibliographical references and index.