rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Cooperative and Graph Signal Processing
    sub title: Principles and Applications
  name:
    name part: Djurić, Petar M.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Richard, Cédric
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2018
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: "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"--
  table of contents: 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.
  table of contents: 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.
  table of contents: 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).
  table of contents: 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.
  table of contents: 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.
  note: Petar M. Djurić, Cédric Richard.
  note: Includes bibliographical references and index.
  subject:
    topic: Signal processing
  subject:
    topic: Image processing
  subject:
    topic: Traitement du signal
  subject:
    topic: Traitement d'images
  subject:
    topic: image processing
  subject:
    topic: TECHNOLOGY & ENGINEERING
    topic: Mechanical
  subject:
    topic: Image processing
  subject:
    topic: Signal processing
  classification: TK5102.9
  classification: 621.3822
  related item:
    identifier: (OCoLC)1011518558
  identifier: 9780128136782
  identifier: 0128136782
  identifier: 9780128136775
  identifier: 0128136774
  identifier: 9780128136782 Ingram Content Group
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: N$T
    record creation date: 180709
    record change date: 20260327153631.6
    record identifier: in00005763624
    language of cataloging:
      language term: eng