大葉大學圖書館 |

Cooperative and graph signal processing :principles and applications /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	621.3822
書名/作者:	Cooperative and graph signal processing : : principles and applications // Petar M. Djurić, Cédric Richard.
作者:	Djurić, Petar M.,
其他作者:	Richard, Cédric,
面頁冊數:	1 online resource
標題:	Signal processing.
標題:	Image processing.
ISBN:	9780128136782
ISBN:	0128136782
ISBN:	9780128136775
ISBN:	0128136774
書目註:	Includes bibliographical references and index.
內容註:	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.
摘要、提要註:	"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"--
電子資源:	https://www.sciencedirect.com/science/book/9780128136775

Cooperative and graph signal processing :principles and applications /
Djurić, Petar M.,

Cooperative and graph signal processing :principles and applications /Petar M. Djurić, Cédric Richard. - 1 online resource

Includes bibliographical references and index.

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.

"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"--

ISBN: 9780128136782Subjects--Topical Terms:

174043
Signal processing.
Index Terms--Genre/Form:

336502
Electronic books.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Cooperative and graph signal processing :principles and applications /
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245 1 0 $a Cooperative and graph signal processing : $b principles and applications / $c Petar M. Djurić, Cédric Richard.
264 1 $a London, United Kingdom : $b Academic Press, an imprint of Elsevier, $c [2018]
264 4 $c ©2018
300 $a 1 online resource
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504 $a Includes bibliographical references and index.
505 0 $a 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.
505 8 $a 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.
505 8 $a 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).
505 8 $a 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.
505 8 $a 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.
520 $a "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"-- $c Provided by publisher.
650 0 $a Signal processing. $3 174043
650 0 $a Image processing. $3 342096
655 4 $a Electronic books. $2 local $3 336502
700 1 $a Richard, Cédric, $e author. $3 716128
856 4 0 $u https://www.sciencedirect.com/science/book/9780128136775