Cooperative and Graph Signal Processing : Principles and Applications
Language: English
Published by Elsevier Inc Jun 2018, 2018
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Neuware - 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.…
Seller Inventory # 9780128136775
- Title
- Cooperative and Graph Signal Processing : Principles and Applications
- Author
- Petar Djuric
- Publisher
- Elsevier Inc Jun 2018
- Publication year
- 2018
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0128136774
- ISBN 13
- 9780128136775
- Item weight
- 1,460 grams
- Dimensions
- 235x191x43 mm
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 covered
- Includes 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
"Synopsis" may belong to another edition of this title.
About the Author
Cédric Richard received the Dipl.-Ing. and the M.S. degrees in 1994, and the Ph.D. degree in 1998, from Compiègne University of Technology, France, all in Electrical and Computer Engineering. He is a Full Professor at the Université Nice Sophia Antipolis, France. He was a junior member of the Institut Universitaire de France in 2010-2015. His current research interests include adaptation and learning, statistical signal processing, and network science. Cédric Richard is the author of over 250 papers. He was the General Co-Chair of the IEEE SSP Workshop that was held in Nice, France, in 2011. He was the Technical Co-Chair of EUSIPCO 2015 that was held in Nice, France, and of the IEEE CAMSAP Workshop 2015 that was held in Cancun, Mexico. He serves as a Senior Area Editor of the IEEE Transactions on Signal Processing and as an Associate Editor of the IEEE Transactions on Signal and Information Processing over Networks since 2015. He is also an Associate Editor of Signal Processing Elsevier since 2009. Cédric Richard is a member of the IEEE Machine Learning for Signal Processing (IEEE MLSP TC) Technical Committee, and served as member of the IEEE Signal Processing Theory and Methods (IEEE SPTM TC) Technical Committee in 2009-2014.
"About the title" may belong to another edition of this title.
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