Cooperative and Graph Signal Processing - 1st Edition - ISBN: 9780128136775

Cooperative and Graph Signal Processing

1st Edition

Principles and Applications

Editors: Petar Djuric Cedric Richard
Paperback ISBN: 9780128136775
Imprint: Academic Press
Published Date: 1st June 2018
Page Count: 824
Sales tax will be calculated at check-out Price includes VAT/GST

Institutional Access

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.


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.

Key Features

  • 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


Researchers and graduate students in signal and information processing over networks

Table of Contents

1. Adaptation and learning
2. Detection, estimation and filtering
3. Bayesian approach to inference
4. Optimization and control
5. Machine learning
6. Game-theoretic learning
7. Discrete-time signal processing
8. Sampling and recovery of graph signals
9. Design of graph filters and filter banks
10. Statistical signal processing
11. Inference of graph topology
12. Prediction and learning
13. Monitoring and sensing
14. Optimal network signal processing and resource allocation
15. Network security and privacy
16. Cooperative and coordinated multi-cell techniques
17. Source and channel decoding
18. Scheduling and queuing protocols
19. Learning, distributed decision-making, estimation, and filtering
20. Consensus and coordination
21. Dynamic social networks
22. Information diffusion and rumor spreading
23. Multi-layered social networks
24. Marketing and advertising, trend prediction, recommendation systems, and crowdsourcing
25. Genomics and systems biology
26. Media and video
27. Smart grids
28. Internet of Things
29. Wireless health
30. Neuroscience


No. of pages:
© Academic Press 2018
Academic Press
Paperback ISBN:

About the Editor

Petar Djuric

Petar M. Djurić received the B.S. and M.S. degrees in electrical engineering from the University of Belgrade, Belgrade, Yugoslavia, respectively, and the Ph.D. degree in electrical engineering from the University of Rhode Island, Kingston, RI, USA. He is currently a Professor with the Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA. His research has been in the area of signal and information processing with primary interests in the theory of signal modeling, detection, and estimation; Monte Carlo-based methods; signal and information processing over networks; machine learning, RFID and the IoT. He has been invited to lecture at many universities in the United States and overseas. Prof. Djurić was a recipient of the IEEE Signal Processing Magazine Best Paper Award in 2007 and the EURASIP Technical Achievement Award in 2012. In 2008, he was the Chair of Excellence of Universidad Carlos III de Madrid-Banco de Santander. From 2008 to 2009, he was a Distinguished Lecturer of the IEEE Signal Processing Society. He has been on numerous committees of the IEEE Signal Processing Society and of many professional conferences and workshops. He is the Editor-in-Chief of the IEEE Transactions on Signal and Information Processing over Networks. Prof. Djurić is a Fellow of IEEE and EURASIP.

Affiliations and Expertise

Stony Brook University, Stony Brook, NY, USA

Cedric Richard

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.

Affiliations and Expertise

Université Nice Sophia Antipolis, Nice, France

Ratings and Reviews