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Cooperative and Graph Signal Processing
Principles and Applications
Provides a comprehensive understanding of the principles of cooperative and graph signal processing and how to apply them
Petar Djuric (Edited by), Cédric Richard (Edited by)
9780128136775
Paperback / softback, published 20 June 2018
866 pages
23.4 x 19 x 5.2 cm, 1.83 kg
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.
PART 1 BASICS OF INFERENCE OVER NETWORKS 1. Asynchronous Adaptive Networks 2. Estimation and Detection Over Adaptive Networks 3. Multitask Learning Over Adaptive Networks With Grouping Strategies 4. Bayesian Approach to Collaborative Inference in Networks of Agents 5. Multiagent Distributed Optimization 6. Distributed Kalman and Particle Filtering 7. Game Theoretic Learning PART 2 SIGNAL PROCESSING ON GRAPHS 8. Graph Signal Processing 9. Sampling and Recovery of Graph Signals 10. Bayesian Active Learning on Graphs 11. Design of Graph Filters and Filterbanks 12. Statistical Graph Signal Processing: Stationarity and Spectral Estimation 13. Inference of Graph Topology 14. Partially Absorbing Random Walks: A Unified Framework for Learning on Graphs PART 3 DISTRIBUTED COMMUNICATIONS, NETWORKING, AND SENSING 15. Methods for Decentralized Signal Processing With Big Data 16. The Edge Cloud: A Holistic View of Communication, Computation, and Caching 17. Applications of Graph Connectivity to Network Security 18. Team Methods for Device Cooperation in Wireless Networks 19. Cooperative Data Exchange in Broadcast Networks 20. Collaborative Spectrum Sensing in the Presence of Byzantine Attack PART 4 SOCIAL NETWORKS 21. Dynamics of Information Diffusion and Social Sensing 22. Active Sensing of Social Networks: Network Identification From Low-Rank Data 23. Dynamic Social Networks: Search and Data Routing 24. Information Diffusion and Rumor Spreading 25. Multilayer Social Networks 26. Multiagent Systems: Learning, Strategic Behavior, Cooperation, and Network Formation PART 5 APPLICATIONS 27. Genomics and Systems Biology 28. Diffusion Augmented Complex Extended Kalman Filtering for Adaptive Frequency Estimation in Distributed Power Networks 29. Beacons and the City: Smart Internet of Things 30. Big Data 31. Graph Signal Processing on Neuronal Networks
