Cooperative and Graph Signal Processing Principles and Applications [Paperback]

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

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 Thl3,