Introduction to graph signal processing / Antonio Ortega, University of Southern California.
By: Ortega, Antonio [author.]
.
Material type: 
BookPublisher: Cambridge : Cambridge University Press, 2022Description: 1 online resource (xvii, 301 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781108552349 (ebook).Subject(s): Signal processingAdditional physical formats: Print version: : No titleDDC classification: 621.382/2 Online resources: Click here to access online Summary: An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.
Title from publisher's bibliographic system (viewed on 07 Apr 2022).
An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.
There are no comments for this item.