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Introduction to graph signal processing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Introduction to graph signal processingAntonio Ortega, University of Southern California.
作者:	Ortega, Antonio.
出版者:	Cambridge :Cambridge University Press,2022.
面頁冊數:	xvii, 301 p. :ill., digital ;25 cm.
附註:	Title from publisher's bibliographic system (viewed on 07 Apr 2022).
標題:	Signal processing.
電子資源:	https://doi.org/10.1017/9781108552349
ISBN:	9781108552349$q(electronic bk.)

Introduction to graph signal processing
Ortega, Antonio.

Introduction to graph signal processing[electronic resource] /Antonio Ortega, University of Southern California. - Cambridge :Cambridge University Press,2022. - xvii, 301 p. :ill., digital ;25 cm.

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.

ISBN: 9781108552349$q(electronic bk.)Subjects--Topical Terms:

182382
Signal processing.

LC Class. No.: TK5102.9 / .O77 2022

Dewey Class. No.: 621.3822

Introduction to graph signal processing
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500 $a Title from publisher's bibliographic system (viewed on 07 Apr 2022).
520 $a 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.
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