소장자료
LDR | 02078cam a2200000 a | ||
001 | 0100723683▲ | ||
005 | 20230808165327▲ | ||
007 | ta ▲ | ||
008 | 210809s2021 nyu b 001 0 eng c▲ | ||
010 | ▼a 2021038900▲ | ||
020 | ▼a9781108428132▼q(hbk.)▲ | ||
035 | ▼a(KERIS)REF000019666936▲ | ||
040 | ▼aDLC▼beng▼cDLC▼d221016▲ | ||
042 | ▼apcc▲ | ||
050 | 0 | 0 | ▼aTK5102.9▼b.O77 2021▲ |
082 | 0 | 4 | ▼a621.382/2▼223▲ |
090 | ▼a621.3822▼bO77i▲ | ||
100 | 1 | ▼aOrtega, Antonio,▼d1965-▲ | |
245 | 1 | 0 | ▼aIntroduction to graph signal processing /▼cAntonio Ortega.▲ |
260 | ▼aNew York, NY :▼bCambridge University Press,▼c2021.▲ | ||
300 | ▼axvii, 301 p. ;▼c25 cm▲ | ||
504 | ▼aIncludes bibliographical references and index.▲ | ||
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"--▼cProvided by publisher.▲ | ||
650 | 0 | ▼aSignal processing.▲ |
Introduction to graph signal processing
자료유형
국외단행본
서명/책임사항
Introduction to graph signal processing / Antonio Ortega.
발행사항
New York, NY : Cambridge University Press , 2021.
형태사항
xvii, 301 p. ; 25 cm
서지주기
Includes bibliographical references and index.
요약주기
"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"-- Provided by publisher.
ISBN
9781108428132
청구기호
621.3822 O77i
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