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Vertex-frequency analysis of graph s...

Sejdic, Ervin.

Vertex-frequency analysis of graph signals

Record Type:	Electronic resources : Monograph/item
Title/Author:	Vertex-frequency analysis of graph signalsedited by Ljubisa Stankovic, Ervin Sejdic.
other author:	Stankovic, Ljubisa.
Published:	Cham :Springer International Publishing :2019.
Description:	xv, 507 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Vertex detectors.
Online resource:	https://doi.org/10.1007/978-3-030-03574-7
ISBN:	9783030035747$q(electronic bk.)

Vertex-frequency analysis of graph signals
Vertex-frequency analysis of graph signals[electronic resource] /edited by Ljubisa Stankovic, Ervin Sejdic. - Cham :Springer International Publishing :2019. - xv, 507 p. :ill., digital ;24 cm. - Signals and communication technology,1860-4862. - Signals and communication technology..

Introduction to Graph Signal Processing -- Oversampled Graph Laplacian Matrix for Graph Filter Banks -- Toward an Uncertainty Principle for Weighted Graphs -- Graph Theoretic Uncertainty and Feasibility -- Signal-Adapted Tight Frames on Graphs -- Local Spectral Analysis of the Cerebral Cortex: New Gyrification Indices -- Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets.

This book introduces new methods to analyze vertex-varying graph signals. In many real-world scenarios, the data sensing domain is not a regular grid, but a more complex network that consists of sensing points (vertices) and edges (relating the sensing points) Furthermore, sensing geometry or signal properties define the relation among sensed signal points. Even for the data sensed in the well-defined time or space domain, the introduction of new relationships among the sensing points may produce new insights in the analysis and result in more advanced data processing techniques. The data domain, in these cases and discussed in this book, is defined by a graph. Graphs exploit the fundamental relations among the data points. Processing of signals whose sensing domains are defined by graphs resulted in graph data processing as an emerging field in signal processing. Although signal processing techniques for the analysis of time-varying signals are well established, the corresponding graph signal processing equivalent approaches are still in their infancy. This book presents novel approaches to analyze vertex-varying graph signals. The vertex-frequency analysis methods use the Laplacian or adjacency matrix to establish connections between vertex and spectral (frequency) domain in order to analyze local signal behavior where edge connections are used for graph signal localization. The book applies combined concepts from time-frequency and wavelet analyses of classical signal processing to the analysis of graph signals. Covering analytical tools for vertex-varying applications, this book is of interest to researchers and practitioners in engineering, science, neuroscience, genome processing, just to name a few. It is also a valuable resource for postgraduate students and researchers looking to expand their knowledge of the vertex-frequency analysis theory and its applications. The book consists of 15 chapters contributed by 41 leading researches in the field.

ISBN: 9783030035747$q(electronic bk.)

Standard No.: 10.1007/978-3-030-03574-7doiSubjects--Topical Terms:

833171
Vertex detectors.

LC Class. No.: QC787.V45 / V478 2019

Dewey Class. No.: 621.4830153

Vertex-frequency analysis of graph signals
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245 0 0 $a Vertex-frequency analysis of graph signals $h [electronic resource] / $c edited by Ljubisa Stankovic, Ervin Sejdic.
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490 1 $a Signals and communication technology, $x 1860-4862
505 0 $a Introduction to Graph Signal Processing -- Oversampled Graph Laplacian Matrix for Graph Filter Banks -- Toward an Uncertainty Principle for Weighted Graphs -- Graph Theoretic Uncertainty and Feasibility -- Signal-Adapted Tight Frames on Graphs -- Local Spectral Analysis of the Cerebral Cortex: New Gyrification Indices -- Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets.
520 $a This book introduces new methods to analyze vertex-varying graph signals. In many real-world scenarios, the data sensing domain is not a regular grid, but a more complex network that consists of sensing points (vertices) and edges (relating the sensing points) Furthermore, sensing geometry or signal properties define the relation among sensed signal points. Even for the data sensed in the well-defined time or space domain, the introduction of new relationships among the sensing points may produce new insights in the analysis and result in more advanced data processing techniques. The data domain, in these cases and discussed in this book, is defined by a graph. Graphs exploit the fundamental relations among the data points. Processing of signals whose sensing domains are defined by graphs resulted in graph data processing as an emerging field in signal processing. Although signal processing techniques for the analysis of time-varying signals are well established, the corresponding graph signal processing equivalent approaches are still in their infancy. This book presents novel approaches to analyze vertex-varying graph signals. The vertex-frequency analysis methods use the Laplacian or adjacency matrix to establish connections between vertex and spectral (frequency) domain in order to analyze local signal behavior where edge connections are used for graph signal localization. The book applies combined concepts from time-frequency and wavelet analyses of classical signal processing to the analysis of graph signals. Covering analytical tools for vertex-varying applications, this book is of interest to researchers and practitioners in engineering, science, neuroscience, genome processing, just to name a few. It is also a valuable resource for postgraduate students and researchers looking to expand their knowledge of the vertex-frequency analysis theory and its applications. The book consists of 15 chapters contributed by 41 leading researches in the field.
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650 2 4 $a Applications of Graph Theory and Complex Networks. $3 759901
650 2 4 $a Neurosciences. $3 211508
700 1 $a Stankovic, Ljubisa. $3 833170
700 1 $a Sejdic, Ervin. $3 576907
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