000			03978nam a2200409 i 4500
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007			cr nn nnnaamaa
710	2		_aSpringerLink (Online service) _9106996
999			_c110725 _d110725 _x1
001			110725
003			ES-MaUEC
005			20230102113435.0
008			181130s2019 gw a o |||| 0|eng d
020			_a9783030035747
024	7		_a10.1007/978-3-030-03574-7 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQC787 .C6 _b2019 EB
245	0	0	_aVertex-Frequency Analysis of Graph Signals _cedited by Ljubiša Stanković, Ervin Sejdić
264		1	_aCham _bSpringer _c2019
300			_a1 recurso en línea (XV, 507 páginas) _b196 ilustraciones, 170 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aSignals and Communication Technology _x1860-4862
505	0		_aIntroduction 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	3		_aThis 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.
988			_aPrimersemestre_2019_Engineering
650		7	_9671318 _aContadores nucleares _2embne
700	1		_aSejdić, Ervin. _eeditor literario _997617
700	1		_aStanković, Ljubiša. _eeditor literario
776	0	8	_iPrinted edition: _z9783030035730
776	0	8	_iPrinted edition: _z9783030035754
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-03574-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b11/2019 _eu _zSI