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Produktbild: Vertex-Frequency Analysis of Graph Signals
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Vertex-Frequency Analysis of Graph Signals

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

29.05.2026

Abbildungen

XIV, 555 p. 211 illus., 183 illus. in color.

Herausgeber

Ljubiša Stanković + weitere

Verlag

Springer

Seitenzahl

555

Maße (L/B/H)

24,1/16/3,5 cm

Gewicht

1118 g

Auflage

Second Edition 2026

Sprache

Englisch

ISBN

978-3-032-16588-6

Beschreibung

Portrait


Prof. Ljubiša Stanković was born in Montenegro in 1960. He received the B.S. degree in EE from the University of Montenegro (UoM) with the Best Student at the University award, winning twice the EE student competition in mathematics in Yugoslavia. He obtained the M.S. degree in communications from the University of Belgrade and the Ph.D. in theory of electromagnetic waves from the UoM.


As Fulbright Grantee, he spent 1984–1985 academic year at the Worcester Polytechnic Institute, USA. In 1997–1999, he was on leave at the Ruhr University Bochum, Germany, supported by the Alexander von Humboldt Foundation. At the beginning of 2001, he was at the Technische Universiteit Eindhoven, The Netherlands, as Visiting Professor. He was Visiting Academic at the Imperial College, London, in 2012-2013. He was Rector of the UoM 2003-2008 and Ambassador of Montenegro to the United Kingdom, Ireland, and Iceland from 2011 to 2015. Prof. Stanković is a Life Fellow of IEEE and a recipient of numerous awards, including the Best Paper Award from the European Association for Signal Processing in 2017 for a paper published in the Signal Processing, the IEEE Signal Processing Magazine Best Column Award for 2020, and the Outstanding Paper Award at IEEE ICASSP 2021.


Prof. Ervin Sejdić received B.E.Sc. and Ph.D. degrees in electrical engineering from the University of Western Ontario, London, Ontario, Canada, in 2002 and 2008, respectively. From 2008 to 2010, he was Postdoctoral Fellow at the University of Toronto with a cross-appointment at Bloorview Kids Rehab, Canada’s largest children’s rehabilitation teaching hospital. From 2010 until 2011, he was Research Fellow at Harvard Medical School with a cross-appointment at Beth Israel Deaconess Medical Center. In 2011, Prof. Sejdić joined the Department of Electrical and Computer Engineering at the University of Pittsburgh (Pittsburgh, PA, USA). In 2021, he joined the University of Toronto as Faculty Member. He is also Research Chair in Artificial Intelligence for Health Outcomes at North York General Hospital in Toronto.


Prof. Sejdić is Senior Member of IEEE and Recipient of many awards. In February 2016, President Obama named Dr. Sejdić as a recipient of the Presidential Early Career Award for Scientists and Engineers. In 2017, Dr. Sejdić was awarded the National Science Foundation CAREER Award. In 2018, he was awarded the Chancellor’s Distinguished Research Award at the University of Pittsburgh.

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

29.05.2026

Abbildungen

XIV, 555 p. 211 illus., 183 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

555

Maße (L/B/H)

24,1/16/3,5 cm

Gewicht

1118 g

Auflage

Second Edition 2026

Sprache

Englisch

ISBN

978-3-032-16588-6

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Vertex-Frequency Analysis of Graph Signals

  • Part I: Introduction.- Introduction to graph signal processing.- Part II: Vertex-frequency theory.- Transformation from graphs to signals and back.- The spectral graph wavelet transform: Fundamental theory and fast computation.- Signal-adapted decomposition of graph signals.- Wavelets on graphs via deep learning.- Oversampled transforms for graph signals.- Spectral analysis on directed acyclic graphs.- Matched filtering on graphs.- A filtering framework for time-varying graph signals.- Vertex-frequency energy distributions.- Part III: Applications.- Flexible graph encoder-decoder for unsupervised anomaly detection.- Estimating the complexity of the cerebral cortex folding with a local shape spectral analysis.- Wavelet-based visual data exploration.- Graph-based wavelet multiresolution modeling of multivariate terrain data.