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pro vyhledávání: '"Miljan Petrovic"'
Autor:
Miljan Petrovic, Thomas A. W. Bolton, Maria Giulia Preti, Raphaël Liégeois, Dimitri Van De Ville
Publikováno v:
Network Neuroscience, Vol 3, Iss 3, Pp 807-826 (2019)
Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions—for example, based
Externí odkaz:
https://doaj.org/article/87cb364a729c40d380d3d512106e0fa1
Publikováno v:
IEEE Signal Processing Magazine, Vol. 37, No 6 (2020) pp. 150-159
The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0371cdc10a7dacc9cff65e3b92b7229e
https://archive-ouverte.unige.ch/unige:156900
https://archive-ouverte.unige.ch/unige:156900
Akademický článek
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Autor:
Miljan Petrovic, Milica Jovanovic
Publikováno v:
Serbian Journal of Electrical Engineering, Vol 13, Iss 1, Pp 59-70 (2016)
This paper presents mathematical modeling and performance evaluation of different realizations of universal periodic digital signal generator based on infinite impulse response filter. The development kit used was Spartan 3E-Starter Board. Using Xili
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7711bd1e7df1a37c5c15715b72179758
https://doi.org/10.2298/sjee1601059p
https://doi.org/10.2298/sjee1601059p
Autor:
Miljan Petrovic, Thomas A. W. Bolton, Maria Giulia Preti, Raphaël Liégeois, Dimitri Van De Ville
Publikováno v:
Network Neuroscience, Vol. 3, No 3 (2019) pp. 807-826
Network Neuroscience
Network Neuroscience, Vol 3, Iss 3, Pp 807-826 (2019)
Network Neuroscience
Network Neuroscience, Vol 3, Iss 3, Pp 807-826 (2019)
Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions-e.g., based on wavele
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b92274a412b9a8f9d6a6f1ca795cae3a
Autor:
Miljan Petrovic, Dimitri Van De Ville
Publikováno v:
Proceedings of the SPIE Conference on Optical Engineering + Applications: Wavelets and Sparsity XVIII
Proceedings of the SPIE Conference on Optical Engineering + Applications: Wavelets and Sparsity XVIII P. 111380D
Proceedings of the SPIE Conference on Optical Engineering + Applications: Wavelets and Sparsity XVIII P. 111380D
Joint localization of graph signals in vertex and spectral domain is achieved in Slepian vectors calculated by either maximizing energy concentration (mu) or minimizing modified embedded distance (xi) in the subgraph of interest. On the other hand, g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::218f708efbc54a069bc52975e55b3160
https://infoscience.epfl.ch/record/275526
https://infoscience.epfl.ch/record/275526