SPARSAM

Sparse Sampling: Theory, Algorithms and Applications

 Coordinatore ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE 

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 Nazionalità Coordinatore Switzerland [CH]
 Totale costo 1˙839˙174 €
 EC contributo 1˙839˙174 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2009-AdG
 Funding Scheme ERC-AG
 Anno di inizio 2010
 Periodo (anno-mese-giorno) 2010-05-01   -   2015-04-30

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

 Organization address address: BATIMENT CE 3316 STATION 1
city: LAUSANNE
postcode: 1015

contact info
Titolo: Ms.
Nome: Caroline
Cognome: Vandevyver
Email: send email
Telefono: +41 21 693 4977
Fax: +41 21 693 5585

CH (LAUSANNE) hostInstitution 1˙839˙174.00
2    ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

 Organization address address: BATIMENT CE 3316 STATION 1
city: LAUSANNE
postcode: 1015

contact info
Titolo: Prof.
Nome: Martin
Cognome: Vetterli
Email: send email
Telefono: 41216935634
Fax: 41216934312

CH (LAUSANNE) hostInstitution 1˙839˙174.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

inverse    algorithms    compression    discrete    time    theory    sparse    signals    sampling    physical    communications    bases    signal    central    continuous   

 Obiettivo del progetto (Objective)

Signal representations with Fourier and wavelet bases are central to signal processing and communications. Non-linear approximation methods in such bases are key for problems like denoising, compression and inverse problems. Recently, the idea that signals that are sparse in some domain can be acquired at low sampling density has generated strong interest, under various names like compressed sensing, compressive sampling and sparse sampling. We aim to study the central problem of acquiring continuous-time signals for discrete-time processing and reconstruction using the methods of sparse sampling. Solving this involves developing theory and algorithms for sparse sampling, both in continuous and discrete time. In addition, in order to acquire physical signals, we plan to develop a sampling theory for signals obeying physical laws, like the wave and diffusion equation, and light fields. Together, this will lead to a sparse sampling theory and framework for signal processing and communications, with applications from analog-to-digital conversion to new compression methods, to super-resolution data acquisition and to inverse problems in imaging. In sum, we aim to develop the theory and algorithms for sparse signal processing, with impact on a broad range of applications.

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