SPARSAM
Sparse Sampling: Theory, Algorithms and Applications
| Coordinatore | ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| 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
| # | ||||
|---|---|---|---|---|
| 1 |
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Organization address
address: BATIMENT CE 3316 STATION 1 contact info |
CH (LAUSANNE) | hostInstitution | 1˙839˙174.00 |
| 2 |
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Organization address
address: BATIMENT CE 3316 STATION 1 contact info |
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.
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.