RECOSAMP
Sampling and Reconstruction driven by Sparsity Models with Applications in Sensor Networks and Neuroscience
| Coordinatore | IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | United Kingdom [UK] |
| Totale costo | 1˙451˙162 € |
| EC contributo | 1˙451˙162 € |
| 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-2011-StG_20101014 |
| Funding Scheme | ERC-SG |
| Anno di inizio | 2011 |
| Periodo (anno-mese-giorno) | 2011-11-01 - 2016-10-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD contact info |
UK (LONDON) | hostInstitution | 1˙451˙162.00 |
| 2 |
IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD contact info |
UK (LONDON) | hostInstitution | 1˙451˙162.00 |
Mappa
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Obiettivo del progetto (Objective)
'The problem of reconstructing or estimating partially observed or sampled signals is animportant one that finds application in many areas of signal processing and communications. Traditional acquisition and reconstruction approaches are heavily influences by classical Shannon sampling theory which gives an exact sampling and interpolation formula for bandlimited signals. Recently, the emerging theory of sparse sampling has challenged the way we think about signal acquisition and has demonstrated that, by using more sophisticated signal models, it is possible to break away from the need to sample signals at the Nyquist rate. The insight that sub-Nyquist sampling can, under some circumstances, allow perfect reconstruction is revolutionizing signal processing, communications and inverse problems. Given the ubiquity of the sampling process, the implications of these new research developments are far reaching.
This project is based on the applicant's recent work on the sampling of sparse continuous-time signals and aims to extend the existing theory to include more general signal models that are closer to the physical characteristics of real data, to explore new domains where sparsity and sampling can be effectively used and to provide a set of new fast algorithms with clear and predictable performance. As part of this work, he will also consider timely important problems such as the localization of diffusive sources in sensor networks and the analysis of neuronal signals of the brain. He will, for the first time, pose these as sparse sampling problems and in this way he expects to develop technologies with a step change in performance.'