RECOSAMP

Sampling and Reconstruction driven by Sparsity Models with Applications in Sensor Networks and Neuroscience

Coordinatore	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE    more 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

#	participant	country	role	EC contrib. [€]
1	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Dr. Nome: Pier Luigi Cognome: Dragotti Email: send email Telefono: +44 207 594 6192 Fax: +44 207 594 6234	UK (LONDON)	hostInstitution	1˙451˙162.00
2	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Mr. Nome: Shaun Cognome: Power Email: send email Telefono: +44 207 594 8773 Fax: +44 207 594 8609	UK (LONDON)	hostInstitution	1˙451˙162.00

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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.'