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Professor Dragotti


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

Project summary


Project Members

The problem of reconstructing or estimating partially observed or sampled signals is an important 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.

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