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.
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.
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
Field of science
- /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/signal processing/compressed sensing
- /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/sensors/smart sensors
Call for proposal
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