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» Reconstructing Boolean Models of Signaling
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ICASSP
2008
IEEE
13 years 11 months ago
Reconstructing sparse signals from their zero crossings
Classical sampling records the signal level at pre-determined time instances, usually uniformly spaced. An alternative implicit sampling model is to record the timing of pre-deter...
Petros Boufounos, Richard G. Baraniuk
ICASSP
2008
IEEE
13 years 11 months ago
Distributed compressed sensing: Sparsity models and reconstruction algorithms using annihilating filter
Consider a scenario where a distributed signal is sparse and is acquired by various sensors that see different versions. Thus, we have a set of sparse signals with both some commo...
Ali Hormati, Martin Vetterli
ICASSP
2011
IEEE
12 years 9 months ago
Data driven model based least squares image reconstruction for radio astronomy
Image reconstruction problems in radio astronomy and other fields like biomedical imaging are often ill-posed and some form of regularization is required. This imposes user speci...
Stefan J. Wijnholds, Alle-Jan van der Veen
BCBGC
2008
13 years 6 months ago
BMA - Boolean Matrices as Model for Motif Kernels
We introduce the data model BM, which specifies kernels of motifs by means of Boolean matrices. Different from position frequency matrices these only specify which bases can appea...
Jan Schröder, Manfred Schimmler, Heiko Schr&o...
TSP
2010
13 years 5 hour ago
Distributed sampling of signals linked by sparse filtering: theory and applications
We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin ...
Ali Hormati, Olivier Roy, Yue M. Lu, Martin Vetter...