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This paper studies the stability of some reconstruction algorithms for compressed sensing in terms of the bit precision. Considering the fact that practical digital systems deal with discretized signals, we motivate the importance of the total number of ac ...
We introduce a new signal model, called (K,C)-sparse, to capture K-sparse signals in N dimensions whose nonzero coefficients are contained within at most C clusters, with C < K < N. In contrast to the existing work in the sparse approximation and compress ...
This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal ...
We present a method for multimodal fusion based on the estimated reliability of each individual modality. Our method uses an information theoretic measure, the entropy derived from the state probability distribution for each stream, as an estimate of relia ...
This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via ℓ1 minimisation. The problem is to identify a dictionary \dico from a set of training samples \Y knowing that \Y=\dico\X ...
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Instead of taking N periodic samples, we measure M < N inner products with random vectors and then recover the signal via a sparsity-seeki ...
Structural investigations of several minerals belonging to the calaverite group with composition Au1–xAgxTe2 (x = 0.00, 0.02, 0.05, 0.09, 0.19, and 0.33) indicate that Ag is randomly distributed on the Au sites. This suppresses the valence fluctuation of A ...
We consider the task of recovering correlated vectors at a central decoder based on fixed linear measurements obtained by distributed sensors. A general formulation of the problem is proposed, under both a universal and an almost sure reconstruction requir ...
Distributed compressed sensing is the extension of compressed sampling (CS) to sensor networks. The idea is to design a CS joint decoding scheme at a central decoder (base station) that exploits the inter-sensor correlations, in order to recover the whole ...
This paper addresses the problem of correct recovery of multiple sparse correlated signals using distributed thresholding. We consider the scenario where multiple sensors capture the same event, but observe different signals that are correlated by local tr ...
