We consider the sensor broadcast problem: in our setup, sensors measure each one pixel of an image that unfolds over a field, and broadcast a rate constrained encoding of their me...
We attempt to quantify the possible gains that can be achieved by examining a rate-distortion competition between a conventional and a compressive sampling solution to data rate r...
In compressed sensing we seek to gain information about vector x ∈ RN from d << N nonadaptive linear measurements. Candes, Donoho, Tao et. al. ( see e.g. [2, 4, 8]) propos...
In this paper, we introduce a novel bayesian compressive sensing (CS) technique for phonetic classification. CS is often used to characterize a signal from a few support training...
Tara N. Sainath, Avishy Carmi, Dimitri Kanevsky, B...
We initiate the study of sparse recovery problems under the Earth-Mover Distance (EMD). Specifically, we design a distribution over m × n matrices A such that for any x, given A...