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Subsampled (or partial) Fourier matrices were originally introduced in the compressive sensing literature by Candes et al. Later, in papers by Candes and Tao and Rudelson and Vershynin, it was shown that (random) subsampling of the rows of many other classes of unitary matrices also yield effective sensing matrices. The key requirement is that the rows of U, the unitary matrix, must be highly incoherent...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recovered from a relatively small number of samples in the form of random projections. However, in severely resource-constrained settings even CS techniques may fail, and thus, a less aggressive goal of partial signal recovery is reasonable. This paper describes a simple data-adaptive procedure that efficiently...
A multi-step adaptive resampling procedure is proposed, and shown to be an effective approach when detecting high-dimensional sparse signals in noise. Each step of the proposed procedure refines an estimate of the true signal subspace, allowing sensing energy to be focused more directly into the subspace of interest and significantly improving the performance of the final detection test. Large-sample...
This paper studies a generalization of the classic binary search problem of locating a desired value within a sorted list. The classic problem can be viewed as determining the correct one-dimensional, binary-valued threshold function from a finite class of such functions based on queries taking the form of point samples of the function. The classic problem is also equivalent to a simple binary encoding...
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