Compressive sensing aims to recover a sparse or compressible signal from a small set of projections onto random vectors; conventional solutions involve linear programming or greed...
Marco F. Duarte, Michael B. Wakin, Richard G. Bara...
Given two strings (a text t of length n and a pattern p) and a natural number w, window subsequence problems consist in deciding whether p occurs as a subsequence of t and/or findi...
In this paper, we derive concentration of measure inequalities for compressive Toeplitz matrices (having fewer rows than columns) with entries drawn from an independent and identic...
Borhan Molazem Sanandaji, Tyrone L. Vincent, Micha...
Compressive Sensing is an emerging field based on the revelation that a small group of non-adaptive linear projections of a compressible signal contains enough information for rec...
Michael B. Wakin, Jason N. Laska, Marco F. Duarte,...
Abstract. Classification of hyperspectral images is paramount to an increasing number of user applications. With the advent of more powerful technology, sensed images demand for la...