458 citations · 893 across the 37 of their papers we have counts for
5 papers · 1 filter
On the Fundamental Limits of Adaptive Sensing
Ery Arias-Castro, Emmanuel J. Candes, Mark Davenport
Suppose we can sequentially acquire arbitrary linear measurements of an n-dimensional vector x resulting in the linear model y = Ax + z, where z represents measurement noise. If th…
On the stability and accuracy of least squares approximations
Albert Cohen, Mark A. Davenport, Dany Leviatan
We consider the problem of reconstructing an unknown function on a domain from samples of at randomly chosen points with respect to a given measure . Given a s…
Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences
Mark A. Davenport, Michael B. Wakin
Compressive sensing (CS) has recently emerged as a framework for efficiently capturing signals that are sparse or compressible in an appropriate basis. While often motivated as an…
How well can we estimate a sparse vector?
Emmanuel J. Candès, Mark A. Davenport
The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on th…
The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range
Mark A. Davenport, Jason N. Laska, John R. Treichler +1
Compressive sensing (CS) exploits the sparsity present in many signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, in theor…