458 citations · 893 across the 37 of their papers we have counts for
10 papers · 1 filter
Estimation of Poisson arrival processes under linear models
Michael G. Moore, Mark A. Davenport
In this paper we consider the problem of estimating the parameters of a Poisson arrival process where the rate function is assumed to lie in the span of a known basis. Our goal is…
ROAST: Rapid Orthogonal Approximate Slepian Transform
Zhihui Zhu, Santhosh Karnik, Michael B. Wakin +2
In this paper, we provide a Rapid Orthogonal Approximate Slepian Transform (ROAST) for the discrete vector that one obtains when collecting a finite set of uniform samples from a b…
The Eigenvalue Distribution of Discrete Periodic Time-Frequency Limiting Operators
Zhihui Zhu, Santhosh Karnik, Mark A. Davenport +2
Bandlimiting and timelimiting operators play a fundamental role in analyzing bandlimited signals that are approximately timelimited (or vice versa). In this paper, we consider a ti…
An overview of low-rank matrix recovery from incomplete observations
Mark A. Davenport, Justin Romberg
Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, thes…
Constrained adaptive sensing
Mark A. Davenport, Andrew K. Massimino, Deanna Needell +1
Suppose that we wish to estimate a vector from a small number of noisy linear measurements of the form , where…
Signal Space CoSaMP for Sparse Recovery with Redundant Dictionaries
Mark A. Davenport, Deanna Needell, Michael B. Wakin
Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. The bulk of the CS literature has focused on the case where the acquired signal…
Compressive binary search
Mark A. Davenport, Ery Arias-Castro
In this paper we consider the problem of locating a nonzero entry in a high-dimensional vector from possibly adaptive linear measurements. We consider a recursive bisection method…
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…