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Universal compressed sensing [PDF]

open access: yes2016 IEEE International Symposium on Information Theory (ISIT), 2016
In this paper, the problem of developing universal algorithms for compressed sensing of stochastic processes is studied. First, R nyi's notion of information dimension (ID) is generalized to analog stationary processes. This provides a measure of complexity for such processes and is connected to the number of measurements required for their accurate ...
H. Vincent Poor, Shirin Jalali
openaire   +4 more sources

From compression to compressed sensing [PDF]

open access: yes2013 IEEE International Symposium on Information Theory, 2013
Can compression algorithms be employed for recovering signals from their underdetermined set of linear measurements? Addressing this question is the first step towards applying compression algorithms for compressed sensing (CS). In this paper, we consider a family of compression algorithms $\mathcal{C}_r$, parametrized by rate $r$, for a compact class ...
Arian Maleki, Shirin Jalali
openaire   +5 more sources

“Compressed” compressed sensing

open access: yes2010 IEEE International Symposium on Information Theory, 2010
The field of compressed sensing has shown that a sparse but otherwise arbitrary vector can be recovered exactly from a small number of randomly constructed linear projections (or samples). The question addressed in this paper is whether an even smaller number of samples is sufficient when there exists prior knowledge about the distribution of the ...
Galen Reeves, Michael Gastpar
openaire   +4 more sources

Algebraic compressed sensing

open access: yesApplied and Computational Harmonic Analysis, 2023
We introduce the broad subclass of algebraic compressed sensing problems, where structured signals are modeled either explicitly or implicitly via polynomials. This includes, for instance, low-rank matrix and tensor recovery. We employ powerful techniques from algebraic geometry to study well-posedness of sufficiently general compressed sensing ...
Breiding, Paul   +3 more
openaire   +2 more sources

Compression-Based Compressed Sensing [PDF]

open access: yesIEEE Transactions on Information Theory, 2017
Modern compression algorithms exploit complex structures that are present in signals to describe them very efficiently. On the other hand, the field of compressed sensing is built upon the observation that "structured" signals can be recovered from their under-determined set of linear projections.
Farideh E. Rezagah   +3 more
openaire   +2 more sources

Practical challenges in data‐driven interpolation: Dealing with noise, enforcing stability, and computing realizations

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary In this contribution, we propose a detailed study of interpolation‐based data‐driven methods that are of relevance in the model reduction and also in the systems and control communities. The data are given by samples of the transfer function of the underlying (unknown) model, that is, we analyze frequency‐response data.
Quirin Aumann, Ion Victor Gosea
wiley   +1 more source

Compressed Sensing in Astronomy [PDF]

open access: yesIEEE Journal of Selected Topics in Signal Processing, 2008
Recent advances in signal processing have focused on the use of sparse representations in various applications. A new field of interest based on sparsity has recently emerged: compressed sensing. This theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory.
Jérôme Bobin   +2 more
openaire   +4 more sources

Kinetic Compressive Sensing [PDF]

open access: yes2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2017
5 pages, 6 figures, Submitted to the Conference Record of "IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE NSS-MIC) 2017"
Scipioni Michele   +6 more
openaire   +4 more sources

Quantization and Compressive Sensing [PDF]

open access: yes, 2015
35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications ...
Boufounos, Petros   +3 more
openaire   +4 more sources

Compressed wavefront sensing [PDF]

open access: yesOptics Letters, 2014
We report on an algorithm for fast wavefront sensing that incorporates sparse representation for the first time in practice. The partial derivatives of optical wavefronts were sampled sparsely with a Shack-Hartman wavefront sensor (SHWFS) by randomly subsampling the original SHWFS data to as little as 5%.
Ryan P. McNabb   +3 more
openaire   +3 more sources

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