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Aldroubi, Akram; Huang, Longxiu; Krishtal, Ilya; Ledeczi, Akos; Lederman, Roy R; Volgyesi, Peter
Dynamical sampling with additive random noise Technical Report
no. arXiv:1807.10866 [math], 2018, (arXiv: 1807.10866).
Abstract | Links | BibTeX | Tags: Mathematics - Numerical Analysis
@techreport{aldroubi_dynamical_2018,
title = {Dynamical sampling with additive random noise},
author = {Akram Aldroubi and Longxiu Huang and Ilya Krishtal and Akos Ledeczi and Roy R Lederman and Peter Volgyesi},
url = {http://arxiv.org/abs/1807.10866},
year = {2018},
date = {2018-01-01},
urldate = {2020-08-13},
number = {arXiv:1807.10866 [math]},
abstract = {Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution.},
note = {arXiv: 1807.10866},
keywords = {Mathematics - Numerical Analysis},
pubstate = {published},
tppubtype = {techreport}
}
Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution.