Cart (Loading....) | Create Account
Close category search window
 

Reconstruction of streams of impulses from quantized samples using a stochastic algorithm based on Genetic Algorithms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Erdozain, A. ; CEIT, Univ. de Navarra, Donostia ; Crespo, Pedro M.

Works in the last decades have shown that a large class of parametric non-bandlimited signals can be exactly re-constructed from samples of their filtered versions. In particular, signals x(t) that are linear combinations of a finite number of Diracs per unit of time can be acquired by linear filtering followed by uniform sampling. Nevertheless, when the samples are distorted by noise, many of the early proposed schemes can become ill-conditioned. Recently, a stochastic algorithm that recovers the filtered signal z(t) of x(t), but which fails in the reconstruction of x(t) has been presented. In the present paper, a novel stochastic algorithm which blends together concepts of evolutionary algorithms with those of Gibbs sampling and which successes in recovering x(t) is proposed. This algorithm is adapted to the case where the samples are distorted by quantization noise.

Published in:

Sarnoff Symposium, 2009. SARNOFF '09. IEEE

Date of Conference:

March 30 2009-April 1 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.