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Author Kyriakides, I. ♦ Pribic, R.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Bayes methods ♦ Signal processing algorithms ♦ Monte Carlo methods ♦ Compressed sensing ♦ Noise measurement ♦ Computational modeling ♦ Indexes ♦ Bayesian Compressive Sensing ♦ Monte Carlo methods
Abstract Bayesian compressive sensing using Monte Carlo methods is able to handle non-linear, non-Gaussian signal models. The computational expense associated with Monte Carlo methods is, however, a concern especially in scenarios requiring real-time processing. In this work, a theoretical model is derived that provides insight on the relationship between performance and computational expense for a Monte Carlo Bayesian compressive sensing algorithm. The theoretical model is shown to accurately describe the practical performance of the algorithm. Additionally, the theoretical model is able to inexpensively project the algorithm's performance characteristics for various SNRs and computational complexity levels. The model is then useful in assessing the method's performance under different operational requirements.
Description Author affiliation: Dept. of Electr. Eng., Univ. of Nicosia, Nicosia, Cyprus (Kyriakides, I.) || Sensors Adv. Developments, Thales Nederland Delft, Delft, Netherlands (Pribic, R.)
ISBN 9781479914814
ISSN 2151870X
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-06-22
Publisher Place Spain
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 230.19 kB
Page Count 4
Starting Page 397
Ending Page 400

Source: IEEE Xplore Digital Library