Detection of burst noise using the chi-squared goodness of fit test

Detection of burst noise using the chi-squared goodness of fit test

Statistically more test samples obtained from a single chip would give a better picture of the various noise processes present. Increasing the number of samples while testing one chip would however lead to an increase in the testing time, decreasing the overall throughput. The aim of this report is...

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Bibliographic Details
Main Author: Marwaha, Shubra
Format: Others
Language:English
Published: 2010
Subjects:
Burst Noise
Thermal Noise
Chi-Squared Distribution
Gaussian
Pearson's Goodness-of-fit
Online Access:http://hdl.handle.net/2152/ETD-UT-2009-08-180
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spelling ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-ETD-UT-2009-08-1802015-09-20T16:53:58ZDetection of burst noise using the chi-squared goodness of fit testMarwaha, ShubraBurst NoiseThermal NoiseChi-Squared DistributionGaussianPearson's Goodness-of-fitStatistically more test samples obtained from a single chip would give a better picture of the various noise processes present. Increasing the number of samples while testing one chip would however lead to an increase in the testing time, decreasing the overall throughput. The aim of this report is to investigate the detection of non-Gaussian noise (burst noise) in a random set of data with a small number of samples. In order to determine whether a given set of noise samples has non-Gaussian noise processes present, a Chi-Squared ‘Goodness of Fit’ test on a modeled set of random data is presented. A discussion of test methodologies using a single test measurement pass as well as two passes is presented from the obtained simulation results.text2010-06-04T14:48:46Z2010-06-04T14:48:46Z2009-082010-06-04T14:48:46ZAugust 2009thesisapplication/pdfhttp://hdl.handle.net/2152/ETD-UT-2009-08-180eng
collection NDLTD
language English
format Others
sources NDLTD
topic Burst Noise
Thermal Noise
Chi-Squared Distribution
Gaussian
Pearson's Goodness-of-fit
spellingShingle Burst Noise
Thermal Noise
Chi-Squared Distribution
Gaussian
Pearson's Goodness-of-fit
Marwaha, Shubra
Detection of burst noise using the chi-squared goodness of fit test
description Statistically more test samples obtained from a single chip would give a better picture of the various noise processes present. Increasing the number of samples while testing one chip would however lead to an increase in the testing time, decreasing the overall throughput. The aim of this report is to investigate the detection of non-Gaussian noise (burst noise) in a random set of data with a small number of samples. In order to determine whether a given set of noise samples has non-Gaussian noise processes present, a Chi-Squared ‘Goodness of Fit’ test on a modeled set of random data is presented. A discussion of test methodologies using a single test measurement pass as well as two passes is presented from the obtained simulation results. === text
author Marwaha, Shubra
author_facet Marwaha, Shubra
author_sort Marwaha, Shubra
title Detection of burst noise using the chi-squared goodness of fit test
title_short Detection of burst noise using the chi-squared goodness of fit test
title_full Detection of burst noise using the chi-squared goodness of fit test
title_fullStr Detection of burst noise using the chi-squared goodness of fit test
title_full_unstemmed Detection of burst noise using the chi-squared goodness of fit test
title_sort detection of burst noise using the chi-squared goodness of fit test
publishDate 2010
url http://hdl.handle.net/2152/ETD-UT-2009-08-180
work_keys_str_mv AT marwahashubra detectionofburstnoiseusingthechisquaredgoodnessoffittest
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