Using a new uncertainty measure to determine optimal bases for signal representations

We use a new uncertainty measure, H/sub p/, that predicts the compactness of digital signal representations to determine a good (non-orthogonal) set of basis vectors. The measure uses the entropy of the signal and its Fourier transform in a manner that is similar to the use of the signal and its Fou...

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Hauptverfasser:	Przebinda, T., DeBrunner, V., Ozaydin, M.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Biomedical signal processing Electric variables measurement Entropy Filter bank Fourier transforms Mathematics Measurement uncertainty Signal processing algorithms Signal representations Time frequency analysis
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creator	Przebinda, T. DeBrunner, V. Ozaydin, M.
description	We use a new uncertainty measure, H/sub p/, that predicts the compactness of digital signal representations to determine a good (non-orthogonal) set of basis vectors. The measure uses the entropy of the signal and its Fourier transform in a manner that is similar to the use of the signal and its Fourier transform in the Heisenberg uncertainty principle. The measure explains why the level of discretization of continuous basis signals can be very important to the compactness of representation. Our use of the measure indicates that a mixture of (non-orthogonal) sinusoidal and impulsive or "blocky" basis functions may be best for compactly representing signals.
doi_str_mv	10.1109/ICASSP.1999.756234
format	Conference Proceeding
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Our use of the measure indicates that a mixture of (non-orthogonal) sinusoidal and impulsive or "blocky" basis functions may be best for compactly representing signals.</description><subject>Biomedical signal processing</subject><subject>Electric variables measurement</subject><subject>Entropy</subject><subject>Filter bank</subject><subject>Fourier transforms</subject><subject>Mathematics</subject><subject>Measurement uncertainty</subject><subject>Signal processing algorithms</subject><subject>Signal representations</subject><subject>Time frequency analysis</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>0780350413</isbn><isbn>9780780350410</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1999</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkM1qwzAQhEV_oGmaF8hJL2B3pZVs61hC_yDQQhroLcjROqgkcpAUSt6-gnQuA8swfDuMzQXUQoB5fF88rVaftTDG1K1uJKorNpHYmkoY-L5m99B2gBqUwBs2EVpC1Qhl7tgspR8oUlpDixO2XicfdtzyQL_8FLYUs_Uhn_mBbDpF4nnkjjLFgw_Ex2P2B7vnvU2U-DBGnvwulEOkY6REIdvsx5Ae2O1g94lm_z5l65fnr8Vbtfx4LejLykvAXJHqaJBtD64zFlx5xm4dFjaLWgoHyipNQ2-B-g6VHFBtqUHpTMk7hwqnbH7p9US0OcYCF8-byyD4B1NUVJE</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Przebinda, T.</creator><creator>DeBrunner, V.</creator><creator>Ozaydin, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>1999</creationdate><title>Using a new uncertainty measure to determine optimal bases for signal representations</title><author>Przebinda, T. ; DeBrunner, V. ; Ozaydin, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-e48ef27b0d89a0d999acd3045a3521d04a45efba0eb8342f34ce632d9d89dd343</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Biomedical signal processing</topic><topic>Electric variables measurement</topic><topic>Entropy</topic><topic>Filter bank</topic><topic>Fourier transforms</topic><topic>Mathematics</topic><topic>Measurement uncertainty</topic><topic>Signal processing algorithms</topic><topic>Signal representations</topic><topic>Time frequency analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Przebinda, T.</creatorcontrib><creatorcontrib>DeBrunner, V.</creatorcontrib><creatorcontrib>Ozaydin, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Przebinda, T.</au><au>DeBrunner, V.</au><au>Ozaydin, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Using a new uncertainty measure to determine optimal bases for signal representations</atitle><btitle>1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)</btitle><stitle>ICASSP</stitle><date>1999</date><risdate>1999</risdate><volume>3</volume><spage>1365</spage><epage>1368 vol.3</epage><pages>1365-1368 vol.3</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>0780350413</isbn><isbn>9780780350410</isbn><abstract>We use a new uncertainty measure, H/sub p/, that predicts the compactness of digital signal representations to determine a good (non-orthogonal) set of basis vectors. The measure uses the entropy of the signal and its Fourier transform in a manner that is similar to the use of the signal and its Fourier transform in the Heisenberg uncertainty principle. The measure explains why the level of discretization of continuous basis signals can be very important to the compactness of representation. 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subjects	Biomedical signal processing Electric variables measurement Entropy Filter bank Fourier transforms Mathematics Measurement uncertainty Signal processing algorithms Signal representations Time frequency analysis
title	Using a new uncertainty measure to determine optimal bases for signal representations
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