Staff View: Time-frequency distribution kernel design over a discrete powers-of-two space.

Time-frequency distribution kernel design over a discrete powers-of-two space.

Bibliographic Details
Main Authors:	Venkatesan, Gopal T., Amin, Moeness G.
Format:	Villanova Faculty Authorship
Language:	English
Published:	1996
Online Access:	http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:173747

Main Authors:

Venkatesan, Gopal T., Amin, Moeness G.

Format:

Villanova Faculty Authorship

Language:

English

Published:

1996

Online Access:

http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:173747

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spelling	Time-frequency distribution kernel design over a discrete powers-of-two space. Venkatesan, Gopal T. Amin, Moeness G. We introduce a new class of powers-of-two (PFT) kernels for fast real-time implementations of time-frequency (t-f) distributions. In this class, the local autocorrelation function is computed using a series of shifting and addition operations. PFT filter design techniques can be applied to produce fixed kernels or to design data-dependent kernels suitable for specific operating environments. In the t-f context, where the task is to identify the signal autoterms in the t-f domain, a discretized PFT kernel shows little or no difference in performance from its infinite precision counterpart. 1996 Villanova Faculty Authorship vudl:173747 IEEE SIGNAL PROCESSING LETTERS, VOL. 3, NO. 12, DECEMBER 1996. en
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dc.creator_txt_mv	Venkatesan, Gopal T. Amin, Moeness G.
dc.description_txt_mv	We introduce a new class of powers-of-two (PFT) kernels for fast real-time implementations of time-frequency (t-f) distributions. In this class, the local autocorrelation function is computed using a series of shifting and addition operations. PFT filter design techniques can be applied to produce fixed kernels or to design data-dependent kernels suitable for specific operating environments. In the t-f context, where the task is to identify the signal autoterms in the t-f domain, a discretized PFT kernel shows little or no difference in performance from its infinite precision counterpart.
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author	Venkatesan, Gopal T. Amin, Moeness G.
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title	Time-frequency distribution kernel design over a discrete powers-of-two space.
title_full	Time-frequency distribution kernel design over a discrete powers-of-two space.
title_fullStr	Time-frequency distribution kernel design over a discrete powers-of-two space.
title_full_unstemmed	Time-frequency distribution kernel design over a discrete powers-of-two space.
title_short	Time-frequency distribution kernel design over a discrete powers-of-two space.
title_sort	time-frequency distribution kernel design over a discrete powers-of-two space.
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Time-frequency distribution kernel design over a discrete powers-of-two space.
Venkatesan, Gopal T.
Amin, Moeness G.
We introduce a new class of powers-of-two (PFT) kernels for fast real-time implementations of time-frequency (t-f) distributions. In this class, the local autocorrelation function is computed using a series of shifting and addition operations. PFT filter design techniques can be applied to produce fixed kernels or to design data-dependent kernels suitable for specific operating environments. In the t-f context, where the task is to identify the signal autoterms in the t-f domain, a discretized PFT kernel shows little or no difference in performance from its infinite precision counterpart.
1996
Villanova Faculty Authorship
vudl:173747
IEEE SIGNAL PROCESSING LETTERS, VOL. 3, NO. 12, DECEMBER 1996.
en

dc.title_txt_mv

Time-frequency distribution kernel design over a discrete powers-of-two space.

dc.creator_txt_mv

Venkatesan, Gopal T.
Amin, Moeness G.

dc.description_txt_mv

We introduce a new class of powers-of-two (PFT) kernels for fast real-time implementations of time-frequency (t-f) distributions. In this class, the local autocorrelation function is computed using a series of shifting and addition operations. PFT filter design techniques can be applied to produce fixed kernels or to design data-dependent kernels suitable for specific operating environments. In the t-f context, where the task is to identify the signal autoterms in the t-f domain, a discretized PFT kernel shows little or no difference in performance from its infinite precision counterpart.

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1996

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Venkatesan, Gopal T.
Amin, Moeness G.

spellingShingle

Venkatesan, Gopal T.
Amin, Moeness G.
Time-frequency distribution kernel design over a discrete powers-of-two space.

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Amin, Moeness G.

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Time-frequency distribution kernel design over a discrete powers-of-two space.

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Time-frequency distribution kernel design over a discrete powers-of-two space.

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Time-frequency distribution kernel design over a discrete powers-of-two space.

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time-frequency distribution kernel design over a discrete powers-of-two space.

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Time-frequency distribution kernel design over a discrete powers-of-two space.

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