2005Unpublished venueRequires access

Recognition of time-varying signals in the time-frequency domain by means of the Wigner distribution

B. Bouachache, F.J. Jimenez Rodriguez

Open publisher page 32 citations

Abstract

Among the time-frequency distributions, the Wigner distribution (WD) is found to be one of the most powerful tools for time-frequency analysis. We have therefore developed an interactive simulator for time-frequency analysis based on a discrete version of the WD. The WD behavior is then studied for any frequency modulated and time limited signal. The properties of the WD make possible the detection of time-varying signals in the time-frequency domain. An application is presented where the problem is to automatically detect moving bubbles formed in the human blood under hyperbaric environments. We show that time-frequency analysis by means of the WD is an appropriate method to discriminate the bubble signals from the noise. We present some of our results.

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What this paper is about

Among the time-frequency distributions, the Wigner distribution (WD) is found to be one of the most powerful tools for time-frequency analysis. We have therefore developed an interactive simulator for time-frequency analysis based on a discrete version of the WD. The WD behavior is then studied for any frequency modulated and time limited signal. The properties of the WD make possible the detection of time-varying signals in the time-frequency domain. An application is presented where the problem is to automatically detect moving bubbles formed in the human blood under hyperbaric environments. We show that time-frequency analysis by means of the WD is an appropriate method to discriminate the bubble signals from the noise. We present some of our results.

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OpenAlex reports 32 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Among the time-frequency distributions, the Wigner distribution (WD) is found to be one of the most powerful tools for time-frequency analysis. We have therefore developed an interactive simulator for time-frequency analysis based on a discrete version of the WD. The WD behavior is then studied for any frequency modulated and time limited signal. The properties of the WD make possible the detection of time-varying signals in the time-frequency domain. An application is presented where the problem is to automatically detect moving bubbles formed in the human blood under hyperbaric environments. We show that time-frequency analysis by means of the WD is an appropriate method to discriminate the bubble signals from the noise. We present some of our results.

Key concepts: Time–frequency analysis, Frequency domain, Wigner distribution function, Time domain, Computer science, SIGNAL (programming language), Noise (video), Distribution (mathematics)

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