Performance comparison of multi-component signals using WVD and Cohen's class variants

Thomas, Merin Sara; Lethakumary, B.; Jacob, Roshen

doi:10.1109/icceet.2012.6203869

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…The kernel of WVD, which is the simplest and most important of the Cohen class bilinear TFDs, is Φ(v,τ)=1 and is expressed as in Eq. (3) [22][23][24].…”

Section: Spectral Amplitudes Of the Eeg Signalmentioning

confidence: 99%

Determination of the Appropriate Kernel Structure in Electroencephalography Analysis of Alcoholic Subjects

Akgun¹

2020

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Alcoholism is one of the major health problems in the world. The organ most affected by alcohol is the brain. It has been shown that alcohol causes neuronal loss in the brain and reduces brain blood flow and oxygen use. Electroencephalography is a method that measures the instantaneous electrical activity of the brain. It is known that valuable information can be obtained by observing the biological effects of alcohol through EEG. As their methods of signal processing and analysis have evolved, Electroencephalography signals have attracted the attention of researchers in this field. In this study, methods of the time-frequency analysis were applied to Electroencephalography signals obtained from normal and alcoholic subjects. For this purpose, the Cohen’s class distribution was examined. Ambiguity function analysis, which was in the structure of the distribution, was applied to the signals. Then, from the kernel structure inside the distribution, the Wigner-Ville distribution, which was very common, was reached and this distribution was examined. The inadequacy of the distribution resolution was seen and analysis of the new time-frequency distributions, which were obtained by making convolution with 4 types of kernel functions (nonseparable, separable, Doppler independent, lag independent), was performed. As a result, it was shown that the resolution of time-frequency distributions could be improved with proper kernel functions. Thus, at the end of these analyses, changes that alcohol caused in brain functions were revealed.

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“…The kernel of WVD, which is the simplest and most important of the Cohen class bilinear TFDs, is Φ(v,τ)=1 and is expressed as in Eq. (3) [22][23][24].…”

Section: Spectral Amplitudes Of the Eeg Signalmentioning

confidence: 99%

Determination of the Appropriate Kernel Structure in Electroencephalography Analysis of Alcoholic Subjects

Akgun¹

2020

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“…The processing gain is As (2) shows the WVD of LFM signals has desirable TF concentration and it focuses on the line indicating the changing of instantaneous frequency. Meanwhile, the frequency resolution can be derived from the following equation [27].…”

Section: Theoretical Analysis Of Snr Resolution and Computational Costmentioning

confidence: 99%

“…As (2) shows the WVD of LFM signals has desirable TF concentration and it focuses on the line indicating the changing of instantaneous frequency. Meanwhile, the frequency resolution can be derived from the following equation [27].

normalΔ f = \sqrt{Δ f_{normalFT}^{2} + Δ f_{i k}^{2}} = \sqrt{f_{s}^{2} / N f f t^{2} + k^{2} N_{normalleng}^{2} / f_{s}^{2}}

where

normalΔ f_{FT}

is the frequency resolution of FFT ,

normalΔ f_{i k}

is the inherent resolution,

N_{leng}

is the length of signal, k is the slope of frequency modulation, Nfft is the points of FT and

f_{normals}

is the sampling frequency.…”

Section: Performance Analysis and Improvementmentioning

confidence: 99%

Elimination of cross‐terms in the Wigner–Ville distribution of multi‐component LFM signals

2017

IET signal process.

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A novel method is put forward to remove cross-terms in the Wigner-Ville distribution (WVD) of multicomponent linear frequency modulation (LFM) signals. For the difference of the amplitude of auto-terms and cross-terms, the WVD matrix is filtered using low-pass filter in the direction of frequency modulation ratio. Furthermore, a novel technique called energy weight is proposed for the cross-terms with low-oscillation frequency which cannot be eliminated because of the limitation of filters. The gain of signal to noise ratio (SNR) after filtering is derived while the resolution and computational cost are also analysed. The simulation and experimental results prove that this method improves the SNR along with the removal of cross-terms with maintaining high resolution.

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Blind source separation based on time–frequency morphological characteristics for rigid acoustic scattering by underwater objects

Yang

2016

J. Marine. Sci. Appl.

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Performance comparison of multi-component signals using WVD and Cohen's class variants

Cited by 6 publications

References 8 publications

Determination of the Appropriate Kernel Structure in Electroencephalography Analysis of Alcoholic Subjects

Determination of the Appropriate Kernel Structure in Electroencephalography Analysis of Alcoholic Subjects

Elimination of cross‐terms in the Wigner–Ville distribution of multi‐component LFM signals

Blind source separation based on time–frequency morphological characteristics for rigid acoustic scattering by underwater objects

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