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These techniques are based on time, frequency, non-stationary and nonlinear characteristics of EEG signals.
To address these challenges, a new SI technique based on Time–Frequency Blind Source Separation (TFBSS) is proposed.
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The first method is based on time-frequency analysis and the second one is based on time-scale analysis.
The performance of the sparse signal reconstruction-based methods is also compared against traditional methods based on time-frequency analysis for motion parameter estimation of two closely located ground moving targets.
Briassouli and Ahuja [36] provided a method based on time-frequency analysis of the video sequence.
The former method is based on time-frequency transformation [2, 8, 9].
The proposed procedure is based on time-frequency speech signal representation using wavelet packet decomposition.
In this paper, a novel algorithm of wavelet coefficient threshold (WCT) based on time-frequency adaptation is proposed.
The advanced algorithms and methods in the area of information retrieval based on time-frequency representation have been investigated.
This paper describes some alternative methods based on time-frequency distributions from a Cohen's class point of view.
These peaks were then paired off and combinatorial hash values were computed based on time-frequency pairs, which also increased robustness.
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