In this paper, our goal is to compare different recent time-frequency (TF) approaches retrieve the modes of multicomponent signals (MCSs). While it acknowledged that synchrosqueezing transform (SST) improves readability representation (TFR) MCSs, and SST-based demodulation (DSST) more efficient than SST itself for mode retrieval (MR), unclear whether DSST outperforms downsampled short-time Four...

OBJECTIVE While extracting the temporal dynamical features based on the time-frequency analyses, like the reassignment and synchrosqueezing transform, attracts more and more interest in biomedical data analysis, we should be careful about artifacts generated by interpolation schemes, in particular when the sampling rate is not significantly higher than the frequency of the oscillatory component...

With widespread non-linear loads and the increasing penetration of distributed generations in power system, harmonic pollution has become a great concern. The causes not only include integer harmonics, but also interharmonics, which exacerbate complexity analysis. In addition, output variability highly renewables such as electric arc furnaces photovoltaic solar or wind generation may lead to we...

EEG is widely used to record the electrical activity of the brain for detecting various kinds of diseases and disorders of the human brain. EEG signals are contaminated with several unwanted artifacts during EEG recording and these artifacts make the analysis of EEG signal difficult by hiding some valuable information. Time-frequency representation of electroencephalogram (EEG) signal provides ...

In many applications, it is necessary to retrieve the sub-signal building blocks of a multi-component signal, which usually non-stationary in real-world and real-life applications. Empirical mode decomposition (EMD), synchrosqueezing transform (SST), signal separation operation (SSO), iterative filtering (IFD) have been proposed developed for this purpose. However, these computational methods a...