Abstract Synchrosqueezing transform (SST) and its improved algorithm are used to process time-varying signals have been widely developed in the field of signal processing recently. However, strong is still a tricky problem. Multisynchrosqueezing (MSST) an excellent time-frequency (TF) analysis technique for signals. some TF points will not be rearranged using this method. So we propose new name...

Abstract Recently, the synchrosqueezing transform (SST) was developed as an alternative to the empirical mode decomposition scheme to separate a non-stationary signal with time-varying amplitudes and instantaneous frequencies (IFs) into a superposition of frequency components that each have well-defined IFs. The continuous wavelet transform (CWT)-based SST sharpens the time-frequency representa...

In recent years, the synchrosqueezing transform (SST) has gained popularity as a method for the analysis of signals that can be broken down into multiple components determined by instantaneous amplitudes and phases. One such version of SST, based on the short-time Fourier transform (STFT), enables the sharpening of instantaneous frequency (IF) information derived from the STFT, as well as the s...

An accurate time-frequency representation (TFR) can provide useful information in non stationary data analysis and processing. The traditional methods like short-time Fourier transform (STFT) and wavelet transform (WT) based TFR approaches are leads to a tradeoff between time and frequency resolution. A recently proposed synchrosqueezing transform (SST), which is an extension of the WT, has bee...

a r t i c l e i n f o a b s t r a c t Keywords: Time–frequency analysis Windowed Fourier transform Wavelet transform Synchrosqueezing Time–frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed versions (SWFT, SWT), provide powerful analysis tools. Here we present a thorough review of these TFRs, summarizing al...

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and base...