Time-frequency analysis (TFA) plays an important role in various engineering and
biomedical fields. For a non-stationary time series, a common practice is to divide data into …

This paper presents a new time–frequency distribution which uses a time-dependent two-
sided linear predictor model. The current sample is estimated as a weighted sum of the past …

Most real-life signals exhibit non-stationary characteristics. Processing of such signals
separately in the time-domain or in the frequency-domain does not provide sufficient …

Time series research in academic and industrial fields has attracted wide attention.
However, the frequency information contained in time series still lacks effective modeling …

Time-frequency analysis has been intensively investigated and developed in the last two
decades. A variety of timefrequency distributions have been developed to provide efficient …

Most of the real-world signals in nature are non-stationary, ie, their statistics are time variant.
Extracting the time-varying frequency characteristics of a signal is very important in …

Motivated by analyzing complicated time series, nonlinear-type time–frequency analysis has
become an active research topic in the past decades. Those developed tools have been …

The nonstationary nature of signals and nonlinear systems require the time-frequency
representation. In time-domain signal, frequency information is derived from the phase of the …

Abstract Analysis of nonstationary signals is a challenging task. True nonstationary signal
analysis involves monitoring the frequency changes of the signal over time (ie, monitoring …

The goal of signal processing is to estimate the contained frequencies and extract subtle
changes in the signals. In this paper, a new adaptive multiple signal classification-empirical …