Analysis and extraction of strongly frequency modulated signals have been a challenging
problem for adaptive data analysis methods, eg, empirical mode decomposition [NE Huang …

In this paper, we propose a time-frequency analysis method to obtain instantaneous
frequencies and the corresponding decomposition by solving an optimization problem. In …

We introduce a new adaptive method for analyzing nonlinear and nonstationary data. This
method is inspired by the empirical mode decomposition (EMD) method and the recently …

Adaptive data analysis provides an important tool in extracting hidden physical information
from multiscale data that arise from various applications. In this paper, we review two data …

The empirical mode decomposition (EMD) has been developed by NE Huang et al. in 1998
as an iterative method to decompose a nonlinear and nonstationary univariate function …

Rolling element bearings are important parts of rotating machinery, and they are also one of
the most fault-prone parts in rotating machinery. Therefore, many new algorithms have been …

In this paper, we consider multiple signals sharing the same instantaneous frequencies. This
kind of data is very common in scientific and engineering problems. To take advantage of …

This contribution deals with extraction and analysis of the transient component of a
deployable structure vibration based on the sparse decomposition with mixed norms. The …

Time-frequency analysis for non-linear and non-stationary signals is extraordinarily
challenging. To capture features in these signals, it is necessary for the analysis methods to …

In this paper, we introduce a new adaptive data analysis method to study trend and
instantaneous frequency of nonlinear and nonstationary data. This method is inspired by the …