Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …

Signal processing methods are essential in scientific research, and time-frequency analysis
techniques such as Fourier Transform constitute an important progress in data analysis, but …

We present a generic extension of variational mode decomposition (VMD) algorithm to
multivariate or multichannel data. The proposed method utilizes a model for multivariate …

Variational mode decomposition (VMD), a recently introduced method for adaptive data
analysis, has aroused much attention in various fields. However, the VMD is formulated …

Stock market forecasting is a vital component of financial systems. However, the stock prices
are highly noisy and non-stationary due to the fact that stock markets are affected by a …

The goal of this article is to promote the use of fixed point strategies in data science by
showing that they provide a simplifying and unifying framework to model, analyze, and solve …

This paper introduces a novel EMD interval thresholding (EMD-IT) denoising, where
relevant modes are selected using al 2-norm measure between the probability density …

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 …

Based on a preconditioned version of the randomized block-coordinate forward-backward
algorithm recently proposed in [Combettes, Pesquet, 2014], several variants of block …

The general aim of this paper is to introduce the concept of synchrosqueezing transforms
(SSTs) that was developed to sharpen linear time–frequency representations (TFRs), like …