One of the most important aspects of time series is their degree of stochasticity vs. chaoticity.
Since the discovery of chaotic maps, many algorithms have been proposed to discriminate …

Nonlinear techniques have found an increasing interest in the dynamical analysis of various
kinds of systems. Among these techniques, entropy-based metrics have emerged as …

Complexity measures are important to understand and analyze systems with one
dimensional data. However, extension of these methods to images (two dimensional data) …

[EN] Muscle fatigue is an important field of study in sports medicine and occupational health.
Several studies in the literature have proposed methods for predicting muscle fatigue in …

The main motivation of this paper is to introduce the permutation Jensen-Shannon distance,
a symbolic tool able to quantify the degree of similarity between two arbitrary time series …

The development of new measures and algorithms to quantify the entropy or related
concepts of a data series is a continuous effort that has brought many innovations in this …

Since Bandt and Pompe's seminal work, permutation entropy has been used in several
applications and is now an essential tool for time series analysis. Beyond becoming a …

Entropy metrics (for example, permutation entropy) are nonlinear measures of irregularity in
time series (one-dimensional data). Some of these entropy metrics can be generalised to …

Abstract Dispersion Lempel-Ziv complexity (DLZC) has been introduced into the field of
underwater acoustic with great performance, but it only reflects complexity information from …

This work presents a comparison between different approaches for the model-free
estimation of information-theoretic measures of the dynamic coupling between short …