Signal processing is a very useful field of study in the interpretation of signals in many
everyday applications. In the case of applications with time-varying signals, one possibility is …

We present a novel method, called Dispersion Entropy for Graph Signals, DEG, as a
powerful tool for analysing the irregularity of signals defined on graphs. DEG generalizes the …

Recent studies proposed the use of Total Correlation to describe functional connectivity
among brain regions as a multivariate alternative to conventional pairwise measures such …

Bearing faults are one kind of primary failure in rotatory machines. To avoid economic loss
and casualties, it is important to diagnose bearing faults accurately. Vibration-based …

The study focuses on the fault signals of rolling bearings, which are characterized by
nonlinearity, periodic impact, and low signal-to-noise ratio. The advantages of entropy …

Abstracts Identifying scientific disruption is consistently recognized as challenging, and more
so is to predict it. We suggest that better predictions are hindered by the inability to integrate …

Entropy metrics are nonlinear measures to quantify the complexity of time series. Among
them, permutation entropy is a commonly used metric due to its robustness and fast …

Multi-scale permutation entropy (MPE) is an interesting tool for analyzing signal internal
structures and quantifying complexity. The most commonly used MPEs involve a linear …

Permutation entropy and its associated frameworks are remarkable examples of physics-
inspired techniques adept at processing complex and extensive datasets. Despite …

Using a graph-based approach, we propose a multiscale permutation entropy to explore the
complexity of multivariate time series over multiple time scales. This multivariate multiscale …