In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …

Inspired by recent successes of deep learning in computer vision, we propose a novel
framework for encoding time series as different types of images, namely, Gramian Angular …

Tool condition monitoring (TCM) has become essential to achieve high-quality machining as
well as cost-effective production. Identification of the cutting tool state during machining …

Despite considerable progress in genome-and proteome-based high-throughput screening
methods and in rational drug design, the increase in approved drugs in the past decade did …

In 1980 and 1981, two pioneering papers laid the foundation for what became known as
nonlinear time-series analysis: the analysis of observed data—typically univariate—via …

Recurrence quantification analysis Understanding Complex Systems Charles L. Webber, Jr.
Norbert Marwan Editors Recurrence Quanti cation Analysis Theory and Best Practices Page 2 …

Recurrence plots and recurrence quantification analysis have become popular in the last
two decades. Recurrence based methods have on the one hand a deep foundation in the …

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 …

Electroencephalogram (EEG)-based Motor imagery (MI) is a key topic in the brain-computer
interface (BCI). The EEG-based real execution and motor imagery multi-class classification …