Around a third of the total population of Europe suffers from mental disorders. The use of
electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose …

Artificial intelligence and expert system remains a key technology in the 21st century. Using
active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such …

Time-Frequency Signal Analysis and Processing (TFSAP) is a collection of theory,
techniques and algorithms used for the analysis and processing of non-stationary signals …

Automated analysis of physiological time series is utilized for many clinical applications in
medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural …

The goal of signal processing is to estimate the contained frequencies and extract subtle
changes in the signals. In this paper, a new adaptive multiple signal classification-empirical …

In this work, an attempt has been made to classify emotional states using Electrodermal
Activity (EDA) signals and Convolutional Neural Network (CNN) learned features. The EDA …

Measured signals obtained by sensors during dynamic events such as earthquake, wind,
and wave contain nonlinear, nonstationary, and noisy properties. In this paper, a new …

Rotating speed fluctuation is a key problem that affects the fault diagnosis performance of
mechanical equipment. Deep learning theory can use deep neural networks to realize …

Time–frequency representation (TFR) is useful for non‐stationary signal analysis as it
provides information about the time‐varying frequency components. This study proposes a …

Time-frequency (TF) based machine learning methodologies can improve the design of
classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF …