This paper deals with the problem of extracting information from non-stationary signals in the
form of features that can be used for effective decision-making in both data analysis and …

Detecting epileptic seizures in electroencephalography (EEG) signals is a challenging task
due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected …

This paper proposes a new performance evaluation process for time-frequency distributions
(TFD) by designing a reference optimal TFD and novel accuracy and resolution measures …

Chatter is a self-excited vibration that affects the part quality and tool life in the machining
process. This paper introduces an intelligent chatter detection method based on image …

The rapid detection of the pathogenic bacteria in patient samples is crucial to expedient
patient care. The proposed approach reports the development of a novel lab-on-a-chip …

Chatter is a cause of low surface quality and productivity in milling and crucial features need
to be extracted for accurate chatter detection and suppression. This paper introduces a …

This paper proposes a new feature extraction method using time–frequency image data for
fault diagnosis of variable-speed rotating machinery. Time-frequency representation (TFR) is …

The electroencephalography (EEG), which is a method for monitoring the brain signals, is a
common method used to diagnose the epileptic seizures. In this study, some features are …

With the ever-progressing development in the field of computational and analytical science
the last decade has seen a big improvement in the accuracy of electroencephalography …

Due to the increasing number of chronic disease patients, continuous health monitoring has
become the top priority for health-care providers and has posed a major stimulus for the …