This paper provides a review of the Teager–Kaiser (TK) energy operator and its extensions
for signals and images processing. This class of operators possesses simplicity and good …

Bearing faults are one of the most common inducements for machine failures. Therefore, it is
very important to perform bearing fault diagnosis reliably and rapidly. However, it is …

Dynamic health monitoring (DHM) and performance degradation assessment (PDA) is
critical for mechanical bearings throughout their long in-service life. For this issue, it is …

Hemodynamic changes during neonatal transition increase the vulnerability of the preterm
brain to injury. Real-time monitoring of brain function during this period would help identify …

Signal processing techniques for non-stationary and noisy signals have recently attracted
considerable attentions. Among them, the empirical mode decomposition (EMD) which is an …

The aim of this study was to develop methods for detecting the nonstationary periodic
characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates …

Group-sparse mode decomposition (GSMD) is a decomposition method designed based on
the group sparse property of signals in frequency domain. It is proved to be highly efficient …

A new method is proposed in the present work for identifying fault severity in the ball
bearings. Proposed method named as multi-scale refined composite standard deviation …

Objective. Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform
discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased …

The diagnosis of low-speed bearing faults remains a challenging issue because
background noise is often present and the impulse signal is prone to being masked. In this …