Rotating machines have been widely used in industrial engineering. The fault diagnosis of
rotating machines plays a vital important role to reduce the catastrophic failures and heavy …

Epilepsy is the neurological disorder of the brain which is difficult to diagnose visually using
Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using …

One of the most powerful tools to assess the dynamical characteristics of time series is
entropy. Sample entropy (SE), though powerful, is not fast enough, especially for long …

Lempel-Ziv complexity (LZC) is one of the most important metrics for detecting dynamic
changes in non-linear signals, but due to its dependence on binary conversion, LZC tends to …

Intelligent fault diagnosis of rotating machinery is essentially a pattern recognition problem.
Meanwhile, effective feature extraction from the raw vibration signal is an important …

Recently, coded permutation entropy has been proposed, which enhances the noise
immunity by quadratic partitioning on the basis of permutation entropy. However, coded …

Entropy, as a complexity measure, has been widely applied for time series analysis. One
preeminent example is the design of machine condition monitoring and industrial fault …

Background Sleep staging is a critical step in a range of electrophysiological signal
processing pipelines used in clinical routine as well as in sleep research. Although the …

Entropy is a powerful tool for quantification of the brain function and its information
processing capacity. This is evident in its broad domain of applications that range from …

Health condition identification of planetary gearboxes is crucial to reduce the downtime and
maximize productivity. This paper aims to develop a novel fault diagnosis method based on …