With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …

Background modeling for foreground detection is often used in different applications to
model the background and then detect the moving objects in the scene like in video …

In recent years, deep learning techniques have been proved a promising tool for bearing
fault diagnosis. However, to extract deep features with better representative ability, how to …

The operation conditions of the rotating machinery are always complex and variable, which
makes it difficult to automatically and effectively capture the useful fault features from the …

Traditional Kalman filter (KF) is derived under the well-known minimum mean square error
(MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals …

The steady-state excess mean square error (EMSE) of the adaptive filtering under the
maximum correntropy criterion (MCC) has been studied. For Gaussian noise case, we …

An exhaustive review of adaptive signal processing schemes which are robust, sparsity-
aware and robust as well as sparsity-aware has been carried out in this paper. Conventional …

The maximum correntropy criterion (MCC) has received increasing attention in signal
processing and machine learning due to its robustness against outliers (or impulsive …

Correntropy is a local similarity measure defined in kernel space, hence can combat large
outliers in robust signal processing and machine learning. So far, many robust learning …

Sparse adaptive channel estimation problem is one of the most important topics in
broadband wireless communications systems due to its simplicity and robustness. So far …