Non-Gaussian randomness widely exists in complex dynamical systems, in which the
traditional mean-variance index cannot fully reflect the systematic characteristics. To …

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 Volterra filter is proven to be a structurally simple method for nonlinear active noise
control (NLANC). However, its wide application is deeply constrained by computational …

The least mean square (LMS) algorithm of the graph signal processing (GSP) based on the
mean square error criterion has a poor reconstruction effect when the graph sampling signal …

We propose a constrained least lncosh (CLL) adaptive filtering algorithm, which, as we
show, provides better performance than other algorithms in impulsive noise environment …

Time-series prediction is a popular application that relies on the collection of historical data
via sensors, which is then leveraged by predictive models to forecast future values or trends …

The constrained least mean square algorithm is extensively used for adaptive filtering
applications which need to satisfy a set of linear constraints. However, it is not robust when …

Efficient and robust online processing techniques for irregularly structured data are crucial in
the current era of data abundance. In this paper, we propose a graph/network version of the …

Wireless sensor network (WSN) is an important part of the Internet of Things (IoT) and has
emerged in various new forms, such as smart home, smart city, and intelligent manufacturing …

The Cauchy loss (CL) is a high-order loss function which has been successfully used to
overcome large outliers in kernel adaptive filters. The squared error in the CL is then …