Sixth-generation (6G) mobile communication networks are expected to have dense
infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified …

Abstract The Internet of Things (IoT) is expected to require more effective and efficient
wireless communications than ever before. For this reason, techniques such as spectrum …

There are many excellent toolkits which provide support for develo** machine learning
software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library …

Online learning from a signal processing perspective There is increased interest in kernel
learning algorithms in neural networks and a growing need for nonlinear adaptive …

Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …

With the wide application of industrial robots in the field of precision machining, reliability
analysis of positioning accuracy becomes increasingly important for industrial robots. Since …

The combination of the famed kernel trick and the least-mean-square (LMS) algorithm
provides an interesting sample-by-sample update for an adaptive filter in reproducing kernel …

In this paper, we propose a quantization approach, as an alternative of sparsification, to curb
the growth of the radial basis function structure in kernel adaptive filtering. The basic idea …

Kernel-based algorithms have been a topic of considerable interest in the machine learning
community over the last ten years. Their attractiveness resides in their elegant treatment of …

Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust
estimation method for the parameters of a model contaminated by a sizable percentage of …