The local polynomial Fourier transform (LPFT), as a high-order generalization of the short-
time Fourier transform (STFT), has been developed and used for many different applications …

Separation of signal from noise is the most fundamental problem in data analysis, and arises
in many fields, for example, signal processing, econometrics, acturial science, and …

As one of the most important tasks in biomedical imaging, image segmentation provides the
foundation for quantitative reasoning and diagnostic techniques. A large variety of different …

The shape-adaptive discrete cosine transform (SA-DCT) transform can be computed on a
support of arbitrary shape, but retains a computational complexity comparable to that of the …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of point cloud denoising techniques. In this article, we first provide a …

We review the evolution of the nonparametric regression modeling in imaging from the local
Nadaraya-Watson kernel estimate to the nonlocal means and further to transform-domain …

Interest in parameterized time-frequency analysis for non-stationary signal processing is
increasing steadily. An important advantage of such analysis is to provide highly …

With the promises of smart grids, power can be more efficiently and reliably generated,
transmitted, and consumed over conventional electricity systems. Through the two-way flow …

Accurate estimation of long term wind speed probability distribution is a fundamental and
challenging task in wind energy planning. This paper proposes a nonparametric kernel …

Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and
reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over …