This paper provides an overview of the support vector machine (SVM) methodology and its
applicability to real‐world engineering problems. Specifically, the aim of this study is to …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

Existing image super-resolution algorithms still suffer from the problems of not extracting rich
image features and losing realistic high-frequency details. In order to solve these problems …

Spatiotemporal signal reconstruction from samples randomly gathered in a multidimensional
space with uncertainty is a crucial problem for a variety of applications. Such a problem …

Dynamic Mode Decomposition (DMD) is a data-driven reduced order method, which is
known for its power to capture the basic features of dynamical systems. In fluid dynamics …

Due to the proliferation of ubiquitous computing services, locating a device in indoor
scenarios has received special attention during recent years. A variety of algorithms are …

A modification of the conventional Lagrange interpolator is proposed in this paper, that
allows one to approximate a band-limited signal from its own nonuniform samples with high …

In this paper, we propose a method for efficient signal reconstruction from non-uniformly
spaced samples collected using level-crossing sampling. Level-crossing (LC) sampling …

In this study, the estimation of fast‐fading long term evolution (LTE) downlink channels in
high‐speed applications of LTE advanced is investigated by the authors. A robust channel …

This paper presents a review in the form of a unified framework for tackling estimation
problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The …