Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new
window onto the organizational principles of brain function. Using state-of-the-art signal …

A very fast noniterative algorithm is proposed for denoising or smoothing one-dimensional
discrete signals, by solving the total variation regularized least-squares problem or the …

In automated heart sound analysis and diagnosis, a set of clinically valued parameters
including sound intensity, frequency content, timing, duration, shape, systolic and diastolic …

This paper seeks to combine linear time-invariant (LTI) filtering and sparsity-based
denoising in a principled way in order to effectively filter (denoise) a wider class of signals …

Confirmatory approaches to fMRI data analysis look for evidence for the presence of pre-
defined regressors modeling contributions to the voxel time series, including the BOLD …

Feature extraction plays an essential role in bearing fault detection. However, the measured
vibration signals are complex and non-stationary in nature, and meanwhile impulsive …

Background Prodromal positive psychotic symptoms and anxiety are two strong risk factors
for schizophrenia in 22q11. 2 deletion syndrome (22q11DS). The analysis of large-scale …

This paper introduces a novel method to combine total variation and ℓ2 regularizations to
reconstruct piecewise smooth signals. The main idea is to consider the signal as a sum of …

This paper describes an extension to total variation denoising wherein it is assumed the first-
order difference function of the unknown signal is not only sparse, but also that large values …

Spontaneous fluctuations in the blood oxygenation level dependent signal measured
through resting-state functional magnetic resonance imaging have been corroborated to …