Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Recherche

Afficher tous les résultats pour

Accueil

Concept

Signal separation

Applied sciences
Information engineering
Science des données
Réduction de la dimensionna...

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (25)

Page 2 sur 2

Sparsity-Promoting Spatiotemporal Regularization for Data Mining in Functional Magnetic Resonance Imaging

Fikret Isik Karahanoglu

Magnetic resonance imaging (MRI) has opened unprecedented avenues to observe the human brain non-invasively. In particular, for about two decades, functional MRI (fMRI) has enabled to monitor brain function using the blood-oxygen-level-dependent (BOLD) con ...

EPFL2013

Sparsity and the Bayesian perspective

Anaïs Marie Rassat

Sparsity has recently been introduced in cosmology for weak-lensing and cosmic microwave background (CMB) data analysis for different applications such as denoising, component separation, or inpainting (i.e., filling the missing data or the mask). Although ...

Edp Sciences S A2013

On The Optimality Of Operator-Like Wavelets For Sparse Ar(1) Processes

Michaël Unser, Pedram Pad

Sinusoidal transforms such as the DCT are known to be optimal-that is, asymptotically equivalent to the Karhunen-Loeve transform (KLT)-for the representation of Gaussian stationary processes, including the classical AR(1) processes. While the KLT remains a ...

Ieee2013

A Tractable Framework for Estimating and Combining Spectral Source Models for Audio Source Separation

Rémi Gribonval

The underdetermined blind audio source separation (BSS) problem is often addressed in the time-frequency (TF) domain assuming that each TF point is modeled as an independent random variable with sparse distribution. On the other hand, methods based on stru ...

2012

Distributed static linear Gaussian models using consensus

Pavle Belanovic

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for stat ...

Elsevier2012