We develop a probabilistic modeling framework for multiway arrays. Our framework exploits the link between graphical models and tensor factorization models and it can realize any ...
The problem of estimating a sparse channel, i.e. a channel with a few non-zero taps, appears in many fields of communication including acoustic underwater or wireless transmissions...
Rad Niazadeh, Massoud Babaie-Zadeh, Christian Jutt...
We propose a framework for blind multiple filter estimation from convolutive mixtures, exploiting the time-domain sparsity of the mixing filters and the disjointness of the sources...
Most of audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the m...
We describe in this paper an advanced protocol for the discrimination and the classification of neuronal spike waveforms within multichannel electrophysiological recordings. Sparse...
Vincent Vigneron, Hsin Chen, Yen-Tai Chen, Hsin-Yi...
In this work, we look at single user and multiuser Multiple-Input Multiple-Output (MIMO) beamforming networks with Channel Distribution Information (CDI). CDI does not need to be ...
The resource allocation problem in wireless multi-user decode-and-forward (DF) relay networks is considered. The conventional resource allocation schemes based on the equal distri...
Xiaowen Gong, Sergiy A. Vorobyov, Chintha Tellambu...
We present a novel weighted approach for shrinkage functions learning in image denoising. The proposed approach optimizes the shape of the shrinkage functions and maximizes denois...
In this paper, we propose a symmetrical EEG/fMRI fusion algorithm which combines EEG and fMRI by means of a common generative model. The use of a total variation (TV) prior as wel...
Martin Luessi, S. Derin Babacan, Rafael Molina, Ja...
The energy-distortion function (E(D)) for a network is defined as the minimum total energy required to achieve a target distortion D at the receiver without putting any restricti...