13 accepted papers
This paper introduces TenSOFO, a novel tensor-based method specifically designed for blind source separation (BSS). Ten-SOFO presents a new efficient alternating direction method of multipliers framework, allowing for simultaneous decomposition of two symmetric third-order tensors under the individu…
Gearbox condition monitoring and quality surveillance are crucial techniques to ensure safe and cost-efficient machine operations. In condition monitoring, the interpretation of the different vibration spectrum elements is still an open question, many works show that some predefined vibration models…
In this paper, we investigate the problem of convolutive blind source separation (BSS) via tensor decomposition. A fundamental link between convolutive BSS and block-term decomposition (BTD) is established, forming the basis for our novel tensor-based convolutive BSS method, namely TCBSS. Specifical…
We studied the problem of robust subspace tracking (RST) in contaminated environments. Leveraging the fast approximated power iteration and α-divergence, a novel robust algorithm called αFAPI was developed for tracking the underlying principal subspace of streaming data over time. αFAPI is fast and…
We studied the problem of sparse subspace tracking in the high-dimensional regime where the dimension is comparable to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm called OPIT was derived for tracking the sparse principal subspace…
In this paper, we introduce a fast adaptive algorithm for CAN- DECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator t…
This paper addresses the problem of adaptive blind sparse source separation in the time domain of an over-determined instantaneous noisy mixture. A two-step approach is proposed: first, the data are projected on the signal subspace estimated using the principal subspace tracker FAPI. In the second s…
This paper deals with semi-blind channel estimation in Single-Input Single-Output (SISO) Orthogonal Frequency Division Multiplexing (OFDM) communications system. The proposed algorithm proceeds in two main stages. The first one addresses the pilot-based Time-Of-Arrival (TOA) estimation using subspac…
Dictionary learning algorithms have been successfully applied to a number of signal and image processing problems. In some applications however, the observed signals may have a multi-subpsace structure that enables block-sparse signal representations. Based on the observation that the observed signa…
This paper deals with semi-blind (SB) channel estimation of Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) wireless communications system in the uplink transmission. Herein, we propose a new channel estimation approach using the well known EM technique. More pr…
We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has…
The issue of blind Multiple-Input and Multiple-Output (MIMO) deconvolution of communication system is addressed. Two new iterative Blind Source Separation (BSS) algorithms are presented, based on the minimization of Multi-Modulus (MM) criterion. A pre-whitening filter is utilized to transform the pr…
This paper introduces a new PARAFAC algorithm for a class of third-order tensors. Particularly, the proposed algorithm is based on subspace estimation and solving a non-symmetrical joint diagonalization problem. To deal with large scale problem, a procedure for overcoming scale and permutation ambig…