In this letter, a two-step learning scheme for the optimal selection of time lags is proposed for a typical temporal blind source separation (TBSS), Temporal Decorrelation source SEParation algorithm (abbreviated as TDSEP). Given the time lags, the time-delayed second-order correlation matrices are first diagonalized simultaneously. Then, a genetic algorithm is used to update the time lags. Fin...

In this paper we investigate the application of Blind Source Separation (BSS) algorithms for the decoding of linearly precoded MIMO communication systems and for the design of limited feedback channels that send the Channel Status Information (CSI) from the receiver to the transmitter. The advantage of using BSS is that the MIMO channel can be continuously tracked without the need of pilot symb...

Multidimensional proton nuclear magnetic resonance spectra of biomolecules dissolved in aqueous solutions are usually contaminated by an intense water artifact. We discuss the application of a generalized eigenvalue decomposition (GEVD) method using a matrix pencil to solve the blind source separation (BSS) problem of removing the intense solvent peak and related artifacts. The method explores ...

We present a general theory for the closed-form parametric estimation of the unitary mixing matrix after prewhitening in the blind separation of two source signals from two noiseless instantaneous linear mixtures. The proposed methodology is based on the algebraic formalism of bicomplex numbers and is able to treat both real and complex valued mixtures indiscriminately. Existing analytic method...

For blind source separation, when the Fisher information matrix is used as the Riemannian metric tensor for the parameter space, the steepest descent algorithm to maximize the likelihood function in this Riemannian parameter space becomes the serial updating rule with equivariant property. This algorithm can be further simplified by using the asymptotic form of the Fisher information matrix aro...

Conventional blind source separation (BSS) algorithms are applicable when the number of sources equals to that of observations; however, they are inapplicable when the number of sources is larger than that of observations. Most underdetermined BSS algorithms have been developed based on an assumption that all sources have sparse distributions. These algorithms are applicable to separate speech ...