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A novel scheme for selecting projection vectors in bivariate empirical mode decomposition (BEMD) is proposed in order to enable accurate signal decomposition at lower computational complexity. Unlike existing algorithms which use a static uniform scheme for the distribution of projection vectors, the proposed scheme examines local curvature in multidimensional spaces to produce a data-adaptive set of direction vectors for taking signal projections. This is achieved by aligning the density of projection vectors according to the empirical distributions of angles where the signal exhibits highest local dynamics. We show that the proposed methodology outperforms the existing schemes for a small number of signal projections. The proposed algorithm is verified via illustrative simulations demonstrating accurate local mean estimation and mode extraction. [ABSTRACT FROM AUTHOR]

The problem of blind source separation of complex-valued sources from a linear mixture is addressed. We propose a deflationary algorithm for the sequential recovery of a set of communication signals, where each source is extracted by performing a Bounded Component Analysis of the linear mixture. The contribution of each recovered source to the observations is removed by minimizing its convex perimeter, without using second-order statistics. This implies to run a gradient descent algorithm several times. In order to accelerate the convergence, we have derived a fast step size that exploits the second-order information of the cost function by means of the augmented Hessian matrix. Computer simulations show that the proposed method is able to blindly separate even dependent sources, as long as they satisfy the BCA separability conditions. Also, the speed of convergence of this novel step size is compared with other classical approaches. [ABSTRACT FROM AUTHOR]

This letter proposes a novel stochastic gradient algorithm for the online adaptation of the pole position in Laguerre filters. The proposed algorithm exploits the inherent relationship between the values of the filter coefficients and the value of the Laguerre pole. This leads to an unbiased solution and, hence, a more accurate estimate of the error gradient. Simulations in a system identification setting support the analysis. [ABSTRACT FROM AUTHOR]

A generalized normalized gradient descent (GNGD) algorithm for linear finite-impulse response (FIR) adaptive filters is introduced. The GNGD represents an extension of the normalized least mean square (NLMS) algorithm by means of an additional gradient adaptive term in the denominator of the learning rate of NLMS. This way, GNGD adapts its learning rate according to the dynamics of the input signal, with the additional adaptive term compensating for the simplifications in the derivation of NLMS. The performance of GNGD is bounded from below by the performance of the NLMS, whereas it converges in environments where NLMS diverges. The GNGD is shown to be robust to significant variations of initial values of its parameters. Simulations in the prediction setting support the analysis. [ABSTRACT FROM AUTHOR]