the infomax algorithm is a popular method in blind source separation problem. in this article an extension of the infomax algorithm is proposed that is able to separate mixed signals with any sub- or super-gaussian distributions. this ability is the results of using two different nonlinear functions and new coefficients in the learning rule. in this paper we show how we can use the distribution...

The reconstruction of sparse signals requires the solution of an `0-norm minimization problem in Compressed Sensing. Previous research has focused on the investigation of a single candidate to identify the support (index of nonzero elements) of a sparse signal. To ensure that the optimal candidate can be obtained in each iteration, we propose here an iterative greedy reconstruction algorithm (G...

A simple approach for numerically exact simulations of nonadiabatic quantum dynamics in multidimensional systems is introduced and applied to the description of the photoabsorption spectroscopy of pyrazine. The propagation scheme generalizes the recently developed matching-pursuit/split-operator-Fourier-transform (MP/SOFT) method [Y. Wu and V. S. Batista, J. Chem. Phys. 121, 1676 (2004)] to sim...

The paper considers the task of recognizing environmental sounds for the understanding of a scene or context surrounding an audio sensor. A variety of features have been proposed for audio recognition, including the popular Mel-frequency cepstral coefficients (MFCCs) which describe the audio spectral shape. Environmental sounds, such as chirpings of insects and sounds of rain which are typicall...

We consider a problem in signal processing which deals with the recovery of a high-dimensional sparse signal based on a small number of measurements. Our goal is to apply the least absolute deviations (LAD) method in an algorithm that would essentially follow the steps of the orthogonal matching pursuit (OMP) algorithm that has been used mostly in this setting. OMP can recover the signal with h...

Compressed sensing is a developing field aiming at reconstruction of sparse signals acquired in reduced dimensions, which make the recovery process under-determined. The required solution is the one with minimum l0 norm due to sparsity, however it is not practical to solve the l0 minimization problem. Commonly used techniques include l1 minimization, such as Basis Pursuit (BP) and greedy pursui...

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary com...