Fathi M. Salam

Learn More
This paper integrates our contributions in the domain of blind source separation (BSS) and blind source deconvolution (BSD) both in static and dynamic environments. We focus on the use of the state space formulation and the development of a generalized optimization framework, using Kullback-Liebler divergence as the performance measure subject to the(More)
We present a novel performance index to measure the statistical independence of data sequences and apply it to the framework of blind source recovery (BSR) that includes blind source separation, deconvolution and equalization. This performance index is capable of measuring the mutual independence of data sequences directly from the data. This information(More)
The paper discusses State Space Blind Source Recovery (BSR) for minimum phase and non-minimum phase mixtures of gaussian and non-gaussian distributions. The State Space Natural Gradient approach results in compact iterative update laws for BSR. Two separate state space algorithms for minimum phase and non-minimum phase mixing environments are presented. The(More)
Blind Source Recovery (BSR) is an interesting autonomous and unsupervised stochastic adaptation problem that includes the well-known blind adaptive problems of Blind Source Separation (BSS), Deconvolution (BSD) and Equalization (BSE). BSR includes also the nonlinear case and hences focuses on reproducing or estimating the source signals even if environment(More)
We report the successful use of continuous wavelet transforms applied to atomic force microscope data sets for landmark recognition of biological features. The data sets were images of mixed red and white blood cells. Contrast enhancement followed by continuous wavelet transform of the data was used to successfully distinguish erythrocytes from neutrophil(More)