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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)
The paper discusses Blind Source Recovery (BSR) of minimum phase and non-minimum phase mixtures of multiple source distributions using an adaptive score function. This proposed parametric score function is derived from the generalized gaussian distribution model. An adaptive algorithm to determine the tuning parameter for the proposed score function using(More)
This paper presents two separate structures for the blind source recovery (BSR) of stochastically independent signal sources. We hypothesize linear state space models for both the mixing environment and the demixing (i.e. recovering) adaptive network. Separate algorithms for adaptive estimation of parameters for the feedforward and feedback recovering(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)
This paper discusses the implementation of our proposed algorithms for Blind source Recovery based on constrained optimization using the state-space framework. Two simulation examples are presented where the mixing environment is modeled as FIR and IIR, respectively. The rate of convergence using the proposed implementation for these particular 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)
With the advancement in technology, many products in the market use images for control and display. Image compression is one of the primary image processing techniques that are embedded in all electronic products. In this paper, hybrid architecture comprising of DWT and Neural Network is combined together to compress and decompress image. The hybrid(More)