Masayuki Kawamata

Analytical Approach for Synthesis of Minimum L2-Sensitivity Realizations for State-Space Digital Filters

On the fixed-point implementation of digital filters, undesirable finite-word-length (FWL) effects arise due to the coefficient truncation and arithmetic roundoff. These FWL effects must be reduced as small as possible because such effects may cause serious degradation of characteristic of digital filters. L2-sensitivity is one of the evaluation functions which evaluate the coefficient quantization effects of state-space digital filters [1–14]. The L2-sensitivity minimization is quite beneficial technique for the synthesis of high-accuracy digital filter structures, which achieves quite low-coefficient quantization error.

A Characteristic Function Based Contrast Function for Blind Extraction of Statistically Independent Signals*This work was supported by the Monbukagakusho Government of Japan.

In this paper, we propose to employ a characteristic function based non-Gaussianity measure as a one-unit contrast function for independent component analysis. This non-Gaussianity measure is a weighted distance between the characteristic function of a random variable and a Gaussian characteristic function at some adequately chosen sample points. Independent component analysis of an observed random vector is performed by optimizing the above mentioned contrast function (for different units) using a fixed-point algorithm. Moreover, in order to obtain a better separation performance, we employ a mechanism to choose appropriate sample points from an initially selected sample vector. Finally, some computer simulations are presented to demonstrate the validity and effectiveness of the proposed method.