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Digital filter design optimization counts as one of the most challenging problems in circuit development. Furthermore, the bit-width allocation has a crucial impact on the efficiency and accuracy of fixed-point digital filters. The conventional implementation procedure involves studying the bit-width allocation problem after determining the filter coefficients. This sequential procedure leads frequently to an excessive bit-width allocation, hence an extra implementation cost. The main contribution of this paper is to propose a new implementation procedure based on the simultaneous handling of both design issues and the bit-width optimization for the infinite impulse response filter. In this study, the problem is formalized by a multi-objective programming technique using the non-dominated sorting genetic algorithm II. The joint consideration of the two problems offers a better trade-off between design and bit-width allocation. The output of our approach is compared to the results of existing algorithms. Simulation results show that the multi-objective method significantly reduces the implementation cost while ensuring filter stability and an acceptable mean square error (MSE) compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the precision analysis of filters in the parallel form framework. The precision analysis consists of determining suitable fractional bit-widths to set a tradeoff between resource consumption and computational accuracy. Although the stability of the parallel form is relatively better controlled than the related direct form, its bit-width optimization did not receive much consideration in the literature, despite its significant contribution to the optimization of the filter's physical implementation. To carry out the needed bit-width optimization, we present two heuristics based on the Estimation of Distribution Algorithm, which falls within the category of probabilistic model-building genetic algorithm. The performance of the proposed approach is discussed, and compared to chosen benchmarks, the results show that our hardware implementations reduce the cost of the resulted circuits up to 37 % . [ABSTRACT FROM AUTHOR]

In this paper, we propose analytical formulas that involve second-order statistics for separating two signals. The method utilizes source decorrelation and correlation function diversity. In particular, the proposed SOBAS (second-order blind analytical separation) algorithm differs from the ASOBI (analytical second-order blind identification) algorithm in that it does not require prior knowledge or estimation of the noise variance. Computer simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study proposes a process of assessment of wind potential using descriptive statistics and time-frequency analysis. Via the time-frequency analysis, the variation of the spectral content of wind speed with respect to time is analyzed by the determination of the frequencies, the durations and the occurrence-time of significant fluctuations. The study is carried out for six sites in Algeria during a time interval of 1 year and over a frequency range from 1.1 × 10 to 2.4 cycles/day (i.e., periods from 10 h to about 90 days). The study has allowed to deduce that the windiest regions are located in the southern part of the country with annual mean from 5 to 6 m/s. The time-frequency analysis reveals the presence of three types of oscillations. In the warm season, diurnal oscillations have been distinguished especially in the costal and mountainous regions. However, in the cold season, synoptic oscillations of periods around 9-13 days have been observed. The Southern regions are characterized by long oscillations of periods around 33-50 days. [ABSTRACT FROM AUTHOR]

This paper deals with the extraction of signals from their instantaneous linear mixtures using time-frequency distributions. Fundamentally, this problem is a signal synthesis from the time-frequency (t-f) plane. However with the incorporation of the spatial information provided by a multisensor array, the problem can be posed as special case of blind source separation. So far, the blind source separation has been solved using only statistical information available on the source signals. Herein, we propose to solve the aforementioned problem using time-frequency signal representations and the spatial array aperture. The proposed approach relies on the difference in the t-f signatures of the sources to be separated. It is based on the diagonalization of a combined set of spatial time-frequency distribution matrices. A numerical example is provided to illustrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]