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Identification of Hammerstein nonlinear models has received much attention due to its ability to describe a wide variety of nonlinear systems. A novel identification approach based on intelligent optimal weighted least squares SVM (WLSSVM) is proposed for Hammerstein system, where a new adaptive particle swarm optimization algorithm (APSO) using the evolutionary state estimation technique and mutation operator is applied. The proposed method not only has fast convergence to the global optimal solution but also has good identification results. The comparison researches are carried out among the proposed method, WLSSVM, LSSVM and RIV methods in detail. The research results show the effectiveness of proposed APSO-WLSSVM method. [ABSTRACT FROM AUTHOR]

This paper presents a tracking and detection engine for 2 \times 2 spatial-multiplexing multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems under highly time-selective fading channels. A recursive least-squares (RLS) adaptive algorithm is adopted to track time-varying channel frequency response. Besides, iterative MIMO signal detection is used, including inter-carrier interference (ICI) cancellation, to enhance system performance. To avoid error propagation in the decision-directed RLS adaptation, ordered-selection mechanism and signal strength criteria are incorporated so that more reliable detection results can be selected for calculating channel statistics. The scheduling strategies and hardware reduction techniques are proposed for implementation of the computation-intensive RLS adaptive algorithm and ICI cancellation. This baseband processor has been fabricated in 90 nm CMOS technology and has 1023K gates. At the nominal operating frequency of 15.36 MHz, it provides throughput of 76.8 Mbps with 64-QAM up to 240 Km/hr. For constellation of 16-QAM and QPSK, the mobility of 360 Km/hr is supported. The power consumption is 11.8 mW at 0.7 V supply voltage. The operating frequency can be raised up to 103 MHz to offer 515 Mbps throughput with power consumption of 202 mW at 1.2V. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the Artificial Bee Colony (ABC) algorithm is applied to construct Adaptive Noise Canceller (ANC) for electroencephalogram (EEG)/Event Related Potential (ERP) filtering with modified range selection, described as Bounded Range ABC (BR-ABC). ERP generated due to hand movement is filtered through Adaptive Noise Canceller (ANC) from the EEG signals. ANCs are also implemented with Least Mean Square (LMS) and Recursive Least Square (RLS) algorithm. Performance of the algorithms is evaluated in terms of Signal-to-Noise Ratio (SNR) in dB, correlation between resultant and template ERP, and mean value difference. Testing of their noise attenuation capability is done on contaminated ERP with white noise at different SNR levels. A comparative study of the performance of conventional gradient based methods like LMS, RLS, and ABC algorithm is also made which reveals that ABC algorithm gives better performance in highly noisy environment. [Copyright &y& Elsevier]

Abstract: A new method is proposed for finding the inverse of the covariance (or correlation) matrix. The technique is based on a previous method known as automatic variance control. The technique naturally gives rise to the inverse due to the properties of negative feedback itself rather than by traditional invertion methods. The method can be applied to the non-stationary problem and can form part of a recursive parameter estimation scheme. [Copyright &y& Elsevier]

The article provides information on the Web site provided by the Restless Legs Syndrome (RLS) Foundation in the U.S. The Foundation is a non-profit organization which aims to increase awareness about RLS. Contents of the Web site include a section dedicated to educating users about RLS. The site also contains a portable document format (PDF) containing information about RLS treatment options.