A New State-Regularized QRRLS Algorithm with Variable Forgetting Factor

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Performance analysis and design of FxLMS algorithm in broadband ANC system with online secondary-path modeling

The filtered-x LMS (FxLMS) algorithm has been widely used in active noise control (ANC) systems, where the secondary path is usually estimated online by injecting auxiliary noises. In such an ANC system, the ANC controller and the secondary-path estimator are coupled with each other, which make it difficult to analyze the performance of the entire system. Therefore, a comprehensive performance analysis of broadband ANC systems is not available currently to our best knowledge. In this paper, the convergence behavior of the FxLMS algorithm in broadband ANC systems with online secondary-path modeling is studied. Difference equations which describe the mean and mean square convergence behaviors of the adaptive algorithms are derived. Using these difference equations, the stability of the system is analyzed. Finally, the coupled equations at the steady state are solved to obtain the steady-state excess mean square errors (EMSEs) for the ANC controller and the secondary-path estimator. Computer simulations are conducted to verify the agreement between the simulated and theoretically predicted results. Moreover, using the proposed theoretical analysis, a systematic and simple design procedure for ANC systems is proposed. The usefulness of the theoretical results and design procedure is demonstrated by means of a design example. © 2012 IEEE.published_or_final_versio

General control for boost PFC converter from a sliding mode viewpoint

Author name used in this publication: Chi K. TseRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

A new regularized transform-domain NLMS adaptive filtering algorithm

The transform domain normalized LMS (TD-NLMS)-adaptive filtering algorithm is an efficient adaptive filter with fast convergence speed and reasonably low arithmetic complexity. However, it is sensitive to the level of the excitation signal, which may vary significantly over time in speech and audio signals. This paper proposes a new regularized transform domain NLMS (R-TDNLMS) algorithm and studies its mean and mean square convergence performance. The proposed algorithm extends the conventional TDNLMS algorithm by imposing a regularization term on the coefficients to reduce the variance of the estimator. The mean and mean square convergence behaviors of the proposed algorithm are studied to characterize its convergence condition and steady-state excess mean squares error (MSE). It shows that regularization can help to reduce the MSE for coloured inputs by trading slight bias for variance. Moreover, the immunity to varying input signal level is significantly reduced. Computer simulations are conducted to examine the effectiveness of the proposed algorithm and they are in good agreement with the theoretical analysis. © 2010 IEEE.published_or_final_versionThe 2010 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS 2010), Kuala Lumpur, Malaysia, 6-9 December 2010. In Proceedings of APCCAS, 2010, p. 696-69

A new regularized QRD recursive least M-estimate algorithm: Performance analysis and applications

Proceedings of the International Conference on Green Circuits and Systems, 2010, p. 190-195This paper proposes a new regularized QR decomposition based recursive least M-estimate (R-QRRLM) adaptive filtering algorithm and studies its mean and mean square convergence performance and application to acoustic echo cancellation (AEC). The proposed algorithm extends the conventional RLM algorithm by imposing a weighted L2 regularization term on the coefficients to reduce the variance of the estimator. Moreover, a QRD-based algorithm is employed for efficient recursive implementation and improved numerical property. The mean convergence analysis shows that a bias solution to the classical Wiener solution will be introduced due to the regularization. The steady-state excess mean square error (EMSE) is derived and it suggests that the variance will decrease while the bias will increase with the regularization parameter. Therefore, regularization can help to trade bias for variance. In this study, the regularization parameter can be adaptively selected and the resultant variable regularization parameter QRRLM (VR-QRRLM) algorithm can obtain both high immunity to input variation and low steady-state EMSE values. The theoretical results are in good agreement with simulation results. Computer simulation results on AEC show that the R-QRRLM and VR-QRRLM algorithms considerably outperform the traditional RLS algorithm when the input signal level is low or during double talk. © 2010 IEEE.published_or_final_versio

A new transform-domain regularized recursive least M-estimate algorithm for a robust linear estimation

This brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR-decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system coefficients when they are correlated. Furthermore, a QR-based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated. © 2006 IEEE.published_or_final_versio

A new recursive algorithm for time-varying autoregressive (TVAR) model estimation and its application to speech analysis

This paper proposes a new state-regularized (SR) and QR decomposition based recursive least squares (QRRLS) algorithm with variable forgetting factor (VFF) for recursive coefficient estimation of time-varying autoregressive (AR) models. It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance and bias over traditional regularized RLS algorithm. It also increases the tracking speed by introducing a new measure of convergence status to control the FF. Simulations using synthetic and real speech signals show that the proposed method has improved tracking performance and reduced estimation error variance than conventional TVAR modeling methods during rapid changing of AR coefficients. © 2012 IEEE.published_or_final_versionThe 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 1026-102

Stability analysis of two-stage PFC power supplies

Author name used in this publication: Chi K. TseRefereed conference paper2005-2006 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Characteristics of nonmethane hydrocarbons (NMHCs) in industrial, industrial-urban, and industrial-suburban atmospheres of the Pearl River Delta (PRD) region of south China

In a study conducted in late summer 2000, a wide range of volatile organic compounds (VOCs) were measured throughout five target cities in the Pearl River Delta (PRD) region of south China. Twenty-eight nonmethane hydrocarbons (NMHCs; 13 saturated, 9 unsaturated, and 6 aromatic) are discussed. The effect of rapid industrialization was studied for three categories of landuse in the PRD: Industrial, industrial-urban, and industrial-suburban. The highest VOC mixing ratios were observed in industrial areas. Despite its relatively short atmospheric lifetime (2-3 days), toluene, which is largely emitted from industrial solvent use and vehicular emissions, was the most abundant NMHC quantified. Ethane, ethene, ethyne, propane, n-butane, i-pentane, benzene, and m-xylene were the next most abundant VOCs. Direct emissions from industrial activities were found to greatly impact the air quality in nearby neighborhoods. These emissions lead to large concentration variations for many VOCs in the five PRD study cities. Good correlations between isoprene and several short-lived combustion products were found in industrial areas, suggesting that in addition to biogenic sources, anthropogenic emissions may contribute to urban isoprene levels. This study provides a snapshot of industrial, industrial-urban, and industrial-suburban NMHCs in the five most industrially developed cities of the PRD. Increased impact of industrial activities on PRD air quality due to the rapid spread of industry from urban to suburban and rural areas, and the decrease of farmland, is expected to continue until effective emission standards are implemented. Copyright 2006 by the American Geophysical Union

A New Variable Regularized Transform Domain NLMS Adaptive Filtering Algorithm-Acoustic Applications and Performance Analysis

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