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

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Calculating real-time computer-generated holograms for holographic 3D displays through deep learning

© OSA 2019 © 2019 The Author(s) A deep learning method is proposed to calculate holograms in real-time. After training, it can generate holograms for all R/G/B channels within 10 msec. Simulation results confirm successfully reconstruct the target training and testing images

A new regularized TVAR-based algorithm for recursive detection of nonstationarity and its application to speech signals

This paper develops a new recursive nonstationarity detection method based on time-varying autoregressive (TVAR) modeling. A local likelihood estimation approach is introduced which gives more weights to observations near the current time instant but less to those distance apart. It thus allows the Wald test to be computed based on RLS-type algorithms with low computational cost. A reliable and efficient state regularized variable forgetting factor (VFF) QR decomposition (QRD)-based RLS (SR-VFF-QRRLS) algorithm is adopted for estimation for its asymptotically unbiased property and immunity to lacking of excitation. Advantages of the proposed approach over conventional approaches are 1) it provides continuous parameter estimates and the corresponding stationary intervals with low complexity, 2) it mitigates low excitation problems using state regularization, and 3) stationarity at different scales can be detected by appropriately choosing a certain window size. The effectiveness of the proposed algorithm is evaluated by testing vocal tract changes in real speech signals. © 2012 IEEE.published_or_final_versio

A New Variable Regularized QR Decomposition-Based Recursive Least M-Estimate Algorithm-Performance Analysis and Acoustic Applications

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