• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.30-32
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• 2006

An adaptive IIR filter in ANC(Active Noise Control) systems is more effective than an adaptive FIR filter when acoustic feedback exists, in which cause an order of an adaptive FIR filter must be very large if some of poles of the ideal control filter are near the unit circle. But the IIR filters may have stability problems especially when the adaptive algorithm for adaptive filters is not yet converged. In this paper, a stabilized multi-channel recursive LMS (MCRLMS) algorithm for an adaptive multi-channel IIR filter is presented. RLMS algorithms usually diverge before the algorithm is not yet converged. So, in the beginning of the ANC system, the stability of the RLMS algorithms could be Improved by pulling the poles of the IIR filter to the center of the unit circle, and returning the poles to their original positions after the filter converges. Computer simulations and experiments for dipole ducts using a TMS320C32 digital signal processor have performed to show the effectiveness of a proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.81-89
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• 1999

Image obtained by incomplete communication always include noise, blur and distortion, etc. In this paper, we propose and apply the new spatial filter algorithm, called an optimal adaptive generalized stack filter(AGSF), which optimizes adaptive generalized stack filter(AGSF) using neural network weight learning algorithm of back-propagation learning algorithm for improving noise removal and edge preservation rate. AGSF divides into two parts: generalized stack filter(GSF) and adaptive multistage median filter(AMMF), GSF improves the ability of stack filter algorithm and AMMF proposes the improved algorithm for reserving the sharp edge. Applied to neural network theory, the proposed algorithm improves the performance of the AGSF using two weight learning algorithms, such as the least mean absolute(LAM) and least mean square (LMS) algorithms. Simulation results of the proposed filter algorithm are presented and discussed.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.250-255
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• 2005

In this paper, we propose a new fast running FIR filter structure that improves the convergence speed of adaptive signal processing and reduces the computational complexity. The proposed filter is applied to wavelet based adaptive algorithm. Actually we compared the performance of the proposed algorithm with other algorithm using computer simulation of adaptive noise canceler based on synthesis speech. As the result, the frequency domain algorithm is prefer than the existent time domain. we analyzed the Wavelet algorithm, short-length fast running FIR algorithm, fast-short-length fast running FIR algorithm and proposed algorithm.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.18-27
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• 2007

The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first- and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a non-stationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.5
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• pp.12-19
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• 1982

In general the design methods of adaptive filter are divided into two categories, one is based upon the local parameter optimization theory and the other is based upon stability theory. Among the various design techniques, the LMS algorithm by steepest-descent method which is based upon local parameter optimization theory is used widely. In designing the adaptive filter, the most important factor is the convergence rate of the algorithm. In this paper a new algorithm is proposed to improve the convergence rate of adaptive firter compared with the commonly used LMS algorithm. The faster convergence rate is obtained by adjusting the adaptation gain of LMS algorithm. And various aspects of improvement of the adaptive filter characteristics are discussed in detail.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.56.3-56
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• 2001

This study describes the jump error smoothing scheme with fuzzy logic based on the scalar adaptive filter. The scalar adaptive filter is an useful algorithm for smoothing abrupt jump errors. However, the performances of scalar adaptive algorithm depend on the variance of real signal. So to design an effective algorithm, many informations of real and jump signal are required. In this paper, the fuzzy rules are designed by the analysis of scalar adaptive filter, and then the improved and simplified scheme is developed for smoothing the jump error. Simulations to INS/GPS integrated system show that the proposed method is effective.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.23-28
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• 1992

The purpose of this paper is to develop a new adaptive echo canceller improving convergence speed and near-end-talker detection performance of the conventional echo canceller. In a conventional adaptive echo canceller, an adaptive digital filter with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to cote the coefficients, and NET detector using energy comparison method prevents the adaptive digital filter to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF(Adaptive Digital Filter) output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yield more accurate detection of the start point of the NET signal.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3
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• pp.221-227
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• 2004

This paper presents the subband affine projection algorithm(SAPA). The improved performance of SAPA is achieved by applying the affine projection algorithm to the subband adaptive structure. In this algorithm, the weight updating formula of adaptive filter is simply derived by using the orthogonal quadrature filter(OQF) as an analysis filter bank for subband filtering. The derived SAPA has the fast convergence speed and small computational complexity. The efficiency of the proposed algorithm for colored input signal is evaluated through some experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1379-1382
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• 1997

We introduce a fast implementation of the adaptive transversal filter which uses least-mean-square(LMS) algorithm. The fast Hadamard transform(FHT) is used for the implementation of the filter. By using the proposed filter we can get the significant time reduction in computatioin over the conventional time domain LMS filter at the cost of a little performance. By computer simulation, we show the comparison of the propsed Hadamard-domain filter and the time domain filter in the view of multiplication time, mean-square error and robustness for noise.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.370-378
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• 2002

Fuzzy inference which combines numerical data and linguistic data has been used to design adaptive filter algorithms. In adaptive IIR filter design, the fuzzy prefilter is taken account, and applied to both direct and lattice structure. As for the fuzzy inference of the fuzzy filter, the Sugeno's method is employed. As membership functions and inference rules are recursively generated through neural network, the accuracy can be improved. The proposed adaptive algorithm, adaptive IIR filter with fuzzy prefilter, has been applied to adaptive system identification for the purposed of performance test. The evaluations have been carried out with viewpoints of convergence property and tracking properties of the parameter estimation. As a result, the faster convergence and the better coefficients tracking performance than those of the conventional algorithm are shown in case of direct structures.