• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.4
• /
• pp.691-698
• /
• 2003

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-l, we may compute the updated estimate of this vector at iteration n upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RLS algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the B times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.551-554
• /
• 2005

The method of the reduction of the automotive induction noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the LMS (Least-Mean-Square) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, When the Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm goes bad when the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. Thus Normalized FXLMS algorithm was developed to improve the control performance under the rapid acceleration. The advantage of Normalized FXLMS algorithm is that the step size is no longer constant. Instead, it varies with time. But there is one additional practical difficulty that can arise when a nonstationary input is used. If the input is zero for consecutive samples, then the step size becomes unbounded. So, in order to solve this problem. the Correlation FXLMS algorithm was developed. The Correlation FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Correlation FXLMS Is presented in comparison with that of the other FXLMS algorithms based on computer simulations.

• The Journal of the Acoustical Society of Korea
• /
• v.18 no.7
• /
• pp.45-55
• /
• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1713-1719
• /
• 2022

As the noise canceler with a small processing delay increases the sampling frequency, a better-quality output can be obtained. For a single buffer, processing delay occurs because it is impossible to write new data while the processor is processing the data. When synthesizing with anti-noise and output signal, this processing delay creates additional buffering overhead to match the phase. In this paper, we propose an accelerator structure that minimizes processing delay and increases processing speed by alternately performing read and write operations using the Symmetric Even-Odd-buffer. In addition, we compare the structural differences between the two methods of noise cancellation (Fast Fourier Transform noise cancellation and adaptive Least Mean Square algorithm). As a result, using an Symmetric Even-Odd-buffer the processing delay was reduced by 29.2% compared to a single buffer. The proposed Symmetric Even-Odd-buffer structure has the advantage that it can be applied to various canceling algorithms.

• Journal of the Optical Society of Korea
• /
• v.15 no.3
• /
• pp.232-236
• /
• 2011

We analyze the linear equalizers used in optically amplified on-off-keyed (OOK) systems to combat chromatic dispersion (CD) and polarization mode dispersion (PMD), and we derive the mathematical minimum mean squared error (MMSE) performance of these equalizers. Currently, the MMSE linear equalizer for optical OOK systems is obtained by simulations using adaptive approaches such as least mean squared (LMS) or constant modulus algorithm (CMA), but no theoretical studies on the optimal solutions for these equalizers have been performed. We model the optical OOK systems as square-law nonlinear channels and compute the MMSE equalizer coefficients directly from the estimated optical channel, signal power, and optical noise variance. The accuracy of the calculated MMSE equalizer coefficients and MMSE performance has been verified by simulations using adaptive algorithms.

• The Journal of the Acoustical Society of Korea
• /
• v.12 no.1E
• /
• pp.20-31
• /
• 1993

Berman과 Feuer의 SLMS(smoothed least mean square)알고리즘의 근사적 분석을 행하여 보다 유용한 분석결과를 얻었다. 수렴범위와 misadjustment에 대한 분석에서는 기존의 알고리즘의 분석결과들과 비교할 수 있는 형태로 얻었을뿐만아니라 여러 변수들이 이 알고리즘의 성능에 미치는 영향을 명확히 알 수 있는 형태로 얻었다. 둘째로 몇몇 서로 유사한 알고리즘들을 비교검토함으로써 서로간의 관계를 밝히고 이 결과들을 해석하였다. 이어서 위의 분석결과들이 유효함을 실험을 통하여 밝혔다. 수렴한계 근처에서 LMS알고리즘보다 안정됨을 보였다. 이들 아고리즘의 비정상특성(nonstationary characteristics)에 대하여서도 살펴보았는데, SLMS알고리즘의 경우 추적능력의 별다른 희생 없이도 가중계수(weight)의 잡음을 줄일 수 있음을 보였다.

• Proceedings of the IEEK Conference
• /
• 2000.06b
• /
• pp.238-241
• /
• 2000

This paper describes an equalizer using the DFE (Decision Feedback Equalizer) structure, CMA. (Constant Modulus Algorithm) and LMS (Least Mean Square) algorithms. We employ high speed multipliers, square logics and many CSAs (Carry Save Adder) for high speed operations. We have developed floating-point models and fixed-point models using the COSSAP$\^$TM/ CAD tool and developed VHDL models. We have peformed logic synthesis using the SYNOPSYS$\^$TM/ CAD tool and the SAMSUNG 0.5 $\mu\textrm{m}$ standard cell library (STD80). The total number of gates is about 130,000.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.12
• /
• pp.57-63
• /
• 2002

In this paper, the feedforward control with least mean square (LMS) adaptive algorithm is proposed and examined to reduce rotating error by runout of an active magnetic bearing system. Using eddy-current type gap sensor fur control, the electrical runout caused by non-uniform material properties of sensor target produces rotational error amplified in feedback control loop, so this runout should be eliminated to increase rotating accuracy. The adaptive feedforward controller is designed and examined its tracking and stability performances numerically with established frequency response function. The tested grinding spindle system is manufactured with a 5.5 ㎾ internal motor and 5-axis active magnetic bearing system including 5 eddy current gap sensors which have approximately 15 ~ 30 ${\mu}{\textrm}{m}$ of electrical runout. According to the experimental analysis, the error signal in radial bearings is reduced to less than 5 ${\mu}{\textrm}{m}$ when it is rotating up to 50,000 rpm due to applying the feedforward control for first order harmonic frequency, and vibration of the spindle base is also reduced about same frequency.

• The Journal of the Acoustical Society of Korea
• /
• v.14 no.2
• /
• pp.40-49
• /
• 1995

Application of the filtered-x LMS adaptive filter to active noise cancellation requires to estimate the transfer charactersitics between the output and the error signal of the adaptive canceler. In this paper, we derive the filtered-x adaptive noise cancellation algorithm and analyze its convergence behavior when the acoustic noise consists of multiple sinusoids. The results of the convergence analysis of the filtered-x LMS algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to strongly affected by the accuracy of the phase response estimate. Simulation results are presented to support the theoretical convergence analysis.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.9
• /
• pp.62-70
• /
• 1995

In some practical applications of the LMS Algorithm the coefficient adaptation can be performed only after some fixed delay. The resulting algorithm is known as the Delayed Least Mean Square (DLMS) algorithm in the literature. There exist analyses for this algorithm, but most of them are based on the unrealistic independence assumption between successive input vectors. Inthis paper we consider the DLMS algorithm with decreasing step size .mu.(n)=n/a, a>0 and prove the almost-sure convergence ofthe weight vector W(n) to the Wiener solution W$_{opt}$ as n .rarw. .inf. under the mixing unput condition and the satisfaction of the law of large numbers. Computer simulations for decision-directed adaptive equalizer with decoding delay are performed to demonstrate the functioning of the proposed algorithm.m.