• Journal of Industrial Technology
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• v.18
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• pp.317-322
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• 1998

The purpose of this present experiments was to simulate the Active noise control system using MATLAB Tool kit. The Least-Mean-Square algorithm is the most applicable one to optimize the ANC systems, even it has tight limitation. This paper shows the influence of choosing step size to the performance of the LMS adaptive filters. In addition to the simulation, this paper describes the method to design the filtered LMS algorithm to get the better performance in Active noise control. It contains the secondary-path modeling to realize the real Active noise control system in the requesting fields.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2833-2835
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• 1999

이 논문에서는 임의의 시스템 동정에 사용되는 적응필터의 계수를 최적화시키는 방법으로 광범위하게 사용되어지고 있는 기존의 적응 알고리즘인 Least Mean Square(LMS)방법과 최근들어 다양한 최적화 문제에 응용되고 있는 유전자 알고리즘(GA)을 합성한 하이브리드 형태의 적응 알고리즘을 사용한다. 이 알고리즘은 TIR 필터를 설계하는데 있어, 경사하강법의 개념을 사용함으로써 야기되는 지역 수렴문제의 단점을 보완하기 위해, 미분과 같은 결정론적인 규칙없이 단지 확률적인 연산자만으로 진행하는 유전자 알고리즘을 이용한다. 그리고 유전자 알고리즘에 있어서 확률적인 연산을 사용함으로써 발생하는 많은 계산량과 느린 수렴속도 문제를 LMS의 경사하강법을 이용하여 보완한다. 이처럼 유전자 알고리즘이 지닌 장점과 LMS 알고리즘이 갖는 장점을 이용하여 각 알고리즘이 지니는 단점을 서로 보완함으로써 알고리즘의 성능을 향상시키고 이 향상된 알고리즘을 이용하여 최적 필터계수를 찾는다 이렇게 얻은 필터계수값을 이용하여 적응 필터의 성능을 확인 평가한다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.3
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• pp.69-75
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• 2008

The objective of this paper is to increase the performance by selecting the optimal value in outputs of two algorithms that are Generlized-Lagrange and LMS(Least Mean Square). This paper presents superior performance by mixed two of conventional algorithm arbitrary. Based on the analysis from various simulation, it is observed that proposed method is better performance in terms of practical WLL(Wireless Local Loop) environment.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.8 s.338
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• pp.17-24
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• 2005

This paper proposes a Decision Feedback Equalizer (DFT) with an error feedback filter for blind channel equalization. The proposed equalizer uses Least Mean Square(LMS) Algorithm and Multi-Modulus Algorithm (MMA), and has been designed for 64/256 QAM constellations. The existing MMA equalizer uses either two transversal filters or feedforward and feedback filers, while the proposed equalizer uses feedforward, feedback and error feedback filters to improve the channel adaptive performance and to reduce the number of taps. The proposed equalizer has been simulated using the $SPW^{TM}$ tool and it shows performance improvement. It has been modeled by VHDL and logic synthesis has been performed using the $0.25\;\mu m$ Faraday CMOS standard cell library. The total number of gates is about 190,000 gates. The proposed equalizer operates at 15 MHz. In addition, FPGA vertification has been performed using FPGA emulation board.

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

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.33-40
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• 1996

In this paper, we resolved a trade-off problem of the blind equalizer based on the stop-and-go algorithm that is commonly used for QAM demodulation in CATV receiver. The stop-and-go algorithm has used the LMS(least mean square) algorithm in the updating operation of tap weights so that the structure of equalizer is simple, but there is a trade-off between convergence speed and steady state error as in the typical LMS algorithm. We used the variable step size algrithm to improve the convergence speed with the steady state error in the constant level. With respect to the same level of the steady state error, the variable step size stop-and-go algortihm improved convergence speed by about $36%{\sim}56%$ as compared with that of the constant step size algortihm.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.285-291
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• 2017

Many methods have been studied to estimate the time delay between incoming signals to two receivers. In the case of the method based on the channel estimation technique, the relative delay between the input signals of the two receivers is estimated as an impulse response of the channel between the two signals. In this case, the characteristic of the channel has sparsity. Most of the existing methods do not take advantage of the channel sparseness. In this paper, we propose a time delay estimation method using BPD (Basis Pursuit Denoising) optimization technique, which is one of the sparse signal optimization methods, in order to utilize the channel sparseness. Compared with the existing GCC (Generalized Cross Correlation) method, adaptive eigen decomposition method and RZA-LMS (Reweighted Zero-Attracting Least Mean Square), the proposed method shows that it can mitigate the threshold phenomenon even under a white Gaussian source, a colored signal source and oceanic mammal sound source.

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

• ETRI Journal
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• v.28 no.1
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• pp.59-66
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• 2006

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF-SVD-SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine-tuning of centers and widths still shows ill-behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center-gradient variance of the RBFN-SVD-SG algorithm. We found analytically that the steadystate weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center-gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady-state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.

• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.21-29
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• 1996

Industrial equipment, such as power plant, is required to operate reliably, continuously and economically under rather severe conditions of temperature, stress, and enbironment. To test structural integrity and fitness, ultrasonic nondestructive testing is used because of effectiveness and simplicity. In this paper, wavelet transform based least mean square(LMS) algorithm is applied to reduce the influence of the interference occurring between randomly positioned small scatters. The RUN test is performed to check the nonstationarity of the speckle noise signal. The performance of this new approach is compared with that of the time domain LMS algorithm by means of condition numbers, signal-to-noise ratio and 3-D image. As a result, the wavelet transform based LMS algorithm shows better performance than the time domain LMS algorithm in this experiment.