• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.1028-1035
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• 1993

The signed regressor (SR) algorithm employs one bit quantization on the input regressor (or tap input) in such a way that the quantized input sequences become +1 or -1. The algorithm is computationally more efficient by nature than the popular least mean square (LMS) algorithm. The behavior of the SR algorithm unfortunately is heavily dependent on the characteristics of the input signal, and there are some Inputs for which the SR algorithm becomes unstable. It is known, however, that such a stability problem does not take place with the SR algorithm when the input signal is Gaussian, such as in the case of speech processing. In this paper, we explore a statistical analysis of the SR algorithm. Under the assumption that signals involved are zero-mean and Gaussian, and further employing the commonly used independence assumption, we derive a set of nonlinear evolution equations that characterizes the mean and mean-squared behavior of the SR algorithm. Experimental results that show very good agreement with our theoretical derivations are also presented.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.10
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• pp.1712-1719
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• 2003

This paper concerns on application of active magnetic bearing(AMB) system to levitate the elevation axis of an electro-optical sight mounted on moving vehicles. In such a system, it is desirable to retain the elevation axis within the predetermined air-gap while the vehicle is moving. An optimal base acceleration feedforward control is proposed to reduce the base motion response. In the consideration of the uncertainty of the system model, a filtered-x least-mean-square(FXLMS) algorithm is used to estimate the frequency response function of the feedforward control which cancels base motions. The frequency response function is fitted to an optimal feedforward control. Experimental results demonstrate that the proposed control reduces the air-gap deviation to 27.7% that by feedback control alone.

• 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.

• Journal of Communications and Networks
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• v.6 no.1
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• pp.19-25
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• 2004

In this paper, we present a trellis-based blind channel estimation and equalization technique coupling two kinds of adaptive Viterbi algorithms. First, the initial blind channel estimation is accomplished by incorporating the list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved simultaneously and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, the best channel estimate from the above operation will be adopted to set up the whole trellis. The conventional adaptive Viterbi algorithm is then applied to detect the signal and further update the channel estimate alternately. A small delay is introduced for the symbol detection and the decision feedback to smooth the noise impact. An automatic switch between the above two operations is also proposed by exploiting the evolution of path metrics and the linear constraint inherent in the trellis mapping. Simulation has shown an overall excellent performance of the proposed scheme in terms of mean square error (MSE) for channel estimation, robustness to the initial channel guess, computational complexity, and channel equalization.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.245-250
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• 2009

In communications and signal processing area, a number of least mean square adaptive algorithms have been used because of simplicity and robustness. However the LMS algorithm is known to have slow and non-uniform convergence. Various variable step size LMS adaptive algorithms have been introduced and researched to speed up the convergence rate. A variable step size LMS algorithm using the error difference for updating the step size is proposed. Compared with other algorithms, simulation results show that the proposed LMS algorithm has a fast convergence. The theoretical performance of the proposed algorithm is also analyzed for the steady state.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.288-295
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• 2013

In this paper, we compare the existing channel estimation technique for 4S (Ship to Ship, Ship to Shore) maritime communications under AWGN channel model, Rician fading channel model, and Rayleigh fading channel model respectively. In general, the received signal is corrupted by multipath and ISI (Inter Symbol Interference). The estimation of a time-varying multipath fading channel is a difficult task for the receiver. Its performance can be improved if an appropriate channel estimation filter is used. The simulation is performed in MATLAB. In this simulation, we use the popular estimation algorithms, LMS (Least Mean Square) and RLS (Recursive Least-Squares) are compared with respect to AWGN, Rician and Rayleigh channels.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.28-38
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• 2013

In this paper, we propose a improved turbo equalizer which generates a feedback signal through a simple calculation to improve performance in single carrier system with the LMS(least mean square) algorithm based equalizer and LDPC(low density parity check) codes. LDPC codes can approach the Shannon limit performance closely. However, computational complexity of LDPC codes is greatly increased by increasing the repetition of the LDPC codes and using a long parity check matrix in harsh environments. Turbo equalization based on LDPC code is used for improvement of system performance. In this system, there is a disadvantage of very large amount of computation due to the increase of the repetition number. To less down the amount of this complicated calculation, The proposed improved turbo equalizer adjusts the adoptive equalizer after the soft decision and the LDPC code. Through the simulation results, it's confirmed that performance of improved turbo equalizer is close to the SISO-MMSE(soft input soft output minimum mean square error) turbo equalizer based on LDPC code with the smaller amount of calculation.

• The Journal of the Acoustical Society of Korea
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• v.34 no.5
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• pp.377-384
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• 2015

An active noise control (ANC) algorithm for noise canceling headphone is proposed. In this study, the feedback ANC operated with the filtered-x least mean square algorithm (FxLMS) algorithm is used to attenuate the undesired noise. Also an adaptive residual music canceller (RMC) is proposed for enhancing the accuracy of the reference signal of the feedback ANC. Simulation results show that a high quality of music sound can be consistently achieved in a time-varying secondary path situation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.333-338
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• 2002

자기베어링의 회전정밀도에 영향을 미치는 인자로 PWM 전력증폭기, 위치 센서 등과 같은 자기베어링 구성 장치의 동특성 및 정밀도, 시스템의 정확한 모델링, 제어기법, 런아웃 등이 있다. 본 연구에서는 능동 자기베어링을 제어하기 위해 자기베어링의 PWM 전력증폭기와 회전축을 모델링하고 이를 바탕으로 능동 자기베어링 제어를 위한 PID 제어기를 구성하였으며, 변위 센서의 부착위치 및 회전축의 진원도의 영향으로 발생하는 주기적인 런아웃 요소를 첨가하여 런아웃의 영향을 확인하였으며, 런아웃 (Runout)에 의해 발생하는 에러(Error)를 효과적으로 제어하여 자기베어링의 제어 정밀도를 향상시키기 위한 방법으로 기본적인 PID 제어기에 최소평균자승(Least Mean Square, LMS) 알고리즘을 적용한 적응 피드포워드 제어기를 구성하여 자기베어링의 능동 제어에서 발생하는 주기적인 런아웃을 효과적으로 제어할 수 있음을 MATLAB을 통한 시뮬레이션을 통해 확인하였다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.69-77
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• 2002

Speaker verification can be classified in two categories, text-dependent speaker verification and text-independent speaker verification. In this paper, we discuss text-dependent speaker verification. Text-dependent speaker verification system determines whether the sound characteristics of the speaker are equal to those of the specific person or not. In this paper we obtain the speaker data using a sound card in various noisy conditions, apply a new Hidden LMS (Least Mean Square) adaptive algorithm to it, and extract LPC (Linear Predictive Coding)-cepstrum coefficients as feature vectors. Finally, we use a competitive learning neural network for speaker verification. The proposed hidden LMS adaptive filter using a neural network reduces noise and enhances features in various noisy conditions. We construct a separate neural network for each speaker, which makes it unnecessary to train the whole network for a new added speaker and makes the system expansion easy. We experimentally prove that the proposed method improves the speaker verification performance.