• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.26-30
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• 2009

We propose an adaptive Recursive Least Rank(RLR) L-filter which uses an L-estimator in order statistics and is based on rank estimate in robust statistics. The proposed RLR L-filter is a non-linear adaptive filter using non-linear adaptive algorithm and adapts itself to optimal filter in the sense of least dispersion measure of errors with non-homogeneous step size. Therefore the filter may be suitable for applications when the transmission channel is nonlinear channels such as Gaussian noise or impulsive noise, or when the signal is non-stationary such as image signal.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1210-1216
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• 2009

In this paper we analyze the convergence behavior of the recursive least rank (RLR) L-filter. The RLR L-filter is an order statistics filter, filter coefficients of which are the weights according to the order of magnitude of inputs. And RLR L-filter is a non-linear adaptive filter, that uses RLR algorithm for coefficient updating. The RLR algorithm is a non-linear adaptive algorithm based on rank estimates in Robust statistics. The mean and mean-squared convergence behavior of the RLR L-filter is examined with variable step-sizes. The RLR L-filter adapts the median filter type to the heavy-tailed distribution function of impulse noise, and adapts the average filter type to Gaussian noises.

• Journal of IKEEE
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• v.24 no.2
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• pp.570-578
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• 2020

In this paper, we propose an efficient method for implementing the adaptive second-order Volterra filter. To reduce computational load, the UCFD-SVF has been proposed. The UCFD-SVF, however, shows deteriorated convergence performance. We propose a new method that initializes the adaptive filter weights periodically on the fact that the energy of the filter weights is slowly increased. Furthermore, we propose another method that the interval for the weight initialization is variable to guarantee the performance and we shows the method gives the better performance under the non-stationary environment through the computer simulation for the adaptive system identification.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.167-170
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• 1993

matched filter는 신호와 잡음의 통계적 값을 알고 있을 때 신호대 잡음비를 최대로 하는 filter이다. 그런데, matched filter가 최적화 되려면 잡음이 white noise이어야한다. 그러나 ECG신호에 존재하는 잡음은 여러가지 성분이 공존하는 서로 연관되어있는 잡음이다. 따라서 whitening filter를 사용하여 잡음을 whitening시킨후에 matched filter를 통과 시켜야한다. 본 논문에서는 QRS complex를 검출하기 위한 matched filter에 있어서 LMS방법을 이용한 linear whitening filter와 neural network을 이용한 non-linear whitening filter의 특성을 비교하였다.

• Journal of Biomedical Engineering Research
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• v.29 no.3
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• pp.239-248
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• 2008

In this study, a new method using a capacitive sensor and an adaptive filter was proposed to deal with artifacts contaminating an oscillation signal in oscilometric blood pressure measurement. The proposed method makes use of a variation of the capacitance between an electrode fixed to a cuff and an external object to detect artifacts caused by the external object bumping into the cuff. The proposed method utilizes the adaptive filter based on linear prediction to remove the detected artifacts. The conventional method using linear interpolation and the proposed method using the adaptive filter were applied to three types of the artifact-contaminated oscillation signals(no overlap, non-consecutive overlap, and consecutive overlap between artifacts and oscillations) to compare them in terms of the artifact reduction performance. The proposed method was more robust than the conventional method in the case of consecutive overlap between artifacts and oscillations. The proposed method could be useful for measuring blood pressure in such a noisy environment that the subject is being transported.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.2
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• pp.145-151
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• 2010

This paper proposes a reverse filtering system of sound field obtaining a state of sound field transmitted from two sounds, using an adaptive filter and neural network. The proposed system uses the reverse filtering method with calculating and renewing a coefficient of a filter, using least mean square. Based on training the neural network, experiments confirm that the proposed system is effective for a simple waveform with non-linear distortion, by using neural network and adaptive filtering method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9A
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• pp.830-837
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• 2005

ISI is caused by delay spread in the multipath channel environment. There are two kinds of channel equalizer: Linear and Non-Linear type according to the structures. In this paper, we propose an improved adaptive linear equalizer to mitigate ISI. The proposed adaptive equalizer is constructed by using asymmetrical Dsmvenu filter based on USE sub-optimal receiver. Asymmetrical structure of the transversal filter is realized by moving the main tap position from center to side. If this structure is used, we can divide ISI to precusor and postcusor. As a result the proposed equalizer has a larger extended compensation range than conventional adaptive linear equalizer. In computer simulation, we compare the bit error rate performance of the proposed linear equalizer with the conventional one on the S-V channel which is modeled for WB systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.231-238
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• 2004

This paper presents the high speed noise reduction processing system using the modified discrete fourier transform(MDFT) on the frequency domain. The proposed filter uses the linear prediction coefficients of the adaptive line enhance(ALE) method based on the Sign algorithm The signals with a random noise tracking performance are examined through computer simulations. It is confirmed that the fast adaptive digital filter is realized by the high speed adaptive noise reduction(HANR) algorithm with rapid convergence on the frequency domain(FD).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.360-364
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• 1994

This paper is concerned with extracting the diagnostic signature for motor-operated valves (MOV's) noninvasively. A torque estimator is developed and tested to obtain electical torque of the induction motors which are attached to the MOV's. Inverse filter is used to recover the gear meshing forces from the measured actuator housing vibration, which contain the gear rotation information. Frequency demodulation techniques are performed and an adaptive linear bandpass filter is implemented to improve signal-to-noise ratio. Finally, stand-alone valve experiments are carried out to validate the signature extraction scheme.

• Journal of Communications and Networks
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• v.5 no.1
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• pp.33-42
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• 2003

In this paper a two-stage maximum-likelihood (ML) detection structure for group detection in DS/CDMA systems is presented. The first stage of the receiver is a linear filter, aimed at suppressing the effect of the unwanted (i.e., out-of-grout) users' signals, while the second stage is a non-linear block, implementing a ML detection rule on the set of desired users signals. As to the linear stage, we consider both the decorrelating and the minimum mean square error approaches. Interestingly, the proposed detection structure turns out to be a generalization of Varanasi's group detector, to which it reduces when the system is synchronous, the signatures are linerly independent and the first stage of the receiver is a decorrelator. The issue of blind adaptive receiver implementation is also considered, and implementations of the proposed receiver based on the LMS algorithm, the RLS algorithm and subspace-tracking algorithms are presented. These adaptive receivers do not rely on any knowledge on the out-of group users' signals, and are thus particularly suited for rejection of out-of-cell interference in the base station. Simulation results confirm that the proposed structure achieves very satisfactory performance in comparison with previously derived receivers, as well as that the proposed blind adaptive algorithms achieve satisfactory performance.