• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.242-242
• /
• 2000

Comparing to the conventional adaptive filters using LMS algorithm, the proposed adaptive filters can reduce the amounts of computation and have robustness to variance of characteristics of input signals. LMS algorithm is performed in the domain of Hadamard transform after a reference signal and input signal are transformed by fast Hadamard transformation. As a transformation from time domain to Hadamard transformed domain, the proposed filter not only maintains the performance of estimating an input signal but also greatly reduces the number of multiplication. Moreover, the effect of characteristic changes of input signal is decreased. Computer simulation shows the stability and robustness of the proposed filter.

• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.548-551
• /
• 2003

The adaptive filter is proposed for removing EOG from measured EEG on the frontal lobe. The proposed adaptive filter has been implemented and the feedback output control algorithm has been employed to control the alpha wave ratio on the basis of TMS320C31 DSP board with the on-line and real time performance. The feedback algorithm controls the input voltage of stimulating devices on the portable bio-feedback system. The EEG data are acquired at the $F_{p1}$ and $F_{p2}$ localization and are processed by the proposed adaptive filter. We demonstrated that the proposed adaptive filter could effectively remove EOG from the measured EEG on the frontal lobe and the feedback algorithm is proper to control the output voltage of DSP board using the ratio of the alpha wave.

• Journal of Biomedical Engineering Research
• /
• v.16 no.4
• /
• pp.517-524
• /
• 1995

This paper is a study on the design of the cascade adaptive filter (CAF) for baseline wandering elimination in order to enhance the performance of the detection of ST segments in ECG. The CAF using Least Mean Square (LMS) algorithm consists of two filters. The primary adaptive filter which has the cutoff frequency of 0.3Hz eliminates the baseline wandering in raw ECG The secondary adaptive filter removes the remnant baseline wandering which is not eliminated by the primary adaptive filter. The performance of the CAF was compared with the standard filter, the recursive filter, and the adaptive impulse correlated filter (AICF). As a result, the CAF showed a lower signal distortion than the standard filter and the AICF. Also, the CAF showed a better perf'ormance in noise elimination than the standard filter and the recursive filter. In conclusion, considering the characteristics of the noise elimination and the signal distortion, the CAF shows a better performance in the removal of the baseline wandering than the other three Otters and suggests the high performance in the detection of ST segment.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.24 no.1
• /
• pp.33-39
• /
• 2019

We propose a mechanical parameter estimation algorithm for surface-mounted permanent magnet synchronous motors (SPMSMs) using a sliding-mode observer (SMO) and an adaptive filter. The SMO estimates system disturbances in real time, which contain the information on mechanical parameters. A desirable feature that distinguishes the proposed estimation algorithm from other existing mechanical parameter estimators is that the adaptive filter estimates electromagnetic torque to improve the estimation performance. Moreover, the SMO acts as a low-pass filter to suppress the chattering effect, which enables the smooth output signals of the SMO. We verify the mechanical parameter estimation performance for SPMSM by conducting extensive experiments for the proposed algorithm.

• Journal of Korea Multimedia Society
• /
• v.7 no.12
• /
• pp.1650-1656
• /
• 2004

In this paper, a multi-adaptive filter is proposed for removing EOG and the 60 Hz power supply noise from EEG measured in the frontal lobe and the feedback output control method is implemented for biofeedback. The multi-adaptive filter has been implemented on the TMS320C6711 DSP system and the feedback output control algorithm has been realized by calculating the ratio of alpha wave on the TMS320C31 DSP system with real time performance. Through the experiment using the implemented multi-adaptive filter and feedback output controller, we demonstrate that the proposed adaptive filter effectively removes EOG and the 60 Hz power supply noise from the measured EEG in the frontal lobe and the feedback algorithm controls the level of stimulation by the ratio of the alpha wave.

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

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.377-380
• /
• 2002

When it calculates the optimum price of filter coefficient, the many operation quantity is necessary. Is like that the real-time control is difficult and the hardware embodiment expense is big. The case which does not know advance information of input signal or the case where the statistical nature changes with change of surroundings environment is necessary the adaptive filter. Every hour to change a coefficient automatically and system in order to reach to the condition of optimum oneself, the fact that is the adaptive filter. When it does not the quality of input signal or it does not know the environment of surroundings every hour changing, it does not emit not to be, in order to collect, the fact that is the adaptive filter. The case of the Acoustic Echo Canceler does thousands filter coefficients in necessity. It reduces a many calculation quantity to respect, it uses the IIR filter from hour territory. Also it uses the block adaptive filter which has a block input signal and a block output signal. The former there is a weak point where the stability discrimination is always demanded. Consequently, The block adaptive filter is researched plentifully. This dissertation planned the block MADF adaptive filter used to MADf algorithm.

• Proceedings of the KIEE Conference
• /
• 1987.07b
• /
• pp.1379-1382
• /
• 1987

IN ADAPTIVE LINEAR PREDICTION, AN ADAPTIVE CAPABILITY IS BUILT INTO THE PROCESSOR SUCH THAT AS THE IMAGE STATISTICS CHANGE, THE PREDICTION FILTER COEFFICIENTS THEMSELVES CHANGE, PRODUCING A NEW FILTER MORE CLOSELY OPTIMIZED TO THE NEW SET OF IMAGES STATISTICS. THE LMS ALGORITHM MAY BE USED TO ADAPT THE COEFFICIENT OF AN ADAPTIVE PREDICTION FILTER FOR IMAGE SOURCE ENCODING. IN THIS PAPER, TWO CODING SYSTEMS USING DPCM AND LMS ALGORITHMS RESPECTIVELY FOR OBTAINING THE FIRST TRANSFORMED COEFFICIENT IN HYBRID CODING ARE COMPARED.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 1991.10a
• /
• pp.111-114
• /
• 1991

This paper proposes an efficient adaptive echo canceller using pilot filter approach to achieve improved convergence speed. The pilot filter is an adaptive filter with only a few filter coefficients to filter the received signal for the purpose of whitening the signal. Thus the convergence speed of the main LMS-TDL filter combined with the pilot filter is improved. In the proposed echo canceller, an adaptive lattice predictor as the pilot filter is used and its inverse filter is used to equalize the distorted near end talker signal. Simulation results for colored signal show that the convergence speed of the proposed echo cancellation algorithm is faster than that of the conventional LMS-TDL echo cancellation algorithm.

• Proceedings of the IEEK Conference
• /
• 2000.11d
• /
• pp.115-118
• /
• 2000

We present a new variable step size LMS algorithm using the correlation between reference input and error signal of adaptive filter. The proposed algorithm updates each weight of filter by different step size at same sample time. We applied this algorithm to adaptive multip]e-notch filter. Simulation results are presented to compare the performance of the proposed algorithm with the usual LMS algorithm and another variable step algorithm.