Jae Jin Jeong

A Variable Step Size for Normalized Subband Adaptive Filters

A normalized subband adaptive filter algorithm uses a fixed step size, which is chosen as a trade-off between the steady-state error and the convergence rate. In this letter, a variable step size for normalized subband adaptive filters is derived by minimizing the mean-square deviation between the optimal weight vector and the estimated weight vector at each instant of time. The variable step size is presented in terms of error variance. Therefore, the proposed algorithm is capable of tracking in non-stationary environments. The simulation results show good tracking ability and low misalignment of the proposed algorithm in system identification.

Steady-state mean-square deviation analysis of improved normalized subband adaptive filter

A new minimization criterion for the normalized subband adaptive filter (NSAF), which is called improved NSAF (INSAF), was introduced recently to improve the performance of the steady-state mean-square deviation (MSD). However, the steady-state MSD analysis of the INSAF was not studied. Therefore, this paper proposes a general solution of steady-sate MSD analysis of the INSAF algorithm, which is based on the substitution of the past weight error vector in the weight error vector. The simulation shows that our theoretical results correspond closely with the computer simulation results in various environments.

Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors

Non-intrusive electrocardiogram (ECG) monitoring has many advantages: easy to measure and apply in daily life. However, motion noise in the measured signal is the major problem of non-intrusive measurement. This paper proposes a method to reduce the noise and to detect the R peaks of ECG in a stable manner in a sitting arrangement using non-intrusive sensors. The method utilizes two capacitive ECG sensors (cECGs) to measure ECG, and another two cECGs located adjacent to the sensors for ECG are added to obtain the information on motion. Then, active noise cancellation technique and the motion information are used to reduce motion noise. To verify the proposed method, ECG was measured indoors and during driving, and the accuracy of the detected R peaks was compared. After applying the method, the sum of sensitivity and positive predictivity increased 8.39% on average and 26.26% maximally in the data. Based on the results, it was confirmed that the motion noise was reduced and that more reliable R peak positions could be obtained by the proposed method. The robustness of the new ECG measurement method will elicit benefits to various health care systems that require noninvasive heart rate or heart rate variability measurements.

Mean-Square Deviation Analysis of Multiband-Structured Subband Adaptive Filter Algorithm

A multiband-structured subband adaptive filter (MSAF) algorithm was introduced to achieve a fast convergence rate for the correlated input signal. The convergence analysis of the adaptive filter algorithm is an important concept because it provides a guideline to design the adaptive filter. However, the convergence analysis of the MSAF algorithm has not been researched as extensively as that of the normalized least-mean-square algorithm. Therefore, it needs to be researched. In this paper, we present a new approach to the mean-square deviation (MSD) analysis of the MSAF algorithm by using the persistently exciting input and the practical assumption that the stopband attenuation of the prototype filter is high. Unlike the previous analysis, the proposed analysis is possible to be applied to the long-length adaptive filter such as the acoustic echo cancellation. The proposed analysis is also applied to a non-stationary model with a random walk of the optimal weight vector. The simulation results match with the theoretical results in both the transient-state and steady-state MSD.

Arc stability index using phase electrical power in AC electric arc furnace

In AC electric arc furnace, the state of the arc is a major concern to save the electrical power and to increase the productivity. There have been several researches about the arc stability index, such as the index based on the direct measurements of arc current and the index using the three-phase to ground voltage. In this paper, a modified index, which considers the concepts of both voltages and currents, is introduced. This index is the mapped phase electrical power by a mapping function, and the electrical power is calculated from the phase to ground voltages and currents. The simulations of various cases are performed, and the modified index shows a good performance in analyzing the arc state compared to existing index.

Fast communication: Variable regularization for normalized subband adaptive filter

To overcome the performance degradation of least mean square (LMS)-type algorithms when input signals are correlated, the normalized subband adaptive filter (NSAF) was developed. In the NSAF, the regularization parameter influences the stability and performance. In addition, there is a trade-off between convergence rate and steady-state mean square deviation (MSD) according to the change of the parameter. Therefore, to achieve both fast convergence rate and low steady-state MSD, the parameter should be varied. In this paper, a variable regularization scheme for the NSAF is derived on the basis of the orthogonality between the weight-error vector and weight vector update, and by using the calculated MSD. The performance of the variable regularization algorithm is evaluated in terms of MSD. Our simulation results exhibit fast convergence and low steady-state MSD when using the proposed algorithm.

Robust convex combination of affine projection-type algorithms using an impulsive noise indicator

A novel adaptive filter combining the affine projection algorithm (APA) and the affine projection sign algorithm (APSA) is proposed using an impulsive noise indicator. This indicator is proposed to use the APA as the component filter in impulsive noise environments, and it is easily obtained with convex combination schemes. From this, the proposed algorithm achieves robustness against impulsive noise regardless of the convergence state. In addition, the proposed algorithm exhibits a fast convergence rate of the APA for various noise environments. Simulation results verify that the proposed algorithm adequately combines the advantages of the two component filters for system identification scenarios. HighlightsThis paper is the first approach combining the APA and the APSA.A novel impulsive noise indicator is introduced to adopt APA with impulsive noise.The indicator is derived using the difference between two filter output errors.Proposed algorithm converges as fast as the APA even with impulsive noise.Proposed algorithm shows robustness in various noise environments.

Variable step-size affine projection sign algorithm using selective input vectors

Affine projection sign algorithm (APSA) is a useful adaptive filter for a highly correlated input signal in the presence of impulsive noise. In this study, a novel variable step-size APSA is proposed using selective input vectors to achieve both fast convergence rate and low steady-state mean-square deviation (MSD) with low computational cost. The selective input vectors and step size are chosen so as to maximize the theoretical MSD difference derived using Price?s theorem. The simulation results show that the proposed algorithm has the fastest convergence rate and lowest steady-state MSD when compared with recent variable step-size APSAs. Moreover, it effectively reduces computational cost. HighlightsWe derive the MSD using Price?s theorem to obtain the input selection strategy.We obtain the optimal step-size to maximize the iterative MSD difference.We successfully combine the input selection strategy and the optimal step size.Proposed algorithm has reduced averaged computational cost than conventional APSA.Proposed algorithm shows the best performance compared to recent VSS-APSAs.

Two-Dimensional Soft Output Viterbi Algorithm with a Variable Reliability Factor for Holographic Data Storage

In a practical holographic data storage system, the reconstruction process for a data page should account for the processing time as well as the bit error rate (BER) performance. To improve both aspects, we introduce two-dimensional (2D) partial response maximum likelihood composed of a 2D partial response (PR) target including diagonal elements and a 2D soft output Viterbi algorithm (SOVA) with a variable reliability factor. The 2D SOVA performs two one-dimensional (1D) SOVAs in structural accordance with the 2D PR target where extrinsic information uses the expected value calculated on a synchronization pattern. Finally, the 2D SOVA exports a weighted average using the reliability factor that is updated similarly as the optimization scheme for each page. The simulation results show that the proposed method has superior BER performance, despite using only two 1D SOVAs as compared with the modified 2D SOVA composed of four 1D SOVAs.

Regularization parameter of normalized subband adaptive filter

The stability and performance of the normalized subband adaptive filter (NSAF) algorithm is influenced by the regularization parameter. However, in various noise environments, the regularization parameter is difficult to be determined. The basic idea of this paper is to eliminate the effects of the noise in filter estimation. Simulation results show the proposed method has valid results in various noise environment.