JinWoo Yoo

Variable Step-Size Sign Subband Adaptive Filter

This letter proposes a variable step-size sign subband adaptive filter (SSAF) based on the minimization of mean-square deviation (MSD). In the process of minimizing the MSD, because it is not feasible to know the exact value of the MSD, the step size is derived by minimizing the upper bound of the MSD in each iteration. The proposed algorithm uses this step size in the SSAF update equation so as to improve the filter performance in terms of the convergence rate and the steady-state estimation error. The proposed algorithm is tested in a system-identification scenario that includes impulsive noise. Simulation results show that the proposed algorithm performs better than the previous algorithms.

Variable Step-Size Affine Projection Sign Algorithm

This brief proposes a novel variable step-size affine projection sign algorithm (APSA), which is characterized by its robustness against impulsive noises. To obtain a step size reasonably, the proposed algorithm investigates the mean-square deviation (MSD) of APSA. Because it is impossible to accurately compute the MSD of APSA, the proposed algorithm derives the upper bound of the MSD using the upper bound of the L1-norm of the measurement noise. The optimal step size is calculated at each iteration by minimizing the upper bound of the MSD, which improves the filter performance, with respect to the convergence rate and the steady-state estimation error. The simulation results demonstrate that the proposed algorithm improves the filter performance in a system-identification scenario in the presence of impulsive noises.

Fast communication: A band-dependent variable step-size sign subband adaptive filter

This letter proposes a band-dependent variable step-size sign subband adaptive filter using the concept of mean-square deviation (MSD) minimization. Since it is difficult to obtain the value of the MSD accurately, the proposed step size is derived by minimizing the upper bound of the conditional MSD with given input. By assigning the different step size in each band, the filter performance can be improved. Moreover, we suggest the estimation method of the measurement-noise variance in an impulsive-noise environment, because the proposed algorithm needs the measurement-noise variance to calculate the step size. The reset algorithm is also applied for maintaining the filter performance when a system change occurs suddenly. Simulation results demonstrate that the proposed algorithm performs better than the existing algorithms in aspects of the convergence rate and the steady-state estimation error.

An Improved NLMS Algorithm in Sparse Systems Against Noisy Input Signals

This brief proposes a novel normalized least mean square algorithm that is characterized by robustness against noisy input signals. To compensate for the bias caused by the input noise that is added at the filter input, a derivation method based on reasonable assumptions finds a bias-compensating vector. Moreover, the proposed algorithm has a fast convergence rate when applied to sparse systems, owing to its L0-norm cost in the proposed update equation. The simulation results verify that the proposed algorithm improves the performance of the filter, in terms of system identification in sparse systems, in the presence of noisy input signals.

Vision-based camber measurement system in the endless hot rolling process

In the endless hot rolling process, camber is a significant defect where camber is flatness asymmetries produced by nonuniform distribution of rolling pressure. This kind of defect may not only cause clogging of the finishing mill, but also visible defects, such as bar edge folds, edge cracks, holes, and scrapes. It is important to measure the exact camber values of the overall shape and head/tail part of steel bars to reduce camber for hot-rolled steel bars. We introduce a vision-based camber measurement system based on two-area-scan charge-coupled device (CCD) cameras and proposes a camber-detection algorithm to achieve the optimal cutting-line, the overall bar shape, and camber values from the obtained image. The proposed algorithm consists of three parts: optimal cutting-line detection, image-stitching, and camber detection. The optimal cutting-line detection part determines the cutting line of the head/tail part of the bar for continuous joining between steel bars. The image-stitching part obtains the overall shape of a hot-rolled steel bar with continuous image sequence obtained by two-area-scan CCD cameras. The camber-detection part measures head camber, tail camber, and whole camber of the steel bar. Through the proposed camber-detection algorithm, the system not only satisfies a time constraint for detecting the camber of the steel bar but also obtains the optimal cutting line, the overall bar shape, and the camber values.

Variable step-size sign algorithm against impulsive noises

This paper proposes a new variable step-size sign algorithm through the minimisation of mean-square deviation (MSD). As it is difficult to obtain the MSD accurately, the upper bound of the MSD is derived for calculating the step size at each iteration. The proposed algorithm is not only robust to impulsive noises, but also has improved filter performance in aspects of convergence rate and steady-state estimation error owing to the proposed variable step-size strategy. The simulation results verify that the proposed algorithm has better performance than the existing algorithms in a system-identification scenario in the presence of impulsive noises.

A defect inspection method for TFT panel using the compute unified device architecture (CUDA)

In this paper, a parallel algorithm to inspect defects of thin film transistor liquid crystal displays (TFT-LCD) panel is presented. The algorithm is implemented on a graphics processing unit (GPU) using the compute unified device architecture (CUDA) development environment. The result shows better performance compared to the one implemented on a CPU.

A variable step-size diffusion affine projection algorithm

This paper presents a new variable step-size diffusion affine projection algorithm VSS-DAPA to advance the filter performance of the diffusion affine projection algorithm DAPA. The proposed VSS strategy is developed for the DAPA, which can solve the distributed estimation problem over diffusion networks well. To obtain the optimal step size reasonably, we seek the update recursion of mean-square deviation MSD that is suitable for the DAPA. The step size is optimally given through the minimization for the MSD of the DAPA at each iteration. The derived step size through the MSD minimization improves the filter performance with respect to the convergence and the estimation error in steady state. The results based on simulations demonstrate that the proposed VSS-DAPA performs better than the existing algorithms with a system-identification scenario in diffusion network. Copyright

Adaptive regularisation for normalised subband adaptive filter: mean-square performance analysis approach

The normalised subband adaptive filter (NSAF) is a useful adaptive filter, which improves the convergence rate compared with the normalised least mean-square algorithm. Most analytical results of the NSAF set the regularisation parameter set to zero or present only steady-state mean-square error performance of the regularised NSAF (e-NSAF). This study presents a mean-square performance analysis of e-NSAF, which analyses not only convergence behaviour but also steady-state behaviour. Furthermore, a novel adaptive regularisation for NSAF (AR-NSAF) is also developed based on the proposed analysis approach. The proposed AR-NSAF selects the optimal regularisation parameter that leads to improving the performance of the adaptive filter. Simulation results comparing the proposed analytical results with the results achieved from the simulation are presented. In addition, these results verify that the proposed AR-NSAF outperforms the previous algorithms in a system-identification and acoustic echo-cancellation scenarios.

A robust affine projection sign algorithm against the high power of measurement noises

Summary This paper proposes a novel robust affine projection sign algorithm (R-APSA) through a modified criterion that consists of the Euclidean norm of the sum of the difference between the present weight vector and the previous weight vectors. Because the filter update equation of the proposed R-APSA is obtained from the modified criterion, it has robustness against the high power of measurement noises. Moreover, due to the characteristic inherent in the original APSA, the proposed R-APSA performs well even though impulsive noises occur. Simulation results verify that the proposed R-APSA attains smaller steady-state errors than the existing released algorithms. Copyright