Adaptive Step-size LMS Research Articles

Investigating the Effectiveness of Adaptive Step Size LMS Algorithms for the Use with VOIP Applications

Investigating the Effectiveness of Adaptive Step Size LMS Algorithms for the Use with VOIP Applications

Online secondary path modeling for active sound quality control systems

The algorithms for online secondary path modeling, with the injection of auxiliary noise, have always been applied to active noise control (ANC) systems to deal with the problem of when the secondary path is time-varying. The applications of ANC in vehicles for active sound quality control may pose new challenges for the current online secondary path modeling algorithms due to the disturbance noise signal from unconcerned frequencies. Therefore, this paper presents a framework of online secondary path modeling for active sound quality control systems. On the basis of current algorithms, an adaptive step size least mean square (ASS-LMS) algorithm of the secondary path modeling with interference signals is proposed. The ASS-LMS algorithm improves the stability of iteration without reducing convergence speed. Furthermore, a modified strategy for scheduling the auxiliary noise power is also proposed. The proposed modification reduces the degradation of the sound quality control performance caused by auxiliary noise. Simulations results are provided to verify the effectiveness of the algorithms proposed in this paper.

Adaptive step size LMS improves ECG detection during MRI at 1.5 T and 3 T

We describe a new real-time filter to reduce artefacts on electrocardiogram (ECG) due to magnetic field gradients during MRI. The proposed filter is a Least Mean Square (LMS) filter able to continuously adapt its step size according to the gradient signal of the ongoing MRI acquisition. We implemented this filter and compared it, within two databases (at 1.5 T and 3 T) with over 6000 QRS complexes, to five real-time filtering strategies (no filter, low pass filter, standard LMS, and two other filters optimized within the databases: optimized LMS, and optimized Kalman filter) The energy of the remaining noise was significantly reduced (26% vs 68%, p < 0.001) with the new filter vs standard LMS. The detection error of our ventricular complex (QRS) detector was: 11% with our method vs 25% with raw ECG, 35% with low pass filter, 17% with standard LMS, 12% with optimized Kalman filter, and 11% with optimized LMS filter ( Fig. 1 ). The adaptive step size LMS improves ECG denoising during MRI. QRS detection has the same F1 score with this filter than with filters optimized within the database.

Adaptive step size LMS improves ECG detection during MRI at 1.5 and 3T.

We describe a new real-time filter to reduce artefacts on electrocardiogram (ECG) due to magnetic field gradients during MRI. The proposed filter is a least mean square (LMS) filter able to continuously adapt its step size according to the gradient signal of the ongoing MRI acquisition. We implemented this filter and compared it, within two databases (at 1.5 and 3T) with over 6000 QRS complexes, to five real-time filtering strategies (no filter, low pass filter, standard LMS, and two other filters optimized within the databases: optimized LMS, and optimized Kalman filter). The energy of the remaining noise was significantly reduced (26 vs. 68%, p<0.001) with the new filter vs. standard LMS. The detection error of our ventricular complex (QRS) detector was: 11% with our method vs. 25% with raw ECG, 35% with low pass filter, 17% with standard LMS, 12% with optimized Kalman filter, and 11% with optimized LMS filter. The adaptive step size LMS improves ECG denoising during MRI. QRS detection has the same F1 score with this filter than with filters optimized within the database.

Signed-gradient adaptive step size LMS algorithm for biomedical applications.

Gradient adaptive step size adaptive filters have been widely used to adapt different biomedical application environments and obtain useful life signals from serious ambient noise and interferences. In order to further improve the signal-to-noise ratio (SNR) of the life signals, this paper presents a class of signed-gradient adaptive step size least mean square (LMS) adaptive filters. The proposed algorithms introduce a sign function to replace the gradient of squared error in the step size updating process of the gradient adaptive step size LMS adaptive filters. The performance of both gradient and signed-gradient algorithms with dual adaptive filters is compared by extracting heartbeat signals from ambient noise in stethoscopes. Simulation results demonstrate that though the signed-gradient adaptive step size LMS algorithm converges at a slower rate at the early stage of iteration, it has a smaller mean squared error (MSE) at the stage of convergence, thus achieves a higher SNR.

A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter

The performance of pulse oximeters is highly influenced by motion artifacts (MAs) in photoplethysmographic (PPG) signals. In this paper, we propose a simple and efficient approach based on adaptive step-size least mean squares (AS-LMS) adaptive filter for reducing MA in corrupted PPG signals. The presented method is an extension to our prior work on efficient use of adaptive filters for reduction of MA in PPG signals. The novelty of the method lies in the fact that a synthetic noise reference signal for an adaptive filtering process, representing MA noise, is generated internally from the MA-corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Thus, the generated noise reference signal is then applied to the AS-LMS adaptive filter for artifact removal. While experimental results proved the efficacy of the proposed scheme, the merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal. In addition to arterial oxygen saturation estimation, the artifact reduction method facilitated the waveform contour analysis on artifact-reduced PPG, and the conventional parameters were evaluated for assessing the arterial stiffness.

Blind Frequency Offset Estimation Using Adaptive Step-Size LMS Algorithm for OFDM Communications

In this Letter, we propose a least mean square (LMS) with adaptive step-size (AS) algorithm for adaptive blind carrier frequency offset (CFO) estimation in the orthogonal frequency division multiplexing system. In conjunction with the closed-loop estimate structure, the proposed algorithm eliminates the inter-carrier interference caused by time varying CFO. To improve the convergence performance of the fixed step-size LMS estimator, the regular AS LMS algorithm offers faster convergence speed and more accuracy to the CFO estimate. Several computer simulation examples are presented for illustrating the effectiveness of the proposed algorithm.

Adaptive Step-size Constant Modulus Algorithm for blind multiuser detection in DS-CDMA systems

Blind Adaptive Step-size Constant Modulus Algorithm (AS-CMA) for multiuser detection in DS-CDMA systems is presented. It combines the CMA and the concept of variable step-size, uses a second LMS algorithm for the step size. It adjusts the step-size according to the minimum output-energy principle within a specified range, thus overcomes the problems of bad effect of fixed step-size LMS algorithm. Compared with Adaptive Step-size LMS (AS-LMS) algorithm, through simulations, this algorithm can adapt the changes of the environment, suppress multiple access interference in the dynamic environment and the stability of Signal to Interference Ratio (SIR) is superior to that of AS-LMS.