WLMS Algorithm Research Articles

A robust adaptive hybrid feedback cancellation scheme for hearing aids in the presence of outliers

It has been demonstrated that the Filtered-x Wilcoxon LMS (FxWLMS) based adaptive filter mitigates the effect of the outliers acquired by the microphone signal of hearing aids by minimizing the Wilcoxon norm and hence shows better cancellation performance than the existing Filtered-x LMS (FxLMS) algorithm. The prediction error method based adaptive feedback canceller (PEMAFC) reduces the bias present in the estimate of the feedback path due to the continuous adaptive filtering (CAF). However, the impulse response of the measured feedback path is close to zero for the first many samples due to the delay introduced by ADC converters and then contains few significant values, which results in slow convergence rate when an adaptive filter is used to model the same. To overcome this limitation, we propose a proportionate normalized WLMS (PNWLMS) algorithm based PEMAFC (P-PNWLMS) for feedback cancellation in hearing aid in the presence of outliers. Further, with an objective to improve the convergence rate and performance accuracy simultaneously, this paper proposes a novel convex PNWLMS (CPNWLMS) algorithm which incorporates convex combination of PNWLMS and WLMS algorithms. The weight update equations are derived for PEMAFC trained by PNWLMS (P-PNWLMS) and CPNWLMS (P-CPNWLMS) algorithms respectively. The results of the simulation study show improved performance of the proposed CPNWLMS based adaptive filter over its component filters.

Evaluating and comparing algorithms for respiratory motion prediction

In robotic radiosurgery, it is necessary to compensate for systematic latencies arising from target tracking and mechanical constraints. This compensation is usually achieved by means of an algorithm which computes the future target position. In most scientific works on respiratory motion prediction, only one or two algorithms are evaluated on a limited amount of very short motion traces. The purpose of this work is to gain more insight into the real world capabilities of respiratory motion prediction methods by evaluating many algorithms on an unprecedented amount of data. We have evaluated six algorithms, the normalized least mean squares (nLMS), recursive least squares (RLS), multi-step linear methods (MULIN), wavelet-based multiscale autoregression (wLMS), extended Kalman filtering, and ε-support vector regression (SVRpred) methods, on an extensive database of 304 respiratory motion traces. The traces were collected during treatment with the CyberKnife (Accuray, Inc., Sunnyvale, CA, USA) and feature an average length of 71 min. Evaluation was done using a graphical prediction toolkit, which is available to the general public, as is the data we used. The experiments show that the nLMS algorithm—which is one of the algorithms currently used in the CyberKnife—is outperformed by all other methods. This is especially true in the case of the wLMS, the SVRpred, and the MULIN algorithms, which perform much better. The nLMS algorithm produces a relative root mean square (RMS) error of 75% or less (i.e., a reduction in error of 25% or more when compared to not doing prediction) in only 38% of the test cases, whereas the MULIN and SVRpred methods reach this level in more than 77%, the wLMS algorithm in more than 84% of the test cases. Our work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation. Additionally, we have seen that the structure of a patient’s respiratory motion trace has strong influence on the outcome of prediction. Further work is needed to determine a priori the suitability of an individual’s respiratory behaviour to motion prediction.

A Comparative Analysis for WLMS Algorithm in System Identification

System Identification is an important area of research in signal processing to design an unknown system. The identification task covers almost all the areas of engineering application such as problem of building models of systems. When insignificant prior information is available and system's properties are known up to a few parameters, identification is most useful. This paper approaches the system identification problem using WLMS Algorithm (Wilcoxon based LMS) in presence of outliers. Also the result is compared with the conventional LMS. In addition to it, the error is analyzed for the deviation factor at the time of analysis. The result shows an excellent performance with minimum training samples.

OC-0023 GATED MRI-LINAC (MRL) TREATMENT FOR KIDNEYS: INTER BREATH HOLD VARIATIONS

Conclusions: In summary all algorithms predict movement with high accuracy and lead to a significa compared to no prediction. However the evaluation cle the wLMS algorithm is superior for both patient categ to the MULIN and the currently implemented CyberK With the new wLMS algorithm the treatment acc smoothness of the robot motion control can [1] Ernst F (2007), Prediction of Respiratory Motion based Multiscale Autoregression, MICCAI 2007. [2 ]Ernst F (2008), Predicting respiratory motion sig guided radiotherapy usingmulti-step linearmethods Comput Assist Radiol Surg

Predicting the outcome of respiratory motion prediction

Prediction of respiratory motion traces has become an important research topic. Especially for motion compensated radiotherapy, compensation of the latencies arising from mechanical constraints and signal processing is necessary. In recent years, many algorithms have been developed and evaluated. It is, however, still unclear how well a specific patient will be suited to motion prediction before the treatment actually starts. In this work, we have analyzed 304 respiratory motion traces with an average duration of 71 min. A total of 21 features characterizing these signals (12 from the frequency domain and 9 from the time domain) have been determined for each motion trace. The correlation between these features and the overall prediction quality for three different algorithms (based on wavelet-based multiscale autoregression, support vector regression, and linear expansion of the prediction error) has been analyzed and six dominant features have been identified (three each from the time and frequency domains). Additionally, the optimized results of the multistep-linear method (MULIN) prediction algorithm on the first 300 s of motion data have been used as a seventh, independent feature. Assessing the prediction algorithms' quality was done by calculating the relative root mean squared (RMS(rel)) error, i.e., the ratio between the RMS error of the prediction output and the RMS error of the delayed signal (the RMS error obtained when doing no prediction). Then, for each algorithm, the signals themselves were grouped into four classes according to the quality of prediction: relative RMS less than 0.8 (C1), between 0.8 and 0.9 (C2), between 0.9 and 1.0 (C3), and over 1.0 (C4). The goal of this work is to identify, prior to treatment, those patients whose respiratory behavior indicates probable (RMS(rel) ≥ 0.9) or certain (RMS(rel) ≥ 1.0) failure of respiratory motion prediction. Consequently, all signals from C4 must be identified and rejected and no signals from C1 may be falsely rejected. The restriction on C2 and C3 is slightly weaker: C2 are those signals that should be kept and C3 are those signals that should be rejected. Rejecting all signals from C4 and C3, keeping as many signals from C1 and as few from C2 as possible, has been achieved for the wLMS algorithm when using six feature pairs and the result of prediction on the short signal. Here, the false rejectance rate for C1 was less than 13% and the false acceptance rate for C2 was 15%. For the SVRpred and MULIN algorithms, the results are somewhat worse: in both cases, signals from C3 were falsely accepted (25.0% and 14.3%, respectively) but all signals from C4 were rejected. The false rejectance rate for C1 was 11.4% (MULIN) and 26.3% (SVRpred). In general, it has been shown that pretreatment classification of the quality of respiratory motion prediction is possible and that signals with high relative RMS error can be identified with great reliability. This is especially true for the wLMS algorithm, which has also been identified as the most precise and robust of the presented methods.

Forecasting pulsatory motion for non-invasive cardiac radiosurgery: an analysis of algorithms from respiratory motion prediction

Recently, radiosurgical treatment of cardiac arrhythmia, especially atrial fibrillation, has been proposed. Using the CyberKnife, focussed radiation will be used to create ablation lines on the beating heart to block unwanted electrical activity. Since this procedure requires high accuracy, the inevitable latency of the system (i.e., the robotic manipulator following the motion of the heart) has to be compensated for. We examine the applicability of prediction algorithms developed for respiratory motion prediction to the prediction of pulsatory motion. We evaluated the MULIN, nLMS, wLMS, SVRpred and EKF algorithms. The test data used has been recorded using external infrared position sensors, 3D ultrasound and the NavX catheter systems. With this data, we have shown that the error from latency can be reduced by at least 10 and as much as 75% (44% average), depending on the type of signal. It has also been shown that, although the SVRpred algorithm was successful in most cases, it was outperformed by the simple nLMS algorithm, the EKF or the wLMS algorithm in a number of cases. We have shown that prediction of cardiac motion is possible and that the algorithms known from respiratory motion prediction are applicable. Since pulsation is more regular than respiration, more research will have to be done to improve frequency-tracking algorithms, like the EKF method, which performed better than expected from their behaviour on respiratory motion traces.

Forecasting respiratory motion with accurate online support vector regression (SVRpred)

To accurately deliver radiation in image-guided robotic radiosurgery, highly precise prediction algorithms are required. A new prediction method is presented and evaluated. SVRpred, a new prediction method based on support vector regression (SVR), has been developed and tested. Computer-generated data mimicking human respiratory motion with a prediction horizon of 150 ms was used for lab tests. The algorithm was subsequently evaluated on a respiratory motion signal recorded during actual radiosurgical treatment using the CyberKnife. The algorithm's performance was compared to the MULIN prediction methods and Wavelet-based multi scale autoregression (wLMS). The SVRpred algorithm clearly outperformed both the MULIN and the wLMS algorithms on both real (by 15 and 16 percentage points, respectively) and noise-corrupted simulated data (by 13 and 48 percentage points, respectively). Only on noise-free artificial data, the SVRpred algorithm did perform as well as the MULIN algorithms but not as well as the wLMS algorithm. This new algorithm is a feasible tool for the prediction of human respiratory motion signals significantly outperforming previous algorithms. The only drawback is the high computational complexity and the resulting slow prediction speed. High performance computers will be needed to use the algorithm in live prediction of signals sampled at a high resolution.