In this study, the authors propose a closed-form time-of-arrival source localisation method and justify the employment of the invariance property of the maximum likelihood (ML) estimator in the source localisation con...
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In this study, the authors propose a closed-form time-of-arrival source localisation method and justify the employment of the invariance property of the maximum likelihood (ML) estimator in the source localisation context with multiple samples. The magnitude of the bias of the proposed sample vector function (the statistic that consists of the multiple observations set) using the invariance property of the ML estimator is smaller than that based on the sample mean. Therefore, the mean squared error (mse) of the weighted least squares estimate using the proposed sample vector function is smaller than that based on the sample mean when the variances of both sample vector functions are the same. Furthermore, the authors investigate a situation in which sensors have erroneous position information. The simulation results show that the averaged mse performance of the proposed method is superior to that of the existing methods irrespective of the number of samples.
Many algorithms used for the analysis of physiological signals include hyper-parameters that must be selected by the investigator. The ultimate choice of these parameter values can have a dramatic impact on the perfor...
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ISBN:
(纸本)9781479943463
Many algorithms used for the analysis of physiological signals include hyper-parameters that must be selected by the investigator. The ultimate choice of these parameter values can have a dramatic impact on the performance of the approach. Addressing this issue often requires investigators to manually tune parameters for their particular data-set. In this study, we illustrate the importance of global optimization techniques for the automated determination of parameter values in the multi-scale entropy (mse) algorithm. Importantly, we demonstrate that global optimization techniques provide an effective, and automated framework for tuning parameters of such algorithms, and easily improve upon the default settings selected by experts.
作者：
Lee, JSKo, JHKim, ESKimpo Coll
Div Elect & Informat Sci Kimpo Si Kyounggi Do South Korea Kwangwoon Univ
Sch Elect Engn Natl Res Lab Dimens Media 3 Nowon Gu Seoul 139701 South Korea
In this paper, we proposed a new adaptive stereo object tracking system that can control the convergence angle and pan/tilt of cameras by using optical binary phase extraction joint transform correlator and can extrac...
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In this paper, we proposed a new adaptive stereo object tracking system that can control the convergence angle and pan/tilt of cameras by using optical binary phase extraction joint transform correlator and can extract the tracking object from a complex background and foreground noises by using the block-based mean square error algorithm. From the experimental results, the proposed stereo tracking system is found to track the object adaptively under the complex circumstances and changing background noises and the possibility of real-time implementation of the proposed system by using the optical system is also suggested. (C) 2001 Published by Elsevier Science B.V.
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