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72 results on '"Cui, Zongmin"'

61. Underwater optical communication performance under the influence of the eddy diffusivity ratio

Author

Yue, Peng, Wu, Mengjie, Yi, Xiang, Cui, Zongmin, and Luan, Xiaohui

Abstract

Generally, the eddy diffusivity ratio of salinity to temperature is not equal to one, especially in the upper and mid-to-high latitude ocean. In this paper, the performance of practical underwater optical communication (UOC) systems is investigated by considering the influence of the eddy diffusivity ratio other than one. Specifically, using the Rytov theory in weak turbulence, the aperture-averaged scintillation indices for the plane and spherical waves are derived. The typical performance criteria including the mean signal-to-noise ratio and bit error rate are further studied. It is found that the scintillation index and the associated UOC performance differ between the cases of the unity and variable eddy diffusivity ratio. Such a difference becomes smaller as the receiving aperture increases.

Published
2019

69. Remarkable local resampling based on particle filter for visual tracking.

Author

Zhao, Zhiqiang, Wang, Tianjiang, Liu, Fang, Choe, Gwangmin, Yuan, Caihong, and Cui, Zongmin

Subjects
TRACKING & trailing, RESAMPLING (Statistics), NONPARAMETRIC statistics, MONTE Carlo method, NONLINEAR functions
Abstract

Generally, particle filters need a large number of particles to approximate the posterior for the purpose of ideal effect. Previous methods extract remarkable particles from the particles at time t-1 by nonlinear function. Those methods use the remarkable particles to reduce the number of particles and improve the accuracy of particle filter. However, the nonlinear function extracts the remarkable particles, which will weaken or even ignore useful remarkable local particles. Thus this paper presents a new resampling scheme to extract remarkable local particles. We propose a weight threshold and a distance threshold to extract remarkable local particles from particles at time t-1. Meanwhile, we use these remarkable local particles to track the target analytically. Besides, we propose a global transition model to improve the accuracy of the particle filter. Based on remarkable local resampling scheme and the global transition model, we propose a new framework of particle filter. Finally, experiments show that our framework has higher efficiency than previous methods in the case of fewer particles. [ABSTRACT FROM AUTHOR]

Published
2017
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