Improved hull walking algorithms for two-dimensional percolation are proposed. In these algorithms a walker explores the external perimeter of percolation clusters. With our modifications very large systems (size L) c...
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Improved hull walking algorithms for two-dimensional percolation are proposed. In these algorithms a walker explores the external perimeter of percolation clusters. With our modifications very large systems (size L) can be studied with finite and small memory requirement and in computation time tau approximately L7/4. Applications in determining spanning probabilities, continuum percolation, and percolation with nonuniform occupation probability arc pointed out.
This paper presents a method to reconstruct the nonuniform background for camera-based quantitative evaluation of thin-layer chromatography (TLC). After analyzing the concave distribution feature of illumination produ...
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This paper presents a method to reconstruct the nonuniform background for camera-based quantitative evaluation of thin-layer chromatography (TLC). After analyzing the concave distribution feature of illumination produced by a linear light source on a plane, the paper then makes use of the feature and convex hull algorithm to find points belonging to the background. After that, B-spline is employed to reconstruct the background. An experiment is also made to test the performance of the method, which shows that the correlation coefficient between the linear samples is 0.9949 after removing the estimated background. (c) 2006 Optical society of America.
A method of environment mapping using laser-based light detection and ranging （LIDAR） is proposed in this paper. This method not only has a good detection performance in a wide range of detection angles, but also fa...
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A method of environment mapping using laser-based light detection and ranging （LIDAR） is proposed in this paper. This method not only has a good detection performance in a wide range of detection angles, but also facilitates the detection of dynamic and hollowed-out obstacles. Essentially using this method, an improved clustering algorithm based on fast search and discovery of density peaks （CBFD） is presented to extract various obstacles in the environment map. By comparing with other cluster algorithms, CBFD can obtain a favorable number of clusterings automatically. Furthermore, the experiments show that CBFD is better and more robust in functionality and performance than the K-means and iterative self-organizing data analysis techniques algorithm （ISODATA）.
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