In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar ...
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In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the xy, yz, and zx planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method.
This paper is devoted to an octagonal cut algorithm and a hexadecagonal cut algorithm for finding the convex hull of n points in R-2, where some octagon and hexadecagon are used for discarding most of the given points...
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This paper is devoted to an octagonal cut algorithm and a hexadecagonal cut algorithm for finding the convex hull of n points in R-2, where some octagon and hexadecagon are used for discarding most of the given points interior to these polygons. In this way, the scope of the given problem can be reduced significantly. In particular, the convex hull of n points distributed b(least)-b(most)-boundedly in some rectangle can be determined with the complexity O (n). Computational experiments demonstrate that our algorithms outperform the quickhull algorithm by a significant factor of up to 47 times when applied to the tested data sets. The speedup compared to the CGAL library is even more pronounced.
The mechanical response of granular materials has been investigated widely using discontinuous modeling, such as the discrete-element method (DEM). Contact detection and contact resolution have been critical issues wh...
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The mechanical response of granular materials has been investigated widely using discontinuous modeling, such as the discrete-element method (DEM). Contact detection and contact resolution have been critical issues when modeling multiple body contacts, especially for arbitrary polyhedral blocks. In this study, the contact overlap calculation algorithms, including polyhedron-polyhedron and polyhedron-boundary contact, were developed to calculate the contact characteristics. For polyhedron-polyhedron contact, the contact overlap volume algorithm is developed based on the geometric dualization theory. The Gilbert-Johnson-Keerthi (GJK) and quickhull algorithms are used to calculate the overlap polyhedron. The contact characteristics, such as normal direction (n), contact area (a), and penetration depth (u(n)) could be extracted from the contact overlap volume. For polyhedron-boundary contact, a novel and effective algorithm is presented, where the polyhedron-boundary contact is transformed into polyhedron-triangle contact. Then, two types of benchmarks are used to verify the previously mentioned algorithms, which demonstrated that the algorithms could handle the complicated contact types and maintained contact continuity even from face to edge contact. As a complex benchmark, the failure process in a masonry structure is simulated and compared with the model test.
The quickhull algorithm for determining the convex hull of a finite set of points was independently conducted by Eddy in 1977 and Bykat in 1978. Inspired by the idea of this algorithm, we present a new efficient algor...
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The quickhull algorithm for determining the convex hull of a finite set of points was independently conducted by Eddy in 1977 and Bykat in 1978. Inspired by the idea of this algorithm, we present a new efficient algorithm, for determining the connected orthogonal convex hull of a finite set of points through extreme points of the hull, that still keeps advantages of the quickhull algorithm. Consequently, our algorithm runs faster than the others (the algorithms introduced by Montuno and Fournier in 1982 and by An, Huyen and Le in 2020). We also show that the expected complexity of the algorithm is O(n log n), where n is the number of points. (C) 2022 Elsevier Inc. All rights reserved.
In this paper, we present some modifications of the quickhull algorithm finding the convex hull of a finite set of planar points. The underlying ideas are to reduce the number of the fundamental operations of the Quic...
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In this paper, we present some modifications of the quickhull algorithm finding the convex hull of a finite set of planar points. The underlying ideas are to reduce the number of the fundamental operations of the quickhull algorithm calculating orientation and to decrease the size of input data by preprocessing and separating the original problem into smaller problems. Our numerical experiments show that the modifications reduce the computation time of the original quickhull algorithm by a factor of three on average.
An important problem in distance geometry is of determining the position of an unknown point in a given convex set such that its longest distance to a set of finite number of points is shortest. In this paper we prese...
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An important problem in distance geometry is of determining the position of an unknown point in a given convex set such that its longest distance to a set of finite number of points is shortest. In this paper we present an algorithm based on subgradient method and convex hull computation for solving this problem. A recent improvement of quickhull algorithm for computing the convex hull of a finite set of planar points is applied to fasten up the computations in our numerical experiments.
In this paper, a new algorithm based on the quickhull algorithm is proposed to find convex hulls for 3-D objects using neighbor trees. The neighbor tree is the data structure by which all visible facets to the selecte...
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In this paper, a new algorithm based on the quickhull algorithm is proposed to find convex hulls for 3-D objects using neighbor trees. The neighbor tree is the data structure by which all visible facets to the selected furthest outer point can be found. The neighboring sequence of ridges on the outer boundary of all visible facets also can be found directly from the neighbor tree. This new algorithm is twice as efficient as Barber's algorithm.
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