A genetic algorithm for the minimum weight triangulation

In this paper, a new method for the minimum weight triangulation of points on a plane, called genetic minimum weight triangulation (GMWT), is presented based on the rationale of genetic algorithms. Polygon crossover and its algorithm for triangulations are proposed. New adaptive genetic operators, or adaptive crossover and mutation operators, are introduced. It is shown that the new method for the minimum weight triangulation can obtain more optimal results of triangulations than the greedy algorithm.published_or_final_versio

Hydrogen : a metastable donor in TiO₂ single crystals

2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Surface segmentation for improved remeshing

Many remeshing techniques sample the input surface in a meaningful way and then triangulate the samples to produce an output triangulated mesh. One class of methods samples in a parametrization of the surface. Another class samples directly on the surface. These latter methods must have sufficient density of samples to achieve outputs that are homeomorphic to the input. In many datasets samples must be very dense even in some nearly planar regions due to small local feature size. We present an isotropic remeshing algorithm called κCVT that achieves topological correctness while sampling sparsely in all flat regions, regardless of local feature size. This is accomplished by segmenting the surface, remeshing the segmented subsurfaces individually and then stitching them back together. We show that κCVT produces quality meshes using fewer triangles than other methods. The output quality meshes are both homeomorphic and geometrically close to the input surface.postprin

Optimized synthesis of art patterns and layered textures

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Visibility-based coverage of mobile sensors in non-convex domains

The area coverage problem of mobile sensor networks has attracted much attention recently, as mobile sensors find many important applications in remote and hostile environments. However, the deployment of mobile sensors in a non-convex domain is nontrivial due to the more general shape of the domain and the attenuation of sensing capabilities caused by the boundary walls or obstacles. We consider the problem of exploration and coverage by mobile sensors in an unknown non-convex domain. We propose the definition of 'visibility-based Voronoi diagram' and extend the continuous-time Lloyd's method, which only works for convex domains, to deploy the mobile sensors in the unknown environments in a distributed manner. Our simulations show the effectiveness of the proposed algorithms. © 2011 IEEE.published_or_final_versionThe 8th International Symposium on Voronoi Diagrams in Science and Engineering (ISVD2011), Qingdao, China, 28-30 June 2011. In Proceedings of the 8th ISVD, 2011, p. 105-11

CVT-based 2D motion planning with maximal clearance

Maximal clearance is an important property that is highly desirable in multi-agent motion planning. However, it is also inherently difficult to attain. We propose a novel approach to achieve maximal clearance by exploiting the ability of evenly distributing a set of points by a centroidal Voronoi tessellation (CVT). We adapt the CVT framework to multi-agent motion planning by adding an extra time dimension and optimize the trajectories of the agents in the augmented domain. As an optimization framework, our method can work naturally on complex regions. We demonstrate the effectiveness of our algorithm in achieving maximal clearance in motion planning with some examples.published_or_final_versionThe 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9-13 May 2011. In Proceedings of the IEEE-ICRA, 2011, p. 2281-228

Planar hexagonal meshing for architecture

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Hydrogen-related dynamic dielectric behavior of barium titanate single crystals

2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Global Optimization of Centroidal Voronoi Tessellation with Monte Carlo Approach

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Autonomous deployment for load balancing k-surface coverage in sensor networks

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