nazanin hadiniya | The University of Birjand (original) (raw)
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Papers by nazanin hadiniya
arXiv (Cornell University), Aug 12, 2023
Delaunay Triangulation(DT) is one of the important geometric problems that is used in various bra... more Delaunay Triangulation(DT) is one of the important geometric problems that is used in various branches of knowledge such as computer vision, terrain modeling, spatial clustering and networking. Kinetic data structures has become very important in computational geometry for dealing with moving objects. However, when dealing with moving points, maintaining a dynamically changing Delaunay triangulation can be challenging. So, In this case, we have to update triangulation repeatedly. If the points move so far, it's better to rebuild the triangulation. One approach to handle moving points is to use an incremental algorithm. For the case that points move slowly, we can give a faster algorithm than rebuilding. Furthermore, sequential algorithms can be computationally expensive for large datasets. So one way to compute as fast as possible is parallelism. In this paper, we propose a parallel algorithm for moving points. we propose an algorithm that divides datasets into equal partitions and give every partition to one block. Each block satisfay the Delaunay constraints after each time step and uses delete and insert algorithms to do this. We show this algorithm works faster than serial algorithms.
2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)
arXiv (Cornell University), Aug 12, 2023
Delaunay Triangulation(DT) is one of the important geometric problems that is used in various bra... more Delaunay Triangulation(DT) is one of the important geometric problems that is used in various branches of knowledge such as computer vision, terrain modeling, spatial clustering and networking. Kinetic data structures has become very important in computational geometry for dealing with moving objects. However, when dealing with moving points, maintaining a dynamically changing Delaunay triangulation can be challenging. So, In this case, we have to update triangulation repeatedly. If the points move so far, it's better to rebuild the triangulation. One approach to handle moving points is to use an incremental algorithm. For the case that points move slowly, we can give a faster algorithm than rebuilding. Furthermore, sequential algorithms can be computationally expensive for large datasets. So one way to compute as fast as possible is parallelism. In this paper, we propose a parallel algorithm for moving points. we propose an algorithm that divides datasets into equal partitions and give every partition to one block. Each block satisfay the Delaunay constraints after each time step and uses delete and insert algorithms to do this. We show this algorithm works faster than serial algorithms.
2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)