Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Theory and Algorithms
Degree Constrained Triangulation, Roshan Gyawali
Degree Constrained Triangulation, Roshan Gyawali
UNLV Theses, Dissertations, Professional Papers, and Capstones
Triangulation of simple polygons or sets of points in two dimensions is a widely investigated problem in computational geometry. Some researchers have considered variations of triangulation problems that include minimum weight triangulation, de-launay triangulation and triangulation refinement. In this thesis we consider a constrained version of the triangulation problem that asks for triangulating a given domain (polygon or point sites) so that the resulting triangulation has an increased number of even degree vertices. This problem is called Degree Constrained Triangulation (DCT). We propose four algorithms to solve DCT problems. We also present experimental results based on the implementation of the …
Improved Algorithms For Ear-Clipping Triangulation, Bartosz Kajak
Improved Algorithms For Ear-Clipping Triangulation, Bartosz Kajak
UNLV Theses, Dissertations, Professional Papers, and Capstones
We consider the problem of improving ear-slicing algorithm for triangulating a simple polygon. We propose two variations of ear-slicing technique for generating “good-quality” triangulation. The first approach is based on searching for the best triangle along the boundary. The second approach considers polygon partitioning on a pre-process before applying the ear-slicing. Experimental investigation reveals that both approaches yield better quality triangulation than the standard ear-slicing method.
Implementation Of Hidden Semi-Markov Models, Nagendra Abhinav Dasu
Implementation Of Hidden Semi-Markov Models, Nagendra Abhinav Dasu
UNLV Theses, Dissertations, Professional Papers, and Capstones
One of the most frequently used concepts applied to a variety of engineering and scientific studies over the recent years is that of a Hidden Markov Model (HMM). The Hidden semi-Markov model (HsMM) is contrived in such a way that it does not make any premise of constant or geometric distributions of a state duration. In other words, it allows the stochastic process to be a semi-Markov chain. Each state can have a collection of observations and the duration of each state is a variable. This allows the HsMM to be used extensively over a range of applications. Some of …