Abstract: We have devised and implemented a tessellation algorithm with two typical space filling curves, Hilbert curve and Sierpinski curve, and applied the tessellation algorithm into the construction of 3D fractal models. In this paper we shall describe our tessellation algorithm with Hilbert curve and Sierpinski curve, and provide the method how to map the two tiled planes on the surface of models. Some result of verification experiments will be provided also.

• Publication Date: 13-Jan-2019
• DOI: 10.15224/978-1-63248-165-8-06
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Abstract: Orthogonal matching pursuit (OMP) is a commonly used algorithm in compressed sensing (CS) for estimating a sparse vector/signal x from linear measurements y m , where m n . There are two generally stopping criteria adopted in the iterative OMP. One, assuming the number of nonzero entries of the sparse vector x is known, stop the algorithm after exactly K iterations. The other halt the pursuit if the strength of the residual is smaller than some threshold. These two criteria respectively rely on certain knowledge about the signal and the environment/noise. We propose a normalized residual strength based stopping criterion, which can be employed without the information mentioned above. Numerical results show that under some circumstances, the proposed criterion leads to a smaller normalized signal reconstruction error as compared to that achieved by OMP with exact K iterations and the conventional residual strength based stopping criterion.

• Publication Date: 21-Jul-2019
• DOI: 10.15224/978-1-63248-172-6-01
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