نتایج جستجو برای: marching

تعداد نتایج: 2109  

Journal: :Progress In Electromagnetics Research C 2016

Journal: :ATZelectronics worldwide 2021

2008
P. A. Armitage S. Munoz Maniega M. E. Bastin

Introduction: Neighborhood tractography offers the potential to improve the reliability and reproducibility of automated white matter pathway extraction and associated measurements of connectivity and tract integrity. Neighborhood tractography frameworks have been proposed for local tractography methods, particularly those based on streamline, or probabilistic streamline algorithms [1,2]. In th...

Journal: :SIAM J. Scientific Computing 2015
Pieter D. Boom David W. Zingg

This article extends the theory of classical finite-difference summation-by-parts (FD-SBP) timemarching methods to the generalized summation-by-parts (GSBP) framework. Dual-consistent GSBP time-marching methods are shown to retain: A and L-stability, as well as superconvergence of integral functionals when integrated with the quadrature associated with the discretization. This also implies that...

2007
Hamish A. Carr

Isosurfaces, one of the most fundamental volumetric visualization tools, are commonly rendered using the wellknown Marching Cubes cases that approximate contours of trilinearly-interpolated scalar fields. While a complete set of cases has recently been published by Nielson, the formal proof that these cases are the only ones possible and that they are topologically correct is difficult to follo...

2012
Daniel Jönsson Per Ganestam Michael C. Doggett Anders Ynnerman Timo Ropinski

A major challenge when designing general purpose graphics hardware is to allow efficient access to texture data. Although different rendering paradigms vary with respect to their data access patterns, there is no flexibility when it comes to data caching provided by the graphics architecture. In this paper we focus on volume ray-casting, and show the benefits of algorithm-aware data caching. Ou...

2008
Henrik R. Nagel

In this article is presented an efficient implementation of the Marching Cubes Algorithm using nVidia’s CUDA technology, which can handle datasets that are so large that they cannot be loaded into the working memory of the used computer. Two kinds of data are considered: a single 3D grid that is too large to fit in memory and many, temporal 3D grids that individually fit in memory, but combined...

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