Grid computing research focuses on building large-scale computing infrastructures by linking computing facilities at many distributed locations. Significant effort has been spent in the design and implementation of middleware for enabling computational Grids. These software packages have been successfully deployed and it is now possible to build clusters beyond the boundaries of a single local ...

Dynamic load balancing is considered in the context of adaptive multilevel methods for partial diierential equations on distributed memory multiproces-sors. An approach that periodically repartitions the grid is taken. The important properties of a partitioning algorithm are presented and discussed in this context. A partitioning algorithm based on the reenement tree of the adaptive grid is pre...

The purpose of this study is to incorporate adaptive ontology into ubiquitous learning grid to achieve seamless learning environment. Ubiquitous learning grid uses ubiquitous computing environment to infer and determine the most adaptive learning contents and procedures in anytime, any place and with any device. To achieve the goal, an ontology-based ubiquitous learning grid (OULG) was proposed...

In this paper we consider the adaptive finite element solution of a general class of variational problems using a combination of node insertion, node movement and edge swapping. The adaptive strategy that is proposed is based upon the construction of a hierarchy of locally optimal meshes starting with a coarse grid for which the location and connectivity of the nodes is optimized. This grid is ...

This paper suggests a cooperative spectrum sensing using dynamic double thresholds energy detection and adaptive grid search to obtain the highest probability detection. The proposed double thresholds are adaptive to noise fluctuation. A new fusion method combined of weighting and voting is used in cooperative sensing. Further, in order to obtain the best sensing performance, we firstly use ada...

Adaptive algorithms are an important technique to achieve portable high performance. They choose among solution methods and optimizations according to expected performance on a particular machine. Grid environments make the adaptation problem harder, because the optimal decision may change across runs and even during runtime. Therefore, the performance model used by an adaptive algorithm must b...

We will present an approach to numerical simulation on recursively structured adaptive discretisation grids. The respective grid generation process is based on recursive bisection of triangles along marked edges. The resulting refinement tree is sequentialised according to a Sierpinski space-filling curve, which leads to both minimal memory requirements and inherently cache-efficient processing...

Cloth has traditionally been simulated on a static mesh composed of a uniform grid of nodes. Since a coarse mesh cannot generally capture fine wrinkles, realistic cloth simulation relies on a fine grid of nodes that is expensive to simulate. However, fine wrinkles are often localized to relatively small portions of a mesh. We introduce an adaptive method that locally refines cloth where wrinkle...

An adaptive hybrid prismatic/tetrahedral grid generation method is described and applied to complex geometries for diverse industrial applications. The method consists of using a surface mesh generator to triangulate the geometry to be modeled. The volume mesh is then created using prismatic and tetrahedral elements. The prisms cover the region close to each body's surface for better resolution...

In this paper we discuss a two-dimensional adaptive grid method that is based on a tensor-product approach. Adaptive grids are a commonly used tool for increasing the accuracy and reducing computational costs when solving both Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs). A traditional and widely used form of adaptivity is the concept of equidistribution, whi...