Narrow Results

Evaluating the intersection of two rational parametric surfaces is a recurring operation in solid modeling. However, surface intersection is not an easy problem and continues to be an active topic of research. The main reason lies in the fact that any good surface intersection technique has to balance three conflicting goals of accuracy, robustness and efficiency. In this paper, we formulate the problems of curve and surface intersections using algebraic sets in a higher dimensional space. Using results from Elimination theory, we project the algebraic set to a lower dimensional space. The projected set can be expressed as a matrix determinant. The matrix itself, rather than its symbolic determinant, is used as the representation for the algebraic set in the lower dimensional space. This is a much more compact and efficient representation. Given such a representation, we perform matrix operations for evaluation and use results from linear algebra for geometric operations on the intersection curve. Most of the operations involve evaluating numeric determinants and computing the rank, kernel and eigenvalues of matrices. The accuracy of such operations can be improved by pivoting or other numerical techniques. We use this representation for inversion operation, computing the intersection of curves and surfaces and tracing the intersection curve of two surfaces in lower dimension.

Generalized Hypercube-Connected-Cycles (GHCC), is a challenging interconnection network, proposed earlier in the literature. In this paper, we discuss how some important, useful algorithms, like, matrix transpose, matrix multiplication and sorting can efficiently be implemented on GHCC. Matrix transpose and matrix-by-matrix multiplication of matrices of order n × n, , takes O(l) and time, respectively, on GHCC(l,m), with lm^l processors. Using the same number of processors, a list of m^l numbers can be sorted in O(l²log³ m) time.