A sampling theorem is developed to reduce integration error in matrix-vector and linear multiplexing processors that perform discrete versions of continuous linear operations. By simply filtering the operation kernel before sampling, one can perform integration-error-free processing on inputs sampled at their Nyquist rate. Example applications to Laplace and Hilbert transformation are presented.

In this work, we propose computational models and algorithms for point cloud registration with non-rigid transformation. First, point clouds sampled from manifolds originally embedded in some Euclidean space RD are transformed to new point clouds embedded in Rn by Laplace-Beltrami(LB) eigenmap using the n leading eigenvalues and corresponding eigenfunctions of LB operator defined intrinsically ...