We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
Recently, the problem of intrinsic shape matching has received a lot of attention. A number of algorithms have been proposed, among which random-sampling-based techniques have bee...
Art Tevs, Alexander Berner, Michael Wand, Ivo Ihrk...
Cost pressure is driving vendors of safety-critical systems to integrate previously distributed systems. One natural approach we have previous introduced is On-Demand Redundancy (...
Brett H. Meyer, Benton H. Calhoun, John Lach, Kevi...