Results 1 
3 of
3
Computing Nearest Neighbors In Real Time
 Proceedings of the Fifteenth Conference on Parallel and Distributed Computing and Systems, Marina Del Rey
, 2001
"... The nearestneighbor method can successfully be applied to correct possible errors induced into bit strings transmitted over noisy communication channels or to classify samples into a predefined set of categories. These two applications are investigated under realtime constraints, when the deadl ..."
Abstract

Cited by 4 (4 self)
 Add to MetaCart
The nearestneighbor method can successfully be applied to correct possible errors induced into bit strings transmitted over noisy communication channels or to classify samples into a predefined set of categories. These two applications are investigated under realtime constraints, when the deadlines imposed can dramatically alter the quality of the solution unless a parallel model of computation (in these cases, a linear array of processors) is used. We also study a class of realtime computations, referred to as reactive realtime systems, that are particularly sensitive to the first time constraint imposed. 1
On the importance of parallelism for quantum computation and the concept of a universal computer
 Proceedings of the Fourth International Conference on Unconventional Computation
, 2005
"... COMPUTER \Lambda ..."
INHERENTLY PARALLEL GEOMETRIC COMPUTATIONS
 PARALLEL PROCESSING LETTERS
, 2004
"... A new computational paradigm is described which offers the possibility of superlinear (and sometimes unbounded) speedup, when parallel computation is used. The computations involved are subject only to given mathematical constraints and hence do not depend on external circumstances to achieve superl ..."
Abstract
 Add to MetaCart
A new computational paradigm is described which offers the possibility of superlinear (and sometimes unbounded) speedup, when parallel computation is used. The computations involved are subject only to given mathematical constraints and hence do not depend on external circumstances to achieve superlinear performance. The focus here is on geometric transformations. Given a geometric object A with some property, it is required to transform A into another object B which enjoys the same property. If the transformation requires several steps, each resulting in an intermediate object, then each of these intermediate objects must also obey the same property. We show that in transforming one triangulation of a polygon into another, a parallel algorithm achieves a superlinear speedup. In the case where a convex decomposition of a set of points is to be transformed, the improvement in performance is unbounded, meaning that a parallel algorithm succeeds in solving the problem as posed, while all sequential algorithms fail.