Constraint violations in stochastically generated data: Detection and correction strategies

We consider the generation of stochastic data under constraints where the constraints can be expressed in terms of different parameter sets. Obviously, the constraints and the generated data must remain the same over each parameter set. Otherwise, the parameters and/or the generated data would be inconsistent. We consider how to avoid or detect and then correct such inconsistencies under three proposed classifications: (1) data versus characteristic parameters, (2) macro- versus microconstraint scopes, and (3) intra- versus intervariable relationships. We propose several strategies and a heuristic for generating consistent stochastic data. Experimental results show that these strategies and heuristic generate more consistent data than the traditional discard-and-replace methods. Since generating stochastic data under constraints is a very common practice in many areas, the proposed strategies may have wide-ranging applicability.Scopu

Flexible parallel implementation of logic gates using chaotic elements

We demonstrate the basic principles for the direct and flexible implementation of all basic logical operations utilizing low dimensional chaos. Then we generalize the concept to high dimensional chaotic systems, and show the parallelism inherent in such systems. As a case study we implement the proposed parallel computing architecture to obtain parallelized bit-by-bit addition with a two-dimensional chaotic neuronal and a three-dimensional chaotic laser model

Parallel computing with extended dynamical systems

We discuss the scope of parallelism based on extended dynamical systems, in particular, arrays of chaotic elements. As a case study we demonstrate the rapid solution of the Deutsch-Jozsa problem, utilizing the collective properties of such systems