Generic systolic array for genetic algorithms

The authors present a systolic design for a simple GA mechanism which provides high throughput and unidirectional pipelining by exploiting the inherent parallelism in the genetic operators. The design computes in O(N+G) time steps using O(N2) cells where N is the population size and G is the chromosome length. The area of the device is independent of the chromosome length and so can be easily scaled by replicating the arrays or by employing fine-grain migration. The array is generic in the sense that it does not rely on the fitness function and can be used as an accelerator for any GA application using uniform crossover between pairs of chromosomes. The design can also be used in hybrid systems as an add-on to complement existing designs and methods for fitness function acceleration and island-style population management

Application of personal computers to eddy current inspections

This presentation describes a system for measuring claddings as an example of the many possible advantages to be obtained by applying a personal computer to eddy current testing. A theoretical model and a learning algorithm are integrated into an instrument. They are supported in the PC, and serve to simplify and enhance multiparameter testing. The PC gives additional assistance by simplifying set-up procedures and data logging etc.

The representation of delayed intentions: a prospective subject-performed task?

To-be-enacted material is more accessible in tests of recognition and lexical decision than material not intended for action (T. Goschke J. Kuhl, 1993; R. L. Marsh, J. L. Hicks, & M. L. Bink, 1998). This finding has been attributed to the superior status of intention-related information. The current article explores an alternative (action-superiority) account that draws parallels between the intended enactment effect (IEE) and the subject-performed task effect. Using 2 paradigms, the authors observed faster recognition latencies for both enacted and to-be-enacted material. It is crucial to note that there was no evidence of an IEE for items that had already been executed during encoding. The IEE was also eliminated when motor processing was prevented after verbal encoding. These findings suggest an overlap between overt and intended enactment and indicate that motor information may be activated for verbal material in preparation for subsequent execution.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.