Composition and decomposition in bimanual dynamic learning.

Our ability to skillfully manipulate an object often involves the motor system learning to compensate for the dynamics of the object. When the two arms learn to manipulate a single object they can act cooperatively, whereas when they manipulate separate objects they control each object independently. We examined how learning transfers between these two bimanual contexts by applying force fields to the arms. In a coupled context, a single dynamic is shared between the arms, and in an uncoupled context separate dynamics are experienced independently by each arm. In a composition experiment, we found that when subjects had learned uncoupled force fields they were able to transfer to a coupled field that was the sum of the two fields. However, the contribution of each arm repartitioned over time so that, when they returned to the uncoupled fields, the error initially increased but rapidly reverted to the previous level. In a decomposition experiment, after subjects learned a coupled field, their error increased when exposed to uncoupled fields that were orthogonal components of the coupled field. However, when the coupled field was reintroduced, subjects rapidly readapted. These results suggest that the representations of dynamics for uncoupled and coupled contexts are partially independent. We found additional support for this hypothesis by showing significant learning of opposing curl fields when the context, coupled versus uncoupled, was alternated with the curl field direction. These results suggest that the motor system is able to use partially separate representations for dynamics of the two arms acting on a single object and two arms acting on separate objects.

HYDROGENATED AMORPHOUS SILICON THIN-FILM DEPOSITION BY DIRECT PHOTO-ENHANCED DECOMPOSITION OF SILANE USING AN INTERNAL HYDROGEN DISCHARGE LAMP.

Hydrogenated amorphous silicon (a-Si:H) thin films have been deposited from silane using a novel photo-enhanced decomposition technique. The system comprises a hydrogen discharge lamp contained within the reaction vessel; this unified approach allows high energy photon excitation of the silane molecules without absorption by window materials or the need for mercury sensitisation. The film growth rates (exceeding 4 Angstrom/s) and material properties obtained are comparable to those of films produced by plasma-enhanced CVD techniques. The reduction of energetic charged particles in the film growth region should enable the fabrication of cleaner semiconductor/insulator interfaces in thin-film transistors.

On the domain decomposition and transmission line modelling finite element method for time-domain induction motor analysis

This paper demonstrates how a finite element model which exploits domain decomposition is applied to the analysis of three-phase induction motors. It is shown that a significant gain in cpu time results when compared with standard finite element analysis. Aspects of the application of the method which are particular to induction motors are considered: the means of improving the convergence of the nonlinear finite element equations; the choice of symmetrical sub-domains; the modelling of relative movement; and the inclusion of periodic boundary conditions. © 1999 IEEE.

Application of domain decomposition and transmission line modelling techniques to 2D, time-domain, finite element problems

In this paper a recently published finite element method, which combines domain decomposition with a novel technique for solving nonlinear magnetostatic finite element problems is described. It is then shown how the method can be extended to, and optimised for, the solution of time-domain problems. © 1999 IEEE.

Applications of the dynamic mode decomposition

The decomposition of experimental data into dynamic modes using a data-based algorithm is applied to Schlieren snapshots of a helium jet and to time-resolved PIV-measurements of an unforced and harmonically forced jet. The algorithm relies on the reconstruction of a low-dimensional inter-snapshot map from the available flow field data. The spectral decomposition of this map results in an eigenvalue and eigenvector representation (referred to as dynamic modes) of the underlying fluid behavior contained in the processed flow fields. This dynamic mode decomposition allows the breakdown of a fluid process into dynamically revelant and coherent structures and thus aids in the characterization and quantification of physical mechanisms in fluid flow. © 2010 Springer-Verlag.