Bistable composite slit tubes. I. A beam model

Composite slit tubes with a circular cross-section show an interesting variety in their large-deformation behaviour, that depends on the layup of the surface that is used: tubes made from many antisymmetric laminae are bistable, and have a compact coiled configuration, tubes made from similar, but symmetric, laminae do not have a compact coiled state, and indeed may not be bistable, while tubes made from an isotropic sheet are not bistable. A simple model is presented here that is able to distinguish between these behaviours; it assumes that the cross-section remains circular, but allows twist, which is shown to be the key to making the distinction between the behaviours described. © 2004 Elsevier Ltd. All rights reserved.

Focused ion beam etching of GaN

We have investigated the use of focused ion beam (FIB) etching for the fabrication of GaN-based devices. Although work has shown that conventional reactive ion etching (RIE) is in most cases appropriate for the GaN device fabrication, the direct write facility of FIB etching - a well-established technique for optical mask repair and for IC failure analysis and repair - without the requirement for depositing an etch mask is invaluable. A gallium ion beam of about 20nm diameter was used to sputter GaN material. The etching rate depends linearly on the ion dose per area with a slope of 3.5×10 -4μm3/pC. At a current of 3nA, for example, this corresponds to an etch rate of 1.05μm3/s. Good etching qualities have been achieved with a side wall roughness significantly below 0.1μm. Changes in the roughness of the etched surface plane stay below 8nm.

Free space adaptive optical interconnect, using a ferroelectric liquid crystal SLM for beam steering

A free-space, board-to-board, adaptive optical interconnect demonstrator has been developed. Binary phase gratings displayed on a Ferroelectric Liquid Crystal Spatial Light Modulator are used to maintain data transfer at 1.25Gbps, given varying optical misalignment © 2005 Optical Society of America.

Gaussian process change point models

We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets. Copyright 2010 by the author(s)/owner(s).

Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.