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.

Image sequence segmentation using mixture-models with uniqueness and spatio-temporal consistency constraints

The use of mixture-model techniques for motion estimation and image sequence segmentation was discussed. The issues such as modeling of occlusion and uncovering, determining the relative depth of the objects in a scene, and estimating the number of objects in a scene were also investigated. The segmentation algorithm was found to be computationally demanding, but the computational requirements were reduced as the motion parameters and segmentation of the frame were initialized. The method provided a stable description, in whichthe addition and removal of objects from the description corresponded to the entry and exit of objects from the scene.

Properties of grain boundaries in bulk, melt processed YB2Cu3O7-δ fabricated using bridge-shaped seeds

Single grain REBa2C3uO7 ((RE)BCO, where RE is a rare earth element or yttrium) bulk superconducting materials have significant potential for a variety of engineering applications due to their ability to trap high magnetic fields. However, it is well known that the presence of grain boundaries coupled with a high angle of misorientation (typically 5�) significantly reduces the critical current density, J c , in all forms of high temperature superconducting materials. It is of considerable fundamental and technological interest, therefore, to investigate the grain boundary properties of bulk, film and tape (RE)BCO. We report a successful multi-seeding technique for the fabrication of fully aligned, artificial (0��misalignment) grain boundaries within large grain YBCO bulk superconductors using bridge-shaped seeds. The microstructure and critical current densities of the grain boundaries produced by this technique have been studied in detail.

Ferroelectric liquid crystal mixture integrated into optical waveguides

We report an on-chip integrated ferroelectric liquid crystal (FLC) waveguide structure suitable for telecommunication applications. Single gaps with different widths of 5, 10, and 20 μ m inside individual silica waveguides were filled with an FLC mixture. The waveguide devices operate as a binary switch or an attenuator in a temperature range from 30 °C to 60 °C. The FLC mixture exhibited a good alignment quality in these gaps without alignment layers. A good extinction ratio of up to 33.9 dB and a low insertion loss of <4.3 dB at λ = 1550 nm were observed. Switching times of <100 μs were obtained for the low electric fields applied in this experiment. © 2012 IEEE.

The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios. © 2012 Elsevier Ltd. All rights reserved.

YBCO single grains seeded by 45° - 45° bridge-seeds of different lengths

Single grain, (RE)BCO bulk superconductors in large or complicated geometries are required for a variety of potential applications, such as motors and generators and magnetic shielding devices. As a result, top, multi-seeded, melt growth (TMSMG) has been investigated over the past two years in an attempt to enlarge the size of (RE)BCO single grains specifically for such applications. Of these multi-seeding techniques, so-called bridge seeding provides the best alignment of two seeds in a single grain growth process. Here we report, for the first time, the successful growth of YBCO using a special, 45{\deg} - 45{\deg}, arrangement of bridge-seeds. The superconducting properties, including trapped field, of the multi-seeded YBCO grains have been measured for different bridge lengths of the 45{\deg}- 45{\deg} bridge-seeds. The boundaries at the impinging growth front and the growth features of the top, multi-seeded surface and cross-section of the multi-seeded, samples have been analysed using optical microscopy. The results suggest that an impurity-free boundary between the two seeds of each leg of the bridge-seed can form when 45{\deg}- 45{\deg} bridge-seeds are used to enlarge the size of YBCO grains.