Bayesian Online Changepoint Detection

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.

Infrared Magnitude-Redshift Relations for Luminous Radio Galaxies

Infrared magnitude-redshift relations for the 3CR and 6C samples of radio galaxies are presented for a wide range of plausible cosmological models, including those with non-zero cosmological constant OmegaLambda. Variations in the galaxy formation redshift, metallicity and star formation history are also considered. The results of the modelling are displayed in terms of magnitude differences between the models and no-evolution tracks, illustrating the amount of K-band evolution necessary to account for the observational data. Given a number of plausible assumptions, the results of these analyses suggest that: (i) cosmologies which predict T_0xH_0>1 (where T_0 denotes the current age of the universe) can be excluded; (ii) the star formation redshift should lie in the redshift interval 5massive) than those nearby in models with finite OmegaLambda, including the favoured model with Omega=0.3, OmegaLambda=0.7. For cosmological models with larger values of T_0xH_0, the conclusions are the same regardless of whether any adjustments are made or not. The implications of these results for cosmology and models of galaxy formation are discussed.

A tool for assessing reconfigurability of distributed manufacturing systems

In recent years, a large number of approaches to developing distributed manufacturing systems has been proposed. One of the principles reasons for these development has been to enhance the reconfigurability of a manufacturing operation; allowing it to readily adapt to changes over time. However, to date, there has only been a limited assessment of the resulting reconfigurability properties and hence it remains inconclusive as to whether a distributed manufacturing system design approach does in fact improve reconfigurability. This paper represents part of a study which investigates this issue. It proposes an assessment tool - the so called "Design Structure Matrix" as a means of assessing the modularity of elements in a manufacturing system. (Modularity has been shown to be a key characteristic of a reconfigurable manufacturing system.) The use of the Design Structure Matrix is illustrated in assessing a robot assembly cell designed on distributed manufacturing system principles. Copyright © 2006 IFAC.

Adaptive BDDC in Three Dimensions

The adaptive BDDC method is extended to the selection of face constraints in three dimensions. A new implementation of the BDDC method is presented based on a global formulation without an explicit coarse problem, with massive parallelism provided by a multifrontal solver. Constraints are implemented by a projection and sparsity of the projected operator is preserved by a generalized change of variables. The effectiveness of the method is illustrated on several engineering problems.

BarraCUDA - a fast short read sequence aligner using graphics processing units.

BACKGROUND: With the maturation of next-generation DNA sequencing (NGS) technologies, the throughput of DNA sequencing reads has soared to over 600 gigabases from a single instrument run. General purpose computing on graphics processing units (GPGPU), extracts the computing power from hundreds of parallel stream processors within graphics processing cores and provides a cost-effective and energy efficient alternative to traditional high-performance computing (HPC) clusters. In this article, we describe the implementation of BarraCUDA, a GPGPU sequence alignment software that is based on BWA, to accelerate the alignment of sequencing reads generated by these instruments to a reference DNA sequence. FINDINGS: Using the NVIDIA Compute Unified Device Architecture (CUDA) software development environment, we ported the most computational-intensive alignment component of BWA to GPU to take advantage of the massive parallelism. As a result, BarraCUDA offers a magnitude of performance boost in alignment throughput when compared to a CPU core while delivering the same level of alignment fidelity. The software is also capable of supporting multiple CUDA devices in parallel to further accelerate the alignment throughput. CONCLUSIONS: BarraCUDA is designed to take advantage of the parallelism of GPU to accelerate the alignment of millions of sequencing reads generated by NGS instruments. By doing this, we could, at least in part streamline the current bioinformatics pipeline such that the wider scientific community could benefit from the sequencing technology.BarraCUDA is currently available from http://seqbarracuda.sf.net.

Dead-time minimisation using active voltage control gate drive for high-power IGBT converters

High-power converters usually need longer dead-times than their lower-power counterparts and a lower switching frequency. Also due to the complicated assembly layout and severe variations in parasitics, in practice the conventional dead-time specific adjustment or compensation for high-power converters is less effective, and usually this process is time-consuming and bespoke. For general applications, minimising or eliminating dead-time in the gate drive technology is a desirable solution. With the growing acceptance of power electronics building blocks (PEBB) and intelligent power modules (IPM), gate drives with intelligent functions are in demand. Smart functions including dead time elimination/minimisation can improve modularity, flexibility and reliability. In this paper, a dead-time minimisation using Active Voltage Control (AVC) gate drive is presented. © 2012 IEEE.

Controlling Sub-nm Gaps in Plasmonic Dimers using Graphene

Graphene is used as the thinnest possible spacer between gold nanoparticles and a gold substrate. This creates a robust, repeatable, and stable sub-nanometre gap for massive plasmonic field enhancements. White light spectroscopy of single 80 nm gold nanoparticles reveals plasmonic coupling between the particle and its image within the gold substrate. While for a single graphene layer, spectral doublets from coupled dimer modes are observed shifted into the near infra-red, these disappear for increasing numbers of layers. These doublets arise from plasmonic charge transfer, allowing the direct optical measurement of out-of-plane conductivity in such layered systems. Gating the graphene can thus directly produce plasmon tuning.

Vortex migration caused by travelling magnetic wave in a 2 in. diameter YBa2Cu3O7-δ thin film

We provide experimental evidence for a vortex migration phenomenon in YBa2Cu3O7-δ (YBCO) thin film caused by travelling magnetic wave. The experiment is carried out on a 2 in. diameter YBCO thin film with a circular-type magnetic flux pump. We found that the travelling wave helps the vortices migrate into the centre of the sample: after the zero-field cooling process, the increase of the flux density in the centre is four times larger than the amplitude of the travelling wave. The reason for this massive vortex migration is probably due to the magnetic stress variation caused by the travelling wave: the magnetic stress increases locally in the crest region while decreases locally in the trough region, which could help the vortices to move locally. A comparison shows that the magnetization by standing wave can be easily predicted by Bean's model while travelling wave causes vortex migration generally much larger than the prediction of Bean's model. It is possible that travelling magnetic wave can be an effective way to magnetize a type II superconductor in considering this unusual vortex dynamics. © 2013 AIP Publishing LLC.

Control of aircraft in the terminal manoeuvring area using parallelised sequential Monte Carlo

This paper reports on the use of a parallelised Model Predictive Control, Sequential Monte Carlo algorithm for solving the problem of conflict resolution and aircraft trajectory control in air traffic management specifically around the terminal manoeuvring area of an airport. The target problem is nonlinear, highly constrained, non-convex and uses a single decision-maker with multiple aircraft. The implementation includes a spatio-temporal wind model and rolling window simulations for realistic ongoing scenarios. The method is capable of handling arriving and departing aircraft simultaneously including some with very low fuel remaining. A novel flow field is proposed to smooth the approach trajectories for arriving aircraft and all trajectories are planned in three dimensions. Massive parallelisation of the algorithm allows solution speeds to approach those required for real-time use.