Linear gaussian computations for near-exact Bayesian Monte Carlo inference in skewed alpha-stable time series models

In this paper we study parameter estimation for time series with asymmetric α-stable innovations. The proposed methods use a Poisson sum series representation (PSSR) for the asymmetric α-stable noise to express the process in a conditionally Gaussian framework. That allows us to implement Bayesian parameter estimation using Markov chain Monte Carlo (MCMC) methods. We further enhance the series representation by introducing a novel approximation of the series residual terms in which we are able to characterise the mean and variance of the approximation. Simulations illustrate the proposed framework applied to linear time series, estimating the model parameter values and model order P for an autoregressive (AR(P)) model driven by asymmetric α-stable innovations. © 2012 IEEE.

Series-parallel six-element synthesis of biquadratic impedances

The object of this paper is to give a complete treatment of the realizability of positive-real biquadratic impedance functions by six-element series-parallel networks comprising resistors, capacitors, and inductors. This question was studied but not fully resolved in the classical electrical circuit literature. Renewed interest in this question arises in the synthesis of passive mechanical impedances. Recent work by the authors has introduced the concept of a regular positive-real functions. It was shown that five-element networks are capable of realizing all regular and some (but not all) nonregular biquadratic positive-real functions. Accordingly, the focus of this paper is on the realizability of nonregular biquadratics. It will be shown that the only six-element series-parallel networks which are capable of realizing nonregular biquadratic impedances are those with three reactive elements or four reactive elements. We identify a set of networks that can realize all the nonregular biquadratic functions for each of the two cases. The realizability conditions for the networks are expressed in terms of a canonical form for biquadratics. The nonregular realizable region for each of the networks is explicitly characterized. © 2004-2012 IEEE.

Series-connected IGBTs using active voltage control technique

With series insulated-gate bipolar transistor (IGBT) operation, well-matched gate drives will not ensure balanced dynamic voltage sharing between the switching devices. Rather, it is IGBT parasitic capacitances, mainly gate-to-collector capacitance Cgc, that dominate transient voltage sharing. As Cgc is collector voltage dependant and is significantly larger during the initial turn-off transition, it dominates IGBT dynamic voltage sharing. This paper presents an active control technique for series-connected IGBTs that allows their dynamic voltage transition dV\ce/dt to adaptively vary. Both switch ON and OFF transitions are controlled to follow a predefined dVce/dt. Switching losses associated with this technique are minimized by the adaptive dv /dt control technique incorporated into the design. A detailed description of the control circuits is presented in this paper. Experimental results with up to three series devices in a single-ended dc chopper circuit, operating at various low voltage and current levels, are used to illustrate the performance of the proposed technique. © 2012 IEEE.

IGBT series connection under Active Voltage Control with temporary clamp

This paper presents the use of an Active Voltage Control (AVC) technique for balancing the voltages in a series connection of Insulated Gate Bipolar Transistors (IGBTs). The AVC technique can control the switching trajectory of an IGBT according to a pre-set reference signal. In series connections, every series connected IGBT follows the reference and so that the dynamic voltage sharing is achieved. For the static voltage balancing, a temporary clamp technique is introduced. The temporary clamp technique clamps the collector-emitter voltage of all the series connected IGBTs at the ideal voltage so that the IGBTs will share the voltage evenly. © 2012 IEEE.

Two problems with variational expectation maximisation for time-series models

Variational methods are a key component of the approximate inference and learning toolbox. These methods fill an important middle ground, retaining distributional information about uncertainty in latent variables, unlike maximum a posteriori methods (MAP), and yet generally requiring less computational time than Monte Carlo Markov Chain methods. In particular the variational Expectation Maximisation (vEM) and variational Bayes algorithms, both involving variational optimisation of a free-energy, are widely used in time-series modelling. Here, we investigate the success of vEM in simple probabilistic time-series models. First we consider the inference step of vEM, and show that a consequence of the well-known compactness property of variational inference is a failure to propagate uncertainty in time, thus limiting the usefulness of the retained distributional information. In particular, the uncertainty may appear to be smallest precisely when the approximation is poorest. Second, we consider parameter learning and analytically reveal systematic biases in the parameters found by vEM. Surprisingly, simpler variational approximations (such a mean-field) can lead to less bias than more complicated structured approximations.

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.

Flow field measurements of a series of turbulent premixed and stratified methane/air flames

This paper presents flow field measurements for the turbulent stratified burner introduced in two previous publications in which high resolution scalar measurements were made by Sweeney et al. [1,2] for model validation. The flow fields of the series of premixed and stratified methane/air flames are investigated under turbulent, globally lean conditions (φg=0.75). Velocity data acquired with laser Doppler anemometry (LDA) and particle image velocimetry (PIV) are presented and discussed. Pairwise 2-component LDA measurements provide profiles of axial velocity, radial velocity, tangential velocity and corresponding fluctuating velocities. The LDA measurements of axial and tangential velocities enable the swirl number to be evaluated and the degree of swirl characterized. Power spectral density and autocorrelation functions derived from the LDA data acquired at 10kHz are optimized to calculate the integral time scales. Flow patterns are obtained using a 2-component PIV system operated at 7Hz. Velocity profiles and spatial correlations derived from the PIV and LDA measurements are shown to be in very good agreement, thus offering 3D mapping of the velocities. A strong correlation was observed between the shape of the recirculation zones above the central bluff body and the effects of heat release, stoichiometry and swirl. Detailed analyses of the LDA data further demonstrate that the flow behavior changes significantly with the levels of swirl and stratification, which combines the contributions of dilatation, recirculation and swirl. Key turbulence parameters are derived from the total velocity components, combining axial, radial and tangential velocities. © 2013 The Combustion Institute.

Multilinear function factorisation for time series feature extraction

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, a great deal of work has centred around designing time series classifiers using more and more complex feature extraction and machine learning schemes. This paper argues that complex learners and domain specific feature extraction schemes of this type are not necessarily needed for time series classification, as excellent classification results can be obtained by simply applying a number of existing matrix factorisation or linear projection techniques, which are simple and computationally inexpensive. We highlight this using a geometric separability measure and classification accuracies obtained though experiments on four different high dimensional multivariate time series datasets. © 2013 IEEE.

High resolution cortical bone thickness measurement from clinical CT data.

The distribution of cortical bone in the proximal femur is believed to be a critical component in determining fracture resistance. Current CT technology is limited in its ability to measure cortical thickness, especially in the sub-millimetre range which lies within the point spread function of today's clinical scanners. In this paper, we present a novel technique that is capable of producing unbiased thickness estimates down to 0.3mm. The technique relies on a mathematical model of the anatomy and the imaging system, which is fitted to the data at a large number of sites around the proximal femur, producing around 17,000 independent thickness estimates per specimen. In a series of experiments on 16 cadaveric femurs, estimation errors were measured as -0.01+/-0.58mm (mean+/-1std.dev.) for cortical thicknesses in the range 0.3-4mm. This compares with 0.25+/-0.69mm for simple thresholding and 0.90+/-0.92mm for a variant of the 50% relative threshold method. In the clinically relevant sub-millimetre range, thresholding increasingly fails to detect the cortex at all, whereas the new technique continues to perform well. The many cortical thickness estimates can be displayed as a colour map painted onto the femoral surface. Computation of the surfaces and colour maps is largely automatic, requiring around 15min on a modest laptop computer.

Influence of cluster-assembly parameters on the field emission properties of nanostructured carbon films

Supersonic cluster beam deposition has been used to produce films with different nanostructures by controlling the deposition parameters such as the film thickness, substrate temperature and cluster mass distribution. The field emission properties of cluster-assembled carbon films have been characterized and correlated to the evolution of the film nanostructure. Threshold fields ranging between 4 and 10 V/mum and saturation current densities as high as 0.7 mA have been measured for samples heated during deposition. A series of voltage ramps, i.e., a conditioning process, was found to initiate more stable and reproducible emission. It was found that the presence of graphitic particles (onions, nanotube embryos) in the films substantially enhances the field emission performance. Films patterned on a micrometer scale have been conditioned spot by spot by a ball-tip anode, showing that a relatively high emission site density can be achieved from the cluster-assembled material. (C) 2002 American Institute of Physics.

Optical and micro-structural properties of high photoluminescence efficiency InGaN/AlInGaN quantum well structures

We have studied the optical properties of a series of InGaN/AlInGaN 10-period multiple quantum wells (MQW) with differing well thickness grown by metal-organic vapor-phase epitaxy that emit at around 380 nm. The aim of this investigation was to optimise the room temperature internal quantum efficiency, thus the quantum well (QW) thicknesses were accordingly chosen so that the overlap of the electron/hole wave function was maximised. At low temperature, we observed a reduction of the photo luminescence decay time with decreasing well width in line with the theoretical predictions. For a structure with well thicknesses of 1.5 nm, we measured a photoluminescence internal quantum efficiency of 67% at room temperature with a peak emission wavelength of 382 nm. (c) 2006 Elsevier B.V. All rights reserved.

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.

Structure-flexoelastic properties of bimesogenic liquid crystals

In this paper, we report on the flexoelastic and viscoelastic ratios for a number of bimesogens compounds with the same generic structure. Values are obtained indirectly by measuring the flexoelectro-optic response in the chiral nematic phase. By varying the molecular structure we alter the bend angle, transverse dipole moment, and length of the molecule. First, to examine the influence of the bend angle we use a homologous series whereby the only alteration in the molecular structure is the number of methylene units in the aliphatic spacer, n. Results show that the flexoelastic ratio, e K, and the effective flexoelectric coefficient, e, both exhibit an odd-even effect with values for n=odd being greater than that for n=even. This is understood in terms of an increase in the bend angle of the molecule and an increase in the transverse dipole moment. Second, in order to investigate the impact of the dipole moment, we have altered the mesogenic units so as to vary the longitudinal dipole moment and used different linkages in the aliphatic spacer in an attempt to alter the transverse dipole moment. Qualitatively, the results demonstrate that the odd-spaced bimesogen with larger transverse dipole moments exhibit larger flexoelastic ratios. © 2007 The American Physical Society.