Robust video stabilisation algorithm using feature point selection and delta optical flow

In this study, the authors propose a novel video stabilisation algorithm for mobile platforms with moving objects in the scene. The quality of videos obtained from mobile platforms, such as unmanned airborne vehicles, suffers from jitter caused by several factors. In order to remove this undesired jitter, the accurate estimation of global motion is essential. However it is difficult to estimate global motions accurately from mobile platforms due to increased estimation errors and noises. Additionally, large moving objects in the video scenes contribute to the estimation errors. Currently, only very few motion estimation algorithms have been developed for video scenes collected from mobile platforms, and this paper shows that these algorithms fail when there are large moving objects in the scene. In this study, a theoretical proof is provided which demonstrates that the use of delta optical flow can improve the robustness of video stabilisation in the presence of large moving objects in the scene. The authors also propose to use sorted arrays of local motions and the selection of feature points to separate outliers from inliers. The proposed algorithm is tested over six video sequences, collected from one fixed platform, four mobile platforms and one synthetic video, of which three contain large moving objects. Experiments show our proposed algorithm performs well to all these video sequences.

The development and validation of Australian indices of child development - Part II : validity support

The two outcome indices described in a companion paper (Sanson et al., Child Indicators Research, 2009) were developed using data from the Longitudinal Study of Australian Children (LSAC). These indices, one for infants and the other for 4 year to 5 year old children, were designed to fill the need for parsimonious measures of children’s developmental status to be used in analyses by a broad range of data users and to guide government policy and interventions to support young children’s optimal development. This paper presents evidence from Wave 1data from LSAC to support the validity of these indices and their three domain scores of Physical, Social/Emotional, and Learning. Relationships between the indices and child, maternal, family, and neighborhood factors which are known to relate concurrently to child outcomes were examined. Meaningful associations were found with the selected variables, thereby demonstrating the usefulness of the outcome indices as tools for understanding children’s development in their family and socio-cultural contexts. It is concluded that the outcome indices are valuable tools for increasing understanding of influences on children’s development, and for guiding policy and practice to optimize children’s life chances.

The development and validation of Australian Indices of Child Development — Part I: conceptualisation and development

The Longitudinal Study of Australian Children (LSAC) is a major national study examining the lives of Australian children, using a cross-sequential cohort design and data from parents, children, and teachers for 5,107 infants (3–19 months) and 4,983 children (4–5 years). Its data are publicly accessible and are used by researchers from many disciplinary backgrounds. It contains multiple measures of children’s developmental outcomes as well as a broad range of information on the contexts of their lives. This paper reports on the development of summary outcome indices of child development using the LSAC data. The indices were developed to fill the need for indicators suitable for use by diverse data users in order to guide government policy and interventions which support young children’s optimal development. The concepts underpinning the indices and the methods of their development are presented. Two outcome indices (infant and child) were developed, each consisting of three domains—health and physical development, social and emotional functioning, and learning competency. A total of 16 measures are used to make up these three domains in the Outcome Index for the Child Cohort and six measures for the Infant Cohort. These measures are described and evidence supporting the structure of the domains and their underlying latent constructs is provided for both cohorts. The factorial structure of the Outcome Index is adequate for both cohorts, but was stronger for the child than infant cohort. It is concluded that the LSAC Outcome Index is a parsimonious measure representing the major components of development which is suitable for non-specialist data users. A companion paper (Sanson et al. 2010) presents evidence of the validity of the Index.

The applicability of biased estimation in model and model order selection

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance of the minimum description length (MDL) criterion based on biased and unbiased estimation and compare it against modern model selection criteria such as Kay's conditional model order estimator (CME), the bootstrap and the more recently proposed hook-and-loop resampling based model selection. The advantages and limitations of the considered techniques are discussed. The results indicate that, in some cases, biased estimators can slightly improve the selection of the correct model. We also give an example for which the CME with an unbiased estimator fails, but could regain its power when a biased estimator is used.

Power system stability analysis based on descriptive study of electrical indices

In a power network, when a propagation energy wave caused by a disturbance hits a weak link, a reflection is appeared and some of energy is transferred across the link. In this work, an analytical descriptive methodology is proposed to study the dynamical stability of a large scale power system. For this purpose, the measured electrical indices (angle, or voltage/frequency) following a fault in different points among the network are used, and the behaviors of the propagated waves through the lines, nodes and buses are studied. This work addresses a new tool for power system stability analysis based on a descriptive study of electrical measurements. The proposed methodology is also useful to detect the contingency condition and synthesis of an effective emergency control scheme.

Data-driven background dataset selection for SVM-based speaker verification

The recently proposed data-driven background dataset refinement technique provides a means of selecting an informative background for support vector machine (SVM)-based speaker verification systems. This paper investigates the characteristics of the impostor examples in such highly-informative background datasets. Data-driven dataset refinement individually evaluates the suitability of candidate impostor examples for the SVM background prior to selecting the highest-ranking examples as a refined background dataset. Further, the characteristics of the refined dataset were analysed to investigate the desired traits of an informative SVM background. The most informative examples of the refined dataset were found to consist of large amounts of active speech and distinctive language characteristics. The data-driven refinement technique was shown to filter the set of candidate impostor examples to produce a more disperse representation of the impostor population in the SVM kernel space, thereby reducing the number of redundant and less-informative examples in the background dataset. Furthermore, data-driven refinement was shown to provide performance gains when applied to the difficult task of refining a small candidate dataset that was mis-matched to the evaluation conditions.

Exploiting multiple feature sets in data-driven impostor dataset selection for speaker verification

This study assesses the recently proposed data-driven background dataset refinement technique for speaker verification using alternate SVM feature sets to the GMM supervector features for which it was originally designed. The performance improvements brought about in each trialled SVM configuration demonstrate the versatility of background dataset refinement. This work also extends on the originally proposed technique to exploit support vector coefficients as an impostor suitability metric in the data-driven selection process. Using support vector coefficients improved the performance of the refined datasets in the evaluation of unseen data. Further, attempts are made to exploit the differences in impostor example suitability measures from varying features spaces to provide added robustness.

Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. Applying the model to the stock and bond futures data, we discover that the correlation pattern between them has dramatically changed around the turn of the century. The model is also applied to a selection of world stock indices, and we find evidence for an increasing degree of integration in the capital markets.

The home stigma : adverse selection in ADRs and the home capital market environment

We investigate whether characteristics of the home country capital environment, such as information disclosure and investor rights protection continue to affect ADRs cross-listed in the U.S. Using microstructure measures as proxies for adverse selection, we find that characteristics of the home markets continue to be relevant, especially for emerging market firms. Less transparent disclosure, poorer protection of investor rights and weaker legal institutions are associated with higher levels of information asymmetry. Developed market firms appear to be affected by whether or not home business laws are common law or civil law legal origin. Our finding contributes to the bonding literature. It suggests that cross-listing in the U.S. should not be viewed as a substitute for improvement in the quality of local institutions, and attention must be paid to improve investor protection in order to achieve the full benefits of improved disclosure. Improvement in the domestic capital market environment can attract more investors even for U.S. cross-listed firms.

Selection and optimization of piezoelectric polyvinylidene fluoride polymers for adaptive optics in space environments

Various piezoelectric polymers based on polyvinylidene fluoride (PVDF) are of interest for large aperture space-based telescopes. Dimensional adjustments of adaptive polymer films depend on charge deposition and require a detailed understanding of the piezoelectric material responses which are expected to deteriorate owing to strong vacuum UV, � -, X-ray, energetic particles and atomic oxygen exposure. We have investigated the degradation of PVDF and its copolymers under various stress environments detrimental to reliable operation in space. Initial radiation aging studies have shown complex material changes with lowered Curie temperatures, complex material changes with lowered melting points, morphological transformations and significant crosslinking, but little influence on piezoelectric d33 constants. Complex aging processes have also been observed in accelerated temperature environments inducing annealing phenomena and cyclic stresses. The results suggest that poling and chain orientation are negatively affected by radiation and temperature exposure. A framework for dealing with these complex material qualification issues and overall system survivability predictions in low earth orbit conditions has been established. It allows for improved material selection, feedback for manufacturing and processing, material optimization/stabilization strategies and provides guidance on any alternative materials.

A new multilevel converter configuration for high power and high quality applications

The Queensland University of Technology (QUT) allows the presentation of theses for the Degree of Doctor of Philosophy in the format of published or submitted papers, where such papers have been published, accepted or submitted during the period of candidature. This thesis is composed of ten published /submitted papers and book chapters of which nine have been published and one is under review. This project is financially supported by an Australian Research Council (ARC) Discovery Grant with the aim of investigating multilevel topologies for high quality and high power applications, with specific emphasis on renewable energy systems. The rapid evolution of renewable energy within the last several years has resulted in the design of efficient power converters suitable for medium and high-power applications such as wind turbine and photovoltaic (PV) systems. Today, the industrial trend is moving away from heavy and bulky passive components to power converter systems that use more and more semiconductor elements controlled by powerful processor systems. However, it is hard to connect the traditional converters to the high and medium voltage grids, as a single power switch cannot stand at high voltage. For these reasons, a new family of multilevel inverters has appeared as a solution for working with higher voltage levels. Besides this important feature, multilevel converters have the capability to generate stepped waveforms. Consequently, in comparison with conventional two-level inverters, they present lower switching losses, lower voltage stress across loads, lower electromagnetic interference (EMI) and higher quality output waveforms. These properties enable the connection of renewable energy sources directly to the grid without using expensive, bulky, heavy line transformers. Additionally, they minimize the size of the passive filter and increase the durability of electrical devices. However, multilevel converters have only been utilised in very particular applications, mainly due to the structural limitations, high cost and complexity of the multilevel converter system and control. New developments in the fields of power semiconductor switches and processors will favor the multilevel converters for many other fields of application. The main application for the multilevel converter presented in this work is the front-end power converter in renewable energy systems. Diode-clamped and cascade converters are the most common type of multilevel converters widely used in different renewable energy system applications. However, some drawbacks – such as capacitor voltage imbalance, number of components, and complexity of the control system – still exist, and these are investigated in the framework of this thesis. Various simulations using software simulation tools are undertaken and are used to study different cases. The feasibility of the developments is underlined with a series of experimental results. This thesis is divided into two main sections. The first section focuses on solving the capacitor voltage imbalance for a wide range of applications, and on decreasing the complexity of the control strategy on the inverter side. The idea of using sharing switches at the output structure of the DC-DC front-end converters is proposed to balance the series DC link capacitors. A new family of multioutput DC-DC converters is proposed for renewable energy systems connected to the DC link voltage of diode-clamped converters. The main objective of this type of converter is the sharing of the total output voltage into several series voltage levels using sharing switches. This solves the problems associated with capacitor voltage imbalance in diode-clamped multilevel converters. These converters adjust the variable and unregulated DC voltage generated by renewable energy systems (such as PV) to the desirable series multiple voltage levels at the inverter DC side. A multi-output boost (MOB) converter, with one inductor and series output voltage, is presented. This converter is suitable for renewable energy systems based on diode-clamped converters because it boosts the low output voltage and provides the series capacitor at the output side. A simple control strategy using cross voltage control with internal current loop is presented to obtain the desired voltage levels at the output voltage. The proposed topology and control strategy are validated by simulation and hardware results. Using the idea of voltage sharing switches, the circuit structure of different topologies of multi-output DC-DC converters – or multi-output voltage sharing (MOVS) converters – have been proposed. In order to verify the feasibility of this topology and its application, steady state and dynamic analyses have been carried out. Simulation and experiments using the proposed control strategy have verified the mathematical analysis. The second part of this thesis addresses the second problem of multilevel converters: the need to improve their quality with minimum cost and complexity. This is related to utilising asymmetrical multilevel topologies instead of conventional multilevel converters; this can increase the quality of output waveforms with a minimum number of components. It also allows for a reduction in the cost and complexity of systems while maintaining the same output quality, or for an increase in the quality while maintaining the same cost and complexity. Therefore, the asymmetrical configuration for two common types of multilevel converters – diode-clamped and cascade converters – is investigated. Also, as well as addressing the maximisation of the output voltage resolution, some technical issues – such as adjacent switching vectors – should be taken into account in asymmetrical multilevel configurations to keep the total harmonic distortion (THD) and switching losses to a minimum. Thus, the asymmetrical diode-clamped converter is proposed. An appropriate asymmetrical DC link arrangement is presented for four-level diode-clamped converters by keeping adjacent switching vectors. In this way, five-level inverter performance is achieved for the same level of complexity of the four-level inverter. Dealing with the capacitor voltage imbalance problem in asymmetrical diodeclamped converters has inspired the proposal for two different DC-DC topologies with a suitable control strategy. A Triple-Output Boost (TOB) converter and a Boost 3-Output Voltage Sharing (Boost-3OVS) converter connected to the four-level diode-clamped converter are proposed to arrange the proposed asymmetrical DC link for the high modulation indices and unity power factor. Cascade converters have shown their abilities and strengths in medium and high power applications. Using asymmetrical H-bridge inverters, more voltage levels can be generated in output voltage with the same number of components as the symmetrical converters. The concept of cascading multilevel H-bridge cells is used to propose a fifteen-level cascade inverter using a four-level H-bridge symmetrical diode-clamped converter, cascaded with classical two-level Hbridge inverters. A DC voltage ratio of cells is presented to obtain maximum voltage levels on output voltage, with adjacent switching vectors between all possible voltage levels; this can minimize the switching losses. This structure can save five isolated DC sources and twelve switches in comparison to conventional cascade converters with series two-level H bridge inverters. To increase the quality in presented hybrid topology with minimum number of components, a new cascade inverter is verified by cascading an asymmetrical four-level H-bridge diode-clamped inverter. An inverter with nineteen-level performance was achieved. This synthesizes more voltage levels with lower voltage and current THD, rather than using a symmetrical diode-clamped inverter with the same configuration and equivalent number of power components. Two different predictive current control methods for the switching states selection are proposed to minimise either losses or THD of voltage in hybrid converters. High voltage spikes at switching time in experimental results and investigation of a diode-clamped inverter structure raised another problem associated with high-level high voltage multilevel converters. Power switching components with fast switching, combined with hard switched-converters, produce high di/dt during turn off time. Thus, stray inductance of interconnections becomes an important issue and raises overvoltage and EMI issues correlated to the number of components. Planar busbar is a good candidate to reduce interconnection inductance in high power inverters compared with cables. The effect of different transient current loops on busbar physical structure of the high-voltage highlevel diode-clamped converters is highlighted. Design considerations of proper planar busbar are also presented to optimise the overall design of diode-clamped converters.

A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition

Web service composition is an important problem in web service based systems. It is about how to build a new value-added web service using existing web services. A web service may have many implementations, all of which have the same functionality, but may have different QoS values. Thus, a significant research problem in web service composition is how to select a web service implementation for each of the web services such that the composite web service gives the best overall performance. This is so-called optimal web service selection problem. There may be mutual constraints between some web service implementations. Sometimes when an implementation is selected for one web service, a particular implementation for another web service must be selected. This is so called dependency constraint. Sometimes when an implementation for one web service is selected, a set of implementations for another web service must be excluded in the web service composition. This is so called conflict constraint. Thus, the optimal web service selection is a typical constrained ombinatorial optimization problem from the computational point of view. This paper proposes a new hybrid genetic algorithm for the optimal web service selection problem. The hybrid genetic algorithm has been implemented and evaluated. The evaluation results have shown that the hybrid genetic algorithm outperforms other two existing genetic algorithms when the number of web services and the number of constraints are large.