Clustering with random indexing K-tree and XML structure

This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.

Spherical image-based visual servo and structure estimation

This paper presents a formulation of image-based visual servoing (IBVS) for a spherical camera where coordinates are parameterized in terms of colatitude and longitude: IBVSSph. The image Jacobian is derived and simulation results are presented for canonical rotational, translational as well as general motion. Problems with large rotations that affect the planar perspective form of IBVS are not present on the sphere, whereas the desirable robustness properties of IBVS are shown to be retained. We also describe a structure from motion (SfM) system based on camera-centric spherical coordinates and show how a recursive estimator can be used to recover structure. The spherical formulations for IBVS and SfM are particularly suitable for platforms, such as aerial and underwater robots, that move in SE(3).

Polymersomes, smaller than you think : ferrocene as a TEM probe to determine core structure

By incorporating ferrocene into the hydrophobic membrane of PEG-b-PCL polymersome nanoparticles it is possible to selectively visualize their core using Transmission Electron Microscopy (TEM). Two different sizes of ferrocene-loaded polymersomes with mean hydrodynamic diameters of approximately 40 and 90 nm were prepared. Image analysis of TEM pictures of these polymersomes found that the mean diameter of the core was 4–5 times smaller than the mean hydrodynamic diameter. The values obtained also allow the surface diameter and internal volume of the core to be calculated.

A connective model for the practice-led research exegesis : an analysis of content and structure

Since the formal recognition of practice-led research in the 1990s, many higher research degree candidates in art, design and media have submitted creative works along with an accompanying written document or ‘exegesis’ for examination. Various models for the exegesis have been proposed in university guidelines and academic texts during the past decade, and students and supervisors have experimented with its contents and structure. With a substantial number of exegeses submitted and archived, it has now become possible to move beyond proposition to empirical analysis. In this article we present the findings of a content analysis of a large, local sample of submitted exegeses. We identify the emergence of a persistent pattern in the types of content included as well as overall structure. Besides an introduction and conclusion, this pattern includes three main parts, which can be summarized as situating concepts (conceptual definitions and theories); precedents of practice (traditions and exemplars in the field); and researcher’s creative practice (the creative process, the artifacts produced and their value as research). We argue that this model combines earlier approaches to the exegesis, which oscillated between academic objectivity, by providing a contextual framework for the practice, and personal reflexivity, by providing commentary on the creative practice. But this model is more than simply a hybrid: it provides a dual orientation, which allows the researcher to both situate their creative practice within a trajectory of research and do justice to its personally invested poetics. By performing the important function of connecting the practice and creative work to a wider emergent field, the model helps to support claims for a research contribution to the field. We call it a connective model of exegesis.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Physiological investigation of periosteum structure and its application in periosteum tissue engineering

Although many different materials, techniques and methods, including artificial or engineered bone substitutes, have been used to repair various bone defects, the restoration of critical-sized bone defects caused by trauma, surgery or congenital malformation is still a great challenge to orthopedic surgeons. One important fact that has been neglected in the pursuit of resolutions for large bone defect healing is that most physiological bone defect healing needs the periosteum and stripping off the periosteum may result in non-union or non-healed bone defects. Periosteum plays very important roles not only in bone development but also in bone defect healing. The purpose of this project was to construct a functional periosteum in vitro using a single stem cell source and then test its ability to aid the repair of critical-sized bone defect in animal models. This project was designed with three separate but closely-linked parts which in the end led to four independent papers. The first part of this study investigated the structural and cellular features in periostea from diaphyseal and metaphyseal bone surfaces in rats of different ages or with osteoporosis. Histological and immunohistological methods were used in this part of the study. Results revealed that the structure and cell populations in periosteum are both age-related and site-specific. The diaphyseal periosteum showed age-related degeneration, whereas the metaphyseal periosteum is more destructive in older aged rats. The periosteum from osteoporotic bones differs from normal bones both in terms of structure and cell populations. This is especially evident in the cambial layer of the metaphyseal area. Bone resorption appears to be more active in the periosteum from osteoporotic bones, whereas bone formation activity is comparable between the osteoporotic and normal bone. The dysregulation of bone resorption and formation in the periosteum may also be the effect of the interaction between various neural pathways and the cell populations residing within it. One of the most important aspects in periosteum engineering is how to introduce new blood vessels into the engineered periosteum to help form vascularized bone tissues in bone defect areas. The second part of this study was designed to investigate the possibility of differentiating bone marrow stromal cells (BMSCs) into the endothelial cells and using them to construct vascularized periosteum. The endothelial cell differentiation of BMSCs was induced in pro-angiogenic media under both normoxia and CoCl2 (hypoxia-mimicking agent)-induced hypoxia conditions. The VEGF/PEDF expression pattern, endothelial cell specific marker expression, in vitro and in vivo vascularization ability of BMSCs cultured in different situations were assessed. Results revealed that BMSCs most likely cannot be differentiated into endothelial cells through the application of pro-angiogenic growth factors or by culturing under CoCl2-induced hypoxic conditions. However, they may be involved in angiogenesis as regulators under both normoxia and hypoxia conditions. Two major angiogenesis-related growth factors, VEGF (pro-angiogenic) and PEDF (anti-angiogenic) were found to have altered their expressions in accordance with the extracellular environment. BMSCs treated with the hypoxia-mimicking agent CoCl2 expressed more VEGF and less PEDF and enhanced the vascularization of subcutaneous implants in vivo. Based on the findings of the second part, the CoCl2 pre-treated BMSCs were used to construct periosteum, and the in vivo vascularization and osteogenesis of the constructed periosteum were assessed in the third part of this project. The findings of the third part revealed that BMSCs pre-treated with CoCl2 could enhance both ectopic and orthotopic osteogenesis of BMSCs-derived osteoblasts and vascularization at the early osteogenic stage, and the endothelial cells (HUVECs), which were used as positive control, were only capable of promoting osteogenesis after four-weeks. The subcutaneous area of the mouse is most likely inappropriate for assessing new bone formation on collagen scaffolds. This study demonstrated the potential application of CoCl2 pre-treated BMSCs in the tissue engineering not only for periosteum but also bone or other vascularized tissues. In summary, the structure and cell populations in periosteum are age-related, site-specific and closely linked with bone health status. BMSCs as a stem cell source for periosteum engineering are not endothelial cell progenitors but regulators, and CoCl2-treated BMSCs expressed more VEGF and less PEDF. These CoCl2-treated BMSCs enhanced both vascularization and osteogenesis in constructed periosteum transplanted in vivo.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Multilevel converter structure and control

In recent years, multilevel converters are becoming more popular and attractive than traditional converters in high voltage and high power applications. Multilevel converters are particularly suitable for harmonic reduction in high power applications where semiconductor devices are not able to operate at high switching frequencies or in high voltage applications where multilevel converters reduce the need to connect devices in series to achieve high switch voltage ratings. This thesis investigated two aspects of multilevel converters: structure and control. The first part of this thesis focuses on inductance between a DC supply and inverter components in order to minimise loop inductance, which causes overvoltages and stored energy losses during switching. Three dimensional finite element simulations and experimental tests have been carried out for all sections to verify theoretical developments. The major contributions of this section of the thesis are as follows: The use of a large area thin conductor sheet with a rectangular cross section separated by dielectric sheets (planar busbar) instead of circular cross section wires, contributes to a reduction of the stray inductance. A number of approximate equations exist for calculating the inductance of a rectangular conductor but an assumption was made that the current density was uniform throughout the conductors. This assumption is not valid for an inverter with a point injection of current. A mathematical analysis of a planar bus bar has been performed at low and high frequencies and the inductance and the resistance values between the two points of the planar busbar have been determined. A new physical structure for a voltage source inverter with symmetrical planar bus bar structure called Reduced Layer Planar Bus bar, is proposed in this thesis based on the current point injection theory. This new type of planar busbar minimises the variation in stray inductance for different switching states. The reduced layer planar busbar is a new innovation in planar busbars for high power inverters with minimum separation between busbars, optimum stray inductance and improved thermal performances. This type of the planar busbar is suitable for high power inverters, where the voltage source is supported by several capacitors in parallel in order to provide a low ripple DC voltage during operation. A two layer planar busbar with different materials has been analysed theoretically in order to determine the resistance of bus bars during switching. Increasing the resistance of the planar busbar can gain a damping ratio between stray inductance and capacitance and affects the performance of current loop during switching. The aim of this section is to increase the resistance of the planar bus bar at high frequencies (during switching) and without significantly increasing the planar busbar resistance at low frequency (50 Hz) using the skin effect. This contribution shows a novel structure of busbar suitable for high power applications where high resistance is required at switching times. In multilevel converters there are different loop inductances between busbars and power switches associated with different switching states. The aim of this research is to consider all combinations of the switching states for each multilevel converter topology and identify the loop inductance for each switching state. Results show that the physical layout of the busbars is very important for minimisation of the loop inductance at each switch state. Novel symmetrical busbar structures are proposed for multilevel converters with diode-clamp and flying-capacitor topologies which minimise the worst case in stray inductance for different switching states. Overshoot voltages and thermal problems are considered for each topology to optimise the planar busbar structure. In the second part of the thesis, closed loop current techniques have been investigated for single and three phase multilevel converters. The aims of this section are to investigate and propose suitable current controllers such as hysteresis and predictive techniques for multilevel converters with low harmonic distortion and switching losses. This section of the thesis can be classified into three parts as follows: An optimum space vector modulation technique for a three-phase voltage source inverter based on a minimum-loss strategy is proposed. One of the degrees of freedom for optimisation of the space vector modulation is the selection of the zero vectors in the switching sequence. This new method improves switching transitions per cycle for a given level of distortion as the zero vector does not alternate between each sector. The harmonic spectrum and weighted total harmonic distortion for these strategies are compared and results show up to 7% weighted total harmonic distortion improvement over the previous minimum-loss strategy. The concept of SVM technique is a very convenient representation of a set of three-phase voltages or currents used for current control techniques. A new hysteresis current control technique for a single-phase multilevel converter with flying-capacitor topology is developed. This technique is based on magnitude and time errors to optimise the level change of converter output voltage. This method also considers how to improve unbalanced voltages of capacitors using voltage vectors in order to minimise switching losses. Logic controls require handling a large number of switches and a Programmable Logic Device (PLD) is a natural implementation for state transition description. The simulation and experimental results describe and verify the current control technique for the converter. A novel predictive current control technique is proposed for a three-phase multilevel converter, which controls the capacitors' voltage and load current with minimum current ripple and switching losses. The advantage of this contribution is that the technique can be applied to more voltage levels without significantly changing the control circuit. The three-phase five-level inverter with a pure inductive load has been implemented to track three-phase reference currents using analogue circuits and a programmable logic device.