Context-Based Modelling of Interferometric Signals for the Assessment of Tear-Film Suface Quality

Modelling of interferometric signals related to tear film surface quality is considered. In the context of tear film surface quality estimation in normal healthy eyes, two clinical parameters are of interest: the build-up time, and the average interblink surface quality. The former is closely related to the signal derivative while the latter to the signal itself. Polynomial signal models, chosen for a particular set of noisy interferometric measurements, can be optimally selected, in some sense, with a range of information criteria such as AIC, MDL, Cp, and CME. Those criteria, however, do not always guarantee that the true derivative of the signal is accurately represented and they often overestimate it. Here, a practical method for judicious selection of model order in a polynomial fitting to a signal is proposed so that the derivative of the signal is adequately represented. The paper highlights the importance of context-based signal modelling in model order selection.

Online estimation of distorted power system signal parameters

An algorithm based on the concept of Kalman filtering is proposed in this paper for the estimation of power system signal attributes, like amplitude, frequency and phase angle. This technique can be used in protection relays, digital AVRs, DSTATCOMs, FACTS and other power electronics applications. Furthermore this algorithm is particularly suitable for the integration of distributed generation sources to power grids when fast and accurate detection of small variations of signal attributes are needed. Practical considerations such as the effect of noise, higher order harmonics, and computational issues of the algorithm are considered and tested in the paper. Several computer simulations are presented to highlight the usefulness of the proposed approach. Simulation results show that the proposed technique can simultaneously estimate the signal attributes, even if it is highly distorted due to the presence of non-linear loads and noise.

Wireless channel modelling for indoor populated environments : statistical characterization of narrowband channels at 2.45 Ghz and 5.2 Ghz

The recent development of indoor wireless local area network (WLAN) standards at 2.45 GHz and 5 GHz has led to increased interest in propagation studies at these frequency bands. Within the indoor environment, human body effects can strongly reduce the quality of wireless communication systems. Human body effects can cause temporal variations and shadowing due to pedestrian movement and antenna- body interaction with portable terminals. This book presents a statistical characterisation, based on measurements, of human body effects on indoor narrowband channels at 2.45 GHz and at 5.2 GHz. A novel cumulative distribution function (CDF) that models the 5 GHz narrowband channel in populated indoor environments is proposed. This novel CDF describes the received envelope in terms of pedestrian traffic. In addition, a novel channel model for the populated indoor environment is proposed for the Multiple-Input Multiple-Output (MIMO) narrowband channel in presence of pedestrians at 2.45 GHz. Results suggest that practical MIMO systems must be sufficiently adaptive if they are to benefit from the capacity enhancement caused by pedestrian movement.

Latent variable models in statistical genetics

Understanding the complexities that are involved in the genetics of multifactorial diseases is still a monumental task. In addition to environmental factors that can influence the risk of disease, there is also a number of other complicating factors. Genetic variants associated with age of disease onset may be different from those variants associated with overall risk of disease, and variants may be located in positions that are not consistent with the traditional protein coding genetic paradigm. Latent Variable Models are well suited for the analysis of genetic data. A latent variable is one that we do not directly observe, but which is believed to exist or is included for computational or analytic convenience in a model. This thesis presents a mixture of methodological developments utilising latent variables, and results from case studies in genetic epidemiology and comparative genomics. Epidemiological studies have identified a number of environmental risk factors for appendicitis, but the disease aetiology of this oft thought useless vestige remains largely a mystery. The effects of smoking on other gastrointestinal disorders are well documented, and in light of this, the thesis investigates the association between smoking and appendicitis through the use of latent variables. By utilising data from a large Australian twin study questionnaire as both cohort and case-control, evidence is found for the association between tobacco smoking and appendicitis. Twin and family studies have also found evidence for the role of heredity in the risk of appendicitis. Results from previous studies are extended here to estimate the heritability of age-at-onset and account for the eect of smoking. This thesis presents a novel approach for performing a genome-wide variance components linkage analysis on transformed residuals from a Cox regression. This method finds evidence for a dierent subset of genes responsible for variation in age at onset than those associated with overall risk of appendicitis. Motivated by increasing evidence of functional activity in regions of the genome once thought of as evolutionary graveyards, this thesis develops a generalisation to the Bayesian multiple changepoint model on aligned DNA sequences for more than two species. This sensitive technique is applied to evaluating the distributions of evolutionary rates, with the finding that they are much more complex than previously apparent. We show strong evidence for at least 9 well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least 7 classes in an alignment of four mammals, including human. A pattern of enrichment and depletion of genic regions in the profiled segments suggests they are functionally significant, and most likely consist of various functional classes. Furthermore, a method of incorporating alignment characteristics representative of function such as GC content and type of mutation into the segmentation model is developed within this thesis. Evidence of fine-structured segmental variation is presented.

Empirical derivation and validation of a wandering typology

OBJECTIVES: To develop and validate a wandering typology. ---------- DESIGN: Cross-sectional, correlational descriptive design. ---------- SETTING:: Twenty-two nursing homes and six assisted living facilities. ---------- PARTICIPANTS: One hundred forty-two residents with dementia who spoke English, met Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition, criteria for dementia, scored less than 24 on the Mini-Mental State Examination (MMSE), were ambulatory (with or without assistive device), and maintained a stable regime of psychotropic medications were studied. ---------- MEASUREMENTS: Data on wandering were collected using direct observations, plotted serially according to rate and duration to yield 21 parameters, and reduced through factor analysis to four components: high rate, high duration, low to moderate rate and duration, and time of day. Other measures included the MMSE, Minimum Data Set 2.0 mobility items, Cumulative Illness Rating Scale—Geriatric, and tympanic body temperature readings. ---------- RESULTS: Three groups of wanderers were identified through cluster analysis: classic, moderate, and subclinical. MMSE, mobility, and cardiac and upper and lower gastrointestinal problems differed between groups of wanderers and in comparison with nonwanderers. ---------- CONCLUSION: Results have implications for improving identification of wanderers and treatment of possible contributing factors.

Building a successful partnership to increase affordable rental housing supply in Queensland

Accessibility to housing for low to moderate income groups in Australia has been experiencing a severe decline since 2001. On the supply side, the public sector has been reducing its commitment to the direct provision of public housing. Despite high demand for affordable housing, there has been limited supply generated by non-government housing providers. One possible solution to promote an increase in affordable housing supply, like other infrastructure, is through the development of multi-stakeholder partnerships and private financing. This research aims to identify current issues underlying decision-making criteria for building multi-stakeholder partnerships to deliver affordable housing projects. It also investigates strategies for minimising risk and ensuring the financial outcomes of these partnership arrangements. A mix of qualitative in-depth interviews and quantitative surveys has been used as the main method to explore stakeholder experiences regarding their involvement in partnership arrangements in the affordable housing sector in Queensland. Two sets of interviews were conducted following an exploratory pilot study: one set in 2003-2004 and the other in 2007-2008. There were nineteen respondents representing government, private and not-for-profit organisations in the first stage interviews and surveys. The second stage interviews were focussed on twenty-two housing providers in South East Queensland. Initial analyses have been conducted using thematic and statistical analyses. This study extends the use of existing decision making tools and combines the use of a Soft System Framework to analyse the ideal state questionnaires using qualitative thematic analysis. Soft System Methodology (SSM) has been used to analyse this unstructured complex problem by using systematic thinking to develop a conceptual model and carrying it to the real world situations to solve the problem. This research found that the diversity of stakeholder capability and their level of risk acceptance will allow partnerships to develop the best synergies and a degree of collaboration which achieves the required financial return within acceptable risk parameters. However, some of the negativity attached to future commitment to such partnerships has been found to be the anticipation of a worse outcome than that expected from independent action. Many interviewees agree that housing providers' fear of financial risk and community rejection has been central to dampening their enthusiasm for entering such investment projects. The creation of a mixed-use development structure will mitigate both risk and return as the commercial income will subsidise the affordable housing development and will normalise concentration of marginalised low-income people who live in a prime location with an award winning design. In addition, tenant support schemes and rent-to-buy incentive programs will encourage them to secure their tenancies and significantly reduce the risk of rent arrears and property damage. There is also a breakthrough investment vehicle offered by the social developer which sells the non-physical but financial product to individual and institutional investors to mitigate further financial risk. Finally, this study recommends modification of the current value-for-money framework in favour of broader partnership arrangements which are more closely aligned with risk minimisation strategies.

Computed tomography analysis of sagittal plane parameters in adolescent idiopathic scoliosis treated by video assisted thoracoscopic spinal fusion and instrumentation

One of the primary treatment goals of adolescent idiopathic scoliosis (AIS) surgery is to achieve maximum coronal plane correction while maintaining coronal balance. However maintaining or restoring sagittal plane spinal curvature has become increasingly important in maintaining the long-term health of the spine. Patients with AIS are characterised by pre-operative thoracic hypokyphosis, and it is generally agreed that operative treatment of thoracic idiopathic scoliosis should aim to restore thoracic kyphosis to normal values while maintaining lumbar lordosis and good overall sagittal balance. The aim of this study was to evaluate CT sagittal plane parameters, with particular emphasis on thoracolumbar junctional alignment, in patients with AIS who underwent Video Assisted Thoracoscopic Spinal Fusion and Instrumentation (VATS). This study concluded that video-assisted thoracoscopic spinal fusion and instrumentation reliably increases thoracic kyphosis while preserving junctional alignment and lumbar lordosis in thoracic AIS.

Establishing indigeneity in African pluralities using PRO169 parameters and a case study for measuring their inclusivity

The concept of the indigenous person or group in Africa is a contentious one. The current argument is that there exist no indigenous people in Africa because all Africans are indigenous. The obverse considers those Africans who have not been touched by colonialism and lost their traditional cultures commensurate with attachments to the lands or a distinguishable traditional lifestyle to be indigenous. This paper argues in favor of the latter. People who live in the global telos and do not participate in a distinct traditional culture that has been attached to the land for centuries are not indigenous. It is argued that this cultural divergence between modern and traditional is the major identifying point to settle the indigenous-non indigenous African debate. Finally, the paper looks at inclusive development and provides a new political analysis model for quantifying inclusivity so as to measure the inclusivity of indigenous peoples.

A simple and systematic approach to assigning Denavit-Hartenberg Parameters

This paper presents a simple and intuitive approach to determining the kinematic parameters of a serial-link robot in Denavit– Hartenberg (DH) notation. Once a manipulator’s kinematics is parameterized in this form, a large body of standard algorithms and code implementations for kinematics, dynamics, motion planning, and simulation are available. The proposed method has two parts. The first is the “walk through,” a simple procedure that creates a string of elementary translations and rotations, from the user-defined base coordinate to the end-effector. The second step is an algebraic procedure to manipulate this string into a form that can be factorized as link transforms, which can be represented in standard or modified DH notation. The method allows for an arbitrary base and end-effector coordinate system as well as an arbitrary zero joint angle pose. The algebraic procedure is amenable to computer algebra manipulation and a Java program is available as supplementary downloadable material.

Monitoring and analysis of internet traffic targeting unused address spaces

Today’s evolving networks are experiencing a large number of different attacks ranging from system break-ins, infection from automatic attack tools such as worms, viruses, trojan horses and denial of service (DoS). One important aspect of such attacks is that they are often indiscriminate and target Internet addresses without regard to whether they are bona fide allocated or not. Due to the absence of any advertised host services the traffic observed on unused IP addresses is by definition unsolicited and likely to be either opportunistic or malicious. The analysis of large repositories of such traffic can be used to extract useful information about both ongoing and new attack patterns and unearth unusual attack behaviors. However, such an analysis is difficult due to the size and nature of the collected traffic on unused address spaces. In this dissertation, we present a network traffic analysis technique which uses traffic collected from unused address spaces and relies on the statistical properties of the collected traffic, in order to accurately and quickly detect new and ongoing network anomalies. Detection of network anomalies is based on the concept that an anomalous activity usually transforms the network parameters in such a way that their statistical properties no longer remain constant, resulting in abrupt changes. In this dissertation, we use sequential analysis techniques to identify changes in the behavior of network traffic targeting unused address spaces to unveil both ongoing and new attack patterns. Specifically, we have developed a dynamic sliding window based non-parametric cumulative sum change detection techniques for identification of changes in network traffic. Furthermore we have introduced dynamic thresholds to detect changes in network traffic behavior and also detect when a particular change has ended. Experimental results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach, using both synthetically generated datasets and real network traces collected from a dedicated block of unused IP addresses.

GIS-based mapping and statistical analysis of air pollution and mortality in Brisbane, Australia

In this thesis, the relationship between air pollution and human health has been investigated utilising Geographic Information System (GIS) as an analysis tool. The research focused on how vehicular air pollution affects human health. The main objective of this study was to analyse the spatial variability of pollutants, taking Brisbane City in Australia as a case study, by the identification of the areas of high concentration of air pollutants and their relationship with the numbers of death caused by air pollutants. A correlation test was performed to establish the relationship between air pollution, number of deaths from respiratory disease, and total distance travelled by road vehicles in Brisbane. GIS was utilized to investigate the spatial distribution of the air pollutants. The main finding of this research is the comparison between spatial and non-spatial analysis approaches, which indicated that correlation analysis and simple buffer analysis of GIS using the average levels of air pollutants from a single monitoring station or by group of few monitoring stations is a relatively simple method for assessing the health effects of air pollution. There was a significant positive correlation between variable under consideration, and the research shows a decreasing trend of concentration of nitrogen dioxide at the Eagle Farm and Springwood sites and an increasing trend at CBD site. Statistical analysis shows that there exists a positive relationship between the level of emission and number of deaths, though the impact is not uniform as certain sections of the population are more vulnerable to exposure. Further statistical tests found that the elderly people of over 75 years age and children between 0-15 years of age are the more vulnerable people exposed to air pollution. A non-spatial approach alone may be insufficient for an appropriate evaluation of the impact of air pollutant variables and their inter-relationships. It is important to evaluate the spatial features of air pollutants before modeling the air pollution-health relationships.

Bayesian models for longitudinal data

Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes which evolve over time, and these can be referred to as growth or trajectory models. One of the traditional ways of looking at growth models is to employ either linear or polynomial functional forms to model trajectory shape, and account for variation around an overall mean trend with the inclusion of random eects or individual variation on the functional shape parameters. The identification of distinct subgroups or sub-classes (latent classes) within these trajectory models which are not based on some pre-existing individual classification provides an important methodology with substantive implications. The identification of subgroups or classes has a wide application in the medical arena where responder/non-responder identification based on distinctly diering trajectories delivers further information for clinical processes. This thesis develops Bayesian statistical models and techniques for the identification of subgroups in the analysis of longitudinal data where the number of time intervals is limited. These models are then applied to a single case study which investigates the neuropsychological cognition for early stage breast cancer patients undergoing adjuvant chemotherapy treatment from the Cognition in Breast Cancer Study undertaken by the Wesley Research Institute of Brisbane, Queensland. Alternative formulations to the linear or polynomial approach are taken which use piecewise linear models with a single turning point, change-point or knot at a known time point and latent basis models for the non-linear trajectories found for the verbal memory domain of cognitive function before and after chemotherapy treatment. Hierarchical Bayesian random eects models are used as a starting point for the latent class modelling process and are extended with the incorporation of covariates in the trajectory profiles and as predictors of class membership. The Bayesian latent basis models enable the degree of recovery post-chemotherapy to be estimated for short and long-term followup occasions, and the distinct class trajectories assist in the identification of breast cancer patients who maybe at risk of long-term verbal memory impairment.

Development of pollutant build-up parameters for MIKE URBAN for Southeast Queensland, Australia

Accurate estimation of input parameters is essential to ensure the accuracy and reliability of hydrologic and water quality modelling. Calibration is an approach to obtain accurate input parameters for comparing observed and simulated results. However, the calibration approach is limited as it is only applicable to catchments where monitoring data is available. Therefore, methodology to estimate appropriate model input parameters is critical, particularly for catchments where monitoring data is not available. In the research study discussed in the paper, pollutant build-up parameters derived from catchment field investigations and model calibration using MIKE URBAN are compared for three catchments in Southeast Queensland, Australia. Additionally, the sensitivity of MIKE URBAN input parameters was analysed. It was found that Reduction Factor is the most sensitive parameter for peak flow and total runoff volume estimation whilst Build-up rate is the most sensitive parameter for TSS load estimation. Consequently, these input parameters should be determined accurately in hydrologic and water quality simulations using MIKE URBAN. Furthermore, an empirical equation for Southeast Queensland, Australia for the conversion of build-up parameters derived from catchment field investigations as MIKE URBAN input build-up parameters was derived. This will provide guidance for allowing for regional variations in the estimation of input parameters for catchment modelling using MIKE URBAN where monitoring data is not available.

Analysis of distrubances in large interconnected power systems

World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.