Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.

Financial literacy and superannuation investment decision-making in a choice environment : an exploratory study

This thesis examines superannuation fund members' level of financial literacy and its association with investment choice decisions in the setting of the mandatory superannuation system in Australia. It also investigates a range of contextual and socio-demographic factors in explaining financial literacy and investment choice. The empirical analysis shows that while most survey respondents displayed high levels of self-rated and general financial literacy, fewer scored as well in relation to more advanced literacy regarding superannuation investment options. The findings of this study have important implications for policy-makers and the superannuation industry in educating and engaging with fund members.

Conceptual and statistical framework for a water quality component of the integrated report card for the Great Barrier Reef catchments

This report presents the final deliverable from the project titled Conceptual and statistical framework for a water quality component of an integrated report card’ funded by the Marine and Tropical Sciences Research Facility (MTSRF; Project 3.7.7). The key management driver of this, and a number of other MTSRF projects concerned with indicator development, is the requirement for state and federal government authorities and other stakeholders to provide robust assessments of the present ‘state’ or ‘health’ of regional ecosystems in the Great Barrier Reef (GBR) catchments and adjacent marine waters. An integrated report card format, that encompasses both biophysical and socioeconomic factors, is an appropriate framework through which to deliver these assessments and meet a variety of reporting requirements. It is now well recognised that a ‘report card’ format for environmental reporting is very effective for community and stakeholder communication and engagement, and can be a key driver in galvanising community and political commitment and action. Although a report card it needs to be understandable by all levels of the community, it also needs to be underpinned by sound, quality-assured science. In this regard this project was to develop approaches to address the statistical issues that arise from amalgamation or integration of sets of discrete indicators into a final score or assessment of the state of the system. In brief, the two main issues are (1) selecting, measuring and interpreting specific indicators that vary both in space and time, and (2) integrating a range of indicators in such a way as to provide a succinct but robust overview of the state of the system. Although there is considerable research and knowledge of the use of indicators to inform the management of ecological, social and economic systems, methods on how to best to integrate multiple disparate indicators remain poorly developed. Therefore the objective of this project was to (i) focus on statistical approaches aimed at ensuring that estimates of individual indicators are as robust as possible, and (ii) present methods that can be used to report on the overall state of the system by integrating estimates of individual indicators. It was agreed at the outset, that this project was to focus on developing methods for a water quality report card. This was driven largely by the requirements of Reef Water Quality Protection Plan (RWQPP) and led to strong partner engagement with the Reef Water Quality Partnership.

Statistics critical in securing our food supply

“World food security … is at its lowest in half a century,” wrote Julian Cribb FTSE, a wellknown consultant in science communication and founding editor of www.sciencealert. com.au in the lead article in the 2008 ATSE Focus magazine issue entitled “Food for the world: the nation’s challenge”. Food security continues to be a key national and international concern and it is pleasing to see this issue of Focus again exploring aspects of the topic with the aim of continuing to raise awareness of issues and influencing relevant policy decisions. Statistics (or statistical science, more broadly) has been critical to the information and decision-making value chain needed to optimise agriculture and the food supply chain. The key steps are most often addressed by multidisciplinary research groups including statisticians in collaboration with life and physical scientists, agri-industry personnel and other relevant stakeholders.

"The red dress or the blue?" How do staff perceive that they support decision making for people with dementia living in residential aged care facilities?

Respect for a person's right to make choices and participate in decision making is generally seen as central to quality of life and well-being. When a person moves into a residential aged care facility (RACF), however, decision making becomes more complicated, particularly if the person has a diagnosis of dementia. Little is known about how staff in RACFs perceive that they support decision making for people with dementia within their everyday practice, and this article seeks to address this knowledge gap. The article reports on the findings of a qualitative study conducted in the states of Victoria and Queensland, Australia with 80 direct care staff members. Findings revealed that the participants utilized a number of strategies in their intention to support decision making for people with dementia, and had an overall perception that "a little effort goes a long way."

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Causes of rigidity in SMEs' export commencement decision

Today, small-medium sized enterprises (SMEs) collectively contribute to the largest percentage of job creation in OECD countries. SMEs have become increasingly international since the turn of the century despite being smaller in size in comparison to large multinational firms, and notably, exporting is the most favoured mode of international market entry utilised by SMEs in their internationalisation strategy. Governments around the world have acknowledged the importance of export promotion and have employed policies that are targeted at increasing the export activity of SMEs. However, in many countries, the involvement of SMEs in export operations remains rather low. Within Australia, for example, only about one-third of local SMEs are exporting and this raises an important question as to why there is such a huge percentage of non-exporters. Much scholarly research that focuses on this problem has concentrated on the broad concept of 'export barriers' that act as obstacles to a firm's export development. This paper takes a different approach to previous studies and proposes that a firm's resistance to commence exporting can be better understood through an analysis of the behavioural decision process during its pre-export state. Using a sample of Australian SMEs, the factors that are important in preventing a firm’s initial export commencement decision are categorised and discussed through the use of factor analysis.

Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making

The decision-making processes of early childhood teachers when working with children experiencing parental separation and divorce

In this study, the pedagogical decision-making processes of 21 Australian early childhood teachers working with children experiencing parental separation and divorce were examined. Transcripts from interviews and a focus group with teachers were analysed using grounded theory methodology. The findings showed that as teachers interacted with young children experiencing parental separation and divorce, they reported using strategic, reflexive pedagogical decision-making processes. These processes comprised five stages: (1) teachers constructing their knowledge; (2) teachers thinking about their knowledge; (3) teachers using decision-making schemas; (4) teachers taking action, and; (5) teachers monitoring action and evaluating. This understanding of teachers’ reflexive pedagogical decision-making is useful for identifying how teachers and educational leaders can support children experiencing parental separation and divorce or other life challenges.

Reinforcing guardianship regimes through assisted decision-making : a Scottish perspective

Guardianship laws which provide legal mechanisms for decision-making on behalf of adults with limited or impaired capacity to make decisions have become a feature of most Western jurisdictions. In the UK, the Adults with Incapacity (Scotland) Act 2000 and the English and Welsh Mental Capacity Act 2005 have established a comprehensive framework for authorising medical, financial and welfare decisions to be taken on behalf of adults who are unable to take any or all such decisions themselves. One feature of guardianship regimes has been their tendency to evolve over time. Legal reforms have been instigated by the changes in philosophical views about how society should engage with adults with a decision-making impairment and also modern views about the role human rights play in guardianships regimes. The latest ideas in guardianship regimes around the Western world centre on the possibility of legal recognition of the concept of assisted decision-making...

Decision support for the efficient annotation of bioacoustic events

Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories. We describe two algorithms: (1) a “naïve” decision support tool (16%–64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacoustics projects.

Statistical methods for electromyography data and associated problems

This thesis proposes three novel models which extend the statistical methodology for motor unit number estimation, a clinical neurology technique. Motor unit number estimation is important in the treatment of degenerative muscular diseases and, potentially, spinal injury. Additionally, a recent and untested statistic to enable statistical model choice is found to be a practical alternative for larger datasets. The existing methods for dose finding in dual-agent clinical trials are found to be suitable only for designs of modest dimensions. The model choice case-study is the first of its kind containing interesting results using so-called unit information prior distributions.