The BEE Undergraduate Model for Knowledge Development applied to the QUT Spatial Science Course : Diversity in Surveying Curriculum

This paper provides an overview of the current QUT Spatial Science undergraduate program based in Brisbane, Queensland, Australia. It discusses the development and implementation of a broad-based educational model for the faculty of built environment and engineering courses and specifically to the course structure of the new Bachelor of Urban Development (Spatial Science) study major. A brief historical background of surveying courses is discussed prior to the detailing of the three distinct and complementary learning themes of the new course structure with a graphical course matrix. Curriculum mapping of the spatial science major has been undertaken as the course approaches formal review in late 2010. Work-integrated learning opportunities have been embedded into the curriculum and a brief outline is presented. Some issues relevant to the tertiary surveying/ spatial sector are highlighted in the context of changing higher education environments in Australia.

A University Support Model for eResearch

Queensland University of Technology (QUT) is faced with a rapidly growing research agenda built upon a strategic research capacity-building program. This presentation will outline the results of a project that has recently investigated QUT’s research support requirements and which has developed a model for the support of eResearch across the university. QUT’s research building strategy has produced growth at the faculty level and within its research institutes. This increased research activity is pushing the need for university-wide eResearch platforms capable of providing infrastructure and support in areas such as collaboration, data, networking, authentication and authorisation, workflows and the grid. One of the driving forces behind the investigation is data-centric nature of modern research. It is now critical that researchers have access to supported infrastructure that allows the collection, analysis, aggregation and sharing of large data volumes for exploration and mining in order to gain new insights and to generate new knowledge. However, recent surveys into current research data management practices by the Australian Partnership for Sustainable Repositories (APSR) and by QUT itself, has revealed serious shortcomings in areas such as research data management, especially its long term maintenance for reuse and authoritative evidence of research findings. While these internal university pressures are building, at the same time there are external pressures that are magnifying them. For example, recent compliance guidelines from bodies such as the ARC, and NHMRC and Universities Australia indicate that institutions need to provide facilities for the safe and secure storage of research data along with a surrounding set of policies, on its retention, ownership and accessibility. The newly formed Australian National Data Service (ANDS) is developing strategies and guidelines for research data management and research institutions are a central focus, responsible for managing and storing institutional data on platforms that can be federated nationally and internationally for wider use. For some time QUT has recognised the importance of eResearch and has been active in a number of related areas: ePrints to digitally publish research papers, grid computing portals and workflows, institutional-wide provisioning and authentication systems, and legal protocols for copyright management. QUT also has two widely recognised centres focused on fundamental research into eResearch itself: The OAK LAW project (Open Access to Knowledge) which focuses upon legal issues relating eResearch and the Microsoft QUT eResearch Centre whose goal is to accelerate scientific research discovery, through new smart software. In order to better harness all of these resources and improve research outcomes, the university recently established a project to investigate how it might better organise the support of eResearch. This presentation will outline the project outcomes, which include a flexible and sustainable eResearch support service model addressing short and longer term research needs, identification of resource requirements required to establish and sustain the service, and the development of research data management policies and implementation plans.

Towards a model of the institutional logics of climate change

The proliferation of innovative schemes to address climate change at international, national and local levels signals a fundamental shift in the priority and role of the natural environment to society, organizations and individuals. This shift in shared priorities invites academics and practitioners to consider the role of institutions in shaping and constraining responses to climate change at multiple levels of organisations and society. Institutional theory provides an approach to conceptualising and addressing climate change challenges by focusing on the central logics that guide society, organizations and individuals and their material and symbolic relationship to the environment. For example, framing a response to climate change in the form of an emission trading scheme evidences a practice informed by a capitalist market logic (Friedland and Alford 1991). However, not all responses need necessarily align with a market logic. Indeed, Thornton (2004) identifies six broad societal sectors each with its own logic (markets, corporations, professions, states, families, religions). Hence, understanding the logics that underpin successful –and unsuccessful– climate change initiatives contributes to revealing how institutions shape and constrain practices, and provides valuable insights for policy makers and organizations. This paper develops models and propositions to consider the construction of, and challenges to, climate change initiatives based on institutional logics (Thornton and Ocasio 2008). We propose that the challenge of understanding and explaining how climate change initiatives are successfully adopted be examined in terms of their institutional logics, and how these logics evolve over time. To achieve this, a multi-level framework of analysis that encompasses society, organizations and individuals is necessary (Friedland and Alford 1991). However, to date most extant studies of institutional logics have tended to emphasize one level over the others (Thornton and Ocasio 2008: 104). In addition, existing studies related to climate change initiatives have largely been descriptive (e.g. Braun 2008) or prescriptive (e.g. Boiral 2006) in terms of the suitability of particular practices. This paper contributes to the literature on logics by examining multiple levels: the proliferation of the climate change agenda provides a site in which to study how institutional logics are played out across multiple, yet embedded levels within society through institutional forums in which change takes place. Secondly, the paper specifically examines how institutional logics provide society with organising principles –material practices and symbolic constructions– which enable and constrain their actions and help define their motives and identity. Based on this model, we develop a series of propositions of the conditions required for the successful introduction of climate change initiatives. The paper proceeds as follows. We present a review of literature related to institutional logics and develop a generic model of the process of the operation of institutional logics. We then consider how this is applied to key initiatives related to climate change. Finally, we develop a series of propositions which might guide insights into the successful implementation of climate change practices.

Developing an integrated decision model for the enhancement of sustainability deliverables for Australian road infrastructure projects

Road and highway infrastructure provides the backbone for a nation's economic growth. The versatile dispersion of population in Australia, from sparsely settled communities in remote areas to regenerated inner city suburbs with high density living in metropolitans, calls for continuing development and improvement on roads infrastructure under the current federal government policies and state governments' strategic plans. As road infrastructure projects involve large resources and mechanism, achieving sustainability not only in economic scales but also through environmental and social responsibility becomes a crucial issue. Current efforts are often impeded by different interpretation on sustainability agenda by stakeholders involved in these types of projects. As a result, sustainability deliverables at the project level is not often as transparent and measurable, compared to promises in project briefs and designs. This paper reviews the past studies on sustainable infrastructure construction, focusing on roads and highway projects. Through literature study and consultation with the industry, key sustainability indicators specific to road infrastructure projects have been identified. Based on these findings, this paper introduces an on-going research project aimed at identifying and integrating the different perceptions and priority needs of the stakeholders, and issues that impact on the gap between sustainability foci and its actual realization at project end level. The exploration helps generate an integrated decision-making model for sustainable road infrastructure projects. The research will promote to the industry more systematic and integrated approaches to decision-making on the implementation of sustainability strategies to achieve deliverable goals throughout the development and delivery process of road infrastructure projects in Australia.

The development and evaluation of an innovative nursing practice model to improve undergraduate nursing students' competence in paediatric physical assessment

Introduction The purpose of this study was to develop, implement and evaluate the impact of an educational intervention, comprising an innovative model of clinical decisionmaking and educational delivery strategy for facilitating nursing students‘ learning and development of competence in paediatric physical assessment practices. Background of the study Nursing students have an undergraduate education that aims to produce graduates of a generalist nature who demonstrate entry level competence for providing nursing care in a variety of health settings. Consistent with population morbidity and health care roles, paediatric nursing concepts typically form a comparatively small part of undergraduate curricula and students‘ exposure to paediatric physical assessment concepts and principles are brief. However, the nursing shortage has changed traditional nursing employment patterns and new graduates form the majority of the recruitment pool for paediatric nursing speciality staff. Paediatric nursing is a popular career choice for graduates and anecdotal evidence suggests that nursing students who select a clinical placement in their final year intend to seek employment in paediatrics upon graduation. Although concepts of paediatric nursing are included within undergraduate curriculum, students‘ ability to develop the required habits of mind to practice in what is still regarded as a speciality area of practice is somewhat limited. One of the areas of practice where this particularly impacts is in paediatric nursing physical assessment. Physical assessment is a fundamental component of nursing practice and competence in this area of practice is central to nursing students‘ development of clinical capability for practice as a registered nurse. Timely recognition of physiologic deterioration of patients is a key outcome of nurses‘ competent use of physical assessment strategies, regardless of the practice context. In paediatric nursing contexts children‘s physical assessment practices must specifically accommodate the child‘s different physiological composition, function and pattern of clinical deterioration (Hockenberry & Barrera, 2007). Thus, to effectively manage physical assessment of patients within the paediatric practice setting nursing students need to integrate paediatric nursing theory into their practice. This requires significant information processing and it is in this process where students are frequently challenged. The provision of rules or models can guide practice and assist novice-level nurses to develop their capabilities (Benner, 1984; Benner, Hooper-Kyriakidis & Stannard, 1999). Nursing practice models are cognitive tools that represent simplified patterns of expert analysis employing concepts that suit the limited reasoning of the inexperienced, and can represent the =rules‘ referred to by Benner (1984). Without a practice model of physical assessment students are likely to be uncertain about how to proceed with data collection, the interpretation of paediatric clinical findings and the appraisal of findings. These circumstances can result in ad hoc and unreliable nursing physical assessment that forms a poor basis for nursing decisions. The educational intervention developed as part of this study sought to resolve this problem and support nursing students‘ development of competence in paediatric physical assessment. Methods This study utilised the Context Input Process Product (CIPP) Model by Stufflebeam (2004) as the theoretical framework that underpinned the research design and evaluation methodology. Each of the four elements in the CIPP model were utilised to guide discrete stages of this study. The Context element informed design of the clinical decision-making process, the Paediatric Nursing Physical Assessment model. The Input element was utilised in appraising relevant literature, identifying an appropriate instructional methodology to facilitate learning and educational intervention delivery to undergraduate nursing students, and development of program content (the CD-ROM kit). Study One employed the Process element and used expert panel approaches to review and refine instructional methods, identifying potential barriers to obtaining an effective evaluation outcome. The Product element guided design and implementation of Study Two, which was conducted in two phases. Phase One employed a quasiexperimental between-subjects methodology to evaluate the impact of the educational intervention on nursing students‘ clinical performance and selfappraisal of practices in paediatric physical assessment. Phase Two employed a thematic analysis and explored the experiences and perspectives of a sample subgroup of nursing students who used the PNPA CD-ROM kit as preparation for paediatric clinical placement. Results Results from the Process review in Study One indicated that the prototype CDROM kit containing the PNPA model met the predetermined benchmarks for face validity and the impact evaluation instrumentation had adequate content validity in comparison with predetermined benchmarks. In the first phase of Study Two the educational intervention did not result in statistically significant differences in measures of student performance or self-appraisal of practice. However, in Phase Two qualitative commentary from students, and from the expert panel who reviewed the prototype CD-ROM kit (Study One, Phase One), strongly endorsed the quality of the intervention and its potential for supporting learning. This raises questions regarding transfer of learning and it is likely that, within this study, several factors have influenced students‘ transfer of learning from the educational intervention to the clinical practice environment, where outcomes were measured. Conclusion In summary, the educational intervention employed in this study provides insights into the potential e-learning approaches offer for delivering authentic learning experiences to undergraduate nursing students. Findings in this study raise important questions regarding possible pedagogical influences on learning outcomes, issues within the transfer of theory to practice and factors that may have influenced findings within the context of this study. This study makes a unique contribution to nursing education, specifically with respect to progressing an understanding of the challenges faced in employing instructive methods to impact upon nursing students‘ development of competence. The important contribution transfer of learning processes make to students‘ transition into the professional practice context and to their development of competence within the context of speciality practice is also highlighted. This study contributes to a greater awareness of the complexity of translating theoretical learning at undergraduate level into clinical practice, particularly within speciality contexts.

FPGA implementation of dual-microphone delay-and-sum beamforming for in-car speech enhancement and recognition

In an automotive environment, the performance of a speech recognition system is affected by environmental noise if the speech signal is acquired directly from a microphone. Speech enhancement techniques are therefore necessary to improve the speech recognition performance. In this paper, a field-programmable gate array (FPGA) implementation of dual-microphone delay-and-sum beamforming (DASB) for speech enhancement is presented. As the first step towards a cost-effective solution, the implementation described in this paper uses a relatively high-end FPGA device to facilitate the verification of various design strategies and parameters. Experimental results show that the proposed design can produce output waveforms close to those generated by a theoretical (floating-point) model with modest usage of FPGA resources. Speech recognition experiments are also conducted on enhanced in-car speech waveforms produced by the FPGA in order to compare recognition performance with the floating-point representation running on a PC.

Model round-trip engineering

In the quest for shorter time-to-market, higher quality and reduced cost, model-driven software development has emerged as a promising approach to software engineering. The central idea is to promote models to first-class citizens in the development process. Starting from a set of very abstract models in the early stage of the development, they are refined into more concrete models and finally, as a last step, into code. As early phases of development focus on different concepts compared to later stages, various modelling languages are employed to most accurately capture the concepts and relations under discussion. In light of this refinement process, translating between modelling languages becomes a time-consuming and error-prone necessity. This is remedied by model transformations providing support for reusing and automating recurring translation efforts. These transformations typically can only be used to translate a source model into a target model, but not vice versa. This poses a problem if the target model is subject to change. In this case the models get out of sync and therefore do not constitute a coherent description of the software system anymore, leading to erroneous results in later stages. This is a serious threat to the promised benefits of quality, cost-saving, and time-to-market. Therefore, providing a means to restore synchronisation after changes to models is crucial if the model-driven vision is to be realised. This process of reflecting changes made to a target model back to the source model is commonly known as Round-Trip Engineering (RTE). While there are a number of approaches to this problem, they impose restrictions on the nature of the model transformation. Typically, in order for a transformation to be reversed, for every change to the target model there must be exactly one change to the source model. While this makes synchronisation relatively “easy”, it is ill-suited for many practically relevant transformations as they do not have this one-to-one character. To overcome these issues and to provide a more general approach to RTE, this thesis puts forward an approach in two stages. First, a formal understanding of model synchronisation on the basis of non-injective transformations (where a number of different source models can correspond to the same target model) is established. Second, detailed techniques are devised that allow the implementation of this understanding of synchronisation. A formal underpinning for these techniques is drawn from abductive logic reasoning, which allows the inference of explanations from an observation in the context of a background theory. As non-injective transformations are the subject of this research, there might be a number of changes to the source model that all equally reflect a certain target model change. To help guide the procedure in finding “good” source changes, model metrics and heuristics are investigated. Combining abductive reasoning with best-first search and a “suitable” heuristic enables efficient computation of a number of “good” source changes. With this procedure Round-Trip Engineering of non-injective transformations can be supported.

Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

Facilitating unlearning during implementation of new technology

Purpose – One of the critical issues for change management, particularly in relation to the implementation of new technologies, is the existence of prior knowledge and established mental models which may hinder change efforts. Understanding unlearning and how it might assist during organizational change is a way to address this resistance. The purpose of this paper is to present research designed to identify specific factors that facilitate unlearning. Design/methodology/approach – Drawing together issues identified as potential influencers of unlearning, a survey questionnaire was developed and administered in an Australian corporation undergoing large-scale change due to the implementation of an enterprise information system. The results were analyzed to identify specific factors that impact on unlearning. Findings – Findings from this paper identify factors that hinder or help the unlearning process during times of change including understanding the need for change, the level of organizational support and training, assessment of the change, positive experience and informal support, the organization's history of change, individual's prior outlooks, and individuals' feelings and expectations. Research limitations/implications – The use of only one organization does not allow for comparisons between organizations of different sizes, cultures or industries and therefore extension of this research is recommended. Practical implications – For practitioners, this paper provides specific elements at both the level of individuals and the organization that need to be considered for optimal unlearning during times of change. Originality/value – Previous literature on unlearning has been predominantly conceptual and theoretical. These empirical findings serve to further an earlier model based on qualitative research into potential influencers of unlearning.

An integrated information management model for ecologically sustainable development

Ecologically sustainable development has become a major feature of legal systems at the international, national and local levels throughout the world. In Australia, governments have responded to environmental crises by enacting legislation imposing obligations and restrictions over privately-owned land. Whilst these obligations and restrictions may well be necessary to achieve sustainability, the approach to management of information concerning these instruments is problematic. For example, management of information concerning obligations and restrictions in Queensland is fragmented, with some instruments registered or recorded on the land title register, some on external registers, and some information only available in the legislation itself. This approach is used in most Australian jurisdictions. This fragmented approach has led to two separate but interconnected problems. First, the Torrens system is no longer meeting its goal of providing a complete and accurate picture of title. Second, this uncoordinated approach to the management of land titles, and obligations and restrictions on land use, has created a barrier to sustainable management of natural resources. This is because compliance with environmental laws is impaired in the absence of easily accessible and accurate information. These problems demonstrate a clear need for reform in this area. To determine how information concerning these obligations and restrictions may be most effectively managed, this thesis will apply a comparative methodology and consider three case studies, which each utilise different models for management of this information. These jurisdictions will be assessed according to a set of guidelines for comparison to identify which features of their systems provide for effective management of information concerning obligations and restrictions on title and use. Based on this comparison, this thesis will devise a series of recommendations for an effective system for the management of information concerning obligations and restrictions on land title and use, taking into account any potential legal issues and barriers to implementation. This series of recommendations for reform will be supplemented by suggested draft legislative provisions.

Detection and prevention of distributed denial of services attacks by collaborative effort of software agents, first prototype implementation

Distributed Denial of Services DDoS, attacks has become one of the biggest threats for resources over Internet. Purpose of these attacks is to make servers deny from providing services to legitimate users. These attacks are also used for occupying media bandwidth. Currently intrusion detection systems can just detect the attacks but cannot prevent / track the location of intruders. Some schemes also prevent the attacks by simply discarding attack packets, which saves victim from attack, but still network bandwidth is wasted. In our opinion, DDoS requires a distributed solution to save wastage of resources. The paper, presents a system that helps us not only in detecting such attacks but also helps in tracing and blocking (to save the bandwidth as well) the multiple intruders using Intelligent Software Agents. The system gives dynamic response and can be integrated with the existing network defense systems without disturbing existing Internet model. We have implemented an agent based networking monitoring system in this regard.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

An integrated rail track degradation model

There has been a worldwide trend to increase axle loads and train speeds. This means that railway track degradation will be accelerated, and track maintenance costs will be increased significantly. There is a need to investigate the consequences of increasing traffic load. The aim of the research is to develop a model for the analysis of physical degradation of railway tracks in response to changes in traffic parameters, especially increased axle loads and train speeds. This research has developed an integrated track degradation model (ITDM) by integrating several models into a comprehensive framework. Mechanistic relationships for track degradation hav~ ?een used wherever possible in each of the models contained in ITDM. This overcc:mes the deficiency of the traditional statistical track models which rely heavily on historical degradation data, which is generally not available in many railway systems. In addition statistical models lack the flexibility of incorporating future changes in traffic patterns or maintenance practices. The research starts with reviewing railway track related studies both in Australia and overseas to develop a comprehensive understanding of track performance under various traffic conditions. Existing railway related models are then examined for their suitability for track degradation analysis for Australian situations. The ITDM model is subsequently developed by modifying suitable existing models, and developing new models where necessary. The ITDM model contains four interrelated submodels for rails, sleepers, ballast and subgrade, and track modulus. The rail submodel is for rail wear analysis and is developed from a theoretical concept. The sleeper submodel is for timber sleepers damage prediction. The submodel is developed by modifying and extending an existing model developed elsewhere. The submodel has also incorporated an analysis for the likelihood of concrete sleeper cracking. The ballast and subgrade submodel is evolved from a concept developed in the USA. Substantial modifications and improvements have been made. The track modulus submodel is developed from a conceptual method. Corrections for more global track conditions have been made. The integration of these submodels into one comprehensive package has enabled the interaction between individual track components to be taken into account. This is done by calculating wheel load distribution with time and updating track conditions periodically in the process of track degradation simulation. A Windows-based computer program ~ssociated with ITDM has also been developed. The program enables the user to carry out analysis of degradation of individual track components and to investigate the inter relationships between these track components and their deterioration. The successful implementation of this research has provided essential information for prediction of increased maintenance as a consequence of railway trackdegradation. The model, having been presented at various conferences and seminars, has attracted wide interest. It is anticipated that the model will be put into practical use among Australian railways, enabling track maintenance planning to be optimized and potentially saving Australian railway systems millions of dollars in operating costs.

Development and implementation of the areawide Dynamic ROad traffic NoisE (DRONE) simulator

This paper discusses the areawide Dynamic ROad traffic NoisE (DRONE) simulator, and its implementation as a tool for noise abatement policy evaluation. DRONE involves integrating a road traffic noise estimation model with a traffic simulator to estimate road traffic noise in urban networks. An integrated traffic simulation-noise estimation model provides an interface for direct input of traffic flow properties from simulation model to noise estimation model that in turn estimates the noise on a spatial and temporal scale. The output from DRONE is linked with a geographical information system for visual representation of noise levels in the form of noise contour maps.

RatSLAM : a hippocampal model for simultaneous localization and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.