Implementation of a survey form marking system using image processing techniques on an Imputer

This paper describes the feasibility of the application of an Imputer in a multiple choice answer sheet marking system based on image processing techniques.

Chronological age and age-related cognitive deficits are associated with an increase in multiple types of driving errors in late life

Objective: Older driver research has mostly focused on identifying that small proportion of older drivers who are unsafe. Little is known about how normal cognitive changes in aging affect driving in the wider population of adults who drive regularly. We evaluated the association of cognitive function and age, with driving errors. Method: A sample of 266 drivers aged 70 to 88 years were assessed on abilities that decline in normal aging (visual attention, processing speed, inhibition, reaction time, task switching) and the UFOV® which is a validated screening instrument for older drivers. Participants completed an on-road driving test. Generalized linear models were used to estimate the associations of cognitive factor with specific driving errors and number of errors in self-directed and instructor navigated conditions. Results: All errors types increased with chronological age. Reaction time was not associated with driving errors in multivariate analyses. A cognitive factor measuring Speeded Selective Attention and Switching was uniquely associated with the most errors types. The UFOV predicted blindspot errors and errors on dual carriageways. After adjusting for age, education and gender the cognitive factors explained 7% of variance in the total number of errors in the instructor navigated condition and 4% of variance in the self-navigated condition. Conclusion: We conclude that among older drivers errors increase with age and are associated with speeded selective attention particularly when that requires attending to the stimuli in the periphery of the visual field, task switching, errors inhibiting responses and visual discrimination. These abilities should be the target of cognitive training.

Cellulosic ethanol from sugarcane bagasse in Australia : exploring industry feasibility through systems analysis, techno-economic assessment and pilot plant development

Overcoming many of the constraints to early stage investment in biofuels production from sugarcane bagasse in Australia requires an understanding of the complex technical, economic and systemic challenges associated with the transition of established sugar industry structures from single product agri-businesses to new diversified multi-product biorefineries. While positive investment decisions in new infrastructure requires technically feasible solutions and the attainment of project economic investment thresholds, many other systemic factors will influence the investment decision. These factors include the interrelationships between feedstock availability and energy use, competing product alternatives, technology acceptance and perceptions of project uncertainty and risk. This thesis explores the feasibility of a new cellulosic ethanol industry in Australia based on the large sugarcane fibre (bagasse) resource available. The research explores industry feasibility from multiple angles including the challenges of integrating ethanol production into an established sugarcane processing system, scoping the economic drivers and key variables relating to bioethanol projects and considering the impact of emerging technologies in improving industry feasibility. The opportunities available from pilot scale technology demonstration are also addressed. Systems analysis techniques are used to explore the interrelationships between the existing sugarcane industry and the developing cellulosic biofuels industry. This analysis has resulted in the development of a conceptual framework for a bagassebased cellulosic ethanol industry in Australia and uses this framework to assess the uncertainty in key project factors and investment risk. The analysis showed that the fundamental issue affecting investment in a cellulosic ethanol industry from sugarcane in Australia is the uncertainty in the future price of ethanol and government support that reduces the risks associated with early stage investment is likely to be necessary to promote commercialisation of this novel technology. Comprehensive techno-economic models have been developed and used to assess the potential quantum of ethanol production from sugarcane in Australia, to assess the feasibility of a soda-based biorefinery at the Racecourse Sugar Mill in Mackay, Queensland and to assess the feasibility of reducing the cost of production of fermentable sugars from the in-planta expression of cellulases in sugarcane in Australia. These assessments show that ethanol from sugarcane in Australia has the potential to make a significant contribution to reducing Australia’s transportation fuel requirements from fossil fuels and that economically viable projects exist depending upon assumptions relating to product price, ethanol taxation arrangements and greenhouse gas emission reduction incentives. The conceptual design and development of a novel pilot scale cellulosic ethanol research and development facility is also reported in this thesis. The establishment of this facility enables the technical and economic feasibility of new technologies to be assessed in a multi-partner, collaborative environment. As a key outcome of this work, this study has delivered a facility that will enable novel cellulosic ethanol technologies to be assessed in a low investment risk environment, reducing the potential risks associated with early stage investment in commercial projects and hence promoting more rapid technology uptake. While the study has focussed on an exploration of the feasibility of a commercial cellulosic ethanol industry from sugarcane in Australia, many of the same key issues will be of relevance to other sugarcane industries throughout the world seeking diversification of revenue through the implementation of novel cellulosic ethanol technologies.

UHT milk contains multiple forms of αS1-casein that undergo degradative changes during storage

Milk proteins are susceptible to chemical changes during processing and storage. We used proteomic tools to analyse bovine αS1-casein in UHT milk. 2-D gels of freshly processed milk αS1-casein was presented as five or more spots due to genetic polymorphism and variable phosphorylation. MS analysis after phosphopeptide enrichment allowed discrimination between phosphorylation states and genetic variants. We identified a new alternatively-spliced isoform with a deletion of exon 17, producing a new C-terminal sequence, K164SQVNSEGLHSYGL177, with a novel phosphorylation site at S174. Storage of UHT milk at elevated temperatures produced additional, more acidic αS1-casein spots on the gels and decreased the resolution of minor forms. MS analysis indicated that non-enzymatic deamidation and loss of the N-terminal dipeptide were the major contributors to the changing spot pattern. These results highlight the important role of storage temperature in the stability of milk proteins and the utility of proteomic techniques for analysis of proteins in food.

Biodiversity–productivity relationships in small-scale mixed-species plantations using native species in Leyte province, Philippines

In this study, we investigate the relationship between tree species diversity and production in 18 mixed-species plantations established under the Rainforestation Farming system in Leyte province, the Philippines. The aim was to quantify productivity in the mixed-species plantations in comparison to the monocultures, and identify key drivers of productivity including environmental conditions, stand structural characteristics and surrogate measures of biodiversity, i.e. species richness, Shannon’s diversity index and functional groups. We found that monocultures had a much higher productivity than mixtures of the same and other species. In the mixtures, biodiversity and productivity did not have a simple relationship. Instead the proportion of exotic and native species, and the proportion of fast-growing species had a marginally significant positive effect on stand productivity, but no significant relationship was found with species richness or Shannon’s diversity. Instead stand structural characteristics such as density and age were the strongest drivers of increased productivity. Production levels within the mixed-species plantations varied significantly between sites. Overall, we found that the productivity of mixed species plantations was driven more by the characteristics of species present and stand structural characteristics then by simply the number and abundance of species, which suggests management practices are key for balancing multiple objectives to meet sustainable development needs.

Towards persistent localization and mapping with a continuous appearance-based topology

Appearance-based localization can provide loop closure detection at vast scales regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale not only with the size of the environment but also with the operation time of the platform. Additionally, repeated visits to locations will develop multiple competing representations, which will reduce recall performance over time. These properties impose severe restrictions on long-term autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. In this paper we present a graphical extension to CAT-SLAM, a particle filter-based algorithm for appearance-based localization and mapping, to provide constant computation and memory requirements over time and minimal degradation of recall performance during repeated visits to locations. We demonstrate loop closure detection in a large urban environment with capped computation time and memory requirements and performance exceeding previous appearance-based methods by a factor of 2. We discuss the limitations of the algorithm with respect to environment size, appearance change over time and applications in topological planning and navigation for long-term robot operation.

Multiple cameras for audio-visual speech recognition in an automotive environment

Audio-visualspeechrecognition, or the combination of visual lip-reading with traditional acoustic speechrecognition, has been previously shown to provide a considerable improvement over acoustic-only approaches in noisy environments, such as that present in an automotive cabin. The research presented in this paper will extend upon the established audio-visualspeechrecognition literature to show that further improvements in speechrecognition accuracy can be obtained when multiple frontal or near-frontal views of a speaker's face are available. A series of visualspeechrecognition experiments using a four-stream visual synchronous hidden Markov model (SHMM) are conducted on the four-camera AVICAR automotiveaudio-visualspeech database. We study the relative contribution between the side and central orientated cameras in improving visualspeechrecognition accuracy. Finally combination of the four visual streams with a single audio stream in a five-stream SHMM demonstrates a relative improvement of over 56% in word recognition accuracy when compared to the acoustic-only approach in the noisiest conditions of the AVICAR database.

Forecasting success on large projects : developing reliable scales to predict multiple perspectives by multiple stakeholders over multiple time frames

Our aim is to develop a set of leading performance indicators to enable managers of large projects to forecast during project execution how various stakeholders will perceive success months or even years into the operation of the output. Large projects have many stakeholders who have different objectives for the project, its output, and the business objectives they will deliver. The output of a large project may have a lifetime that lasts for years, or even decades, and ultimate impacts that go beyond its immediate operation. How different stakeholders perceive success can change with time, and so the project manager needs leading performance indicators that go beyond the traditional triple constraint to forecast how key stakeholders will perceive success months or even years later. In this article, we develop a model for project success that identifies how project stakeholders might perceive success in the months and years following a project. We identify success or failure factors that will facilitate or mitigate against achievement of those success criteria, and a set of potential leading performance indicators that forecast how stakeholders will perceive success during the life of the project's output. We conducted a scale development study with 152 managers of large projects and identified two project success factor scales and seven stakeholder satisfaction scales that can be used by project managers to predict stakeholder satisfaction on projects and so may be used by the managers of large projects for the basis of project control.

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

The Multidimensional Loss Scale : validating a cross cultural instrument for measuring loss in Burmese refugees

The Multidimensional Loss Scale: Initial Development and Psychometric Evaluation The Multidimensional Loss Scale (MLS) represents the first instrument designed specifically to measure loss in refugee populations. Researchers developed initial items of the Multidimensional Loss Scale to assess Experience of Loss Events and Loss Distress in a culturally sensitive manner across multiple domains (social, material, intra-personal and cultural). A sample of 70 recently settled Burmese adult refugees completed a battery of questionnaires, including new scale items. Analyses explored the scale’s factor structure, internal consistency, convergent validity and divergent validity. Principal Axis Factoring supported a five-factor model: Loss of Symbolic Self, Loss of Interdependence, Loss of Home, Interpersonal Loss, and Loss of Intrapersonal Integrity. Chronbach’s Alphas indicated satisfactory internal consistency for Experience of Loss Events (.85) and Loss Distress (.92). Convergent and divergent validity of Loss Distress were supported by moderate correlations with interpersonal grief and trauma symptoms and weak correlations with depression and anxiety. The new scale was well received by people from refugee backgrounds and shows promise for application in future research and practice

Speaker attribution of multiple telephone conversations using a complete-linkage clustering approach

In this paper we propose and evaluate a speaker attribution system using a complete-linkage clustering method. Speaker attribution refers to the annotation of a collection of spoken audio based on speaker identities. This can be achieved using diarization and speaker linking. The main challenge associated with attribution is achieving computational efficiency when dealing with large audio archives. Traditional agglomerative clustering methods with model merging and retraining are not feasible for this purpose. This has motivated the use of linkage clustering methods without retraining. We first propose a diarization system using complete-linkage clustering and show that it outperforms traditional agglomerative and single-linkage clustering based diarization systems with a relative improvement of 40% and 68%, respectively. We then propose a complete-linkage speaker linking system to achieve attribution and demonstrate a 26% relative improvement in attribution error rate (AER) over the single-linkage speaker linking approach.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data synchronization error and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. This paper first presents a brief review of the most inherent uncertainties of the SHM-oriented WSN platforms and then investigates their effects on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when employing merged data from multiple tests. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and Data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Experimental accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as clean data before being contaminated by different data pollutants in sequential manner to simulate practical SHM-oriented WSN uncertainties. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with SHM-WSN uncertainties. Finally, the use of the measurement channel projection for the time-domain OMA techniques and the preferred combination of the OMA techniques to cope with the SHM-WSN uncertainties is recommended.

Taxonomy and palaeoecology of Quaternary faunas from caves in eastern tropical Queensland : a record of broad-scale environmental change

Analysis of fossils from cave deposits at Mount Etna (eastern-central Queensland) has established that a species-rich rainforest palaeoenvironment existed in that area during the middle Pleistocene. This unexpected finding has implications for several fields (e.g., biogeography/phylogeography of rainforest-adapted taxa, and the impact of climate change on rainforest communities), but it was unknown whether the Mount Etna sites represented a small refugial patch of rainforest or was more widespread. In this study numerous bone deposits in caves in north-east Queensland are analysed to reconstruct the environmental history of the area during the late Quaternary. Study sites are in the Chillagoe/Mitchell Palmer and Broken River/Christmas Creek areas. The cave fossil records in these study areas are compared with dated (middle Pleistocene-Holocene) cave sites in the Mount Etna area. Substantial taxonomic work on the Mount Etna faunas (particularly dasyurid marsupials and murine rodents) is also presented as a prerequisite for meaningful comparison with the study sites further north. Middle Pleistocene sites at Mount Etna contain species indicative of a rainforest palaeoenvironment. Small mammal assemblages in the Mount Etna rainforest sites (>500-280 ka) are unexpectedly diverse and composed almost entirely of new species. Included in the rainforest assemblages are lineages with no extant representatives in rainforest (e.g., Leggadina), one genus previously known only from New Guinea (Abeomelomys), and forms that appear to bridge gaps between related but morphologically-divergent extant taxa ('B-rat' and 'Pseudomys C'). Curiously, some taxa (e.g., Melomys spp.) are notable for their absence from the Mount Etna rainforest sites. After 280 ka the rainforest faunas are replaced by species adapted to open, dry habitats. At that time the extinct ‘rainforest’ dasyurids and rodents are replaced by species that are either extant or recently extant. By the late Pleistocene all ‘rainforest’ and several ‘dry’ taxa are locally or completely extinct, and the small mammal fauna resembles that found in the area today. The faunal/environmental changes recorded in the Mount Etna sites were interpreted by previous workers as the result of shifts in climate during the Pleistocene. Many samples from caves in the Chillagoe/Mitchell-Palmer and Broken River/Christmas Creek areas are held in the Queensland Museum’s collection. These, supplemented with additional samples collected in the field as well as samples supplied by other workers, were systematically and palaeoecologically analysed for the first time. Palaeoecological interpretation of the faunal assemblages in the sites suggests that they encompass a similar array of palaeoenvironments as the Mount Etna sites. ‘Rainforest’ sites at the Broken River are here interpreted as being of similar age to those at Mount Etna, suggesting the possibility of extensive rainforest coverage in eastern tropical Queensland during part of the Pleistocene. Likewise, faunas suggesting open, dry palaeoenvironments are found at Chillagoe, the Broken River and Mount Etna, and may be of similar age. The 'dry' faunal assemblage at Mount Etna (Elephant hole Cave) dates to 205-170 ka. Dating of one of the Chillagoe sites (QML1067) produced a maximum age for the deposit of approximately 200 ka, and the site is interpreted as being close to that age, supporting the interpretation of roughly contemporaneous deposition at Mount Etna and Chillagoe. Finally, study sites interpreted as being of late Pleistocene-Holocene age show faunal similarities to sites of that age near Mount Etna. This study has several important implications for the biogeography and phylogeography of murine rodents, and represents a major advance in the study of the Australian murine fossil record. Likewise the survey of the northern study areas is the first systematic analysis of multiple sites in those areas, and is thus a major contribution to knowledge of tropical Australian faunas during the Quaternary. This analysis suggests that climatic changes during the Pleistocene affected a large area of eastern tropical Queensland in similar ways. Further fieldwork and dating is required to properly analyse the geographical extent and timing of faunal change in eastern tropical Queensland.

The effect of juice composition on the scaling rate and scale composition in sugar mill evaporators

To date, the formation of deposits on heat exchanger surfaces is the least understood problem in the design of heat exchangers for processing industries. Dr East has related the structure of the deposits to solution composition and has developed predictive models for composite fouling of calcium oxalate and silica in sugar factory evaporators.

Achieving high reliability for ambiguity resolutions with multiple GNSS constellations

Global Navigation Satellite Systems (GNSS)-based observation systems can provide high precision positioning and navigation solutions in real time, in the order of subcentimetre if we make use of carrier phase measurements in the differential mode and deal with all the bias and noise terms well. However, these carrier phase measurements are ambiguous due to unknown, integer numbers of cycles. One key challenge in the differential carrier phase mode is to fix the integer ambiguities correctly. On the other hand, in the safety of life or liability-critical applications, such as for vehicle safety positioning and aviation, not only is high accuracy required, but also the reliability requirement is important. This PhD research studies to achieve high reliability for ambiguity resolution (AR) in a multi-GNSS environment. GNSS ambiguity estimation and validation problems are the focus of the research effort. Particularly, we study the case of multiple constellations that include initial to full operations of foreseeable Galileo, GLONASS and Compass and QZSS navigation systems from next few years to the end of the decade. Since real observation data is only available from GPS and GLONASS systems, the simulation method named Virtual Galileo Constellation (VGC) is applied to generate observational data from another constellation in the data analysis. In addition, both full ambiguity resolution (FAR) and partial ambiguity resolution (PAR) algorithms are used in processing single and dual constellation data. Firstly, a brief overview of related work on AR methods and reliability theory is given. Next, a modified inverse integer Cholesky decorrelation method and its performance on AR are presented. Subsequently, a new measure of decorrelation performance called orthogonality defect is introduced and compared with other measures. Furthermore, a new AR scheme considering the ambiguity validation requirement in the control of the search space size is proposed to improve the search efficiency. With respect to the reliability of AR, we also discuss the computation of the ambiguity success rate (ASR) and confirm that the success rate computed with the integer bootstrapping method is quite a sharp approximation to the actual integer least-squares (ILS) method success rate. The advantages of multi-GNSS constellations are examined in terms of the PAR technique involving the predefined ASR. Finally, a novel satellite selection algorithm for reliable ambiguity resolution called SARA is developed. In summary, the study demonstrats that when the ASR is close to one, the reliability of AR can be guaranteed and the ambiguity validation is effective. The work then focuses on new strategies to improve the ASR, including a partial ambiguity resolution procedure with a predefined success rate and a novel satellite selection strategy with a high success rate. The proposed strategies bring significant benefits of multi-GNSS signals to real-time high precision and high reliability positioning services.