Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

The Large Allen Cognitive Level Screen as an indicator for medication adherence among adults accessing community mental health services

Background: Medication remains the cornerstone treatment for mental illness. Cognition is one of the strongest predictors of non-adherence. The aim of this preliminary investigation was to examine the association between the Large Allen Cognitive Level Screen (LACLS) and medication adherence among a small sample of mental health service users to determine whether the LACLS has potential as a screening tool for capacity to manage medication regimens. Method: Demographic and clinical information was collected from a small sample of people who had recently accessed community mental health services. Participants then completed the LACLS and the Medication Adherence Rating Scale (MARS) at a single time point. The strength of association between the LACLS and MARS was examined using Spearman rank-order correlation. Results: A strong positive correlation between the LACLS and medication adherence (r = 0.71, p = 0.01) was evident. No participants reported the use of medication aids despite evidence of impaired cognitive functioning. Conclusion: This investigation has provided the first empirical evidence indicating that the LACLS may have utility as a screening instrument for capacity to manage medication adherence among this population. While promising, this finding should be interpreted with caveats given its preliminary nature.

Numerical simulation of anomalous diffusion with application to medical imaging

The first objective of this project is to develop new efficient numerical methods and supporting error and convergence analysis for solving fractional partial differential equations to study anomalous diffusion in biological tissue such as the human brain. The second objective is to develop a new efficient fractional differential-based approach for texture enhancement in image processing. The results of the thesis highlight that the fractional order analysis captured important features of nuclear magnetic resonance (NMR) relaxation and can be used to improve the quality of medical imaging.

A microscopic simulation model to estimate bus rapid transit station bus capacity

The Bus Rapid Transit (BRT) station is the interface between passengers and services. The station is crucial to line operation as it is typically the only location where buses can pass each other. Congestion may occur here when buses maneuvering into and out of the platform lane interfere with bus flow, or when a queue of buses forms upstream of the platform lane blocking the passing lane. Further, some systems include operation where express buses do not observe the station, resulting in a proportion of non-stopping buses. It is important to understand the operation of the station under this type of operation and its effect on BRT line capacity. This study uses microscopic traffic simulation modeling to treat the BRT station operation and to analyze the relationship between station bus capacity and BRT line bus capacity. First, the simulation model is developed for the limit state scenario and then a statistical model is defined and calibrated for a specified range of controlled scenarios of dwell time characteristics. A field survey was conducted to verify the parameters such as dwell time, clearance time and coefficient of variation of dwell time to obtain relevant station bus capacity. The proposed model for BRT bus capacity provides a better understanding of BRT line capacity and is useful to transit authorities in BRT planning, design and operation.

Simulation of retrofitted unreinforced concrete masonry unit walls under blast loading

This paper describes an investigation into the effectiveness of using spray-on nano-particle reinforced polymer and aluminium foam as new types of retrofit material to prevent the breaching and collapse of unreinforced concrete masonry walls subjected to blast over a whole range of dynamic and impulsive regimes. Material models from the LSDYNA material library were used to model the behaviors of each of the materials and its interface for retrofitted and unretrofitted masonry walls. Available test data were used to validate the numerical models. Using the validated LS-DYNA numerical models, the pressure-impulse diagrams for retrofitted concrete masonry walls were constructed. The efficiency of using these retrofits to strengthen the unreinforced concrete masonry unit (CMU) walls under various pressures and impulses was investigated using pressure-impulse diagrams. Comparisons were made to find the most efficient retrofits for masonry walls against blasts.

Sequential decision fusion of multibiometrics applied to text-dependent speaker verification for controlled errors

Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.

Dynamic simulation of a light-weight, low-drag, hybrid-electric sports coupe

The University of Queensland UltraCommuter concept is an ultra- light, low-drag, hybrid-electric sports coupe designed to minimize energy consumption and environmental impact while enhancing the performance, styling, features and convenience that motorists enjoy. This paper presents a detailed simulation study of the vehicle's performance and fuel economy using ADVISOR, including a detailed description of the component models and parameters assumed. Results from the study include predictions of a 0-100 kph acceleration time of ≺9s, and top speed of 170 kph, an electrical energy consumption of ≺67 Wh/km in ZEV mode and a petrol-equivalent fuel consumption of ≺2.5 L/100 km in charge-sustaining HEV mode. Overall, the results of the ADVISOR modelling confirm the UltraCommuter's potential to achieve high performance with high efficiency, and the authors look forward to a confirmation of these estimates following completion of the vehicle.

Computer simulation of the fatigue behaviour of roof cladding during the passage of a tropical cyclone

This paper describes the development of an analytical model used to simulate the fatigue behaviour of roof cladding during the passage of a tropical cyclone. The model incorporated into a computer program uses wind pressure data from wind tunnel tests in combination with time history information on wind speed and direction during a tropical cyclone, and experimental fatigue characteristics data of roof claddings. The wind pressure data is analysed using a rainflow form of analysis, and a fatigue damage index calculated using a modified form of Miner's rule. Some of the results obtained to date and their significance in relation to the review of current fatigue tests are presented. The model appears to be reasonable for comparative estimation of fatigue life, but an improvement of Miner's rule is required for the prediction of actual fatigue life.

The ECOS Project. An interactive simulation to promote an ECO-friendly universe.

Custom designed for display on the Cube Installation situated in the new Science and Engineering Centre (SEC) at QUT, the ECOS project is a playful interface that uses real-time weather data to simulate how a five-star energy building operates in climates all over the world. In collaboration with the SEC building managers, the ECOS Project incorporates energy consumption and generation data of the building into an interactive simulation, which is both engaging to users and highly informative, and which invites play and reflection on the roles of green buildings. ECOS focuses on the principle that humans can have both a positive and negative impact on ecosystems with both local and global consequence. The ECOS project draws on the practice of Eco-Visualisation, a term used to encapsulate the important merging of environmental data visualization with the philosophy of sustainability. Holmes (2007) uses the term Eco-Visualisation (EV) to refer to data visualisations that ‘display the real time consumption statistics of key environmental resources for the goal of promoting ecological literacy’. EVs are commonly artifacts of interaction design, information design, interface design and industrial design, but are informed by various intellectual disciplines that have shared interests in sustainability. As a result of surveying a number of projects, Pierce, Odom and Blevis (2008) outline strategies for designing and evaluating effective EVs, including ‘connecting behavior to material impacts of consumption, encouraging playful engagement and exploration with energy, raising public awareness and facilitating discussion, and stimulating critical reflection.’ Consequently, Froehlich (2010) and his colleagues also use the term ‘Eco-feedback technology’ to describe the same field. ‘Green IT’ is another variation which Tomlinson (2010) describes as a ‘field at the juncture of two trends… the growing concern over environmental issues’ and ‘the use of digital tools and techniques for manipulating information.’ The ECOS Project team is guided by these principles, but more importantly, propose an example for how these principles may be achieved. The ECOS Project presents a simplified interface to the very complex domain of thermodynamic and climate modeling. From a mathematical perspective, the simulation can be divided into two models, which interact and compete for balance – the comfort of ECOS’ virtual denizens and the ecological and environmental health of the virtual world. The comfort model is based on the study of psychometrics, and specifically those relating to human comfort. This provides baseline micro-climatic values for what constitutes a comfortable working environment within the QUT SEC buildings. The difference between the ambient outside temperature (as determined by polling the Google Weather API for live weather data) and the internal thermostat of the building (as set by the user) allows us to estimate the energy required to either heat or cool the building. Once the energy requirements can be ascertained, this is then balanced with the ability of the building to produce enough power from green energy sources (solar, wind and gas) to cover its energy requirements. Calculating the relative amount of energy produced by wind and solar can be done by, in the case of solar for example, considering the size of panel and the amount of solar radiation it is receiving at any given time, which in turn can be estimated based on the temperature and conditions returned by the live weather API. Some of these variables can be altered by the user, allowing them to attempt to optimize the health of the building. The variables that can be changed are the budget allocated to green energy sources such as the Solar Panels, Wind Generator and the Air conditioning to control the internal building temperature. These variables influence the energy input and output variables, modeled on the real energy usage statistics drawn from the SEC data provided by the building managers.

Simulation of Au particle interaction on graphene sheets

The interaction of Au particles with few layer graphene is of interest for the formation of the next generation of sensing devices(1). In this paper we investigate the coupling of single gold nanoparticles to a graphene sheet, and multiple gold nanoparticles with a graphene sheet using COMSOL Multiphysics. By using these simulations we are able to determine the electric field strength and associated hot-spots for various gold nanoparticle-graphene systems. The Au nanoparticles were modelled as 8 nm diameter spheres on 1.5 nm thick (5 layers) graphene, with properties of graphene obtained from the refractive index data of Weber(2) and the Au refractive index data from Palik(3). The field was incident along the plane of the sheet with polarisation tested for both s and p. The study showed strong localised interaction between the Au and graphene with limited spread; however the double particle case where the graphene sheet separated two Au nanoparticles showed distinct interaction between the particles and graphene. An offset was introduced (up to 4 nm) resulting in much reduced coupling between the opposed particles as the distance apart increased. Findings currently suggest that the graphene layer has limited interaction with incident fields with a single particle present whilst reducing the coupling region to a very fine area when opposing particles are involved. It is hoped that the results of this research will provide insight into graphene-plasmon interactions and spur the development of the next generation of sensing devices.

Workflow resource pattern simulation and visualization

This thesis addresses the process simulation and validation in Business Process Management. It proposes that the hybrid Multi Agent System (MAS) / 3D Virtual World approach is a valid method for better simulating the behaviour of human resources in business processes, supporting a wide range of rich visualization applications that can facilitate communication between business analysts and stakeholders. It is expected that the findings of this thesis may be fruitfully extended from BPM to other application domains, such as social simulation in video games and computer-based training animations.

Computational simulation of the early stage of bone healing under different configurations of locking compression plates

Flexible fixation or the so-called ‘biological fixation’ has been shown to encourage the formation of fracture callus, leading to better healing outcomes. However, the nature of the relationship between the degree of mechanical stability provided by a flexible fixation and the optimal healing outcomes has not been fully understood. In this study, we have developed a validated quantitative model to predict how cells in fracture callus might respond to change in their mechanical microenvironment due to different configurations of locking compression plate (LCP) in clinical practice, particularly in the early stage of healing. The model predicts that increasing flexibility of the LCP by changing the bone–plate distance (BPD) or the plate working length (WL) could enhance interfragmentary strain in the presence of a relatively large gap size (.3 mm). Furthermore, conventional LCP normally results in asymmetric tissue development during early stage of callus formation, and the increase of BPD or WL is insufficient to alleviate this problem.

Is foot ulcer simulation training (FUST) really effective? Participants’ supervisors speak out

Background Foot ulcers are a common reason for diabetes-related hospitalisation. Foot ulcer simulation training (FUST) programs have increased podiatry participants self-confidence to manage foot ulcers. However, supervisors’ perspectives on their participants attending these simulation programs have not been investigated. This mixed method (quantitative and qualitative) study aimed to investigate home clinical supervisors’ perspectives on any changes to their participants’ competence and practice following FUST. Methods Clinical supervisors of fifteen podiatrists, who participated in a two-day Foot Ulcer Simulation Training (FUST) course, were recruited. Supervisors completed quantitative surveys evaluating their participants’ foot ulcer competence pre-FUST and 6-months post-FUST, via a purposed designed 21-item survey using a five-point Likert scale (1=Very limited, 5=Highly competent). Supervisors also attended a semi-structured qualitative group interview to investigate supervisors’ perspectives on FUST. Results Supervisors surveys returned were pre-FUST (n=10) and post-FUST (n=12). Significant competence improvements were observed at the 6-month survey (mean scores 2.84 cf. 3.72, p < 0.05). Five supervisors attended the group interview. Five sub-themes emerged: i) FUST provided a good foundation for future learning, ii) FUST modelled good clinical behaviour, iii) clinical practice improvement was evident in most participants, iv) clinical improvements were dependent on participant’s willingness to change and existing workplace culture, v) FUST needs to be reinforced back in the home clinic. Conclusion Overall, supervisors of FUST participants indicated that the course improved their participants’ competence and clinical practice. However, the degree of improvement appears dependant on the participants’ home workplace culture and willingness to embrace change.

Foot ulcer simulation training (FUST) : are podiatrists FUST with long-term clinical confidence?

Background Foot ulcers are a leading cause of diabetes-related hospitalisations. Clinical training has been shown to be beneficial in foot ulcer management. Recently, improved self-confidence in podiatrists was reported immediately after foot ulcer simulation training (FUST) pilot programs. This study aimed to investigate the longer-term impacts of the FUST program on podiatrists’ self-confidence over 12 months in a larger sample. Methods Participants were podiatrists attending a two-day FUST course comprising web-based interactive learning, low-fidelity part-tasks and high-fidelity full clinical scenarios. Primary outcome measures included participants’ self-confidence measured pre-, (immediately) post-, 6-month post- and 12-month post-course via a purpose designed 21-item survey using a five-point Likert scale (1=Very limited, 5=Highly confident). Participants’ perceptions of knowledge gained, satisfaction, relevance and fidelity were also investigated. ANOVA and post hoc tests were used to test any differences between groups. Results Thirty-four participants completed FUST. Survey response rates were 100% (pre), 82% (post), 74% (6-month post), and 47% (12-month post). Overall mean scores were 3.13 (pre), 4.49 (post), 4.35 (6-month post) and 4.30 (12-month post) (p < 0.05); post hoc tests indicated no differences between the immediately, 6-month and 12-month post group scores (p > 0.05). Satisfaction, knowledge, relevance and fidelity were all rated highly. Conclusion This study suggests that significant short-term improvements in self-confidence to manage foot ulcers via simulation training are retained over the longer term. It is likely that improved self-confidence leads to improved foot ulcer clinical practice and outcomes; although this requires further research.