Social capital in a tolerant society? a response to treasurer Costello’s presentation to the Sydney Institute

On 16 July 2003 the Australian Government’s Treasurer, the Honourable Peter Costello, addressed the Sydney Institute on his vision for a tolerant Australian society. Drawing from Putnam’s research Costello argued that tolerance will flow from an active voluntary sector. However, Costello’s proposed model for a “tolerant” society fails to acknowledge that all behaviour is strategic and that voluntary association based on disinterested action is not possible. Ultimately, Treasurer Costello’s notion of tolerant society fuelled by an active voluntary sector may not lead to his desired end. Indeed, the promotion of voluntary association may contribute to the ongoing exclusion of citizens with low social capital. Amid the dynamism of the modern era some citizens such as ageing workers, slow or ineffective learners and women may be especially at risk. Accordingly, rather than advocating a policy approach that seeks to build inclusiveness through an active voluntary sector this paper recommends a policy approach that explores ways in which to strengthen learning and the wider involvement of individuals through both formal and informal means.

Protecting the protectors : legal liabilities from the use of Web 2.0 for Australian disaster response

The traditional model for information dissemination in disaster response is unidirectional from official channels to the public. However recent crises in the US, such as Hurricane Katrina and the Californian Bushfires show that civilians are now turning to Web 2.0 technologies as a means of sharing disaster related information. These technologies present enormous potential benefits to disaster response authorities that cannot be overlooked. In Australia, the Victorian Bushfires Royal Commission has recently recommended that Australian disaster response authorities utilize information technologies to improve the dissemination of disaster related, bushfire information. However, whilst the use of these technologies has many positive attributes, potential legal liabilities for disaster response authorities arise. This paper identifies some potential legal liabilities arising from the use of Web 2.0 technologies in disaster response situations thereby enhancing crisis related information sharing by highlighting legal concerns that need to be addressed.

Airborne vision-based collision-detection system

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and TCAS). This paper describes the development and evaluation of a real-time vision-based collision detection system suitable for fixed-wing aerial robotics. Using two fixed-wing UAVs to recreate various collision-course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We overcame the challenge of achieving real-time computational speeds by exploiting the parallel processing architectures of graphics processing units found on commercially-off-the-shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real-time processing of 1024 by 768 pixel image frames at a rate of approximately 30Hz. Flight trials using manned Cessna aircraft where all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms.

Sex on premises venue (SOPV) health promotion project in response to sustained increases in HIV notifications

Background: Due to increases in HIV notifications in Queensland, a health promotion outreach project was developed with sex on premises venues (SOPV) in Brisbane. Methods: A health promotion officer (HPO) promoted safer sex behaviours among SOPV patrons over 14 months, including providing information, counselling and skills to enhance safer sexual behaviours and providing referrals. Surveys were introduced to facilitate discussions regarding HIV/sexually transmissible infections, testing and safer sex practices. Results: The project demonstrated feasibility within this highly sexualised environment, and was enhanced by careful monitoring and revising the procedure to improve patron/staff responses to the project. The introduction of a survey instrument was a significant contributor to the project’s effectiveness, providing opportunities for patrons to discuss a variety of key sexual health issues. Conclusions: This initiative reflected effective partnering between the Health Department, a community HIV/lesbian, gay, bisexual and transgender organisation and private industry. Despite initial difficulties, the presence of a health worker within an SOPV was acceptable to patrons and allowed for brief interventions to be conducted. This project was deemed effective for a limited time period and within certain constraints.

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.

Low fat loss response after medium-term supervised exercise in obese is associated with exercise-induced increase in food reward

Objective: To examine exercise-induced changes in the reward value of food during medium-term supervised exercise in obese individuals. ---------- Subjects/Methods: The study was a 12-week supervised exercise intervention prescribed to expend 500 kcal/day, 5 d/week. 34 sedentary obese males and females were identified as responders (R) or non-responders (NR) to the intervention according to changes in body composition relative to measured energy expended during exercise. Food reward (ratings of liking and wanting, and relative preference by forced choice pairs) for an array of food images was assessed before and after an acute exercise bout. ---------- Results. 20 responders and 14 non-responders were identified. R lost 5.2 kg±2.4 of total fat mass and NR lost 1.7 kg±1.4. After acute exercise, liking for all foods increased in NR compared to no change in R. Furthermore, NR showed an increase in wanting and relative preference for high-fat sweet foods. These differences were independent of 12-weeks regular exercise and weight loss. ---------- Conclusion. Individuals who showed an immediate post-exercise increase in liking and increased wanting and preference for high-fat sweet foods displayed a smaller reduction in fat mass with exercise. For some individuals, exercise increases the reward value of food and diminishes the impact of exercise on fat loss.