Cutting edge web

The artwork describes web as a network environment and a space where people are connected and as a result, it can reshape you as an interactive participant who is able to regenerate an object as a new form through a truly collaborative and cooperative interactions with others. The artwork has been created based on the research findings of characteristic of web: 1) Participatory (Slater 2002, p.536), 2) Communicational (Rheingold 1993), 3) Connected (Jordan 1999, 80), and 4) Stylising (Jordan 1999, 69). The artwork has conceptualised and visualised those characteristics of web based on principles of graphic design and visual communication.

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.

Theoretical analysis of serrated chip formation based on ideal models in high speed cutting

In this paper, two ideal formation models of serrated chips, the symmetric formation model and the unilateral right-angle formation model, have been established for the first time. Based on the ideal models and related adiabatic shear theory of serrated chip formation, the theoretical relationship among average tooth pitch, average tooth height and chip thickness are obtained. Further, the theoretical relation of the passivation coefficient of chip's sawtooth and the chip thickness compression ratio is deduced as well. The comparison between these theoretical prediction curves and experimental data shows good agreement, which well validates the robustness of the ideal chip formation models and the correctness of the theoretical deducing analysis. The proposed ideal models may have provided a simple but effective theoretical basis for succeeding research on serrated chip morphology. Finally, the influences of most principal cutting factors on serrated chip formation are discussed on the basis of a series of finite element simulation results for practical advices of controlling serrated chips in engineering application.

Outsourcing and cutting choice in private hospital menus – does it impact on patient foodservice satisfaction?

Patient satisfaction with foodservices is multidimensional. It is well recognised that food and other aspects of foodservice delivery are important elements of patients overall perception of the hospital experience. This study aimed to determine whether menu changes in 2008 at an acute private hospital, considered negative by the dietetic staff, would affect patient satisfaction with the foodservice. Changes to the menu, secondary to the refurbishment of the foodservice facilities decreased the number of choices at breakfast from six to four, and altered the dessert menu to include a larger proportion of commercially produced products. The Acute Care Hospital Foodservice Patient Satisfaction Questionnaire (ACHFPSQ) was utilised to assess patient satisfaction with the menu changes, as it has proven accuracy and reliability in measuring patient satisfaction. Results of the survey (n=306) were compared to data with previous ACHFPSQ surveys conducted annually since 2003. Data analysed included overall foodservice satisfaction and four dimensions of foodservice satisfaction: food quality, meal service quality, staff/service issues and the physical environment. Satisfaction targets were set at 4 (scale 1–5) for each foodservice dimension. Analysis showed that despite changes to the menu, overall foodservice satisfaction rated high, with a score of 4.3. Eighty-six percent of patients rated the foodservice as either ‘very good’ or ‘good’. The four foodservice dimensions were rated highly (4.2–4.8). Findings were consistent with previous survey results, demonstrating a high level of patient satisfaction across all dimensions of the foodservice, despite changes to the menu. The annual ACHFPSQ was of value to this practice question.

Beef carcasses with larger eye muscle areas, lower ossification scores and improved nutrition have a lower incidence of dark cutting

This study evaluated the effect of eye muscle area (EMA), ossification, carcass weight, marbling and rib fat depth on the incidence of dark cutting (pH u > 5.7) using routinely collected Meat Standards Australia (MSA) data. Data was obtained from 204,072 carcasses at a Western Australian processor between 2002 and 2008. Binomial data of pH u compliance was analysed using a logit model in a Bayesian framework. Increasing eye muscle area from 40 to 80 cm 2, increased pH u compliance by around 14% (P < 0.001) in carcasses less than 350 kg. As carcass weight increased from 150 kg to 220 kg, compliance increased by 13% (P < 0.001) and younger cattle with lower ossification were also 7% more compliant (P < 0.001). As rib fat depth increased from 0 to 20 mm, pH u compliance increased by around 10% (P < 0.001) yet marbling had no effect on dark cutting. Increasing musculature and growth combined with good nutrition will minimise dark cutting beef in Australia.