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.

Adapting a generic BEAMnrc model of the BrainLAB m3 micro-multileaf collimator to simulate a local collimation device

This work is focussed on developing a commissioning procedure so that a Monte Carlo model, which uses BEAMnrc’s standard VARMLC component module, can be adapted to match a specific BrainLAB m3 micro-multileaf collimator (μMLC). A set of measurements are recommended, for use as a reference against which the model can be tested and optimised. These include radiochromic film measurements of dose from small and offset fields, as well as measurements of μMLC transmission and interleaf leakage. Simulations and measurements to obtain μMLC scatter factors are shown to be insensitive to relevant model parameters and are therefore not recommended, unless the output of the linear accelerator model is in doubt. Ultimately, this note provides detailed instructions for those intending to optimise a VARMLC model to match the dose delivered by their local BrainLAB m3 μMLC device.

Predictions of road traffic noise on residential balconies using a specular and diffusion model

A combined specular reflection and diffusion model using the radiosity technique was developed to calculate road traffic noise level on residential balconies. The model is capable of numerous geometrical configurations for a single balcony situated in the centre of a street canyon. The geometry of the balcony and the street can be altered with width,length and height. The model was used to calculate for three different geometrical and acoustic absorption characteristics for a balcony. The calculated results are presented in this paper.

An intelligent building model of sustainable architecture in a mechanism of open systems evolution

This paper argues a model of open systems evolution based on evolutionary thermodynamics and complex system science, as a design paradigm for sustainable architecture. The mechanism of open system evolution is specified in mathematical simulations and theoretical discourses. According to the mechanism, the authors propose an intelligent building model of sustainable design by a holistic information system of the end-users, the building and nature. This information system is used to control the consumption of energy and material resources in building system at microscopic scale, to adapt the environmental performance of the building system to the natural environment at macroscopic scale, for an evolutionary emergence of sustainable performance of buildings.

Maternal administration of erythromycin fails to eradicate intrauterine ureaplasma infection in an ovine model

Erythromycin is the standard antibiotic used for treatment of Ureaplasma species during 3 pregnancy; however, maternally administered erythromycin may be ineffective at eliminating 4 intra-amniotic ureaplasma infections. We asked if erythromycin would eradicate intra-amniotic 5 ureaplasma infections in pregnant sheep. At 50 days of gestation (d, term=150d) pregnant ewes 6 received intra-amniotic injections of erythromycin-sensitive U. parvum serovar 3 (n=16) or 10B 7 medium (n=16). At 100d, amniocentesis was performed; five fetal losses (ureaplasma group: 8 n=4; 10B group: n=1) had occurred by this time. Remaining ewes were allocated into treatment 9 subgroups: medium only (M, n=7); medium and erythromycin (M/E, n=8); ureaplasma only (Up, 10 n=6) or ureaplasma and erythromycin (Up/E, n=6). Erythromycin was administered intra11 muscularly (500 mg), eight-hourly for four days (100d-104d). Amniotic fluid samples were 12 collected at 105d. At 125d preterm fetuses were surgically delivered and specimens were 13 collected for culture and histology. Erythromycin was quantified in amniotic fluid by liquid 14 chromatography-mass spectrometry. Ureaplasmas were isolated from the amniotic fluid, 15 chorioamnion and fetal lung of animals from the Up and Up/E groups, however, the numbers of 16 U. parvum recovered were not different between these groups. Inflammation in the 17 chorioamnion, cord and fetal lung was increased in ureaplasma-exposed animals compared to 18 controls, but was not different between the Up and Up/E groups. Erythromycin was detected in 19 amniotic fluid samples, although concentrations were low (<10-76 ng/mL). This study 20 demonstrates that maternally administered erythromycin does not eradicate chronic, intra- amniotic ureaplasma infections or improve fetal outcomes in an ovine model, potentially due to 22 the poor placental passage of erythromycin.

CYR61 (CCN1) protein expression during fracture healing in an ovine tibial model and its relation to the mechanical fixation stability

The formation of new blood vessels is a prerequisite for bone healing. CYR61 (CCN1), an extracellular matrix-associated signaling protein, is a potent stimulator of angiogenesis and mesenchymal stem cell expansion and differentiation. A recent study showed that CYR61 is expressed during fracture healing and suggested that CYR61 plays a significant role in cartilage and bone formation. The hypothesis of the present study was that decreased fixation stability, which leads to a delay in healing, would lead to reduced CYR61 protein expression in fracture callus. The aim of the study was to quantitatively analyze CYR61 protein expression, vascularization, and tissue differentiation in the osteotomy gap and relate to the mechanical fixation stability during the course of healing. A mid-shaft osteotomy of the tibia was performed in two groups of sheep and stabilized with either a rigid or semirigid external fixator, each allowing different amounts of interfragmentary movement. The sheep were sacrificed at 2, 3, 6, and 9 weeks postoperatively. The tibiae were tested biomechanically and histological sections from the callus were analyzed immunohistochemically with regard to CYR61 protein expression and vascularization. Expression of CYR61 protein was upregulated at the early phase of fracture healing (2 weeks), decreasing over the healing time. Decreased fixation stability was associated with a reduced upregulation of the CYR61 protein expression and a reduced vascularization at 2 weeks leading to a slower healing. The maximum cartilage callus fraction in both groups was reached at 3 weeks. However, the semirigid fixator group showed a significantly lower CYR61 immunoreactivity in cartilage than the rigid fixator group at this time point. The fraction of cartilage in the semirigid fixator group was not replaced by bone as quickly as in the rigid fixator group leading to an inferior histological and mechanical callus quality at 6 weeks and therefore to a slower healing. The results supply further evidence that CYR61 may serve as an important regulator of bone healing.

The Canadian ‘Model Forest’ approach : a way forward for Tasmania?

Forest policy and forestry management in Tasmania have undergone a number of changes in the last thirty years, many explicitly aimed at improving industry sustainability, job security, and forest biodiversity conservation. Yet forestry remains a contentious issue in Tasmania, due to a number of interacting factors, most significant of which is the prevalence of a ‘command and control’ governance approach by policymakers and managers. New approaches such as multiple-stakeholder decision-making, adaptive management, and direct public participation in policymaking are needed. Such an approach has been attempted in Canada in the last decade, through the Canadian Model Forest Program, and may be suitable for Tasmania. This paper seeks to describe what the Canadian Model Forest approach is, how it may be implemented in Tasmania, and what role it may play in the shift to a new forestry paradigm. Until such a paradigm shift occurs contentions and confrontations are likely to continue.

Towards a model for the analysis and description of mathematical knowledge and understanding

Mathematics education literature has called for an abandonment of ontological and epistemological ideologies that have often divided theory-based practice. Instead, a consilience of theories has been sought which would leverage the strengths of each learning theory and so positively impact upon contemporary educational practice. This research activity is based upon Popper’s notion of three knowledge worlds which differentiates the knowledge shared in a community from the personal knowledge of the individual, and Bereiter’s characterisation of understanding as the individual’s relationship to tool-like knowledge. Using these notions, a re-conceptualisation of knowledge and understanding and a subsequent re-consideration of learning theories are proposed as a way to address the challenge set by literature. Referred to as the alternative theoretical framework, the proposed theory accounts for the scaffolded transformation of each individual’s unique understanding, whilst acknowledging the existence of a body of domain knowledge shared amongst participants in a scientific community of practice. The alternative theoretical framework is embodied within an operational model that is accompanied by a visual nomenclature with which to describe consensually developed shared knowledge and personal understanding. This research activity has sought to iteratively evaluate this proposed theory through the practical application of the operational model and visual nomenclature to the domain of early-number counting, addition and subtraction. This domain of mathematical knowledge has been comprehensively analysed and described. Through this process, the viability of the proposed theory as a tool with which to discuss and thus improve the knowledge and understanding with the domain of mathematics has been validated. Putting of the proposed theory into practice has lead to the theory’s refinement and the subsequent achievement of a solid theoretical base for the future development of educational tools to support teaching and learning practice, including computer-mediated learning environments. Such future activity, using the proposed theory, will advance contemporary mathematics educational practice by bringing together the strengths of cognitivist, constructivist and post-constructivist learning theories.

An entrepreneurial model of economic and environmental co-evolution

A basic tenet of ecological economics is that economic growth and development are ultimately constrained by environmental carrying capacities. It is from this basis that notions of a sustainable economy and of sustainable economic development emerge to undergird the “standard model” of ecological economics. However, the belief in “hard” environmental constraints may be obscuring the important role of the entrepreneur in the co-evolution of economic and environmental relations, and hence limiting or distorting the analytic focus of ecological economics and the range of policy options that are considered for sustainable economic development. This paper outlines a co-evolutionary model of the dynamics of economic and ecological systems as connected by entrepreneurial behaviour. We then discuss some of the key analytic and policy implications.