Characterization of sky-region morphological-temporal airborne collision detection

Automated airborne collision-detection systems are a key enabling technology for facilitat- ing the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety-critical systems must be sensitive enough to provide timely warnings of genuine air- borne collision threats, but not so sensitive as to cause excessive false-alarms. Hence, an accurate characterisation of detection and false alarm sensitivity is essential for understand- ing performance trade-offs, and system designers can exploit this characterisation to help achieve a desired balance in system performance. In this paper we experimentally evaluate a sky-region, image based, aircraft collision detection system that is based on morphologi- cal and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter back- ground). A novel collection methodology for collecting realistic airborne collision-course target footage in both head-on and tail-chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540m in 3 flight test cases with no false alarm events in 14.14 hours of non-target data (under cloudy conditions, the system achieved detection ranges greater than 1170m in 4 flight test cases with no false alarm events in 6.63 hours of non-target data). Importantly, this paper is the first documented presentation of detection range versus false alarm curves generated from airborne target and non-target image data.

Bayesian algorithms with applications

Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be considered by the applied scientist. Such sampling algorithms are particularly relevant to Bayesian statistics, since the target function is the posterior distribution of the unobservables given the observables. In this thesis we develop, adapt and apply Bayesian algorithms, whilst addressing substantive applied problems in biology and medicine as well as other applications. For an increasing number of high-impact research problems, the primary models of interest are often sufficiently complex that the likelihood function is computationally intractable. Rather than discard these models in favour of inferior alternatives, a class of Bayesian "likelihoodfree" techniques (often termed approximate Bayesian computation (ABC)) has emerged in the last few years, which avoids direct likelihood computation through repeated sampling of data from the model and comparing observed and simulated summary statistics. In Part I of this thesis we utilise sequential Monte Carlo (SMC) methodology to develop new algorithms for ABC that are more efficient in terms of the number of model simulations required and are almost black-box since very little algorithmic tuning is required. In addition, we address the issue of deriving appropriate summary statistics to use within ABC via a goodness-of-fit statistic and indirect inference. Another important problem in statistics is the design of experiments. That is, how one should select the values of the controllable variables in order to achieve some design goal. The presences of parameter and/or model uncertainty are computational obstacles when designing experiments but can lead to inefficient designs if not accounted for correctly. The Bayesian framework accommodates such uncertainties in a coherent way. If the amount of uncertainty is substantial, it can be of interest to perform adaptive designs in order to accrue information to make better decisions about future design points. This is of particular interest if the data can be collected sequentially. In a sense, the current posterior distribution becomes the new prior distribution for the next design decision. Part II of this thesis creates new algorithms for Bayesian sequential design to accommodate parameter and model uncertainty using SMC. The algorithms are substantially faster than previous approaches allowing the simulation properties of various design utilities to be investigated in a more timely manner. Furthermore the approach offers convenient estimation of Bayesian utilities and other quantities that are particularly relevant in the presence of model uncertainty. Finally, Part III of this thesis tackles a substantive medical problem. A neurological disorder known as motor neuron disease (MND) progressively causes motor neurons to no longer have the ability to innervate the muscle fibres, causing the muscles to eventually waste away. When this occurs the motor unit effectively ‘dies’. There is no cure for MND, and fatality often results from a lack of muscle strength to breathe. The prognosis for many forms of MND (particularly amyotrophic lateral sclerosis (ALS)) is particularly poor, with patients usually only surviving a small number of years after the initial onset of disease. Measuring the progress of diseases of the motor units, such as ALS, is a challenge for clinical neurologists. Motor unit number estimation (MUNE) is an attempt to directly assess underlying motor unit loss rather than indirect techniques such as muscle strength assessment, which generally is unable to detect progressions due to the body’s natural attempts at compensation. Part III of this thesis builds upon a previous Bayesian technique, which develops a sophisticated statistical model that takes into account physiological information about motor unit activation and various sources of uncertainties. More specifically, we develop a more reliable MUNE method by applying marginalisation over latent variables in order to improve the performance of a previously developed reversible jump Markov chain Monte Carlo sampler. We make other subtle changes to the model and algorithm to improve the robustness of the approach.

Detecting conversing groups of chatters : a model, algorithms, and tests

Chatrooms, for example Internet Relay Chat, are generally multi-user, multi-channel and multiserver chat-systems which run over the Internet and provide a protocol for real-time text-based conferencing between users all over the world. While a well-trained human observer is able to understand who is chatting with whom, there are no efficient and accurate automated tools to determine the groups of users conversing with each other. A precursor to analysing evolving cyber-social phenomena is to first determine what the conversations are and which groups of chatters are involved in each conversation. We consider this problem in this paper. We propose an algorithm to discover all groups of users that are engaged in conversation. Our algorithms are based on a statistical model of a chatroom that is founded on our experience with real chatrooms. Our approach does not require any semantic analysis of the conversations, rather it is based purely on the statistical information contained in the sequence of posts. We improve the accuracy by applying some graph algorithms to clean the statistical information. We present some experimental results which indicate that one can automatically determine the conversing groups in a chatroom, purely on the basis of statistical analysis.

Image Region Salience for Improving Appearance-Based Place Recognition using a Supervised Classifier System

Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.

Protection of Refugees and Displaced Persons in the Asia Pacific Region

The chapters in this book explore the impact of recent shifts in global and regional power and the subsequent development and enforcement of international refugee protection standards in the Asia Pacific region. Drawing on their expertise across a number of jurisdictions, the contributors assess the challenges confronting the implementation of international law in the region, as well as new opportunities for extending protection norms into national and regional dialogues. The case studies span key jurisdictions across the region and include a comparative analysis with China, Indonesia, Thailand, Myanmar, Malaysia, Bangladesh and Australia. This topical and important book raises critical questions for the Asia Pacific region and sheds light on the challenges confronting the protection of refugees and displaced persons in this area. Interdisciplinary in its approach, it will be of interest to academics, researchers, students and policy-makers concerned with the rights and protection of refugees.

ACE research vignette 024 : New firm formation and job creation by type of firm an type of region.

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. This vignette, written by Mr. Darren Kavanagh and Professor Per Davidsson, takes a closer look at job creation by new firms.

Adaptive dynamic path re-planning RRT algorithms with game theory for UAVs

The main aim of this paper is to describe an adaptive re-planning algorithm based on a RRT and Game Theory to produce an efficient collision free obstacle adaptive Mission Path Planner for Search and Rescue (SAR) missions. This will provide UAV autopilots and flight computers with the capability to autonomously avoid static obstacles and No Fly Zones (NFZs) through dynamic adaptive path replanning. The methods and algorithms produce optimal collision free paths and can be integrated on a decision aid tool and UAV autopilots.

Growing profitable or growing from profits : putting the horse in front of the cart?

Top lists of and praise for the economy's fastest growing firms abound in business media around the world. Similarly, in academic research there has been a tendency to equate firm growth with business success. This tendency appears to be particularly pronounced in-but not confined to­ entrepreneurship research. In this study we critically examine this tendency to portray firm growth as more or less universally favorable. While several theories suggest that growth drives profitability we first show that the available empirical evidence does not support the existence of a general, positive relation­ ship between growth and profitability. Using the theoretical lens of the Resource-Based View (RBV) we then argue that sound growth usually starts with achieving sufficient levels of profitability. In summary, our theoretical argument is as follows: In a population of SMEs, superior profitability is likely to be indicative of having built a resource-based competitive advantage. Building such a valuable and hard to-copy advantage may at first constrain growth. However, the underlying advantage itself and the financial resources generated through high profitability make it possible for firms in this situation to now achieve sound and sustainable growth - which may require building a series of temporary advantages- without having to sacrifice profitability. By contrast, when firms strive for high growth starting from low profitability, the latter often indicates lack of competitive advantage. Therefore growth must be achieved in head-to-head competition with equally attractive alternatives, leading to profitability deterioration rather than improvement. In addition, these low profitability firms are unlikely to be able to finance strategies toward building valuable and difficult-to-imitate advantages while growing.

UNCLOS and regional action for protection of the marine environment : perspectives of the South Asian Seas region

In very clear language the United Nations Convention on the Law of the Sea (UNCLOS) calls upon the parties to initiate regional action for protection of marine environment. Although the UNCLOS gives special recognition in various ways to developing countries, the South Asian developing countries continue to encounter some bottlenecks in complying with the provisions of the Convention relating to marine environment. Against this backdrop, this paper tends to examine the need for a regional approach towards conservation of marine environment. Moreover, the paper aims to explore possible ways to establish a regional legal framework for conservation of marine environment in South Asian region. In doing so, the paper critically examines existing mechanisms already in place including the South Asian Seas Programme and South Asian Seas Action Plan

The growing demand for emergency healthcare

Emergency health is a critical component of health systems; one increasingly congested from growing demand and blocked access to care. The Emergency Health Services Queensland (EHSQ) study aimed to identify the factors driving increased demand for emergency healthcare. This study examined data on patients treated by the ambulance service and Emergency Departments across Queensland. Data was derived from the Queensland Ambulance Service’s (QAS) Ambulance Information Management System and electronic Ambulance Report Form and from the Emergency Department Information System (EDIS). Data was obtained for the period 2001-02 through to 2009-10. A snapshot of users for the 2009-10 year was used to describe the characteristics of users and comparisons made with the year 2003-04 to identify trends. Per capita demand for EDs has increased by 2% per annum over the decade and for ambulance by 3.7% per annum. The growth in ED demand is most significant in more urgent triage categories with decline in less urgent patients. The growth is most prominent amongst patients suffering injuries and poisoning, amongst both men and women and across all age groups. Patients from lower socioeconomic areas appear to have higher utilisation rates and the utilisation rate for indigenous people exceeds those of other backgrounds. The utilisation rates for immigrant people is less than Australian born however it has not been possible to eliminate the confounding impact of age and socioeconomic profiles. These findings contribute to an understanding of the growth in demand for emergency health. It is evident that the growth is amongst patients in genuine need of emergency healthcare and public rhetoric that congested emergency health services is due to inappropriate attendees is unsustainable. The growth in demand over the last decade reflects not only on changing demographics of the Australian population but also changes in health status, standards of acute health care and other social factors.

Numerical solution of an optimal control model of dendritic cell treatment of a growing tumour

A new optimal control model of the interactions between a growing tumour and the host immune system along with an immunotherapy treatment strategy is presented. The model is based on an ordinary differential equation model of interactions between the growing tu- mour and the natural killer, cytotoxic T lymphocyte and dendritic cells of the host immune system, extended through the addition of a control function representing the application of a dendritic cell treat- ment to the system. The numerical solution of this model, obtained from a multi species Runge–Kutta forward-backward sweep scheme, is described. We investigate the effects of varying the maximum al- lowed amount of dendritic cell vaccine administered to the system and find that control of the tumour cell population is best effected via a high initial vaccine level, followed by reduced treatment and finally cessation of treatment. We also found that increasing the strength of the dendritic cell vaccine causes an increase in the number of natural killer cells and lymphocytes, which in turn reduces the growth of the tumour.

Urban agriculture : a growing field of research workshop at INTERACT 2013

Growing food presents diverse challenges and opportunities within the urban environment. As cities develop, population density rises, land prices rise, and the opportunity to use land for traditional farming and gardening diminishes. Counter to this trend there are a growing number of both community gardens, city farms, guerrilla gardening, rooftop and vertical gardens, pot plants, windowsill herbs, and other balcony or backyard gardens cropping up in different cities, all with a purpose to produce food. This workshop brings to-gether practitioners and researchers in the field of urban agriculture and Hu-man-Computer Interaction to explore and opportunities for technology design to support the different forms of growing practice and foster local food production in cities. This 1-day workshop will serve as an active forum for researchers and practi-tioners across various fields including, but not limited to, agriculture and gar-dening, education, urban planning, human-computer interaction, and communi-ty engagement. This workshop has three distinct points of focus: i) Individual and small-scale gardening and food production, and how to connect like minded people who are involved in these practices to share their knowledge ii) Com-munities involved in urban agriculture, either through community gardens, city farms, or grassroots movements, often dependant on volunteer participation, providing the challenge of managing limited resources iii) Environmental and sociocultural sustainability through urban agriculture. The participants will have an opportunity to present their own work. This will be followed by a visit to a nearby city farm, which will provide a local context for a group design exercise. Finally the workshop will conclude with panel dis-cussions to review opportunities for further research and collaborations beyond the conference. For more information, please visit the workshop website, at http://www.urbaninformatics.net/resources/interact2013cfp/

An analysis of corporate related-party disclosure in the Asia-Pacific region

Related-party (RP) transactions are said to be commonly used opportunistically in business and contribute to corporate failures. While periodic disclosure is widely accepted as an effective means of monitoring such transactions, research is scant, particularly in countries where business dealings may be more susceptible to corruption. This study investigates the nature and extent of corporate RP disclosures across six countries in the Asia-Pacific region. The key finding indicates that companies in countries with stronger regulatory enforcement, shareholders’ protection, and control for corruption, have more transparent RP disclosures. This evidence potentially contributes to reforms aimed at strengthening RP disclosure and compliance.

Knowledge that matters : identifying regional knowledge assets of tampere region

The extant literature considers knowledge as one of the key drivers of regional development. The idiosyncratic nature of regional knowledge is also acknowledged: each region possesses its unique knowledge assets which act as the basis of value creation. However, what is currently not well-known is how the region-specific knowledge assets can be identified, for example, for the purposes of managing and developing them. Thus, this paper aims, first, to explore how the relevant knowledge assets can be identified for a given region and, second, to describe what the context-specific knowledge assets are. These objectives are pursued using a qualitative case approach. As a case region, this study focuses on Tampere Region in Finland. This study makes a contribution by providing new insight regarding the contextual identification of regional knowledge assets and by illustrating the key knowledge assets of the case region. These insights are considered valuable for regional actors who are responsible for carrying out similar initiatives in their regions.

Interim financial reporting in the Asia-Pacific region : a review of regulatory requirements

This paper addresses the question of how interim financial reporting regulation varies across the Asia-Pacific region. Using a content analysis method, the study investigates the relevant regulations in eight selected countries in the Asia-Pacific region which differ in a number of country-level attributes. We find that the regulations in the region show considerable variation in terms of the form of regulatory enforcement, reporting lag, audit requirements, and reporting form. By providing the first in-depth review of the nature of differences in interim financial reporting in key countries in the Asia-Pacific region, the findings of this study will be of interest to investors, regulators and researchers in their quest for international “convergence” in financial reporting practices.