Fat and thin adaptive HMM filters for vision based detection of moving targets

Computer vision is an attractive solution for uninhabited aerial vehicle (UAV) collision avoidance, due to the low weight, size and power requirements of hardware. A two-stage paradigm has emerged in the literature for detection and tracking of dim targets in images, comprising of spatial preprocessing, followed by temporal filtering. In this paper, we investigate a hidden Markov model (HMM) based temporal filtering approach. Specifically, we propose an adaptive HMM filter, in which the variance of model parameters is refined as the quality of the target estimate improves. Filters with high variance (fat filters) are used for target acquisition, and filters with low variance (thin filters) are used for target tracking. The adaptive filter is tested in simulation and with real data (video of a collision-course aircraft). Our test results demonstrate that our adaptive filtering approach has improved tracking performance, and provides an estimate of target heading not present in previous HMM filtering approaches.

Exploratory investigation of atmospheric total suspended particulate matter and heavy metals in urban air

Atmospheric concentration of total suspended particulate matter (TSP) and associated heavy metals are a great concern due to their adverse health impacts and contribution to stormwater pollution. This paper discusses the outcomes of a study which investigated the variation of atmospheric TSP and heavy metal concentrations with traffic and land use characteristics during weekdays and weekends. Data for this study was gathered from fifteen sites at the Gold Coast, Australia using a high volume air sampler. The study detected consistently high TSP concentrations during weekdays compared to weekends. This confirms the significant influence of traffic related sources on TSP loads during weekdays. Both traffic and land use related sources equally contribute to TSP during weekends. Almost all the measured heavy metals showed high concentration on weekdays compared to weekends indicating significant contributions from traffic related emissions. Among the heavy metals, Zn concentration was the highest followed by Pb. It is postulated that re-suspension of previously deposited reserves was the main Pb source. Soil related sources were the main contributors of Mn.

Identifying differences in safe roads and crash prone roads using clustering data mining

Road asset managers are overwhelmed with a high volume of raw data which they need to process and utilise in supporting their decision making. This paper presents a method that processes road-crash data of a whole road network and exposes hidden value inherent in the data by deploying the clustering data mining method. The goal of the method is to partition the road network into a set of groups (classes) based on common data and characterise the class crash types to produce a crash profiles for each cluster. By comparing similar road classes with differing crash types and rates, insight can be gained into these differences that are caused by the particular characteristics of their roads. These differences can be used as evidence in knowledge development and decision support.

Demonstration of closed-­loop airborne sense-­and-­avoid using machine vision

This paper describes a vision-based airborne collision avoidance system developed by the Australian Research Centre for Aerospace Automation (ARCAA) under its Dynamic Sense-and-Act (DSA) program. We outline the system architecture and the flight testing undertaken to validate the system performance under realistic collision course scenarios. The proposed system could be implemented in either manned or unmanned aircraft, and represents a step forward in the development of a “sense-and-avoid” capability equivalent to human “see-and-avoid”.

Business leadership development in Iran

Throughout the twentieth century the economics of the Middle East rose and fell many times in response to the external environment, including European de-colonization and the US and former USSR competing to provide military and economic aid after World War II. Throughout these upheavals the Middle East has remained internationally significant politically and economically not least for the region's large reserves of oil and gas, as discussed in the Introduction to this volume. In recent decades, Western nations have moved to invest into the Middle East in the rapidly developing technology, tourism and education industries that have proliferated. For its part, Iran has been the world's fourth largest provider of petroleum and second largest provider of natural gas and, despite years of political unrest, has made rapid expansion into information technology and telecommunications. Increased involvement in the global economy has meant that Iran has invested heavily in education and training and moved to modernize its management practices. Hitherto there has been little academic research into management in either Western or local organizations in Iran. This chapter seeks to address that gap in knowledge by exploring business leadership in Iran, with particular reference to cultural and institutional impacts.

Digital reflections on learning : a monograph by students of Digital Pedagogies, Semester 2, 2011

Learning a digital tool is often a hidden process. We tend to learn new tools in a bewildering range of ways. Formal, informal, structured, random, conscious, unconscious, individual, group strategies, may all play a part, but are often lost to us in the complex and demanding processes of learning. But when we reflect carefully on the experience, some patterns and surprising techniques emerge. This monograph presents the thinking of four students in MDN642, Digital Pedagogies, where they have deliberately reflected on the mental processes at work as they learnt a digital technology of their choice.

A descriptive approach for power system stability and security assessment

Power system dynamic analysis and security assessment are becoming more significant today due to increases in size and complexity from restructuring, emerging new uncertainties, integration of renewable energy sources, distributed generation, and micro grids. Precise modelling of all contributed elements/devices, understanding interactions in detail, and observing hidden dynamics using existing analysis tools/theorems are difficult, and even impossible. In this chapter, the power system is considered as a continuum and the propagated electomechanical waves initiated by faults and other random events are studied to provide a new scheme for stability investigation of a large dimensional system. For this purpose, the measured electrical indices (such as rotor angle and bus voltage) following a fault in different points among the network are used, and the behaviour of the propagated waves through the lines, nodes, and buses is analyzed. The impact of weak transmission links on a progressive electromechanical wave using energy function concept is addressed. It is also emphasized that determining severity of a disturbance/contingency accurately, without considering the related electromechanical waves, hidden dynamics, and their properties is not secure enough. Considering these phenomena takes heavy and time consuming calculation, which is not suitable for online stability assessment problems. However, using a continuum model for a power system reduces the burden of complex calculations

Face recognition across pose on video using eigen light-fields

We propose an approach to employ eigen light-fields for face recognition across pose on video. Faces of a subject are collected from video frames and combined based on the pose to obtain a set of probe light-fields. These probe data are then projected to the principal subspace of the eigen light-fields within which the classification takes place. We modify the original light-field projection and found that it is more robust in the proposed system. Evaluation on VidTIMIT dataset has demonstrated that the eigen light-fields method is able to take advantage of multiple observations contained in the video.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

Strategic project evaluation for open pit mining ventures using real options and allied econometric techniques

Open pit mine operations are complex businesses that demand a constant assessment of risk. This is because the value of a mine project is typically influenced by many underlying economic and physical uncertainties, such as metal prices, metal grades, costs, schedules, quantities, and environmental issues, among others, which are not known with much certainty at the beginning of the project. Hence, mining projects present a considerable challenge to those involved in associated investment decisions, such as the owners of the mine and other stakeholders. In general terms, when an option exists to acquire a new or operating mining project, , the owners and stock holders of the mine project need to know the value of the mining project, which is the fundamental criterion for making final decisions about going ahead with the venture capital. However, obtaining the mine project’s value is not an easy task. The reason for this is that sophisticated valuation and mine optimisation techniques, which combine advanced theories in geostatistics, statistics, engineering, economics and finance, among others, need to be used by the mine analyst or mine planner in order to assess and quantify the existing uncertainty and, consequently, the risk involved in the project investment. Furthermore, current valuation and mine optimisation techniques do not complement each other. That is valuation techniques based on real options (RO) analysis assume an expected (constant) metal grade and ore tonnage during a specified period, while mine optimisation (MO) techniques assume expected (constant) metal prices and mining costs. These assumptions are not totally correct since both sources of uncertainty—that of the orebody (metal grade and reserves of mineral), and that about the future behaviour of metal prices and mining costs—are the ones that have great impact on the value of any mining project. Consequently, the key objective of this thesis is twofold. The first objective consists of analysing and understanding the main sources of uncertainty in an open pit mining project, such as the orebody (in situ metal grade), mining costs and metal price uncertainties, and their effect on the final project value. The second objective consists of breaking down the wall of isolation between economic valuation and mine optimisation techniques in order to generate a novel open pit mine evaluation framework called the ―Integrated Valuation / Optimisation Framework (IVOF)‖. One important characteristic of this new framework is that it incorporates the RO and MO valuation techniques into a single integrated process that quantifies and describes uncertainty and risk in a mine project evaluation process, giving a more realistic estimate of the project’s value. To achieve this, novel and advanced engineering and econometric methods are used to integrate financial and geological uncertainty into dynamic risk forecasting measures. The proposed mine valuation/optimisation technique is then applied to a real gold disseminated open pit mine deposit to estimate its value in the face of orebody, mining costs and metal price uncertainties.

Combined time- and space-resolved Raman spectrometer for the non-invasive depth profiling of chemical hazards

A time-resolved inverse spatially offset Raman spectrometer was constructed for depth profiling of Raman-active substances under both the lab and the field environments. The system operating principles and performance are discussed along with its advantages relative to traditional continuous wave spatially offset Raman spectrometer. The developed spectrometer uses a combination of space- and time-resolved detection in order to obtain high-quality Raman spectra from substances hidden behind coloured opaque surface layers, such as plastic and garments, with a single measurement. The time-gated spatially offset Raman spectrometer was successfully used to detect concealed explosives and drug precursors under incandescent and fluorescent background light as well as under daylight. The average screening time was 50 s per measurement. The excitation energy requirements were relatively low (20 mW) which makes the probe safe for screening hazardous substances. The unit has been designed with nanosecond laser excitation and gated detection, making it of lower cost and complexity than previous picosecond-based systems, to provide a functional platform for in-line or in-field sensing of chemical substances.

Sociocultural theories and their application in information literacy research and education

Drawing on the example of a recent study (Wang, 2010), this paper discusses the application of a sociocultural approach to information literacy research and curriculat design. First, it describes the foundation of this research approach in sociocultural theories, in particular Vygotsky's sociocultural theory. Then it presents key theoretical principles arising from the research and describes how the sociocultural approach enabled the establishment of collaborative partnerships between information professionals and academic and teaching support staff in a community of practice for information literacy integration.

Learning English in an English speaking world : examining opportunities for intercultural understandings and connectedness through representations of identities in English language textbooks

With the recognition that language both reflects and constructs culture and English now widely acknowledged as an international language, the cul-tural content of language teaching materials is now being problematised. Through a quantitative analysis, this chapter focuses on opportunities for intercultural understanding and connectedness through representations of the identities that appear in two leading English language textbooks. The analyses reveal that the textbooks orientate towards British and western identities with representations of people from non-European/non-Western backgrounds being notable for their absence, while others are hidden from view. Indeed there would appear to be a neocolonialist orientation in oper-ation in the textbooks, one that aligns English with the West. The chapter proposes arguments for the consideration of cultural diversity in English language teaching (ELT) textbook design, and promoting intercultural awareness and acknowledging the contexts in which English is now being used. It also offers ways that teachers can critically reflect on existing ELT materials and proposes arguments for including different varieties of Eng-lish in order to ensure a level of intercultural understanding and connect-edness.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

Considering the importance of a systems approach to integrating ESD with campus operations

Society faces an unprecedented global education challenge to equip professionals with the knowledge and skills to address emerging 21st Century challenges, spanning climate change mitigation through to adaptation measures to deal with issues such as temperature and sea level rise, and diminishing fresh water and fossil fuel reserves. This paper discusses the potential for systemic and synergistic integration of curriculum with campus operations to accelerate curriculum renewal towards ESD, drawing on the authors' experiences within engineering education. The paper begins by a providing a brief overview of the need for timely curriculum renewal towards ESD in tertiary education. The paper then highlights some examples of academic barriers that need to be overcome for integration efforts to be successful, and opportunities for promoting the benefits of such integration. The paper concludes by discussing the rational for planning green campus initiatives within a larger system of curriculum renewal considerations, including awareness raising and developing a common understanding, identifying and mapping graduate attributes, curriculum auditing, content development and strategic renewal, and bridging and outreach.