Description:
Recent research as part of the Co-operative Research Centre in Predictive Mineral Discovery has addressed a number of issues pertaining to the giant base-metal deposits of the Mount Isa Region of northern Australia.
Description:
The paper will report on an ongoing research project being conducted by the authors, on behalf of the Australian National VET Equity Advisory Council (NVEAC), in which we are required to conduct “a review and analysis of effective models and underpinning principles for gathering and responding to feedback from learners, particularly disadvantaged learners”. The term “learner voice” is used throughout the NVEAC documentation to describe engagement with students of vocational education and training. But the “voice” that has unashamedly dominated the policy discourse in vocational and adult education and training in recent decades has been that of business and industry. Recently, however, particularly in England during the final term of the New Labour administration, and increasingly is some Scandinavian and European countries, a renewed emphasis on policies of social inclusion has introduced the notion of “learner voice” into policy considerations. Especially important are the voices of learners who are perceived to be disadvantaged or marginalised. In Australia, too, discourses of both inclusion and human capital have led to policies of involving students, their interests and their views in some way in the education project. The engagement of students with the tertiary education sector and institutions has come to be regarded as a way of promoting students’ learning by making their education and training more relevant to, and inclusive of, their “needs” while simultaneously contributing to the more efficient utilisation of human capital in an increasingly competitive national economy. Such inclusiveness, therefore, is promoted as facilitating the twin virtues of equity and efficiency, and is seen by some as having the potential to empower learners and transform their learning experience, and also to transform and expand Vocational Education and Training (VET) and Adult and Community Education (ACE). The paper will critically examine the dynamics of the vet policy framework and the range current practice in relation to learner voice. It will particularly emphasise contradictions in both practice and policy in relation to who speaks and with what authority, and who listens to what effect.
Description:
This paper reports on research conducted by the authors on behalf of the Australian National VET Equity Advisory Council (NVEAC), which was established in 2009 to provide independent advice to the Standing Council on Tertiary Education, Skills and Employment (SCOTESE) on how disadvantaged learners can achieve better outcomes from vocational education and training (VET). The paper draws on interviews conducted with more than 60 VET managers and staff, students and student organisations, and a range of VET stakeholders in all Australian states and territories. The authors were required to conduct a review and analysis of effective models and underpinning principles for gathering and responding to feedback from learners, particularly disadvantaged learners. Participants were asked particularly about learner voice regulatory frameworks and provider accountability for acting on feedback from learners, particularly disadvantaged learners.
Description:
Contextual information is important for interpreting human actions especially when actions exhibit interactive relationship with their context. Contextual clues become even more crucial when videos are captured in unfavorable conditions like extreme low light nighttime scenarios. These conditions encourage the use of multi-senor imagery and context enhancement. In this paper, we explore the importance of contextual knowledge for recognizing human actions in multi-sensor nighttime videos. Information fusion is utilized for encapsulating visual information about actions and their context. Space-time action information is contained using 3D fourier transform of fused action silhouette volume. In parallel, SIFT context images are extracted and fused using principal component analysis based feature fusion for each action class. Contextual dissimilarity is penalized by minimizing context SIFT flow energy. The action dataset comprises multi-sensor night vision video data from infra-red and visible spectrum. Experimental results show that fused contextual action information boost action recognition performance as compared to the baseline action recognition approac
Description:
The task of classifying the genre of polyphonic music signals is traditionally done using only low level features of the signal. In this paper high level features have been applied to improve the task of music genre classification. The use of statistical chord features and chord progression information in conjunction with low level features are proposed in this paper. The chord progression information is manifested in genre probability descriptors calculated using a pattern matching algorithm. Our proposed method provides an improvement of 12.4% in the classification results over a commonly compared technique.
Description:
Timber bridges are significant asset in bridge inventory. However, managing aging timber bridges stock is a major challenge. Most of the timber bridges has already reached its design service life, and needs to be replaced. The limited financial resources to maintain these bridges and the depleting supply of quality hardwood timber have created a need for a cost effective alternative for the replacement of these timber bridges. Fibre composite girders are a viable solution, but manufacturing cost needs to be reduced to make it commercially viable. A major research and development project on fibre composite alternative girders to replace existing hard-wood timber girders was carried out by the Centre of Excellence in Engineered Fibre Composites (CEEFC). This project was in collaboration with the Queensland Department of Transport and Main Roads (DTMR) and two industry partners. This project aims to develop commercially viable fibre composite bridge girders for the Australian market through the use of advanced manufacturing processes. These full-scale girders have undergone rigours testing including proof load testing, fatigue testing and ultimate strength testing to prove its viability as a suitable replacement girder. This paper presents the performance of these girders and discusses important issues encountered in the production of these fibre composite replacement girders. It is concluded that both fibre composite bridge girder concepts developed by the industry partners meet the set design criteria.
Description:
Automatic music genre classification is an important component in Music Information Retrieval (MIR). It has gained lot of attention lately due to the rapid growth in the use of digital music. Past work in this area has already produced a number of audio features and classification techniques; however, genre classification still remains an unsolved problem. In this paper we explore a hybrid unsupervised/supervised top-down hierarchical classification approach. Most existing work on hierarchical music genre classification relies on human built trees and taxonomies; however these hierarchies may not always translate well into machine classification problems. Therefore, we explore an automatic approach to construct a classification tree through subspace cluster analysis. Experimental results validate the tree building algorithm and provide a new research direction for automatic genre classification. We also addressed the issue of scarcity in publicly available music datasets, by introducing a new dataset containing genre, artist and album labels.
Description:
Music genre classification has attracted a lot of research interest due to the rapid growth of digital music. Despite the availability of a vast number of audio features and classification techniques, genre classification still remains a challenging task. In this work we propose a class centric feature and classifier ensemble selection method which deviates from the conventional practice of employing a single, or an ensemble of classifiers trained with a selected set of audio features. We adopt a binary decomposition technique to divide the multiclass problem into a set of binary problems which are then treated in a class specific manner. This differs from the traditional techniques which operate on the naive assumption that a specific set of features and/or classifiers can perform equally well in identifying all the classes. Experimental results obtained on a popular genre dataset and a newly created dataset suggest significant improvements over traditional techniques.
Description:
Optimum selection of sensors in target tracking applications has a great potential to maintain right trade-off between energy consumption and quality of tracking. In this paper, we propose a dynamic sensor selection scheme to achieve energy efficiency while ensuring the required quality of tracking. To this end, relative information utility projection of a target on sensors' observation is used in niche overlap measurements. Niche overlap measures are used to assess the similarity in information utilities where information utility is inversely proportional to error in target's state estimation based on prior distribution. The proposed scheme is a greedy approach in which sensor nodes are selected such that the overall niche overlap of all the selected nodes is maximized until the required level of accuracy is achieved. Our simulation results show significant improvement in tracking accuracy and network's lifetime over the existing methods.
Description:
Complex target tracking applications require active sensor nodes to collaboratively track multiple moving targets, which can balance the trade-off between the quality of tracking and network's lifetime. In this paper, we develop a distributed sensor-selection protocol (DSSP) to activate dynamic number of sensors based on the cost metrics. Cost metrics contains energy-aware leadership cost and eagerness-based tracking cost; which selects sensors with higher energy resources and information utilities. DSSP enables an even distribution of energy consumption among the nodes to prolong the network lifetime. Our results show that the proposed scheme can significantly improve the network lifetime while maintaining the high tracking accuracy as compared to the other schemes.
Description:
Efficient target tracking applications require active sensor nodes to track a cluster of moving targets. Clustering could lead to significant cost improvement as compared to tracking individual targets. This paper presents accurate clustering of targets for both coherent and incoherent movement patterns. We propose a novel clustering algorithm that utilises an implicit dynamic time frame to assess the relational history of targets in creating a weighted graph of connected components. The proposed algorithm employs key features of localisation algorithms in target tracking, namely, estimated current and predicted locations to determine the relational directions and distances of moving targets. Our simulation results show a significant improvement on the clustering accuracy and computation time by dynamically adjusting the history-window size and predicting the relationships among targets.
Description:
Efficient target tracking applications use active sensor nodes collaboratively to track multiple moving targets by balancing the trade-off between the quality of tracking and network's lifetime. In this paper, we propose a low-energy dynamic sensor selection (LEDS) scheme to track multiple targets by estimating energy consumption of sensors and information utility projection of the targets on sensors to calculate the eagerness in tracking. Eagerness represents the eligibility of a sensor node to be selected for tracking, considering relative profiles of other sensors and location of all the targets in its vicinity. LEDS enables an even distribution of energy consumption among the nodes to prolong their remaining energies. Our results show that the proposed scheme can significantly improve the network lifetime over the existing methods while maintaining the high tracking accuracy in congested areas where multiple concurrent targets overlap.
Description:
Rapid, accurate and non-subjective grain tests help growers and processors agree on a fair price and ensure that the grain is used for the most appropriate purpose. Common tests include protein, moisture, test (hectolitre) weight, screenings and dockages for pests, disease and foreign matter (Vicgrain, 2000). Each test contributes to a more comprehensive assessment of the grain. The tests assist breeders when selecting promising new cultivars. Plant breeders and maltsters also use the thousand kernel weight (TKW) test, which provides additional information on seed morphology. The test indicates the average kernel weight, with the units expressed as grams per thousand seeds. TKWs are valuable to maltsters and millers as high TKW kernels are plumper, malt and/or mill more evenly and have a higher proportion of endosperm than small kernels. The high TKW grains also produce more attractive malt (Stuart, 1998). TKWs assist breeders in selecting large kernel cultivars and permit growers to calculate their optimum sowing rates (Schwarz and Horsley, 1995).
Description:
Rapid, accurate and non-subjective grain tests help growers and processors agree on a fair price and ensure that the grain is used for the most appropriate purpose. Common tests include protein, moisture, test (hectolitre) weight, screenings and dockages for pests, disease and foreign matter (Vicgrain, 2000). Each test contributes to a more comprehensive assessment of the grain. The tests assist breeders when selecting promising new cultivars. Plant breeders and maltsters also use the thousand kernel weight (TKW) test, which provides additional information on seed morphology. The test indicates the average kernel weight, with the units expressed as grams per thousand seeds. TKWs are valuable to maltsters and millers as high TKW kernels are plumper, malt and/or mill more evenly and have a higher proportion of endosperm than small kernels. The high TKW grains also produce more attractive malt (Stuart, 1998). TKWs assist breeders in selecting large kernel cultivars and permit growers to calculate their optimum sowing rates (Schwarz and Horsley, 1995).