20 resultados para Eaton Papers.

em Queensland University of Technology - ePrints Archive


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This thesis locates the origins of modern secular knowledge in late medieval theology. Problems with modern and postmodern knowledge which arise from these theological origins are then tackled theologically, and the manner in which secular ways of understanding knowledge are embedded in specific university, political and hospital contexts are then described and evaluated from a post-secular theological standpoint. The theoretical component of this thesis looks at knowledge itself and finds that without faith there can be no knowledge. The applied component of this thesis does two things. Firstly it explores how our conception of knowledge shapes the assumptions, operational norms, belief frames and tacit values of some characteristically modern and secular institutions. Secondly the applied component evaluates those contexts from the theologically premised conception of knowledge which was argued for in the theoretical component of this thesis.

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This volume represents teh second collection of working papers and articles by participants in the Higher Education Policy Project (HEPP), a project funded by the Australian Research Council and based in the Graduate School of Education at the University of Queensland. The first volume, 'Higher Education in Transition: Working Papers of the Higher Education Policy Project (Bella, McCollow and Knight, 1993), took the broad theme of "higher education in transition" in order to introduce readers the HEPP and give them some idea of the breadth of the research being pursued by the HEPP research team itself and by the cohort of post-graduate students also associated with the project. Since then, higher education has remained in transition. Stubborn and resurgent questions continue: such as what a university ought to be, what forms of research should be supported in a mass system, and how institutional accountability can be demonstrated. In differing ways and using a variety of research perspectives and methodologies, the contributors to this volume explore these and other questions of relevance to higher education today.

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I grew up in academic heaven. At least for me it was. Not only was Sweden in the late 1980s paradise for any kind of empirical research, with rich and high-quality business statistics being made available to researchers without them having to sign away their lives; 70+ percent response rates achieved in mail surveys to almost any group (if you knew how to do them), and boards of directors opening their doors to more qualitatively orientated researchers to sit in during their meetings. In addition, I perceived an environment with a very high degree of academic freedom, letting me do whatever I found interesting and important. I’m sure for others it was sheer hell, with very unclear career paths and rules of the game. Career progression (something which rarely entered my mind) meant that you tried as best you could and then you put all your work – reports, books, book chapters, conference papers, maybe even published articles – in a box and had some external committee of professors look at it. If you were lucky they liked what they saw for whatever reasons their professorial wisdom dictated, and you got hired or promoted. If you were not so lucky you wouldn’t get the job or the promotion, without quite knowing why. So people could easily imagine an old boys club – whose members were themselves largely unproven in international, peer review publishing – picking whoever they wanted by whatever criteria they choose to apply. Neither the fact that assessors were external nor the presence of an appeals system might have completely appeased your suspicious and skeptical mind, considering the balance of power.

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Robust descriptor matching across varying lighting conditions is important for vision-based robotics. We present a novel strategy for quantifying the lighting variance of descriptors. The strategy works by utilising recovered low dimensional mappings from Isomap and our measure of the lighting variance of each of these mappings. The resultant metric allows different descriptors to be compared given a dataset and a set of keypoints. We demonstrate that the SIFT descriptor typically has lower lighting variance than other descriptors, although the result depends on semantic class and lighting conditions.

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Timely and comprehensive scene segmentation is often a critical step for many high level mobile robotic tasks. This paper examines a projected area based neighbourhood lookup approach with the motivation towards faster unsupervised segmentation of dense 3D point clouds. The proposed algorithm exploits the projection geometry of a depth camera to find nearest neighbours which is time independent of the input data size. Points near depth discontinuations are also detected to reinforce object boundaries in the clustering process. The search method presented is evaluated using both indoor and outdoor dense depth images and demonstrates significant improvements in speed and precision compared to the commonly used Fast library for approximate nearest neighbour (FLANN) [Muja and Lowe, 2009].

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This paper presents a pose estimation approach that is resilient to typical sensor failure and suitable for low cost agricultural robots. Guiding large agricultural machinery with highly accurate GPS/INS systems has become standard practice, however these systems are inappropriate for smaller, lower-cost robots. Our positioning system estimates pose by fusing data from a low-cost global positioning sensor, low-cost inertial sensors and a new technique for vision-based row tracking. The results first demonstrate that our positioning system will accurately guide a robot to perform a coverage task across a 6 hectare field. The results then demonstrate that our vision-based row tracking algorithm improves the performance of the positioning system despite long periods of precision correction signal dropout and intermittent dropouts of the entire GPS sensor.

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This paper presents the design of μAV, a palm size open source micro quadrotor constructed on a single Printed Circuit Board. The aim of the micro quadrotor is to provide a lightweight (approximately 86g) and cheap robotic research platform that can be used for a range of robotic applications. One possible application could be a cheap test bed for robotic swarm research. The goal of this paper is to give an overview of the design and capabilities of the micro quadrotor. The micro quadrotor is complete with a 9 Degree of Freedom Inertial Measurement Unit, a Gumstix Overo® Computer-On-Module which can run the widely used Robot Operating System (ROS) for use with other research algorithms.

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Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

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This paper introduces an improved line tracker using IMU and vision data for visual servoing tasks. We utilize an Image Jacobian which describes motion of a line feature to corresponding camera movements. These camera motions are estimated using an IMU. We demonstrate impacts of the proposed method in challenging environments: maximum angular rate ~160 0/s, acceleration ~6m /s2 and in cluttered outdoor scenes. Simulation and quantitative tracking performance comparison with the Visual Servoing Platform (ViSP) are also presented.

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This paper presents a new multi-scale place recognition system inspired by the recent discovery of overlapping, multi-scale spatial maps stored in the rodent brain. By training a set of Support Vector Machines to recognize places at varying levels of spatial specificity, we are able to validate spatially specific place recognition hypotheses against broader place recognition hypotheses without sacrificing localization accuracy. We evaluate the system in a range of experiments using cameras mounted on a motorbike and a human in two different environments. At 100% precision, the multiscale approach results in a 56% average improvement in recall rate across both datasets. We analyse the results and then discuss future work that may lead to improvements in both robotic mapping and our understanding of sensory processing and encoding in the mammalian brain.

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In this paper we present a novel place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images (although we use a cohort normalization score to exploit temporal frame information), alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We demonstrate the algorithm on the challenging Alderley sunny day – rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. The system is able to achieve 21.24% recall at 100% precision, matching drastically different day and night-time images of places while successfully rejecting match hypotheses between highly aliased images of different places. The results provide a new benchmark for single image, condition-invariant place recognition.

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An important aspect of robotic path planning for is ensuring that the vehicle is in the best location to collect the data necessary for the problem at hand. Given that features of interest are dynamic and move with oceanic currents, vehicle speed is an important factor in any planning exercises to ensure vehicles are at the right place at the right time. Here, we examine different Gaussian process models to find a suitable predictive kinematic model that enable the speed of an underactuated, autonomous surface vehicle to be accurately predicted given a set of input environmental parameters.

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In light of the time available today, I will limit my comments to addressing that aspect of Professor Fletcher’s paper in which he refers to the 2012 report he co-authored with Professor Wessels of the Netherlands for the American Law Institute (ALI) and the International Insolvency Institute (III) on Transnational Insolvency: Global Principles for Cooperation in International Insolvency Cases. I will comment on the potential benefits for Australian courts as well as insolvency administrators and their advisers in referring to the ALI-III Report - in light of Australia’s adoption of the UNCITRAL Model Law. In so doing, I would like to acknowledge the support of the Australian Academy of Law, under the leadership of The Hon Dr Kevin Lindgren for the research project underpinning these comments, as well as to acknowledge the contributions of my colleagues Associate Professor Sheryl Jackson and Mark Wellard.