Nambour : the model rural school

This paper examines the Rural Schools of Queensland. Starting with Nambour in 1917, the scheme incorporated thirty schools, and operated for over forty years. The rhetoric of the day was that boys and girls from the senior classes of primary school would be provided with elementary instruction of a practical character. In reality, the subjects taught were specifically tailored to provide farm skills to children in rural centres engaged in farming, dairying or fruit growing. Linked to each Rural School was a number of smaller surrounding schools, students from which travelled to the Rural School for special agricultural or domestic instruction. Through this action, the Queensland Department of Public Instruction left no doubt it intended to provide educational support for agrarian change and development within the state; in effect, they had set in motion the creation of a Queensland yeoman class. The Department’s intention was to arrest or reverse the trend toward urbanisation — whilst increasing agricultural productivity — through the making of a farmer born of the land and accepting of the new scientific advances in agriculture.

The public health challenges of mental health problems and associated difficulties

Mental health is a major global health issue. Neuropsychiatric conditions are the most significant cause of disability worldwide, and account for 14% of the global burden of disease. Depression in particular places a huge burden on society, with the Global Burden of Disease 2000 study listing it as the fourth leading cause of disease burden worldwide and the largest non-fatal disease burden. In Australia, mental disorders are startlingly common and related to significant disability. The 2007 National Survey of Mental Health and Wellbeing revealed that the lifetime prevalence of any mental disorder was 45%, and within the last 12 months 20% of Australians met criteria for a mental disorder. Many of the articles in this issue explore mental health issues in young people. Indeed, mental health issues account for a large proportion of the disease burden in young people. Across the globe, mental health disorders caused the greatest number of years lost to disability(YLDs) amongst young people aged 10 to 24 years (45% of total YLDs). Depression caused the highest number of disability-adjusted life-years (DALYs) across this age group, accounting for 8. 2% of DALYs alone.6 It is clear that mental health is a critical area of focus for researchers, practitioners, and policy makers.

Children with autistic spectrum disorder have an associated risk of depression

Over recent years there has been an increase in the literature examining youth with Autism Spectrum Disorders (ASD). The growth in this area of research has highlighted a significant gap in our understanding of suitable interventions for people with ASD and the treatment of co-occurring psychiatric disorders.1-3 Children with ASD are at increased risk of experiencing depressive symptoms and developing depression; however with very few proven interventions available for preventing and treating depression in children with ASD, there is a need for further research in this area.

MediaClub : learning and hanging out with friends

MediaClub is an after-school digital literacy activity for Year 4-7 students at Waterford West State School. Since it began in 2010, as part of the URL project, the club has provided approximately 18 students from the school each term with a structured program of media production opportunities. Here we describe the aims and organisation of the club and student experiences and outcomes.

Integrating robotics in primary school activities

This article outlines the integration of robotics in two settings in a primary school. This initiative was part of an Australian Research Council project which was undertaken at this school. The article highlights how robotics was integrated in a technology unit in a year four class. It also explains how it was embedded into an after-school program which catered for students from years five to seven. From these experiences further possibilities of engaging with robotics are also discussed.

Embedding information and communication technologies in the early childhood classroom

New technologies and the pace of change in modern society mean changes for classroom teaching and learning. Information and communication technologies (ICTs) feature in everyday life and provide ample opportunities for enhancing classroom programs. This article outlines how ICTs complement curriculum implementation in one year two classroom. It suggests practical strategies demonstrating how teachers can make ICTs work for them and progressively teach children how to make ICTs work for them.

Skype : a communications framework for robotics

This paper describes an architecture for robotic telepresence and teleoperation based on the well known tools ROS and Skype. We discuss how Skype can be used as a framework for robotic communication and can be integrated into a ROS/Linux framework to allow a remote user to not only interact with people near the robot, but to view maps, sensory data, robot pose and to issue commands to the robot’s navigation stack. This allows the remote user to exploit the robot’s autonomy, providing a much more convenient navigation interface than simple remote joysticking.

100Hz onboard vision for quadrotor state estimation

This paper introduces a high-speed, 100Hz, visionbased state estimator that is suitable for quadrotor control in close quarters manoeuvring applications. We describe the hardware and algorithms for estimating the state of the quadrotor. Experimental results for position, velocity and yaw angle estimators are presented and compared with motion capture data. Quantitative performance comparison with state-of-the-art achievements are also presented.

STALKERBOT : learning to navigate dynamic human environments by following people

Service robots that operate in human environments will accomplish tasks most efficiently and least disruptively if they have the capability to mimic and understand the motion patterns of the people in their workspace. This work demonstrates how a robot can create a humancentric navigational map online, and that this map re ects changes in the environment that trigger altered motion patterns of people. An RGBD sensor mounted on the robot is used to detect and track people moving through the environment. The trajectories are clustered online and organised into a tree-like probabilistic data structure which can be used to detect anomalous trajectories. A costmap is reverse engineered from the clustered trajectories that can then inform the robot's onboard planning process. Results show that the resultant paths taken by the robot mimic expected human behaviour and can allow the robot to respond to altered human motion behaviours in the environment.

CAT-GRAPH+ : towards odometry-driven place consolidation in changing environments

Changing environments present a number of challenges to mobile robots, one of the most significant being mapping and localisation. This problem is particularly significant in vision-based systems where illumination and weather changes can cause feature-based techniques to fail. In many applications only sections of an environment undergo extreme perceptual change. Some range-based sensor mapping approaches exploit this property by combining occasional place recognition with the assumption that odometry is accurate over short periods of time. In this paper, we develop this idea in the visual domain, by using occasional vision-driven loop closures to infer loop closures in nearby locations where visual recognition is difficult due to extreme change. We demonstrate successful map creation in an environment in which change is significant but constrained to one area, where both the vanilla CAT-Graph and a Sum of Absolute Differences matcher fails, use the described techniques to link dissimilar images from matching locations, and test the robustness of the system against false inferences.

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.

Towards brain-based sensor fusion for navigating robots

Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce “good enough” maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition during a navigation task, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.

Locally weighted learning model predictivek control for elastic joint robots

This paper proposes an efficient and online learning control system that uses the successful Model Predictive Control (MPC) method in a model based locally weighted learning framework. The new approach named Locally Weighted Learning Model Predictive Control (LWL-MPC) has been proposed as a solution to learn to control complex and nonlinear Elastic Joint Robots (EJR). Elastic Joint Robots are generally difficult to learn to control due to their elastic properties preventing standard model learning techniques from being used, such as learning computed torque control. This paper demonstrates the capability of LWL-MPC to perform online and incremental learning while controlling the joint positions of a real three Degree of Freedom (DoF) EJR. An experiment on a real EJR is presented and LWL-MPC is shown to successfully learn to control the system to follow two different figure of eight trajectories.

Understanding QC and EQA : an authentic learning model for undergraduate students (Conference Abstract)

Introduction QC and EQA are integral to good pathology laboratory practice. Medical Laboratory Science students undertake a project exploring internal QC and EQA procedures used in chemical pathology laboratories. Each student represents an individual lab and the class group represents the peer group of labs performing the same assay using the same method. Methods Using a manual BCG assay for serum albumin, normal and abnormal controls are run with a patient sample over 7 weeks. The QC results are assessed each week using calculated z-scores and both 2S & 3S control rules to determine whether a run is ‘in control’. At the end of the 7 weeks a completed LJ chart is assessed using the Westgard Multirules. Students investigate causes of error and the implications for both lab practice and patient care if runs are not ‘in control’. Twice in the 7 weeks two EQA samples (with target values unknown) are assayed alongside the weekly QC and patient samples. Results from each student are collated and form the basis of an EQA program. ALP are provided and students complete a Youden Plot, which is used to analyse the performance of each ‘lab’ and the method to identify bias. Students explore the concept of possible clinical implications of a biased method and address the actions that should be taken if a lab is not in consensus with the peer group. Conclusion This project is a model of ‘real world’ practice in which student demonstrate an understanding of the importance of QC procedures in a pathology laboratory, apply and interpret statistics and QC rules and charts, apply critical thinking and analytical skills to quality performance data to make recommendations for further practice and improve their technical competence and confidence.