2,056 research outputs found
Addressing Australia's Housing Shortage Through Improved Housing Utilisation
Australia currently suffers from a well documented housing shortage, particularly in the area of affordable housing. the reasons for this shortage are equally well documented, with supply-side constraints being generally regarded as the root of the problem. Governments at all levels have made various attempts to relieve the acuity of the problem, which has become something of a policy debate football, with most responses on the need to create more housing. However, the creation of additional new housing stock is highly costly and can involve considerable time lags, through various barriers to completion. New home construction can also be quite environmentally unsustainable, having a high carbon footprint. Therefore, it is proposed that another, although partial, solution may lie in addressing the demanside for housing, via the utilisation of existing housing stock. This paper explores the current level of utilisation of Australia's existing housing stock and identifies significant underutilisation by international standards. Using detached housing in the Sydney metropolitan area as a proxy, examination of 2006 Census data has shown that there is substantial underutilisation of housing stock. The need to explore policy initiatives that can encourage a higher utilisation of existing housing stock is highlighted, including removing the existing barriers to housing substitution for retirees. A central conclusion of this research is that if a significantly higher housing stock utilisation could be achieved the potential time and capital savings, and importantly, carbon footprint reduction, could be significant by comparison to the construction of new dwellings
Augmented Reality
Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning
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Swayed by sound: sonic guidance as a neurorehabilitation strategy in the cerebellar ataxias
Cerebellar disease leads to problems in controlling movement. The most common
difficulties are dysmetria and instability when standing.
Recent understanding of cerebellar function has expanded to include non -motor
aspects such as emotional, cognitive and sensory processing. Deficits in the
acquisition and processing of sensory information are one explanation for the
movement problems observed in cerebellar ataxia. Sensory deficits result in an
inability to make predictions about future events; a primary function of the
cerebellum. A question therefore, is whether augmenting or replacing sensory
information can improve motor performance in cerebellar disease. This question is
tested in this thesis by augmenting sensory information through the provision of an
auditory movement guide.A variable described in motor control theory (tau) was used to develop auditory
guides that were continuous and dynamic. A reaching experiment using healthy
individuals showed that the timing of peak velocity, audiomotor coordination
accuracy, and velocity of approach, could be altered in line with the movement
parameters embedded in the auditory guides. The thesis then investigated the use of
these sonic guides in a clinical population with cerebellar disease. Performance on
neurorehabilitation exercises for balance control was tested in twenty people with
cerebellar atrophy, with and without auditory guides. Results suggested that
continuous, predictive, dynamic auditory guidance is an effective way of improving
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movement smoothness in ataxia (as measured by jerk). In addition, generating and
swaying with imaginary auditory guides was also found to increase movement
smoothness in cerebellar disease.Following the tests of instantaneous effects, the thesis then investigated the longterm consequences on motor behaviour of following a two -month exercise with
auditory guide programme. Seven people with cerebellar atrophy were assessed pre - and post -intervention using two measures, weight -shifting and walking. The results
of the weight -shifting test indicated that the sonic -guide exercise programme does
not initiate long -term changes in motor behaviour. Whilst there were minor,
improvements in walking, because of the weight -shifting results, these could not be
attributed to the sonic guides. This finding confirms the difficulties of motor
rehabilitation in people with cerebellar disease.This thesis contributes original findings to the field of neurorehabilitation by first
showing that on -going and predictive stimuli are an appropriate tool for improving
motor behaviour. In addition, the thesis is the first of its kind to apply externally
presented guides that convey continuous meaningful information within a clinical
population. Finally, findings show that sensory augmentation using the auditory
domain is an effective way of improving motor coordination in some forms of
cerebellar disease
Human Activity Annotation based on Active Learning
Human activity recognition algorithms have been increasingly sought due to their broad
application, in areas such as healthcare, safety and sports. Current works focusing on
human activity recognition are based majorly on Supervised Learning algorithms and
have achieved promising results. However, high performance is achieved at the cost of a large amount of labelled data required to train and learn the model parameters, where a high volume of data will increase the algorithmâs performance and the classifierâs ability to generalise correctly into new, and previously unseen data. Commonly, the labelling process of ground truth data, which is required for supervised algorithms, must be done manually by the user, being tedious, time-consuming and difficult.
On this account, we propose a Semi-Supervised Active Learning technique able to
partly automate the labelling process and reduce considerably the labelling cost and the labelled data volume required to obtain a highly performing classifier. This is achieved through the selection of the most relevant samples for annotation and propagation of their label to similar samples. In order to accomplish this task, several sample selection strategies were tested in order to find the most valuable sample for labelling to be included in the classifierâs training set and create a representative set of the entire dataset. Followed by a semi-supervised stage, labelling with high confidence unlabelled samples, and augmenting the training set without any extra labelling effort from the user. Lastly, five stopping criteria were tested, optimising the trade-off between the classifierâs performance and the percentage of labelled data in its training set.
Experimental results were performed on two different datasets with real data, allowing
to validate the proposed method and compare it to literature methods, which were
replicated. The developed model was able to reach similar accuracy values as supervised learning, with a reduction in the required labelled data of more than 89% for the two datasets, respectively
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