Fine-grained Y2Cu2O5 powder from a co-precipitated precursor

A co-precipitation process is utilized to manufacture Y2Cu2O5 precursor powders. Upon calcination at high temperatures, such as 800 degrees C, the co-precipitated powder transforms to Y2Cu2O5. By selective variation of calcination parameters, grain-growth can be controlled to yield different sized Y2Cu2O5 powder, including sub-micron average sizes. ICP analysis, X-ray diffraction, electron microscopy, a.c. magnetic susceptibility and FT Raman are used to characterize phase development, morphology and purity of the powders.

Manufacture of fine-grained Y2BaCuO5 powder by coprecipitation

Quantities of Y2BaCuO5 powder greater than 500g have been manufactured by a co-precipitation process. By suitable heat treatments, the particle size of these powders can be varied from 5µm to less than 500nm. Sub-micrometer size powders may, under some conditions, have a duller green colour which is attributed to <2% unreacted material. However, after re-grinding and re-firing of this powder, high-purity powders can be achieved without significant grain growth. Inductively coupled plasma (ICP) spectroscopy is used to measure the stoichiometry of the powders and X-ray diffraction is used to determine phase purity. In both cases, the bulk composition is consistent with Y2BaCuO5 and phase purity is considered better than 95%.

Production of fine-grained particles

A method of producing particles having nano-sized grains comprises the steps of: (a) prepg. a soln. contg. one or more metal cations; (b) mixing the soln. from step (a) with one or more surfactants to form a surfactant/liq. mixt. and (c) heating the mixt. from step (b) above to form the particles. [on SciFinder(R)]

Production of fine-grained particles

A method of producing particles having nano-sized grains comprises the steps of: (a) prepg. a soln. contg. one or more metal cations; (b) mixing the soln. from step (a) with one or more surfactants to form a surfactant/liq. mixt. and (c) heating the mixt. from step (b) above to form the particles. [on SciFinder(R)]

Evaluation of features for leaf classification in challenging conditions

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-theart performance with an average accuracy of 97:3%�0:6% compared to traditional features which obtain an average accuracy of 91:2%�1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5:7% for all of the evaluated condition variations.

Fine-grained plant classification using convolutional neural networks for feature extraction

We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014.

Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

Achieving medium-high density in low scale development: The Queensland experience in innovative ‘fine-grained’ urbanism

Worldwide population growth and economic agglomeration is driving increasing urban density within larger metropolitan conurbations. Population growth and housing diversity and affordability issues in Queensland have seen an increasing demand for more diverse and higher density development. Under Queensland’s flexible planning regulatory provisions, a level of ‘medium’ to ‘high density’ is being achieved by a focus on fine-grained urban design, low scale development, lot diversity, and delivery of single dwelling products. This for Queensland (and Australia) has been an unprecedented innovation in urban and dwelling design. Dwellings are being delivered on lots with zero regulatory minimum sizes providing for a range of new products including ‘apartments on the ground’. This paper reviews recent and nascent demonstrations of EDQ’s fine-grained urbanism principles, identifiable with historical ‘vernacular suburbanism’. The paper introduces and defines a concept of a ‘natural density’ linking human scale built form with walkability. The paper challenges the notion that (sub)urban development, outside major city centres, needs to be of a higher scale to achieve density and diversity aspirations. ‘Natural density’ provides a means of achieving the increasing demand for more diverse and higher density development.