Developments in texture-based vector field visualisation techniques

Vector field visualisation is one of the classic sub-fields of scientific data visualisation. The need for effective visualisation of flow data arises in many scientific domains ranging from medical sciences to aerodynamics. Though there has been much research on the topic, the question of how to communicate flow information effectively in real, practical situations is still largely an unsolved problem. This is particularly true for complex 3D flows. In this presentation we give a brief introduction and background to vector field visualisation and comment on the effectiveness of the most common solutions. We will then give some examples of current development on texture-based techniques, and given practical examples of their use in CFD research and hydrodynamic applications.

Digital image processing techniques for pavement macro-texture analysis

Road surface macro-texture is an indicator used to determine the skid resistance levels in pavements. Existing methods of quantifying macro-texture include the sand patch test and the laser profilometer. These methods utilise the 3D information of the pavement surface to extract the average texture depth. Recently, interest in image processing techniques as a quantifier of macro-texture has arisen, mainly using the Fast Fourier Transform (FFT). This paper reviews the FFT method, and then proposes two new methods, one using the autocorrelation function and the other using wavelets. The methods are tested on pictures obtained from a pavement surface extending more than 2km's. About 200 images were acquired from the surface at approx. 10m intervals from a height 80cm above ground. The results obtained from image analysis methods using the FFT, the autocorrelation function and wavelets are compared with sensor measured texture depth (SMTD) data obtained from the same paved surface. The results indicate that coefficients of determination (R2) exceeding 0.8 are obtained when up to 10% of outliers are removed.

The effect of high concentrations of calcium hydroxide in neutralised synthetic supernatant liquor : implications for alumina refinery residues

The presence of calcium hydroxide (Ca(OH)2) in Bayer residue slurry inhibits the effectiveness of the seawater neutralisation process to reduce the pH and aluminium concentration in the residue. An increase in the slurry pH (reversion), after seawater neutralisation, is caused by the dissolution of calcium hydroxide and hydrocalumite (solid components found in bauxite refinery residue). Reversion was not observed when the final solution pH was greater than 10.5, due to hydrocalumite being in a state of equilibrium at high pH. Hydrocalumite has been found to form during the neutralisation process when high concentrations of calcium hydroxide are present in the residue liquor. The dissolution of hydrocalumite releases hydroxyl (OH-) and aluminium ions back into solution after the seawater neutralisation (SWN) process, which causes pH and aluminium reversion to occur. This investigation looks at the effect of Ca(OH)2 and subsequently hydrocalumite on the pH and aluminium concentration in bauxite refinery residue liquors after the SWN process.

Frequency decomposition techniques for increased discriminative 3D facial information capture

Eigen-based techniques and other monolithic approaches to face recognition have long been a cornerstone in the face recognition community due to the high dimensionality of face images. Eigen-face techniques provide minimal reconstruction error and limit high-frequency content while linear discriminant-based techniques (fisher-faces) allow the construction of subspaces which preserve discriminatory information. This paper presents a frequency decomposition approach for improved face recognition performance utilising three well-known techniques: Wavelets; Gabor / Log-Gabor; and the Discrete Cosine Transform. Experimentation illustrates that frequency domain partitioning prior to dimensionality reduction increases the information available for classification and greatly increases face recognition performance for both eigen-face and fisher-face approaches.

Addressing sustainable living using a collaborative approach and multi-disciplinary techniques

In order to achieve meaningful reductions in individual ecological footprints, individuals must dramatically alter their day to day behaviours. Effective interventions will need to be evidence based and there is a necessity for the rapid transfer or communication of information from the point of research, into policy and practice. A number of health disciplines, including psychology and public health, share a common mission to promote health and well-being and it is becoming clear that the most practical pathway to achieving this mission is through interdisciplinary collaboration. This paper argues that an interdisciplinary collaborative approach will facilitate research that results in the rapid transfer of findings into policy and practice. The application of this approach is described in relation to the Green Living project which explored the psycho-social predictors of environmentally friendly behaviour. Following a qualitative pilot study, and in consultation with an expert panel comprising academics, industry professionals and government representatives, a self-administered mail survey was distributed to a random sample of 3000 residents of Brisbane and Moreton Bay (Queensland, Australia). The Green Living survey explored specific beliefs which included attitudes, norms, perceived control, intention and behaviour, as well as a number of other constructs such as environmental concern and altruism. This research has two beneficial outcomes. First, it will inform a practical model for predicting sustainable living behaviours and a number of local councils have already expressed an interest in making use of the results as part of their ongoing community engagement programs. Second, it provides an example of how a collaborative interdisciplinary project can provide a more comprehensive approach to research than can be accomplished by a single disciplinary project.

Novel synthetic bio-mimic polymers for potential cell delivery therapy

Cell-based therapy is one of the major potential therapeutic strategies for cardiovascular, neuronal and degenerative diseases in recent years. Synthetic biodegradable polymers have been utilized increasingly in pharmaceutical, medical and biomedical engineering. Control of the interaction of living cells and biomaterials surfaces is one of the major goals in the design and development of new polymeric biomaterials in tissue engineering. The aims of this study is to develop a novel bio-mimic polymeric materials which will facilitate the delivery cells, control cell bioactivities and enhance the focal integration of graft cells with host tissues.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Preliminary characterisation of the surface of cartilage following exposure to saturated and unsaturated synthetic lipids

Articular cartilage is covered by a microscopic structure known as surface amorphous layer. This surface structure is often the first victim of attack during cartilage degeneration, thereby resulting in a gross impairment in cartilage function such as lubrication and load bearing. We hypothesize that incubation of degraded cartilage in solutions of different species of synthetic surface active phospholipids (saturated and unsaturated species) can remodel this lost surface structure. To test this hypothesis, the structural configuration of the surface of articular cartilage was studied and characterised with the lipid filled surface amorphous layer intact using the AFM. The results were then compared with those obtained following a systematic removal (delipidization) and replacement (relipidization) of this layer. Our results show that the unsaturated surfactant partially restored the lost surface amorphous layer while the saturated surfactant specie settled on the surface due to its poor solubility in aqueous solution.