Attraction and image for the Australian rail industry

Despite the global financial downturn, the Australian rail industry is in a period of expansion. Reports indicate that the industry is not attracting sufficient entry level and mid-career engineers and skilled technicians from within the Australian labour market and is facing widespread retirements from an ageing workforce. This paper reports on a completed qualitative study that explores the perceptions of engineering students, their lecturers, careers advisors and recruitment consultants regarding rail as a brand and of careers in the rail industry. Findings are presented about career knowledge, job characteristic preferences, branding and image and indicate that rail as a brand has a dated image, that young people and their influencers have little knowledge of rail careers and that rail could better focus its image and recruitment strategies. Conclusions include suggestions for more effective attraction and image strategies for the industry and for further research.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Synthesis and characterisation of cobalt hydroxy carbonate Co2CO3(OH)2 Nanomaterials

In an attempt to make nanofibres based upon cobalt oxides, a novel compound a hydrated cobalt hydroxy carbonate was formed. This compound is related to the minerals of the rosasite mineral group. X-ray diffraction showed that the formed compound was a cobalt hydroxy carbonate and SEM displayed bundles of fibres on the micron scale in length and nanoscale in width. The morphology was compared with that of the rosasite mineral group. XPS proved two bond energies for cobalt and three for oxygen in the compound. The compound was characterised by vibrational spectroscopy and the spectra related to minerals of the rosasite mineral group. The stability of the synthetic mineral was limited to temperatures below 200°C.

A Taxonomy of image matching techniques for stereo vision

Stereo vision is a method of depth perception, in which depth information is inferred from two (or more) images of a scene, taken from different perspectives. Applications of stereo vision include aerial photogrammetry, autonomous vehicle guidance, robotics, industrial automation and stereomicroscopy. A key issue in stereo vision is that of image matching, or identifying corresponding points in a stereo pair. The difference in the positions of corresponding points in image coordinates is termed the parallax or disparity. When the orientation of the two cameras is known, corresponding points may be projected back to find the location of the original object point in world coordinates. Matching techniques are typically categorised according to the nature of the matching primitives they use and the matching strategy they employ. This report provides a detailed taxonomy of image matching techniques, including area based, transform based, feature based, phase based, hybrid, relaxation based, dynamic programming and object space methods. A number of area based matching metrics as well as the rank and census transforms were implemented, in order to investigate their suitability for a real-time stereo sensor for mining automation applications. The requirements of this sensor were speed, robustness, and the ability to produce a dense depth map. The Sum of Absolute Differences matching metric was the least computationally expensive; however, this metric was the most sensitive to radiometric distortion. Metrics such as the Zero Mean Sum of Absolute Differences and Normalised Cross Correlation were the most robust to this type of distortion but introduced additional computational complexity. The rank and census transforms were found to be robust to radiometric distortion, in addition to having low computational complexity. They are therefore prime candidates for a matching algorithm for a stereo sensor for real-time mining applications. A number of issues came to light during this investigation which may merit further work. These include devising a means to evaluate and compare disparity results of different matching algorithms, and finding a method of assigning a level of confidence to a match. Another issue of interest is the possibility of statistically combining the results of different matching algorithms, in order to improve robustness.

Design Led Innovation : exploring the synthesis of needs, technologies and business models

The term Design is used to describe a wide range of activities. Like the term innovation, it is often used to describe both an activity and an outcome. Many products and services are often described as being designed, as they describe a conscious process of linking form and function. Alternatively, the many and varied processes of design are often used to describe a cost centre of an organisation to demonstrate a particular competency. However design is often not used to describe the ‘value’ it provides to an organisation and more importantly the ‘value’ it provides to both existing and future customers. Design Led Innovation bridges this gap. Design Led Innovation is a process of creating a sustainable competitive advantage, by radically changing the customer value proposition. A conceptual model has been developed to assist organisations apply and embed design in a company’s vision, strategy, culture, leadership and development processes.

Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.

New palladium catalysed reactions of bromoporphyrins: synthesis and crystal structures of nickel(II) complexes of primary 5-aminoporphyrin, 5,5’-bis(porphyrinyl) secondary amine, and 5-hydroxyporphyrin

Primary aminoporphyrin, secondary bis(porphyrinyl)amine and hydroxyporphyrin complexes have been isolated and characterised both spectroscopically and crystallographically from the reaction of 5-bromo-10,15,20-triphenylporphyrinato-nickel(II) with hydrazine under palladium catalysis.

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.

An efficient approach for the direct synthesis of lithium niobate powders

Lithium niobate powders from the raw powders of Li2 O5 are directly synthesized by a combustion method with urea fuel. The synthesis parameters (e.g. the calcination temperature, calcination time, and urea-to-(Li2 CO3 + Nb2 O5) quantity ratio) are studied to reveal the optimized synthesis conditions for preparing high-quality lithium niobate powders. In our present work, it is found that a urea-to-(Li2 CO3 + Nb2 O5) ratio close to 3, calcination temperature at 550-600 degrees and reaction time around 2.5h may lead to high-quality lithium niobate powsers. The microstructure of synthesized powders is further studied; a possible mechanism of the involved reactions is also proposed.

Facile synthesis of Lithium Niobate squares by a combustion route

A combustion synthesis of lithium niobate (LN) squares from activated niobium oxide (Nb2 O5.nH2O) and Li2CO3 was studied to understand all the chemical reactions involved, and the nucleation and square-growth mechanisms. It was found that first the lithium ions react with the fuel (urea), then niobium ions of Nb2 O5.nH2O begin a continuous reaction with the fuel to form metal-organic complexes. LN nuclei are formed by the solid-state reaction of Li- and Nb-organic complexes at 430 degrees celcius. Lithium niobate squares are obtained in the crystallization stasge at 700 degrees celcius, which go on the grow into larger squares at 850 degrees celcius because of the agglomeration effect.

The synthesis and spectroscopic characterisation of hydrotalcite formed from aluminate solutions

Raman spectroscopy has been used to characterise nine hydrotalcites prepared from aluminate and magnesium solutions (magnesium chloride and seawater). The aluminate hydrotalcites are proposed to have the following formula Mg6Al2(OH)16(CO32-).xH2O, Mg6Al2(OH)16(CO32-,SO42-).xH2O, and Mg6Al2(OH)16(SO42-).xH2O. The synthesis of these hydrotalcites using seawater results in the intercalation of sulfate anions into the hydrotalcite interlayer. The spectra have been used to assess the molecular assembly of the cations and anions in the hydrotalcite structures. The spectra have been conveniently subdivided into spectral features based upon the carbonate anion, the hydroxyl units and water units. This investigation has shown the ideal conditions to form hydrotalcite from aluminate solutions is at pH 14 using magnesium chloride. Changes in synthesis conditions resulted in the formation of impurity products aragonite, thenardite, and gypsum.

Synthesis and vibrational spectroscopy of halotrichite and bilinite

Near infrared (NIR), infrared (IR) spectroscopy and X-ray diffraction (XRD) have been applied to halotrichites of the formula FeAl2(SO4)4∙22H2O and Fe2+Fe23+(SO4)4∙22H2O. Comparison of the halotrichites and their starting materials has been used to give a better understanding of the bonding involved in these types of minerals. The vibrational spectroscopy data has shown that Fe2+ oxidises during the formation of halotrichite, no preventative measures were implemented to prevent oxidation, and this has been clearly shown by the position and broadness of electronic bands of transition metals in the NIR spectra (12500 to 7500 cm-1). It is apparent from this region that Fe3+ substitutes for Al3+ in the synthesis of halotrichite. Due to the oxidation of Fe2+ to Fe3+ the halotrichite sample contains a small portion of bilinite. This has been confirmed by XRD, peaks at 9 and 14° 2θ were observed in the halotrichite sample and are identical to the XRD pattern obtained for bilinite. Substitution of aluminium for Fe3+ has resulted in significant changes in the overall infrared and NIR spectral profiles. However, the lower wavenumber regions of the NIR spectra have very similar spectral profiles, which indicate a similar structure to halotrichite has formed for bilinite. This work has shown that iron halotrichites can be synthesised and characterised by infrared and NIR spectroscopy.

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.

Modeling object identification and tracking errors on image-based safety assessment of earthmoving operations

Object identification and tracking have become critical for automated on-site construction safety assessment. The primary objective of this paper is to present the development of a testbed to analyze the impact of object identification and tracking errors caused by data collection devices and algorithms used for safety assessment. The testbed models workspaces for earthmoving operations and simulates safety-related violations, including speed limit violations, access violations to dangerous areas, and close proximity violations between heavy machinery. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of device and algorithm errors were investigated for safety planning purposes.

Synthesis of proteoglycan 4 by chondrocyte subpopulations in cartilage explants, monolayer cultures, and resurfaced cartilage cultures

Objective: To quantify the levels of proteoglycan 4 (PRG4) expression by subpopulations of chondrocytes from superficial, middle, and deep layers of normal bovine calf cartilage in various culture systems. Methods: Bovine calf articular cartilage discs or isolated cells were used in I of 3 systems of chondrocyte culture: explant, monolayer, or transplant, for 1-9 days. PRG4 expression was quantified by enzyme-linked immunosorbent assay of spent medium and localized by immunohistochemistry at the articular surface and within chondrocytes in explants and cultured cells. Results: Superficial chondrocytes secreted much more PRG4 than did middle and deep chondrocytes in all cultures. The pattern of PRG4 secretion into superficial culture medium varied with the duration of culture, decreasing with time in explant culture (from similar to25 mug/cm(2)/day on days 0-1 to similar to3 mug/cm(2)/day on days 5-9), while increasing in monolayer culture (from similar to1 pg/cell/day on days 0-1 to similar to7 pg/cell/day on days 7-9) and tending to increase in transplant culture (reaching similar to2 mug/cm(2)/day by days 7-9). In all of the culture systems, inclusion of ascorbic acid stimulated PRG4 secretion, and the source of PRG4 was immunolocalized to superficial cells. Conclusion: The results described here indicate that the phenotype of PRG4 secretion by chondrocytes in culture is generally maintained, in that PRG4 is expressed to a much greater degree by chondrocytes from the superficial zone than by those from the middle and deep zones. The marked up-regulation of PRG4 synthesis by ascorbic acid may have implications for cartilage homeostasis and prevention of osteoarthritic disease. Transplanting specialized cells that secrete PRG4 to a surface may impart functional lubrication and be generally applicable to many tissues in the body.