Eye-safe UV stand-off Raman spectroscopy for explosive detection in the field

This project focused on maximising the detection range of an eye-safe stand-off Raman system for use in detecting explosives. Investigation of the effect on detection range through differing laser parameters in this thesis provided optimal laser settings to achieve the largest possible detection range of explosives, while still remaining under the eye-safe limit.

Binding properties of peptidic affinity ligands for plasmid DNA capture and detection

Peptides constructed from α-helical subunits of the Lac repressor protein (LacI) were designed then tailored to achieve particular binding kinetics and dissociation constants for plasmid DNA purification and detection. Surface plasmon resonance was employed for quantification and characterization of the binding of double stranded Escherichia coli plasmid DNA (pUC19) via the lac operon (lacO) to "biomimics" of the DNA binding domain of LacI. Equilibrium dissociation constants (K D), association (k a), and dissociation rates (k d) for the interaction between a suite of peptide sequences and pUC19 were determined. K D values measured for the binding of pUC19 to the 47mer, 27mer, 16mer, and 14mer peptides were 8.8 ± 1.3 × 10 -10 M, 7.2 ± 0.6 × 10 -10 M, 4.5 ± 0.5 × 10 -8 M, and 6.2 ± 0.9 × 10 -6 M, respectively. These findings show that affinity peptides, composed of subunits from a naturally occurring operon-repressor interaction, can be designed to achieve binding characteristics suitable for affinity chromatography and biosensor devices.

Corner detection in images under different noise levels

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.

The competitive market for assurance engagements on greenhouse gas statements : is there a role for assurers from the accounting profession?

In response to current and increasing demand for assurance on greenhouse gas statements, the International Auditing and Assurance Standards Board (IAASB) released an exposure draft of a new assurance standard, ISAE 3410 'Assurance on a Greenhouse Gas Statement' (IFAC 2011), to provide comprehensive guidance on these types of greenhouse gas (GHG) assurance engagements. Internationally, approximately 50 percent of GHG statements are independently assured. The related assurance market is competitive, with the accounting profession and those outside the profession currently holding approximately equal shares. This paper highlights the characteristics of GHG assurance engagements that warrant multi-disciplinary teamwork, the unique and interdependent skill-sets that different practitioners bring to these engagements, and the market forces that create a demand for diverse providers.

Developing an international assurance standard on greenhouse gas statements

Worldwide public concern over climate change and the need to limit greenhouse gas (hereafter, GHG) emissions has increasingly motivated public officials to consider more stringent environmental regulation and standards. The authors argue that the development of a new international assurance standard on GHG disclosures is an appropriate response by the auditing and assurance profession to meet these challenges. At its December 2007 meeting, the International Auditing and Assurance Standards Board (hereafter, IAASB) approved a project to consider the development of such a standard aimed at promoting trust and confidence in disclosures of GHG emissions, including disclosures required under emissions trading schemes. The authors assess the types of disclosures that can be assured, and outline the issues involved in developing an international assurance standard on GHG emissions disclosures. The discussion synthesizes the insights gained from four international roundtables on the proposed IAASB assurance standard held in Asia-Pacific, North America, and Europe during 2008, and an IAASB meeting addressing this topic in December 2008.

Analysing governance of Australia's system of landscape-based greenhouse gas abatement

Healthy governance systems are key to delivering effective outcomes in any broad domain of natural resource management (NRM). One of Australia's emerging NRM governance domains is our national framework for greenhouse gas abatement (GGA), as delivered through a wide range of management practices in the Australian landscape. The emerging Landscape-Based GGA Domain represents an innovative governance space that straddles both the nation's broader NRM Policy and Delivery Domain and Australia's GGA Domain. As a point-in-time benchmark, we assess the health of this hybrid domain as it stood at the end of 2013. At that time, the domain was being progressed through the Australian government's Clean Energy Package and, more particularly, its Carbon Farming Initiative (CFI). While significant changes are currently under development by a new Australian government, this paper explores key areas of risk within the governance system underpinning this emerging hybrid domain at that point in time. We then map some potential reform or continuous improvement pathways required (from national to paddock scale) with the view to securing improved landscape outcomes over time through widespread GGA activities.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Green-fluorescent protein facilitates rapid in vivo detection of genetically transformed plant cells

Early detection of plant transformation events is necessary for the rapid establishment and optimization of plant transformation protocols. We have assessed modified versions of the green fluorescent protein (GFP) from Aequorea victoria as early reporters of plant transformation using a dissecting fluorescence microscope with appropriate filters. Gfp-expressing cells from four different plant species (sugarcane, maize, lettuce, and tobacco) were readily distinguished, following either Agrobacterium-mediated or particle bombardment-mediated transformation. The identification of gfp-expressing sugarcane cells allowed for the elimination of a high proportion of non-expressing explants and also enabled visual selection of dividing transgenic cells, an early step in the generation of transgenic organisms. The recovery of transgenic cell clusters was streamlined by the ability to visualize gfp-expressing tissues in vitro.

Application of anomaly technique in wind turbine bearing fault detection

Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

Optical gas sensing properties of nanoporous Nb2O5 films

Nanoporous Nb2O5 has been previously demonstrated to be a viable electrochromic material with strong intercalation characteristics. Despite showing such promising properties, its potential for optical gas sensing applications, which involves the production of ionic species such as H+, has yet to be explored. Nanoporous Nb2O5 can accommodate a large amount of H+ ions in a process that results in an energy bandgap change of the material, which induces an optical response. Here, we demonstrate the optical hydrogen gas (H¬2) sensing capability of nanoporous anodic Nb2O5 with a large surface-to-volume ratio prepared via a high temperature anodization method. The large active surface area of the film provides enhanced pathways for efficient hydrogen adsorption and dissociation, which are facilitated by a thin layer of Pt catalyst. We show that the process of H2 sensing causes optical modulations that are investigated in terms of response magnitudes and dynamics. The optical modulations induced by the intercalation process and sensing properties of nanoporous anodic Nb2O5 shown in this work can potentially be used for future optical gas sensing systems.