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.

NEU-MODEL : a multi-level object-oriented dynamic emulation laboratory

Computational neuroscience aims to elucidate the mechanisms of neural information processing and population dynamics, through a methodology of incorporating biological data into complex mathematical models. Existing simulation environments model at a particular level of detail; none allow a multi-level approach to neural modelling. Moreover, most are not engineered to produce compute-efficient solutions, an important issue because sufficient processing power is a major impediment in the field. This project aims to apply modern software engineering techniques to create a flexible high performance neural modelling environment, which will allow rigorous exploration of model parameter effects, and modelling at multiple levels of abstraction.

Multi-level neural modelling : an application of object-oriented software engineering

Neu-Model, an ongoing project aimed at developing a neural simulation environment that is extremely computationally powerful and flexible, is described. It is shown that the use of good Software Engineering techniques in Neu-Model’s design and implementation is resulting in a high performance system that is powerful and flexible enough to allow rigorous exploration of brain function at a variety of conceptual levels.

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.

Moving forwards with the aim of going backwards fast : high performance rowing as a learning environment

Background and Purpose: - This paper focuses on the learning culture within the high performance levels of rowing. In doing so, we explore the case of an individual’s learning as he moves across athletic, coaching and administrative functions. This exploration draws on a cultural learning framework and complementary theorisings related to reflexivity. Method - This study makes use of an intellectually, morally and collaboratively challenging approach whereby one member of the research team was also the sole participant of this study. The participant’s careers as a high performance athlete, coach and administrator, coupled with his experience in conducting empirical research presented a rare opportunity to engage in collaborative research (involving degrees of insider and outsider status for each of the research team). We acknowledge that others have looked to combine roles of coach / athlete / administrator with that of researcher however few (if any) have attempted to combine them all in one project. Moreover, coupled with the approach to reflexivity adopted in this study and the authorship contributions we consider this scholarly direction uncommon. Data were comprised of recorded research conversations, a subsequently constructed learning narrative, reflections on the narrative, a stimulated reflective piece from the participant, and a final (re)construction of the participant’s story. Accordingly, data were integrated through an iterative process of thematic analysis. Results - The cultural (i.e., the ways things get done) and structural (e.g., the rules and regulations) properties of high performance rowing were found to shape both the opportunities to be present (e.g., secure a place in the crew) and to learn (e.g., learn the skills required to perform at an Olympic level). However, the individual’s personal properties were brought to bear on re-shaping the constraints such that many limitations could be overcome. In keeping with the theory of learning cultures, the culture of rowing was found to position individuals (a coxswain in this case) differentially. In a similar manner, a range of structural features was found to be important in shaping the cultural and personal elements in performance contexts. For example, the ‘field of play’ was found to be important as a structural feature (i.e., inability of coach to communicate with athletes) in shaping the cultural and personal elements of learning in competition (e.g., positioning the coxswain as an in-boat coach and trusted crewmate). Finally, the cultural and structural elements in rowing appeared to be activated by the participant’s personal elements, most notably his orientation towards quality performance. Conclusion - The participant in this study was found to be driven by the project that he cares about most and at each turn he has bent his understanding of his sport back on itself to see if he can find opportunities to learn and subsequently explore ways to improve performance. The story here emphasises the importance of learner agency, and this is an aspect that has often been missing in recent theorising about learning. In this study, we find an agent using his ‘personal emergent powers to activate the resources in the culture and structure of his sport in an attempt to improve performance. We conclude from this account that this particular high performance rowing culture is one that provided support but nonetheless encouraged those involved, to ‘figure things out’ for themselves – be it as athletes, coaches and/or administrators.

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.

Moving identities : multiplicity, embodiment and the contemporary dancer

Currently, across dance studies, choreographies are usually discussed as representational of the choreographer, with little attention focused on the dancers who also bring the work into being. As well as devaluing the contribution that the dancer makes to the choreographic process, the dancer’s elision from mainstream discourse deprives the art form of a rich source of insight into the incorporating practices of dance. This practice-based research offers a new perspective on choreographic process through the experiential viewpoint of the participating dancer. It involves encounters with contemporary choreographers Rosemary Butcher (UK), John Jasperse (US), Jodi Melnick (US) and Liz Roche (Ire). Utilizing a mixed-mode research structure, it covers the creative process and performance of three solo dance pieces in Dublin in 2008, as well as an especially composed movement treatise, all of which are documented on the attached DVD. The main hypothesis presented is that the dancer possesses a moving identity which is a composite of past dance experience, anatomical structures and conditioned human movement. This is supported by explorations into critical theory on embodiment, including Pierre Bourdieu’s concept of ‘the habitus’. The moving identity is identified as accumulative, altering through encounters with new choreographic movement patterns in independent contemporary dance practice. The interior space of the dancer’s embodied experience is made explicit in chapter 3, through four discussions that outline the dancer’s creative labour in producing each choreographic work. Through adopting a postmodern critical perspective on human subjectivity, supported by Gilles Deleuze, Félix Guattari and Alain Badiou, among others, the thesis addresses the inherent challenges which face independent contemporary dancers within their multiple embodiments as they move between different choreographic processes. In identifying an emergent paradigmatic shift in the role of dancer within dance- making practices, this research forges a new direction that invites further dancer-led initiatives in practice-based research.

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.