The evolution of technology has led the research into new emerging wearable devices such as the Smart Glasses. This technology provides with new visualization techniques. Augmented Reality is an advanced technology that could significantly ease the execution of much complex operations. Augmented Reality is a combination of both Virtual and Actual Reality, making accessible to the user new tools to safeguard in the transfer of knowledge in several environments and for several processes. This thesis explores the development of an android based image recognition application. The feature point detectors and descriptors are used as they can deal great with the correspondence problems. The selection of best image recognition technique on the smart glasses is chosen based on the time taken to retrieve the results and the amount of power consumed in the process. As the smart glasses are equipped with the limited resources, the selected approach should use low computation on it by making the device operations uninterruptable. The effective and efficient method for detection and recognition of the safety signs from images is selected. The ubiquitous SIFT and SURF feature detectors consume more time and are computationally complex and require very high-level hardware components for processing. The binary descriptors are taken into account as they are light weight and can support low power devices in a much effective style. A comparative analysis is being done on the working of binary descriptors like BRIEF, ORB, AKAZE, FREAK, etc., on the smart glasses based on their performance and the requirements. ORB is the most efficient among the binary descriptors and has been more effective for the smart glasses in terms of time measurements and low power consumption.

As the globalisation process accelerates there is a growing need for individual countries to understand the bases for effective performance in international trade. Because it makes up such a large share of world trade, it is especially important to understand what determines effectiveness in exporting. Despite much empirical research, especially over recent decades, the state of knowledge on this topic remains fragmented, unclear and unsatisfactory. The motivation for the present study was therefore twofold: dissatisfaction with the present state of knowledge in this vital area and the importance to the UK economy of improving its export performance in a world of increasing competition. Its aim was to contribute to the resolution of both. In addition to finding what appeared to be quite serious methodological problems in a group of earlier studies, our review of the literature indicated that the best prospects for identifying the determinants of effective exporting were to be found, not at national or sectoral level but at that of the individual firm. Accordingly, an empirical survey research project was developed. To minimise unquantifiable inter-sectoral variability, it was focused on a single sector of industry. For a range of reasons, including the limited amount of information available about its current export activity and prospects, the UK knitted apparel industry was chosen.. Special care having been taken to assemble the fullest possible sampling frame and to develop a suitable instrument (which included an export performance model), a mail survey in the form of a stratified random sample of exporting UK manufacturers of knitted apparel was carried through from late 2000. Persistent follow-up by mail and telephone generated a response rate of 70 per cent, comprising close to half of the sampling frame, that was representative of all company size bands, levels of exporting and products. The overall quality of the responses was good; tests of non-response did not find any indications of non-response bias. Data analysis, designed to test thoroughly our 10 export-determinants hypotheses, relied primarily on Pearsonian correlation at the bivariate level then sequentially on Multiple Regression Analysis, Canonical Correlation Analysis and Partial Least Squares. A perhaps slightly novel aspect of the research was that it was not solely cross-sectional in format; a longitudinal element was provided by drawing on the researcher¿s earlier surveys ; and a panel element by following-up, in 2007, the main 2000 field survey. Where possible, these data were drawn upon in the analysis and interpretation. There did not appear to be any conflict between the three multivariate techniques employed and indeed their findings were not dissimilar. The outcome of the data analysis was to uphold, to varying degrees, most of our hypotheses about the determinants of effective/ or successful exporting. Those that did not find support were three: firm size, product adaptation, and price determination method. Most strongly supported as determinants were promotional intensity, serving many markets and visits to trade fairs/ exhibitions; others which were statistically significant, included management commitment, special staff skills and the use of Commission Agents. While the conclusions must remain a bit tentative they are encouraging.

Random sampling is a well-known technique for approximate processing of large datasets. We introduce a set of algorithms for incremental maintenance of large random samples on secondary storage. We show that the sample maintenance cost can be reduced by refreshing the sample in a deferred manner. We introduce a novel type of log file which follows the intuition that only a “sample” of the operations on the base data has to be considered to maintain a random sample in a statistically correct way. Additionally, we develop a deferred refresh algorithm which updates the sample by using fast sequential disk access only, and which does not require any main memory. We conducted an extensive set of experiments and found, that our algorithms reduce maintenance cost by several orders of magnitude.

La présente étude visait à développer un protocole de fixation et d'échantillonnage pour le poumon équin suivant les directives publiées sur l’utilisation d’une approche stéréologique de type « design-based ». Les poumons gauches de chevaux contrôles et atteints du souffle ont été fixés avec du formaldéhyde 10% pendant 48h à une pression constante de 25-30 cm d’H2O. Les poumons ont été sectionnés en 20-21 tranches d’une épaisseur d'environ 2,5 cm chacune; de 10-11 tranches ont été sélectionnées de façon aléatoire et systématique pour la mesure du volume de référence avec la méthode de Cavalieri. Un protocole d’échantillonnage systématique, aléatoire et uniforme utilisant le principe du « smooth fractionator » et un poinçon à biopsie de 17 mm ont été utilisés pour échantillonner une fraction représentative de chaque poumon. Les méthodes d’échantillonnage de sections verticales, uniformes et aléatoires (VUR) et d’échantillonnage isotropique, uniforme et aléatoire (IUR) ont toutes deux été effectuées pour comparer le nombre de voies respiratoires en coupe perpendiculaire obtenues à partir de chaque méthode. L'architecture globale et la qualité des tissus fixés ont également été évaluées. Des spécimens pulmonaires équins ont été échantillonnés avec succès selon un protocole visant à produire des données morphométriques valides. Les tissus ont été fixés avec un minimum d'artéfacts et contenaient une quantité suffisante de voies respiratoires en coupe perpendiculaire dans les deux types d’échantillons. En conclusion, un protocole de fixation et d'échantillonnage adapté au poumon équin permettant l'utilisation d'une approche stéréologique de type « design-based » a été élaboré pour l’étude du remodelage des voies respiratoires. / The present study aimed at developing a fixation and sampling protocol for the horse lung in agreement with recent published guidelines for a design-based stereology approach. The left lungs from control and from heaves-affected horses were fixed in 10% formaldehyde for 48hr at a controlled constant pressure of 25-30 cm H2O. Lungs were cut into 20-21 slices of a thickness of approximately 2.5cm each; 10-11 slices were then randomly and systematically selected for the measurement of the reference volume using the Cavalieri method. A systematic, uniform and random sampling (SURS) protocol using a 17 mm punch biopsy and the smooth fractionator principle was used to select a representative fraction of each lung. The vertical uniform random (VUR) and isotropic uniform random (IUR) sampling methods were both performed to compare the number of perpendicular airways obtained with each method. The general architecture and the quality of the fixed tissues were also evaluated. Equine lung tissues were successfully sampled with a protocol designed to yield accurate morphometric data. The tissues were fixed with minimal artifacts and contained an adequate amount of perpendicular airways in both VUR and IUR sections. In conclusion, we developed a fixation and sampling protocol adapted to the equine lung allowing the use of a design-based stereology approach to study airway remodeling.

Dans les examens médicaux et les actes de thérapie, les techniques minimalement invasives sont de plus en plus utilisées. Des instruments comme des aiguilles de biopsie, ou des électrodes sont utilisés pour extraire des échantillons de cellules ou pour effectuer des traitements. Afin de réduire les traumatismes et de faciliter le suivi visuelle de ces interventions, des systèmes d’assistance par imagerie médicale, comme par exemple, par l’échographie 2D, sont utilisés dans la procédure chirurgicale. Nous proposons d’utiliser l’échographie 3D pour faciliter la visualisation de l’aiguille, mais en raison de l’aspect bruité de l’image ultrasonore (US) et la grande quantité de données d’un volume 3D, il est difficile de trouver l’aiguille de biopsie avec précision et de suivre sa position en temps réel. Afin de résoudre les deux principaux problèmes ci-dessus, nous avons proposé une méthode basée sur un algorithme RANSAC et un filtre de Kalman. De même l’étude est limitée à une région d’intérêt (ROI) pour obtenir une localisation robuste et le suivi de la position de l’aiguille de biopsie en temps réel. La méthode ROI-RK se compose de deux étapes: l’étape d’initialisation et l’étape de suivi. Dans la première étape, une stratégie d’initialisation d’une ROI en utilisant le filtrage de ligne à base de matrice de Hesse est mise en œuvre. Cette étape permet de réduire efficacement le bruit de granularité du volume US, et de renforcer les structures linéaires telles que des aiguilles de biopsie. Dans la deuxième étape, après l’initialisation de la ROI, un cycle de suivi commence. L’algorithme RK localise et suit l’aiguille de biopsie dans une situation dynamique. L’algorithme RANSAC est utilisé pour estimer la position des micro-outils et le filtrage de Kalman permet de mettre à jour la région d’intérêt et de corriger la localisation de l’aiguille. Une stratégie d’estimation de mouvement est également appliquée pour estimer la vitesse d’insertion de l’aiguille de biopsie. Des volumes 3D US avec un fond inhomogène ont été simulés pour vérifier les performances de la méthode ROI-RK. La méthode a été testée dans des conditions variables, telles que l’orientation d’insertion de l’aiguille par rapport à l’axe de la sonde et le niveau de contraste (CR). La précision de la localisation est de moins de 1 mm, quelle que soit la direction d’insertion de l’aiguille. Ce n’est que lorsque le CR est très faible que la méthode proposée peut échouer dans le suivi d’une structure incomplète de l’aiguille. Une autre méthode, utilisant l’algorithme RANSAC avec apprentissage automatique a été proposée. Cette méthode vise à classer les voxels en se basant non seulement sur l’intensité, mais aussi sur les caractéristiques de la structure de l’aiguille de biopsie. Les résultats des simulations montrent que l’algorithme RANSAC avec apprentissage automatique peut séparer les voxels de l’aiguille et les voxels de tissu de fond avec un CR faible. / In medical examinations and surgeries, minimally invasive technologies are getting used more and more often. Some specially designed surgical instruments, like biopsy needles, or electrodes are operated by radiologists or robotic systems and inserted in human’s body for extracting cell samples or delivering radiation therapy. To reduce the risk of tissue injury and facilitate the visual tracking, some medical vision assistance systems, as for example, ultrasound (US) systems can be used during the surgical procedure. We have proposed to use the 3D US to facilitate the visualization of the biopsy needle, however, due to the strong speckle noise of US images and the large calculation load involved as soon as 3D data are involved, it is a challenge to locate the biopsy needle accurately and to track its position in real time in 3D US. In order to solve the two main problems above, we propose a method based on the RANSAC algorithm and Kalman filter. In this method, a region of interest (ROI) has been limited to robustly localize and track the position of the biopsy needle in real time. The ROI-RK method consists of two steps: the initialization step and the tracking step. In the first step, a ROI initialization strategy using Hessian based line filter measurement is implemented. This step can efficiently reduce the speckle noise of the ultrasound volume, and enhance line-like structures as biopsy needles. In the second step, after the ROI is initialized, a tracking loop begins. The RK algorithm can robustly localize and track the biopsy needles in a dynamic situation. The RANSAC algorithm is used to estimate the position of the micro-tools and the Kalman filter helps to update the ROI and auto-correct the needle localization result. Because the ROI-RK method is involved in a dynamic situation, a motion estimation strategy is also implemented to estimate the insertion speed of the biopsy needle. 3D US volumes with inhomogeneous background have been simulated to evaluate the performance of the ROI-RK method. The method has been tested under different conditions, such as insertion orientations angles, and contrast ratio (CR). The localization accuracy is within 1 mm no matter what the insertion direction is. Only when the CR is very low, the proposed method could fail to track because of an incomplete ultrasound imaging of the needle. Another methodology, i.e. RANSAC with machine learning (ML) algorithm has been presented. This method aims at classifying the voxels not only depending on their intensities, but also using some structure features of the biopsy needle. The simulation results show that the RANSAC with ML algorithm can separate the needle voxels and background tissue voxels with low CR.

This pilot study consists of two parts. The first part investigates the extent to which the domestic electricity consumers intend to use and use energy efficiently using the Theory of Planned Behaviour. The second part investigates the extent to which the Energy @ Home educational intervention changed the domestic electricity consumers’ behaviour. For the first part of the study an advertisement was published and a convenience stratified sample of 61 domestic electricity consumers were selected from the 290 respondents. Data was collected from the domestic electricity consumers via a questionnaire and a telephone response log. The co-relational research design was used to investigate the relationship between the predictor variables the independent variables in the constructs of the Theory of Planned Behaviour. Simple linear regression analysis resulted in F statistic for the predicted behavioural intention was 29.74 with a p value less than 0.0001 which indicates significant statistical evidence of a linear relation between the predictor variables and the independent variables. The r2 of 0.87 implies that data points that fall closely along the best fit line. Therefore the predictor variables were good predictors of the response variable. All the participants that intended to use electricity efficiently confirmed via the telephone that they were using electricity efficiently. In the second part of the study 11 out of the 61 participants were chosen to participate in the Energy @ Home educational intervention and television program. Data was collected via the Energy audit log and the electricity consumption log. The participants intended to save between 2% and 35% of their electricity consumption and the actual electricity consumption savings were between 2% and 30%. / Science and Technology Education / M. Sc. (Mathematics, Physics & Technology Education (Physics Education))

This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used. The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform. The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images. The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration.

This pilot study consists of two parts. The first part investigates the extent to which the domestic electricity consumers intend to use and use energy efficiently using the Theory of Planned Behaviour. The second part investigates the extent to which the Energy @ Home educational intervention changed the domestic electricity consumers’ behaviour. For the first part of the study an advertisement was published and a convenience stratified sample of 61 domestic electricity consumers were selected from the 290 respondents. Data was collected from the domestic electricity consumers via a questionnaire and a telephone response log. The co-relational research design was used to investigate the relationship between the predictor variables the independent variables in the constructs of the Theory of Planned Behaviour. Simple linear regression analysis resulted in F statistic for the predicted behavioural intention was 29.74 with a p value less than 0.0001 which indicates significant statistical evidence of a linear relation between the predictor variables and the independent variables. The r2 of 0.87 implies that data points that fall closely along the best fit line. Therefore the predictor variables were good predictors of the response variable. All the participants that intended to use electricity efficiently confirmed via the telephone that they were using electricity efficiently. In the second part of the study 11 out of the 61 participants were chosen to participate in the Energy @ Home educational intervention and television program. Data was collected via the Energy audit log and the electricity consumption log. The participants intended to save between 2% and 35% of their electricity consumption and the actual electricity consumption savings were between 2% and 30%. / Science and Technology Education / M. Sc. (Mathematics, Physics & Technology Education (Physics Education))