This thesis provides a baseline study for eye tracking user interface design for controlling a mobile robot. The baseline study is an experiment involving the use of a radio controller (RC) to drive the robot, while gaze data is collected from each subject monitoring the position of robot on the remote screen that displays the view for the turret-mounted video camera on the robot. Initial data from the experiment provides a foundation for interface design of actual control of the mobile robot by gaze interaction. Such an interface may provide Tele-presence for the disable. Patients with motor disability cannot use their hands and legs but only use their eye motions. Such applications of an eye tracking system can provide patients with much flexibility and freedom for search and identification of objects. / Muhammad Asghar Jan (+46-700183140) syedmaji@hotmail.com (+46-736805771)

Input Output frameworks have been widely used to study the emissions of industrial sectors either in specific economies or globally but usually focus on aggregated measures under production or consumption accounting principles (PAP and CAP). This leads to a lack of transparency in terms of the structure of the emissions and provides limited information on what are the main drivers of the emissions allocated to each sector under PAP and CAP. This information gap limits the options of policy makers to interventions on whole sectors, rather than the components of their supply chains that hold the major shares of the total embodied emissions. In this thesis we argue in favour of a more disaggregated, a ‘hot-spot’, approach that provides a better understanding of the structure of emissions under both of these headline measures. We develop a methodology to identify CO2 ‘hot-spots’ in downstream and upstream supply chains, both domestic and global. The methodology is applied first to a Single Region Input Output framework for China in 2005 identifying ‘Electricity, Gas and Water Supply’ as the Chinese sector with the highest direct emissions. Examination of the sector’s domestic downstream supply chain reveals that the majority of emissions are generated to support the final demand of other domestic sectors. Of these ‘Construction’ is the main driver and it also is the Chinese sector that is found to have the largest domestic CO2 footprint, with several emissions ‘hot-spots’ in its domestic upstream supply chain. The ‘hot-spot’ methodology is then extended to a global Inter-Regional Input Output framework to consider ‘hot-spots’ in a global supply chain context. By focusing on 2009 (the year for which appropriate data are most recently available) and UK total final demand we find that Chinese ‘Electricity, Gas and Water Supply’ is the non-UK sector with the largest direct emissions driven by UK total final demand. Studying this sector’s downstream supply chain outside China reveals that a large share of the sector’s emissions is ultimately generated to support several UK-based sectors’ domestic final demand. Furthermore, the UK ‘Health and Social Work’ sector is identified as the UK sector with the second largest global footprint to support domestic final demand. We identified a number of ‘hot-spots’ in the international part of its upstream supply chain, with a key finding being its dependence on the activity and the embodied emissions in global chemicals production. Finally, the thesis goes on to demonstrate how conducting ‘hot-spot’ analysis on disaggregated regional/sub-national Input Output tables can provide more detailed local level analysis of ‘hot-spot’ findings from the Inter-Regional Input Output framework. The key finding in this respect is the importance of introducing region specific emissions data where possible, as non-region specific data can lead in incorrect estimation of the embodied emissions in any component of the supply chains of any sectors. In general, through this research project we developed a methodology that can enhance the policy makers understanding of the structure and the drivers of the emissions generated throughout the economy. This additional information on the emissions structure, when combined with familiar IO analysis on employment and value-added for example, has the potential to lead to more targeted/focused policies, which result in significant emissions reduction with the minimum employment, resources and value-added cost.

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Le présent travail a pour but le développement d’une caméra gamma à masque codé permettant d’estimer la position tridimensionnelle (3D) des sources radioactives. Cela est d’un intérêt considérable dans le cadre d’un grand nombre d'applications, de la reconstruction de la forme 3D des objets radioactifs aux systèmes de réalité augmentée appliqués à la radioprotection. Les caméras gamma portables actuelles ne fournissent que la position angulaire relative des sources gamma à localiser, c'est-à-dire qu'aucune information métrique concernant les sources n’est disponible, comme par exemple leur distance par rapport à la caméra. Dans cette thèse, nous proposons principalement deux approches permettant d’estimer la position 3D des sources. La première approche consiste à étalonner la caméra gamma avec un capteur de profondeur à lumière structurée. La seconde approche permet d'estimer la distance source-détecteur par une méthode d’imagerie gamma stéréoscopique. Pour aligner géométriquement les images obtenues par la caméra gamma, le capteur de profondeur, et la caméra optique, une procédure d'étalonnage n’utilisant qu’une seule source ponctuelle radioactive a été conçue et mise en œuvre. Les résultats expérimentaux démontrent que les approches proposées permettent d'obtenir une précision inférieure au pixel, tant pour l’erreur de reprojection que pour la superposition des images gamma et optiques. Ces travaux présentent également une analyse quantitative de la précision et de la résolution relatives à l’estimation de la distance source-détecteur. De plus, les résultats obtenus ont validé le choix de la géométrie du modèle sténopé pour les caméras gamma à masque codé. / A coded aperture gamma camera for retrieving the three-dimensional (3-D) position of radioactive sources is presented. This is of considerable interest for a wide number of applications, ranging from the reconstruction of the 3-D shape of radioactive objects to augmented reality systems. Current portable γ-cameras only provide the relative angular position of the hotspots within their field of view. That is, they do not provide any metric information concerning the located sources. In this study, we propose two approaches to estimate the distance of the surrounding hotspots, and to autonomously determine if they are occluded by an object. The first consists in combining and accurately calibrating the gamma camera with a structured-light depth sensor. The second approach allows the estimation of the source-detector distance by means of stereo gamma imaging. To geometrically align the images obtained by the gamma, depth, and optical cameras used, a versatile calibration procedure has been designed and carried out. Such procedure uses a calibration phantom intentionally easy to build and inexpensive, allowing the procedure to be performed with only one radioactive point source. Experimental results showed that our calibration procedure yields to sub-pixel accuracy both in the re-projection error and the overlay of radiation and optical images. A quantitative analysis concerning the accuracy and resolution of the retrieved source-detector distance is also provided, along with an insight into the respective most influential factors. Moreover, the results obtained validated the choice of the geometry of the pinhole model for a coded aperture gamma camera.

In the US, the roaded landscape has had serious ecological effects. We studied wildlife-vehicle collisions occurring on the 248 state routes in Utah from 1992 to 2002. We tracked trends and patterns in deer-vehicle collisions, evaluated all routes for frequency of deer kills, and identified "hotspots" ( segments of road with high concentrations of collisions per mile). We found pronounced patterns: e.g., 61.15% of all collisions occurred on only 10 routes. We studied the effects of posted speed limit and annual average daily traffic flow and found that no relationship existed between traffic volume and/or posted speed limit and the number of wildlife-vehicle collisions that occurred. We put the economic costs associated with wildlife vehicle collisions into a public safety perspective and confirmed that associated costs, damage, injuries, and loss of resources are significant aspects ofDVCs that require attention and justify mitigation.

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Tennessee has a long history of meteorological hazards that have caused property damage and loss of life. Given climate change and variability, it is imperative to look at trends to ascertain changes spatiotemporally. Space-time cubes, a novel geographic tool, were used to analyze historical heavy precipitation (1-, 2-, and 5-year returns), floods, and flash flood data in Tennessee counties to assess the trends, identify emerging hotspots/cold spots and display changes over space and time. For all return periods, trends analysis revealed that heavy precipitation events are increasing in several counties across the state, with middle Tennessee identified as a hotspot. While floods and flash flood event trends are mixed (with both increases and decreases) across the state counties, related property damages are increasing, especially in middle Tennessee. This study is an important step to understanding spatiotemporal trends and will be useful in federal, state, and county hazard mitigation planning.

This study examines the effectiveness of using the Normalized Difference Vegetation Index (NDVI) derived from 1326 different Landsat Thematic Mapper and Enhanced Thematic Mapper images in finding isolated housing starts within the Commonwealth of Virginia's forests. Individual NDVI images were stacked by year for the years 1995-2011 and the yearly maximum for each pixel was extracted, resulting in a 17-year image stack of all yearly maxima (a 98.7% data reduction). Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to derive vegetation index thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent, F-statistic = 0.86) into housing starts and other forest disturbances using a classification tree and only two variables from the Disturbance Detection and Diagnostics (D3) algorithm: the maximum NDVI in the available recovery period and the slope between the NDVI value at the time of the disturbance and the maximum NDVI in the available recovery period. Landsat time series stacks thus show promise for identifying even the small changes associated with exurban development. / Master of Science / The objective of this study was to determine whether low-density development in previously forested areas can be identified using a time series of maximum annual vegetation greenness derived from the Landsat earth observing satellite missions. The study area was the Commonwealth of Virginia, USA. This study used 1326 different Landsat satellite images from the years 1995 through 2011. Each image contained over 34 million pixels, which were converted to a value between 0 and 1 that indicated how vegetated they were (a higher value being more vegetated). When houses are constructed trees are removed, thus lowering (at least temporarily) the overall greenness in a given area. Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to develop greenness thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent) into housing starts and other (non-housing) disturbances using a classification tree and the highest greenness a pixel attained in the available years after being disturbed (indicating how much vegetation returned in the recovery period) as well as the slope from the year of the identified disturbance to the year that had the highest value in the available recovery period.