The performance of existing and planned millimeter and submillimeter astronomical arrays is limited by fluctuations in the amount of atmosperic water vapor along the atenna's line of sight. Correcting the resulting phase distortion of the received signals is seen as a significant technological challenge. Measurements of the variation in the line-of-sight water vapor abundance at the level of 1 micron precipitable water vapor on a time scale of 1 second and at arbitrary antenna positions are required. This thesis describes the design of, and preliminary results obtained with, a water vapor monior operating at abundance at the level 1 micron precipitable water vapor on a time scale of 1 second and at arbitrary antenna positions are required. This thesis describes the design of, and preliminary results obtained with, a water vapor monitor operating at infrared wavelengths which shows considerable promise for this application. Improvements in, and future plans for, the second generation water vapor monitor currently under development are also discussed. / xiii, 167 leaves : ill. ; 28 cm.

The IRMA III infrared radiometer is a passive atmospheric water vapor detector designed for use with interferometric submillimeter arrays as a method of phase correction. The IRMA III instrument employs a distributed, multi-tasking software control system permitting precise fine-grained control at remote locations over a low-bandwidth network connection. IRMA's software is divided among three processors tasked with performing three primary functions: command interpretation, data collection and motor control of IRMA's Alt-Az mount. IRMA's hardware control and communication functionality is based on compact, low cost, energy efficient Rabbit 2000 microcontroller modules, selected to meet IRMA's limited space and power requirements. IRMA accepts scripts defined in a custom, high level control language as its method of control, which the operator can write or dynamically generated by a separate GUI front-end program. / xi, 193 leaves : ill. ; 28 cm.

Our group has developed a 20 μm passive atmospheric water vapour monitor. The Infrared Radiometer for Millimetre Astronomy (IRMA) has been commissioned and deployed for site testing for the Thirty Meter Telescope (TMT) and the Giant Magellan Telescope (GMT). Measuring precipitable water vapour (PWV) requires both a sophisticated atmospheric model (BTRAM) and an instrument (IRMA). Atmospheric models depend on atmospheric profiles. Most profiles are generic in nature, representing only a latitude in some cases. Site-specific atmospheric profiles are required to accurately simulate the atmosphere above any location on Earth. These profiles can be created from publicly available archives of radiosonde data, that offer nearly global coverage. Having created a site-specific profile and model, it is necessary to determine the PWV sensitivity to the input parameter uncertainties used in the model. The instrument must also be properly calibrated. In this thesis, I describe the radiometric calibration of the IRMA instrument, and the creation and analysis of site-specific atmospheric models for use with the IRMA instrument in its capacity as an atmospheric water vapour monitor for site testing. / xii, 135 leaves : ill. ; 28 cm. --

The Infrared Radiometer for Millimetre Astronomy (IRMA) is a passive atmospheric water vapour monitor developed at the University of Lethbridge. It is a compact, robust, and autonomous instrument, which is capable of being operated remotely. The latest model is based on a PC/104 running an AMD 133 MHz SC520 processor, which allows for more flexible control of the unit. The modifications and upgrades to the software required for the transition to this new platform are discussed in this thesis. In addition to software optimisation, a new calibration method has been developed as the unit has become better understood. This method has been verified through test campaigns carried out in Lethbridge and Chile. The results of these tests are included in this thesis. / xii, 141 leaves : ill. (some col.) ; 28 cm. --

La biométrie est une technologie émergente qui propose de nouvelles méthodes de contrôle, d’identification et de sécurité. Les systèmes biométriques sont souvent victimes de menaces. La reconnaissance faciale est populaire et plusieurs approches existantes utilisent des images dans le spectre visible. Ces systèmes traditionnels opérant dans le spectre visible souffrent de plusieurs limitations dues aux changements d’éclairage, de poses et d’expressions faciales. La méthodologie présentée dans cette thèse est basée sur de la reconnaissance faciale multispectrale utilisant l'imagerie infrarouge et visible, pour améliorer la performance de la reconnaissance faciale et pallier les insuffisances du spectre visible. Les images multispectrales utilisées cette étude sont obtenues par fusion d’images visibles et infrarouges. Les différentes techniques de reconnaissance sont basées sur l’extraction de caractéristiques telles que la texture et les points d’intérêt par les techniques suivantes : une extraction hybride de caractéristiques, une extraction binaire de caractéristiques, une mesure de similarité tenant compte des caractéristiques extraites. / Biometrics is an emerging technology that proposes new methods of control, identification and security. Biometric systems are often subject to risks. Face recognition is popular and several existing approaches use images in the visible spectrum. These traditional systems operating in the visible spectrum suffer from several limitations due to changes in lighting, poses and facial expressions. The methodology presented in this thesis is based on multispectral facial recognition using infrared and visible imaging, to improve the performance of facial recognition and to overcome the deficiencies of the visible spectrum. The multispectral images used in this study are obtained by fusion of visible and infrared images. The different recognition techniques are based on features extraction such as texture and points of interest by the following techniques: a hybrid feature extraction, a binary feature extraction, a similarity measure taking into account the extracted characteristics.

An accurate estimation of biomass burning emissions is in part limited by the lack of knowledge of fire burning phase (smoldering/flaming). In recent years, several fire detection products have been developed to provide information of fire radiative power (FRP), location, size, and temperature of fire pixels, but no information regarding fire burning phase is retrieved. The Day-Night band (DNB) aboard Visible Infrared Imaging Radiometer Suite (VIIRS) is sensitive to visible light from flaming fires in the night. In contrast, VIIRS 4 µm moderate resolution band #13 (M13), though capable to detect fires at all phases, has no direct sensitivity for discerning fire phase. However, the hybrid usage of VIIRS DNB and M-bands data is hampered due to their different scanning technology and spatial resolution. In this study, we present a novel method to rapidly and accurately resample DNB pixel radiances to M-band pixels’ footprint that is based on DNB and M-band’s respective characteristics in their onboard schemes for detector aggregation and bow-tie effect removals. Subsequently, the visible energy fraction (VEF) as an indicator of fire burning phase is introduced and is calculated as the ratio of visible light power (VLP) and FRP for each fire pixel retrieved from VIIRS 750 m active fire product. A global distribution of VEF values, and thereby the fire phase, is quantitatively obtained, showing mostly smoldering wildfires such as peatland fires (with smaller VEF values) in Indonesia, flaming wildfires (with larger VEF values) over grasslands and savannahs in sub-Sahel region, and gas fares with largest VEF values in the Middle East. VEF is highly correlated with modified combustion efficiency (MCE) for different land cover types or regions. These results together with a case study of the 2018 California Campfire show that the VEF has the potential to be an indicator of fire combustion phase for each fire pixel, appropriate for estimating emission factors at the satellite pixel level.

On regroupe sous l’expression navigation autonome l’ensemble des méthodes visantà automatiser les déplacements d’un robot mobile. Les travaux présentés seconcentrent sur la problématique de la localisation en milieu extérieur, urbain etpériurbain, et approchent la problématique de la localisation visuelle soumise à lafois à un changement de capteurs (géométrie et modalité) ainsi qu’aux changementsde l’environnement à long terme, contraintes combinées encore très peu étudiéesdans l’état de l’art. Les recherches menées dans le cadre de cette thèse ont porté surl’utilisation exclusive de capteurs de vision. La contribution majeure de cette thèseporte sur la phase de description et compression des données issues des images sousla forme d’un histogramme de mots visuels que nous avons nommée PHROG (PluralHistograms of Restricted Oriented Gradients). Les expériences menées ont été réaliséessur plusieurs bases d’images avec différentes modalités visibles et infrarouges. Lesrésultats obtenus démontrent une amélioration des performances de reconnaissance descènes comparés aux méthodes de l’état de l’art. Par la suite, nous nous intéresseronsà la nature séquentielle des images acquises dans un contexte de navigation afin defiltrer et supprimer des estimations de localisation aberrantes. Les concepts d’un cadreprobabiliste Bayésien permettent deux applications de filtrage probabiliste appliquéesà notre problématique : une première solution définit un modèle de déplacementsimple du robot avec un filtre d’histogrammes et la deuxième met en place un modèleplus évolué faisant appel à l’odométrie visuelle au sein d’un filtre particulaire.123 / Autonomous navigation field gathers the set of algorithms which automate the moves of a mobile robot. The case study of this thesis focuses on the outdoor localisation issue with additionnal constraints : the use of visual sensors only with variable specifications (geometry, modality, etc) and long-term apparence changes of the surrounding environment. Both types of constraints are still rarely studied in the state of the art. Our main contribution concerns the description and compression steps of the data extracted from images. We developped a method called PHROG which represents data as a visual-words histogram. Obtained results on several images datasets show an improvment of the scenes recognition performance compared to methods from the state of the art. In a context of navigation, acquired images are sequential such that we can envision a filtering method to avoid faulty localisation estimation. Two probabilistic filtering approaches are proposed : a first one defines a simple movement model with a histograms filter and a second one sets up a more complex model using visual odometry and a particules filter.