Community and Regional Planning / In the past two decades, Austin has tremendous population growth, job opportunity in the downtown core and transportation challenges associated with that. Public transit, and particularly rail, often is regarded as a strategy to help reduce urban traffic congestion. The Urban Rail, which combines features of streetcars and light rail, is introduced into Austin as a new transit rail. The City of Austin, Capital Metro and Lone Star Rail are actively studying routing, financial, environmental and community elements associated with a first phase of Urban Rail. This thesis collected 2010 Origin and Destination Rail Transit Survey data from Capital Metropolitan Transportation Authority. The research focuses on the rail transit ridership. Two regression models are applied to analyze the factors influencing Austin rail transit ridership. One model is focusing on the socioeconomic characteristics. One model is focusing on the spatial factors. Our model shows that demographic factors have more significant effect than spatial factors. In addition, this work also tries to analyze the correlations between those factors and make recommendations based on the analysis result. / text

The economic evaluation of a new airport investment requires the use of estimated future air passenger demand.Today it is well known that air passenger demand is basicly dependent on various socioeconomic factors of the country and the region where the planned airport would serve. This study is focused on estimating the future air passenger demand for planned airports in Turkey where the historical air passsenger data is not available.For these purposses, neural networks and multi-linear regression were used to develop forecasting models. As independent variables,twelve socioeconomic parameters are found to be significant and used in models. The available data for the selected indicators are statistically analysed and it is observed that most of the data is highly volatile, heteroscedastic and show no definite patterns. In order to develop more reliable models, various methods like data transformation, outlier elimination and categorization are applied to the data.Only seven of total twelve indicators are used as the most significant in the regression model whereas in neural network approach the best model is achieved when all the twelve indicators are included. Both models can be used to predict air passenger demand for any future year for Or-Gi and Zafer Airports and future air passenger demand for similar airports. Regression and neural models are tested by using various statistical test methods and it is found that neural network model is superior to regression model for the data used in this study.

Le langage parlé complété (LPC) est un système de communication visuel qui utilise des formes de main placés dans différentes positions près du visage, en combinaison avec le discours de la lecture labiale naturel, pour améliorer la perception de la parole à partir de l'entrée visuelle pour les personnes sourdes. Cependant l'un des défis importants est la question de la communication de la parole entre les personnes normo-entendant qui ne pratiquent pas LPC mais produisent discours acoustique et les personnes sourdes qui utilisent la lecture labiale complété par code LPC pour la perception de la parole sans audition résiduelle. Dans notre travail, nous appliquons la méthode de régression linéaire multiple (MLR) et modèle gaussien de mélange (GMM) approche pour mapper des paramètres spectraux acoustiques à la position de la main dans LPC et la forme de la lèvre d’accompagnement. Nous donc contribué à la mise au point d'un système de traduction automatique dans le cadre de la synthèse de la parole visuelle.Cela prouve que l'approche MLR est bonne pour l'estimation des paramètres pour les lèvres à partir des paramètres spectraux car il y a forte corrélation linéaire entre les paramètres des lèvres et des paramètres spectraux. Cependant, la performance de l'approche MLR pour estimer la position de la main est faible car il n'y a pas de relation entre les positions de la main et des paramètres spectraux. En introduisant un espace intermédiaire, il s'avère que la structure de topologie similaire est la clé de la MLR. Afin de libérer de la contrainte linéaire de l'approche MLR, nous appliquons la méthode de cartographie basée sur GMM qui possède à la fois les propriétés de classification et de régression. Les paramètres de GMM sont estimés par les méthodes de formation supervisées, non supervisées et semi- supervisés séparément dans la vue de la théorie de l'apprentissage de la machine. La méthode de formation supervisée montre une grande efficacité et une bonne robustesse. Le Minimum Mean Square Error (MMSE) et Maximum A Posteriori Probabilité (MAP) sont utilisés comme critères de régression séparément dans l'approche de la cartographie basée sur GMM. Cela prouve que l'approche MLR est un cas particulier de l'approche de GMM lorsque le nombre de gaussiennes est égal à un. Ainsi, l'approche de la cartographie sur GMM peut améliorer la performance significative en comparaison avec le MLR en augmentant le nombre de gaussiennes. Enfin, les différentes approches de cartographie utilisées dans ce travail sont comparées dans une transition continue. Il montre que l'approche sur GMM peut effectuer bien grâce à la propriété de classification lorsque les données source et cible n'a pas de " relation" comme dans le cas de l'estimation de la position de la main, et il peut également améliorer les performances par la propriété de régression local lorsque la source et les données cible a forte corrélation comme dans le cas du paramètre de lèvre estimation. En outre, une prédiction directe de la géométrie des lèvres comporte de l'image naturelle de la bouche région d'intérêt (ROI) sur la base de la 2D transformée en cosinus discrète (DCT) combinée à une analyse en composante principale (ACP) est proposé. Les résultats montrent la possibilité d'estimer les caractéristiques géométriques de la lèvre avec une bonne précision en utilisant un ensemble réduit de prédicteurs dérivés des coefficients DCT. / Cued Speech (CS) is a visual communication system that uses hand shapes placed in different positions near the face, in combination with the natural speech lip-reading, to enhance speech perception from visual input for deaf people. However one of the important challenges is the question of speech communication between normal hearing people who do not practice CS but produce acoustic speech and deaf people who use lip-reading complemented by CS code for speech perception with no residual audition. In our work, we apply the multi-linear regression approach (MLR) and Gaussian Mixture Model (GMM)-based mapping approach to map acoustic spectral parameters to the hand position in CS and the accompanying lip shape. We hence contributed to the development of automatic translation system in the framework of visual speech synthesis. It proves that the MLR approach is good for estimating the lip parameters from the spectral parameters since there is strong linear correlation between the lip parameters and spectral parameters. However, the performance of MLR approach for estimating the hand position is poor since there is no relationship between the hand positions and spectral parameters. By introducing an intermediate space, it proves that the similar topology structure is the key of the MLR. In order to release the linear constraint of the MLR approach, we apply the GMM-based mapping approach which has both the classification-partition and regression properties. The parameters of GMM are estimated by the supervised, unsupervised and semi-supervised training methods separately in the view of the machine learning theory. The supervised training method shows high efficiency and good robustness. The Minimum Mean Square Error (MMSE) and Maximum A Posteriori Probability (MAP) are used as regression criteria separately in GMM-based mapping approach. It proves that the MLR approach is a special case of GMM-based mapping approach when the number of the Gaussians equals to one. Thus the GMM-based mapping approach can improve the performance significantly in comparison with the MLR by increasing the number of the Gaussians. Finally, a continuous transition achieved by the linear interpolation in the acoustic space is introduced to compare the different mapping approaches used in this work. It shows that the GMM-based mapping approach can perform well thanks to the classification-partitioning property when the source and target data has “no relationship” such as the case of the hand position estimation; and it can also improve the performance by the local regression property when the source and target data has strong correlation such as the case of the lip parameter estimation. Besides, a direct prediction of lip geometry features from the natural image of mouth region-of-interest (ROI) based on the 2D Discrete Cosine Transform (DCT) combined with a Principal Component Analysis (PCA) is proposed. The results show the possibility to estimate the geometric lip features with good accuracy using a reduced set of predictors derived from the DCT coefficients.

The use of audio and video communication has increased exponentially over the last decade and has gone from speech over GSM to HD resolution video conference between continents on mobile devices. As the use becomes more widespread the interest in delivering high quality media increases even on devices with limited resources. This includes both development and enhancement of the communication chain but also the topic of objective measurements of the perceived quality. The focus of this thesis work has been to perform enhancement within speech encoding and video decoding, to measure influence factors of audio and video performance, and to build methods to predict the perceived video quality. The audio enhancement part of this thesis addresses the well known problem in the GSM system with an interfering signal generated by the switching nature of TDMA cellular telephony. Two different solutions are given to suppress such interference internally in the mobile handset. The first method involves the use of subtractive noise cancellation employing correlators, the second uses a structure of IIR notch filters. Both solutions use control algorithms based on the state of the communication between the mobile handset and the base station. The video enhancement part presents two post-filters. These two filters are designed to improve visual quality of highly compressed video streams from standard, block-based video codecs by combating both blocking and ringing artifacts. The second post-filter also performs sharpening. The third part addresses the problem of measuring audio and video delay as well as skewness between these, also known as synchronization. This method is a black box technique which enables it to be applied on any audiovisual application, proprietary as well as open standards, and can be run on any platform and over any network connectivity. The last part addresses no-reference (NR) bitstream video quality prediction using features extracted from the coded video stream. Several methods have been used and evaluated: Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Least Square Support Vector Machines (LS-SVM), showing high correlation with both MOS and objective video assessment methods as PSNR and PEVQ. The impact from temporal, spatial and quantization variations on perceptual video quality has also been addressed, together with the trade off between these, and for this purpose a set of locally conducted subjective experiments were performed.

Dans le contexte du réchauffement et du changement climatique, il est important d’étudier les distributions, les cycles saisonniers et les tendances des gaz à l’état de trace dans l’atmosphère tels que la vapeur d’eau. L'Observatoire de Physique du Globe de Clermont-Ferrand a en charge plusieurs dispositifs d’observation dont le site instrumenté Cézeaux, Opme et Puy de Dôme (CO-PDD) situés dans le centre de la France (45◦ N, 3◦ E). Le site des Cézeaux dispose d’un LIDAR Rayleigh – Mie - Raman fournissant en routine des profils verticaux de vapeur d’eau et de paramètres optiques caractérisant les cirrus. Le site du puy de Dôme est équipé d’un spectroscope à cavité optique (CRDS-Picarro). Des mesures de colonnes totales de vapeur d’eau sont disponibles sur tous ces sites par GPS. Le site d’Opme est équipé d’un pluviomètre. Enfin Météo-France effectue le travail de contrôle qualité des données météorologiques sur les stations de mesure en France et ces données ont été utilisées dans cette thèse. La validation des données sur le site du puy de Dôme a été la première la première exploitation dans cette thèse. Des comparaisons des données sur le puy de Dôme ont montré un très bon accord entre les données de vapeur d’eau extraites de la station météorologique du puy de Dôme, de Météo France et les donnes CRDS du puy de Dôme, avec une corrélation de 0.94 et 0.98 respectivement. Les profils verticaux obtenus par LIDAR ont permis de sélectionner une anomalie de vapeur d’eau et d’identifier une intrusion stratosphère-troposphère en analysant les processus dynamique associés à cette anomalie. Les données OLR ont montré que cette intrusion est accompagnée de convection profonde et enfin LACYTRAJ confirme l'origine stratosphérique d’une partie de la masse d'air présente au-dessus de Clermont-Ferrand au cours de l’anomalie. Les longues séries d’observations (ex : Puy de Dôme 1995-2015) et des ré-analyse ECMWF ERA-Interim (1979-2017) et la diversité des sources de données (ex : satellites AIRS et COSMIC), nous permettent de documenter, analyser et comparer la variabilité, les cycles et la tendance de la vapeur d'eau à la surface et dans la troposphère, à différentes échelles de temps et déterminer les processus géophysiques responsables des distributions de vapeur d'eau sur le site CO-PDD. Le cycle annuel de la vapeur d'eau est clairement établi pour les deux sites de différentes altitudes et pour tous les types de mesure. Les sites de Cézeaux et du puy de Dôme ne présentent presque aucun cycle diurne, suggérant que la variabilité de la vapeur d'eau à la surface sur ces deux sites est plus influencée par les systèmes météorologique sporadiques que par les variations diurnes régulières. Les données LIDAR montrent une plus grande variabilité mensuelle de la distribution verticale que les produits satellites COSMIC et AIRS. La colonne totale de vapeur d'eau GPS sur le site des Cézeaux présente une tendance positive (0,42 ± 0,45 g/kg*décade entre 2006-2017). L'analyse par régressions multi-linéaires montre que les forçages continentaux (East Atlantic, East Atlantic-West Russia) ont une plus grande influence que le forçage océanique (Nord Atlantic Oscillation) sur les variations de vapeur d'eau. / In the context of global warming and climate change, it is important to study the distributions, seasonal cycles and trends of trace gases in the atmosphere such as water vapor. of the Observatoire de Physique du Globe de Clermont-Ferrand is in charge of several observation devices including the instrumented site Cézeaux, Opme and Puy de Dôme (CO-PDD) located near the center of France (45◦ N, 3◦ E). The site of Cézeaux is instrumented by a Rayleigh - Mie–LIDAR Raman providing routine vertical profiles of water vapor mixing ratio and optical parameters characterizing cirrus clouds. The puy de Dôme site is equipped with an optical cavity spectroscope (CRDS-Picarro). Measurements of total water vapor columns are available on all these sites by GPS. The Opme site is equipped with rain gauges. Finally, Météo-France performs the quality control work and of data on meteorological stations in France and these data were used in this thesis. The validation of the puy de Dôme data was the first the first task in this thesis. Comparisons between the puy de Dôme data sets showed a very good agreement between the water vapor datafrom the OPGC meteorological station of Puy de Dôme, Météo France and CRDS data with a correlation of 0.94 and 0.98 respectively. The vertical profiles deduced from the LIDAR allowed to identify a water vapor anomaly and a stratosphere-troposphere intrusion associated with this anomaly. OLR data showed that this intrusion could be linked with deep convection and LACYTRAJ confirms the stratospheric origin of a part of the air mass present above Clermont-Ferrand. Long series of observations (eg Puy de Dôme 1995-2015) and ECMWF ERA-Interim re-analysis (1979-2017) and the diversity of data sources (eg AIRS and COSMIC satellites), allowed us to document, analyze and compare the variability, cycles and trend of surface and tropospheric water vapor at different time scales and determine the geophysical processes responsible for water vapor distributions at the site of CO-PDD. The annual cycle of water vapor is clearly established for the two sites of different altitudes and for all types of measurement. Cézeaux and puy de Dôme present almost no diurnal cycle, suggesting that the variability of surface water vapor at this site is more influenced by a sporadic meteorological system than by regular diurnal variations. The LIDAR dataset shows a greater monthly variability of the vertical distribution than the COSMIC and AIRS satellite products. The Cézeaux site presents a positive trend for the GPS water vapor total column (0.42 ± 0.45 g/kg*decade during 2006–2017) and a significant negative trend for the surface water vapor mixing ratio (−0.16 ± 0.09 mm/decade during 2002–2017). The multi-linear regression analysis shows that continental forcings (East Atlantic Pattern and East Atlantic-West Russia Pattern) have a larger influence than oceanic forcing (North Atlantic Oscillation) on the water vapor variations.