Les objectifs de cette thèse sont la validation d’une méthodologie de détermination de la température de surface (LST) et de l’émissivité de surface (LES) à partir des radiances hyperspectrales du capteur IASI à bord du satellite METOP. Il s’agit de montrer la possibilité d’extraire ces deux paramètres d’un signal hyperspectral IRT télédétecté dans une approche physique. Le domaine spectral d'étude s'étend de 750 à 1250 cm-1 (8 à 13,3 μm) et la résolution spectrale est de l'ordre du 0,25 cm-1, inscrivant ainsi ce travail dans le giron de la radiométrie à très haute résolution spectrale infrarouge. Après une étude des méthodes de séparation existantes, la méthode SpSm (Spectral Smothness), est validée. Une étude de sensibilité aux erreurs aux bruits atmosphérique et instrumental est menée. La méthode SpSm est appliquée aux données IASI en conditions réelles pour l’année 2008 dans une zone spatiale couvrant l’Europe et le Nord d’ Afrique. Les résultats sont validés d’une part avec les produits MODIS et SEVIRI, et d’autre part avec les paramètres température et émissivité obtenus à partir des radiances SEVIRI et l’algorithme TISI. / This thesis focuses on the validation of a methodology for determining the surface temperature (LST) and surface emissivity (LES) from hyperspectral radiances from the IASI sensor on board of the European satellite METOP. We show that it is possible to extract these two parameters from a remotely sensed TIR signal using a physical approach. The spectral range under study extends from 750 to 1250 cm-1 (8 to 13.3 μm) and the spectral resolution is 0.25 cm-1, placing this work in the context of very high spectral resolution infrared radiometry. After studying the existing methods of separation, the SpSm method (Spectral Smothness), is validated. A study of sensitivity to atmospheric and instrumental noise is conducted. The SpSm method is applied to the IASI data in real conditions in 2008 in a spatial area that covers Europeand North Africa. The results are validated on one hand with the MODIS and SEVIRI products, and on the otherhand with temperatures and emissivities obtained from the SEVIRI radiances and the TISI algorithm.

Neste trabalho, primeiramente, exploramos certos módulos de suavidade e K - funcionais definidos na esfera unitária m - dimensional e suas propriedades, dando prioridade a suas equivalências assintóticas e comparação com o erro de melhor aproximação. Uma das principais referências utilizadas foi (DAI; XU, 2010). Posteriormente, consideramos um módulo de suavidade e um K-funcional em espaços mais gerais, os espaços compactos 2-homogêneos, classe de espaços esta que contém a classe das esferas. A relação entre estes objetos e o raio de aproximação do operador translação (translação esférica, no contexto esférico) foi estudada. As principais referências foram (PLATONOV, 2009) e (PLATONOV, 1997). / In this work, we firstly explored certain moduli of smoothness and K - functionals defined on the m-dimensional unit sphere and their properties, mainly their asymptotic equivalence and relation to the best approximation error. The main reference is (DAI; XU, 2010). Later we consider a moduli of smoothness and a K-functional on a general setting, namely two-point homogeneous spaces, which has the unit spheres as one of its classes. Relations between those tools and the rate of approximation of the shiffting operator were studied. The main references here were (PLATONOV, 2009) and (PLATONOV, 1997).

The task of automatically segmenting 3-D surfaces representing object boundaries is important in quantitative analysis of volumetric images, which plays a vital role in numerous biomedical applications. For the diagnosis and management of disease, segmentation of images of organs and tissues is a crucial step for the quantification of medical images. Segmentation finds the boundaries or, limited to the 3-D case, the surfaces, that separate regions, tissues or areas of an image, and it is essential that these boundaries approximate the true boundary, typically by human experts, as closely as possible. Recently, graph-based methods with a global optimization property have been studied and used for various applications. Sepecifically, the state-of-the-art graph search (optimal surface segmentation) method has been successfully used for various such biomedical applications. Despite their widespread use for image segmentation, real world medical image segmentation problems often pose difficult challenges, wherein graph based segmentation methods in its purest form may not be able to perform the segmentation task successfully. This doctoral work has a twofold objective. 1)To identify medical image segmentation problems which are difficult to solve using existing graph based method and develop novel methods by employing graph search as a building block to improve segmentation accuracy and efficiency. 2) To develop a novel multiple surface segmentation strategy using deep learning which is more computationally efficient and generic than the exisiting graph based methods, while eliminating the need for human expert intervention as required in the current surface segmentation methods. This developed method is possibly the first of its kind where the method does not require and human expert designed operations. To accomplish the objectives of this thesis work, a comprehensive framework of graph based and deep learning methods is proposed to achieve the goal by successfully fulfilling the follwoing three aims. First, an efficient, automated and accurate graph based method is developed to segment surfaces which have steep change in surface profiles and abrupt distance changes between two adjacent surfaces. The developed method is applied and validated on intra-retinal layer segmentation of Spectral Domain Optical Coherence Tomograph (SD-OCT) images of eye with Glaucoma, Age Related Macular Degneration and Pigment Epithelium Detachment. Second, a globally optimal graph based method is developed to attain subvoxel and super resolution accuracy for multiple surface segmentation problem while imposing convex constraints. The developed method was applied to layer segmentation of SD-OCT images of normal eye and vessel walls in Intravascular Ultrasound (IVUS) images. Third, a deep learning based multiple surface segmentation is developed which is more generic, computaionally effieient and eliminates the requirement of human expert interventions (like transformation designs, feature extrraction, parameter tuning, constraint modelling etc.) required by existing surface segmentation methods in varying capacities. The developed method was applied to SD-OCT images of normal and diseased eyes, to validate the superior segmentaion performance, computation efficieny and the generic nature of the framework, compared to the state-of-the-art graph search method.

This quantitative study investigated the effect of client self-disclosure on the physiological arousal of the therapist and subsequent ratings of the therapeutic alliance, session smoothness, and session depth. Three therapists and 10 clients participated in a 40-minute videotaped therapy session while being attached to sensors that measured heart rate and skin conductance. The participants completed self-report questionnaires designed to assess the therapeutic alliance and session smoothness and depth immediately following the therapy session. The videotaped therapy sessions were later transcribed and coded by two independent coders for the occurrence of client self-disclosure. Correlation analyses were utilized to determine whether or not a relationship existed between client self-disclosure and the physiological arousal of the therapist. No significant relationships were found to exist between client self-disclosure and the physiological arousal of the therapist. Positive correlations were found to exist between the occurrence of client self-disclosure and the physiological arousal of the therapist as well as between the occurrence of client self-disclosure and the therapeutic alliance. The physiological arousal of the therapist was also found to be positively correlated with the strength of the therapeutic alliance.

Neste trabalho, primeiramente, exploramos certos módulos de suavidade e K - funcionais definidos na esfera unitária m - dimensional e suas propriedades, dando prioridade a suas equivalências assintóticas e comparação com o erro de melhor aproximação. Uma das principais referências utilizadas foi (DAI; XU, 2010). Posteriormente, consideramos um módulo de suavidade e um K-funcional em espaços mais gerais, os espaços compactos 2-homogêneos, classe de espaços esta que contém a classe das esferas. A relação entre estes objetos e o raio de aproximação do operador translação (translação esférica, no contexto esférico) foi estudada. As principais referências foram (PLATONOV, 2009) e (PLATONOV, 1997). / In this work, we firstly explored certain moduli of smoothness and K - functionals defined on the m-dimensional unit sphere and their properties, mainly their asymptotic equivalence and relation to the best approximation error. The main reference is (DAI; XU, 2010). Later we consider a moduli of smoothness and a K-functional on a general setting, namely two-point homogeneous spaces, which has the unit spheres as one of its classes. Relations between those tools and the rate of approximation of the shiffting operator were studied. The main references here were (PLATONOV, 2009) and (PLATONOV, 1997).

Vehicle trajectory data has played a critical role in the recent history of traffic flow and CAV operations-related studies. However, available trajectories have limited coverage, either spatial or temporal. The implementation of CAV technology is expected to produce a large-scale trajectory dataset. However, at the initial implementation level, the trajectory data produced is expected to have gaps in terms of completeness. This research develops a data model for large-scale trajectory data that can be built on CAV-collected trajectories and easily manipulated to produce traffic parameters for CAV control and operation research. A benchmarking process has been applied to test a trajectory reconstruction approach to develop a population database from partial trajectories to fill the expected data gap in CAV feedback. The large-scale trajectory data is then used in CAV operations-related studies focusing on CAV's integration with human drivers and developing performance matrices for CAV-controlled optimized trajectories. This research used large-scale vehicle trajectory data from Wide Area Motion Imagery (WAMI) developed by PVLabs for modeling and analyzing traffic characteristics as a surrogate of CAV-collected trajectories. This timestamped location data capture provides trajectory information at an interval of one second. Trajectories from an approximate area of four-square kilometers in downtown Hamilton, Canada, are used to develop a data model to extract and store traffic characteristics. The video data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like other moving object detection-based algorithms, this data suffers from false-positive detection, false-negative detection, and other positional inaccuracies caused by faulty image registration. A context-based trajectory filtering algorithm has been developed and validated against ten minutes of vehicle counts from actual WAMI images. The filtered data provides a sample of trajectories over the area, including complete and partial vehicle trajectories, excluding undetected ones. The missing trajectory reconstruction process using a dynamic state estimation process is developed to reconstruct partial and missing trajectories. A data analytics approach predicts the number of missing trajectories between two successive detections in the traffic stream on a roadway lane. A benchmarking test of the performance of the missing trajectory prediction algorithm is conducted using the NGSIM I80 database. A frame-by-frame learning method is developed to join the identified missing trajectories. This data analytics approach preserves the naturalistic property of the trajectory, which was a concern of previous traffic-flow model-based approaches. Joining partial/split trajectories provides a more comprehensive picture of the trajectory population. Due to data structure similarities, including the nature of the split and missing trajectories, the methods developed in this study to recover trajectories can be adopted for future CAV feedback data in a mixed traffic scenario. The applicability of using the large-scale trajectory data model is explored in two performance areas of CAV operations. The first is a scenario-based testing process, which evaluates the "intelligence" of a CAV in handling interactions with Human driven Vehicles (HV) by artificially replacing an HV in the traffic stream with a CAV. Scenario-based testing is conducted for a particular Operational Design Domain (ODD). The ODD is defined as operating conditions under which particular driver assistance or automated control systems are designed to function. Existing literature on scenario-based testing primarily focuses on CAV-HV interaction on highways as large-scale naturalistic trajectory data are available to facilitate such studies. This research explores car-following and lane-changing aspects of arterial CAV testing. The large-scale trajectory data model generates testing scenarios and calibrates the surrogate model for CAV operation. The modification to the trajectory data model to accommodate the scenario-based testing is illustrated. The second consists of using the large-scale trajectory data model to estimate a new trajectory smoothness parameter that can indicate the impact of intersection stop-and-go movement on the smoothness of the entire trajectory. This smoothness parameter can be applied as an optimization variable in future trajectory control-based intersection management. Long-duration trajectories from the large-scale trajectory data are used to estimate the spectral arc length parameter for trajectory smoothness. This research only estimates smoothness parameters for human-driven vehicles to illustrate its applicability for vehicle trajectories. This research developed a framework for applying expected partial trajectories from CAV technology in estimating near-complete trajectories. The large-scale data application process in two CAV operations-related studies is also provided. / Doctor of Philosophy / The decision-making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior for prevailing and future traffic conditions. The analytical tools for policy, design, decision-making, and safety analysis use aggregated and disaggregated traffic parameters. Traffic parameters are information about the dynamic state of the traffic. In the case of a vehicle, the dynamic state information can be location, speed, acceleration, heading, and spacing with other vehicles in the traffic stream. The sequence of these dynamic parameters is called vehicle trajectories in a broader term. The trajectory information is collected using several direct and indirect collection systems. The implementation of CAV technologies is expected to provide a new source of vehicle trajectory information. Trajectory data are integral to CAV safety, operational evaluation, and optimization control algorithms. Trajectory data are also used to develop, calibrate, and validate the models representing a particular aspect of human driver behavior, and the recent development of CAV has elevated the necessity and application of trajectory data. As a result, a significant demand exists in academia and industry for the procedure to create trajectories of the vehicle population in the traffic stream. The trajectory population represents the dynamic properties of all the vehicles moving over the data collection area. The primary goal of this research is to develop and apply a large-scale trajectory population database. Trajectories are typically stored in a Moving Object Database (MOD). This research leverages a MOD database collected by a new generation of Wide-Area Motion Imagery (WAMI). The WAMI system collects images from a high-altitude moving aerial platform with high-definition cameras at a fixed time interval, which captures the trajectories of vehicles in the collection area. However, validating the created trajectories for completeness and data noise revealed continuity and consistency gaps in trajectories. A multistep data mining process is undertaken to filter, process, and extract sample trajectories with reduced data noise. A trajectory reconstruction task is undertaken to reduce the data gap. A benchmarking performance test for trajectory reconstruction is conducted using NGSIM I80 data because it has been validated in multiple studies and contains trajectories of all vehicles during the collection period (i.e., trajectory population). The trajectory reconstruction methodology developed in this research can be adapted for future CAV-collected partial trajectory data. The development of the trajectory reconstruction methodology and training data created from NGSIM I80 is one of the main contributions of this research in the field of trajectory reconstruction. Several traffic flow measures are then estimated from the sample trajectories that outline the analytical requirements to integrate trajectory data with roadway infrastructure. A data model is developed to store and manipulate dynamic trajectory parameters efficiently. The resulting data processing and integration process can be applied to CAV-collected trajectories to create an analytical trajectory database. The large-scale trajectory database is used to illustrate its capability in evaluating CAV operating models, specifically the car-following and lane-changing models on an arterial network. The car-following model mimics the longitudinal movement of real-world drivers following another vehicle. The lane-changing model predicts lane-changing behavior due to path-planning requirements and navigating surrounding traffic conditions. The overall operational model evaluation process is called accelerated evaluation, in which the naturalistic vehicle movement data is used to measure CAV's operational and safety performance. For a second application of the large-scale trajectory data, long-duration trajectories are used to develop a trajectory smoothness performance measure that can be used to test different trajectory control approaches for intersection movement management. This research is one of the early attempts to leverage large-scale vehicle trajectory datasets in transportation engineering applications. Its primary contribution is the development of a comprehensive trajectory validation methodology that can be applied to future CAV feedback to produce a trajectory population database with enhanced analytical capability. The secondary output of this research is benchmarking results for different analytical methodologies to develop the trajectories that can be used in future research and development as a reference.