In this thesis a new approach for characterisation of digital mobile radio channels is investigated. The new approach is based on recognition of the fact that while the fading which is characteristic of the mobile radio channel is very rapid, the processes underlying this fading may vary much more slowly. The comparative stability of these underlying processes has not been exploited in system designs to date. Channel models are proposed which take account of the stability of the channel. Estimators for the parameters of the models are proposed, and their performance is analysed theoretically and by simulation and measurement. Bounds are derived for the extent to which the mobile channel can be predicted, and the critical factors which define these bounds are identified. Two main applications arise for these channel models. The first is the possibility of prediction of the overall system performance. This may be used to avoid channel fading (for instance by change of frequency), or compensate for it (by change of the signal rate or by power control). The second application is in channel equalisation. An equaliser based on a model which has parameters varying only very slowly can offer improved performance especially in the case of channels which appear to be varying so rapidly that the convergence rate of an equaliser based on the conventional model is not adequate. The first of these applications is explored, and a relationship is derived between the channel impulse response and the performance of a broadband system.

This thesis focuses on two important problems related to DDDAS: interest management (data distribution) and prediction models. In order to reduce communication overhead, we propose a new interest management mechanism for mobile peer-to-peer systems. This approach involves dividing the entire space into cells and using an efficient sorting algorithm to sort the regions in each cell. A mobile landmarking scheme is introduced to implement this sort-based scheme in mobile peer-to-peer systems. The design does not require a centralized server, but rather, every peer can become a mobile landmark node to take a server-like role to sort and match the regions. Experimental results show that the scheme has better computational efficiency for both static and dynamic matching. In order to improve communication efficiency, we present a travel time prediction model based on boosting, an important machine learning technique, and combine boosting and neural network models to increase prediction accuracy. We also explore the relationship between the accuracy of travel time prediction and the frequency of traffic data collection with the long term goal of minimizing bandwidth consumption. Several different sets of experiments are used to evaluate the effectiveness of this model. The results show that the boosting neural network model outperforms other predictors.

The objective of the research described in this dissertation is to improve the travel-time prediction process using machine learning methods for the Advanced Traffic In-formation Systems (ATIS). Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. The increased demand of the traffic flow has motivated the need for development of improved applications and frameworks, which could alleviate the problems arising due to traffic flow, without the need of addition to the roadway infrastructure. In this thesis, the basic building blocks of the travel-time prediction models are discussed, with a review of the significant prior art. The problem of travel-time prediction was addressed by different perspectives in the past. Mainly the data-driven approach and the traffic flow modeling approach are the two main paths adopted viz. a viz. travel-time prediction from the methodology perspective. This dissertation, works towards the im-provement of the data-driven method. The data-driven model, presented in this dissertation, for the travel-time predic-tion on freeways was based on wavelet packet decomposition and support vector regres-sion (WPSVR), which uses the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indi-cate that the wavelet reconstructed coefficients when used as an input to the support vec-tor machine for regression (WPSVR) give better performance (with selected wavelets on-ly), when compared against the support vector regression (without wavelet decomposi-tion). The data used in the model is downloaded from California Department of Trans-portation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5 minute intervals over a distance of 9.13 miles. The results indicate an improvement in accuracy when compared against the classical SVR method. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is present-ed with interchangeable prediction methods along with the details of the Matlab applica-tion used to implement the WPSVR algorithm. The initial results are computed over the set of 42 wavelets. To reduce the compu-tational cost involved in transforming the travel-time data into the set of wavelet packets using all possible mother wavelets available, a methodology of filtering the wavelets is devised, which measures the cross-correlation and redundancy properties of consecutive wavelet transformed values of same frequency band. An alternate configuration of travel-time prediction on freeways using the con-cepts of cloud computation is also presented, which has the ability to interchange the pre-diction modules with an alternate method using the same time-series data. Finally, a graphical user interface is described to connect the Matlab environment with the Caltrans data server for online travel-time prediction using both SVR and WPSVR modules and display the errors and plots of predicted values for both methods. The GUI also has the ability to compute forecast of custom travel-time data in the offline mode.

Les résines époxy/amine obtenues grâce au mélange d’un prépolymère époxy et d’un durcisseur amine, sont utilisées dans divers domaines d’applications : peinture, potting de composés électroniques... L’objectif de cette thèse est la prédiction de la durée de vie de trois résines : DGEBA ou DGEBU/cycloalipahtique diamine, DGEBA/aliphatique diamine, soumises à un vieillissement thermo-oxydant. Pour cela, une étude multi échelle de l’oxydation est réalisée à différentes conditions de température (de 110°C à 200°C) et de pression d’oxygène (0,2 bars et 50 bars). A l’échelle moléculaire, la spectroscopie IRTF a montré la formation d’amides et de carbonyles. A l’échelle macromoléculaire, les coupures de chaînes semblent prédominantes lorsque le durcisseur est une diamine cycloaliphatique. En revanche, lorsque le système contient des séquences méthylènes portées par des segments flexibles, elles peuvent induire un mécanisme de réticulation qui peut prédominer. Ces résultats gouvernent l’évolution des propriétés fonctionnelles : la fragilisation mécanique et la dégradation des propriétés diélectriques de DGEBA/cycloaliphatique diamine se produit pour des temps d’exposition inférieurs à ceux observés pour DGEBA/aliphatique diamine. L’extrapolation des durées de vie est réalisée grâce à une modélisation cinétique basée sur un schéma mécanistique de l’oxydation des trois résines. La résolution de ce schéma cinétique permet la modélisation de l’ensemble des résultats expérimentaux (concentration en produits d’oxydation, coupures de chaînes et réticulation) pour une oxydation homogène ou bien sur des échantillons épais présentant un gradient d’oxydation. Les contraintes mécaniques engendrées lors de l’oxydation d’un échantillon épais (3 mm) de DGEBA/cycloaliphatique diamine ont été simulées afin de prédire la fissuration spontanée. / Epoxy/amine resins are thermoset materials made of epoxy prepolymer and amine hardener. Those materials are used in several industrial applications, such as paint or to encapsulate electronics. The main goal of this work is to predict lifetime of three resins: DGEBA or DGEBU/cycloaliphatic diamine, DGEBA/aliphatic diamine, in thermo-oxidative environment. In order to achieve this, a multi scale study of the oxidation is done, at several temperatures (from 110°C to 200°C) and oxygen partial pressures (0,2 bars et 50 bars). At molecular scale, the formation of amides and carbonyls has been noticed. At macromolecular scale, chain scission has been observed in epoxy/cycloaliphatic diamine but in DGEBA/aliphatic diamine cross linking seems to be predominant. Those properties are directly related to functional properties: mechanical and dielectric break down appear later in DGEBA/aliphatic diamine than in epoxy/cycloaliphatic diamine. The extrapolation of life is possible thank to kinetic modelling, based on chemical mechanistic scheme. The resolution of this kinetic scheme allowed us to model all experimental data (concentration of oxidation products, chain scission and cross linking), either in homogenous oxidation and in thick samples (3 mm). Stresses induced by oxidation in a thick sample of DGEBA/cycloaliphatic diamine have been simulated thanks to Matlab ® and finite elements by Abaqus ®.

Lorsque la densité de véhicules devient trop élevée, le trafic autoroutier est instable, et génère naturellement des accordéons, c'est-à-dire une alternance entre des zones fluides et des zones congestionnées. Ce phénomène n'est pas reproduit par les modèles de trafic standards d'ordre 1, mais peut l'être par des modèles d'ordre supérieurs, aussi bien microscopiques (modèles de loi de poursuite) que macroscopiques (systèmes de lois de conservation).Cette thèse analyse comment différents modèles représentent des états de trafic instables, et les oscillations qui en résultent. Au niveau microscopique, à cause de la concavité du flux, le débit moyen de ces oscillations est inférieur au débit d'équilibre pour une densité équivalente. Un algorithme est proposé pour stabiliser le flux par multi-anticipation, en utilisant un véhicule autonome intelligent.Au niveau macroscopique, cette thèse introduit les modèles moyennés, en partant du principe que l'échelle spatio-temporelle des oscillations est trop petite pour être correctement prédite par une simulation. Le modèle LWR moyenné, composé de deux lois de conservations, permet de représenter au niveau macroscopique la variance de la densité d'un trafic hétérogène, et calcule correctement le débit moyen de ces états. Une comparaison avec le modèle ARZ, également d'ordre 2, montre que le modèle moyenné permet de simuler une chute de capacité de façon plus réaliste.Enfin, cette thèse présente le projet SimulaClaire, de prédiction en temps réel du trafic sur le périphérique toulousain, et en particulier l'algorithme parallélisé d'optimisation en temps réel des paramètres développé pour ce projet / Highway traffic is known to be unstable when the vehicle density becomes too high, and to create stop-and-go waves, with an alternance of free flow and congested traffic. First-order traffic models can't reproduce these oscillations, but higher-order models can, both microscopic (car-following models) and macroscopic (systems of conservation laws).This thesis analyses the representation of unstable traffic states and oscillations in various traffic models. At the microscopic level, because of the flux concavity, the average flow of these oscillations is lower than the equilibrium flow for the same density. An algorithm is given to stabilize the flow with multi-anticipation, using an intelligent autonomous vehicle.At the macroscopic level, this work introduces averaged models, using the fact that the spatio-temporal scale of the oscillations is too small to be correctly predicted by simulations. The averaged LWR model, which consists of two conservation laws, enables a macroscopic representation of the density variance in a heterogeneous traffic, and gives the correct average flow of these states. A comparison with the ARZ model, also of order 2, shows that the averaged model can reproduce a capacity drop in a more realistic way.Finally, this thesis presents the SimulaClaire project of real-time traffic prediction on the ring road of Toulouse, and its parallelized parameter optimization algorithm

The Micro Aerial Vehicless (MAVs) are gaining attention in numerous applications asthese platforms are cheap and can do complex maneuvers. Moreover, most of the commer-cially available MAVs are equipped with a mono-camera. Currently, there is an increasinginterest to deploy autonomous mono-camera MAVs with obstacle avoidance capabilitiesin various complex application areas. Some of the application areas have moving obstaclesas well as stationary, which makes it more challenging for collision avoidance schemes.This master thesis set out to investigate the possibility to avoid moving and station-ary obstacles with a single camera as the only sensor gathering information from thesurrounding environment.One concept to perform autonomous obstacle avoidance is to predict the time near-collision based on a Convolution Neural Network (CNN) architecture that uses the videofeed from a mono-camera. In this way, the heading of the MAV is regulated to maximizethe time to a collision, resulting in the avoidance maneuver. Moreover, another interestingperspective is when due to multiple dynamic obstacles in the environment there aremultiple time predictions for different parts of the Field of View (FoV). The method ismaximizing time to a collision by choosing the part with the largest time to collision.However, this is a complicated task and this thesis provides an overview of it whilediscussing the challenges and possible future directions. One of the main reason was thatthe available data set was not reliable and was not provide enough information for theCNN to produce any acceptable predictions.Moreover, this thesis looks into another approach for avoiding collisions, using objectdetection method You Only Lock Once (YOLO) with the mono-camera video feed. YOLOis a state-of-the-art network that can detect objects and produce bounding boxes in real-time. Because of YOLOs high success rate and speed were it chosen to be used in thisthesis. When YOLO detects an obstacle it is telling where in the image the object is,the obstacle pixel coordinates. By utilizing the images FoV and trigonometry can pixelcoordinates be transformed to an angle, assuming the lens does not distort the image.This position information can then be used to avoid obstacles. The method is evaluated insimulation environment Gazebo and experimental verification with commercial availableMAV Parrot Bebop 2. While the obtained results show the efficiency of the method. To bemore specific, the proposed method is capable to avoid dynamic and stationary obstacles.Future works will be the evaluation of this method in more complex environments with multiple dynamic obstacles for autonomous navigation of a team of MAVs. A video ofthe experiments can be viewed at:https://youtu.be/g_zL6eVqgVM.

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Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. / Ph. D.

La conception en vue de la fiabilité (DFR, Design for Reliability) consiste à simuler le vieillissement électrique des composants élémentaires pour évaluer la dégradation d'un circuit complet. C'est dans ce contexte de fiabilité et de simulation de cette dernière, qu'une stratégie de conception en vue de la fiabilité a été développée au cours de ses travaux. Cette stratégie, intégrant une approche « système » de la simulation, s'appuie sur l'ajout de deux étapes intermédiaires dans la phase de conception. La première étape est une étape de construction de modèles comportementaux compacts à l'aide d'une méthodologie basée sur une approche de modélisation multi niveaux (du niveau transistor au niveau circuit) des dégradations d'un circuit. La seconde étape consiste alors l'analyse descendante de la fiabilité de ce circuit, à l'aide de simulations électriques utilisant ses modèles comportementaux dits « dégradables », afin de déterminer les blocs fonctionnels et/ou les composants élémentaires critiques de l'architecture de ce dernier, vis-à-vis d'un mécanisme de défaillance et un profil de mission donnés. Cette analyse descendante permet aussi d'évaluer l'instant de défaillance de ce circuit. Les dispersions statiques, lies au procédé de fabrication utilisé, sur les performances d'un lot de CIs ont aussi été prises en compte afin d'évaluer leur impact sur la dispersion des instants de défaillance des circuits intégrés. Ces méthodes ont été appliquées à deux mécanismes de dégradation : les porteurs chauds et les radiations. / Design for reliability (DFR) consists in assessing the impact of electrical ageing of each elementary component, using electrical simulations, on performance degradations of a full device. According to DFR concept and reliability simulation, theses works present a new DFR strategy. This strategy based on the integration of two intermediate phases in the ICs and SoC design flow. The first phase is a bottom-up ageing behavioural modelling phase of a circuit (from transistor level to circuit level). The second phase is a « top-down reliability analyses » phase of this circuit, performing electrical simulations using its ageing behavioural models, in order to determine critical functional blocks and / or elementary components of its architecture according to a failure mechanism and a given mission profile. Theses analyses also allow determining the failure time of this circuit. Statistical dispersions on ICs performances, due to the used manufacturing process, have been taking into account in order to assess their impact on failure time dispersions of a ICs lot. The method has been applied on two degradation mechanisms: hot carriers and radiations.

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.