Analysis of Carbon Dioxide Emission from Transportation Sector using Panel Data Method

CO2 emissions and climate change have become a topic of global importance for the international community which should have led to immediate action to remedy this dangerous situation The main objective of this work is to identify the causes and factors that can contribute to the reduction of CO2 emissions in the transportation sector The estimation method based on Panel data for 25 countries around the world has shown that the density of the urban population and the heavy use of private vehicles in many metropolitans are the main causes of CO2 emissions We have demonstrated that the development of renewable energies the development of collective transport systems and sustainable forest management practices are concrete and practical solutions to fight against CO2 emissions in megalopolise

People identification in video sequences by appearance and gait

Avec l’installation généralisée de caméras de surveillance dans les zones urbaines, l’enjeu de cette thèse est la reconnaissance automatique de piétons par l’apparence et la démarche. Cette reconnaissance nécessite des descripteurs robustes pour caractériser et identifier une personne au-delà des modifications d’apparence induites par un changement de point de vue, d’éclairage ou des variations de déplacement. La première partie propose une nouvelle mesure de similarité exploitant l’information intra et inter-personnes dans les histogrammes régionaux couleur. Une étude comparative montre l’intérêt de cette modélisation pour s’affranchir des variations de luminosité et de pose. Une fusion pondérée des descripteurs d’apparence les plus performants permet ensuite d’améliorer le taux d’identification. La seconde partie étudie la reconnaissance par la démarche selon un point de vue latéral puis frontal. Pour une observation latérale du piéton, la dynamique de la marche est modélisée par les variations spatiotemporelles des points d’articulation des deux jambes. Tandis qu’en vue frontale, on analyse la distribution des mouvements dans les silhouettes soit par une carte binaire de l’enveloppe (RED), soit par une carte en niveaux de gris des fréquences (RFD). L’étude expérimentale démontre que les descripteurs proposés font preuve de bonnes performances face à l’état de l’art, et qu’une fusion de plusieurs descripteurs permet toujours d’améliorer notablement les taux de reconnaissance. Enfin, la dernière partie de la thèse introduit un système d’identification complet associant l’apparence et la démarche. Cette approche mixte est évaluée sur une base de séquences vidéo intégrant de fortes variations de point de vue et d’éclairage. Elle offre un bon compromis entre efficacité et temps de calcul pour une application en vidéo surveillance.With the wide-spread installation of cameras in urban areas, this thesis deals with an automatic people identification by appearance and gait. This recognition requires robust descriptors to represent and discriminate each person despite the appearance variations caused by changing view point, lighting or way of moving (speed, carrying a bag…). The first part, dedicated to appearance identification, proposes a new similarity measure using intra and inter-person information with regional color histograms. A comparative study shows the efficiency of this representation to overcome the lighting and pose variations. The recognition rate is then improved by merging the most robust appearance descriptors. The second part is focused on gait recognition by distinguishing lateral and frontal points of view. For lateral observation, a pedestrian is characterized by several spatio-temporal variations of some articulated points of the legs. While, the motion of global silhouette is analyzed using a binary envelope map (RED) and a gray levels frequency map (RFD) in frontal point of view. Experimental test prove that those descriptors provide good performances compared to other state-of-art approaches, and that merging descriptors always improves the recognition rate. The last part introduces an identification system coupling appearance and gait. This merging approach is evaluated on a video sequences database including large points of view and lighting variations. It provides a good compromise between efficiency and processing time for application in video surveillance

Identification de personnes dans un flux vidéo par l’apparence et la démarche

With the wide-spread installation of cameras in urban areas, this thesis deals with an automatic people identification by appearance and gait. This recognition requires robust descriptors to represent and discriminate each person despite the appearance variations caused by changing view point, lighting or way of moving (speed, carrying a bag…). The first part, dedicated to appearance identification, proposes a new similarity measure using intra and inter-person information with regional color histograms. A comparative study shows the efficiency of this representation to overcome the lighting and pose variations. The recognition rate is then improved by merging the most robust appearance descriptors. The second part is focused on gait recognition by distinguishing lateral and frontal points of view. For lateral observation, a pedestrian is characterized by several spatio-temporal variations of some articulated points of the legs. While, the motion of global silhouette is analyzed using a binary envelope map (RED) and a gray levels frequency map (RFD) in frontal point of view. Experimental test prove that those descriptors provide good performances compared to other state-of-art approaches, and that merging descriptors always improves the recognition rate. The last part introduces an identification system coupling appearance and gait. This merging approach is evaluated on a video sequences database including large points of view and lighting variations. It provides a good compromise between efficiency and processing time for application in video surveillance.Avec l’installation généralisée de caméras de surveillance dans les zones urbaines, l’enjeu de cette thèse est la reconnaissance automatique de piétons par l’apparence et la démarche. Cette reconnaissance nécessite des descripteurs robustes pour caractériser et identifier une personne au-delà des modifications d’apparence induites par un changement de point de vue, d’éclairage ou des variations de déplacement. La première partie propose une nouvelle mesure de similarité exploitant l’information intra et inter-personnes dans les histogrammes régionaux couleur. Une étude comparative montre l’intérêt de cette modélisation pour s’affranchir des variations de luminosité et de pose. Une fusion pondérée des descripteurs d’apparence les plus performants permet ensuite d’améliorer le taux d’identification. La seconde partie étudie la reconnaissance par la démarche selon un point de vue latéral puis frontal. Pour une observation latérale du piéton, la dynamique de la marche est modélisée par les variations spatiotemporelles des points d’articulation des deux jambes. Tandis qu’en vue frontale, on analyse la distribution des mouvements dans les silhouettes soit par une carte binaire de l’enveloppe (RED), soit par une carte en niveaux de gris des fréquences (RFD). L’étude expérimentale démontre que les descripteurs proposés font preuve de bonnes performances face à l’état de l’art, et qu’une fusion de plusieurs descripteurs permet toujours d’améliorer notablement les taux de reconnaissance. Enfin, la dernière partie de la thèse introduit un système d’identification complet associant l’apparence et la démarche. Cette approche mixte est évaluée sur une base de séquences vidéo intégrant de fortes variations de point de vue et d’éclairage. Elle offre un bon compromis entre efficacité et temps de calcul pour une application en vidéo surveillance

Performance classification of Tunisian public transport operators

Improving public transport performance can encourage motorists to change their attitude and behavior. This improvement should bring the quality of service and attractiveness of public transport to counterbalance the benefits of the private car and to increase the total number of passengers. In this paper, the performance analysis is conducted in three main stages. The first step in this process is continuously introduced to adjust the sets of indicators and to measure the performance of public transport operators. The indicators selected are related to efficiency, economic and financial efficiency, pertinence and quality of service. With these indicators, managers can quickly identify the performance and implement effective and efficient public transport systems in Tunisian cities. Regarding the second step, we assessed the performance of public transport operators in Tunisia. The multi-criteria evidential reasoning provides the public transport authority with tools to control operators at all levels, to improve the performance of the public transport system and in this case all public transport operators can know their positions in relation to existing local companies. The third step is the development of performance risk management plans. To achieve this goal, we have developed an approach that allows predicting the possible evolution of the performance situation and classifying the progress made in the public transport sector year by year. The results obtained show that the public transport in Tunisia is not attractive. The overall figures of economic and financial efficiency reveal a relatively low performance and the vast majority of cities have a defective public transport network

Faces modeling with a Gabor wavelets network

Nous proposons une nouvelle méthode de codage d'une image d'un visage par un réseau d'ondelettes de Gabor. Ce type de réseau permet d'approcher toute fonction par une combinaison linéaire d'ondelettes de Gabor auxquelles sont associées des poids. Les paramètres du réseau, à savoir les poids et les coeffi°cients intervenant dans la dé"finition des ondelettes de Gabor, sont déterminés par apprentissage. Dans notre méthode, nous associons un indice de con"fiance à chaque ondelette, lequel permet d'accorder plus d'importance aux ondelettes les plus signi"catives. Le coeffi°cient de con"fiance est déterminé en étudiant les évolutions des paramètres de l'ondelette au cours de l'apprentissage, étape qui dans notre cas fait intervenir plusieurs images du visage d'un même individu avec différentes expressions faciales. Le masque obtenu par combinaison des amplitudes des ondelettes pondérées par les coeffi°cients de confi"ance indique les zones du visage contenant des indices caractéristiques "fiables pour la reconnaissance

Intelligent Tuning of Augmented L 1 Adaptive Control for Cerebral Palsy Kids Rehabilitation

International audienceWalking ability can be lost due to several neurological diseases. In this paper, we are interested in the rehabilitation of pediatric patients. For this purpose, an extended L 1 adaptive control has been proposed to control 2 DOF lower limbs exoskele-tons used to rehabilitate kids suffering from Cerebral Palsy (CP). Two methods based on PID controllers have been combined with L 1 adaptive control in order to ensure the limb movement along a predefined trajectory. Then, a comparison study has been established based on tracking errors. Moreover, to improve the robustness of these controllers against uncertainties , several tests with the variations of children masses and lengths have been carried out, with the presence of torque limits using fuzzy logic

Design and Implementation of Low Noise Amplifier Operating at 868 MHz for Duty Cycled Wake-Up Receiver Front-End

The integration of wireless communication, e.g., in real- or quasi-real-time applications, is related to many challenges such as energy consumption, communication range, quality of service, and reliability. The improvement of wireless sensor networks (WSN) performance starts by enhancing the capabilities of each sensor node. To minimize latencies without increasing energy consumption, wake-up receiver (WuRx) nodes have been introduced in recent works since they can be always-on or power-gated with short latencies by a power consumption in the range of some microwatts. Compared to standard receiver technologies, they are usually characterized by drawbacks in terms of sensitivity. To overcome the limitation of the sensitivity of WuRxs, a design of a low noise amplifier (LNA) with several design specifications is required. The challenging task of the LNA design is to provide equitable trade-off performances such as gain, power consumption, the noise figure, stability, linearity, and impedance matching. The design of fast settling LNA for a duty-cycled WuRx front-end operating at a 868 MHz frequency band is investigated in this work. The paper details the trade-offs between design challenges and illustrates practical considerations for the simulation and implementation of a radio frequency (RF) circuit. The implemented LNA competes with many commercialized designs where it reaches single-stage 12 dB gain at a 1.8 V voltage supply and consumes only a 1.6 mA current. The obtained results could be made tunable by working with off-the-shelf components for different wake-up based application exigencies

Limitation of Deep-Learning Algorithm for Prediction of Power Consumption

In recent years, electricity consumption has become high due to the use of several domestic applications in the house. On the other hand, there is a trend of using renewable energy in many houses, such as solar energy, energy-storage systems and electric vehicles. For this reason, forecasting household electricity consumption is essential for managing and planning energy use. Forecasting power consumption is a difficult time-series-forecasting task. Additionally, the electrical load has irregular trend elements, which makes it very difficult to predict the demand for electrical energy using simple forecasting techniques. Therefore, several researchers have worked on intelligent algorithms such as machine-learning and deep-learning algorithms to find a solution for this problem. In this work, we demonstrate that deep-learning algorithms are not always reliable and accurate in predicting power consumption

Design and Implementation of Low Noise Amplifier Operating at 868 MHz for Duty CycledWake-Up Receiver Front-End

The integration of wireless communication, e.g., in real- or quasi-real-time applications, is related to many challenges such as energy consumption, communication range, quality of service, and reliability. The improvement of wireless sensor networks (WSN) performance starts by enhancing the capabilities of each sensor node. To minimize latencies without increasing energy consumption, wake-up receiver (WuRx) nodes have been introduced in recent works since they can be always-on or power-gated with short latencies by a power consumption in the range of some microwatts. Compared to standard receiver technologies, they are usually characterized by drawbacks in terms of sensitivity. To overcome the limitation of the sensitivity ofWuRxs, a design of a low noise amplifier (LNA) with several design specifications is required. The challenging task of the LNA design is to provide equitable trade-off performances such as gain, power consumption, the noise figure, stability, linearity, and impedance matching. The design of fast settling LNA for a duty-cycled WuRx front-end operating at a 868 MHz frequency band is investigated in this work. The paper details the trade-offs between design challenges and illustrates practical considerations for the simulation and implementation of a radio frequency (RF) circuit. The implemented LNA competes with many commercialized designs where it reaches single-stage 12 dB gain at a 1.8 V voltage supply and consumes only a 1.6 mA current. The obtained results could be made tunable by working with off-the-shelf components for different wake-up based application exigencies