his study attempts to obtain a holistic view of ICT application and its impact in the context of a developing economy taking the Lebanese Ministry of Finance as a case study. It draws on the works of Heeks and Stanforth (2007) and Tseng (2008) for the pre-deployment phase of the e-Gov application and the post-implementation phase respectively. Heeks and Stanforth used actor network theory to study the trajectory taken by the Sri Lankan e-Gov project, while Tseng used a form of Structuration theory known as Orlikowski's Model of Technology to gauge the impact of an Electronic Government Information System (EGIS) on the Taiwanese Ministry employees. To the knowledge of the researcher the chosen research site has never been investigated before. This necessitated that the design phase of the study needed to be assessed first in order to get in-depth information about the contingent and local contextual factors and to ascertain the level of progress in the design and deployment of the ICT tools and techniques. For the post- implementation phase, this longitudinal study assessed the perceived effectiveness of the ICT impact on the end users - the employees. In addition, secondary data collected from the Ministry and the International Monetary Fund was used to corroborate the research. The study found that the use of 'trajectory mapping' was a crucial tool for investigating the initial ICT adoption process. This is due to its strength in exposing contextual factors, its ability to identify social and technical determinism at different stages of the investigation and its suitability in revealing political wrangling and identifying the dynamism of power in a public institution. The study's findings also reveal the presence of both technical determinism and social determinism throughout the project, restructuring of the organisation due to the introduction of an ICT unit and job redesign in the whole MoF. The study also found out that ICT resulted in a power shift within the organisation by having the IT unit gain power due to its ICT knowledge. The investigation, however, could not find a direct relationship between the 'degree of success' end point suggested by Heeks and Stanforth (2007) and the sought benefits from the ICT impact. In other words, the proposed 'degree of success' may only explain the design aspect of the EGIS, however, this study found that success or demise depends also on the implementation process and the preparedness of citizens to receive such IT services. Furthermore, the study was able to empirically investigate the applicability of the three layered model suggested by Omoteso et al. (2007) and found out that considering contingency as dynamic is more applicable than the static contingency proposed in the model. The study realised that there is a great need for a continuous, contemporary training process in the ever-changing ICT environment in order to achieve uninterrupted positive results. Finally, the study indicates that lack of vertical communication, as observed in the Lebanese public institution, between users, ICT designers, and decision makers weaken the whole change process. Therefore, it suggests a form of knowledge management application using ICT as the main venue, a transition from the current mechanistic (bureaucratic) structure to an organic (flat) structure.

This master's thesis deals with pedestrian detection using neural networks in a video record captured by drone. Pedestrians are tracked, and their GPS coordinates are calculated using digital elevation models and mapped based on their identity and an information provided by the drone.

Dans cette thèse, nous avons étudier une méthode de classification et d'évaluation des modalités de transport utilisées par les porteurs de mobile durant leurs trajets quotidiens. Les informations de mobilité sont collectées par un opérateur au travers des logs du réseau téléphonique mobile qui fournissent des informations sur les stations de base qui ont été utilisées par un mobile durant son trajet. Les signaux (appels/SMS/3G/4G) émis par les téléphones sont une source d'information pertinente pour l'analyse de la mobilité humaine, mais au-delà de ça, ces données représentent surtout un moyen de caractériser les habitudes et les comportements humains. Bien que l'analyse des metadata permette d'acquérir des informations spatio-temporelles à une échelle sans précédent, ces données présentent aussi de nombreuses problématiques à traiter afin d'en extraire une information pertinente. Notre objectif dans cette thèse est de proposer une solution au problème de déduire la trajectoire réelle sur des réseaux de transport à partir d'observations de position obtenues grâce à l'analyse de la signalisation sur les réseaux cellulaires. Nous proposons « CT-Mapper" pour projecter les données de signalisation cellulaires recueillies auprès de smartphone sur le réseau de transport multimodal. Notre algorithme utilise un modèle de Markov caché et les propriétés topologiques des différentes couches de transport. Ensuite, nous proposons « LCT-Mapper » un algorithme qui permet de déduire le mode de transport utilisé. Pour évaluer nos algorithmes, nous avons reconstruit les réseaux de transport de Paris et de la région (Ile-de-France). Puis nous avons collecté un jeu de données de trajectoires réelles recueillies auprès d'un groupe de volontaires pendant une période de 1 mois. Les données de signalisation cellulaire de l'utilisateur ont été fournies par un opérateur français pour évaluer les performances de nos algorithmes à l'aide de données GPS. Pour conclure, nous avons montré dans ce travail qu'il est possible d'en déduire la trajectoire multimodale des utilisateurs d'une manière non supervisée. Notre réalisation permet d'étudier le comportement de mobilité multimodale de personnes et d'explorer et de contrôler le flux de la population sur le réseau de transport multicouche / Around half of the world population is living in cities where different transportation networks are cooperating together to provide some efficient transportation facilities for individuals. To improve the performance of the multimodal transportation network it is crucial to monitor and analyze the multimodal trajectories, however obtaining the multimodal mobility data is not a trivial task. GPS data with fine accuracy, is extremely expensive to collect; Additionally, GPS is not available in tunnels and underground. Recently, thanks to telecommunication advancement cellular dataset such as Call Data Records (CDRs), is a great resource of mobility data, nevertheless, this kind of dataset is noisy and sparse in time. Our objective in this thesis is to propose a solution to this challenging issue of inferring real trajectory and transportation layer from wholly cellular observation. To achieve these objectives, we use Cellular signalization data which is more frequent than CDRs and despite their spatial inaccuracy, they provide a fair source of multimodal trajectory data. We propose 'CT-Mapper’ to map cellular signalization data collected from smart phones over the multimodal transportation network. Our proposed algorithm uses Hidden Markov Model property and topological properties of different transportation layers to model an unsupervised mapping algorithm which maps sparse cellular trajectories on multilayer transportation network. Later on, we propose ‘LCT-Mapper’ an algorithm to infer the main mode of trajectories. The area of study in this research work is Paris and region (Ile-de-France); we have modeled and built the multimodal transportation network database. To evaluate our proposed algorithm, we use real trajectories data sets collected from a group of volunteers for a period of 1 month. The user's cellular signalization data was provided by a french operator to assess the performance of our proposed algorithms using GPS data as ground truth. An extensive set of evaluation has been performed to validate the proposed algorithms. To summarize, we have shown in this work that it is feasible to infer the multimodal trajectory of users in an unsupervised manner. Our achievement makes it possible to investigate the multimodal mobility behavior of people and explore and monitor the population flow over multilayer transportation network