In this study, a video tracker system for traffic monitoring and analysis is developed. This system is able to detect and track vehicles as they move through the camera&rsquo / s field of view. This provides to perform traffic analysis about the scene, which can be used to optimize traffic flows and identify potential accidents. The scene inspected in this study is assumed stationary to achieve high performance solution to the problem. This assumption provides to detect moving objects more accurately, as well as ability of collecting a-priori information about the scene. A new algorithm is proposed to solve the multi-vehicle tracking problem that can deal with problems such as occlusion, short period object lost or inaccurate object detection. Two different tracking methods are used together in the developed tracking system, namely, the multi-model Kalman tracker and the Markov scene partition tracker. By the combination of these vehicle trackers with the developed occlusion reasoning approach, the continuity of the track is achieved for situations such as target loss and occlusion. The developed system is a system that collects a-priori information about the junction and then used it for scene modeling in order to increase the performance of the tracking system. The proposed system is implemented on real-world image sequences. The simulation results demonstrates that, the proposed multi-vehicle tracking system is capable of tracking a target in a complex environment and able to overcome occlusion and inaccurate detection problems as well as abrupt changes in its trajectory.

The surveillance of large sea areas typically involves  the analysis of huge quantities of heterogeneous data.  In order to support the operator while monitoring maritime traffic, the identification of anomalous behavior or situations that might need further investigation may reduce operators' cognitive load. While it is worth acknowledging that existing mining applications support the identification of anomalies, autonomous anomaly detection systems are rarely used for maritime surveillance. Anomaly detection is normally a complex task that can hardly be solved by using purely visual or purely computational methods. This thesis suggests and investigates the adoption of visual analytics principles to support the detection of anomalous vessel behavior in maritime traffic data. This adoption involves studying the analytical reasoning process that needs to be supported,  using combined automatic and visualization approaches to support such process, and evaluating such integration. The analysis of data gathered during interviews and participant observations at various maritime control centers and the inspection of video recordings of real anomalous incidents lead to a characterization of the analytical reasoning process that operators go through when monitoring traffic. These results are complemented with a literature review of anomaly detection techniques applied to sea traffic. A particular statistical-based technique is implemented, tested, and embedded in a proof-of-concept prototype that allows user involvement in the detection process. The quantitative evaluation carried out by employing the prototype reveals that participants who used the visualization of normal behavioral models outperformed the group without aid. The qualitative assessment shows that  domain experts are positive towards providing automatic support and the visualization of normal behavioral models, since these aids may reduce reaction time, as well as increase trust and comprehensibility in the system. Based on the lessons learned, this thesis provides recommendations for designers and developers of maritime control and anomaly detection systems, as well as guidelines for carrying out evaluations of visual analytics environments. / Maria Riveiro is also affiliated to Informatics Research Centre, Högskolan i Skövde / Information Fusion Research Program, Högskolan i Skövde

Software-Defined Networking (SDN) objetiva aliviar as limitações impostas por redes IP tradicionais dissociando tarefas de rede executadas em cada dispositivo em planos específicos. Esta abordagem oferece vários benefícios, tais como a possibilidade de uso de protocolos de comunicação padrão, funções de rede centralizadas, e elementos de rede mais específicos e modulares, tais como controladores de rede. Apesar destes benefícios, ainda há uma falta de apoio adequado para a realização de tarefas relacionadas à classificação de tráfego, pois (i) as características de fluxo nativas disponíveis no protocolo OpenFlow, tais como contadores de bytes e pacotes, não oferecem informação suficiente para distinguir de forma precisa fluxos específicos; (ii) existe uma falta de suporte para determinar qual é o conjunto ótimo de características de fluxo para caracterizar um dado perfil de tráfego; (iii) existe uma necessidade de estratégias flexíveis para compor diferentes mecanismos relacionados à detecção, classificação e mitigação de anomalias de rede usando abstrações de software; (iv) existe uma necessidade de monitoramento de tráfego em tempo real usando técnicas leves e de baixo custo; (v) não existe um framework capaz de gerenciar detecção, classificação e mitigação de anomalias de uma forma coordenada considerando todas as demandas acima. Adicionalmente, é sabido que mecanismos de detecção e classificação de anomalias de tráfego precisam ser flexíveis e fáceis de administrar, a fim de detectar o crescente espectro de anomalias. Detecção e classificação são tarefas difíceis por causa de várias razões, incluindo a necessidade de obter uma visão precisa e abrangente da rede, a capacidade de detectar a ocorrência de novos tipos de ataque, e a necessidade de lidar com erros de classificação. Nesta dissertação, argumentamos que SDN oferece ambientes propícios para a concepção e implementação de esquemas mais robustos e extensíveis para detecção e classificação de anomalias. Diferentemente de outras abordagens na literatura relacionada, que abordam individualmente detecção ou classificação ou mitigação de anomalias, apresentamos um framework para o gerenciamento e orquestração dessas tarefas em conjunto. O framework proposto é denominado ATLANTIC e combina o uso de técnicas com baixo custo computacional para monitorar tráfego e técnicas mais computacionalmente intensivas, porém precisas, para classificar os fluxos de tráfego. Como resultado, ATLANTIC é um framework flexível capaz de categorizar anomalias de tráfego utilizando informações coletadas da rede para lidar com cada perfil de tráfego de um modo específico, como por exemplo, bloqueando fluxos maliciosos. / Software-Defined Networking (SDN) aims to alleviate the limitations imposed by traditional IP networks by decoupling network tasks performed on each device in particular planes. This approach offers several benefits, such as standard communication protocols, centralized network functions, and specific network elements, such as controller devices. Despite these benefits, there is still a lack of adequate support for performing tasks related to traffic classification, because (i) the native flow features available in OpenFlow, such as packet and byte counts, do not convey sufficient information to accurately distinguish between some types of flows; (ii) there is a lack of support to determine what is the optimal set of flow features to characterize different types of traffic profiles; (iii) there is a need for a flexible way of composing different mechanisms to detect, classify and mitigate network anomalies using software abstractions; (iv) there is a need of online traffic monitoring using lightweight/low-cost techniques; (v) there is no framework capable of managing anomaly detection, classification and mitigation in a coordinated manner and considering all these demands. Additionally, it is well-known that anomaly traffic detection and classification mechanisms need to be flexible and easy to manage in order to detect the ever growing spectrum of anomalies. Detection and classification are difficult tasks because of several reasons, including the need to obtain an accurate and comprehensive view of the network, the ability to detect the occurrence of new attack types, and the need to deal with misclassification. In this dissertation, we argue that Software-Defined Networking (SDN) form propitious environments for the design and implementation of more robust and extensible anomaly classification schemes. Different from other approaches from the literature, which individually tackle either anomaly detection or classification or mitigation, we present a management framework to perform these tasks jointly. Our proposed framework is called ATLANTIC and it combines the use of lightweight techniques for traffic monitoring and heavyweight, but accurate, techniques to classify traffic flows. As a result, ATLANTIC is a flexible framework capable of categorizing traffic anomalies and using the information collected to handle each traffic profile in a specific manner, e.g., blocking malicious flows.

Software-Defined Networking (SDN) objetiva aliviar as limitações impostas por redes IP tradicionais dissociando tarefas de rede executadas em cada dispositivo em planos específicos. Esta abordagem oferece vários benefícios, tais como a possibilidade de uso de protocolos de comunicação padrão, funções de rede centralizadas, e elementos de rede mais específicos e modulares, tais como controladores de rede. Apesar destes benefícios, ainda há uma falta de apoio adequado para a realização de tarefas relacionadas à classificação de tráfego, pois (i) as características de fluxo nativas disponíveis no protocolo OpenFlow, tais como contadores de bytes e pacotes, não oferecem informação suficiente para distinguir de forma precisa fluxos específicos; (ii) existe uma falta de suporte para determinar qual é o conjunto ótimo de características de fluxo para caracterizar um dado perfil de tráfego; (iii) existe uma necessidade de estratégias flexíveis para compor diferentes mecanismos relacionados à detecção, classificação e mitigação de anomalias de rede usando abstrações de software; (iv) existe uma necessidade de monitoramento de tráfego em tempo real usando técnicas leves e de baixo custo; (v) não existe um framework capaz de gerenciar detecção, classificação e mitigação de anomalias de uma forma coordenada considerando todas as demandas acima. Adicionalmente, é sabido que mecanismos de detecção e classificação de anomalias de tráfego precisam ser flexíveis e fáceis de administrar, a fim de detectar o crescente espectro de anomalias. Detecção e classificação são tarefas difíceis por causa de várias razões, incluindo a necessidade de obter uma visão precisa e abrangente da rede, a capacidade de detectar a ocorrência de novos tipos de ataque, e a necessidade de lidar com erros de classificação. Nesta dissertação, argumentamos que SDN oferece ambientes propícios para a concepção e implementação de esquemas mais robustos e extensíveis para detecção e classificação de anomalias. Diferentemente de outras abordagens na literatura relacionada, que abordam individualmente detecção ou classificação ou mitigação de anomalias, apresentamos um framework para o gerenciamento e orquestração dessas tarefas em conjunto. O framework proposto é denominado ATLANTIC e combina o uso de técnicas com baixo custo computacional para monitorar tráfego e técnicas mais computacionalmente intensivas, porém precisas, para classificar os fluxos de tráfego. Como resultado, ATLANTIC é um framework flexível capaz de categorizar anomalias de tráfego utilizando informações coletadas da rede para lidar com cada perfil de tráfego de um modo específico, como por exemplo, bloqueando fluxos maliciosos. / Software-Defined Networking (SDN) aims to alleviate the limitations imposed by traditional IP networks by decoupling network tasks performed on each device in particular planes. This approach offers several benefits, such as standard communication protocols, centralized network functions, and specific network elements, such as controller devices. Despite these benefits, there is still a lack of adequate support for performing tasks related to traffic classification, because (i) the native flow features available in OpenFlow, such as packet and byte counts, do not convey sufficient information to accurately distinguish between some types of flows; (ii) there is a lack of support to determine what is the optimal set of flow features to characterize different types of traffic profiles; (iii) there is a need for a flexible way of composing different mechanisms to detect, classify and mitigate network anomalies using software abstractions; (iv) there is a need of online traffic monitoring using lightweight/low-cost techniques; (v) there is no framework capable of managing anomaly detection, classification and mitigation in a coordinated manner and considering all these demands. Additionally, it is well-known that anomaly traffic detection and classification mechanisms need to be flexible and easy to manage in order to detect the ever growing spectrum of anomalies. Detection and classification are difficult tasks because of several reasons, including the need to obtain an accurate and comprehensive view of the network, the ability to detect the occurrence of new attack types, and the need to deal with misclassification. In this dissertation, we argue that Software-Defined Networking (SDN) form propitious environments for the design and implementation of more robust and extensible anomaly classification schemes. Different from other approaches from the literature, which individually tackle either anomaly detection or classification or mitigation, we present a management framework to perform these tasks jointly. Our proposed framework is called ATLANTIC and it combines the use of lightweight techniques for traffic monitoring and heavyweight, but accurate, techniques to classify traffic flows. As a result, ATLANTIC is a flexible framework capable of categorizing traffic anomalies and using the information collected to handle each traffic profile in a specific manner, e.g., blocking malicious flows.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Para melhorias do sistema rodoviÃrio, tanto no que se refere à infra-estrutura quanto à operaÃÃo, à necessÃrio a realizaÃÃo de estudos e planejamento, buscando a melhor utilizaÃÃo dos recursos existentes. Para tanto, faz-se o uso de uma importante medida de trÃfego, o volume veicular. Os dados de trÃfego sÃo coletados por meio manuais ou eletrÃnicos, porÃm, ambos podem apresentar falhas e nÃo coletar os dados em sua totalidade. No caso dos equipamentos eletrÃnicos de contagem, a coleta contÃnua pode formar uma sÃrie histÃrica, que, devido a nÃo coleta, gera falhas ao longo da base de dados, as quais podem comprometer os estudos embasados nestas informaÃÃes. Este trabalho busca, portanto, realizar anÃlises de mÃtodos empregados para estimaÃÃo destes valores faltosos, buscando conhecer o modelo mais eficaz para a variÃvel Volume MÃdio DiÃrio dos dados obtidos pelos postos de contagem contÃnua instalados nas rodovias estaduais do CearÃ. Os modelos de estimaÃÃo aplicados neste trabalho sÃo os modelos ARIMA de anÃlise de sÃries temporais, e modelos simples, que apresentam aplicaÃÃo menos complexa e processamento mais rÃpido, enquanto que o ARIMA demanda maior conhecimento especÃfico do profissional que o utiliza. Assim, o mÃtodo mais eficaz aqui considerado foi o que obteve menores erros apÃs aplicaÃÃo do modelo. Para estas aplicaÃÃes foram selecionados quatro postos permanentes, de acordo com o percentual de dados vÃlidos e sua localizaÃÃo, buscando a utilizaÃÃo de postos em pontos representativos do estado. O melhor modelo encontrado foi o ARIMA (1,0,1)7 (com erro mÃdio de 1,816%), porÃm, um dos modelos simples, o MS2, obteve resultados prÃximos aos do ARIMA (erro mÃdio 1,837%), e tambÃm pode ser considerado satisfatÃrio para aplicaÃÃo na imputaÃÃo de valores faltosos. / In order to improve the road system, with regard to its infrastructure and operation, it is necessary to perform studies and planning, by seeking the best use of existing resources. Therefore an important traffic measure is used, i.e., vehicle volume. Traffic data is collected either manually or electronically; however both ways can fail and not collect all data. In the case of electronic counting equipment, the continuous data collection may form a time series, which produces failures in the database due to non-collection, which can compromise the studies based on this information. Therefore this work aims to perform analysis of methods used to estimate these missing values, by trying to know the most effective model for the Average Daily Volume variable of the data obtained by the continuous counting stations installed in the state highways of CearÃ. The estimation models used in this work are the ARIMA models for time series analysis, and simple models, which present a less complex application and a faster processing, while the ARIMA requires more specific knowledge of the professional who uses it. The most effective method considered herein was the one that obtained smaller errors after the application of the models. Four permanent counting stations were selected for these applications, according to the percentage of valid data and its location, by seeking the use of stations in representative points of the state. The best model found was ARIMA (1,0,1)7 (with an average error of 1.816%), however one of the simplest models, MS2, produced results similar to those of ARIMA (an average error of 1.837%), and it can also be considered suitable for application in the allocation of missing values.

The aim of this Master's thesis is to explore different attitudes and to design various monitoring and detection concepts of volumetric DDoS attacks in core networks. The thesis deals with data flow control protocols with an emphasis on a modern technology of Software Defined Networks. The last part of the thesis describes verification of the theory by setting up a laboratory environment for volumetric DDoS UDP Flood simulation, detection and automated mitigation.

Bridges are vital components of the U.S. transportation network. However, every year, the transportation agencies report a large number of aging bridges that are structurally damaged. Also, evolving traffic and particularly the overloaded traversing traffic can threaten the bridges' integrity and safety further. Bridge weight-in-motion (BWIM) is a system that takes the instrumented bridges as a scale and uses the structure response to compute the trucks' weights with no interruption in the traffic. In a particular type of BWIM, called nothing-on-road BWIM (NOR-BWIM), only a few weighing sensors should be installed under the bridge top slab. Since nothing will be installed on the road surface, NOR-BWIM addresses some of the main challenges of pavement-based WIM and traditional BWIM systems. These include lane closure, interruption to the traveling traffic, and sensitivity to daily tire impacts and harsh weather conditions. It also provides a portable solution with a less labor-intense installation process. Additionally, previous studies have shown that BWIM systems are versatile candidates for overcoming the critical challenges of structural health monitoring (SHM) across various types of bridges. The integration of the two systems is more cost-effective with improved performance; thus, it is more attractive to practitioners. However, the current BWIMs have serious shortcomings that make the integrated SHM-BWIM systems impractical in real-world long-span bridges. In the first two phases of this study, these shortcomings are addressed and a novel BWIM system is proposed. Then, the novel BWIM system is used for SHM in the third phase of the study. These shortcomings are explained as follows. Most studies are performed on short/medium-span T-beam and slab-on-girder bridges. However, longer span lengths, construction methods, different slab properties (e.g., stiffness), etc., can affect the efficacy of the NOR-BWIM. Thus, there is a need to further evaluate this technique on other bridges, such as concrete-box-girder bridges with longer spans, in an effort to ascertain whether or not NOR-BWIM systems would still work effectively on such bridges. Thus, the first phase presents an experimental investigation conducted for a long-span concrete-box-girder bridge (144 m span) called the Smart Road bridge. A total of 18 experimental tests were performed on the bridge. Moreover, a cost-effective sensor placement was developed. It was found that the number of axles is detectable with an accuracy of 100%. Moreover, the estimated mean-absolute-error for axle spacing, vehicle speed, and gross vehicle weight were 4.6%, 2.6%, and 4.6%, respectively. Lastly, it was also demonstrated that the developed cost-effective NOR-BWIM system is capable of lane identification and truck position detection. The second main issue with the existing BWIM approaches is their limited suitability for simultaneous multiple-vehicle cases on multiple-lane bridges. To address this limitation, in the second phase of this study, a novel BWIM approach is proposed. The approach is built around the removal of the non-localized portion of the strain response. Keeping the localized portion of the strain response, which is not sensitive to nearby loads, allowing for enhanced detection. The superiority of this approach stems from its capability to handle multiple-vehicle cases. These may present with an arbitrary number of trucks and light-weight vehicles, simultaneously passing the bridge in any arbitrary pattern or configuration. To show the applicability of the approach, a finite element (FE) model of a long-span concrete-box-girder bridge was simulated. The model was validated against the experimental data collected under known large events. The FE model was then used to consider single-truck events (for proof-of-concept) as well as complex multiple-truck traffic cases. These included in-one-row trucks, zigzag patterns, side-by-side trucks, and a combination of several trucks with several light-weight vehicles present. The results demonstrated that the proposed BWIM approach is capable of detecting the axle weights and gross vehicle weight (GVW) of the traversing trucks. Based on all complex multiple-truck cases, the overall mean absolute errors for GVW and axle weight estimations were 4.5% and 11.3%, respectively. In the last phase, a multiple-presence dual-purpose (MPDP) SHM approach was proposed to monitor the integrity of bridges using the BWIM system existing sensors. This approach centers on the influence line (IL) change and uses a developed multiple-presence IL (MP-IL) technique (in the second phase) for SHM application. This can effectively handle the multiple presence issue of the current integrated SHM-BWIM systems to make them more practical. Also, unlike many SHM-BWIM studies, noise and transverse position change (defined as false damage indicators) were included in the proposed procedure to provide a more realistic bridge health monitoring approach. To show the applicability of the approach, a similar FE model simulated in the second phase was used. The model was then used to evaluate the MPDP approach under single and multiple truck events. Eleven damage scenarios were simulated, and three SHM trucks (a 3-axle, a 4-axle, and a 5-axle) were used to improve the SHM accuracy. Also, an updated sensor placement was proposed to effectively work for both BWIM and SHM applications in both single and multiple-truck events. According to the results, the MPDP SHM procedure coupled with the novel MP-IL and the proposed sensor placement could effectively detect the damage scenarios in both single and multiple-truck events. Also, it was shown that using several independent SHM trucks can make the monitoring process more effective. / Doctor of Philosophy / Every year, the transportation agencies report a large number of aging bridges that are structurally damaged. Also, evolving traffic and particularly overloaded traffic can threaten the bridges' integrity and safety further. Bridge weight-in-motion (BWIM) is a traffic system that takes the instrumented bridges as a scale and uses the structure response to compute the trucks' weights with no interruption in the traffic. In a particular type of BWIM, called nothing-on-road BWIM (NOR-BWIM), only a few weighing sensors should be installed under the road surface. Since nothing will be installed on the road surface, NOR-BWIM addresses some of the main challenges of pavement-based WIM and traditional BWIM systems. These include lane closure, interruption to the traveling traffic, and sensitivity to daily tire impacts and harsh weather conditions. It also provides a portable solution with a less labor-intense installation process. Additionally, previous studies have shown that BWIM systems are versatile candidates for overcoming the critical challenges of structural health monitoring (SHM) across various types of bridges. The integration of the two systems is more attractive to practitioners because it brings improved performance at a lower cost. However, the current BWIMs have serious shortcomings that make the integrated SHM-BWIM systems impractical in real-world long-span bridges. In the first two phases of this study, these shortcomings are addressed and a novel BWIM system is proposed. Then, the novel BWIM system is used for SHM in the third phase of the study. These shortcomings are explained as follows. Most studies are performed on short/medium-span bridges with particular types of structures. However, longer span lengths, construction methods, different bridge components' properties, etc., can affect the efficacy of the NOR-BWIM. Thus, there is a need to further evaluate this technique on other bridges with longer spans and different structural systems to ascertain whether or not NOR-BWIM systems would still work effectively on such bridges. Thus, the first phase presents an experimental investigation conducted for a long-span concrete-box-girder bridge (a different structural system than the literature) with 144-m spans. A total of 18 experimental tests were performed on the bridge. Moreover, a cost-effective sensor placement was developed. It was found that the number of axles is detectable with no error. Moreover, the estimated error for axle spacing, vehicle speed, and gross vehicle weight were all low. Lastly, it was also demonstrated that the developed cost-effective NOR-BWIM system is capable of lane identification and truck position detection. The second main issue with the existing BWIM approaches is their limited suitability for simultaneous multiple vehicles on multiple-lane bridges. To address this limitation, in the second phase of this study, a novel BWIM approach is proposed. The superiority of this approach stems from its capability to handle multiple-vehicle cases. These may present with an arbitrary number of trucks and light-weight vehicles, simultaneously passing the bridge in any arbitrary pattern or configuration. To show the applicability of the approach, a model of the long-span bridge was simulated. The model was validated against the experimental data collected under known traffic events. The model was then used to consider single-truck events and complex multiple-truck traffic cases. The results demonstrated that the proposed BWIM approach can detect the axle weights and gross vehicle weight (GVW) of the traversing trucks. Based on all complex multiple-truck cases, the overall errors for GVW and axle weight estimations were 4.5% and 11.3%, respectively. In the last phase, a novel SHM approach was proposed to monitor the integrity of bridges using the existing sensors for BWIM. This approach uses the proposed BWIM system for SHM application. This can effectively handle the multiple presence issue of the current integrated SHM-BWIM systems to make them more practical. Also, unlike many SHM-BWIM studies, noise and transverse position change were included in the proposed procedure to provide a more realistic bridge health monitoring approach. A similar model simulated in the second phase was used to show the applicability of the approach. The model was then used to evaluate the MPDP approach under single and multiple truck events. Eleven damage scenarios were simulated. Also, an updated sensor placement was proposed to work effectively for both BWIM and SHM applications in single and multiple-truck events. According to the results, the proposed SHM procedure coupled with the novel BWIM and the proposed sensor placement could effectively detect the damage scenarios in both single and multiple-truck events.

La fiabilité et la sécurité des réseaux de communication nécessitent des composants efficaces pour analyser finement le trafic de données. La diversification des services ainsi que l'augmentation des débits obligent les systèmes d'analyse à être plus performants pour gérer des débits de plusieurs centaines, voire milliers de Gigabits par seconde. Les solutions logicielles communément utilisées offrent une flexibilité et une accessibilité bienvenues pour les opérateurs du réseau mais ne suffisent plus pour répondre à ces fortes contraintes dans de nombreux cas critiques.Cette thèse étudie des solutions architecturales reposant sur des puces programmables de type Field-Programmable Gate Array (FPGA) qui allient puissance de calcul et flexibilité de traitement. Des cartes équipées de telles puces sont intégrées dans un flot de traitement commun logiciel/matériel afin de compenser les lacunes de chaque élément. Les composants du réseau développés avec cette approche innovante garantissent un traitement exhaustif des paquets circulant sur les liens physiques tout en conservant la flexibilité des solutions logicielles conventionnelles, ce qui est unique dans l'état de l'art.Cette approche est validée par la conception et l'implémentation d'une architecture de traitement de paquets flexible sur FPGA. Celle-ci peut traiter n'importe quel type de paquet au coût d'un faible surplus de consommation de ressources. Elle est de plus complètement paramétrable à partir du logiciel. La solution proposée permet ainsi un usage transparent de la puissance d'un accélérateur matériel par un ingénieur réseau sans nécessiter de compétence préalable en conception de circuits numériques. / The reliability and the security of communication networks require efficient components to finely analyze the traffic of data. Service diversification and through put increase force network operators to constantly improve analysis systems in order to handle through puts of hundreds,even thousands of Gigabits per second. Commonly used solutions are software oriented solutions that offer a flexibility and an accessibility welcome for network operators, but they can no more answer these strong constraints in many critical cases.This thesis studies architectural solutions based on programmable chips like Field-Programmable Gate Arrays (FPGAs) combining computation power and processing flexibility. Boards equipped with such chips are integrated into a common software/hardware processing flow in order to balance short comings of each element. Network components developed with this innovative approach ensure an exhaustive processing of packets transmitted on physical links while keeping the flexibility of usual software solutions, which was never encountered in the previous state of theart.This approach is validated by the design and the implementation of a flexible packet processing architecture on FPGA. It is able to process any packet type at the cost of slight resources over consumption. It is moreover fully customizable from the software part. With the proposed solution, network engineers can transparently use the processing power of an hardware accelerator without the need of prior knowledge in digital circuit design.

The field of bridge monitoring is one of rapid development. Advances in sensor technologies, in data communication and processing algorithms all affect the possibilities of Structural Monitoring in Bridges. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining their serviceability and deterioration state. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening works. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring. This thesis consists of an extended summary and five appended papers. The thesis presents advances in sensor technology, damage identification algorithms, Bridge Weigh-In-Motion systems, and other techniques used in bridge monitoring. Four case studies are presented. In the first paper, a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. In the second paper, the seasonal variability of a ballasted railway bridge is studied and characterized in its natural variability. In the third, the non-linear characteristic of a ballasted railway bridge is studied and described stochastically. In the fourth, a novel damage detection algorithm based in Bridge Weigh-In-Motion data and machine learning algorithms is presented and tested on a numerical experiment. In the fifth, a bridge and traffic monitoring system is implemented in a suspension bridge to study the cause of unexpected wear in the bridge bearings. Some of the major scientific contributions of this work are: 1) the development of a B-WIM for railway traffic capable of estimating the load on individual axles; 2) the characterization of in-situ measured railway traffic in Stockholm, with axle weights and train configuration; 3) the quantification of a hitherto unreported environmental behaviour in ballasted bridges and possible mechanisms for its explanation (this behaviour was shown to be of great importance for monitoring of bridges located in colder climate) 4) the statistical quantification of the nonlinearities of a railway bridge and its yearly variations and 5) the integration of B-WIM data into damage detection techniques. / <p>QC 20140910</p>

Rising from the “excess demand” modern societies and economies place on limited road resources, congestion causes increased vehicle emissions, decreases national efficiency, and wastes time (Downs, 2004). In order to minimise congestion’s impacts, traffic management systems gather traffic data and use it to implement efficient management algorithms (Downs, 2004). This dissertation’s purpose has been the development of a distributable vehicle presence detection sensor, which will wirelessly provide vehicle presence information in real time. To address the sensor’s wireless power requirements, the feasibility of self-powering the device via harvested energy has been investigated. Piezoelectric, electrostatic, and electromagnetic energy harvesting devices’ principles of operation and underlying theory has been investigated in detail and an overview presented alongside a literature review of previous vibration energy harvesting research. An electromagnetic energy harvesting device was designed, which consists of: a nylon reinforced rubber bladder, hydraulic piston, neodymium magnets, and wire-wound coil housing. Preliminary testing demonstrated a harvested energy between 100mJ and 205mJ per axle. This amount is able to be transferred to a 100O load when driven over at speeds between 10km/h and 50km/h. Combined with an embedded circuit, the energy harvester facilitated the development of a passive sensor, which is able to wirelessly transmit a vehicle’s presence signal to a host computer. The vehicle detected event is displayed via a graphical user interface. Energy harvesting’s ability to power the embedded circuit’s wireless transmission, demonstrated the feasibility of developing systems capable of harvesting energy from their environment and using it to power discrete electronic components. The ability to wirelessly transmit a vehicle’s presence facilitates the development of distributable traffic monitoring systems, allowing for remote traffic monitoring and management.