Background: The main purpose of this research is to design and develop a cost-effective system for detection of Foreign Object Debris (FOD), dedicated to airports. FOD detection has been a significant problem at airports as it can cause damage to aircraft. Developing such a device to detect FOD may require complicated hardware and software structures. The proposed solution is based on a computer vision system, which comprises of flexible off the shelf components such as a Raspberry Pi and Camera Module, allowing the simplistic and efficient way to detect FOD. Methods: The solution to this research is achieved through User-centered design, which implies to design a system solution suitably and efficiently. The system solution specifications, objectives and limitations are derived from this User-centered design. The possible technologies are concluded from the required functionalities and constraints to obtain a real-time efficient FOD detection system. Results: The results are obtained using background subtraction for FOD detection and implementation of SSD (single-shot multi-box detector) model for FOD classification. The performance evaluation of the system is analysed by testing the system to detect FOD of different size for different distances. The web design is also implemented to notify the user in real-time when there is an occurrence of FOD. Conclusions: We concluded that the background subtraction and SSD model are the most suitable algorithms for the solution design with Raspberry Pi to detect FOD in a real-time system. The system performs in real-time, giving the efficiency of 84% for detecting medium-sized FOD such as persons at a distance of 75 meters and 72% efficiency for detecting large-sized FOD such as cars at a distance of 125 meters, and the average frame per second (fps) that is the system ’s performance in recording and processing frames of the area required to detect FOD is 0.95.

Automotive and industrial embedded systems are increasingly dependent on real-time capabilities. TSN aims to offer flexibility of the traffic by providing Ethernet with hard and soft real-time capabilities which allows for integration with other protocols in legacy systems. TSN requires the network to be fully synchronized to achieve high performance. However, there are cases where legacy systems are not able to synchronize with TSN. These systems might nonetheless be able to synchronize with each other through their legacy synchronization mechanisms. In this thesis, we have investigated effects in terms of jitter and clock drift in endpoints by synchronizing them with each other and passing communication through an unsynchronized intermediary TSN switch. Our results revealed that with the introduction of TSN, jitter was reduced, while clock drift between endpoints and the TSN switch was introduced. The results show that negative clock drift leads to packets missing their scheduled TSN windows and positive drift leads to packets being dropped in the switch buffer queues. We proposed two solutions in order to manage the experienced clock drift. In one solution we statically changed the switch cycle, and in the other, we let the receiver node dynamically update the sending period in the sender node. In the static solution, the clock drift was reduced from negative eight microseconds per second to two nanoseconds per second. In the dynamic solution, a packet error rate of one per 100 seconds was reduced to zero errors in 19 hours.

Being predictable with respect to time is, by definition, a fundamental requirement for any real-time system. Modern multiprocessor systems impose a challenge in this context, due to resource sharing conflicts causing memory transfers to become unpredictable. In this thesis, we present a framework for achieving predictability for real-time applications running on multiprocessor system-on-chip platforms. Using a TDMA bus, worst-case execution time analysis and scheduling are done simultaneously. Since the worst-case execution times are directly dependent on the bus schedule, bus access design is of special importance. Therefore, we provide an efficient algorithm for generating bus schedules, resulting in a minimized worst-case global delay. We also present a new approach considering the average-case execution time in a predictable context. Optimization techniques for improving the average-case execution time of tasks, for which predictability with respect to time is not required, have been investigated for a long time in many different contexts. However, this has traditionally been done without paying attention to the worst-case execution time. For predictable real-time applications, on the other hand, the focus has been solely on worst-case execution time optimization, ignoring how this affects the execution time in the average case. In this thesis, we show that having a good average-case global delay can be important also for real-time applications, for which predictability is required. Furthermore, for real-time applications running on multiprocessor systems-on-chip, we present a technique for optimizing for the average case and the worst case simultaneously, allowing for a good average case execution time while still keeping the worst case as small as possible. The proposed solutions in this thesis have been validated by extensive experiments. The results demonstrate the efficiency and importance of the presented techniques.

Les véhicules modernes sont de plus en plus informatisés pour satisfaire les exigences de sureté les plus strictes et pour fournir de meilleures expériences de conduite. Par conséquent, le nombre d'unités de contrôle électronique (ECU) dans les véhicules modernes a augmenté de façon continue au cours des dernières années. En outre, les applications à calcul complexe offrent une demande de calcul plus élevée sur les ECU et ont des contraintes de temps-réel dures et souples, d'où le besoin d’une approche unifiée traitant les deux types de contraintes. Les architectures multi-cœur permettent d'intégrer plusieurs niveaux de criticité de sureté sur la même plate-forme. De telles applications ont été conçues à l'aide d'approches statiques; cependant, les approches dites statiques ne sont plus réalisables dans des environnements très dynamiques en raison de la complexité croissante et les contraintes de coûts strictes, d’où la nécessite des solutions plus souples. Cela signifie que, pour faire face aux environnements dynamiques, un système automobile doit être adaptatif; c'est-à-dire qu'il doit pouvoir adapter sa structure et / ou son comportement à l'exécution en réponse à des changements fréquents dans son environnement. Ces nouvelles exigences ne peuvent être confrontées aux approches actuelles des systèmes et logiciels automobiles. Ainsi, une nouvelle conception de l'architecture électrique / électronique (E / E) d'un véhicule doit être développé. Récemment, l'industrie automobile a convenu de changer la plate-forme AUTOSAR actuelle en "AUTOSAR Adaptive Platform". Cette plate-forme est développée par le consortium AUTOSAR en tant que couche supplémentaire de la plate-forme classique. Il s'agit d'une étude de faisabilité continue basée sur le système d'exploitation POSIX qui utilise une communication orientée service pour intégrer les applications dans le système à tout moment. L'idée principale de cette thèse est de développer de nouveaux concepts d'architecture basés sur l'adaptation pour répondre aux besoins d'une nouvelle architecture E / E pour les véhicules entièrement électriques (VEF) concernant la sureté, la fiabilité et la rentabilité, et les intégrer à AUTOSAR. Nous définissons l'architecture ASLA (Adaptive System Level in AUTOSAR), qui est un cadre qui fournit une solution adaptative pour AUTOSAR. ASLA intègre des fonctions de reconfiguration au niveau des tâches telles que l'addition, la suppression et la migration des tâches dans AUTOSAR. La principale différence entre ASLA et la plate-forme Adaptive AUTOSAR est que ASLA permet d'attribuer des fonctions à criticité mixtes sur le même ECU ainsi que des adaptations bornées temps-réel, tant dis que Adaptive AUTOSAR sépare les fonctions temps réel critiques (fonctionnant sur la plate-forme classique) des fonctions temps réel non critiques (fonctionnant sur la plate-forme adaptative). Pour évaluer la validité de notre architecture proposée, nous fournissons une implémentation prototype de notre architecture ASLA et nous évaluons sa performance à travers des expériences. / Modern vehicles have become increasingly computerized to satisfy the more strict safety requirements and to provide better driving experiences. Therefore, the number of electronic control units (ECUs) in modern vehicles has continuously increased in the last few decades. In addition, advanced applications put higher computational demand on ECUs and have both hard and soft timing constraints, hence a unified approach handling both constraints is required. Moreover, economic pressures and multi-core architectures are driving the integration of several levels of safety-criticality onto the same platform. Such applications have been traditionally designed using static approaches; however, static approaches are no longer feasible in highly dynamic environments due to increasing complexity and tight cost constraints, and more flexible solutions are required. This means that, to cope with dynamic environments, an automotive system must be adaptive; that is, it must be able to adapt its structure and/or behaviour at runtime in response to frequent changes in its environment. These new requirements cannot be faced by the current state-of-the-art approaches of automotive software systems. Instead, a new design of the overall Electric/Electronic (E/E) architecture of a vehicle needs to be developed. Recently, the automotive industry agreed upon changing the current AUTOSAR platform to the “AUTOSAR Adaptive Platform”. This platform is being developed by the AUTOSAR consortium as an additional product to the current AUTOSAR classic platform. This is an ongoing feasibility study based on the POSIX operating system and uses service-oriented communication to integrate applications into the system at any desired time. The main idea of this thesis is to develop novel architecture concepts based on adaptation to address the needs of a new E/E architecture for Fully Electric Vehicles (FEVs) regarding safety, reliability and cost-efficiency, and integrate these in AUTOSAR. We define the ASLA (Adaptive System Level in AUTOSAR) architecture, which is a framework that provides an adaptive solution for AUTOSAR. ASLA incorporates tasks-level reconfiguration features such as addition, deletion and migration of tasks in AUTOSAR. The main difference between ASLA and the Adaptive AUTOSAR platform is that ASLA enables the allocation of mixed critical functions on the same ECU as well as time-bound adaptations while adaptive AUTOSAR separates critical, hard real-time functions (running on the classic platform) from non-critical/soft-real-time functions (running on the adaptive platform). To assess the validity of our proposed architecture, we provide an early prototype implementation of ASLA and evaluate its performance through experiments.

Ce document présente nos contributions aux algorithmes d'ordonnancement à criticité mixte pour multi-processeurs. La correction de l'exécution des applications temps réel critiques est assurée par l'utilisation d'un ordonnancement vérifié à la conception. Dans ce contexte, le dimensionnement des plate-formes d'exécution vise à minimiser le nombre de processeurs nécessaires pour assurer un ordonnancement correct. Ce dimensionnement est affecté par les exigences de sûreté de fonctionnement. Ces exigences poussent à surestimer le temps nécessaire garantissant l'exécution correcte des applications. Il en découle un dimensionnement assez coûteux. Les méthodes d'ordonnancement des systèmes à criticité mixte proposent des compromis sur les garanties d'exécution des applications améliorant le dimensionnement. Différents compromis ont été proposés mais tous reposent sur la notion de mode d'exécution. Les modes sont ordonnés, et les tâches voient leur temps d'exécution requis croître avec les modes. Cependant, afin de diminuer le dimensionnement du système, seul l'ordonnancement des tâches les plus critiques est garanti. Ce modèle est appelé "discarding". La majorité des algorithmes proposés se limitent à deux modes d'exécutions par simplicité. De plus, les algorithmes les plus efficaces pour multi-processeurs exhibent un nombre élevé de préemptions, ce qui constitue un frein à leur adoption. Finalement, ces algorithmes sont rarement généralisables. Pourtant, la prise en compte de plus de deux modes, ou de tâches aux périodes élastiques permettrait une adoption plus large par le milieu industriel. L'approche proposée repose sur la séparation des préoccupations entre la prise en compte des modes de fonctionnement, et l'ordonnancement des tâches sur multi-processeurs. Cette méthode permet de concevoir une politique d'ordonnancement efficace et adaptable à différents modèles de systèmes à criticité mixte. Notre approche consiste à transformer un lot de tâches à criticité mixte en un lot de tâches qui n'est plus à criticité mixte. Ceci nous permet d'utiliser un algorithme d'ordonnancement temps réel optimal engendrant peu de préemptions et de migrations, à savoir RUN. Cette approche, appliquée en premier pour le modèle discarding avec deux modes d'exécution, rempli son objectif d'efficacité. Nous illustrons sa généricité en utilisant le même principe pour ordonnancer des systèmes discarding avec plus de deux modes d'exécution. Enfin, une démarche reposant sur la décomposition de tâche permet de généraliser l'approche au cas des tâches élastiques. / This thesis focuses on the scheduling of mixed-criticality scheduling algorithms for multi-processors. The correctness of the execution of the real-time applications is ensured by a scheduler and is checked during the design phase. The execution platform sizing aims at minimising the number of processors required to ensure this correct scheduling. This sizing is impacted by the safety requirements. Indeed, these requirements tend to overestimate the execution times of the applications to ensure their correct executions. Consequently, the resulting sizing is costly. The mixed-criticality scheduling theory aims at proposing compromises on the guarantees of the execution of the applications to reduce this over-sizing. Several models of mixed-criticality systems offering different compromises have been proposed but all are based on the use of execution modes. Modes are ordered and tasks have non decreasing execution times in each mode. Yet, to reduce the sizing of the execution platform, only the execution of the most critical tasks is ensured. This model is called the discarding model. For simplicity reasons, most of the mixed-criticality scheduling algorithms are limited to this model. Besides, the most efficient scheduling policies for multi-processors entail too many preemptions and migrations to be actually used. Finally, they are rarely generalised to handle different models of mixed-criticality systems. However, the handling of more than two execution modes or of tasks with elastic periods would make such solutions more attractive for the industry. The approach proposed in this thesis is based on the separation of concerns between handling the execution modes and the scheduling of the tasks on the multi-processors. With this approach, we achieve to design an efficient scheduling policy that schedules different models of mixed-criticality systems. It consists in performing the transformation of a mixed-criticality task set into a non mixed-criticality one. We then schedule this task set by using an optimal hard real-time scheduling algorithm that entails few preemptions and migrations: RUN. We first apply our approach on the discarding model with two execution modes. The results show the efficiency of our approach for such model. Then, we demonstrate the versatility of our approach by scheduling systems of the discarding model with more than two execution modes. Finally, by using a method based on the decomposition of task execution, our approach can schedule systems based on elastic tasks.

The shift from uniprocessor to multi-core architectures has made it difficult to design predictable hard real-time systems (HRTS) since guaranteeing deadlines while achieving high processor utilization remains a major challenge. In addition, due to increasing demands, energy efficiency has become an important design metric in HRTS. To obtain energy savings, most multi-core systems use dynamic voltage and frequency scaling (DVFS) to reduce dynamic power consumption when the system is underloaded. However, in many multi-core systems, DVFS is implemented using voltage and frequency islands (VFI), implying that individual cores cannot independently select their voltage and frequency (v/f) pairs, thus resulting in less energy savings when existing energy-aware task assignment and scheduling techniques are used. In this thesis, we present an analysis of the increase in energy consumption in the presence of VFI. Further, we propose a semi-partitioned approach called EDF-hv to reduce the energy consumption of HRTS on multi-core systems with VFI. Simulation results revealed that when workload imbalance among the cores is sufficiently high, EDF-hv can reduce system energy consumption by 15.9% on average.

Computer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person’s cognitive and affective state vary over time of day and seasonally, individualized models have had differing difficulties. The simultaneous creation and execution of an individualized model, in real time, represents the last option for modeling such cognitive and affective states. This dissertation presents and evaluates four differing techniques for the creation of cognitive and affective models that are created on-line and in real time for each individual user as alternatives to generalized models. Each of these techniques involves making predictions and modifications to the model in real time, addressing the real time datastream problems of infinite length, detection of new concepts, and responding to how concepts change over time. Additionally, with the knowledge that a user is physically present, this work investigates the contribution that the occasional direct user query can add to the overall quality of such models. The research described in this dissertation finds that the creation of a reasonable quality affective model is possible with an infinitesimal amount of time and without “ground truth” knowledge of the user, which is shown across three different emotional states. Creation of a cognitive model in the same fashion, however, was not possible via direct AI modeling, even with all of the “ground truth” information available, which is shown across four different cognitive states.

Systems controlled by embedded computers become indispensable in our lives and can be found in avionics, automotive industry, home appliances, medicine, telecommunication industry, mecatronics, space industry, etc. Fast, accurate and flexible performance estimation tools giving feedback to the designer in every design phase are a vital part of a design process capable to produce high quality designs of such embedded systems. In the past decade, the limitations of models considering fixed task execution times have been acknowledged for large application classes within soft real-time systems. A more realistic model considers the tasks having varying execution times with given probability distributions. No restriction has been imposed in this thesis on the particular type of these functions. Considering such a model, with specified task execution time probability distribution functions, an important performance indicator of the system is the expected deadline miss ratio of tasks or task graphs. This thesis proposes two approaches for obtaining this indicator in an analytic way. The first is an exact one while the second approach provides an approximate solution trading accuracy for analysis speed. While the first approach can efficiently be applied to monoprocessor systems, it can handle only very small multi-processor applications because of complexity reasons. The second approach, however, can successfully handle realistic multiprocessor applications. Experiments show the efficiency of the proposed techniques. / <p>Report code: LiU-Tek-Lic-2002:58.</p>

Colonoscopy is essential for colorectal cancer screening and disease surveillance. It can remove pre-cancerous colon polyps to reduce a patient’s cancer risk. This thesis aims to improve colonoscopy’s localization using motion tracking and colonoscopy’s coverage using surface mapping. Chapter 4 describes an endoscope motion tracker that records the scope’s insertion length, rotation, and speed during a colonoscopy. The endoscope tracker’s motion record can be combined with the endoscope’s video to localize colon polyps or cancers. In the future, the device could record highly skilled manoeuvres performed by endoscopists to help train medical residents. It is difficult to image the colon’s mucosa because the colonoscope’s camera has a limited field of view. Chapter 3 uses a 180° fisheye camera to unwrap high resolution panoramas of a colon phantom. The panoramas are then combined into a mosaic map of the colon phantom’s surface. The colon’s surface is approximated as a cylinder. Follow up experiments could test our mapping algorithm using imagery from a wide-angle, high-definition colonoscope. Chapter 2 describes another technique to localize locations where polyps have been removed—blood vessel landmarks. Colonic blood vessels from a pig were imaged to determine if they could be used to fingerprint locations on the colon’s wall. Blood vessels are also useful image features for surface mapping. The proof-of-concept experiments successfully imaged large arteries but further work is needed to image the small capillaries in the colonic mucosa and to image the veins. In summary, we have visualized colonic blood vessels to test if they could be useful landmarks, tested using an extended field of view camera to create an unwrapped map of the colon wall, and designed an endoscope tracker to help localize abnormal tissue. Combining the endoscope tracker with the other two techniques should make is possible to accurately map the colon. / Thesis / Doctor of Philosophy (PhD) / Colonoscopy is a powerful tool for colon cancer screening. A colonoscopy can decrease the chance of developing advanced cancers by removing pre-cancerous polyps before they grow. This research works to improve colonoscopy’s localization using motion tracking and its coverage using surface mapping. We have developed an endoscope motion tracker that records the scope’s insertion length, rotation, and speed during a colonoscopy. It is In described in Chapter 4. The recorded motion can be combined with the endoscope’s video to improve colon cancer localization. Next, it is difficult to image the colon’s mucosa because the colonoscope’s camera has a limited field of view. Chapter 3 uses a 180° fisheye camera to unwrap high resolution panoramas of a colon phantom. The panoramas are then combined into a cylindrical surface map. Finally, Chapter 2 images the colon’s blood vessels to determine if they can fingerprint locations on the colon’s wall.

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