Addressing the shortage of healthcare professionals is critical for improving access to safe surgery in low- and middle-income countries. Patient safety precautions in surgical training include restricting the number of trainees present in the operating room, imposing limitations on how many surgeons can be trained simultaneously. The use of head-mounted cameras is suggested as a means to enhance and make surgical training more accessible by enabling remote viewing of surgical procedures from the surgeon's point-of-view. This research explores healthcare professionals' acceptance of incorporating a head-mounted smart camera as a complementary tool in surgical training. The case study, conducted during three surgical training workshops in Kenya and Ethiopia, utilized a modified UTAUT model. Questionnaires and five semi-structured interviews were employed, revealing a high level of technology acceptance. While some drawbacks were noted, overall positivity was expressed. The smart camera did in many ways align well with the needs of surgeons and anesthesiologists. Challenges mainly revolved around facilitating conditions such as poor connectivity and costs. Although the smart camera in many aspects enhanced the efficiency of surgical training, the findings highlight its inability to fully replace direct observation in the operating room.

Dans plusieurs tâches de la robotique, la vision est considérée comme l’élément essentiel avec lequel la perception de l’environnement ou l’interaction avec d’autres utilisateurs peut se réaliser. Néanmoins, les artefacts potentiellement présents dans les images capturées rendent la tâche de reconnaissance et d’interprétation de l’information visuelle extrêmement compliquée. Il est de ce fait, très important d’utiliser des primitives robustes, stables et ayant un taux de répétabilité élevé afin d’obtenir de bonnes performances. Cette thèse porte sur les problèmes de localisation et de détection de fermeture de boucle d’un robot mobile en utilisant la saillance visuelle. Les résultats en termes de précision et d’efficacité des applications de localisation et de détection de fermeture sont évalués et comparés aux résultats obtenus avec des approches de l’état de l’art sur différentes séquences d’images acquises en milieu extérieur. Le principal inconvénient avec les modèles proposés pour l’extraction de zones de saillance est leur complexité de calcul, ce qui conduit à des temps de traitement important. Afin d’obtenir un traitement en temps réel, nous présentons dans ce mémoire l’implémentation du détecteur de régions saillantes sur la plate forme reconfigurable DreamCam. / In several tasks of robotics, vision is considered to be the essential element by which the perception of the environment or the interaction with other users can be realized. However, the potential artifacts in the captured images make the task of recognition and interpretation of the visual information extremely complicated. It is therefore very important to use robust, stable and high repeatability rate primitives to achieve good performance. This thesis deals with the problems of localization and loop closure detection for a mobile robot using visual saliency. The results in terms of accuracy and efficiency of localization and closure detection applications are evaluated and compared to the results obtained with the approaches provided in literature, both applied on different sequences of images acquired in outdoor environnement. The main drawback with the models proposed for the extraction of salient regions is their computational complexity, which leads to significant processing time. To obtain a real-time processing, we present in this thesis also the implementation of the salient region detector on the reconfigurable platform DreamCam.

Suffisante pour exécuter les algorithmes à la cadence de ces capteurs d’images performants, tout en gardant une faible consommation d’énergie. Les systèmes monoprocesseur n’arrivent plus à satisfaire les exigences de ce domaine. Ainsi, grâce aux avancées technologiques et en s’appuyant sur de précédents travaux sur les machines parallèles, les systèmes multiprocesseurs sur puce (MPSoC) représentent une solution intéressante et prometteuse. Dans de précédents travaux à cette thèse, la cible technologique pour développer de tels systèmes était les FPGA. Or les résultats ont montré les limites de cette cible en terme de ressource matérielles et en terme de performance (vitesse notamment). Ce constat nous amène à changer de cible c’est-à-dire à passer sur cible ASIC nécessitant ainsi de retravailler profondément l’architecture et les IPs qui existaient autour de la méthode existante (appelée HNCP, pour Homogeneous Network of Communicating Processors). Afin de bénéficier de la performance offerte par la cible ASIC, les systèmes multiprocesseurs proposés s’appuient sur la flexibilité de son architecture. Combinés à des squelettes de parallélisation facilitant la programmabilité de l’architecture, les circuits proposés permettent d’offrir des systèmes supportant le portage en temps réels de différentes classes d’algorithme de traitement d’images. Le résultat de ce travail a abouti à la fabrication d’un circuit intégré à base d’un seul processeur et de ses périphériques en technologie ST CMOS 65nm dont la surface est d’environ 1 mm² et à la définition de 2 architectures multiprocesseurs flexibles basées sur le concept des squelettes de parallélisation (une architecture de 16 coeurs de processeur en technologie ST CMOS 65 nm et une deuxième architecture de 64 coeurs de processeur en technologie ST CMOS FD-SOI 28 nm). / Smart sensors today require processing components with sufficient power to run algorithms at the rate of these high-performance image sensors, while maintaining low power consumption. Monoprocessor systems are no longer able to meet the requirements of this field. Thus, thanks to technological advances and based on previous works on parallel computers, multiprocessor systems on chip (MPSoC) represent an interesting and promising solution. Previous works around this thesis have used FPGA as technological target. However, results have shown the limits of this target in terms of hardware resources and in terms of performance (speed in particular). This observation leads us to change the target from FPGA to ASIC. This migration requires deep rework at the architecture level. Particularly, existing IPs around the method (called HNCP for Homogeneous Network of Communicating Processors) have to be revisited. To take advantage of the performance offered by the ASIC target, proposed multiprocessor systems are based on the flexibility of its architecture. Combined with parallel skeletons that ease programmability of the architecture, the proposed circuits allow to offer systems that support various real-time image processing algorithms. This work has led to the fabrication of an integrated circuit based on a single processor and its peripheral using ST CMOS 65nm technology with an area around 1 mm². Moreover, two flexible multiprocessor architectures based on the concept of parallel skeletons have been proposed (a 16 cores 65 nm CMOS multiprocessors and a 64 cores 28 nm FD-SOI CMOS multiprocessors).

A vehicles ability to detect and estimate its surroundings is important for ensuring the safety of the vehicle and passengers regardless of the level of vehicle autonomy. With an improved road and lane estimation, advanced driver-assistance systems will be able to provide earlier and more accurate warnings and actions to prevent a possible accident. Current lane boundary estimations rely on camera and inertial sensor data to detect and estimate relevant lane boundaries in the vehicles surroundings. The current lane boundary estimation system struggles to provide correct estimations at distances exceeding 75 meters and has a performance which is affected by environmental effects. The methods in this thesis show how map data, together with sensor fusion with radar, camera, inertial measurement unit and global navigation satellite system data is able to provide an improvement to the lane boundary estimations. The map based estimation system is implemented and evaluated for high speed roads (highways and country roads) where lane boundary estimations for distances above 75 meters are needed. The results are conducted in a simulate environment and show how the map based system is able to correct unreliable sensor input to provide more precise boundary estimations. The map based system is also able to provide an up to 36% relative increase in correctly identified objects within ego vehicles lane between 12.5-150 meters in front of ego vehicle. The results indicate the ability to extend the horizon in which driver-assistance functions are able to operate, thus increasing the safety of future autonomous or semi-autonomous vehicles. Future work within the subject is needed to apply map based estimations on urban areas. The precision of such an system also relies on precise positional data. Incorporation of more precise global navigation data would be able to show an increased performance. / Ett fordons förmåga att upptäcka och uppskatta sin omgivning är viktig för att säkerställa fordonets och passagerarnas säkerhet oavsett fordonets autonominivå. Med en förbättrad väg- och körfältsuppskattning kommer avancerade förarassistanssystem att kunna ge tidigare och mer exakta varningar och åtgärder för att förhindra en eventuell olycka. Aktuella estimeringar av körfältsgränser är beroende av kamera och tröghetssensordata för att upptäcka och uppskatta relevanta körfältsgränser i fordonets omgivning. Det nuvarande estimerings-systemet upvisar inkorrekta uppskattningar på avstånd över 75 meter och har en prestanda som påverkas av den omgivande miljön. Metoderna i detta examensarbete visar hur kartdata, tillsammans med sensorfusion av radar, kamera, tröghetsmätenhet och globala satellitnavigeringsdata, kan ge en förbättrad estimering av körfältsgränser. Det kartbaserade systemet är implementerat och utvärderat för höghastighetsvägar (motorvägar och landsvägar) där estimeringar av körfältsgränser för avstånd över 75 meter behövs. Resultaten utförs i en simulerad miljö och visar hur det kartbaserade systemet kan korrigera opålitlig sensorinmatning för att ge mer exakta gränsuppskattningar. Systemet kan också ge en upp till 36% relativ ökning av korrekt identifierade objekt inom ego-fordonets körfält mellan 12.5-150 meter framför ego-fordonet. Resultaten indikerar förmågan att förlänga horisonten som förarassistansfunktioner kan fungera i, vilket ökar säkerheten för framtida autonoma eller halvautonoma fordon. Framtida arbeten inom ämnet behövs för att tillämpa kartbaserade uppskattningar på tätorter. Precisionen hos ett sådant system är också beroende av mer exakt positionsdata. Inkorporering av mer exakt global navigationsdata skulle i detta fall kunna visa en ökad sytemprestanda.

With no online pupillary analysis methods today, both the medical and the research fields are left to carry out a lengthy, manual and often faulty examination. A real-time, intelligent, embedded systems solution to pupillary analysis would help reduce faulty diagnosis, speed-up the analysis procedure by eliminating the human expert operator and in general, provide a versatile and highly adaptable research tool. Therefore, this thesis has sought to investigate, develop and test possible system designs for pupillary analysis, with the aim for caffeine detection. A pair of LED manipulator glasses have been designed to standardize the illumination method across testing. A data analysis method of the raw pupillary data has been established offline and then adapted to a real-time platform. ANN was chosen as classification algorithm. The accuracy of the ANN from the offline analysis was 94% while for the online classification the obtained accuracy was 17%. A realtime data communication and synchronization method has been developed. The resulting system showed reliable and fast execution times. Data analysis and classification took no longer than 2ms, faulty data detection showed consistent results. Data communication suffered no message loss. In conclusion, it is reported that a real-time, intelligent, embedded solution is feasible for pupillary analysis.