État de l'art des méthodes de correction des déformations cérébrales per-opératoires

L'utilisation croissante de systèmes de navigation pour l'aide à la chirurgie a permis de faciliter les interventions ainsi que la planification des gestes chirurgicaux. Néanmoins, dans le cas de la neurochirurgie où le geste opératoire doit être très précis, les systèmes actuels sont limités à cause de déformations per-opératoires nommées ``Brain Shift''. Le terme de 'Brain Shift' traduit le mouvement des structures cérébrales arrivant après ouverture de la boite crânienne (jusqu'à 25mm). Le recalage rigide réalisé par le système de neuronavigation entre les examens préopératoires et la position du patient en salle d'opération est donc entaché d'une imprécision. Ainsi, les informations fournies par le système de navigation deviennent partiellement obsolètes. Ce document propose une présentation des différentes techniques de mesure et de compensation du 'Brain Shift'. Les avantages et inconvénients de chaque approche seront soulignés avant de conclure par une brève présentation des méthodes de validation existantes. / Navigation systems become a very attractive tool in surgical planning and procedure. However, the accuracy and usefulness of such systems is limited in presence of soft-tissue deformations. In neurosurgery, this phenomenon is called ``Brain Shift''. The ``Brain shift'' is the motion of cerebral structures occurring after the craniotomy (up to 25mm). The neuronavigation system matches rigidly the pre-operative images with the surgical field. The hypothesis of a rigid registration is no longer valid because of deformations. This document presents a survey with classification of published methods to measure and compensate for the brain shift. The various validation framework are also presented

Verified control and estimation for cloud computing

In this thesis we propose formal verification as a way to produce rigorous performance guarantees for resource control and estimation mechanisms in cloud computing. In particular, with respect to control, we focus on an automated resource provisioning mechanism, commonly referred to as auto-scaling, which allows resources to be acquired and released on demand. However, the shared environment, along with the exponentially large space of available parameters, makes the configuration of auto-scaling policies a challenging task. To address this problem, we propose a novel approach based on performance modelling and formal verification to produce performance guarantees on particular rule-based auto-scaling policies. We demonstrate the usefulness and efficiency of our techniques through a detailed validation process on two public cloud providers, Amazon EC2 and Microsoft Azure, targeting two cloud computing models, Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), respectively. We then develop novel solutions for the problem of verifying state estimation algorithms, such as the Kalman filter, in the context of cloud computing. To achieve this, we first tackle the broader problem of developing a methodology for verifying properties related to numerical and modelling errors in Kalman filters. This targets more general applications such as automotive and aerospace engineering, where the Kalman filter has been extensively applied. This allows us to develop a general framework for modelling and verifying different filter implementations operating on linear discrete-time stochastic systems, and ultimately tackle the more specific case of cloud computing