Industrial Control Systems (ICSes), networked through conventional IT infrastructures, are vulnerable to attacks originating from network channels. Perimeter security techniques such as access control and firewalls have had limited success in mitigating such attacks due to the frequent updates required by standard computing platforms, third-party hardware and embedded process controllers. The high level of human-machine interaction also aids in circumventing perimeter defenses, making an ICS susceptible to attacks such as reprogramming of embedded controllers. The Stuxnet and Aurora attacks have demonstrated the vulnerabilities of ICS security and proved that these systems can be stealthily compromised. We present several run-time methods for preemptive intrusion detection in industrial control systems to enhance ICS security against reconfiguration and network attacks. A run-time prediction using a linear model of the physical plant and a neural-network based classifier trigger mechanism are proposed for preemptive detection of an attack. A standalone, safety preserving, optimal backup controller is implemented to ensure plant safety in case of an attack. The intrusion detection mechanism and the backup controller are instantiated in configurable hardware, making them invisible to operating software and ensuring their integrity in the presence of malicious software. Hardware implementation of our approach on an inverted pendulum system illustrates the performance of both techniques in the presence of reconfiguration and network attacks. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / Precision medicine refers to the delivering of customized treatment to patients based on their individual characteristics, and aims to reduce adverse events, improve diagnostic methods, and enhance the efficacy of therapies. Among efforts to achieve the goals of precision medicine, researchers have used observational data for developing predictive modeling to best predict health outcomes according to patients’ variables. Although numerous predictive models have been reported in the literature, not all models present high prediction power, and as the result, not all models may reach clinical settings to help healthcare professionals make clinical decisions at the point-of-care. The lack of generalizability stems from the fact that no comprehensive medical data repository exists that has the information of all patients in the target population. Even if the patients’ records were available from other sources, the datasets may need further processing prior to data analysis due to differences in the structure of databases and the coding systems used to record concepts. This project intends to fill the gap by introducing an interoperable solution that receives patient electronic health records via Health Level Seven (HL7) messaging standard from other data sources, transforms the records to observational medical outcomes partnership (OMOP) common data model (CDM) for population health research, and applies predictive models on patient data to make predictions about health outcomes. This project comprises of three studies. The first study introduces CCD-TOOMOP parser, and evaluates OMOP CDM to accommodate patient data transferred by HL7 consolidated continuity of care documents (CCDs). The second study explores how to adopt predictive model markup language (PMML) for standardizing dissemination of OMOP-based predictive models. Finally, the third study introduces Personalized Health Risk Scoring Tool (PHRST), a pilot, interoperable OMOP-based model scoring tool that processes the embedded models and generates risk scores in a real-time manner. The final product addresses objectives of precision medicine, and has the potentials to not only be employed at the point-of-care to deliver individualized treatment to patients, but also can contribute to health outcome research by easing collecting clinical outcomes across diverse medical centers independent of system specifications.

Patients admitted to intermediate care units require frequent monitoring by hospital personnel. An automatisation of this monitoring would save a considerable amount of resources and could also improve the quality of the treatment. In this thesis, a deep learning-based video action recognition model is through different transfer learning approaches trained to distinguish between behaviours of patients in TV-series and a prediction system which collects, processes and predicts on images in real-time is proposed. The results from the model-training suggest that it is possible to detect behaviours that need human intervention but training on a large-scale, real-life dataset is required to form a solid conclusion. The performance results of the prediction system show that live-streamed predictions are possible at frame rates sufficient for capturing sought events, without GPU acceleration. / Patienter inlagda på intermediärvårdsavdelningar behöver frekvent övervakning av sjukhuspersonal. En automatisering av denna övervakning skulle spara en betydande mängd resurser och även kunna förbättra kvaliteten av behandlingen. I detta examensarbete tränas en djupinlärningsbaserad modell för videohandlingsigenkänning att, genom olika överföringsinlärningsmetoder, skilja på beteenden mellan olika patienter i TV-serier och ett prediktionssystem som insamlar, processerar och predikterar på bilder i realtid presenteras. Resultaten från modellträningen tyder på att det är möjligt att detektera beteenden som kräver mänsklig interaktion men träning på ett storskaligt, realistiskt dataset krävs för att kunna dra en säker slutsats. Prestandaresultaten från prediktionssystemet visar att live-strömmade prediktioner är möjliga vid bilduppdateringsfrekvenser tillräckliga för att fånga de sökta händelserna, utan GPU-acceleration.

No description available.

No description available.

In this thesis a new approach for characterisation of digital mobile radio channels is investigated. The new approach is based on recognition of the fact that while the fading which is characteristic of the mobile radio channel is very rapid, the processes underlying this fading may vary much more slowly. The comparative stability of these underlying processes has not been exploited in system designs to date. Channel models are proposed which take account of the stability of the channel. Estimators for the parameters of the models are proposed, and their performance is analysed theoretically and by simulation and measurement. Bounds are derived for the extent to which the mobile channel can be predicted, and the critical factors which define these bounds are identified. Two main applications arise for these channel models. The first is the possibility of prediction of the overall system performance. This may be used to avoid channel fading (for instance by change of frequency), or compensate for it (by change of the signal rate or by power control). The second application is in channel equalisation. An equaliser based on a model which has parameters varying only very slowly can offer improved performance especially in the case of channels which appear to be varying so rapidly that the convergence rate of an equaliser based on the conventional model is not adequate. The first of these applications is explored, and a relationship is derived between the channel impulse response and the performance of a broadband system.

Lorsque la densité de véhicules devient trop élevée, le trafic autoroutier est instable, et génère naturellement des accordéons, c'est-à-dire une alternance entre des zones fluides et des zones congestionnées. Ce phénomène n'est pas reproduit par les modèles de trafic standards d'ordre 1, mais peut l'être par des modèles d'ordre supérieurs, aussi bien microscopiques (modèles de loi de poursuite) que macroscopiques (systèmes de lois de conservation).Cette thèse analyse comment différents modèles représentent des états de trafic instables, et les oscillations qui en résultent. Au niveau microscopique, à cause de la concavité du flux, le débit moyen de ces oscillations est inférieur au débit d'équilibre pour une densité équivalente. Un algorithme est proposé pour stabiliser le flux par multi-anticipation, en utilisant un véhicule autonome intelligent.Au niveau macroscopique, cette thèse introduit les modèles moyennés, en partant du principe que l'échelle spatio-temporelle des oscillations est trop petite pour être correctement prédite par une simulation. Le modèle LWR moyenné, composé de deux lois de conservations, permet de représenter au niveau macroscopique la variance de la densité d'un trafic hétérogène, et calcule correctement le débit moyen de ces états. Une comparaison avec le modèle ARZ, également d'ordre 2, montre que le modèle moyenné permet de simuler une chute de capacité de façon plus réaliste.Enfin, cette thèse présente le projet SimulaClaire, de prédiction en temps réel du trafic sur le périphérique toulousain, et en particulier l'algorithme parallélisé d'optimisation en temps réel des paramètres développé pour ce projet / Highway traffic is known to be unstable when the vehicle density becomes too high, and to create stop-and-go waves, with an alternance of free flow and congested traffic. First-order traffic models can't reproduce these oscillations, but higher-order models can, both microscopic (car-following models) and macroscopic (systems of conservation laws).This thesis analyses the representation of unstable traffic states and oscillations in various traffic models. At the microscopic level, because of the flux concavity, the average flow of these oscillations is lower than the equilibrium flow for the same density. An algorithm is given to stabilize the flow with multi-anticipation, using an intelligent autonomous vehicle.At the macroscopic level, this work introduces averaged models, using the fact that the spatio-temporal scale of the oscillations is too small to be correctly predicted by simulations. The averaged LWR model, which consists of two conservation laws, enables a macroscopic representation of the density variance in a heterogeneous traffic, and gives the correct average flow of these states. A comparison with the ARZ model, also of order 2, shows that the averaged model can reproduce a capacity drop in a more realistic way.Finally, this thesis presents the SimulaClaire project of real-time traffic prediction on the ring road of Toulouse, and its parallelized parameter optimization algorithm