Over the past few years, the interest of remote wireless sensor networks has increased with the growth of Internet of Things technology. The wireless sensor network applications vary from tracking animal movement to controlling small electrical devices. Wireless sensors deployed in remote areas where the grid is unavailable are normally powered by batteries, inducing a limited lifespan for the sensor. This thesis work presents a solution to implement solar energy harvesting to a wireless sensor network. By gathering energy from the environment and using it in conjunction with an energy storage, the lifetime of a sensor node can be extended while at the same time reducing maintenance costs. To make sensor nodes in a network energy efficient, an adaptive controller of the nodes energy consumption can be used. A network consisting of a client node and a server node was created. The client node was powered by a small solar cell in conjunction with a capacitor. A linear-quadratic tracking algorithm was implemented to adaptively change the transmission rate for a node based on its current and previous battery level and the energy harvesting model. The implementation was done using only integers. To evaluate the system for extended run-times, the battery level was simulated using MATLAB. The system was simulated for different weather conditions. The simulation results show that the system is viable for both cloudy and sunny weather conditions. The integer linear-quadratic algorithm responds to change very abruptly in comparison to a floating point-version.

Real-time embedded systems can be found in a large number of devices we use, including safety-critical systems. Useful for their small size and low power consumption, they are also harder to protect against state-of-the-art attacks than general purpose systems due to their lack of hardware features. Even current defenses may not be applicable since instrumentation added to defend real-time embedded systems may cause them to miss their deadlines, rending them inoperable. We show that the static properties obtained by the scheduling policies can be used as security guarantees for the tasks composing the program. By completely securing a subset of the tasks of the program only using the scheduler policy, we remove the need to add external instrumentation on these tasks, reducing the amount of extra instructions needed to entirely protect the system. With less instrumentation, the overhead added by the defenses is reduced and can therefore be applied to a larger number of systems.

"A combined hardware and software platform for ambulatory seizure onset detection is presented. The hardware is developed around commercial off-the-shelf components, featuring ADS1299 analog front ends for electroencephalography from Texas Instruments and a Broadcom ARM11 microcontroller for algorithm execution. The onset detection algorithm is a patient-specific support vector machine algorithm. It outperforms a state-of-the-art detector on a reference data set, with 100% sensitivity, 3.4 second average onset detection latency, and on average 1 false positive per 24 hours. The more comprehensive European Epilepsy Database is then evaluated, which highlights several real-world challenges for seizure onset detection, resulting in reduced average sensitivity of 93.5%, 5 second average onset detection latency, and 85.5% specificity. Algorithm enhancements to improve this reduced performance are proposed."

Face Detection has been applied in many fields such as surveillance, human machine interaction, entertainment and health care. Two main reasons for extensive attention on this typical research domain are: 1) a strong need for the face recognition system is obvious due to the widespread use of security, 2) face recognition is more user friendly and faster since it almost requests the users to do nothing. The system is based on ARM Cortex-A8 development board, including transplantation of Linux operating system, the development of drivers, detecting face by using face class Haar feature and Viola-Jones algorithm. In the paper, the face Detection system uses the AdaBoost algorithm to detect human face from the frame captured by the camera. The paper introduces the pros and cons between several popular images processing algorithm. Facial expression recognition system involves face detection and emotion feature interpretation, which consists of offline training and online test part. Active shape model (ASM) for facial feature node detection, optical flow for face tracking, support vector machine (SVM) for classification is applied in this research.

As embedded systems become more connected and more ubiquitous in mission- and safety-critical systems, embedded devices have become a high- value target for hackers and security researchers. Attacks on real-time embedded systems software can put lives in danger and put our critical infrastructure at risk. Despite this, security techniques for embedded systems have not been widely studied. Many existing software security techniques for general purpose computers rely on assumptions that do not hold in the embedded case. This thesis focuses on one such technique, control-flow integrity (CFI), that has been vetted as an effective countermeasure against control-flow hijacking attacks on general purpose computing systems. Without the process isolation and fine-grained memory protections provided by a general purpose computer with a rich operating system, CFI cannot provide any security guarantees. This thesis explores a way to use CFI on ARM Cortex-R devices running minimal real-time operating systems. We provide techniques for protecting runtime structures, isolating processes, and instrumenting compiled ARM binaries with CFI protection.

AUTOSAR (AUTomotive Open System Architecture) est un standard industriel mondial créé en 2003 dans le but de standardiser le développement des architectures logicielles automobiles. Il fournit un ensemble de concepts et définit une méthodologie commune pour le développement des logiciels embarqués automobiles. Les principales caractéristiques de ce standard sont la modularité et la « configurabilité» de logiciels qui permettent la réutilisation fonctionnelle des modules logiciels fournis par des fournisseurs différents. Cependant,le développement d’une application embarquée AUTOSAR nécessite la configuration d’un grand nombre de paramètres liés principalement au grand nombre de composants logiciels (software component« SWC ») de l’application. Cette configuration commence par l’étape d’allocation des SWCs à la plateforme matérielle (calculateursconnectés par des réseaux), jusqu’à l’étape de configuration de chaque calculateur et du réseau de communication. Différentes alternatives sont possibles pendant ces étapes de configuration etc chaque décision de conception peut impacter les performances temporelles du système, d’où la nécessité d’automatiser ces étapes de configuration et de développer un outil d’évaluation d’architectures.Dans ce travail de thèse, nous introduisons une approche holistique d’optimisation afin de synthétiser l’architecture E/E d’un système embarqué AUTOSAR. Cette approche se base sur des méthodes métaheuristique et heuristique. La méthode métaheuristique (i.e. algorithme génétique) a le rôle de trouver les allocations les plus satisfaisantes des SWCs aux calculateurs. A chaque allocation proposée, deux méthodes heuristiques sont développées afin de résoudre le problème de la configuration des calculateurs (le nombre de tâches et ses priorités, allocation des runnables aux tâches, etc.) et des réseaux de communication (le nombre de messages et ses priorités, allocation des « data-elements » aux messages,etc.). Afin d’évaluer les performances de chaque allocation, nous proposons une nouvelle méthode d’analyse pour calculer le temps de réponse des tâches, des runnables, et de bout-en-bout de tâches/runnables. L’approche d’exploration architecturale proposée par cette thèse considère le modèle des applications périodiques et elle est évaluée à l’aide d’applications génériques et industrielles. / AUTOSAR (AUTomotive Open System ARchitecture) has been created by automotive manufacturers, suppliers and tools developers in order to establish an open industry standard for automotive E/E(Electrical/Electronic) architectures. AUTOSAR provides a set of concepts and defines a common methodology to develop automotive software platforms. The key features of this standard are modularity and configurability of automotive software; this allows functional reuse of software modules provided by different suppliers and guarantees interoperability of these modules through standardized interfaces. However, the development of an embedded application according to AUTOSAR necessitates configuring a lot of parameters related to the large number of Software Components (SWCs), their allocations to the hardware platform and then, the configurationof each Electronic Control Unit (ECU). Different alternatives are possible during the design of such systems. Each implementation decision may impact system performance and needs therefore to be evaluated and compared against performance constraints and optimization goals. In this thesis, we introduce a holistic optimization approach to synthesizearchitecture E/E of an embedded AUTOSAR system. This approach is based on heuristic and metaheuristic methods. The metaheuristics (e.g. genetic algorithm) has the role to find the most satisfactory allocations of SWCs to ECUs. Each allocation step, two heuristics are developed to solve the problem of the ECU configuration (the number of tasks and priorities, allocation of runnables to tasks, etc.) and networks configuration (the number of messagesand priorities, allocation of data-elements to messages, etc.). In order to evaluate the performance of each allocation, we propose a new analysis method to calculate the response time of tasks, runnables, and end-to-end paths. The architectural exploration approach proposed by this thesis considers the model for periodic applications and is evaluated using generic and industrial applications.

<p>The boundary scan standard which has been in existence since the early nineties is widely used to test printed circuit boards (PCB). It is primarily aimed at providing increased physical test access to surface mounted devices on printed circuit boards (PCB). Using boundary scan avoids using functional testing and In-circuit-techniques like '<em>bed of nails</em>' for structurally testing PCBs as increasing densities and complexities made opting for them a herculean task. Though the standard has had a revolutionizing effect on board testing conducted during the development and production phases, there is a lack of a standardized mechanism to allow IEEE 1149.1 to be used in a system post installation. This has led to problems typically encountered during field test runs, like the issue of high number of No-Fault-Found (NFF), being left unaddressed. The solution lies in conducting a structural test after a given module has already been installed in the system. This can be done by embedding the programmability features of the boundary scan test mechanism into the Unit under test (UUT) thereby enabling the UUT to conduct boundary scan based self tests without the need of external stimuli. In this thesis, a test and debug framework, which aims to use boundary-scan in post system-installation, is the subject of a study and subsequent enhancement. The framework allows embedding much of the test vector deployment and debug mechanism onto the Unit under test (UUT) to enable its remote testing and debug. The framework mainly consists of a prototype board which, along with the UUT, comprise the 'embedded system'. The following document is a description of the phased development of above said framework and its intended usage in the field.</p>

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This thesis proposes a new bandpass filter prototype modified based on T-type coupled resonator architecture by considering the parasitic shunt capacitance effect. After derivation, the new prototype can be proved equivalent to third-order Chebyshev bandpass filter. It is easy to realize the new prototype circuit by utilizing the inductor and capacitor library established from electromagnetic simulations. The couplings between circuit components can cause some transmission zeros to enhance attenuation rate at stopbands. This thesis designs several bandpass filters embedded in 4-layer laminate package substrate with center frequency at 2.45GHz. The measurement results show that most of these filters can achieve less than 1.7dB insertion loss and more than 14dB return loss at passband, and more than 30dB attenuation at 950MHz, 4.8GHz and 7.2GHz. One of the filters has a size of 1.9¡Ñ2.7mm2, which is the smallest area for the currently reported bandpass filters embedded in the organic package substrate.

When a robot performs a task in an unstructured dynamic environment, it has to account for many factors. It should not only keep the track of where it is and how it should move, but also ensure that the kinematic, dynamic and task specific limitations are observed. It is also important that the robot can effectively avoid collisions with static and moving obstacles. In the current thesis we present design and implementation of an algorithm capable to face all these challenges. The system combines principles of dynamic roadmaps and elastic roadmaps frameworks, both of which are the state-of-art approaches to motion planning problem. The suggested solution is presented in the context of a broad overview of the literature in motion planning domain focusing on methodology of sample-based and feedback planning in dynamic environments. The implemented algorithm is applied to a 7-degree-of-freedom manipulator and is demonstrated and analyzed through a variety of simulated test scenarios. The result is an extensible and future-oriented planning system that can plan and execute movement between a starting and target position while preserving task constraints and reacting to environment changes in real time.