While the characteristic dimensions of electronic systems scale down to micro- or nano-world, their performance is greatly influenced. Micro-fabrication process or variations of the operating situation such as temperature, humidity or pressure are usual cause of dispersion. Therefore, it seems essential to co-integrate self-testing or self-adjustment routines for these microdevices. For this feature, most existing system parameter estimation methods are based on the implementation of high-resolution digital measurements of the system's output. Thus, long design time and large silicon areas are needed, which increases the cost of the micro-fabricated devices. The parameter estimation problems based on binary outputs can be introduced as alternative self-test identification methods, requiring only a 1-bit Analog-to-Digital Converter (ADC) and a 1-bit Digital-to-Analog Converter (DAC).In this thesis, we propose a novel recursive identification method to the problem of system parameter estimation from binary observations. An online identification algorithm with low-storage requirements and small computational complexity is derived. We prove the asymptotic convergence of this method under some assumptions. We show by Monte Carlo simulations that these assumptions do not necessarily have to be met in practice in order to obtain an appropriate performance of the method. Furthermore, we present the first experimental application of this method dedicated to the self-test of integrated micro-electro-mechanical systems (MEMS). The proposed online Built-In Self-Test method is very amenable to integration for the self-testing of systems relying on resistive sensors and actuators, because it requires low memory storage, only a 1-bit ADC and a 1-bit DAC which can be easily implemented in a small silicon area with minimal energy consumption.

Bien que les dimensions caractéristiques des systèmes électroniques aient été réduites aux micro- ou nano-échelles, leur performance reste très sensible à des facteurs extérieurs. Les variations lors du processus de fabrication des microsystèmes et celles dans leurs conditions de fonctionnement (température, humidité, pression) sont la cause habituelle de ces dispersions. Par conséquent, il est important de co-intégrer des routines de self-test ou d'auto-ajustement pour ces micro-dispositifs. La plupart des méthodes d'estimation des paramètres du système existantes sont fondées sur la mise en œuvre de mesures numériques haute résolution de la sortie du système. Leur mise en œuvre nécessite ainsi un long temps de conception et une grande surface de silicium, ce qui augmente le coût de ces micro-dispositifs. Les méthodes d'estimation de paramètres basées sur les observations binaires ont été présentées comme des méthodes d'identification alternatives, nécessitant seulement un Convertisseur Analogique-Numérique (CAN) 1-bit.Dans cette thèse, nous proposons une nouvelle méthode d'identification récursive pour le problème d'estimation des paramètres à partir des observations binaires. Un algorithme d'identification en ligne avec de faibles besoins de stockage et une complexité algorithmique réduite est introduit. Nous prouvons la convergence asymptotique de cette méthode sous certaines hypothèses. Ensuite, nous montrons par des simulations de Monte-Carlo que ces hypothèses ne doivent pas nécessairement être respectées dans la pratique pour obtenir une bonne performance de la méthode. De plus, nous présentons la première application expérimentale de cette méthode dédiée au self-test de MEMS intégrés. La méthode de «Built-In Self-Test» en ligne proposée est très intéressante pour le self-test de capteurs, car elle nécessite des ressources faibles de stockage, un seul CAN 1-bit et un seul CNA 1-bit qui peut être facilement mis en œuvre dans une petite surface de silicium avec une consommation réduite d'énergie. / While the characteristic dimensions of electronic systems scale down to micro- or nano-world, their performance is greatly influenced. Micro-fabrication process or variations of the operating situation such as temperature, humidity or pressure are usual cause of dispersion. Therefore, it seems essential to co-integrate self-testing or self-adjustment routines for these microdevices. For this feature, most existing system parameter estimation methods are based on the implementation of high-resolution digital measurements of the system's output. Thus, long design time and large silicon areas are needed, which increases the cost of the micro-fabricated devices. The parameter estimation problems based on binary outputs can be introduced as alternative self-test identification methods, requiring only a 1-bit Analog-to-Digital Converter (ADC) and a 1-bit Digital-to-Analog Converter (DAC).In this thesis, we propose a novel recursive identification method to the problem of system parameter estimation from binary observations. An online identification algorithm with low-storage requirements and small computational complexity is derived. We prove the asymptotic convergence of this method under some assumptions. We show by Monte Carlo simulations that these assumptions do not necessarily have to be met in practice in order to obtain an appropriate performance of the method. Furthermore, we present the first experimental application of this method dedicated to the self-test of integrated micro-electro-mechanical systems (MEMS). The proposed online Built-In Self-Test method is very amenable to integration for the self-testing of systems relying on resistive sensors and actuators, because it requires low memory storage, only a 1-bit ADC and a 1-bit DAC which can be easily implemented in a small silicon area with minimal energy consumption.

The aim of this diploma thesis is a creation of modal parameter estimation application. Modal properties (natural frequencies, damping factors and mode shapes) are used in many dynamics analysis and their accurate determination is very important therefore the modal parameter estimation is one of the most significant part of the experimental modal analysis. Many methods have been developed for modal parameter estimation, each of them with different assumptions and with different accuracy. In the beginning of this thesis, a theory connected with modal analysis and a theory which is necessary for understanding to presented modal parameter methods are given. Then four different modal parameter estimation methods are presented - Peak Picking, Circle Fit, Least Square method and Eigensystem Realization Algorithm. The application for the modal parameter estimation is the output of this diploma thesis. In addition, the application allows performing all experimental modal analysis such as estimation of frequency response functions, animation of the found mode shapes, different kinds of comparison etc. In the conclusion, three structures are shown on which the application and modal parameter estimation methods were tested.

The research presented in this work addresses open questions regarding (i) the fundamental understanding of powder compaction, and (ii) the complex mechanical response of particle-binder composites under large deformations. This work thus benefits a broad range of industries, from the pharmaceutical industry and its recent efforts on continuous manufacturing of solid tablets, to the defense and energy industries and the recurrent need to predict the performance of energetic materials. Powder compacts and particle-binder composites are essentially confined particulate systems with significant heterogeneity at the meso (particle) scale. While particle mechanics strategies for modeling evolution of mesoscale microstructure during powder compaction depend on the employed contact formulation to accurately predict macroscopic quantities like punch and die wall pressures, modeling of highly nonlinear, strain-path dependent macroscopic response without a distinctive yield surface, typical of particle-binder composites, requires proper constitutive modeling of these complex deformation mechanisms. Moreover, continued loading of particle-binder composites over their operational life may introduce significant undesirable changes to their microstructure and mechanical properties. These challenges are addressed with a combined effort on theoretical, modeling and experimental fronts, namely, (a) novel contact formulations for elasto-plastic particles under high levels of confinement, (b) a multi-scale experimental procedure for assessing changes in microstructure and mechanical behavior of particle-binder composites due to cyclic loading and time-recovery, and (c) a finite strain nonlinear elastic, endochronic plastic constitutive formulation for particle-binder composites.