Дисертації з теми "Hybrid dimensional systems"

Simulation plays a key role in the computer-aided design of systems, primarily because it helps in catching design bugs at an early stage and in quickly ruling out many unreasonable options in the design space. There is a wealth of tools and languages that have been proposed over the years for the design and verification of hybrid systems and most of them provide 3D visualization utility for facilitating design process. However, their visualization systems often rely on some external configuration files written in naive 3D APIs like VRML or Java3D and complicated connections have to be established between the simulation data and the visualization. Numerous efforts have to be invested in visualization related modeling and certain level of understanding of the low level 3D libraries are needed. Thus it is not an easy job for non-professions to build a 3D visualization. In this thesis we explore the idea that in a hybrid system model, almost all the information need for visualization is already in place. This means that very few annotation should be enough to fully specify a useful 3D visualization. Based on this idea, we propose a lightweight 3D visualization framework which allows the user to expresses the three-dimensional object in a high level and abstract way and to take advantage of the data already in the simulation. We evaluate our approaching by building a prototype extension of modeling language called Acumen. The result of this thesis work is included in Acumen's official release version that can be download at www.acumen-language.org. To build a 3D visualization in Acumen, only very few annotations need to be added to the simulation model and are included inside one reserved variable. Non-professions like students who are studying Cyber-physical systems can easily use this tool to visualize their models in Acumen without any prerequisite knowledge regarding 3D visualization technology. We also developed a number of benchmarks to test the correctness and functionality of Acumen3D. Besides, as a lager case study into the expressivity of Acumen and its visualizaiton efficiency, we developed a simplified model of a Ping Pong game. We gained experience about how to model complex hybrid system in Acumen. Moreover by comparing modeling such a hybrid system in Java3D and in Acumen3D, we find concrete evidence that modelling it in Acumen3D is easier in that of less code and less prerequisite knowledge and more supporting built in functions.

In the last decade, two-dimensional (2D) materials have attracted attention both in the nascent field of flexible nanotechnology as well as in more conventional semiconductor technol-ogies. Within the rapidly expanding portfolio of 2D materials, the group of semiconducting transition metal dichalcogenides (TMDCs) has emerged as an intriguing candidate for various optoelectronic applications. The atomically thin profile, favorable bandgap and outstanding electronic properties of TMDCs are unique features that can be explored and applied in novel photodetecting platforms. This thesis presents highly sensitive two-dimensional phototransistors made of sub-nanometre thick TMDC channels. Firstly, an encapsulation route is developed to address the detrimental and, to date, uncontrollable impact of atmospheric adsorbates, which severely deteriorate detector performance. The passivation scheme improves the transport properties of TMDCs, leading to high photoconductive gain with gate dependent responsivity of 10 -10^4 A/W throughout the visible, and temporal response down to 10 ms, which is suitable for imaging applications. The atomic device thickness yields ultra-low dark current operation and record detectivity of 10^11 - 10^12 Jones for TMDC-based detectors is achieved. The use of monolayer TMDCs, however, has disadvantages like limited spectral absorption due to the bandgap and limited absorption efficiency. In order to increase the absorption and to extend the spectral coverage, TMDC channels are covered with colloidal quantum dots to make hybrid phototransistors. This compelling synergy combines strong and size-tunable light absorption within the QD film, efficient charge separation at the TMDC-QD interface and fast carrier transport through the 2D channel. This results in large gain of 10^6 electrons per absorbed photon and creates the basis for extremely sensitive light sensing. Colloidal quan-tum dots are an ideal sensitizer, because their solution-processing and facile implementation on arbitrary substrates allows for low-cost fabrication of hybrid TMDC-QD devices. Moreover, the custom tailored bandgap of quantum dots provides the photodetector with wide spectral tunability. For photodetection in the spectral window of NIR/SWIR, which is still dominated by expensive and complex epitaxy-based technologies, these hybrid detectors have the potential to favorably compete with commercially available systems. The interface of the TMDC-QD hybrid is of paramount importance for sensitive detector operation. A high density of trap states at the interface is shown to be responsible for inefficient gate-control over channel conductivity, which leads to high dark currents. To maintain the unique electrical field-effect modulation in TMDCs upon deposition of colloidal quantum dots, a passivation route of the interface with semiconducting metal-oxide films is developed. The buffer-layer material is selected such that charge transfer from QDs into the channel is favored. The retained field-effect modulation with a large on/off ratio allows operation of the phototransistor at significantly lower dark currents than non-passivated hybrids. A TMDC-QD phototransistor with an engineered interface that exhibits detectivity of 10^12 - 10^13 Jones and response times of 12 ms and less is reported. In summary, this work showcases prototype photodetectors made of encapsulated 2D TMDCs and TMDC-QD hybrids. Plain TMDC-detectors have potential for application as flexible and semi-transparent detector platforms with high sensitivity in the visible. The hybrid TMDC-QD device increases its spectral selectivity to the NIR/SWIR due to the variable absorption of the sensitizing quantum dots and reaches compelling performance thanks to im-proved light-matter interaction and optimized photocarrier generation.
En la última década ha surgido un gran interés por los materiales bidimensionales (2D) tanto para las tecnologías emergentes de dispositivos flexibles, como para las tecnologías de semiconductores tradicionales. Dentro del creciente catálogo de materiales 2D, los semiconductores basados en dicalcogenuros de metales de transición (DCMTs) han surgido como candidatos para aplicaciones optoelectrónicas. Sus características únicas, tales como grosor atómico, banda prohibida y propiedades electrónicas pueden ser examinadas y aplicadas en nuevas plataformas de fotodetección. En esta tesis se presentan nuevos fototransistores bidimensionales ultrasensibles basados en canales de DCMTs subnanométricos. Se presenta una ruta de encapsulación para intentar solucionar el impacto negativo, e incontrolable hasta la fecha, producido por la adsorción de sustancias atmosféricas que degradan el funcionamiento de los detectores. Este proceso mejora el transporte en los DCMTs dando lugar a una gran ganancia fotoconductora, una respuesta, dependiente de la tensión aplicada en el gate, de 10-10^4 A/W en el visible y una respuesta temporal de tan solo 10 ms, todo ello adecuado para aplicaciones de imagen. El grosor atómico de los dispositivos da lugar a corrientes de oscuridad muy bajas y una detectividad de 10^11-10^12 Jones. Sin embargo, el uso de monocapas de DCMTs presenta ciertas desventajas como por ejem-plo una eficiencia en la absorción baja. Con el fin de mejorar la absorción, los canales de DCMTs se han recubierto con puntos cuánticos (QDs) para fabricar fototransistores híbridos. Esta sinergia combina la alta absorción de los QDs, una eficiente separación de cargas en la interfaz DCMT-QD y un rápido transporte de cargas a través del canal 2D. Todo esto resulta en una ganancia de 10^6 electrones por fotón absorbido y crea la base para sensores de luz extremadamente sensibles. Los puntos cuánticos coloidales son sensibizadores ideales ya que su procesado en disolución y su fácil incorporación sobre cualquier sustrato permiten la fabricación de sistemas híbridos DCMT-QD a bajo coste. Además, la posibilidad de modifi-car la banda prohibida, ofrecida por los QDs, proporciona al fotodetector una amplia respuesta espectral. Para fotodetección en la ventana espectral del infrarrojo cercano (NIR/SWIR), estos detectores híbridos presentan el potencial de competir favorablemente con los sistemas comerciales disponibles. La interfaz entre el híbrido DCMT-QD es de la mayor importancia para la sensibilidad del detector. Se ha demostrado que una alta densidad de trampas en la interfaz es la responsable del ineficiente control mediante el gate de la conductividad del canal, dando lugar a corrientes de oscuridad muy altas. Para mantener la excepcional modulación de efecto campo aún después de la deposición de los QDs, se ha desarrollado una ruta de pasivación de la interfaz con óxidos metálicos semiconductores. El material de esta capa amortiguadora (buffer) es seleccionado de tal manera que permita la transferencia de cargas desde los puntos cuánticos hasta el canal DCMT. Esto retiene la modulación de efecto campo con una relación encendido/apagado muy alta, permitiendo el funcionamiento del fototransistor con corrientes de oscuridad significativamente menores que las de los híbridos sin pasivar. Así, se presenta un fototransistor híbrido DCMT-QD, con una interfaz cuidadosamente diseñada, que exhibe una detectividad de 10^12-10^13 Jones. En resumen, este trabajo presenta unos prototipos de fotodetectores basados en DCMT 2D encapsulados y en híbridos DCMT-QD. Los fotodetectores basados en DCMT simples presentan potencial para su aplicación en detectores flexibles y semitransparentes, con gran sensibilidad en el visible. Los híbridos DCMT-QD amplían la selectividad espectral al infrarrojo cercano gracias a la absorción variable ofrecida por los puntos cuánticos y alcanzan un muy interesante rendimiento gracias a una mejor interacción luz-materia.

Thesis (Ph. D.)--Missouri University of Science and Technology, 2008.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 18, 2008) Includes bibliographical references.

Le 21_eme siècle est le siècle de l'explosion informatique, des milliards de Données sont produites, collectées et stockées dans notre vie quotidienne. Les façons de collecter les ensembles de données sont multiples mais toujours en essayant d'optimiser le critère qui consiste _a avoir le maximum d'information dans le minimum de données numérique. Il est préférable de collecter directement l'information, car les informations étant contraintes sont dans un espace plus faible que celui où évolues les données (signaux ou états). Cette méthode est donc appelée \la collecte de l'information", et conceptuellement peut ^être résumée dans les trois étapes suivantes : (1) la modélisation, ceci consiste _a condenser l'information pertinente pour les signaux _a un sous-espace plus petit; (2) l'acquisition, ceci consiste _a collecter et préserver l'information dans un espace inferieur _a la dimension des données et (3) la restauration, ceci consiste _a reconstituer l'information dans son espace d'origine. En suivant cette pensée, les principales contributions de cette thèse, concernant les observateurs et le \Compressive Sensing" (CS) basé sur des modèles bay_esiens peuvent ^être unies dans le cadre de la collecte de l'information : les principaux problèmes concernés par ces deux applications peuvent ^être de façon analogue, scindés en les trois étapes sus- mentionnées. Dans la première partie de la th_ese, le problème réside dans le domaine des systèmes dynamiques où l'objectif est de retrouver l'état du système _a partir de la mesure de la sortie. Il nous faut donc déterminer si les états du système sont récupérables _a partir des mesures de la sortie et de la connaissance partielle ou totale du modèle dynamique, c'est le problème de l'observabilité. Ensuite de transposer notre problème dans une représentation plus appropriée, c'est l'écriture sous forme normale et en récupérer l'information, c'est la phase de synthèse d'observateur. Plus précisément dans cette partie, nous avons considéré une classe de systèmes à commutation haute fréquence allant jusqu'au phénomène de Zénon. Pour ces deux types de commutation les transitions de l'état discret sont considérées trop élevées pour ^être mesurées. Toutefois, la valeur moyenne obtenue par filtrage des transitions peut ^être acquise ce qui donne une connaissance partielle des états discrets. Ici, avec ces seuls informations partielles, nous avons discuté de l'observabilité et ceci par les approches géométrie différentielle et algébrique. Aussi, des observateurs ont été proposes par la suite. Dans la deuxième partie de cette thèse, nous avons abordé de la même manière le thème du CS qui est une alternative efficace à l'acquisition abondante de données faiblement informatives pour ensuite les compresser. Le CS se propose de collecter l'information directement de façon compressée, ici les points clés sont la modélisation du signal en fonction des connaissances a priori dont on dispose, ainsi que la construction d'une matrice de mesure satisfaisant la \restricted isometry property" et finalement la restauration des signaux originaux clairsemés en utilisant des algorithmes d'éparpillement régularisé et d'inversion linéaire. Plus précisément, dans cette seconde partie, en considérant les propriétés du CS liées _a la modélisation, la capture et la restauration, il est proposé : (1) d'exploiter les séquences chaotiques pour construire la matrice de mesure qui est appelée la matrice chaotique de mesure, (2) considérer des types de modèle de signal clairsemé et reconstruire le modèle du signal à partir de ces structures sous-jacentes des modèles clairsemés, et (3) proposer trois algorithmes non paramétriques pour la méthode bayesienne hiérarchique. Dans cette dernière partie, des résultats expérimentaux prouvent d'une part que la matrice chaotique de mesure a des propriétés semblables aux matrices aléatoires sous-gaussienne et d'autre part que des informations supplémentaires sur les structures sous-jacentes clairsemés améliorent grandement les performances de reconstruction du signal et sa robustesse vis-a-vis du bruit
This is the era of information-explosion, billions of data are produced, collected and then stored in our daily life. The manners of collecting the data sets are various but always following the criteria { the less data while the more information. Thus the most favorite way is to directly measure the information, which, commonly, resides in a lower dimensional space than its carrier, namely, the data (signals or states). This method is thus called information measuring, and conceptually can be concluded in a framework with the following three steps: (1) modeling, to condense the information relevant to signals to a small subspace; (2) measuring, to preserve the information in lower dimensional measurement space; and (3) restoring, to reconstruct signals from the lower dimensional measurements. From this vein, the main contributions of this thesis, saying observer and model based Bayesian compressive sensing can be well uni_ed in the framework of information measuring: the main concerned problems of both applications can be decomposed into the above three aspects. In the _rst part, the problem is resided in the domain of control systems where the objective of observer design is located in the observability to determine whether the system states are recoverable and observation of the system states from the lower dimensional measurements (commonly but not restrictively). Speci_cally, we considered a class of switched systems with high switching frequency, or even with Zeno phenomenon, where the transitions of the discrete state are too high to be captured. However, the averaged value obtained through filtering the transitions can be easily sensed as the partial knowledge. Consequently, only with this partial knowledge, we discussed the observability respectively from differential geometric approach and algebraic approach and the corresponding observers are designed as well. At the second part, we switched to the topic of compressive sensing which is objected to sampling the sparse signals directly in a compressed manner, where the central fundamentals are resided in signal modeling according to available priors, constructing sensing matrix satisfying the so-called restricted isometry property and restoring the original sparse signals using sparse regularized linear inversion algorithms. Respectively, considering the properties of CS related to modeling, measuring and restoring, we propose to (1) exploit the chaotic sequences to construct the sensing matrix (or measuring operator) which is called chaotic sensing matrix, (2) further consider the sparsity model and then rebuild the signal model to consider structures underlying the sparsity patterns, and (3) propose three non-parametric algorithms through the hierarchical Bayesian method. And the experimental results prove that the chaotic sensing matrix is with the similar property to sub-Gaussian random matrix and the additional consideration on structures underlying sparsity patterns largely improves the performances of reconstruction and robustness

Les besoins grandissants de prévisions robustes pour des systèmes complexes conduisent à introduire des modèles mathématiques considérant un nombre croissant de paramètres. Au temps s'ajoutent l'espace, l'aléa, les échelles de dynamiques, donnant lieu à des modèles stochastiques multi-échelles avec dépendance spatiale (modèles spatiaux). Cependant, l'explosion du temps de simulation de tels modèles complique leur utilisation. Leur analyse difficile a néanmoins permis, pour les modèles à une échelle, de développer des outils puissants: loi des grands nombres (LGN), théorème central limite (TCL), ..., puis d'en dériver des modèles simplifiés et algorithmes accélérés. Dans le processus de dérivation, des modèles et algorithmes dits hybrides ont vu le jour dans le cas multi-échelle, mais sans analyse rigoureuse préalable, soulevant ainsi la question d'approximation hybride dont la consistance constitue l'une des motivations principales de cette thèse.En 2012, Crudu, Debussche, Muller et Radulescu établissent des critères d'approximation hybride pour des modèles homogènes en espace de réseaux de régulation de gènes. Le but de cette thèse est de compléter leur travail et le généraliser à un cadre spatial.Nous avons développé et simplifié différents modèles, tous des processus de Markov de sauts pures à temps continu. La démarche met en avant, d'une part, des conditions d'approximations déterministes par des solutions d'équations d'évolution (type réaction-advection-diffusion), et, d'autre part, des conditions d'approximations hybrides par des processus stochastiques hybrides. Dans le cadre des réseaux de réactions biochimiques, un TCL est établi. Il correspond à une approximation hybride d'un modèle homogène simplifié à deux échelles de temps (suivant Crudu et al.). Puis, une LGN est obtenue pour un modèle spatial à deux échelles de temps. Ensuite, une approximation hybride est établie pour un modèle spatial à deux échelles de dynamique en temps et en espace. Enfin, des comportements asymptotiques en grandes populations et en temps long sont présentés pour un modèle d'épidémie de choléra, via une LGN suivie d'une borne supérieure pour les sous-ensembles compacts, dans le cadre d'un principe de grande déviation (PGD) correspondant.À l'avenir, il serait intéressant, entre autres, de varier la géométrie spatiale, de généraliser le TCL, de compléter les estimations du PGD, et d'explorer des systèmes complexes issus d'autres domaines
The growing needs of precise predictions for complex systems lead to introducing stronger mathematical models, taking into account an increasing number of parameters added to time: space, stochasticity, scales of dynamics. Combining these parameters gives rise to spatial --or spatially inhomogeneous-- multiscale stochastic models. However, such models are difficult to study and their simulation is extremely time consuming, making their use not easy. Still, their analysis has allowed one to develop powerful tools for one scale models, among which are the law of large numbers (LLN) and the central limit theorem (CLT), and, afterward, to derive simpler models and accelrated algorithms. In that deduction process, the so-called hybrid models and algorithms have arisen in the multiscale case, but without any prior rigorous analysis. The question of hybrid approximation then shows up, and its consistency is a particularly important motivation of this PhD thesis.In 2012, criteria for hybrid approximations of some homogeneous regulation gene network models were established by Crudu, Debussche, Muller and Radulescu. The aim of this PhD thesis is to complete their work and generalize it afterward to a spatial framework.We have developed and simplified different models. They all are time continuous pure jump Markov processes. The approach points out the conditions allowing on the the one hand deterministic approximations by solutions of evolution equations of type reaction-advection-diffusion, and, on the other hand, hybrid approximations by hybrid stochastic processes. In the field of biochemical reaction networks, we establish a CLT. It corresponds to a hybrid approximation of a simplified homogeneous model (due to Crudu et al.). Then a LLN is obtained for a spatial model with two time scales. Afterward, a hybrid approximation is established, for a two time-space scales spatial model. Finally, the asymptotic behaviour in large population and long time are respectively presented for a model of cholera epidemic, through a LLN followed by the upper bound for compact sets, in the context of a corresponding large deviation principle (LDP).Interesting future works would be, among others, to study other spatial geometries, to generalize the CLT, to complete the LDP estimates, and to study complex systems from other fields

The objective of this dissertation is to develop theoretical and computational tools for the study of qualitative changes in the dynamics of systems with discontinuities, also known as nonsmooth or hybrid dynamical systems, under parameter variations. Accordingly, this dissertation is divided into two parts. The analytical section of this dissertation discusses mathematical tools for the analysis of hybrid dynamical systems and their application to a series of model examples. Specifically, qualitative changes in the system dynamics from a nonimpacting to an impacting motion, referred to as grazing bifurcations, are studied in oscillators where the discontinuities are caused by impacts. Here, the study emphasizes the formulation of conditions for the persistence of a steady state motion in the immediate vicinity of periodic and quasiperiodic grazing trajectories in an impacting mechanical system. A local analysis based on the discontinuity-mapping approach is employed to derive a normal-form description of the dynamics near a grazing trajectory. Also, the results obtained using the discontinuity-mapping approach and direct numerical integration are found to be in good agreement. It is found that the instabilities caused by the presence of the square-root singularity in the normal-form description affect the grazing bifurcation scenario differently depending on the relative dimensionality of the state space and the steady state motion at the grazing contact. The computational section presents the structure and applications of a software program, TC-HAT, developed to study the bifurcation analysis of hybrid dynamical systems. Here, we present a general boundary value problem (BVP) approach to locate periodic trajectories corresponding to a hybrid dynamical system under parameter variations. A methodology to compute the eigenvalues of periodic trajectories when using the BVP formulation is illustrated using a model example. Finally, bifurcation analysis of four model hybrid dynamical systems is performed using TC-HAT.
Ph. D.

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.
Higher Committee for Education Development in Iraq

Après une étude théorique, puis modélisée, de l'analyse d'une image dans le plan focal d'un télescope par un détecteur à deux dimensions (chapitre 1) et un bref rappel de physique du solide (chapitre 2) on étudie un détecteur infrarouge bidimensionnel de 32x32 l'IRCCD. La description d'une caméra astronomique basée sur ce détecteur, et des choix effectués dans sa réalisation est donnée (chapitre 3). L'attention est portée sur les problèmes de l'observation sous fort flux de photons du fond. Un chapitre entier est consacré à une étude détaillée de l'IRCCD et de ses caractérisriques (chapitre 4). On décrit ensuite l'ensemble du système monté sur télescope, ainsi que les procédures d'observation adoptées (chapitre 5). Quelques résultats de mesure et de calibration sur télescope sont proposés. Le dernier chapitre est consacré à la présentation de résultats astrophysiques préliminaires.

This thesis poses a feedback control method for obtaining humanlike bipedal walking on a human-inspired hybrid biped model. The end goal was to understand better the fundamental mechanisms that underlie bipedal walking in the hopes that this newfound understanding will facilitate better mechanical and control design for bipedal robots. Bipedal walking is hybrid in nature, characterized by periodic contact between a robot and the environment, i.e., the ground. Dynamic models derived from Lagrangians modeling mechanical systems govern the continuous dynamics while discrete dynamics were handed by an impact model using impulse-like forces and balancing angular momentum. This combination of continuous and discrete dynamics motivated the use of hybrid systems for modeling purposes. The framework of hybrid systems was used to model three-dimensional bipedal walking in a general setup for a robotic model with a hip, knees, and feet with the goal of obtaining stable walking. To achieve three-dimensional walking, functional Routhian reduction was used to decouple the sagittal and coronal dynamics. By doing so, it was possible to achieve walking in the two-dimensional sagittal plane on the three-dimensional model, restricted to operate in the sagittal plane. Imposing this restriction resulted in a reduced-order model, referred to as the sagittally-restricted model. Sagittal control in the form of controlled symmetries and additional control strategies was used to achieve stable walking on the sagittally-restricted model. Functional Routhian reduction was then applied to the full-order system. The sagittal control developed on the reduced-order model was used with reduction to achieve walking in three dimensions in simulation. The control schemes described resulted in walking which was remarkably anthropomorphic in nature. This observation is surprising given the simplistic nature of the controllers used. Moreover, the two-dimensional and three-dimensional dynamics were completely decoupled inasmuch as the dynamic models governing the sagittal motion were equivalent. Additionally, the reduction resulted in swaying in the lateral plane. This motion, which is generally present in human walking, was unplanned and was a side-effect of the decoupling process. Despite the approximate nature of the reduction, the motion was still almost completely decoupled with respect to the sagittal and coronal planes.

The work presented in this thesis concerns the study of quantum many-body physics by making use Bose-Einstein condensates loaded in optical lattices potentials. The first part describes the development of a new experimental strategy for the production of the degenerate atomic sample, the second part concerns the optimal control ground state production and the entanglement characterization on a systems of interacting Bosons across the superfluid - Mott insulator quantum phase transition, and the third part illustrates the study of the dynamical properties of an array of 1D gases performed via Bragg spectroscopy.

碩士
元智大學
電機工程學系甲組
107
In this work, a thermal-performance data preloading and address remapping design (DPAR) is proposed for 3D stacked hybrid memory containing PCM and DRAM. The proposed DPAR can preload the frequently used data to PCM and reduce the write operations to PCM. In the hybrid memory system, the vertical stacking of the active PCM and DRAM may cause the thermal problem. By considering temperature, power, and performance, the data and addresses to DRAM and PCM are remapped by DPAR. Compared to the related works, the 3D hybrid memory system using DPAR can reduce the peak temperature up to 26.25°C and the latency up to 78.23% in our experiments.