L'espérance de vie a augmenté dans les dernières décennies et devrait continuer à croître dans les prochaines années. Cette augmentation entraîne de nouveaux défis concernant l'autonomie et l'indépendance des personnes âgées. Le développement d'habitats intelligents est une piste pour répondre à ces défis et permettre aux personnes de vivre plus longtemps dans un environnement sûr et confortable. Rendre un habitat intelligent consiste à y installer des capteurs, des actionneurs et un contrôleur afin de pouvoir prendre en compte le comportement de ses habitants et agir sur leur environnement, pour améliorer leur sécurité, leur santé et leur confort. La plupart de ces approches s'appuient sur la localisation en temps réel des habitants dans leur habitat. Dans cette thèse, une nouvelle approche complète permettant la localisation d'un nombre a priori inconnu d'habitants basée sur le modèle est proposée. Cette approche tire parti des paradigmes, de la théorie et des outils des Systèmes à Événements Discrets. L'utilisation des automates à états finis pour modéliser le mouvement détectable des habitants ainsi que des méthodes permettant de construire ces modèles ont été développées. A partir de ces modèles automates finis, plusieurs algorithmes permettant de localiser de manière efficace les habitants ont été définis. Enfin, plusieurs approches pour l'évaluation des performances de l'instrumentation d'un habitat intelligent pour un objectif de localisation ont été proposées. La méthode a également été totalement implémentée et mise à l'épreuve. Tout au long de cette thèse, les différentes contributions sont illustrées à l'aide de cas d'étude. / Life expectancy has continuously increased in most countries over the last decades and will probably continue to increase in the future. This leads to new challenges relative to the autonomy and the independence of elderly. The development of Smart Homes is a direction to face these challenges and to enable people to live longer in a safe and comfortable environment. Making a home smart consists in placing sensors, actuators and a controller in the house in order to take into account the behavior of their inhabitants and to act on their environment to improve their safety, health and comfort. Most of these approaches are based on the real-time indoor Location Tracking of the inhabitants. In this thesis, a whole new approach for model-based Location Tracking of an a priori unknown number of inhabitants is proposed. This approach is based on Discrete Event Systems paradigms, theory and tools. The usage of Finite Automata (FA) to model the detectable motion of the inhabitants as well as different methods to create such FA models have been developed. Based on these models, algorithms to perform efficient Location Tracking are defined. Finally, several approaches aiming at evaluating the relevance of the instrumentation of a Smart Home with the objective of Location Tracking are proposed. The approach has also been fully implemented and tested. Throughout the thesis, the different contributions are illustrated on case studies. / In den meisten Industrieländern ist die Lebenserwartung in den letzten Jahrzehnten fortlaufend gestiegen und wird höchstwahrscheinlich noch weiter steigen. Dieser Anstieg führt zu neuen Herausforderungen hinsichtlich der Autonomie und Unabhängigkeit von älteren Menschen. Die Entwicklung von intelligenten Wohnungen ist ein Weg diesen Herausforderungen zu begegnen und es den Menschen zu ermöglichen länger in einer sicheren und komfortablen Umgebung zu leben. Dazu stattet man solcheWohnungen mit Sensoren, Aktoren sowie einem Controller aus. Dies erm öglicht es, in Abhängigkeit vom Verhalten der Bewohner, dieWohnumgebung so zu beein_ussen, dass sich Sicherheit, Gesundheit und Komfort verbessern. Ansätze, die dies zum Ziel haben, basieren meistens auf Methoden, die es ermöglichen Menschen innerhalb ihrer Wohnung in Echtzeit zu lokalisieren. In dieser Dissertation wird daher ein neuer Ansatz für eine modellbasierte Lokalisierung einer a priori unbekannten Anzahl von Bewohnern vorgestellt. Dieser Ansatz fuÿt auf der Theorie, den Paradigmen und den Werkzeugen aus dem Gebiet der ereignisdiskreten Systeme. Es werden endliche Automaten eingesetzt, um die von den Sensoren erfassbaren Bewohnerbewegungen zu modellieren. Verschiedene Verfahren zur Erzeugung solcher Automaten werden gezeigt. Basierend auf diesen Modellen warden Algorithmen de_niert, mittels derer die Bewohner wirksam lokalisiert werden können. Abschlieÿend werden Methoden vorgeschlagen, die dazu dienen die Relevanz der Sensorinstrumentierung für die Lokalisierung zu bewerten. Die entwickelten Verfahren werden in der Dissertation durchgehend anhand von Fallbeispielen erläutert. Der gesamte Ansatz wurde implementiert und erprobt.

O objetivo deste trabalho é apresentar o modelo dos autômatos adaptativos de segunda ordem e mostrar a forte conexão desse modelo com o aprendizado indutivo no limite. Tal modelo é definido com a utilização de um conjunto de transformações sobre autômatos finitos não - determinísticos e a conexão com o aprendizado no limite á estabelecida usando o conceito de mutação composta, onde uma hipótese inicial dá início ao processo de aprendizagem, produzindo, após uma sequência de transformações sofridas por essa primeira hipótese, um modelo final que é o resultado correto do aprendizado. Será apresentada a prova de que um autômato adaptativos de segunda ordem, usado como um aprendiz, pode realizar o processo de aprendizado no limite. O formalismo dos autômatos adaptativos de segunda ordem é desenvolvido sobre o modelo dos autômatos adaptativos de primeira ordem, uma extensão natural do modelo dos autômatos adaptativos clássicos. Embora tenha o mesmo poder computacional, o autômato adaptativo de primeira ordem apresenta uma notação mais simples e rigorosa que o seu antecessor, permitindo derivar novas propriedades. Uma dessas propriedades é justamente sua capacidade de aprendizado. Como consequência, o modelo dos autômatos adaptativos de segunda ordem aumenta a expressividade computacional dos dispositivos adaptativos através da sua notação recursiva, e também através do seu potencial para o uso em aplicações de aprendizado de máquina, ilustrados nesta tese. Uma arquitetura de aprendizado de máquina usando os autômatos adaptativos de segunda ordem é proposto e um modelo de identificação no limite, aplicado em processos de inferência para linguagens livre de contexto, é apresentado. / The purpose of this work is to present the second-order adaptive automaton under an transformation automata approach and to show the strong connection of this model with learning in the limit. The connection is established using the adaptive mutations, in which any hypothesis can be used to start a learning process, and produces a correct final model following a step-by-step transformation of that hypothesis by a second-order adaptive automaton. Second-order adaptive automaton learner will be proved to acts as a learning in the limit. The presented formalism is developed over the first-order adaptive automaton, a natural and unified extension of the classical adaptive automaton. First-order adaptive automaton is a new and better representation for the adaptive finite automaton and to also show that both formulations the original and the newly created have the same computational power. Afterwards both formulations show to be equivalent in representation and in computational power, but the new one has a highly simplified notation. The use of the new formulation actually allows simpler theorem proofs and generalizations, as can be verified in this work. As results, the second-order adaptive automaton enhances the computational expressiveness of adaptive automaton through its recursive notation, and also its skills for the use in machine learning applications were illustrated here. An architecture of machine learning to use the adaptive technology is proposed and the model of identification in limit applied in inference processes for free-context languages.

Uma sequência de imagens de satélite climático é processada aplicando-se um conjunto de operações de filtros, no intuito de extrair padrões de comportamento das nuvens. Caracteres são criados a partir deste tratamento e suas transições são investigadas, explorando a possibilidade de justificar suas ocorrências através de linguagens formais e linguagens bidimensionais, definindo suas gramáticas. Com esta contagem de transições, uma análise de sua forma fractal é iniciada e um paralelo com outras contagens estabelecida, como uma forma de estruturar um modelo computacionalmente menos complexo de prever o tempo, ou o comportamento de qualquer entidade dinâmica que possa ser discretizada. Com estas investigações e experiências, foi possível diminuir a quantidade de símbolos utilizados para justificar as formas das nuvens, bem como criar classes de equivalências para representar conjuntos de símbolos que compartilham as mesmas propriedades, diminuindo ainda mais a complexidade da gramática que se espera encontrar. / A sequence of weather satellite images are processed by applying a set of filtering operations in order to extract the behavior patterns of clouds. Characters are created from this treatment and their transitions are investigated by exploring the possibility of justifying their occurrence across formal languages and two-dimensional languages, defining their grammar. With these count transitions an analysis of their fractals starts and counts a parallel with others established as a way to structure a model less computationally complex to predict the weather, or the behavior of any dynamic entity that could be discretized. With these investigations and experiments, it was possible to reduce the number of symbols used to explain the shapes of clouds and create equivalent classes to represent the symbol sets that share the same properties, further reducing the complexity of the grammar expected to be found.

The increasing need for low power and stunningly fast devices in Complementary Metal Oxide Semiconductor Very large Scale Integration (CMOS VLSI) circuits, directs the stream towards scaling of the same. However scaling at sub-micro level and nano level pose quantum mechanical effects and thereby limits further scaling of CMOS circuits. Researchers look into new aspects in nano regime that could effectively resolve this quandary. One such technology that looks promising at nano-level is the quantum dot cellular automata (QCA). The basic operation of QCA is based on transfer of charge rather than the electrons itself. The wave nature of these electrons and their uncertainty in device operation demands a probabilistic approach to study their operation. The data is assigned to a QCA cell by positioning two electrons into four quantum dots. However the site in which the electrons settles is uncertain and depends on various factors. In an ideal state, the electrons position themselves diagonal to each other, through columbic repulsion, to a low energy state. The quantum cell is said to be polarized to +1 or -1, based on the alignment of the electrons. In this thesis, we put forth a probabilistic model to design sequential QCA in Bayesian networks. The timing constraints inherent in sequential circuits due to the feedback path, makes it difficult to assign clock zones in a way that the outputs arrive at the same time instant. Hence designing circuits that have many sequential elements is time consuming. The model presented in this paper is fast and could be used to design sequential QCA circuits without the need to align the clock zones. One of the major advantages of our model lies in its ability to accurately capture the polarization of each cell of the sequential QCA circuits. We discuss the architecture of some of the basic sequential circuits such as J-K flip flop (FF), RAM memory cell and s27 benchmark circuit designed in QCADesigner. We analyze the circuits using a state-of-art Dynamic Bayesian Networks (DBN). To our knowledge this is the first time sequential circuits are analyzed using DBN. For the first time, Estimated Posterior Importance Sampling Algorithm (EPIS) is used to determine the probabilistic values, to study the effect due to variations in physical dimension and operating temperature on output polarization in QCA circuits.

Most existing computers today are built upon a subset of the arithmetic system which is based upon the foundation of set theory. All formal systems can be expressed in terms of arithmetic and logic on current arithmetic computers through an appropriate model, then work with the model using software manipulation. However, severe speed degradation is the price one must pay for using a software-based approach, making several high-level formal systems impractical. To improve the speed at which computers can implement these high-level systems, one must either design special hardware, implementing specific operations much like math and image processing coprocessors, or execute operations upon multiple processors in a parallel fashion. Due to the increase in developing applications for the manipulation of logic functions, an interest in the logic machine has arisen. Many applications such as logic optimization, simulation, pattern recognition and image processing can be better implemented with a logic machine. This thesis proposes the design, hardware realization, and testing of the iterative logic unit (ILU) of the Cube Calculus Machine II (CCM2). The CCM2 is a general purpose computer with an architecture that emphasizes a data path designed to execute operations of cube calculus, a popular algebraic model used in the minimization of Boolean functions. The ILU is an iterative logic array of cells (ITs) using internal distributed control, enabling the execution of basic cube operations, while the Control Unit (CU) handles global signals from the host computer. The ILU of the CCM2 has been realized in hardware using Xilinx Logic Cell Arrays (LCAs). FPGAs offer the logic density and versatility of gate arrays, with the off-the shelf availability and time-to-market advantages of standard user-programmable devices. These devices can be reconfigured, allowing multiple revisions and future design generations to accommodate the same device, thus saving design and production costs, an ideal solution to the resource and financial problems plaguing the University environment.

This thesis presents a new technique for mapping combinational circuits to Fine-Grain Cellular-Architecture FPGAs. We represent the netlist as the binary tree with decision variables associated with each node of the tree. The functionality of the tree nodes is chosen based on the target FPGA architecture. The proposed tree restructuring algorithms preserve local connectivity and allow direct mapping of the trees to the cellular array, thus eliminating the traditional routing phase. Also, predictability of the signal delays is a very important advantage of the developed approach. The developed bus-assignment algorithm efficiently utilizes the medium distance routing resources (buses). The method is general and can be used for any Fine Grain CA-type FPGA. To demonstrate our techniques, ATMEL 6000 series FPGA was used as a target architecture. The area and delay comparison between our methods and commercial tools is presented using a set of MCNC benchmarks. Final layouts of the implemented designs are included. Results show that the proposed techniques outperform the available commercial tools for ATMEL 6000 FPGAs, both in area and delay optimization.

COPD is characterized by tissue inflammation and impaired remodeling that suggests fibroblast maintenance of structural homeostasis is dysregulated. Thus, we performed in vitro wound healing experiments on normal and diseased human lung fibroblasts and developed an ABM of fibroblasts closing a scratched monolayer using NetLogo to evaluate differences due to COPD or cigarette smoke condensate exposure. This ABM consists of a rule-set governing the healing response, accounting for cell migration, proliferation, death, activation and senescence rates; along with the effects of heterogeneous activation, phenotypic changes, serum deprivation and exposure to cigarette smoke condensate or bFGF. Simulations were performed to calibrate parameter-sets for each cell type using in vitro data of scratch-induced migration, viability, senescence-associated beta-galactosidase and alpha-smooth muscle actin expression. Parameter sensitivities around each calibrated parameter-set were analyzed. This model represents the prototype of a tool designed to explore fibroblast functions in the pathogenesis of COPD and evaluate potential therapies.

Grouping is a vital precursor to object recognition. The complexity of the object recognition process can be reduced to a large extent by using a frontend grouping process. In this dissertation, a grouping framework based on spectral methods for graphs is used. The objects are segmented from the background by means of an associated learning process that decides on the relative importance of the basic salient relationships such as proximity, parallelism, continuity, junctions and common region. While much of the previous research has been focussed on using simple relationships like similarity, proximity, continuity and junctions, this work differenciates itself by using all the relationships listed above. The parameters of the grouping process is cast as probabilistic specifications of Bayesian networks that need to be learned: the learning is accomplished by a team of stochastic learning automata. One of the stages in the grouping process is graph partitioning. There are a variety of cut measures based on which partitioning can be obtained and different measures give different partitioning results. This work looks at three popular cut measures, namely the minimum, average and normalized. Theoretical and empirical insight into the nature of these partitioning measures in terms of the underlying image statistics are provided. In particular, the questions addressed are as follows: For what kinds of image statistics would optimizing a measure, irrespective of the particular algorithm used, result in correct partitioning? Are the quality of the groups significantly different for each cut measure? Are there classes of images for which grouping by partitioning is not suitable? Does recursive bi-partitioning strategy separate out groups corresponding to K objects from each other? The major conclusion is that optimization of none of the above three measures is guaranteed to result in the correct partitioning of K objects, in the strict stochastic order sense, for all image statistics. Qualitatively speaking, under very restrictive conditions when the average inter-object feature affinity is very weak when compared to the average intra-object feature affinity, the minimum cut measure is optimal. The average cut measure is optimal for graphs whose partition width is less than the mode of distribution of all possible partition widths. The normalized cut measure is optimal for a more restrictive subclass of graphs whose partition width is less than the mode of the partition width distributions and the strength of inter-object links is six times less than the intra-object links. The learning framework described in the first part of the work is used to empirically evaluate the cut measures. Rigorous empirical evaluation on 100 real images indicates that in practice, the quality of the groups generated using minimum or average or normalized cuts are statistically equivalent for object recognition, i.e. the best, the mean, and the variation of the qualities are statistically equivalent. Another conclusion is that for certain image classes, such as aerial and scenes with man-made objects in man-made surroundings, the performance of grouping by partitioning is the worst, irrespective of the cut measure.

<p>Centralna tema ovog istraživanje je primena ćelijskog automata računarskog modela u procesu projektovanja školskih zgrada. Istraživanje dominantno obrađuje teme vezane za upotrebu savremenih računarskih tehnologija u procesu arhitektonskog projektovanja, a primenjeno konkretno na školske objekte.</p> / <p>The central theme of this research is the application of the cellular automata computer model in the design of school buildings. Research tends to examine a large number of contemporary architectural design dillemas, foremost the subject of usage of modern informational technologies in architecture, applied to school buildings.</p>

Transducers are finite state automata with an output. In this thesis, we attempt to classify sequences that can be constructed by iteratively applying a transducer to a given word. We begin exploring this problem by considering sequences of words that can be produced by iterative application of a transducer to a given input word, i.e., identifying sequences of words of the form w, t(w), t²(w), . . . We call such sequences transducer recognizable. Also we introduce the notion of "recognition of a sequence in context", which captures the possibility of concatenating prefix and suffix words to each word in the sequence, so a given sequence of words becomes transducer recognizable. It turns out that all finite and periodic sequences of words of equal length are transducer recognizable. We also show how to construct a deterministic transducer with the least number of states recognizing a given sequence. To each transducer t we associate a two-dimensional language L²(t) consisting of blocks of symbols in the following way. The first row, w, of each block is in the input language of t, the second row is a word that t outputs on input w. Inductively, every subsequent row is a word outputted by the transducer when its preceding row is read as an input. We show a relationship of the entropy values of these two-dimensional languages to the entropy values of the one-dimensional languages that appear as input languages for finite state transducers.