An automated signal classifier and a semi-automated signal identifier are designed for collecting the dielectrophoretic signatures of cells flowing through a dielectrophoretic cytometer. In past work, the DEP cytometer signals were manually sorted by going through all recorded signals, which is impractical when analyzing 1000’s of cells per day. In the semi-automated method of collection, signals are automatically identified as events and displayed on the user interface to be accepted or rejected by the user. This approach reduced signal collection time by more than half and produced statistics nearly identical to the manual method. The automated signal classifier based on pattern recognition categorizes detection signals as ‘Accept’ or ‘Reject’. Analyzing large volumes of detection signals is possible in much reduced times and may be approaching real time capability.

This study presents new techniques for the analysis of data acquired by focal plane array Fourier transform infrared (FPA-FTIR) spectroscopy. FPA-FTIR spectrometers are capable of acquiring several orders of magnitude more data than conventional FTIR spectrometers, necessitating the use of novel data analysis techniques to exploit the information-rich nature of these infrared imaging systems. The techniques investigated in this study are demonstrated in the context of bacteria identification by FPA-FTIR spectroscopy. Initially, an examination is made of the image fidelity of three FPA-FTIR instruments and demonstrates the high degree of within-image and between-image variability that is encountered with this technology. This is followed by a description of the development of pixel filtration routines that allow for the extraction of the most representative data from the infrared images of non-uniform samples. A genetic algorithm (GA) approach is introduced for determining the relevancy of spectral features in relation to bacterial classification and is compared to other forms of classifier optimizations. A proof-of-concept study demonstrating the potential use of infrared imaging to detect bacterial samples originating from a mixed culture is then presented. Finally, an overall methodology involving the combination of these data analysis techniques and including additional approaches towards the development, maintenance, and validation of databases based on infrared imaging data is described. This methodology has been developed with an emphasis on accessibility by implementing the elements of an expert system which allows for this technology to be employed by a non-technical user. / Cette étude présente une nouvelle approche d'analyse de données spectrales résultant de l'utilisation de la spectroscopie infrarouge à transformée de Fourier couplée à un détecteur type «matrice à plan focal» (FPA-FTIR) à balayage rapide. Les spectromètres FPA-FTIR ont une capacité de capture de données de plusieurs ordres de grandeur supérieurs aux spectromètres traditionnels et nécessitent donc des techniques avancées d'analyse de données pour exploiter cette mine d'information que représente l'imagerie infrarouge. La spectroscopie FPA-FTIR a été utilisée dans cette étude pour l'identification des bactéries. L'étape initiale, celle de la comparaison de trois spectromètres FPA-FTIR sur les points de vue fidélité de l'image, tant image-image qu'entre images, a révélé de grandes variabilités qui sont propres à cette technologie. Cette étape est suivie du développement de routines de filtration de pixels permettant d'extraire les données caractéristiques de l'imagerie infrarouge des échantillons non-uniformes. Un algorithme génétique (GA) est ensuite introduit pour déterminer la pertinence des caractéristiques spectrales sur le plan de la classification bactérienne et a été comparé à d'autres formes de classification optimisée. Une étude de démonstration de la capacité de la technologie d'imagerie infrarouge pour la détection des échantillons de bactéries provenant de cultures mixtes s'en est suivie. Pour terminer, une méthodologie globale combinant ces techniques d'analyse de données et incluant d'autres étapes telles le développement, la mise à niveau et la validation des bases de données d'imagerie infrarouge a été présentée. Cette méthodologie met l'emphase sur le développement et l'implantation d'un système expert accessible d'utilisation à de non-experts.

In this thesis, we analyze a context-dependent movie recommendation system using a Hierarchical Bayesian Network. Unlike most other recommender systems which either do not consider context or do so using collaborative filtering, our approach is content-based. This allows users to individually interpret contexts or invent their own contexts and continue to get good recommendations. By using a Hierarchical Bayesian Network, we can provide context recommendations when users have only provided a small amount of information about their preferences per context. At the same time, our model has enough degrees of freedom to handle users with different preferences in different contexts. We show on a real data set that using a Bayesian Network to model contexts reduces the error on cross-validation over models that do not link contexts together or ignore context altogether. / Dans cette thèse, nous analysons un système de recommandations de films dépendant du contexte en utilisant un réseau Bayésien hiérarchique. Contrairement à la plupart des systèmes de recommendations qui, soit ne considère pas le contexte, soit le considère en utilisant le filtrage collaboratif, notre approche est basée sur le contenu. Ceci permet aux utilisateurs d'interpréter les contextes individuellement ou d'inventer leurs propres contextes tout en obtenant toujours de bonnes recommandations. En utilisant le rèseau Bayésien hiérarchique, nous pouvons fournir des recommendations en contexte quand les utilisateurs n'ont fourni que quelques informations par rapport à leurs préférences dans différents contextes. De plus, notre modèle a assez de degrés de liberté pour prendre en charge les utilisateurs avec des préférences différentes dans différents contextes. Nous démontrons sur un ensemble de données réel que l'utilisation d'un réseau Bayésien pour modéliser les contextes réduit l'erreur de validation croisée par rapport aux modèles qui ne lient pas les contextes ensemble ou qui ignore tout simplement le contexte.

We explore the use of co-training to improve the performance of image classification in the setting where multiple classifiers are used and several types of features are available. Features are assigned to classifiers in an optimal manner using hierarchical clustering with a distance metric based on conditional mutual information. The effect of increasing the number of classifiers is then evaluated by co-training using the assigned feature sets. Experimental results indicate that the feature assignments chosen by the clustering approach afford superior co-training performance in comparison to other logical assignment choices. The results also indicate that increasing the number of classifiers beyond two leads to improved performance provided that the classifiers are sufficiently independent, and are reasonable well balanced in terms of labeling ability.Additionally, we explore the effect that the initial training set selectionhas on co-training performance. We find that the quality of training imageshas a profound effect on performance and provide recommendations for howbest to select these images. / Nous explorons l'utilisation de la co-formation pour améliorer la performance de classification d'image dans un milieu où multiples classificateurs s'emploient et plusieurs types de caractéristiques sont disponibles. Les caractéristiques sont associés aux classificateurs d'une manière optimal en employant le groupage hiérarchique avec une mesure de distance basée sur l'information mutuelle conditionnelle. L'effet d'augmenter le nombre de classificateurs est alors evalué par la co-formation, en employant les ensembles de caractéristiques attribués. Les résultats de nos expériences indique que si on augmente le nombre de classificateurs au-delà de deux, la performance s'améliore pourvu que les caractéristiques soient suffisamment indépendantes et assez bien équilibrées en termes de compétence d'étiquetage. En plus, nous explorons l'effet de l'ensemble choisi pour l'entraînement initial sur la performance en co-formation. Nous trouvons que la qualité d'images dans l'entraînement a un effet profond sur la performance, et nous fournissons des recommandations sur comment sélectionner ces images pour le meilleur effet.

Since the emergence of the object technology, organizations have accumulated a tremendous amount of object-oriented (OO) code. Instead of continuing to recreate components similar to existing artifacts, and considering the rising costs of development, many organizations would like to decrease software development costs and cycle time by reusing existing OO components. The difficulty of finding reusable components is that reuse is a complex and thus less quantifiable measure. In this research, we first proposed three reuse hypotheses about the impact of three internal characteristics (inheritance, coupling, and complexity) of OO software artifacts on reusability. Corresponding metrics suites were then selected and extracted. We used C4.5, a machine learning algorithm, to build predictive models from the learning data set that we have obtained from a medium sized software system developed in C++. Each predictive models was then verified according to its completeness, correctness and global accuracy. The verification results proved that the proposed hypotheses were correct. The uniqueness of this research work is that we have combined the state of the art of three different subjects (reuse detection and prediction, OO metrics and their extraction, and applied machine learning algorithm) to form a process of finding interesting properties of OO software components that affect reusability.

Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally from experience in unknown systems. A major problem for such learning algorithms is how to balance optimally the exploration of the system, to gather knowledge, and the exploitation of current knowledge, to complete the task. Model-based Bayesian Reinforcement Learning (BRL) methods provide an optimal solution to this problem by formulating it as a planning problem under uncertainty. However, the complexity of these methods has so far limited their applicability to small and simple domains. To improve the applicability of model-based BRL, this thesis presents several extensions to more complex and realistic systems, such as partially observable and continuous domains. To improve learning efficiency in large systems, this thesis includes another extension to automatically learn and exploit the structure of the system. Approximate algorithms are proposed to efficiently solve the resulting inference and planning problems. / L'apprentissage par renforcement a émergé comme une technique utile pour apprendre à accomplir une tâche de façon optimale à partir d'expérience dans les systèmes inconnus. L'un des problèmes majeurs de ces algorithmes d'apprentissage est comment balancer de façon optimale l'exploration du système, pour acquérir des connaissances, et l'exploitation des connaissances actuelles, pour compléter la tâche. L'apprentissage par renforcement bayésien avec modèle permet de résoudre ce problème de façon optimale en le formulant comme un problème de planification dans l'incertain. La complexité de telles méthodes a toutefois limité leur applicabilité à de petits domaines simples. Afin d'améliorer l'applicabilité de l'apprentissage par renforcement bayésian avec modèle, cette thèse presente plusieurs extensions de ces méthodes à des systèmes beaucoup plus complexes et réalistes, où le domaine est partiellement observable et/ou continu. Afin d'améliorer l'efficacité de l'apprentissage dans les gros systèmes, cette thèse inclue une autre extension qui permet d'apprendre automatiquement et d'exploiter la structure du système. Des algorithmes approximatifs sont proposés pour résoudre efficacement les problèmes d'inference et de planification résultants.

An original way to define, analyze and design mechanical systems with inherently lifelike dynamic properties is presented. The construction of robotic manipulators which embody a complete set of technologically relevant biological principles is outlined. The ultimate objective is to develop a new class of mobile, autonomous, and interactive machines which dynamically emulate live musculoskeletal systems. / This study introduces the mathematical models and algorithms to transform and synthesize the results of research in musculoskeletal physiology into explicit engineering design specifications. The application of a new contractile muscle-like viscoelastic motor, as a servomechanical drive for articulated rigid link mechanisms as well as for a novel flexible trunk-like manipulator, is investigated. Key features of the neuromuscular force control by twitch summation are combined to formulate a pulse stream control method suitable for fluid powered mechanisms.

<p> Starting from Edward Edinger's portrayal of Jung's process of individuation as the creation of consciousness, this dissertation asks in what ways the creation of artificial intelligence (AI) can be seen as the recreation of consciousness, and specifically whether the AI's maturation from nonconsciousness to something equivalent to consciousness will have an analogous effect on humanity's development out of unconsciousness toward a greater state of cognitive freedom. Taking a functional perspective, this dissertation asks whether B. F. Skinner's metaphor of the human psyche as a black box, normally seen as expressing the belief that humans are mechanistic and determined, is in fact an attempt to insulate the most intimate of human experiences (the soul) from the intrusive gaze of the scientific mindset. Juxtaposing this black box metaphor with two other metaphors&mdash;that of the box that holds Schrodinger's cat and that of Pandora's box&mdash;this dissertation asks whether the presence of an entirely constructed entity that displays all the signs of soul will cause the artificially intelligent entity to act as a mirror, reflecting humanity's gaze past our inner defenses, to an inner absence where a metaphysical soul was once surmised to be. Although such a change in self-image would initially entail an apparent loss of meaning, this dissertation notes that such a lacuna of meaning is already growing in society and concludes that the loss of this concept would eventually result in a new concept of self that would represent an important milestone for the collective individuation of the species.</p>

This thesis presents a development environment for Design Expert Systems that features a new language where the roles of knowledge and the way it is brought to bear are predefined by the solution method. / The language also allows knowledge to be organized into classes of objects whose dynamic creation corresponds to the structure of the components to be configured. This approach, combined with a specialized control strategy for Design problems, helps in narrowing the representation gap between the expert's knowledge and the system's knowledge. / The environment assists knowledge acquisition by means of a structured editor and by static and dynamic analyses for validation; it is implemented on a micro-computer and includes an internal Production System to execute the Expert Systems that are generated. Two applications to Engineering Design are used to demonstrate the environment's capabilities.

A robotic workcell is a collection of robots, sensors, and other industrial equipment grouped in a cooperative environment to perform various complex tasks. Due to their distributed nature however, the control and programming of robotic workcells is often a difficult task, for which dedicated environments have to be designed and built. / Simulation, especially graphic simulation, can greatly contribute to the development of programs for such integrated robotic applications: the simulator emulates the behavior of the workcell on a computer display, and allows the programmer to test and debug programs without requiring an immediate access to the physical equipment. / This thesis presents the design of a graphic simulator for robotic workcell applications. The simulator is based on SAGE/WRAP, an environment for the programming and run-time control of robotic workcells. Given a WRAP program as input, the simulator displays a top-view of the workcell and animates graphically the execution of the program; the coordination and the flow of operations within the workcell being shown, the programmer can quickly assess the overall validity of the program. / The simulator was developed in C under the X Window System, and is currently implemented as a standalone software; the design was made flexible and modular, to facilitate an eventual integration to the WRAP environment.