The purpose of this study was to investigate the links among children's representations of attachment and their intelligence and academic achievement. John Bowlby's attachment theory is the framework used in this study to understand and explain differences in children's intelligence and academic achievement. Bowlby maintained that the quality of children's attachment to their caregivers exerts a strong influence on their ability and interest in investigating their environment. According to attachment theory, the quality of children's attachment to their primary caregivers would be expected to be associated with their intelligence and scholastic achievement. The findings from this study suggest that attachment patterns are significantly related to children's crystallized intelligence, which involves learning, knowledge and skills that are accumulated from past experiences. Attachment patterns were not significantly related to children's global intelligence or their academic achievement. The results of this study are relevant not only to attachment researchers but also school psychologists, parents, and teachers.

Automated visual surveillance is one of the most actively researched areas in the past decade. Although current behaviour recognition systems provide us with a good understanding on the behaviour of individual moving objects present in an observed scene, they are not able to efficiently recognize the behaviour of groups formed by large numbers of moving objects. In this thesis, we present a HMM-based group behaviour recognition system which is capable of recognizing group behaviours effectively and efficiently. In our approach, we generate synthetic data for the training and validation of our behaviour recognition system. In addition, we use a single feature vector to represent the group dynamics, instead of using one feature vector for each pairwise interaction. Experimental results show accurate classification for both real-life data and simulated data from Lee's dataset. Therefore, we conclude that the proposed approach is a viable and accurate technique to perform group behaviour recognition in both simulated environment and real-life situations. Moreover, the high accuracy of the classification results obtained on real-life data, when only synthetic data was used for the training, suggests that it is possible to develop group behaviour models using synthetic data alone. / La surveillance visuelle automatisée est un domaine de recherche parmi les plus actifs au cours de la dernière décennie. Bien que les systèmes actuels de reconnaissance des comportements nous fournissent une bonne compréhension sur le comportement des objets en mouvement dans une scène observée, ils ne sont pas en mesure de reconnaître efficacement le comportement de groupes formés de plusieurs objets en mouvement. Dans cette mémoire, nous présentons un système de reconnaissance des comportements de groupes basé sur le modèle de Markov caché (MMC). Notre système est capable de reconnaître les comportements de groupe de façon efficace et efficiente. Dans notre approche, nous générons des données synthétiques pour former et valider notre système de reconnaissance des comportements. De plus, nous utilisons un vecteur caractéristique pour représenter la dynamique d'un groupe au lieu d'utiliser un vecteur pour chaque interaction entre deux objets en mouvement. Les résultats expérimentaux montrent une classification précise pour les données réelles et simulées utilisant la base de données de Lee. Par conséquent, nous concluons que l'approche proposée est une solution viable et une technique précise pour effectuer la reconnaissance des comportements de groupes dans un environnement simulé et dans des situations de la vie courante. Les résultats démontrent aussi qu'en utilisant uniquement des données synthétiques pour le former, le système classe avec une grande précision les comportements issues de situations réelles. Cela suggère qu'il est possible de développer des modèles de comportement de groupe en utilisant seulement les don

In this thesis, we applied both numerical methods and artificial intelligence techniques to NMR data processing and analysis. First, a comprehensive study of the Iterative Quadratic Maximum Likelihood (IQML) method applied to NMR spectral parameter estimation is reported. The IQML is compared to other conventional time domain data analysis methods. Extensive simulations demonstrate the superior performance of the IQML method. We also develop a new technique, which uses genetic algorithm with a priori knowledge, to improve the quantification of NMR spectral parameters. The new proposed method outperforms the other conventional methods, especially in the situations that there are signals close in frequencies and the signal-to-noise ratio of the FID is low. / The usefulness of Singular Value Decomposition (SVD) method in NMR data processing is further exploited. A new two dimensional spectral processing scheme based on SVD is proposed for suppressing strong diagonal peaks. The superior performance of this method is demonstrated on an experimental phase-sensitive COSY spectrum. / Finally, we studied the feasibility of using neural network predicted secondary structure information in the NMR data analysis. Protein chemical shift databases are compiled and are used with the neural network predicted protein secondary structure information to improve the accuracy of protein chemical shift prediction. The potential of this strategy for amino acid classification in NMR resonance assignment is explored.

<p> Music composition using Artificial Intelligence is a well-established area of study with research dating back over six decades. From the time the mathematical model of computation was developed by Alan Turing in the 1940s, the question of whether computers can be built to match human level intelligence has been debated. Creativity is certainly considered to be a sign of intelligence, and many areas of Artificial Intelligence have pursued ways to emulate the creative spark found in humans. Music Composition via Artificial Intelligence falls into this category. This thesis explores the application of Artificial Intelligence approaches towards the goal of composing music by implementing three approaches found in Artificial Intelligence and studying their results. </p>

In this study a rationale is provided for the need for integrative sychometric studies in addition to exploratory studies. The psychometric concepts of validity, reliability, test comparability and bias are reviewed. Furthermore the concepts of the nomological network and meta-analysis are reviewed with regard to their relevance for integrating findings of psychometric research. The historical origins of the Intermediate Mental Alertness Test (IMAT) are traced with a view to distinguishing it from a number of related tests. Psychometric information is summarised from a wide cross-section of both published and unpublished studies on the IMAT, and the findings of these studies are reviewed in terms of the reliability and validity of the IMAT across different contexts and groups. It is found that there are an insufficient number of validity studies using similar criteria for the meta-analysis technique to be applied. An attempt is therefore made to interpret trends from the research studies reviewed, and recommendations are provided in terms of reporting standards for future validity studies in order to facilitate integrative research techniques such as meta-analysis. / Thesis (M.A.)-University of Natal, Durban, 1991.

No abstract available. / Thesis (M.A.)-Natal University College, Pietermaritzburg, 1946.

The performance of mineral grinding circuits strongly affects downstream processes such as flotation and cyanidation, and grinding is often the single most expensive unit operation. Hence, optimization efforts must be made on a regular basis to maintain and improve its technical and economic efficiency. The focus of this thesis, off-line optimization of grinding circuits, is based on the mathematical modelling of process units such as ball mills and hydrocyclones. / To complete an optimization task, a mineral process engineer must possess skills and knowledge pertaining to the different stages involved in such effort, available software tools and interpretation of results. A prototype knowledge-based system, Grinding Circuits Optimization Supervisor (GCOS), has been developed in CLIPS (C Language Integrated Production System) to assist a non-expert mineral process engineer to do off-line optimization studies. / Due to the importance of the correct estimation of back-calculated mill selection function in grinding optimization studies, a spline curve fitting algorithm has been used to improve their quality. The linkage of the algorithms for the selection function estimation, spline curve fitting, selection function scaling for different ball sizes and single ball mill simulation has provided a useful tool, Numerical Grinding Optimization Tools in C (NGOTC) for circuit analysis and grinding media size optimization. The smoothed estimated or scaled selection functions can be used subsequently in Ball Milling Circuits Simulator (BMCS) to perform full circuit simulations. / Data from a number of mineral processing plants including Agnico Eagle (La Ronde Division), Les Mine Selbaie, Les Mines Casa Berardi, Lupin Mine, Dome Mine and Louvicourt Mine were used to develop and test NGOTC, BMCS and GCOS. The results of data analysis and circuit simulations of some of these plants are presented, and the impact of some suggested actions is given and discussed.

This thesis explores the use of randomized, performance-based search strategies to improve the generalization of an automatic speech recognition system based on hidden Markov models. We apply simulated annealing and random search to several components of the system, including phoneme model topologies, distribution tying, and the clustering of allophonic contexts. By using knowledge of the speech problem to constrain the search appropriately, we obtain reduced numbers of parameters and higher phonemic recognition results. Performance is measured on both our own data set and the Darpa TIMIT database.

Video surveillance has become of a major research topic recently due to the increasing number of potential applications in public spaces. In particular, there is a demand for automated surveillance applications that detect different types of activities related to public safety, such as in metro stations. Automated video surveillance is intended to be used as an aid to human operators by bringing to their attention certain designated events of interest.This thesis presents a real-time video surveillance system that detects a range of activities in a scene viewed by a single color-video camera. Our contribution in this work is mainly exploiting the properties of the CIELab color space to improve the performance at the low level processing, proposing a multi-level blob matching algorithm to solve the object tracking problem, and using a hierarchy of semantics for detecting events that are of interest to public spaces surveillance.A complete framework of a surveillance system is presented. Objects in an observed scene are modeled by blobs that are detected by means of the adaptive background modeling codebook implementation based on the work of Kim et al. [2]. The implementation uses a dynamically updated codebook in which blobs in the video are characterized in color space, while also dealing with shadows. Collections of blobs, which represent potential objects of interest, are tracked and classified in real-time. For tracking, we employ a simple correlation process based on an elaborate blob matching algorithm. The essence of this algorithm is to find the best blob collection based on matching all potential color histograms from previous frames to those obtained in the current frame. Rules are used to resolve complex cases such as ghosts, occlusion, and lost tracks. Objects are then classified as either being animate persons or inanimate objects. This is essential for providing an accurate description of the scene and drawing the correct inferences about object interaction and events. Given this description of the video, a hierarchical semantic approach is used for event detection. The proposed framework investigates a generalized approach to detecting a spectrum of behaviours based on object interactions and trajectories. These behaviours range from simple single agent events such as loitering, to more complex interactive ones, such as people walking together. Experimental results are presented for standard available videos in order to verify the performance of the system and are compared to existing results in the literature. These results show a significant improvement both in terms of quality and speed, making a step towards a more reliable fully automated surveillance system. / La surveillance vidéo est devenue un sujet de recherche majeur récemment en raison du nombre croissant d'usages potentiels dans les espaces publics. En particulier, il y a une demande pour des applications de surveillance automatisées qui détectent différents types d'activités liées à la sécurité publique, comme dans les stations de métro. La surveillance vidéo automatisée est destinée à être utilisé comme une aide aux opérateurs humains en attirant leur attention à certains événements désignés d'intérêt. Cette thèse présente un système de surveillance vidéo en temps réel qui détecte une gamme d'activités dans une scène captée par une caméra vidéo couleur. Nos contributions sont principalement l'exploitation des propriétés de l'espace couleur CIELab pour améliorer la performance du traitement de bas niveau, la proposition d'un algorithme multi-niveau recherchant les équivalences de 'blob' pour résoudre le problème de suivi d'objets, et l'utilisation d'une hiérarchie de sémantique pour détecter les événements d'intérêt dans la surveillance des espaces publics. Un cadre complet d'un système de surveillance est présenté. Les objets dans une scène observée sont modélisés par des 'blob's qui sont détectés par la mise en œuvre du dictionnaire de modélisation d'arrière-plan adaptative basée sur les travaux de Kim et al. [1]. La mise en œuvre utilise un dictionnaire mis à jour dynamiquement dans lequel les blobs dans la vidéo sont caractérisées dans l'espace couleur, tout en traitant avec les ombres. Les collections de blobs, qui représentent des objets d'intérêt potentiel, sont suivies et classées en temps réel. Pour le suivi, nous employons un processus de corrélation simple basé sur un algorithme complexe de recherche d'équivalences de blob. L'essence de cet algorithme est de trouver la meilleure collection de blob basée sur la correspondance des tous les histogrammes potentiels de couleurs des cadres précédents à ceux obtenus dans le cadre actuel. Des lois sont utilisées pour résoudre les cas complexes comme les 'fantômes', l'occlusion, et les pistes perdues. Les objets sont alors classés comme étant des personnes animées ou des objets inanimés. Cela est essentiel pour fournir une description précise de la scène et pour faire les bonnes déductions par rapport aux événements et aux interactions d'objets. Une approche sémantique hiérarchique est utilisée pour la détection des événements, en partant de cette description. Le cadre proposé étudie une approche généralisée pour détecter une gamme de comportements fondés sur les interactions et les trajectoires d'objets. Ces comportements varient d'événements simples avec un seul agent, comme le flânage, aux événements plus complexes et interactifs, par exemple des personnes qui marchent ensemble. Les résultats expérimentaux sont présentés pour des vidéos standards et disponibles, puis sont comparés aux résultats existants dans la littérature afin de vérifier les performances du système. Ces résultats montrent une amélioration importante en termes de qualité et de vitesse, un pas vers un système plus fiable de surveillance entièrement automatisé.

Epilepsy is a chronic neurological disorder affecting around 50 million people worldwide. It is characterized by the occurrence of seizures; a transient clinical event caused by synchronous and/or abnormal and excessive neuronal activity in the brain. This thesis presents a novel machine learning toolbox that generates personalized epileptic seizure detection algorithms exploiting the information contained in electroencephalographic recordings. A large variety of features designed by the seizure detection/prediction community are implemented. This broad set of features is tailored to specific patients through the use of automated feature selection techniques. Subsequently, the resulting information is exploited by a complex machine learning classifier that is able to detect seizures in real-time. The algorithm generation procedure uses a default set of parameters, requiring no prior knowledge on the patients' conditions. Moreover, the amount of data required during the generation of an algorithm is small. The performance of the toolbox is evaluated using cross-validation, a sound methodology, on subjects present in three different publicly available datasets. We report state of the art results: detection rates ranging from 76% to 86% with median false positive rates under 2 per day. The toolbox, as well as a new dataset, are made publicly available in order to improve the knowledge on the disorder and reduce the overhead of creating derived algorithms. / L'épilepsie est un trouble neurologique cérébral chronique qui touche environ 50 millions de personnes dans le monde. Cette maladie est caractérisée par la présence de crises d'épilepsie; un événement clinique transitoire causé par une activité cérébrale synchronisée et/ou anormale et excessive. Cette thèse présente un nouvel outil, utilisant des techniques d'apprentissage automatique, capable de générer des algorithmes personnalisés pour la détection de crises épileptiques qui exploitent l'information contenue dans les enregistrements électroencéphalographiques. Une grande variété de caractéristiques conçues pour la recherche en détection/prédiction de crises ont été implémentées. Ce large éventail d'information est adapté à chaque patient grâce à l'utilisation de techniques de sélection de caractéristiques automatisées. Par la suite, l'information découlant de cette procédure est utilisée par un modèle de décision complexe, qui peut détecter les crises en temps réel. La performance des algorithmes est évaluée en utilisant une validation croisée sur des sujets présents dans trois ensembles de données accessibles au public. Nous observons des résultats dignes de l'état de l'art: des taux de détections allant de 76% à 86% avec des taux de faux positifs médians en deçà de 2 par jour. L'outil ainsi qu'un nouvel ensemble de données sont rendus publics afin d'améliorer les connaissances sur la maladie et réduire la surcharge de travail causée par la création d'algorithmes dérivés.