University, Diss., 2005--Bonn.

University, Diss., 2005--Bielefeld.

Philosophiae Doctor - PhD / In this thesis, we present a novel hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). Though sequence recognition problems could be potentially modelled through well trained HMMs, they could not provide a reasonable solution to the complicated recognition problems. In contrast, the ability of RNNs to recognize the complex sequence recognition problems is known to be exceptionally good. It should be noted that in the past, methods for applying HMMs into RNNs have been developed by other researchers. However, to the best of our knowledge, no algorithm for processing HMMs through learning has been given. Taking advantage of the structural similarities of the architectural dynamics of the RNNs and HMMs, in this work we analyze the combination of these two systems into the hybrid architecture. To this end, the main objective of this study is to improve the sequence recognition/classi_cation performance by applying a hybrid neural/symbolic approach. In particular, trained HMMs are used as the initial symbolic domain theory and directly encoded into appropriate RNN architecture, meaning that the prior knowledge is processed through the training of RNNs. Proposed algorithm is then implemented on sample test beds and other real time biological applications.

The collection of large speech databases is not a trivial task (if done properly). It is not always possible to collect, segment and annotate large databases for every task or language. It is also often the case that there are imbalances in the databases, as a result of little data being available for a specific subset of individuals. An example of one such imbalance is the fact that there are often more male speakers than female speakers (or vice-versa). If there are, for example, far fewer female speakers than male speakers, then the recognizers will tend to work poorly for female speakers (as compared to performance for male speakers). This thesis focuses on using Bayesian and discriminative training algorithms to improve continuous speech recognition systems in scenarios where there is a limited amount of training data available. The research reported in this thesis can be divided into three categories: • Overspecialization is characterized by good recognition performance for the data used during training, but poor recognition performance for independent testing data. This is a problem when too little data is available for training purposes. Methods of reducing overspecialization in the minimum classification error algo¬rithm are therefore investigated. • Development of new Bayesian and discriminative adaptation/training techniques that can be used in situations where there is a small amount of data available. One example here is the situation where an imbalance in terms of numbers of male and female speakers exists and these techniques can be used to improve recognition performance for female speakers, while not decreasing recognition performance for the male speakers. • Bayesian learning, where Bayesian training is used to improve recognition perfor¬mance in situations where one can only use the limited training data available. These methods are extremely computationally expensive, but are justified by the improved recognition rates for certain tasks. This is, to the author's knowledge, the first time that Bayesian learning using Markov chain Monte Carlo methods have been used in hidden Markov model speech recognition. The algorithms proposed and reviewed are tested using three different datasets (TIMIT, TIDIGITS and SUNSpeech), with the tasks being connected digit recognition and con¬tinuous speech recognition. Results indicate that the proposed algorithms improve recognition performance significantly for situations where little training data is avail¬able. / Thesis (PhD (Electronic Engineering))--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / unrestricted

Most of the chronic diseases have a well-known natural staging system through which the disease progression is interpreted. It is well established that the transition rates from one stage of disease to other stage can be modeled by multi state Markov models. But, it is also well known that the screening systems used to diagnose disease states may subject to error some times. In this study, a simulation study is conducted to illustrate the importance of addressing for misclassification in multi-state Markov models by evaluating and comparing the estimates for the disease progression Markov model with misclassification opposed to disease progression Markov model. Results of simulation study support that models not accounting for possible misclassification leads to bias. In order to illustrate method of accounting for misclassification is illustrated using dementia data which was staged as no cognitive impairment, mild cognitive impairment and dementia and diagnosis of dementia stage is prone to error sometimes. Subjects entered the study irrespective of their state of disease and were followed for one year and their disease state at follow up visit was recorded. This data is used to illustrate that application of multi state Markov model which is an example of Hidden Markov model in accounting for misclassification which is based on an assumption that the observed (misclassified) states conditionally depend on the underlying true disease states which follow the Markov process. The misclassification probabilities for all the allowed disease transitions were also estimated. The impact of misclassification on the effect of covariates is estimated by comparing the hazard ratios estimated by fitting data with progression multi state model and by fitting data with multi state model with misclassification which revealed that if misclassification has not been addressed the results are biased. Results suggest that the gene apoe ε4 is significantly associated with disease progression from mild cognitive impairment to dementia but, this effect was masked when general multi state Markov model was used. While there is no significant relation is found for other transitions.

No description available.

Magister Scientiae (Biodiversity and Conservation Biology) / This study aimed to determine the nutrient content (Co, Cr, F, I, Se and V) of various vegetable based food items collected from the Cape Town area in the Western Cape Province of South Africa. This was done to determine which vegetable crops provided the highest concentrations of essential trace elements, and how much they contribute to the daily recommended intake (DRIs) of these trace elements. It also aimed to assess the effects of the addition of the trace elements (Co, Cr, F, I, Se, Si, Sn and V) on seed germination and root growth under controlled conditions in order to calculate their phytotoxicity, and then to biofortify four vegetable crop species, grown in sand culture, with a composite treatment of the trace elements to determine how the addition of these elements will affect the vegetable crops grown under these experimental conditions. From this study, it was shown that trace element content in vegetable crops in the Western Cape Province of South Africa varied between different geographic locations and that certain trace elements were absent from several items collected from some areas. Although some crop species contained sufficient amounts of certain trace elements to satisfy our daily recommended intakes, most of the crops were found to contain insufficient amounts of many of the trace elements to satisfy our needs. Leafy vegetables and tubers were identified as the better vegetable types to biofortify with essential trace elements as they already contain higher concentrations of several of the essential trace elements and should thus be assessed for their effectiveness as crops to be biofortified. When the trace elements were applied directly to cress and lettuce seeds, it was found that all the trace elements, as well as the composite treatments, exerted phytotoxic effects on cress and/or lettuce seeds when applied athighconcentrations. Lettuce was found to be more prone to the effects of these elements. Seed germination was strongly inhibited by fluoride, while several elements affected root growth. When fluoride was left out of the composite treatment, phytotoxicity only occurred at high concentrations. The addition of the trace elements at the high concentrations to already established spinach, cabbage, lettuce and turnip plants were found to affect the uptake of several essential plant nutrients, but the concentrations of the elements affected generally remained higher than the concentrations needed for adequate growth of agricultural crops. Several of the trace elements supplied to the plants were also found to be retained in the roots of the vegetable crops however, as the concentrations supplied to the plants increased, so did the concentrations found in the edible portions of the crops

Le but de cette recherche est de montrer comment s’effondrent les clichés et comment existe l’inexplicable, qui est, selon Beckett, une des conditions de l’existence avec la lumière et l’obscurité, et qui est aussi le chaos composé par l’art détruisant préjugés et clichés, aperçu par Gilles Deleuze et Félix Guattari. Cette démonstration se propose donc, d’une part, d’examiner la chute des dieux de la création, sources de la vérité, de la sagesse et du progrès, et leur dénaturation en dieux du chaos, et d’autre part, les éléments anormaux de l’écriture, susceptibles d’être nommés monstres, en raison de leur étrangeté et de leur ambiguïté qui empêchent de les définir. Pour ce qui concerne les dieux du chaos, il s’agit de la parodie du Dieu chrétien, de Jésus-Christ et de Prométhée l’inventeur, et de leur dégradation aboutissant à ce que le créateur ne se distingue plus de sa créature. Quant aux monstres inconcevables, leurs conditions et les réactions négatives envers eux, qui sous-entendent paradoxalement la monstrualisation, sont révélées par le thème des fleurs, et deux images mythiques, androgynes et siamoises, dévoilent l’impossibilité de l’identification ou de la signifiance, et l’état de l’entre-deux ou de la confusion. / The purpose of this thesis is to show how clichés collapse and exists the inexplicable that Beckett notices as a condition of existence that coexists with other conditions, light and darkness ; and as chaos, composed by art destroying prejudices and clichés, and seen by Gilles Deleuze and Félix Guattari. This demonstration therefore proposes, on the one hand, to examine the fall of the gods of creation, sources of truth, wisdom and progress, and their denaturation into gods of chaos, and on the other hand, the elements abnormal writing, likely to be named monsters, because of their strangeness and ambiguity that prevent them from being defined. As for the gods of chaos, it will be the parody of the Christian God, Jesus Christ and Prometheus the inventor, and their degradation by which the creator will no longer be distinguished from his creature. As for the inconceivable monsters, the conditions of the monsters and the negative reactions towards them, which paradoxically imply the monstrualization, will be revealed by the theme of flowers, and the two mythical images, androgynous and Siamese, will reveal the impossibility of the identification or the significance, and the state of the interstage or confusion.

In this thesis the Hidden Markov Model (HMM) is used in the process of map matching to investigate the accuracy for road segment map matching. A few HMM algorithms using a Global Navigation Satellite System (GNSS) receiver, odometer and gyroscope sensors are presented. The HMM algorithms are evaluated on four accuracy metrics. Two of these metrics have been seen in previous literature and captures road map match accuracy. The other have not been seen before and captures road segment accuracy. In the evaluation process a dataset is created by simulation to achieve positional ground truth for each sensor measurement. The accuracy distribution for different parts of the map matched trajectory is also evaluated. The result shows that HMM algorithms presented in previous literature, falls short to capture the accuracy for road segment map matching. The results further shows that by using less noisy sensors, as odometer and gyroscope, the accuracy for road segment map matching can be increased.

Dans cette thèse, nous nous intéressons à l'estimation de paramètres dans les chaînes de Markov cachées. Nous considérons tout d'abord le problème de l'estimation en ligne (sans sauvegarde des observations) au sens du maximum de vraisemblance. Nous proposons une nouvelle méthode basée sur l'algorithme Expectation Maximization appelée Block Online Expectation Maximization (BOEM). Cet algorithme est défini pour des chaînes de Markov cachées à espace d'état et espace d'observations généraux. Dans le cas d'espaces d'états généraux, l'algorithme BOEM requiert l'introduction de méthodes de Monte Carlo séquentielles pour approcher des espérances sous des lois de lissage. La convergence de l'algorithme nécessite alors un contrôle de la norme Lp de l'erreur d'approximation Monte Carlo explicite en le nombre d'observations et de particules. Une seconde partie de cette thèse se consacre à l'obtention de tels contrôles pour plusieurs méthodes de Monte Carlo séquentielles. Nous étudions enfin des applications de l'algorithme BOEM à des problèmes de cartographie et de localisation simultanées. La dernière partie de cette thèse est relative à l'estimation non paramétrique dans les chaînes de Markov cachées. Le problème considéré est abordé dans un cadre précis. Nous supposons que (Xk) est une marche aléatoire dont la loi des incréments est connue à un facteur d'échelle a près. Nous supposons que, pour tout k, Yk est une observation de f(Xk) dans un bruit additif gaussien, où f est une fonction que nous cherchons à estimer. Nous établissons l'identifiabilité du modèle statistique et nous proposons une estimation de f et de a à partir de la vraisemblance par paires des observations. / This document is dedicated to inference problems in hidden Markov models. The first part is devoted to an online maximum likelihood estimation procedure which does not store the observations. We propose a new Expectation Maximization based method called the Block Online Expectation Maximization (BOEM) algorithm. This algorithm solves the online estimation problem for general hidden Markov models. In complex situations, it requires the introduction of Sequential Monte Carlo methods to approximate several expectations under the fixed interval smoothing distributions. The convergence of the algorithm is shown under the assumption that the Lp mean error due to the Monte Carlo approximation can be controlled explicitly in the number of observations and in the number of particles. Therefore, a second part of the document establishes such controls for several Sequential Monte Carlo algorithms. This BOEM algorithm is then used to solve the simultaneous localization and mapping problem in different frameworks. Finally, the last part of this thesis is dedicated to nonparametric estimation in hidden Markov models. It is assumed that the Markov chain (Xk) is a random walk lying in a compact set with increment distribution known up to a scaling factor a. At each time step k, Yk is a noisy observations of f(Xk) where f is an unknown function. We establish the identifiability of the statistical model and we propose estimators of f and a based on the pairwise likelihood of the observations.