In this thesis the charge dynamics of correlated electron systems, in which a metallic phase lies in close proximity to an ordered phase, are investigated by means of angle resolved photoemission spectroscopy (ARPES). The analysis of the experimental data is complemented by electronic structure calculations within the framework of density functional theory (DFT). First the charge dynamics of the colossal magnetoresistant bilayer manganites are studied. The analysis of the ARPES spectra based on DFT calculations and a Peierls type charge density wave model, suggests that charge, orbital, spin and lattice degrees of freedom conspire to form a fluctuating two dimensional local order that produces a large pseudo gap of about 450 meV in the ferromagnetic metallic phase and that reduces the expected bilayer splitting. Next, the interplay of Kondo physics and (magnetic) order in the heavy fermion superconductor URu2Si2 is investigated. The low energy electronic structure undergoes strong changes at 17.5 K, where a second order phase transition occurs whose phenomenology is well characterized, but whose order parameter could not yet be unambigeously identified. Below THO, non-dispersive quasi particles with a large scattering rate suddenly acquire dispersion and start to hybridize with the conduction band electrons. Simultaniously the scattering rate drops sinificantly and a large portion of the Fermi surface vanishes due to the opening of a gap within the band of heavy quasi particles. The observed behaviour is in stark contrast to conventional heavy fermion systems where the onset of hybridization between localized and itinerant carriers happens in a crossover type transition rather than abruptly. These experimental results suggest that Kondo screening and the hidden order parameter work together to produce the unusual thermodynamic signatures observed in this compound. Finally, the influence of charge doping and impurity scattering on the superconducting porperties of the transition metal substituted iron pnictide superconductor Ba(Fe1-xTMx)2As2 (TM = Co, Ni) is studied. Here, resonant soft X-ray ARPES is applied to see element selective the contribution of the 3d states of the TM substitute to the Fe 3d host bands. The spectroscopic signatures of the substitution are found to be well reproduced by DFT supercell and model impurity calculations. Namely, the hybridization of the dopant with the host decreases with increasing impurity potential and the electronic states of the impurtiy become increasingly localized. Simultaniously, in all simulated cases a shift of the Fermi level due to electron doping is observed. The magnitude of the shift in the chemical potential that accurs in BaFe2As2, however, is in stark contrast to the marginal doping values obtained for the impurity model, where the shift of the chemical potential is largely compensated by the influence of the increasing impurity potential. This suggests that the rigid band behaviour of TM substituded BaFe2As2 is a peculiarity of the compound, which has strong implications for the developement of superconductivity. / In dieser Arbeit wird die Ladungstraegerdynamik in korrelierten Elektronensystemen, in denen eine metallische Phase in direkter Nachbarschaft zu einer geordneten Phase liegt, mit Hilfe von winkelaufgeloester Photoelektronenspektroskopie (ARPES) untersucht. Die Analyse der experimentellen Daten wird ergaenzt durch lektronenstrukturrechnungen im Rahmen der Dichtefunktionaltheorie (DFT). Zuerst wird die Ladungstraegerdynamik in gemischtvalenten zweischichtmanganaten mit kolossalem Magnetiwiderstand studiert. Die Analyse der Photoemissionsspektren basierend auf DFT Rechnungen und einem Peierls artigem Ladungsdichtewellenmodell, legt nahe, dass die Freiheitsgrade von Ladung, Orbitalen, Spin und des Ionengitters konspirieren, um eine fluktuierende zweidimensionale lokale Ordnung zu bilden, die verantwortlich ist fuer die beobachtete Pseudobandluecke von 450 meV, und die zur Reduktion der erwarteten Zweischichtaufspaltung beitraegt. Als naechstes wird das Zusammenspiel von Kondo Physik und (magnetischer) Ordung im Schwerfermionensupraleiter URu2Si2 untersucht. Die iedrigenergetische elektronische Struktur zeigt starke Veraenderungen bei 17.5 K, wo ein Phasenuebergang zweiter Ordnungstattfindet, der phenomenologisch gut charakterisiert ist, aber dessen Ordungsparameter nocht nicht eindeutig identifiziert werden konnte. Unterhalb von THOerlangen nicht dispergierende Quasiteilchen mit gro en Streuraten abrupt Dispersion und hybridisieren mit den Leitungselektronen. Gleichzeitig sinkt die Streurate und ein gro er Teil der Fermiflaeche verschwindet durch das Oeffnen einer Bandluecke innehalb des Bandes schwerer Quasiteilchen. Das beobachtete Verhalten steht in starkem Kontrast zu dem von konventionellen Schwerfermionensystemen, in denen die Hybridisierung zwischen lokalisierten und itineranten Ladungstraegern in einem kontinuierlichen Uebergang ablaeuft, anstatt abrubt. Diese experimentellen Befunde lassen den Schluss zu, dass das zusammenspiel zwischen Kondo Abschirmung und dem unbekannten Ordnungsparameter die ungewoehnlichen thermodynamischen Signaturen in dieser Verbindung hervorruft. Abschliessend wird das Zusammenwirken von Ladungstraegerdotierung und Streuung an Stoeratomen auf die Supraleitung uebergangsmetalldotierter Eisenpniktid Supraleiter Ba(Fe1-xTMx)2As2 (TM = Co, Ni) untersucht. Mit Hilfe von resonantem Weichenroentgen ARPES gelingt es, elementselektiv den Beitrag der 3d Zustaende des TM Substituenten zu den Eisen 3d Wirtsbaendern zu beobachten. Die spektroskopischen Signaturen der Substitution sind mit Hilfe von DFT Rechnungen und Modelrechnungen mit zufaellig verteilten Stoeratomen gut zu reproduzieren. Insbesondere nimmt die Hybridisierung des dotierten Uebergangsmetalls und der Eisenbaender mit zunehmender Kernladungszahl ab und die elektronischen Zustaende der Stoeratome werden zunehmen lokalisiert. Gleichzeitig wird in allen gerechneten Faellen eine Verschiebung des Fermi Niveaus durch Elektronendotierung beobachtet. Der Betrag der Verschiebung des chemischen Potentials in BaFe2As2 steht allerdings in starkem Kontrast zu den Werten, die man im Falle der Modellrechnungen erhaelt, wo die Verschiebung des Fermi Niveaus durch den Einfluss des Potentials der Stoeratome groesstenteils kompensiert wird. Dies legt nahe, dass das beobachtete "rigid band" Verhalten von TM substituiertem BaFe2As2 eine Besonderheit dieser Verbindung ist, welches starke Auswirkungen auf die Ausbildung von Supraleitung hat.

Both human perceptual studies and automaticmachine-based experiments have shown that visual information from a speaker's mouth region can improve the robustness of automatic speech processing tasks, especially in the presence of acoustic noise. By taking advantage of the complementary nature of the acoustic and visual speech information, audio-visual speech processing (AVSP) applications can work reliably in more real-world situations than would be possible with traditional acoustic speech processing applications. The two most prominent applications of AVSP for viable human-computer-interfaces involve the recognition of the speech events themselves, and the recognition of speaker's identities based upon their speech. However, while these two fields of speech and speaker recognition are closely related, there has been little systematic comparison of the two tasks under similar conditions in the existing literature. Accordingly, the primary focus of this thesis is to compare the suitability of general AVSP techniques for speech or speaker recognition, with a particular focus on synchronous hidden Markov models (SHMMs). The cascading appearance-based approach to visual speech feature extraction has been shown to work well in removing irrelevant static information from the lip region to greatly improve visual speech recognition performance. This thesis demonstrates that these dynamic visual speech features also provide for an improvement in speaker recognition, showing that speakers can be visually recognised by how they speak, in addition to their appearance alone. This thesis investigates a number of novel techniques for training and decoding of SHMMs that improve the audio-visual speech modelling ability of the SHMM approach over the existing state-of-the-art joint-training technique. Novel experiments are conducted within to demonstrate that the reliability of the two streams during training is of little importance to the final performance of the SHMM. Additionally, two novel techniques of normalising the acoustic and visual state classifiers within the SHMM structure are demonstrated for AVSP. Fused hidden Markov model (FHMM) adaptation is introduced as a novel method of adapting SHMMs from existing wellperforming acoustic hidden Markovmodels (HMMs). This technique is demonstrated to provide improved audio-visualmodelling over the jointly-trained SHMMapproach at all levels of acoustic noise for the recognition of audio-visual speech events. However, the close coupling of the SHMM approach will be shown to be less useful for speaker recognition, where a late integration approach is demonstrated to be superior.

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

Magister Scientiae - MSc / Research has shown that five fundamental parameters are required to recognize any sign language gesture: hand shape, hand motion, hand location, hand orientation and facial expressions. The South African Sign Language (SASL) research group at the University of the Western Cape (UWC) has created several systems to recognize sign language gestures using single parameters. These systems are, however, limited to a vocabulary size of 20 – 23 signs, beyond which the recognition accuracy is expected to decrease. The first aim of this research is to investigate the use of two parameters – hand motion and hand shape – to recognise a larger vocabulary of SASL gestures at a high accuracy. Also, the majority of related work in the field of sign language gesture recognition using these two parameters makes use of Hidden Markov Models (HMMs) to classify gestures. Hidden Markov Support Vector Machines (HM-SVMs) are a relatively new technique that make use of Support Vector Machines (SVMs) to simulate the functions of HMMs. Research indicates that HM-SVMs may perform better than HMMs in some applications. To our knowledge, they have not been applied to the field of sign language gesture recognition. This research compares the use of these two techniques in the context of SASL gesture recognition. The results indicate that, using two parameters results in a 15% increase in accuracy over the use of a single parameter. Also, it is shown that HM-SVMs are a more accurate technique than HMMs, generally performing better or at least as good as HMMs.

In Natural Language Processing (NLP), speech and text are parsed and generated with language models and parser models, and translated with translation models. Each model contains a set of numerical parameters which are found by applying a suitable training algorithm to a set of training data. Many such training algorithms are instances of the Expectation-Maximization (EM) algorithm. In [BSV15], a generic EM algorithm for NLP is described. This work presents a particular speech model, the Hidden Markov model, and its standard training algorithm, the Baum-Welch algorithm. It is then shown that the Baum-Welch algorithm is an instance of the generic EM algorithm introduced by [BSV15], from which follows that all statements about the generic EM algorithm also apply to the Baum-Welch algorithm, especially its correctness and convergence properties.

Most quantum algorithms that are efficient as opposed to their equivalent classical algorithms are solving variants of the Hidden Subgroup Problem (HSP), therefore HSP is a central problem in the field of quantum computing. In this thesis, we offer some interesting results about the subgroup and coset structure of certain groups, including the dihedral group. We describe classical algorithms to solve the HSP over various abelian groups and the dihedral group. We also discuss some existing quantum algorithms to solve the HSP and give our own novel algorithms and ideas to approach the HSP for the dihedral groups.

The field of speech recognition has during the last decade left the re- search stage and found its way in to the public market. Most computers and mobile phones sold today support dictation and transcription in a number of chosen languages.  Swedish is often not one of them. In this thesis, which is executed on behalf of the Swedish Radio, an Automatic Speech Recognition model for Swedish is trained and the performance evaluated. The model is built using the open source toolkit Kaldi.  Two approaches of training the acoustic part of the model is investigated. Firstly, using Hidden Markov Model and Gaussian Mixture Models and secondly, using Hidden Markov Models and Deep Neural Networks. The later approach using deep neural networks is found to achieve a better performance in terms of Word Error Rate. / De senaste åren har olika tillämpningar inom människa-dator interaktion och främst taligenkänning hittat sig ut på den allmänna marknaden. Många system och tekniska produkter stöder idag tjänsterna att transkribera tal och diktera text. Detta gäller dock främst de större språken och sällan finns samma stöd för mindre språk som exempelvis svenskan. I detta examensprojekt har en modell för taligenkänning på svenska ut- vecklas. Det är genomfört på uppdrag av Sveriges Radio som skulle ha stor nytta av en fungerande taligenkänningsmodell på svenska. Modellen är utvecklad i ramverket Kaldi. Två tillvägagångssätt för den akustiska träningen av modellen är implementerade och prestandan för dessa två är evaluerade och jämförda. Först tränas en modell med användningen av Hidden Markov Models och Gaussian Mixture Models och slutligen en modell där Hidden Markov Models och Deep Neural Networks an- vänds, det visar sig att den senare uppnår ett bättre resultat i form av måttet Word Error Rate.

Dieser Artikel enthält vier Kapitel. Das erste Kapitel ist berechtigt, '''' lokalen Quantil Regression"und seine Zusammenfassung: Quantil Regression ist eine Technik, bedingte Quantil Kurven zu schätzen. Es bietet ein umfassendes Bild über ein Antwort-Kontingent auf erklärenden Variablen. In einem Rahmen flexible Modellierung ist eine besondere Form der bedingten Quantil-Kurve nicht von vornherein festgelegt. Dies motiviert eine lokale parametrische anstatt einer globalen feste Modell passend Ansatz. Eine nichtparametrische Glättung Schätzung der bedingte Quantil Kurve erfordert, zwischen lokalen Krümmung und stochastische auszugleichen Variabilität. In den ersten Essay empfehlen wir eine lokale Modellauswahl Technik, die eine adaptive Schätzung der bedingte bietet Quantil-Regression-Kurve bei jedem Entwurf-Punkt. Theoretische Ergebnisse behaupten, dass das vorgeschlagene adaptive Verfahren als führt gut als Orakel die würde das Risiko der lokalen Abschätzung für die Aufgabenstellung minimieren. Wir veranschaulichen die Leistung der Trolle. / This article includes four chapters. The first chapter is entitled ``Local Quantile Regression", and its summary: Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables. In a flexible modeling framework, a specific form of the conditional quantile curve is not a priori fixed. This motivates a local parametric rather than a global fixed model fitting approach. A nonparametric smoothing estimate of the conditional quantile curve requires to balance between local curvature and stochastic variability. In the first essay, we suggest a local model selection technique that provides an adaptive estimate of the conditional quantile regression curve at each design point. Theoretical results claim that the proposed adaptive procedure performs as good as an oracle which would minimize the local estimation risk for the problem at hand. We illustrate the performance of the procedure by an extensive simulation study and consider a couple of applications: to tail dependence analysis for the Hong Kong stock market and to analysis of the distributions of the risk factors of temperature dynamics.

Being one of the fastest growing fields of engineering, wireless has gained the attention of researchers and commercial businesses all over the world. Extensive research is underway to improve the performance of existing systems and to introduce cutting edge wireless technologies that can make high speed wireless communications possible. The first part of this dissertation deals with discrete channel models that are used for simulating error traces produced by wireless channels. Most of the time, wireless channels have memory and we rely on discrete time Markov models to simulate them. The primary advantage of using these models is rapid experimentation and prototyping. Efficient estimation of the parameters of a Markov model (including its number of states) is important to reproducing and/or forecasting channel statistics accurately. Although the parameter estimation of Markov processes has been studied extensively, its order estimation problem has been addressed only recently. In this report, we investigate the existing order estimation techniques for Markov chains and hidden Markov models. Performance comparison with semi-hidden Markov models is also discussed. Error source modeling in slow and fast fading conditions is also considered in great detail. Cognitive Radio is an emerging technology in wireless communications that can improve the utilization of radio spectrum by incorporating some intelligence in its design. It can adapt with the environment and can change its particular transmission or reception parameters to execute its tasks without interfering with the licensed users. One problem that CR network usually faces is the difficulty in detecting and classifying its low power signal that is present in the environment. Most of the time traditional energy detection techniques fail to detect these signals because of their low SNRs. In the second part of this thesis, we address this problem by using higher order statistics of incoming signals and classifying them by using the pattern recognition capabilities of HMMs combined with cased-based learning approach. This dissertation also deals with dynamic spectrum allocation in cognitive radio using HMMs. CR networks that are capable of using frequency bands assigned to licensed users, apart from utilizing unlicensed bands such as UNII radio band or ISM band, are also called Licensed Band Cognitive Radios. In our novel work, the dynamic spectrum management or dynamic frequency allocation is performed by the help of HMM predictions. This work is based on the idea that if Markov models can accurately model spectrum usage patterns of different licensed users, then it should also correctly predict the spectrum holes and use these frequencies for its data transmission. Simulations have shown that HMMs prediction results are quite accurate and can help in avoiding CR interference with the primary licensed users and vice versa. At the same time, this helps in sending its data over these channels more reliably. / Ph. D.

Thesis (PhD (Electrical and Electronic Engineering))--University of Stellenbosch, 2007. / Higher-order Markov models are more powerful than first-order models, but suffer from an exponential increase in model parameters with order, which leads to data scarcity problems during training. A more efficient approach is to use mixed-order Markov models, which model data sequences with contexts of different lengths. This study proposes two algorithms for inferring mixed-order Markov chains and hidden Markov models (HMMs), respectively. The basis of these algorithms is the prediction suffix tree (PST), an efficient representation of a mixed-order Markov chain. The smallest encoded context tree (SECT) algorithm constructs PSTs from data, based on the minimum description length principle. It has no user-specifiable parameters to tune, and will expand the depth of the resulting PST as far as the data set allows it, making it a self-bounded algorithm. It is also faster than the original PST inference algorithm. The hidden SECT algorithm replaces the underlying Markov chain of an HMM with a prediction suffix tree, which is inferred using SECT. The algorithm is efficient and integrates well with standard techniques. The properties of the SECT and hidden SECT algorithms are verified on synthetic data. The hidden SECT algorithm is also compared with a fixed-order HMM training algorithm on an automatic language recognition task, where the resulting mixed-order HMMs are shown to be smaller and train faster than the fixed-order models, for similar classification accuracies.