Recent advances in biotechnologies have enabled multiplatform and large-scale quantitative measurements of biomedical events. The need to analyze the produced vast amount of imaging and genomic data stimulates various novel applications of statistical machine learning methods in many areas of biomedical research. The main objective is to assist biomedical investigators to better interpret, analyze, and understand the biomedical questions based on the acquired data. Given the computational challenges imposed by these high-dimensional and complex data, machine learning research finds its new opportunities and roles. In this dissertation thesis, we propose to develop, test and apply novel statistical machine learning methods to analyze the data mainly acquired by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and single nucleotide polymorphism (SNP) microarrays. The research work focuses on: (1) tissue-specific compartmental analysis for dynamic contrast-enhanced MR imaging of complex tumors; (2) computational Analysis for detecting DNA SNP interactions in genome-wide association studies. DCE-MRI provides a noninvasive method for evaluating tumor vasculature patterns based on contrast accumulation and washout. Compartmental analysis is a widely used mathematical tool to model dynamic imaging data and can provide accurate pharmacokinetics parameter estimates. However partial volume effect (PVE) existing in imaging data would have profound effect on the accuracy of pharmacokinetics studies. We therefore propose a convex analysis of mixtures (CAM) algorithm to explicitly eliminate PVE by expressing the kinetics in each pixel as a nonnegative combination of underlying compartments and subsequently identifying pure volume pixels at the corners of the clustered pixel time series scatter plot. The algorithm is supported by a series of newly proved theorems and additional noise filtering and normalization preprocessing. We demonstrate the principle and feasibility of the CAM approach together with compartmental modeling on realistic synthetic data, and compare the accuracy of parameter estimates obtained using CAM or other relevant techniques. Experimental results show a significant improvement in the accuracy of kinetic parameter estimation. We then apply the algorithm to real DCE-MRI data of breast cancer and observe improved pharmacokinetics parameter estimation that separates tumor tissue into sub-regions with differential tracer kinetics on a pixel-by-pixel basis and reveals biologically plausible tumor tissue heterogeneity patterns. This method has combined the advantages of multivariate clustering, convex optimization and compartmental modeling approaches. Interactions among genetic loci are believed to play an important role in disease risk. Due to the huge dimension of SNP data (normally several millions in genome-wide association studies), the combinatorial search and statistical evaluation required to detect multi-locus interactions constitute a significantly challenging computational task. While many approaches have been proposed for detecting such interactions, their relative performance remains largely unclear, due to the fact that performance was evaluated on different data sources, using different performance measures, and under different experimental protocols. Given the importance of detecting gene-gene interactions, a thorough evaluation of the performance and limitations of available methods, a theoretical analysis of the interaction effect and the genetic factors it depends on, and the development of more efficient methods are warranted. Therefore, we perform a computational analysis for detect interactions among SNPs. The contributions are four-fold: (1) developed simulation tools for evaluating performance of any technique designed to detect interactions among genetic variants in case-control studies; (2) used these tools to compare performance of five popular SNP detection methods; and (3) derived analytic relationships between power and the genetic factors, which not only support the experimental results but also gives a quantitative linkage between interaction effect and these factors; (4) based on the novel insights gained by comparative and theoretical analysis, developed an efficient statistically-principled method, namely the hybrid correlation-based association (HCA) to detect interacting SNPs. The HCA algorithm is based on three correlation-based statistics, which are designed to measure the strength of multi-locus interaction with three different interaction types, covering a large portion of possible interactions. Moreover, to maximize the detection power (sensitivity) while suppressing false positive rate (or retaining moderate specificity), we also devised a strategy to hybridize these three statistics in a case-by-case way. A heuristic search strategy is also proposed to largely decrease the computational complexity, especially for high-order interaction detection. We have tested HCA in both simulation study and real disease study. HCA and the selected peer methods were compared on a large number of simulated datasets, each including multiple sets of interaction models. The assessment criteria included several power measures, family-wise type I error rate, and computational complexity. The experimental results of HCA on the simulation data indicate its promising performance in terms of a good balance between detection accuracy and computational complexity. By running on multiple real datasets, HCA also replicates plausible biomarkers reported in previous literatures. / Ph. D.

This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.

In the present thesis, we try to compare the classical boolean complexity with the algebraic complexity, by studying problems related to polynomials. We consider the algebraic models from Valiant and from Blum, Shub and Smale (BSS). To study the algebraic complexity classes, one can start from results and open questions from the boolean case, and look at their translation in the algebraic context. The comparison of the results obtained in the two settings will then boost our understanding of both complexity theories. The first part follows this framework. By considering a polynomial canonically associated to a boolean formula, we get a link between boolean complexity issues on the formula and algebraic complexity problems on the polynomial. We studied the complexity of computing the polynomial in Valiant's model, as a function of the complexity of the boolean formula. We found algebraic counterparts to some boolean results. Along the way, we could also use some algebraic methods to improve boolean results, in particular by getting better counting reductions. Another motivation for algebraic models of computation is to offer an elegant framework to the study of numerical algorithms. The second part of this thesis follows this approach. We started from new algorithms for the search of approximate zeros of complex systems of n polynomials in n variables. Up to now, those were BSS machine algorithms. We studied the implementation of these algorithms on digital computers, and propose an algorithm using floating arithmetic for this problem.

In recent years the industry has been exhorted to increase its use of offsite technologies, or 'Modem Methods of Construction', in order to address the under-supply and poor build quality of housing. Despite the well-rehearsed benefits of such technologies, the take-up within the industry has been slow. Many studies have attempted to scrutinise the barriers and seek solutions, but the perspectives and practices of housebuilders of using offsite remain unclear. This is significant given that housebuilders contribute nearly 90% to the housing unit completions by the industry overall. This thesis aims to provide a strategy for optimising the use of offsite in housebuilding by investigating UK housebuilders' views on, and utilisations of, such technologies. This has been achieved through a combination of a survey of the top 100 firms, a one and-half-year case study of a large housebuilding organisation, and a series of validation interviews and group exercises, within the existing theoretical framework. The thesis reveals that the current low level of offsite usage in large housebuilders was likely to increase given the pressures to improve quality, time, cost, productivity and health & safety. However, a wider take-up was inhibited by perceived higher capital costs, interfacing problems, long lead-in time, delayed planning process and current manufacturing capacity. For addressing this a framework of strategies is presented, surrounding changing the industry's perceptions, improving procurement, providing better cost data, tackling planning and regulations, encouraging political levers, and providing guidance on decision-making and offsite integration. The thesis then contextualises these findings into the organisational and project contexts, examining the historical and current offsite practices within the general housebuilding business. It reveals that housebuilders primarily aspired to improve business efficiency and mitigate financial risks through the use of offsite despite a complicated agenda for such usage. For using offsite housebuilders developed strategies centred on process, procurement, learning & benchmarking, and training. However, the current heuristic approach to decision-making, coupled with the lack of knowledge of build system selection (BSS) for housebuilding, prevents the benefits from offsite being fully realised. A robust, structured and transparent decision support tool for BSS is developed, transferring knowledge in operational and construction management research to the housebuilding context. It provides an improved decision-making process and databases for system selection. Both the process and data were validated within the wider industry and academic domain, by which the tool was claimed as an effective mechanism for optimising the use of offsite and enhancing organisational learning. The thesis contributes to a better theoretical understanding of offsite and provides strategies for its increased take-up in housebuilding. This helps housebuilding organisations to achieve long-term profitability, but also contributes to addressing the current under-supply of housing. The thesis also contributes to knowledge of decisionmaking and construction management research. This has an implication for wider organisational and decision theory.

Made available in DSpace on 2014-07-29T15:21:58Z (GMT). No. of bitstreams: 1 dissertacaonadia.pdf: 556762 bytes, checksum: 6b925b77e88d5393ef916f42ecfc9079 (MD5) Previous issue date: 2007-04-30 / As patient-centered approaches become more popular, increasing attention is given to the effects of various health situations in oral health-related quality of life (OHRQL) and body satisfaction. The aim of this study was to evaluate the association between several aspects related to dental esthetics and body satisfaction, self-evaluation concerning to esthetics, and oral-health related quality of life (OHRQL). This work had a cross-sectional design and its sample comprised subjects aged 13-20 years-old (n = 301; mean age = 16.12; SD = 1.75). The sample was of convenience type, comprising high-school students of a public school from Goiânia. Conditions related to dental esthetics were assessed through a clinical exam following the criteria established by World Health Organization. Self-perception variables were evaluated through the instruments Body Satisfaction Scale (BSS), short-form of Oral Health Impact Profile (OHIP-14) and Psychosocial Impact of Dental Aesthetics Questionnaire (PIDAQ), as well as esthetic self-evaluation and will of change. Chi-square test and multiple logistic regression were used to evaluate the association of variables. There was a low prevalence of dental alterations in the sample assessed. In general, there was a low impact of dental characteristics on OHRQL measures, what may influence on the decision-making process regarding to dental treatment needs. / À medida que as abordagens centradas no paciente se tornam mais difundidas, mais atenção tem sido dada aos efeitos de varias situações de saúde sobre a qualidade de vida relacionada à saúde bucal (QVRSB) e outros aspectos relacionados à auto-percepção dos indivíduos. O objetivo deste estudo foi avaliar a associação entre características relacionadas à estética dentária e a satisfação com o corpo, a auto-avaliação estética e a QVRSB. O estudo teve desenho transversal, incluindo 301 adolescentes de 13 a 20 anos de idade (média= 16,12; DP = 1,75). A amostra do estudo foi de conveniência, compreendendo alunos do ensino médio de uma escola pública de Goiânia. Foram verificadas as condições relacionadas à estética dentária através de exame clínico segundo critérios da Organização Mundial de Saúde. As variáveis relacionadas à auto-percepção foram avaliadas através dos instrumentos Escala de Satisfação Corporal (ESC), versão reduzida do Oral Health Impact Profile (OHIP-14) e o Questionário de Impacto Psicossocial da Estética Dentária (QIPED), além da auto-avaliação estética e desejo de mudança. Para a análise da associação entre as variáveis foi utilizado o teste de Qui-quadrado e regressão logística múltipla. Houve baixa prevalência de alterações dentárias na amostra pesquisada. De modo geral, houve baixa influência das características dentárias sobre medidas para a avaliação da QVRSB, o que pode influenciar na tomada de decisões clínicas relacionadas ao tratamento odontológico.

Η διπλωματική εργασία αυτή αφορά την κατασκευή ενός μικροϋπολογιστικού συστήματος διαχωρισμού σημάτων. Ο διαχωρισμός των σημάτων γίνεται με βάση τη θεωρία της τεχνικής της Ανάλυσης Ανεξάρτητων Συνιστωσών. Αφού παρουσιαστεί η θεωρία της τεχνικής, παρουσιάζεται ο μικροελεγκτής ADuC 7026 που επελέγη για την υλοποίηση. Στη συνέχεια γίνεται η παρουσίαση του λογισμικού προσομοίωσης του μικροελεγκτή και παρατίθενται βασικά παραδείγματα για τον προγραμματισμό του. Τέλος, αναπτύσσονται, χωρίς τη χρήση περιφερειακών, και προσομοιώνονται, με τη χρήση περιφερειακών τρεις αλγόριθμοι, δυο εκδόσεις του FastICA και μια έκδοση του InfoMax. Οι αλγόριθμοι αυτοί αξιολογούνται ως προς τις επιδόσεις τους και εξάγονται τα συμπεράσματα. / This thesis deals with the construction of a microcomputer system to separate signals. The separation of the signals is based on the theory of the technique of Independent Component Analysis. The theory of the technique and the microcontroller ADuC 7026 chosen for implementation are presented. Then, follows the presentation of the software on which the microcontroller is simulated and basic examples of its programming are mentioned. Finally, three algorithms, two versions of FastICA and a version of InfoMax, are developed without the use of peripheral systems and simulated using peripheral systems. These algorithms are evaluated for their performance and conclusions are drawn.

Cette thèse s'inscrit dans le cadre du prétraitement des signaux EEG et s'intéresse plus particulièrement aux perturbations dues à la référence de mesure non nulle. Les perturbations induites par une fluctuation électrique de la référence peuvent engendrer des erreurs d'analyse comme on peut aisément le constater sur les mesures de synchronisation inter-signaux (par exemple la cohérence). Donc, la référence idéale serait une référence nulle. Au cours des travaux développés, nous nous sommes concentrés sur l'estimation des potentiels électriques dit absolus (référence nulle) à partir d'une reformulation du problème inverse. Deux cas sont traités, soit la référence est suffisamment éloignée des sources électrophysiologiques cérébrales et de fait elle peut être considérée comme indépendante, sinon, elle est modélisée comme une combinaison linéaire des autres sources. Grâce à cette modélisation, il a été montré explicitement que les meilleures estimations des potentiels absolus sans aucune information a priori sont les potentiels calculés par rapport à une référencemoyenne. D'autre part, le problème inverse de la référence source-indépendante est résolu dans un contexte de type séparation de sources. Il a été démontré que la meilleure estimation des potentiels absolus sans aucune information a priori est équivalente à l'estimateur MPDR/MVDR (Minimum Power Distortionless Response/Minimum Variance Distortionless Response). Concernant le prétraitement de données EEG, on montre sur signaux simulés et réels que les potentiels mesurés transformés en référence moyenne améliorent certaines méthodes d'analyse utilisées en EEG telles que la séparation aveugle des sources (BSS) et la localisation de sources cérébrales. Au delà des problèmes de référence, la méthode peut être appliquée sous contraintes pour estimer de façon plus robuste des sources singulières telles que les artefacts ou une stimulation électrique exogène déterministe.

L'objectif de ce travail était de tester des méthodes de SAS pour la séparation des spectres complexes RMN de mélanges dans les plus simples des composés purs. Dans une première partie, les méthodes à savoir JADE et NNSC ont été appliqué es dans le cadre de la DOSY , une application aux données CPMG était démontrée. Dans une deuxième partie, on s'est concentré sur le développement d'un algorithme efficace "beta-SNMF" . Ceci s'est montré plus performant que NNSC pour beta inférieure ou égale à 2. Etant donné que dans la littérature, le choix de beta a été adapté aux hypothèses statistiques sur le bruit additif, une étude statistique du bruit RMN de la DOSY a été faite pour obtenir une image plus complète de nos données RMN étudiées. / The objective of the work was to test BSS methods for the separation of the complex NMR spectra of mixtures into the simpler ones of the pure compounds. In a first part, known methods namely JADE and NNSC were applied in conjunction for DOSY , performing applications for CPMG were demonstrated. In a second part, we focused on developing an effective algorithm "beta- SNMF ". This was demonstrated to outperform NNSC for beta less or equal to 2. Since in the literature, the choice of beta has been adapted to the statistical assumptions on the additive noise, a statistical study of NMR DOSY noise was done to get a more complete picture about our studied NMR data.

The objective of the dissertation research is to investigate noise reduction methods for binaural hearing aids based on array and statistical signal processing and inspired by a human auditory model. In digital hearing aids, wide dynamic range compression (WDRC) is the most successful technique to deal with monaural hearing losses. This WDRC processing is usually performed after a monaural noise reduction algorithm. When hearing losses are present in both ears, i.e., a binaural hearing loss, independent monaural hearing aids have been shown not to be comfortable for most users, preferring a processing that involves synchronization between both hearing devices. In addition, psycho-acoustical studies have identified that under hostile environments, e.g., babble noise at very low SNR conditions, users prefer to use linear amplification rather than WDRC. In this sense, the noise reduction algorithm becomes an important component of a digital hearing aid to provide improvement in speech intelligibility and user comfort. Including a wireless link between both hearing aids offers new ways to implement more efficient methods to reduce the background noise and coordinate processing for the two ears. This approach, called binaural hearing aid, has been recently introduced in some commercial products but using very simple processing strategies. This research analyzes the existing binaural noise-reduction techniques, proposes novel perceptually-inspired methods based on blind source separation (BSS) and multichannel Wiener filter (MWF), and identifies different strategies for the real-time implementation of these methods. The proposed methods perform efficient spatial filtering, improve SNR and speech intelligibility, minimize block processing artifacts, and can be implemented in low-power architectures.

Το θέμα της κατασκευής ενός συστήματος αναγνώρισης προτύπων για τα ηχητικά σήματα ενός ανθρώπου που κοιμάται είναι ένα από τα ανοιχτά ζητήματα της Βιοιατρικής. Στην παρούσα διπλωματική εξετάζουμε την εξαγωγή ερμηνεύσιμων σημάτων που αντιστοιχούν στον καρδιακό ρυθμό, την αναπνοή και το ροχαλητό. Χρησιμοποιούμε μεθόδους Ανάλυσης σε Ανεξάρτητες Συνιστώσες και μεθόδους Τυφλού Διαχωρισμού που εκμεταλεύονται Στατιστικές Δεύτερης Τάξης. Συμπεραίνουμε ότι οι δεύτερες είναι οι πλέον κατάλληλες όταν συνοδεύονται από ένα στάδιο προεπεξεργασίας που αφορά ανάλυση σε ζώνες συχνοτήτων. / The design of a non-intrusive Pattern Recognition System to estimate the sleep sounds is an open problem of Bioengineering. We use recordings from body-sensors to estimate the heart beat, the breathing and the snoring. In this thesis we examine the effectiveness of Independent Component Analysis for this Blind Source Separation Problem and we compare it with methods that perform Source Separation using Second Order Statistics. We take into account the temporal structure of the sources as well as the presence of noise. Our system is greatly improved by a preprocessing stage of targeted subband decomposition which uses a priori knowledge about the sources. We propose an efficient solution to this problem which is confirmed by medical data.