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In recent years, researchers have increasingly used magnetic resonance imaging (MRI) to study temporal skeletal muscle changes using gradient echo (GRE) echo planar imaging (EPI). These studies, typically involving exercise or ischemic challenges, have differentiated healthy subjects from athletic or unhealthy populations, such as those with peripheral vascular disease. However, the analysis methodologies have been lacking. In this thesis, two sessions of post-exercise GRE EPI data were collected from six subjects' lower legs using a 3 Tesla MRI scanner and a custom built ergometer. Past studies used common medical imaging software for motion correction. This work shows that such tools degrade leg image data by introducing motion, increasing root mean squared error in rest data by 22%. A new approach decreased it by 12%. EPI distortion correction in muscle images was also achieved, with the correlation ratio of functional and structural images increasing by up to 8%. In addition, a brief but intense artifact in GRE EPI muscle images results from muscle tissue moving in and out of the imaged volume. This through-plane artifact was successfully modelled as a mono-exponential decay for regression analysis, increasing the utility of the residual signal. The regression parameters were also leveraged to produce muscle displacement maps, identifying 44% of voxels as displaced. The maps were validated in a motion phantom and in-vivo using ultrasound. Finally, independent component analysis (ICA) was applied to post-exercise GRE EPI images to detect features in a data-driven, multivariate way and improve on conventional ROI selection methods. ICA produced parametric maps that were spatially correlated to working muscles from every trial (most with |R| > 0.4). The components were also separated from the susceptibility, motion, and blood vessel signals, and temporally reliable within individuals. These methodological advances represent increased rigour in the analysis of muscle GRE EPI images. / Thesis / Doctor of Philosophy (PhD) / Adequate blood circulation to muscles is important for good health. Researchers have used magnetic resonance imaging (MRI) techniques to assess blood and oxygen supply to muscles. The work in this thesis improves upon the analysis methods in prior work, especially in the areas of motion correction of the images and selection of individual muscle regions for analysis. Previous techniques could sometimes make motion in muscle images worse. This work provides valuable motion and distortion correction for muscle imaging, ensuring that measurements truly reflect muscle physiology. It also describes a method to remove an unwanted signal from post-exercise muscle data, and create a map of the internal muscle motion that occurred. Finally, an advanced mathematical technique was used to extract signals of interest and important spatial features from muscle image data automatically. The technique produced reliable results within and among subjects.

Στο πλαίσιο της εργασίας αυτής πραγματοποιείται ανάλυση και εφαρμογή του διαχωρισμού ακουστικών σημάτων, τα οποία έχουν ληφθεί από το ανθρώπινο σώμα, όταν αυτό βρίσκεται σε κατάσταση ύπνου. Τα σήματα αυτά έχουν ληφθεί με τη βοήθεια μιας συσκευής πιεζοκρυστάλλων και ο διαχωρισμός τους επιτυγχάνεται με τη μέθοδο Ανάλυσης Ανεξάρτητων Συνιστωσών (ICA). Κύριος σκοπός όλων των παραπάνω είναι να χρησιμοποιηθεί η εν λόγω μεθοδολογία στη διάγνωση της αποφρακτικής άπνοιας (OSA). Στο πρώτο κεφάλαιο, παρουσιάζεται αναλυτικά η μέθοδος ICA και το μαθηματικό μοντέλο που την περιγράφει, όπως επίσης και όλα τα βήματα προεπεξεργασίας της. Στη συνέχεια αναλύεται διεξοδικά η λειτουργία του αλγορίθμου FastICA και οι ιδιότητες του, με τον οποίο υλοποιείται το πειραματικό μέρος της εργασίας αυτής. Στο δεύτερο κεφάλαιο, μελετάται η ασθένεια της αποφρακτικής άπνοιας (OSA), οι παράγοντες και η παθολογία της καθώς και το κύριο διαγνωστικό σύμπτωμα της: το ροχαλητό. Ύστερα, πραγματεύεται την διάγνωση και τους γνωστότερους τρόπους θεραπείας αυτής της νόσου και τελικά τη μέθοδο του Snoring Detection. Στο τρίτο κεφάλαιο γίνεται μια εισαγωγή στον πιεζοηλεκτρισμό, και μία μελέτη του πιεζοηλεκτρικού φαινομένου και του μαθηματικού του μοντέλου. Ακολουθεί αναφορά των ειδών πιεζοηλεκτρικών αισθητήρων με τους οποίους λαμβάνονται τα σήματα που εξετάζονται σε αυτή την εργασία. Στο επόμενο κεφάλαιο γίνεται μία σύνδεση των δεδομένων θεωρίας που αναφέρονται στα προηγούμενα κεφάλαια και μία εισαγωγή στην πειραματική μέθοδο. Στο κεφάλαιο πέντε παρατίθενται κάποια παραδείγματα εφαρμογής του αλγορίθμου FastICA με τυχαία σήματα, τα οποία έχουν σκοπό να δοκιμάσουν την απόδοση του. Στο κεφάλαιο έξι, 5 γίνεται η πειραματική διαδικασία όπου τώρα τα σήματα που διαχωρίζονται με τον αλγόριθμο FastICA προέρχονται από το ανθρώπινο σώμα. Η υλοποίηση της γίνεται σε Matlab. Έτσι, γίνεται εξαγωγή του ζητούμενου σήματος ροχαλητού και αναγράφονται κάποια συμπεράσματα για την απόδοση του αλγορίθμου. Στο τέλος της εργασίας παρατίθενται σε ένα παράρτημα όλοι οι κώδικες της MATLAB που χρησιμοποιήθηκαν για την ολοκλήρωση του πειραματικού της μέρους στα κεφάλαια πέντε και έξι. / In this particular thesis, analysis and application of separation of acoustic signals is carried out. These signals have been taken from the human body in a sleeping state. They are obtained by means of a piezocrystallic device and their separation is achieved by the method of Independent Component Analysis (ICA). The main purpose of all this is to use this methodology in order to diagnose the Obstructive Sleep Apnea (OSA). The first chapter presents the method of ICA and the mathematical model that describes it as well as all the pre-processing steps. Then it analyses, in detail, the algorithm FastICA, which is used in the experimental part of this thesis and its properties. The second chapter studies the disease of obstructive sleep apnea (OSA), its factors and its pathology and the major diagnostic symptom: snoring. Then, it discusses the diagnosis and the best known ways of treating this disease and eventually the method of Snoring Detection. The third chapter is an introduction to piezoelectricity and a study of the piezoelectric effect and its mathematical description. This is followed by a reference to the types of piezoelectric sensors which are used to obtain the signals used in this paper. In chapter five we have listed some examplesapplications of the FastICA algorithm with random signals, which are designed to test the performance. Section six is where the experimental procedure takes place. The signals derived from the human body are separated by the algorithm FastICA and the implementation is done in Matlab. In addition, some conclusions regarding the performance of the algorithm. At the end of this paper, all the MATLAB codes used for the completion of the experimental part of the chapters five and six are listed in an Annex.

Baigiamajame magistro darbe yra nagrinėjamas bekontaktis pulso matavimo metodas. Darbo tikslams pasiekti naudota Logitech C310 internetinė vaizdo kamera. Įrašomo vaizdo dydis yra 640X480 pikselių. Filmavimo sparta – 15 kadrų per sekundę. Vaizdo įrašo ilgis – 30 sekundžių. Tiriamieji buvo filmuojami apie 0,5 m atstumu nuo kameros. Tiriamųjų amžius nuo 24 iki 64 metų. Vaizdas buvo įrašomas, esant įvairiam apšvietimui: tiek dienos metu, tiek šviečiant skirtingo galingumo lempoms. Rezultatams palyginti buvo naudojamas ant riešo uždedamas pulso matavimo prietaisas ReliOn, kurio veikimas pagrįstas kraujagyslėse pulsuojančio kraujo spaudimo kitimu. Išgautam pulso signalui apdoroti, palyginimui buvo panaudoti du nepriklausomų komponenčių analizės algoritmai: Fast ICA bei stSobi. Eksperimentams atlikti buvo naudojama C# programavimo kalba ir Matlab 2008 matematinis skaičiavimo paketas. / The thesis analyses the non-contact cardiac pulse measurement method. To achieve work main goals Logitech C310 web camera was used. Video resolution was 640X480 pixels. Video capture speed was 15 frames per second. Video length was 30 seconds. Distance from web camera to human face was ~ 0,5 m. Participant age varied from 24 to 64 years old. Video was captured with different light sources: sun, lamps with different power. For results comparison ReliOn handy pulse measurement device was used. Pulse signal was filtered using two independent component analysis algorithms: Fast ICA and stSobi. Experiments have been made using C# programming language and Matlab 2008 mathematical package.

Le développement de techniques à haut débit fournit de nombreuses données sur le fonctionnement de réseaux de régulation. Il devient donc de plus en plus important de développer des techniques qui permettent de déduire la topologie et le fonctionnement des réseaux de régulation à partir des données expérimentales. La plupart des études dans ce domaine se focalisent sur la reconstruction de l'architecture locale du réseau de régulation et la détermination des paramètres qui relient les composants du réseau. Cependant, les réseaux biologiques ne sont jamais entièrement connus. L'absence d'un noeud important dans le réseau de régulation peut facilement conduire à de mauvaises prédictions de la structure du réseau ou des paramètres d'interactions. Dans cette thèse, nous proposons une méthode qui permet d'inférer l'existence, le profile d'expression et la connexion au reste du réseau d'un gène (ou de gènes) manquant. Pour résoudre ce problème difficile, nous devons simplifier la description du réseau de régulation. Nous faisons l'hypothèse communément acceptée que les interactions dans le réseau sont décrites par des fonctions de Hill. Nous approximons ces fonctions trop compliquées par des fonctions de puissance et nous montrons que cette simplification préserve la dynamique du réseau. En prenant le logarithme du système d'équations nous convertissons le système non-linéaire en un système linéaire. De nombreux outils sont disponibles pour analyser des systèmes linéaires. Nous utilisons l'analyse factorielle (FA) et l'analyse de composants indépendants (ICA) pour extraire le profil d'expression du gène inconnu à partir des profils d'expression des parties connues du réseau de régulation. Après avoir estimé le pattern d'expression du gène inconnu, nous explorons les différentes possibilités de connecter ce gène au reste du réseau. Une recherche exhaustive est trop coûteuse pour des grands réseaux de régulation. Nos proposons donc un algorithme de réduction de l'espace de recherche pour diminuer le nombre de calculs nécessaires. L'algorithme proposé est robuste au bruit expérimental et le profil d'expression du gène inconnu est retrouvé avec une probabilité de 80% dans des réseaux de petite taille et avec une probabilité de 60% pour des grands réseaux. FA est plus efficace que ICA pour extraire le profile du gène inconnu. L'algorithme est finalement appliqué à un réseau biologique réel: le réseau de régulation de la transcription du gène acs d'Escherichia coli. Nous prédisons qu'il y a un gène manquant dans ce réseau et les deux méthodes d'extraction du signal trouvent un profil d'expression très similaire pour le gène inconnu. De plus, ce profil d'expression est identique dans trois contextes expérimentaux différents : la souche sauvage, la souche dont l'adénylate cyclase a été délété et cette même souche complémentée par des l'AMPc ajouté au milieu de croissance. Puisque le profil d'expression du gène inconnu reste le mŘme dans les trois souches nous pouvons conclure que ce gène est indépendant de l'AMPc. Les deux méthodes d'extraction du profil d'expression prédisent deux structures différentes du réseau complet. FA prédit que le gène manquant contrôle l'expression de fis, tandis que ICA prédit que le gène inconnu contrôle d'expression de crp. / With the development of hight-throughput technologies, the investigation of the topologies and the functioning of genetic regulatory networks have become an important research topic in recent years. Most of the studies concentrate on reconstructing the local architecture of genetic regulatory networks and the determination of the corresponding interaction parameters. The preferred data sources are time series expression data. However, inevitably one or more important members of the regulatory network will remain unknown. The absence of important members of the genetic circuit leads to incorrectly inferred network topologies and control mechanisms. In this thesis we propose a method to infer the connection and expression pattern of these “missing genes”. In order to make the problem tractable, we have to make further simplifying assumptions. We assume that the interactions within the network are described by Hill-functions. We then approximate these functions by power-law functions. We show that this simplification still captures the dynamic regulatory behaviors of the network. The genetic control system can now be converted to linear model by using a logarithm transformation. In another word, we can analyze the genetic regulatory networks by linear approaches. In the logarithmic space, we propose a procedure for extracting the expression profile of a missing gene within the otherwise defined genetic regulatory network. The algorithm also determines the regulatory connections of this missing gene to the rest of the regulation network. The inference algorithm is based on Factor Analysis, a well-developed multivariate statistical analysis approach that is used to investigate unknown, underlying features of an ensemble of data, in our case the promoter activities and intracellular concentrations of the known genes. We also explore a second blind sources separation method, “Independent Component Analysis”, which is also commonly used to estimate hidden signals. Once the expression profile of the missing gene has been derived, we investigate possible connections of this gene to the remaining network by methods of search space reduction. The proposed method of inferring the expression profile of a missing gene and connecting it to a known network structure is applied to artificial genetic regulatory networks, as well as a real biologicial network studied in the laboratory: the acs regulatory network of Escherichia coli. In these applications we confirm that power-law functions are a good approximation of Hill-functions. Factor Analysis predicts the expression profiles of missing genes with a high accuracy of 80% in small artificial genetic regulatory networks. The accuracy of Factor Analysis of predicting the expression profiles of missing genes of large artificial genetic regulatory networks is 60%. In contrast, Independent Component Analysis is less powerful than Factor Analysis in extracting the expression profiles of missing components in small, as well as large, artificial genetic regulatory networks. Both Factor Analysis and Independent Component suggest that only one missing gene is sufficient to explain the observed expression profiles of Acs, Fis and Crp. The expression profiles of the missing genes in the △cya strain and in the △cya strain supplemented with cAMP estimated by Factor Analysis and Independent Component Analysis are very similar. Factor Analysis suggests that fis is regulated by the missing genes, while Independent Component Analysis suggests that crp is controlled by the missing gene.

Doctor of Philosophy / This thesis is based on research on financial time series analysis using pattern recognition methods. The first part of this research focuses on univariate time series analysis using different pattern recognition methods. First, probabilities of basic patterns are used to represent the features of a section of time series. This feature can remove noise from the time series by statistical probability. It is experimentally proven that this feature is successful for pattern repeated time series. Second, a multiscale Gaussian gravity as a pattern relationship measurement which can describe the direction of the pattern relationship is introduced to pattern clustering. By searching for the Gaussian-gravity-guided nearest neighbour of each pattern, this clustering method can easily determine the boundaries of the clusters. Third, a method that unsupervised pattern classification can be transformed into multiscale supervised pattern classification by multiscale supervisory time series or multiscale filtered time series is presented. The second part of this research focuses on multivariate time series analysis using pattern recognition. A systematic method is proposed to find the independent variables of a group of share prices by time series clustering, principal component analysis, independent component analysis, and object recognition. The number of dependent variables is reduced and the multivariate time series analysis is simplified by time series clustering and principal component analysis. Independent component analysis aims to find the ideal independent variables of the group of shares. Object recognition is expected to recognize those independent variables which are similar to the independent components. This method provides a new clue to understanding the stock market and to modelling a large time series database.

This dissertation deals with dynamics of engineering structures and principally discusses the identification of the modal parameters (i.e., natural frequencies, damping ratios and vibration modes) using output-only information, the excitation sources being considered as unknown and unmeasurable. To solve these kind of problems, a quite large selection of techniques is available in the scientific literature, each of them possessing its own features, advantages and limitations. One common limitation of most of the methods concerns the post-processing procedures that have proved to be delicate and time consuming in some cases, and usually require good users expertise. The constant concern of this work is thus the simplification of the result interpretation in order to minimize the influence of this ungovernable parameter. A new modal parameter estimation approach is developed in this work. The proposed methodology is based on the so-called Blind Source Separation techniques, that aim at reducing large data set to reveal its essential structure. The theoretical developments demonstrate a one-to-one relationship between the so-called mixing matrix and the vibration modes. Two separation algorithms, namely the Independent Component Analysis and the Second-Order Blind Identification, are considered. Their performances are compared, and, due to intrinsic features, one of them is finally identified as more suitable for modal identification problems. For the purpose of comparison, numerous academic case studies are considered to evaluate the influence of parameters such as damping, noise and nondeterministic excitations. Finally, realistic examples dealing with a large number of active modes, typical impact hammer modal testing and operational testing conditions, are studied to demonstrate the applicability of the proposed methodology for practical applications.

We live in a dynamic world full of noises and interferences. The conventional acoustic echo cancellation (AEC) framework based on the least mean square (LMS) algorithm by itself lacks the ability to handle many secondary signals that interfere with the adaptive filtering process, e.g., local speech and background noise. In this dissertation, we build a foundation for what we refer to as the system approach to signal enhancement as we focus on the AEC problem. We first propose the residual echo enhancement (REE) technique that utilizes the error recovery nonlinearity (ERN) to "enhances" the filter estimation error prior to the filter adaptation. The single-channel AEC problem can be viewed as a special case of semi-blind source separation (SBSS) where one of the source signals is partially known, i.e., the far-end microphone signal that generates the near-end acoustic echo. SBSS optimized via independent component analysis (ICA) leads to the system combination of the LMS algorithm with the ERN that allows for continuous and stable adaptation even during double talk. Second, we extend the system perspective to the decorrelation problem for AEC, where we show that the REE procedure can be applied effectively in a multi-channel AEC (MCAEC) setting to indirectly assist the recovery of lost AEC performance due to inter-channel correlation, known generally as the "non-uniqueness" problem. We develop a novel, computationally efficient technique of frequency-domain resampling (FDR) that effectively alleviates the non-uniqueness problem directly while introducing minimal distortion to signal quality and statistics. We also apply the system approach to the multi-delay filter (MDF) that suffers from the inter-block correlation problem. Finally, we generalize the MCAEC problem in the SBSS framework and discuss many issues related to the implementation of an SBSS system. We propose a constrained batch-online implementation of SBSS that stabilizes the convergence behavior even in the worst case scenario of a single far-end talker along with the non-uniqueness condition on the far-end mixing system. The proposed techniques are developed from a pragmatic standpoint, motivated by real-world problems in acoustic and audio signal processing. Generalization of the orthogonality principle to the system level of an AEC problem allows us to relate AEC to source separation that seeks to maximize the independence, hence implicitly the orthogonality, not only between the error signal and the far-end signal, but rather, among all signals involved. The system approach, for which the REE paradigm is just one realization, enables the encompassing of many traditional signal enhancement techniques in analytically consistent yet practically effective manner for solving the enhancement problem in a very noisy and disruptive acoustic mixing environment.

Οι υπνικές άτρακτοι είναι απότομες αλλαγές της ρυθμικής δραστηριότητας που χαρακτηρίζονται από σταδιακή αύξηση και κατόπιν μείωση του πλάτους. Εμφανίζονται κυρίως στα στάδια 2,3 και 4 του υπνικού εγκεφαλογράμματος. Τοπογραφικές αναλύσεις έχουν δείξει την ύπαρξη δύο ξεχωριστών τύπων υπνικών ατράκτων, «αργές» και «ταχείς», περίπου στα 12 και 14 Hz, αντίστοιχα. Υπάρχουν ενδείξεις ότι υπάρχουν τουλάχιστον δύο, λειτουργικά, ξεχωριστές γεννήτριες υπνικών ατράκτων, που αντιστοιχούν στις κλάσεις συχνοτήτων. Ο λόγος της εργασίας αυτής ήταν η επεξεργασία υπνικών ατράκτων με την τεχνική Ανάλυσης Ανεξάρτητων Συνιστωσών (ICA) με σκοπό την έρευνα της πιθανότητας εξαγωγής, στα από την ICA ανακατασκευαζόμενα ηλεκτροεγκεφαλογραφήματα (ΗΕΓ), «συνιστωσών» ατράκτων που αντιστοιχούν σε ξεχωριστές δομές ΗΕΓ, και η μελέτη των πηγών που δημιουργούν αυτές τις συνιστώσες. Χρησιμοποιήθηκαν 8κάναλες καταγραφές υπνικών ατράκτων ΗΕΓ από έναν εξεταζόμενο, που καταγράφηκαν στα πλαίσια του Biopattern Network of Excellence, οι οποίες αρχικά επεξεργάστηκαν με ένα φίλτρο FIR με συχνότητες αποκοπής (-3dB) στα 6 και 21 Hz. Κατόπιν εφαρμόστηκε η ανάλυση ICA και εξάχθηκαν οι ανεξάρτητες συνιστώσες (ICs). Έγινε επιλογή των συνιστωσών οι οποίες θα ανακατασκεύαζαν τα ΗΕΓ και τέλος ανακατασκευάσθηκαν αυτά. Στα ανακατασκευασμένα ΗΕΓ έφαρμόστηκε η ανάλυση LORETA. Πρωτού γίνει όμως αυτό έγινε μία εξομείωση του «ευθύ» και «ανάστροφου» προβλήματος. Αυτό έγινε για να μελετήσουμε κατά πόσον θα μπορούσαμε να εξάγουμε αξιόπιστα αποτελέσματα από την τεχνική LORETA με τόσο μικρό αριθμό καναλιών καταγραφής. Η μελέτη αυτή έδειξε ότι τα αποτελέσματά μας θα μπορούσαν να μας δώσουν αξιόπιστες πληροφορίες όσον αφορά την ευρεία περιοχή παραγωγής των ατράκτων και όχι την ακριβή τους θέση. Τα τελικά αποτελέσματα έδειξαν ότι υπάρχει διαφοροποίηση, όσον αφορά την περιοχή παραγωγής τους, και σταθερότητα των πηγών που σχετίζονται με συνιστώσες ατράκτων που ανακατασκευάζονται από ξεχωριστές ομάδες ανεξαρτήτων συνιστωσών (ICs). / Sleep spindles are bursts of rhythmic activity characterized by progressively increasing, then gradually decreasing amplitude, present predominantly in stages 2, 3 and 4 of the sleep electroencephalogram (EEG). Topographic analyses of sleep spindle incidence suggested the existence of two distinct sleep spindle types, “slow” and “fast” spindles at approximately 12 and 14 Hz respectively. There are indications that there exist at least two functionally separated spindle generators, corresponding to each frequency spectrum class. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, in the ICA-reconstructed EEG, spindle “components” corresponding to separate EEG activity patterns, and to investigate the sources underlying these spindle components. We used 8-channel EEG recordings of sleep spindles of a single subject, recorded in the framework of the Biopattern Network of Excellence, which were processed by a FIR filter with cut-off frequencies (-3 dB) at 6 and 21 Hz. Afterwards, ICA was applied and ICs were extracted. There were a choice of the ICs which would reconstruct the EEG and the EEG were finally reconstructed. Source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied on the reconstructed EEGs. Before that we made a simulation of the “direct” and “inverse” problem. This was made in order to investigate if we would extract reliable results from the LORETA technique with only 8-channel recordings. The investigation stated that the results could give reliable information only for the brain sites at which the spindle generators were located and not for their exact position. Results indicate separability and stability of sources related to sleep spindle components reconstructed from separate groups of Independent Components (ICs).

The task of automatically recognizing targets in IR imagery has a history of approximately 25 years of research and development. ATR is an application of pattern recognition and scene analysis in the field of defense industry and it is still one of the challenging problems. This thesis may be viewed as an exploratory study of ATR problem with encouraging recognition algorithms implemented in the area. The examined algorithms are among the solutions to the ATR problem, which are reported to have good performance in the literature. Throughout the study, PCA, subspace LDA, ICA, nearest mean classifier, K nearest neighbors classifier, nearest neighbor classifier, LVQ classifier are implemented and their performances are compared in the aspect of recognition rate. According to the simulation results, the system, which uses the ICA as the feature extractor and LVQ as the classifier, has the best performing results. The good performance of this system is due to the higher order statistics of the data and the success of LVQ in modifying the decision boundaries.