Parmi les diverses modalités biométriques qui permettent l'identification des personnes, l'iris est considéré comme très fiable, avec un taux d'erreur remarquablement faible. Toutefois, ce niveau élevé de performances est obtenu en contrôlant la qualité des images acquises et en imposant de fortes contraintes à la personne (être statique et à proximité de la caméra). Cependant, dans de nombreuses applications de sécurité comme les contrôles d'accès, ces contraintes ne sont plus adaptées. Les images résultantes souffrent alors de diverses dégradations (manque de résolution, artefacts...) qui affectent négativement les taux de reconnaissance. Pour contourner ce problème, il est possible d’exploiter la redondance de l’information découlant de la disponibilité de plusieurs images du même œil dans la séquence enregistrée. Cette thèse se concentre sur la façon de fusionner ces informations, afin d'améliorer les performances. Dans la littérature, diverses méthodes de fusion ont été proposées. Cependant, elles s’accordent sur le fait que la qualité des images utilisées dans la fusion est un facteur crucial pour sa réussite. Plusieurs facteurs de qualité doivent être pris en considération et différentes méthodes ont été proposées pour les quantifier. Ces mesures de qualité sont généralement combinées pour obtenir une valeur unique et globale. Cependant, il n'existe pas de méthode de combinaison universelle et des connaissances a priori doivent être utilisées, ce qui rend le problème non trivial. Pour faire face à ces limites, nous proposons une nouvelle manière de mesurer et d'intégrer des mesures de qualité dans un schéma de fusion d'images, basé sur une approche de super-résolution. Cette stratégie permet de remédier à deux problèmes courants en reconnaissance par l'iris: le manque de résolution et la présence d’artefacts dans les images d'iris. La première partie de la thèse consiste en l’élaboration d’une mesure de qualité pertinente pour quantifier la qualité d’image d’iris. Elle repose sur une mesure statistique locale de la texture de l’iris grâce à un modèle de mélange de Gaussienne. L'intérêt de notre mesure est 1) sa simplicité, 2) son calcul ne nécessite pas d'identifier a priori les types de dégradations, 3) son unicité, évitant ainsi l’estimation de plusieurs facteurs de qualité et un schéma de combinaison associé et 4) sa capacité à prendre en compte la qualité intrinsèque des images mais aussi, et surtout, les défauts liés à une mauvaise segmentation de la zone d’iris. Dans la deuxième partie de la thèse, nous proposons de nouvelles approches de fusion basées sur des mesures de qualité. Tout d’abord, notre métrique est utilisée comme une mesure de qualité globale de deux façons différentes: 1) comme outil de sélection pour détecter les meilleures images de la séquence et 2) comme facteur de pondération au niveau pixel dans le schéma de super-résolution pour donner plus d'importance aux images de bonnes qualités. Puis, profitant du caractère local de notre mesure de qualité, nous proposons un schéma de fusion original basé sur une pondération locale au niveau pixel, permettant ainsi de prendre en compte le fait que les dégradations peuvent varier d’une sous partie à une autre. Ainsi, les zones de bonne qualité contribueront davantage à la reconstruction de l'image fusionnée que les zones présentant des artéfacts. Par conséquent, l'image résultante sera de meilleure qualité et pourra donc permettre d'assurer de meilleures performances en reconnaissance. L'efficacité des approches proposées est démontrée sur plusieurs bases de données couramment utilisées: MBGC, Casia-Iris-Thousand et QFIRE à trois distances différentes. Nous étudions séparément l'amélioration apportée par la super-résolution, la qualité globale, puis locale dans le processus de fusion. Les résultats montrent une amélioration importante apportée par l'utilisation de la qualité globale, amélioration qui est encore augmentée en utilisant la qualité locale / Among the large number of biometric modalities, iris is considered as a very reliable biometrics with a remarkably low error rate. The excellent performance of iris recognition systems are obtained by controlling the quality of the captured images and by imposing certain constraints on users, such as standing at a close fixed distance from the camera. However, in many real-world applications such as control access and airport boarding these constraints are no longer suitable. In such non ideal conditions, the resulting iris images suffer from diverse degradations which have a negative impact on the recognition rate. One way to try to circumvent this bad situation is to use some redundancy arising from the availability of several images of the same eye in the recorded sequence. Therefore, this thesis focuses on how to fuse the information available in the sequence in order to improve the performance. In the literature, diverse schemes of fusion have been proposed. However, they agree on the fact that the quality of the used images in the fusion process is an important factor for its success in increasing the recognition rate. Therefore, researchers concentrated their efforts in the estimation of image quality to weight each image in the fusion process according to its quality. There are various iris quality factors to be considered and diverse methods have been proposed for quantifying these criteria. These quality measures are generally combined to one unique value: a global quality. However, there is no universal combination scheme to do so and some a priori knowledge has to be inserted, which is not a trivial task. To deal with these drawbacks, in this thesis we propose of a novel way of measuring and integrating quality measures in a super-resolution approach, aiming at improving the performance. This strategy can handle two types of issues for iris recognition: the lack of resolution and the presence of various artifacts in the captured iris images. The first part of the doctoral work consists in elaborating a relevant quality metric able to quantify locally the quality of the iris images. Our measure relies on a Gaussian Mixture Model estimation of clean iris texture distribution. The interest of our quality measure is 1) its simplicity, 2) its computation does not require identifying in advance the type of degradations that can occur in the iris image, 3) its uniqueness, avoiding thus the computation of several quality metrics and associated combination rule and 4) its ability to measure the intrinsic quality and to specially detect segmentation errors. In the second part of the thesis, we propose two novel quality-based fusion schemes. Firstly, we suggest using our quality metric as a global measure in the fusion process in two ways: as a selection tool for detecting the best images and as a weighting factor at the pixel-level in the super-resolution scheme. In the last case, the contribution of each image of the sequence in final fused image will only depend on its overall quality. Secondly, taking advantage of the localness of our quality measure, we propose an original fusion scheme based on a local weighting at the pixel-level, allowing us to take into account the fact that degradations can be different in diverse parts of the iris image. This means that regions free from occlusions will contribute more in the image reconstruction than regions with artefacts. Thus, the quality of the fused image will be optimized in order to improve the performance. The effectiveness of the proposed approaches is shown on several databases commonly used: MBGC, Casia-Iris-Thousand and QFIRE at three different distances: 5, 7 and 11 feet. We separately investigate the improvement brought by the super-resolution, the global quality and the local quality in the fusion process. In particular, the results show the important improvement brought by the use of the global quality, improvement that is even increased using the local quality

Clustering machine learning algorithms have existed for a long time and there are a multitude of variations of them available to implement. Each of them has its advantages and disadvantages, which makes it challenging to select one for a particular problem and application. This study focuses on comparing two algorithms, the K-Means and Gaussian Mixture Model algorithms for outlier detection within public travel data from the travel planning mobile application MobiTime1[1]. The purpose of this study was to compare the two algorithms against each other, to identify differences between their outlier detection results. The comparisons were mainly done by comparing the differences in number of outliers located for each model, with respect to outlier threshold and number of clusters. The study found that the algorithms have large differences regarding their capabilities of detecting outliers. These differences heavily depend on the type of data that is used, but one major difference that was found was that K-Means was more restrictive then Gaussian Mixture Model when it comes to classifying data points as outliers. The result of this study could help people determining which algorithms to implement for their specific application and use case.

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.

Exploring massive amount of user generated data with topics posits a new way to find useful information. The topics are assumed to be “hidden” and must be “uncovered” by statistical methods such as topic modeling. However, the user generated data is typically short and messy e.g. informal chat conversations, heavy use of slang words and “noise” which could be URL’s or other forms of pseudo-text. This type of data is difficult to process for most natural language processing methods, including topic modeling. This thesis attempts to find the approach that objectively give the better topics from short and messy text in a comparative study. The compared approaches are latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words, and a new approach based on previous work named Neural Probabilistic Topic Modeling (NPTM). It could only be concluded that NPTM have a tendency to achieve better topics on short and messy text than LDA and RO-LDA. GMM on the other hand could not produce any meaningful results at all. The results are less conclusive since NPTM suffers from long running times which prevented enough samples to be obtained for a statistical test. / Att utforska enorma mängder användargenererad data med ämnen postulerar ett nytt sätt att hitta användbar information. Ämnena antas vara “gömda” och måste “avtäckas” med statistiska metoder såsom ämnesmodellering. Dock är användargenererad data generellt sätt kort och stökig t.ex. informella chattkonversationer, mycket slangord och “brus” som kan vara URL:er eller andra former av pseudo-text. Denna typ av data är svår att bearbeta för de flesta algoritmer i naturligt språk, inklusive ämnesmodellering. Det här arbetet har försökt hitta den metod som objektivt ger dem bättre ämnena ur kort och stökig text i en jämförande studie. De metoder som jämfördes var latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words samt en egen metod med namnet Neural Probabilistic Topic Modeling (NPTM) baserat på tidigare arbeten. Den slutsats som kan dras är att NPTM har en tendens att ge bättre ämnen på kort och stökig text jämfört med LDA och RO-LDA. GMM lyckades inte ge några meningsfulla resultat alls. Resultaten är mindre bevisande eftersom NPTM har problem med långa körtider vilket innebär att tillräckligt många stickprov inte kunde erhållas för ett statistiskt test.

The field of speech recognition during the last decade has left the research stage and found its way into the public market, and today, speech recognition software is ubiquitous around us. An automatic speech recognizer understands human speech and represents it as text. Most of the current speech recognition software employs variants of deep neural networks. Before the deep learning era, the hybrid of hidden Markov model and Gaussian mixture model (HMM-GMM) was a popular statistical model to solve speech recognition. In this thesis, automatic speech recognition using HMM-GMM was trained on Somali data which consisted of voice recording and its transcription. HMM-GMM is a hybrid system in which the framework is composed of an acoustic model and a language model. The acoustic model represents the time-variant aspect of the speech signal, and the language model determines how probable is the observed sequence of words. This thesis begins with background about speech recognition. Literature survey covers some of the work that has been done in this field. This thesis evaluates how different language models and discounting methods affect the performance of speech recognition systems. Also, log scores were calculated for the top 5 predicted sentences and confidence measures of pre-dicted sentences. The model was trained on 4.5 hrs of voiced data and its corresponding transcription. It was evaluated on 3 mins of testing data. The performance of the trained model on the test set was good, given that the data was devoid of any background noise and lack of variability. The performance of the model is measured using word error rate(WER) and sentence error rate (SER). The performance of the implemented model is also compared with the results of other research work. This thesis also discusses why log and confidence score of the sentence might not be a good way to measure the performance of the resulting model. It also discusses the shortcoming of the HMM-GMM model, how the existing model can be improved, and different alternatives to solve the problem.

Tato práce pojednává o modelování v podprostoru parametrů směsí gaussovských rozložení pro rozpoznávání mluvčího. Práce se skládá ze tří částí. První část je věnována skórovacím metodám při použití sdružené faktorové analýzy k modelování mluvčího. Studované metody se liší převážně v tom, jak se vypořádávají s variabilitou kanálu testovacích nahrávek. Metody jsou prezentovány v souvislosti s obecnou formou funkce pravděpodobnosti pro sdruženou faktorovou analýzu a porovnány jak z hlediska přesnosti, tak i z hlediska rychlosti.  Je zde prokázáno, že použití lineární aproximace pravděpodobnostní funkce dává výsledky srovnatelné se standardním vyhodnocením pravděpodobnosti při dramatickém zjednodušení matematického zápisu a tím i zvýšení rychlosti vyhodnocování. Druhá část pojednává o extrakci tzv. i-vektorů, tedy nízkodimenzionálních reprezentací nahrávek. Práce prezentuje dva přístupy ke zjednodušení extrakce. Motivací pro tuto část bylo jednak urychlení extrakce i-vektorů, jednak nasazení této úspěšné techniky na jednoduchá zařízení typu mobilní telefon, a také matematické zjednodušení umožněňující využití numerických optimalizačních metod pro diskriminativní trénování.  Výsledky ukazují, že na dlouhých nahrávkách je zrychlení vykoupeno poklesem úspěšnosti rozpoznávání, avšak na krátkých nahrávkách, kde je úspěšnost rozpoznávání nízká, se rozdíly úspěšnosti stírají. Třetí část se zabývá diskriminativním trénováním v oblasti rozpoznávání mluvčího. Jsou zde shrnuty poznatky z předchozích prací zabývajících se touto problematikou. Kapitola navazuje na poznatky z předchozích dvou částí a pojednává o diskriminativním trénování parametrů extraktoru i-vektorů.  Výsledky ukazují, že při klasickém trénování extraktoru a následném diskriminatviním přetrénování tyto metody zvyšují úspěšnost.

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Following the significant transition from the traditional production industry to an informationbased economy, the telecommunications industry was faced with an explosion of innovation, resulting in a continuous change in user behaviour. The industry has made efforts to adapt to a more datadriven future, which has given rise to larger and more complex systems. Therefore, troubleshooting systems such as anomaly detection and root cause analysis are essential features for maintaining service quality and facilitating daily operations. This study aims to explore the possibilities, benefits, and drawbacks of implementing cluster analysis for anomaly detection in hybrid fibercoaxial networks. Based on the literature review on unsupervised anomaly detection and an assumption regarding the anomalous behaviour in hybrid fibercoaxial network data, the kmeans, SelfOrganizing Map, and Gaussian Mixture Model were implemented both with and without Principal Component Analysis. Analysis of the results demonstrated an increase in performance for all models when the Principal Component Analysis was applied, with kmeans outperforming both SelfOrganizing Map and Gaussian Mixture Model. On this basis, it is recommended to apply Principal Component Analysis for clusteringbased anomaly detection. Further research is necessary to identify whether cluster analysis is the most appropriate unsupervised anomaly detection approach. / Följt av övergången från den traditionella tillverkningsindustrin till en informationsbaserad ekonomi stod telekommunikationsbranschen inför en explosion av innovation. Detta skifte resulterade i en kontinuerlig förändring av användarbeteende och branschen tvingades genomgå stora ansträngningar för att lyckas anpassa sig till den mer datadrivna framtiden. Större och mer komplexa system utvecklades och således blev felsökningsfunktioner såsom anomalidetektering och rotfelsanalys centrala för att upprätthålla servicekvalitet samt underlätta för den dagliga driftverksamheten. Syftet med studien är att utforska de möjligheterna, för- samt nackdelar med att använda klusteranalys för anomalidetektering inom HFC- nätverk. Baserat på litteraturstudien för oövervakad anomalidetektering samt antaganden för anomalibeteenden inom HFC- data valdes algritmerna k- means, Self- Organizing Map och Gaussian Mixture Model att implementeras, både med och utan Principal Component Analysis. Analys av resultaten påvisade en uppenbar ökning av prestanda för samtliga modeller vid användning av PCA. Vidare överträffade k- means, både Self- Organizing Maps och Gaussian Mixture Model. Utifrån resultatanalysen rekommenderas det således att PCA bör tillämpas vid klusterings- baserad anomalidetektering. Vidare är ytterligare forskning nödvändig för att avgöra huruvida klusteranalys är den mest lämpliga metoden för oövervakad anomalidetektering.

In this dissertation we aim to perform a multi-wavelength analysis of galaxy clusters. We discuss various methods for clustering in order to determine physical parameters of galaxy clusters required for this type of study. A selection of galaxy clusters was chosen from 4 papers, (Popesso et al. 2007b, Yoon et al. 2008, Loubser et al. 2008, Brownstein & Mo at 2006) and restricted by redshift and galactic latitude to reveal a sample of 40 galaxy clusters with 0.0 < z < 0.15. Data mining using Virtual Observatory (VO) and a literature survey provided some background information about each of the galaxy clusters in our sample with respect to optical, radio and X-ray data. Using the Kayes Mixture Model (KMM) and the Gaussian Mixing Model (GMM), we determine the most likely cluster member candidates for each source in our sample. We compare the results obtained to SIMBADs method of hierarchy. We show that the GMM provides a very robust method to determine member candidates but in order to ensure that the right candidates are chosen we apply a select choice of outlier tests to our sources. We determine a method based on a combination of GMM, the QQ Plot and the Rosner test that provides a robust and consistent method for determining galaxy cluster members. Comparison between calculated physical parameters; velocity dispersion, radius, mass and temperature, and values obtained from literature show that for the majority of our galaxy clusters agree within 3 range. Inconsistencies are thought to be due to dynamically active clusters that have substructure or are undergoing mergers, making galaxy member identi cation di cult. Six correlations between di erent physical parameters in the optical and X-ray wavelength were consistent with published results. Comparing the velocity dispersion with the X-ray temperature, we found a relation of T0:43 as compared to T0:5 obtained from Bird et al. (1995). X-ray luminosity temperature and X-ray luminosity velocity dispersion relations gave the results LX T2:44 and LX 2:40 which lie within the uncertainty of results given by Rozgacheva & Kuvshinova (2010). These results all suggest that our method for determining galaxy cluster members is e cient and application to higher redshift sources can be considered. Further studies on galaxy clusters with substructure must be performed in order to improve this method. In future work, the physical parameters obtained here will be further compared to X-ray and radio properties in order to determine a link between bent radio sources and the galaxy cluster environment. / MSc (Space Physics), North-West University, Potchefstroom Campus, 2013

In this dissertation we aim to perform a multi-wavelength analysis of galaxy clusters. We discuss various methods for clustering in order to determine physical parameters of galaxy clusters required for this type of study. A selection of galaxy clusters was chosen from 4 papers, (Popesso et al. 2007b, Yoon et al. 2008, Loubser et al. 2008, Brownstein & Mo at 2006) and restricted by redshift and galactic latitude to reveal a sample of 40 galaxy clusters with 0.0 < z < 0.15. Data mining using Virtual Observatory (VO) and a literature survey provided some background information about each of the galaxy clusters in our sample with respect to optical, radio and X-ray data. Using the Kayes Mixture Model (KMM) and the Gaussian Mixing Model (GMM), we determine the most likely cluster member candidates for each source in our sample. We compare the results obtained to SIMBADs method of hierarchy. We show that the GMM provides a very robust method to determine member candidates but in order to ensure that the right candidates are chosen we apply a select choice of outlier tests to our sources. We determine a method based on a combination of GMM, the QQ Plot and the Rosner test that provides a robust and consistent method for determining galaxy cluster members. Comparison between calculated physical parameters; velocity dispersion, radius, mass and temperature, and values obtained from literature show that for the majority of our galaxy clusters agree within 3 range. Inconsistencies are thought to be due to dynamically active clusters that have substructure or are undergoing mergers, making galaxy member identi cation di cult. Six correlations between di erent physical parameters in the optical and X-ray wavelength were consistent with published results. Comparing the velocity dispersion with the X-ray temperature, we found a relation of T0:43 as compared to T0:5 obtained from Bird et al. (1995). X-ray luminosity temperature and X-ray luminosity velocity dispersion relations gave the results LX T2:44 and LX 2:40 which lie within the uncertainty of results given by Rozgacheva & Kuvshinova (2010). These results all suggest that our method for determining galaxy cluster members is e cient and application to higher redshift sources can be considered. Further studies on galaxy clusters with substructure must be performed in order to improve this method. In future work, the physical parameters obtained here will be further compared to X-ray and radio properties in order to determine a link between bent radio sources and the galaxy cluster environment. / MSc (Space Physics), North-West University, Potchefstroom Campus, 2013