This master thesis investigates the possibility to automatically label and classify radio waves for presence detection, where the objective is to obtain information about the number of people in a room based on channel estimates. Labeling data for machine learning is time consuming and tedious process. To address this two approaches are evaluated. One was to develop a framework to generate labels with the aid of computer vision AI. The other relies on unsupervised learning classifiers complemented with heuristics to generate the labels. The investigation also studies the performance of the classifiers as a function of the TX/RX configuration, SNR, number of consecutive samples in a feature vector, bandwidth and frequency band. When someone moves in a room the propagation environment changes and induces variations in the channel estimates, compared to when the room is empty. These variations are the fundamental concept that is exploited in this thesis. Two methods are suggested to perform classification without the need of training data. The first uses random trees embeddings to construct a random forest without labels and the second using statistical bootstrapping with a random forest classifier. The labels used for annotation indicate whether were zero, one or two people in the room. The performance of binary and non-binary classification is evaluated both for the two blind detection models, as well as the performance of the unsupervised learning techniques Kmeans and self-organizing maps. For classification both supervised and unsupervised learning use random forest classifiers. Results show that random forest classifiers perform well for this kind of problem, and that random tree embeddings are able to extract relational data that could be used for automatic labeling of the data.

Natural disasters like flooding have always been a big problem for countries around the world, but as the global climate changes and the number of people living in cities keeps growing, the threat of flooding has become a lot worse. Even though many studies have been conducted on flood mapping and vulnerability assessment in urban areas, this research addresses a significant knowledge gap in this domain. First, we used a flood depth estimation approach has been used to address the overestimation of urban flood mapping areas using Sentinel-1 images. Ten different combinations of the two initial VH and VV polarizations were used to rapidly and accurately map urban floods within open-source Google Earth Engine platforms using four different methods. The inclusion of flood depth has improved the accuracy of these methods by 7% on average. Next, we focused our research to find out who is most at risk in the floodplain areas. Minority communities, such as African Americans, as a result of socioeconomic constraints, face more difficulties. So, next we conducted an analysis of spatial and temporal changes of demographic patterns (Race) in five southern cities in US. From our analysis we have found that in majority of cities, the minority population within the floodplain has increased over the past two decades, with the exception of Charleston, South Carolina, where the white population has increased while the minority population has decreased. Building upon these insights, we have included more socio-economic and demographic variables in our analysis to find out the more holistic view of the vulnerable people in two of these cities (Jackson and Birmingham). Due to high autocorrelation between explanatory variables, we used Principal Component Analysis (PCA) along with global and local regression techniques to find out how much these variables can explain the vulnerability. According to our findings, the spatial components play a significant role in explaining vulnerabilities in greater detail. The findings of this research can serve as an important resource for policymakers, urban planners, and emergency response agencies to make informed decisions in future events and enhancing overall resilience.

No description available.

Les données sous forme de chaîne de symboles sont très variées (ADN, texte, EEG quantifié,…) et ne sont pas toujours modélisables. Une description universelle des chaînes de symboles indépendante des probabilités est donc nécessaire. La complexité de Kolmogorov a été introduite en 1960 pour répondre à cette problématique. Le concept est simple : une chaîne de symboles est complexe quand il n'en existe pas une description courte. La complexité de Kolmogorov est le pendant algorithmique de l’entropie de Shannon et permet de définir la théorie algorithmique de l’information. Cependant, la complexité de Kolmogorov n’est pas calculable en un temps fini ce qui la rend inutilisable en pratique.Les premiers à rendre opérationnelle la complexité de Kolmogorov sont Lempel et Ziv en 1976 qui proposent de restreindre les opérations de la description. Une autre approche est d’utiliser la taille de la chaîne compressée par un compresseur sans perte. Cependant ces deux estimateurs sont mal définis pour le cas conditionnel et le cas joint, il est donc difficile d'étendre la complexité de Lempel-Ziv ou les compresseurs à la théorie algorithmique de l’information.Partant de ce constat, nous introduisons une nouvelle mesure d’information universelle basée sur la complexité de Lempel-Ziv appelée SALZA. L’implémentation et la bonne définition de notre mesure permettent un calcul efficace des grandeurs de la théorie algorithmique de l’information.Les compresseurs sans perte usuels ont été utilisés par Cilibrasi et Vitányi pour former un classifieur universel très populaire : la distance de compression normalisée [NCD]. Dans le cadre de cette application, nous proposons notre propre estimateur, la NSD, et montrons qu’il s’agit d’une semi-distance universelle sur les chaînes de symboles. La NSD surclasse la NCD en s’adaptant naturellement à davantage de diversité des données et en définissant le conditionnement adapté grâce à SALZA.En utilisant les qualités de prédiction universelle de la complexité de Lempel-Ziv, nous explorons ensuite les questions d’inférence de causalité. Dans un premier temps, les conditions algorithmiques de Markov sont rendues calculables grâce à SALZA. Puis en définissant pour la première l’information dirigée algorithmique, nous proposons une interprétation algorithmique de la causalité de Granger algorithmique. Nous montrons, sur des données synthétiques et réelles, la pertinence de notre approche. / Data in the form of strings are varied (DNA, text, quantify EEG) and cannot always be modeled. A universal description of strings, independent of probabilities, is thus necessary. The Kolmogorov complexity was introduced in 1960 to address the issue. The principle is simple: a string is complex if a short description of it does not exist. The Kolmogorov complexity is the counterpart of the Shannon entropy and defines the algorithmic information theory. Yet, the Kolmogorov complexity is not computable in finit time making it unusable in practice.The first ones to make operational the Kolmogorov complexity are Lempel and Ziv in 1976 who proposed to restrain the operations of the description. Another approach uses the size of the compressed string by a lossless data compression algorithm. Yet these two estimators are not well-defined regarding the joint and conditional complexity cases. So, compressors and Lempel-Ziv complexity are not valuable to estimate algorithmic information theory.In the light of this observation, we introduce a new universal information measure based on the Lempel-Ziv complexity called SALZA. The implementation and the good definition of our measure allow computing efficiently values of the algorithmic information theory.Usual lossless compressors have been used by Cilibrasi and Vitányi to define a very popular universal classifier: the normalized compression distance [NCD]. As part of this application, we introduce our own estimator, called the NSD, and we show that the NSD is a universal semi-distance between strings. NSD surpasses NCD because it gets used to a large data set and uses the adapted conditioning with SALZA.Using the accurate universal prediction quality of the Lempel-Ziv complexity, we explore the question of causality inference. At first, we compute the algorithmic causal Markov condition thanks to SALZA. Then we define, for the first time, the algorithmic directed information and based on it we introduce the algorithmic Granger causality. The relevance of our approach is demonstrated on real and synthetic data.

<p>The Okavango river flows from southern Angola, through the Kavango region of Namibia and into the Okavango Delta in Botswana. The recent peace in Angola hopefully marks the end of the intense suffering that the peoples of the river basin have endured, and the beginning of sustainable decision-making in the area. Informed decision-making however requires knowledge; and there is a need for, and a lack of knowledge regarding basin-wide land cover (LC) changes, and their causes, during the Angolan civil war in the basin. Furthermore, there is a need for, and a lack of knowledge on how expanding large-scale agriculture and urban growth along the Angola-Namibia border affects the water quality of the river.</p><p>The aim of this study was therefore to develop a remote sensing method applicable to the basin (with scant ground-truth data availability) to carry out a systematic historic study of LC changes during the Angolan civil war, to apply the method to the basin, to relate these changes to major societal trends in the region, and to analyse potential impacts of expanding large-scale agriculture and urban growth on the water quality of the river along the Angola-Namibia border.</p><p>A range of remote sensing methods to study historic LC changes in the basin were tried and evaluated against reference data collected during a field visit in Namibia in October 2005. Eventually, two methods were selected and applied to pre-processed Landsat MSS and ETM+ satellite image mosaics of 1973 and 2001 respectively: 1. a combined unsupervised classification and pattern-recognition change detection method providing quantified and geographically distributed binary LC class change trajectory information and, 2. an NDVI (Normalised Difference Vegetation Index) change detection method providing quantified and geographically distributed continuous information on degrees of change in vegetation vigour. In addition, available documents and people initiated in the basin conditions were consulted in the pursuit of discerning major societal trends that the basin had undergone during the Angolan civil war. Finally, concentrations of nutrients (total phosphorous & total nitrogen), bacteria (faecal coliforms & faecal streptococci), conductivity, total dissolved solids, dissolved oxygen, pH, temperature and Secchi depth were sampled at 11 locations upstream and downstream of large-scale agricultural facilities and an urban area during the aforementioned field visit.</p><p>The nature, extent and geographical distribution of LC changes in the study area during the Angolan civil war were determined. The study area (150 922 km²) was the Angolan and Namibian parts of the basin. The results indicate that the vegetation vigour is dynamic and has decreased overall in the area, perhaps connected with precipitation differences between the years. However while the vigour decreased in the northwest, it increased in the northeast, and on more local scales the pattern was often more complex. With respect to migration out of Angola into Namibia, the LC changes followed expectations of more intense use in Namibia close to the border (0-5 km), but not at some distance (10-20 km), particularly east of Rundu. With respect to urbanisation, expectations of increased human impact locally were observed in e.g. Rundu, Menongue and Cuito Cuanavale. Road deterioration was also observed with Angolan urbanisation but some infrastructures appeared less damaged by the war. Some villages (e.g. Savitangaiala de Môma) seem to have been abandoned during the war so that the vegetation could regenerate, which was expected. But other villages (e.g. Techipeio) have not undergone the same vegetation regeneration suggesting they were not abandoned. The areal extent of large-scale agriculture increased 59% (26 km²) during the war, perhaps as a consequence of population growth. But the expansion was not nearly at par with the population growth of the Kavango region (320%), suggesting that a smaller proportion of the population relied on the large-scale agriculture for their subsistence in 2001 compared with 1973.</p><p>No significant impacts were found from the large-scale agriculture and urbanisation on the water quality during the dry season of 2005. Total phosphorous concentrations (with range: 0.067-0.095 mg l^-1) did vary significantly between locations (p=0.013) but locations upstream and downstream of large-scale agricultural facilities were not significantly different (p=0.5444). Neither did faecal coliforms (range: 23-63 counts per 100ml) nor faecal streptococci (range: 8-33 counts per 100ml) vary significantly between locations (p=0.332 and p=0.354 respectively). Thus the impact of Rundu and the extensive livestock farming along the border were not significant at this time. The Cuito river on the other hand significantly decreased both the conductivity (range: 27.2-49.7 μS cm^-1, p<0.0001) and the total dissolved solid concentration (range: 12.7-23.4 mg l^-1, p<0.0001) of the mainstream of the Okavango during the dry season.</p><p>Land cover changes during the Angolan civil war, contributing causes and effects on water quality were studied in this research effort. Many of the obtained results can be used directly or with further application as a knowledge base for sustainable decision-making and management in the basin. Wisely used by institutions charged with that objective, the information can contribute to sustainable development and the ending of suffering and poverty for the benefit of the peoples of the Okavango and beyond.</p>

The Okavango river flows from southern Angola, through the Kavango region of Namibia and into the Okavango Delta in Botswana. The recent peace in Angola hopefully marks the end of the intense suffering that the peoples of the river basin have endured, and the beginning of sustainable decision-making in the area. Informed decision-making however requires knowledge; and there is a need for, and a lack of knowledge regarding basin-wide land cover (LC) changes, and their causes, during the Angolan civil war in the basin. Furthermore, there is a need for, and a lack of knowledge on how expanding large-scale agriculture and urban growth along the Angola-Namibia border affects the water quality of the river. The aim of this study was therefore to develop a remote sensing method applicable to the basin (with scant ground-truth data availability) to carry out a systematic historic study of LC changes during the Angolan civil war, to apply the method to the basin, to relate these changes to major societal trends in the region, and to analyse potential impacts of expanding large-scale agriculture and urban growth on the water quality of the river along the Angola-Namibia border. A range of remote sensing methods to study historic LC changes in the basin were tried and evaluated against reference data collected during a field visit in Namibia in October 2005. Eventually, two methods were selected and applied to pre-processed Landsat MSS and ETM+ satellite image mosaics of 1973 and 2001 respectively: 1. a combined unsupervised classification and pattern-recognition change detection method providing quantified and geographically distributed binary LC class change trajectory information and, 2. an NDVI (Normalised Difference Vegetation Index) change detection method providing quantified and geographically distributed continuous information on degrees of change in vegetation vigour. In addition, available documents and people initiated in the basin conditions were consulted in the pursuit of discerning major societal trends that the basin had undergone during the Angolan civil war. Finally, concentrations of nutrients (total phosphorous &amp; total nitrogen), bacteria (faecal coliforms &amp; faecal streptococci), conductivity, total dissolved solids, dissolved oxygen, pH, temperature and Secchi depth were sampled at 11 locations upstream and downstream of large-scale agricultural facilities and an urban area during the aforementioned field visit. The nature, extent and geographical distribution of LC changes in the study area during the Angolan civil war were determined. The study area (150 922 km2) was the Angolan and Namibian parts of the basin. The results indicate that the vegetation vigour is dynamic and has decreased overall in the area, perhaps connected with precipitation differences between the years. However while the vigour decreased in the northwest, it increased in the northeast, and on more local scales the pattern was often more complex. With respect to migration out of Angola into Namibia, the LC changes followed expectations of more intense use in Namibia close to the border (0-5 km), but not at some distance (10-20 km), particularly east of Rundu. With respect to urbanisation, expectations of increased human impact locally were observed in e.g. Rundu, Menongue and Cuito Cuanavale. Road deterioration was also observed with Angolan urbanisation but some infrastructures appeared less damaged by the war. Some villages (e.g. Savitangaiala de Môma) seem to have been abandoned during the war so that the vegetation could regenerate, which was expected. But other villages (e.g. Techipeio) have not undergone the same vegetation regeneration suggesting they were not abandoned. The areal extent of large-scale agriculture increased 59% (26 km2) during the war, perhaps as a consequence of population growth. But the expansion was not nearly at par with the population growth of the Kavango region (320%), suggesting that a smaller proportion of the population relied on the large-scale agriculture for their subsistence in 2001 compared with 1973. No significant impacts were found from the large-scale agriculture and urbanisation on the water quality during the dry season of 2005. Total phosphorous concentrations (with range: 0.067-0.095 mg l-1) did vary significantly between locations (p=0.013) but locations upstream and downstream of large-scale agricultural facilities were not significantly different (p=0.5444). Neither did faecal coliforms (range: 23-63 counts per 100ml) nor faecal streptococci (range: 8-33 counts per 100ml) vary significantly between locations (p=0.332 and p=0.354 respectively). Thus the impact of Rundu and the extensive livestock farming along the border were not significant at this time. The Cuito river on the other hand significantly decreased both the conductivity (range: 27.2-49.7 μS cm-1, p&lt;0.0001) and the total dissolved solid concentration (range: 12.7-23.4 mg l-1, p&lt;0.0001) of the mainstream of the Okavango during the dry season. Land cover changes during the Angolan civil war, contributing causes and effects on water quality were studied in this research effort. Many of the obtained results can be used directly or with further application as a knowledge base for sustainable decision-making and management in the basin. Wisely used by institutions charged with that objective, the information can contribute to sustainable development and the ending of suffering and poverty for the benefit of the peoples of the Okavango and beyond.

The expression of genetic information, in all organisms, might be characterized as in a constant state of flux with only a fraction of the gene within a genome being expressed at any given time. The genes’ expression pattern reflects the response of cells to stimuli that control growth, development and signal environmental changes. Understanding genes’ expression at the level of transcription and/or other stages of gene regulation at the mRNA level (half life of mRNA, RNA production from primary transcript) might reveal insights into the genes expression mechanisms that control these changes. With the DNA microarray technology researchers are now able to determine, in a single experiment, the gene expression profiles of hundreds to tens of thousands of genes in tissue, tumors, cells or biological fluids. Accordingly, and since the patterns of gene expression are strongly functionally correlated, microarrays might provide unprecedented information both on basic research (e.g. expression profiles of different tissues) and on applied research (e.g. human diseases, drug and hormone action etc). While the simultaneous measurement of thousands of gene expression levels potentially serves as source of profound knowledge, genes quantification (i.e. extraction of the genes expression levels) is confounded by various types of noise originating both from the microarray experimental procedure (e.g. sample preparation) and the probabilistic characteristics of the microarray detection process (e.g. scanning errors). The “noisy” nature of the measured gene expression levels obscures some of the important characteristics of the biological processes of interest. The latter, as a direct effect, renders the extraction of biological meaningful conclusions through microarray experiments difficult and affects the accuracy of the biological inference. Thus, as a major challenge in DNA microarray analysis, and especially for the accurate extraction of genes expression levels, might be considered the effective separation of “true” gene expression values from noise. Noise reduction is an essential process, which has to be incorporated into the microarray image analysis pipeline in order to minimize the “errors” that propagate throughout the microarray analysis pipeline and, consequently, affect the extracted gene expression levels. A possible solution, as proposed in previous studies, for addressing microarray image noise is image enhancement. Results of these studies have indicated a superior quality of the enhanced images, without however examining whether enhancement leads to more accurate spot segmentation or reduces the variability of the extracted gene expression levels. As foresaid, noise also complicates the extraction of meaningful biological conclusions. While more advanced methods have been introduced [28-32] that attempt to prevent the noisy set of genes from being grouped, there is a lack of consensus among experts on the selection of a single method for determining meaningful clusters of genes. The latter, directly affects the biological inference, since different number of clusters are produced when different clustering techniques or either different parameters in the clustering algorithms are utilized. Thus, it turns up that it is not only important to assess the performance of each analysis stage independently (i.e. whether the techniques employed in the microarray analysis pipeline provide accurate extracted gene expression levels or the clustering techniques group biologically related genes) but it is also necessary to ensure an acceptable performance of all steps, as a whole, in terms of biologically meaningful information. This thesis has been carried out towards the development of a complete microarray image processing and analysis framework in order to improve the extraction and, consequently, the quantification of gene expression levels on spotted complementary DNA (cDNA) microarray images. The aims of the present thesis are: a) to model and address the effects of cDNA microarray image noise in such a way that it will increase the accuracy of the extracted gene expression levels, b) to investigate the impact of noise and facilitate genes expression data analysis in order to allow biologists to develop an integrated understanding of the process being studied, c) to introduce a semi-supervised biologically informed criterion for the detection of meaningful biological clusters of genes that answer specific biological questions, d) to investigate the performance and the impact of various state-of-art and novel cDNA microarray image segmentation techniques in the quantification of genes expression levels For exploring all of these aspects, a complete and robust framework of microarray image processing and analysis techniques was designed, built and implemented. The framework incorporated in the microarray analysis pipeline a novel combination of image processing and analysis techniques originating from the comprehensive quantitative investigation of the impact of noise on spot segmentation, intensity extraction and data mining. Additionally, novel formulations of known image segmentation techniques have been introduced, implemented and evaluated in the task of microarray image segmentation. The usefulness of the proposed methods has been validated experimentally on both simulated and real cDNA microarray images. / Η έκφραση της γενετικής πληροφορίας, σε όλους τους οργανισμούς, χαρακτηρίζεται από μια σταθερή κατάσταση «ροής» στην οποία όμως μόνο ένα μέρος του γονιδίου μέσα στο γονιδίωμα (genome) εκφράζεται ανά χρονική στιγμή. Το γονιδιακό μοτίβο έκφρασης (gene expression pattern or gene expression profile) θα μπορούσαμε να πούμε ότι αντανακλά την αντίδραση των κυττάρων στα διάφορα εξωτερικά ερεθίσματα. Για να μπορέσουν να απαντηθούν ερωτήματα σχετικά με τους μηχανισμούς που επηρεάζουν και μεταβάλλουν τη γονιδιακή έκφραση ανάλογα με το εξωτερικό ερέθισμα είναι απαραίτητη η μελέτη της γονιδιακής έκφρασης σε μεταγραφικό επίπεδο (transcription level) ή/και άλλα στάδια (παράγοντες) που ρυθμίζουν τη γονιδιακή έκφραση (gene regulation) σε επίπεδο mRNA. Με τη χρήση της τεχνολογίας των μικροσυστοιχιών, οι ερευνητές έχουν πλέον τη δυνατότητα να μελετήσουν ταυτόχρονα την γονιδιακή έκφραση δεκάδων ή και εκατοντάδων χιλιάδων γονιδίων σε ιστούς, κύτταρα όγκους κλπ με τη χρήση ενός και μόνο πειράματος. Κατά συνέπεια, και από τη στιγμή που τα γονιδιακά μοτίβα έκφρασης συσχετίζονται έντονα λειτουργικά (functionally correlated), η τεχνολογία των μικροσυστοιχιών παρέχει ανεκτίμητης αξίας πληροφορίες που μπορούν να δώσουν ώθηση τόσο στην ανάπτυξη της βασικής έρευνας π.χ. μελέτη των γονιδιακών προφίλ έκφρασης διαφορετικών ιστών όσο και στην ανάπτυξη της εφαρμοσμένης έρευνας π.χ. μελέτη ασθενειών, δράση φαρμάκων και ορμονών κλπ. Παρά τη δυνατότητα που παρέχει η τεχνολογία των μικροσυστοιχιών για την ταυτόχρονη μέτρηση των επιπέδων έκφρασης χιλιάδων γονιδίων, η ποσοτικοποίηση της γονιδιακής έκφρασης (δηλ. η εξαγωγή των επιπέδων έκφρασης των γονιδίων), επηρεάζεται από τους διάφορους τύπους θορύβου που υπεισέρχονται τόσο κατά την πειραματική διαδικασία κατασκευής των μικροσυστοιχιών (π.χ. προετοιμασία δειγμάτων) όσο και από τα πιθανοκρατικά χαρακτηριστικά που διέπουν τη διαδικασία ανίχνευσης (microarray scanning procedure) των μικροσυστοιχιών (π.χ. λάθη ανίχνευσης). Η «θορυβώδης» φύση των γονιδίων και κατά συνέπεια των μετρούμενων γονιδιακών εκφράσεων «κρύβει» (obscure) μερικά από τα πιο σημαντικά χαρακτηριστικά των βιολογικών διαδικασιών ενδιαφέροντος και καθιστά δύσκολη την εξαγωγή χρήσιμων βιολογικών συμπερασμάτων. Από τα παραπάνω διαφαίνεται ότι η μείωση του θορύβου είναι μια πολύ σημαντική διαδικασία η οποία θα πρέπει να ενσωματωθεί στην αλγοριθμική μεθοδολογία που μέχρι στιγμής χρησιμοποιείται για την εξαγωγή των γονιδιακών εκφράσεων από τις εικόνες μικροσυστοιχιών. Με αυτό τον τρόπο θα ελαχιστοποιηθούν τα πιθανά «λάθη» τα οποία μεταφέρονται (propagate) κατά τη διαδικασία εξαγωγής των εντάσεων (μέσω της χρησιμοποιούμενης αλγοριθμικής μεθοδολογίας) και τελικά επηρεάζουν την «ακριβή» εξαγωγή των γονιδιακών εκφράσεων. ‘Ως πιθανή λύση για την αντιμετώπιση του θορύβου στις εικόνες μικροσυστοιχιών, έχει προταθεί στη διεθνή βιβλιογραφία η χρήση τεχνικών αναβάθμισης εικόνας. Τα αποτελέσματα αυτών των επιστημονικών εργασιών συμπεραίνουν ότι με τη χρήση τεχνικών αναβάθμισης η ποιότητα των επεξεργασμένων εικόνων είναι σαφώς καλύτερη. Ωστόσο, καμία από αυτές τις εργασίες δεν μελετάει εάν οι τεχνικές αναβάθμισης οδηγούν στον ακριβέστερο προσδιορισμό των παρυφών των κουκίδων (spot) από τις οποίες εξάγονται οι γονιδιακές εκφράσεις ή εάν βοηθάνε στη μείωση της μεταβλητότητας (variability) των εξαγόμενων γονιδιακών εκφράσεων. Επιπρόσθετα, όπως έχει ήδη προαναφερθεί, ο θόρυβος παρεμποδίζει την εξαγωγή χρήσιμων βιολογικών συμπερασμάτων. Παρά το μεγάλο πλήθος εξελιγμένων μεθόδων που έχουν προταθεί στη διεθνή βιβλιογραφία για την αποτροπή της ομαδοποίησης γονιδίων που χαρακτηρίζονται ως «θορυβώδη», δεν έχει καθοριστεί ακόμα (από τους ειδικούς) μια ενιαία μέθοδος που να βρίσκει και να ομαδοποιεί γονίδια τα οποία θα παρέχουν βιολογικά χρήσιμες πληροφορίες. Αποτέλεσμα αυτής της «ασυμφωνίας» μεταξύ των ειδικών αποτελεί η εξαγωγή διαφορετικών βιολογικών συμπερασμάτων ανάλογα α) με τον αριθμό των δημιουργούμενων γονιδιακών ομάδων (που εξαρτάται άμεσα από τη διαφορετική μέθοδο ομαδοποίησης (clustering)) και β) με τις διαφοροποιήσεις που μπορεί να έχουμε στις παραμέτρους των διαφόρων μεθόδων ομαδοποίησης. H παρούσα διατριβή στοχεύει στη δημιουργία ενός ολοκληρωμένου πλαισίου για την επεξεργασία και ανάλυση εικόνων μικροσυστοιχιών με σκοπό την βελτιστοποίηση της εξαγωγής και κατά συνέπεια της ποσοτικοποίησης των γονιδιακών εντάσεων από εικόνες μικροσυστοιχιών κουκίδων (spotted cDNA microarray images). Οι στόχοι της παρούσας διατριβής συνοψίζονται ως εξής: α) μοντελοποίηση και περιορισμός των επιδράσεων του θορύβου σε εικόνες μικροσυστοιχιών κουκίδων κατά τέτοιο τρόπο ώστε να αυξηθεί η ακρίβεια των εξαγόμενων γονιδιακών εκφράσεων, β) μελέτη της επίδρασης του θορύβου και βελτιστοποίηση των μεθόδων ανάλυσης των γονιδιακών εκφράσεων με σκοπό τη διευκόλυνση των βιολόγων στην εξαγωγής βιολογικών συμπερασμάτων και την καλύτερη κατανόηση της βιολογικής διεργασίας που μελετάται, γ) εισαγωγή ενός ημιεποπτευόμενου (semi-supervised) κριτηρίου που στηριζόμενο σε βιολογικές πληροφορίες θα αποσκοπεί στην ανεύρεση βιολογικά σημαντικών ομάδων γονιδίων τα οποία ταυτόχρονα θα απαντούν σε συγκεκριμένα βιολογικά ερωτήματα ,δ) μελέτη της επίδρασης και της απόδοσης διαφόρων τεχνικών κατάτμησης εικόνων μικροσυστοιχιών κουκίδων, τόσο ανωτάτου επιπέδου (state-of-art) όσο και νέων, στην ποσοτικοποίηση γονιδιακών εκφράσεων. Για την πραγματοποίηση των παραπάνω στόχων σχεδιάστηκε και κατασκευάστηκε μια πλήρως δομημένη μεθοδολογία (a complete and robust framework) που περιελάμβανε αλγοριθμους επεξεργασίας και ανάλυσης εικόνας κουκίδων μικροσυστοιχιών Η προτεινόμενη μεθοδολογία ενσωμάτωσε στην ήδη υπάρχουσα αλγοριθμική μεθοδολογία (microarray analysis pipeline) έναν πρωτότυπο συνδυασμό τεχνικών επεξεργασίας και ανάλυσης εικόνας βασισμένο στην εις βάθος ποσοτική έρευνα της επίδρασης του θορύβου στην κατάτμηση κουκίδων (spot segmentation), στην εξαγωγή εντάσεων και στην εξόρυξη δεδομένων (data mining). Επιπρόσθετα, κατά την παρούσα διατριβή προτάθηκαν, κατασκευάστηκαν και αξιολογήθηκαν νέες τεχνικές κατάτμησης εικόνας από μικροσυστοιχές κουκίδων. Η χρησιμότητα των προτεινόμενων μεθοδολογιών αξιολογήθηκε τόσο σε εικονικές (simulated) όσο και σε πραγματικές εικόνες μικροσυστοιχιών κουκίδων.

Ce travail est construit autour de deux problématiques de santé relatives aux deux plus grandes pandémies qui sévissent en Afrique sub-saharienne : i) l'hétérogénéité rencontrée dans la répartition spatiale et temporelle des vecteurs de paludisme ; ii) la variabilité dans l'observance au traitement antirétroviral par des personnes vivant avec le virus de l'immunodéficience humaine. Sur le plan méthodologique, ces deux problèmes se rapportent à la prise en compte de l'hétérogénéité dans la modélisation de données issues de mesures répétées ; ils nécessitent en outre le développement d'outils statistiques permettant de distinguer à partir des données, des sous-groupes (de localités, d'individus. . .) homogènes indispensables pour rendre plus efficientes les mesures de santé souvent déployer par les praticiens dans le cadre de la lutte contre le paludisme ou le VIH/SIDA. Les modèles de mélanges finis, grâce à leur flexibilité, sont des outils capables de fournir non seulement de bonnes estimations en présence d'une grande hétérogénéité dans les observations mais aussi une bonne partition des unités statistiques. Nous les distinguons, parmi d'autres méthodes, comme étant adaptés aux problématiques du présent travail. Deux applications de ces modèles aux données issues de capture de moustiques ont permis de modéliser la répartition spatiale et temporelle de vecteurs de paludisme et de dégager une méthode simple d'évaluation d'impact de mesures de lutte anti vectorielle. Nous introduisons la notion de _trajectoires de variances_ dans une troisième application portant sur des données d'observance aux traitements antirétroviraux par des personnes vivant avec le virus de l'immunodéficience humaine / This work focuses on two health issues relating to two major pandemics in sub- Saharan Africa : i) the heterogeneity encountered in the spatial and temporal distribution of malaria vectors ; ii) the variability in adherence to antiretroviral treatment by people living with the human immunodeficiency virus. Methodologically, these two problems are related to the consideration of the heterogeneity in the modeling of data from repeated measurements. They also require the development of statistical tools to distinguish from the data, homogeneous clusters of localities, individuals. . . that are needed to make more efficient health measures often deployed by practitioners in the fight against malaria and HIV/AIDS. The finite mixture models, due to their flexibility, are statistical tools that not only provide good estimates in the presence of heterogeneity in the observations but also a good classification of statistical units. We show that they are able to deal with the problematics of our study. The spatial and temporal distributions of malaria vectors are modeled through two different applications of finite mixture models and a simple tool to evaluate the impact of vector control methods is generated. We introduce a ”variance trajectories” method in a third application of finite mixture models to data on adherence to antiretroviral therapy by people living with human immunodeficiency virus

Un sonar latéral de cartographie enregistre les signaux qui ont été rétrodiffusés par le fond marin sur une large fauchée. Les signaux sont ainsi révélateurs de l’interaction entre l’onde acoustique émise et le fond de la mer pour une large plage de variation de l’angle de rasance. L’analyse des statistiques de ces signaux rétrodiffusés montre une dépendance à ces angles de rasance, ce qui pénalise fortement la segmentation des images en régions homogènes. Pour améliorer cette segmentation, l’approche classique consiste à corriger les artefacts dus à la formation de l’image sonar (géométrie d’acquisition, gains variables, etc.) en considérant un fond marin plat et en estimant des lois physiques (Lambert, Jackson, etc.) ou des modèles empiriques. L’approche choisie dans ce travail propose de diviser l’image sonar en bandes dans le sens de la portée ; la largeur de ces bandes étant suffisamment faible afin que l’analyse statistique de la rétrodiffusion puisse être considérée indépendante de l’angle de rasance. Deux types d’analyse de texture sont utilisés sur chaque bande de l’image. La première technique est basée sur l’estimation d’une matrice des cooccurrences et de différents attributs d’Haralick. Le deuxième type d’analyse est l’estimation d’attributs spectraux. La bande centrale localisée à la moitié de la portée du sonar est segmentée en premier par un réseau de neurones compétitifs basé sur l’algorithme SOFM (Self-Organizing Feature Maps) de Kohonen. Ensuite, la segmentation est réalisée successivement sur les bandes adjacentes, jusqu’aux limites basse et haute de la portée sonar. A partir des connaissances acquises sur la segmentation de cette première bande, le classifieur adapte sa segmentation aux bandes voisines. Cette nouvelle méthode de segmentation est évaluée sur des données réelles acquises par le sonar latéral Klein 5000. Les performances de segmentation de l’algorithme proposé sont comparées avec celles obtenues par des techniques classiques. / The sidescan sonar records the energy of an emitted acoustical wave backscattered by the seabed for a large range of grazing angles. The statistical analysis of the recorded signals points out a dependence according grazing angles, which penalizes the segmentation of the seabed into homogeneous regions. To improve this segmentation, classical approaches consist in compensating artifacts due to the sonar image formation (geometry of acquisition, gains, etc.) considering a flat seabed and using either Lambert’s law or an empirical law estimated from the sonar data. The approach chosen in this study proposes to split the sonar image into stripes in the swath direction; the stripe width being limited so that the statistical analysis of pixel values can be considered as independent of grazing angles. Two types of texture analysis are used for each stripe of the image. The first technique is based on the Grey-Level Co-occurrence Matrix (GLCM) and various Haralick attributes derived from. The second type of analysis is the estimation of spectral attributes. The starting stripe at mid sonar slant range is segmented with an unsupervised competitive neural network based on the adaptation of Self- Organizing Feature Maps (SOFM) algorithm. Then, from the knowledge acquired on the segmentation of this first stripe, the classifier adapts its segmentation to the neighboring stripes, allowing slight changes of statistics from one stripe to the other. The operation is repeated until the beginning and the end of the slant range are reached. The study made in this work is validated on real data acquired by the sidescan sonar Klein 5000. Segmentation performances of the proposed algorithm are compared with those of conventional approaches.

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