<p>Rad je posvećen problemu rane i brze detekcije i izolacije neželjenih stanja dinamičkih sistema, sa posebnim naglaskom na rano otkrivanje različitih nepravilnosti u radu i kvarova industrijskih procesa.</p> / <p>The thesys is dedicated to the problem of early and swift detection and isolation of unwanted working regimes of dynamical systems, with particular emphasis on the early detection of various irregularities and failures of industrial processes.</p>

Ad hoc mobile wireless networks are self-configuring infrastructureless networks of mobile devices connected via wireless links. Each device can send and receive data, but it should also forward traffic unrelated to its own use. All need to maintain their autonomy, and effectively preserve their resources (e.g. battery power). Moreover, they can leave the network at any time. Their intrinsic dynamicity and fault tolerance makes them suitable for applications, such as emergency response and disaster relief, when infrastructure is nonexistent or damaged due to natural disasters, such as earthquakes and flooding, as well as more mundane, day-to-day, uses where their flexibility would be advantageous. Routing is the fundamental research issue for such networks and refers to finding and maintaining routes between nodes. Moreover, it involves selecting the best route where many may be available. However, due to the freedom of movement of nodes, new routes need to be constantly recalculated. Most routing protocols use pure broadcasting to discover new routes, which takes up a substantial amount of bandwidth. Intelligent rebroadcasting reduces these overheads by calculating the usefulness of a rebroadcast, and the likelihood of message collisions. Unfortunately, this introduces latency and parts of the network may become unreachable. This dissertation presents a routing protocol that uses a new parallel and distributed guided broadcasting technique to reduce redundant broadcasting and to accelerate the path discovery process, while maintaining a high reachability ratio as well as keeping node energy consumption low. This broadcasting scheme is implemented in a Mobile Ad Hoc Network (MANET) and a Wireless Mesh Network (WMN). To reduce overheads further, a Zone based Routing with Parallel Collision Guided Broadcasting Protocol (ZCG) in MANET is introduced. This uses a one hop clustering algorithm that splits the network into zones led by reliable leaders that are mostly static and have plentiful battery resources. For WMN, a Social-aware Routing Protocol (SCG) is designed that draws upon social network theory to associate longstanding social ties between nodes, using their communication patterns to divide the network into conceptual social groups, which allows cluster members to protect each other from redundant broadcasts by using intelligent rebroadcasting. The performance characteristics of the new protocols are established through simulations that measure their behaviour and by comparing them to other well-known routing protocols, namely the: AODV, DSR, TORA and the OLSR, as appropriate, it emerges that two new protocols, the ZCG and SCG, perform better in certain conditions, with the latter doing consistently well under most circumstances.

Cette thèse vise à faire émerger de nouvelles descriptions de la variabilité des plissements du cortex humain en s’appuyant sur des techniques de fouilles de données. L’objectif principal est la conception d’algorithmes permettant de découvrir des motifs de plissement spécifiques à une sous-population d’individus. Le but final est de réaliser un dictionnaire de ces motifs et de les associer à des particularités cognitives ou architecturales, voire à des pathologies. Deux stratégies de « clustering » sont proposées pour mettre en évidence de tels motifs. La première repose sur des descripteurs de formes globaux correspondant aux invariants de moment 3D, la seconde repose sur l’estimation d’une matrice de distances entre chaque paire d’individus. Un algorithme de clustering dédié est conçu pour détecter les motifs les plus fréquents de manière robuste. Une technique de réduction de dimension est utilisée pour mettre en évidence les transitions entre motifs au sein de la population. Les méthodes algorithmiques proposées sont utilisées pour étudier la forme du cortex sensori-moteur d’une population de gauchers contrariés. Des résultats originaux sur le lien entre la forme du sillon central et la latéralité manuelle sont mis en évidence. Les méthodes développées sont ensuite utilisées pour construire le premier dictionnaire des motifs observés dans les plissements corticaux issu d’une approche algorithmique. / This thesis aims at proposing new descriptions of the variability of the folding of the human cortex using data mining. The main objective is the design of algorithms detecting folding patterns specific to a sub-population. The long term goal is the constitution of an exhaustive dictionary of all the folding patterns enriched with links to cognitive or architectural specificities, or to pathologies. Two clustering strategies are proposed to detect such patterns. The first one is based on global shape descriptors called the 3D moment invariants, the second one implies the computation of a pairwise distance matrix. A dedicated clustering algorithm is designed for robust detection of the most frequent patterns. A dimension reduction strategy is proposed to study the transition from one pattern to another across the population. The proposed framework is applied to the study of the shape of the sensori-motor cortex of a population of left-handers forced to write with the right hand. Original discoveries relating the shape of the central sulcus to handedness are achieved. The framework is finally used to build the first computerized dictionary of the cortical folding patterns.

Bandwidth and resource constraints at the server side is a limitation for deployment of streaming media applications. Resource constraints at the server side often leads to saturation of resources during sudden increase in requests. End System Multicast (ESM) is used to overcome the problem of resource saturation. Resources such as storage, bandwidth available at the end systems are utilized to deliver streaming media. In ESM, the end-systems (also known as peers) form a network which is commonly known as Peer-to-Peer (P2P) network. These peers that receive the stream in turn act as routable components and forward the stream to other requests. These peers do not possess server like characteristics. The peers differ from the server in the following ways: (a) they join and exit the system at will (b) unlike servers, they are not reliable source of media. This induces instability in the network. Therefore, streaming media solution over such unstable peer network is a challenging task. Two kinds of media streaming is supported by ESM, namely, live streaming media and on-demand streaming media. ESM is well studied to support live streaming media. In this thesis we explore the effectiveness of using ESM to support on-demand streaming media over P2P network. There are two major issues to support on-demand streaming video.They are: (a)unlike live streaming, every request should be served from the beginning of the stream and (b) instability in the network due to peer characteristics (particularly transience of peers). In our work, late arriving peers can join the existing stream if the initial segments can be served to these peers. In this scheme, a single stream is used to serve multiple requests and therefore the throughput increases. We propose patching mechanism in which the initial segments of media are temporarily cached in the peers as patches. The peers as they join, contribute storage and this storage space is used to cache the initial segments. The patching mechanism is controlled by Expanding Window Control Protocol (EWCP). EWCP defines a “virtual window” that logically represents the aggregated cache contributed by the peers. The window expands as the peer contribute more resources. Larger the window size more is the number of clients that can be served by a single stream. GAP is formed when contiguous segments of media is lost. GAP limits the expansion of the virtual window. We explore the conditions that lead to the formation of GAP. GAP is formed due to the transience and non-cooperation of peers. Transience of peers coupled with real time nature of the application requires fast failure recovery algorithms and methods to overcome loss of media segments. We propose an efficient peer management protocol that provides constant failure recovery time. We explore several redundancy techniques to overcome the problem of loss of video segments during transience of peers. Peer characteristics (duration, resource contribution etc.) have significant impact on performance.The design of peer management protocol must include peer characteristics to increase its effectiveness. In this thesis we present detailed analysis of the relationship between the peer characteristics and performance. Our results indicate that peer characteristics and realtime nature of the application control the performance of the system. Based on our study, we propose algorithms that considers these parameters and increase the performance of the system. Finally, we bring all the pieces of our work together into a comprehensive system architecture for streaming media over P2P networks. We have implemented a prototype Black-Board System (BBS), a distance program utility that reflects the main concepts of our work. We show that algorithms that exploit peer characteristics performs well in P2P networks.

Naïvement perçu, le processus d’évolution est une succession d’événements de duplication et de mutations graduelles dans le génome qui mènent à des changements dans les fonctions et les interactions du protéome. La famille des hydrolases de guanosine triphosphate (GTPases) similaire à Ras constitue un bon modèle de travail afin de comprendre ce phénomène fondamental, car cette famille de protéines contient un nombre limité d’éléments qui diffèrent en fonctionnalité et en interactions. Globalement, nous désirons comprendre comment les mutations singulières au niveau des GTPases affectent la morphologie des cellules ainsi que leur degré d’impact sur les populations asynchrones. Mon travail de maîtrise vise à classifier de manière significative différents phénotypes de la levure Saccaromyces cerevisiae via l’analyse de plusieurs critères morphologiques de souches exprimant des GTPases mutées et natives. Notre approche à base de microscopie et d’analyses bioinformatique des images DIC (microscopie d’interférence différentielle de contraste) permet de distinguer les phénotypes propres aux cellules natives et aux mutants. L’emploi de cette méthode a permis une détection automatisée et une caractérisation des phénotypes mutants associés à la sur-expression de GTPases constitutivement actives. Les mutants de GTPases constitutivement actifs Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V ont été analysés avec succès. En effet, l’implémentation de différents algorithmes de partitionnement, permet d’analyser des données qui combinent les mesures morphologiques de population native et mutantes. Nos résultats démontrent que l’algorithme Fuzzy C-Means performe un partitionnement efficace des cellules natives ou mutantes, où les différents types de cellules sont classifiés en fonction de plusieurs facteurs de formes cellulaires obtenus à partir des images DIC. Cette analyse démontre que les mutations Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V induisent respectivement des phénotypes amorphe, allongé, rond et large qui sont représentés par des vecteurs de facteurs de forme distincts. Ces distinctions sont observées avec différentes proportions (morphologie mutante / morphologie native) dans les populations de mutants. Le développement de nouvelles méthodes automatisées d’analyse morphologique des cellules natives et mutantes s’avère extrêmement utile pour l’étude de la famille des GTPases ainsi que des résidus spécifiques qui dictent leurs fonctions et réseau d’interaction. Nous pouvons maintenant envisager de produire des mutants de GTPases qui inversent leur fonction en ciblant des résidus divergents. La substitution fonctionnelle est ensuite détectée au niveau morphologique grâce à notre nouvelle stratégie quantitative. Ce type d’analyse peut également être transposé à d’autres familles de protéines et contribuer de manière significative au domaine de la biologie évolutive. / Evolution is a gradual process that gives rise to changes in the form of mutations that are reflected at the protein level. We propose that evolution of new pathways occurs by switching binding partners, hence creating new functions. The different functions encountered in a given family of related proteins have emerged from a common ancestor that has been duplicated and mutated to become implicated in new interactions and to gain new functions. In this study, we will use native and constitutive active mutant variants of the Ras-like family of small GTPases as working model, to explore such gene duplications, followed by neo / sub-functionalization. The reason for choosing this family resides in the fact that it is a defined set of proteins with well known functions that are mediated through multiple protein-protein interactions. The aim of this master is to perform a classification of budding yeast phenotypes using different approaches in order to statistically determine at which level of the population these constitutively active mutations are capable to affect cell morphology. Working with a subset of the Ras-like small GTPases family, we recently developed an approach to catalogue and classify these proteins based on multiple physical and chemical criteria. Using microscopic and bioinformatics methods, we characterized phenotypes associated with over-expression of the native small GTPases of the budding yeast Saccharomyces cerevisiae, showing that an established classification is not very clear. We are interested to investigate how point mutations in small GTPases can affect the cell morphology and their level of impact on asynchronous population. We want to establish a method to determine and quantify mutant and wild type-like phenotypes on these populations using Differential interference contrast microscopy (DIC) images only. As for the first aim of this study, we hypothesize that clustering algorithms can partition mutant cells from wild type cells based on cell shape factor measurements. To prove this hypothesis, we proposed to implement different clustering algorithms to analyze datasets which combines measurements from wild type and respective mutant populations. We created constitutively active forms of these small GTPases and used Cdc42, Rho5, Ras1 and Rsr1 to validate our results. We observed that Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L and Rsr1 G12V mutations induced characteristic amorphous, clumped/elongated, rounded and discrete large phenotypes respectively. This classification allowed us to define a phenotypical classification related to functions. Phenotype classification of the small GTPases has been confirmed using shape factor formulas accompanied with bioinformatics approaches. These approaches which involved different clustering methods allowed an automated quantitative characterization of the phenotypes of up to 7293 mutant cells. Sequence alignment of Cdc42 and Rho5 showed 46.1% identity as well as 62.6% for Ras1 and Rsr1 allowing the identification of diverged residues potentially involved in specific functions and protein-protein interactions. Directed mutagenesis and substitution of these sites from one gene to another have been performed in some positions to test for specificity and involvement in morphology changes. In parallel, interactions observed for native and constitutively active mutants Cdc42 and Rho5 will be assayed with protein-fragment complementation assay (PCA). This will enable us to determine whether a high correlation exists between functions switches and binding partner’s switches. We propose to expand this approach to the whole Ras-like small GTPases family and monitor protein-protein interactions and functions at a network scale. This research will confirm whether enrichment or depletion of residues in specific sites induces a switch of function due to switching binding partners. Understanding the mechanism underlying such correlation is important to gain insight in the biological mechanisms underlying the Ras-like small GTPases and other proteins evolution. Such knowledge is of fundamental importance in biomedical and pharmaceutical fields, since Ras-like small GTPases represent important targets for therapeutic interventions and for the evolutionary biology field.

No description available.

Made available in DSpace on 2014-12-17T14:56:14Z (GMT). No. of bitstreams: 1 LeonardoEBS_DISSERT.pdf: 24615429 bytes, checksum: 65db01cbf658b5c63cee811e9c19bafc (MD5) Previous issue date: 2013-07-29 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Self-organizing maps (SOM) are artificial neural networks widely used in the data mining field, mainly because they constitute a dimensionality reduction technique given the fixed grid of neurons associated with the network. In order to properly the partition and visualize the SOM network, the various methods available in the literature must be applied in a post-processing stage, that consists of inferring, through its neurons, relevant characteristics of the data set. In general, such processing applied to the network neurons, instead of the entire database, reduces the computational costs due to vector quantization. This work proposes a post-processing of the SOM neurons in the input and output spaces, combining visualization techniques with algorithms based on gravitational forces and the search for the shortest path with the greatest reward. Such methods take into account the connection strength between neighbouring neurons and characteristics of pattern density and distances among neurons, both associated with the position that the neurons occupy in the data space after training the network. Thus, the goal consists of defining more clearly the arrangement of the clusters present in the data. Experiments were carried out so as to evaluate the proposed methods using various artificially generated data sets, as well as real world data sets. The results obtained were compared with those from a number of well-known methods existent in the literature / Os mapas auto-organiz?veis (SOM) s?o redes neurais artificiais amplamente utilizadas no campo da minera??o de dados, principalmente por se constitu?rem numa t?cnica de redu??o de dimensionalidade dada a grade fixa de neur?nios associada ? rede. A fim de particionar e visualizar adequadamente a rede SOM, os diversos m?todos existentes na literatura devem ser aplicados em uma etapa de p?s-processamento nos seus neur?nios, visando inferir caracter?sticas relevantes do conjunto de dados. Em geral, tal processamento efetuado sobre os neur?nios da rede, ao inv?s do conjunto de dados em sua totalidade, reduz o custo computacional, dada a quantiza??o vetorial. Este trabalho prop?e p?s-processamentos dos neur?nios da rede SOM nos espa?os de entrada e de sa?da, aliando t?cnicas de visualiza??o a algoritmos baseados na for?a gravitacional e na procura do menor caminho com maior recompensa. Tais m?todos levam em considera??o for?as de liga??o entre neur?nios vizinhos e caracter?sticas de dist?ncias e densidade de padr?es, ambas associadas a posi??o que o neur?nio ocupa no espa?o dos dados ap?s o treinamento da rede. Dessa forma, busca-se definir mais nitidamente a disposi??o dos agrupamentos presentes nos dados. Experimentos foram realizados para avaliar os m?todos propostos utilizando diversos conjuntos de dados gerados artificialmente, assim como conjuntos de dados do mundo real. Os resultados obtidos foram comparados com aqueles provenientes de alguns m?todos bem conhecidos existentes na literatura

La tâche de Segmentation et Regroupement en Locuteurs (SRL), telle que définie par le NIST, considère le traitement des enregistrements d’un corpus comme des problèmes indépendants. Les enregistrements sont traités séparément, et le tauxd’erreur global sur le corpus correspond finalement à une moyenne pondérée. Dans ce contexte, les locuteurs détectés par le système sont identifiés par des étiquettes anonymes propres à chaque enregistrement. Un même locuteur qui interviendrait dans plusieurs enregistrements sera donc identifié par des étiquettes différentes selon les enregistrements. Cette situation est pourtant très fréquente dans les émissions journalistiques d’information : les présentateurs, les journalistes et autres invités qui animent une émission interviennent généralement de manière récurrente. En conséquence, la tâche de SRL a depuis peu été considérée dans un contexte plus large, où les locuteurs récurrents doivent être identifiés de manière unique dans tous les enregistrements qui composent un corpus. Cette généralisation du problème de regroupement en locuteurs va de pair avec l’émergence du concept de collection, qui se réfère, dans le cadre de la SRL, à un ensemble d’enregistrements ayant une ou plusieurs caractéristiques communes. Le travail proposé dans cette thèse concerne le regroupement en locuteurs sur des collections de documents audiovisuels volumineuses (plusieurs dizaines d’heures d’enregistrements). L’objectif principal est de proposer (ou adapter) des approches de regroupement afin de traiter efficacement de gros volumes de données, tout en détectant les locuteurs récurrents. L’efficacité des approches proposées est étudiée sous deux aspects : d’une part, la qualité des segmentations produites (en termes de taux d’erreur), et d’autre part, la durée nécessaire pour effectuer les traitements. Nous proposons à cet effet deux architectures adaptées au regroupement en locuteurs sur des collections de documents. Nous proposons une approche de simplification où le problème de regroupement est représenté par une graphe non-orienté. La décompositionde ce graphe en composantes connexes permet de décomposer le problème de regroupement en un certain nombre de sous-problèmes indépendants. La résolution de ces sous-problèmes de regroupement est expérimentée avec deux approches de regroupements différentes (HAC et ILP) tirant parti des récentes avancées en modélisation du locuteur (i-vector et PLDA). / The task of speaker diarization, as defined by NIST, considers the recordings from a corpus as independent processes. The recordings are processed separately, and the overall error rate is a weighted average. In this context, detected speakers are identified by anonymous labels specific to each recording. Therefore, a speaker appearing in several recordings will be identified by a different label in each of the recordings. Yet, this situation is very common in broadcast news data: hosts, journalists and other guests may appear recurrently. Consequently, speaker diarization has been recently considered in a broader context, where recurring speakers must be uniquely identified in every recording that compose a corpus. This generalization of the speaker partitioning problem goes hand in hand with the emergence of the concept of collections, which refers, in the context of speaker diarization, to a set of recordings sharing one or more common characteristics.The work proposed in this thesis concerns speaker clustering of large audiovisual collections (several tens of hours of recordings). The main objective is to propose (or adapt) clustering approaches in order to efficiently process large volumes of data, while detecting recurrent speakers. The effectiveness of the proposed approaches is discussed from two point of view: first, the quality of the produced clustering (in terms of error rate), and secondly, the time required to perform the process. For this purpose, we propose two architectures designed to perform cross-show speaker diarization with collections of recordings. We propose a simplifying approach to decomposing a large clustering problem in several independent sub-problems. Solving these sub-problems is done with either of two clustering approaches which takeadvantage of the recent advances in speaker modeling.

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There are billions of photos on the internet and as the size of these digital repositories grows, finding target picture becomes more and more difficult. To increase the informational quality of photo albums we propose a new method that selects representative pictures from a group of photographs using computer vision algorithms. The aim of this study is to analyze the issues about image features, image similarity, object clustering and examine the specific characteristics of photographs. Tests show that there is no universal image descriptor that can easily simulate the process of clustering performed by human vision. The thesis proposes a hybrid algorithm that combines the advantages of selected features together using a specialized multiple-step clustering algorithm. The key idea of the process is that the frequently photographed objects are more likely to be representative. Thus, with a random selection from the largest photo clusters certain representative photos are obtained. This selection is further enhanced on the basis of optimization, where photos with better photographic properties are being preferred.