Submitted by Jordan (jordanbiblio@gmail.com) on 2017-05-04T13:38:27Z No. of bitstreams: 1 DISS_2015_Gracyeli Santos Souza Guarienti.pdf: 4160382 bytes, checksum: 066e507b4df1c012a091983043416a9b (MD5) / Approved for entry into archive by Jordan (jordanbiblio@gmail.com) on 2017-05-04T15:41:01Z (GMT) No. of bitstreams: 1 DISS_2015_Gracyeli Santos Souza Guarienti.pdf: 4160382 bytes, checksum: 066e507b4df1c012a091983043416a9b (MD5) / Made available in DSpace on 2017-05-04T15:41:01Z (GMT). No. of bitstreams: 1 DISS_2015_Gracyeli Santos Souza Guarienti.pdf: 4160382 bytes, checksum: 066e507b4df1c012a091983043416a9b (MD5) Previous issue date: 2015-05-27 / CAPES / Variáveis climatológicas podem ser estudadas a partir de seu comportamento temporal. Nesse sentido, este trabalho desenvolveu uma técnica computacional de processamento espaço-temporal de variáveis climatológicas que utiliza busca por similaridade e a possibilidade de comparação em várias resoluções temporais. Para demonstração do uso da técnica e verificação dos resultados, sequências de processamento foram aplicadas em séries de precipitação de um período de quinze anos usando os algoritmos Dynamic Time Warping (DTW) e wavelet em quatro biomas: Amazônia, Cerrado, Pantanal e Mata Atlântica. A técnica foi aplicada nas séries originais e em suas wavelets, com resoluções temporais mensal, semestral, anual e quinze anos de forma a permitir que análises específicas em cada resolução possam ser aplicadas. A flexibilidade e a variedade de resoluções temporais permitidas pela técnica torna possível acrescentar aos processos de monitoramento ambiental novas perspectivas em tomadas de decisão. / Climatic variables can be studied from its temporal behavior. In this sense, this study developed a temporal analysis technique for climatological variables using similarity search and the possibility of comparison in various temporal resolution levels. For the income statement, several processing sequences were applied in series of precipitation a period of fifteen years using the Dynamic Time Warping algorithm (DTW) and wavelet on four biomes: Amazon, Cerrado, Pantanal and Atlantic Forest. The technique was applied to the original data and wavelets, in the temporal resolution of time monthly, semi-annual, annual and fifteen years enable visualization and comparison of data on these different scales. Application the technique developed in this study, provide new perspectives to decision-making in environmental monitoring processes.

Cette étude consiste à étudier l'apport de réseaux de neurones<br />artificiels pour améliorer le contrôle de processus industriels<br />complexes, caractérisés en particulier par leur aspect temporel.<br />Les motivations principales pour traiter des séries temporelles<br />sont la réduction du volume de données, l'indexation pour la<br />recherche de similarités, la localisation de séquences,<br />l'extraction de connaissances (data mining) ou encore la<br />prédiction.<br /><br />Le processus industriel choisi est un four à arc<br />électrique pour la production d'acier liquide au Luxembourg. Notre<br />approche est un concept de contrôle prédictif et se base sur des<br />méthodes d'apprentissage non-supervisé dans le but d'une<br />extraction de connaissances.<br /><br />Notre méthode de codage se base sur<br />des formes primitives qui composent les signaux. Ces formes,<br />composant un alphabet de codage, sont extraites par une méthode<br />non-supervisée, les cartes auto-organisatrices de Kohonen (SOM).<br />Une méthode de validation des alphabets de codage accompagne<br />l'approche.<br /><br />Un sujet important abordé durant ces recherches est<br />la similarité de séries temporelles. La méthode proposée est<br />non-supervisée et intègre la capacité de traiter des séquences de<br />tailles variées.

Le rythme impressionnant auquel les technologies de séquençage progressent est alimenté par leur promesse de révolutionner les soins de santé et la recherche biomédicale. Le séquençage par nanopores est devenu une technologie attrayante pour résoudre des lacunes des technologies précédentes, mais aussi pour élargir nos connaissances sur le transcriptome en générant des lectures longues qui simplifient l’assemblage et la détection de grandes variations structurelles. Au cours du processus de séquençage, les nanopores mesurent les signaux de courant électrique représentant les bases (A, C, G, T) qui se déplacent à travers chaque nanopore. Tous les nanopores produisent simultanément des signaux qui peuvent être analysés en temps réel et traduits en bases par le processus d’appel de bases. Malgré la réduction du coût de séquençage et la portabilité des séquenceurs, le taux d’erreur de l’appel de base entrave leur mise en oeuvre dans la recherche biomédicale. Le but de ce mémoire est de classifier des séquences d’ARNm individuelles en différents groupes d’isoformes via l’élucidation de motifs communs dans leur signal brut. Nous proposons d’utiliser l’algorithme de déformation temporelle dynamique (DTW) pour l’alignement de séquences combiné à la technologie nanopore afin de contourner directement le processus d’appel de base. Nous avons exploré de nouvelles stratégies pour démontrer l’impact de différents segments du signal sur la classification des signaux. Nous avons effectué des analyses comparatives pour suggérer des paramètres qui augmentent la performance de classification et orientent les analyses futures sur les données brutes du séquençage par nanopores. / The impressive rate at which sequencing technologies are progressing is fueled by their promise to revolutionize healthcare and biomedical research. Nanopore sequencing has become an attractive technology to address shortcomings of previous technologies, but also to expand our knowledge of the transcriptome by generating long reads that simplify assembly and detection of large structural variations. During the sequencing process, the nanopores measure electrical current signals representing the bases (A, C, G, T) moving through each nanopore. All nanopores simultaneously produce signals that can be analyzed in real time and translated into bases by the base calling process. Despite the reduction in sequencing cost and the portability of sequencers, the base call error rate hampers their implementation in biomedical research. The aim of this project is to classify individual mRNA sequences into different groups of isoforms through the elucidation of common motifs in their raw signal. We propose to use the dynamic time warping (DTW) algorithm for sequence alignment combined with nanopore technology to directly bypass the basic calling process. We explored new strategies to demonstrate the impact of different signal segments on signal classification. We performed comparative analyzes to suggest parameters that increase classification performance and guide future analyzes on raw nanopore sequencing data.

The subject of this graduation thesis is an application of speech recognition into ATC commands. The selection of methods and approaches to automatic recognition of ATC commands rises from detailed air traffic studies. By the reason that there is not any definite solution in such extensive field like speech recognition, this diploma work is focused just on speech recognizer based on comparison with templates (DTW). This recognizor is in this thesis realized and compared with freely accessible HTK system from Cambrige University based on statistic methods making use of Hidden Markov models. The usage propriety of both methods is verified by practical testing and results evaluation.

With the arrival of 5G networks, telecommunication systems are becoming more intelligent, integrated, and broadly used. This thesis focuses on predicting the upcoming traffic to efficiently promote resource allocation, guarantee stability and reliability of the network. Since networks modeled as graphs potentially capture more information than tabular data, the construction of the graph and choice of the model are key to achieve a good prediction. In this thesis traffic prediction is based on a time-evolving graph, whose node and edges encode the structure and activity of the system. Edges are created by dynamic time-warping (DTW), geographical distance, and $k$-nearest neighbors. The node features contain different temporal information together with spatial information computed by methods from topological data analysis (TDA). To capture the temporal and spatial dependency of the graph several dynamic graph methods are compared. Throughout experiments, we could observe that the most successful model GConvGRU performs best for edges created by DTW and node features that include temporal information across multiple time steps. / Med ankomsten av 5G nätverk blir telekommunikationssystemen alltmer intelligenta, integrerade, och bredare använda. Denna uppsats fokuserar på att förutse den kommande nättrafiken, för att effektivt hantera resursallokering, garantera stabilitet och pålitlighet av nätverken. Eftersom nätverk som modelleras som grafer har potential att innehålla mer information än tabulär data, är skapandet av grafen och valet av metod viktigt för att uppnå en bra förutsägelse. I denna uppsats är trafikförutsägelsen baserad på grafer som ändras över tid, vars noder och länkar fångar strukturen och aktiviteten av systemet. Länkarna skapas genom dynamisk time warping (DTW), geografisk distans, och $k$-närmaste grannarna. Egenskaperna för noderna består av dynamisk och rumslig information som beräknats av metoder från topologisk dataanalys (TDA). För att inkludera såväl det dynamiska som det rumsliga beroendet av grafen, jämförs flera dynamiska grafmetoder. Genom experiment, kunde vi observera att den mest framgångsrika modellen GConvGRU presterade bäst för länkar skapade genom DTW och noder som innehåller dynamisk information över flera tidssteg.