Time series prediction has many applications. In cases with simultaneous series (like measurements of weather from multiple stations, or multiple stocks on the stock market)it is not unlikely that these series from different measurement origins behave similarly, or respond to the same contextual signals. Training input to a prediction model could be constructed from all simultaneous measurements to try and capture the relations between the measurement origins. A generalized approach is to train a prediction model on samples from any individual measurement origin. The data mass is the same in both cases, but in the first case, fewer samples of a larger width are used, while the second option uses a higher number of smaller samples. The first, high-width option, risks over-fitting as a result of fewer training samples per input variable. The second, general option, would have no way to learn relations between the measurement origins. Amending the general model with contextual information would allow for keeping a high samples-per-variable ratio without losing the ability to take the origin of the measurements into account. This thesis presents a vector embedding method for measurement origins in an environment with shared response to contextual signals. The embeddings are based on multi-variate time series from the origins. The embedding method is inspired by co-occurrence matrices commonly used in Natural Language Processing. The similarity measures used between the series are Dynamic Time Warping (DTW), Step-wise Euclidean Distance, and Pearson Correlation. The dimensionality of the resulting embeddings is reduced by Principal Component Analysis (PCA) to increase information density, and effectively preserve variance in the similarity space. The created embedding system allows contextualization of samples, akin to the human intuition that comes from knowing where measurements were taken from, like knowing what sort of company a stock ticker represents, or what environment a weather station is located in. In the embedded space, embeddings of series from fundamentally similar measurement origins are closely located, so that information regarding the behavior of one can be generalized to its neighbors. The resulting embeddings from this work resonate well with existing clustering methods in a weather dataset, and partially in a financial dataset, and do provide performance improvement for an LSTM network acting on said financial dataset. The similarity embeddings also outperform an embedding layer trained together with the LSTM.

The objective of this thesis is to design a system that can be used to retrieve the music songs by acoustic input. The system listens to the melody or the partial song singing by the Karaoke users, and then prompts them the whole song paragraphs. Note segmentation is completed by both the magnitude of the song and the k-Nearest Neighbor technique. In order to speed up our system, the pitch period estimation algorithm is rewritten by a theory in communications. Besides, a large popular music database is built to make this system more practical.

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.

Extracting information from acoustic signals is a common task in signal processing and pattern recognition. Broadly speaking, the processing system has, as initial task, to obtain a low-dimensional representation of the acoustic signal, extracted trough computational methods called feature extractors. This representation aims to present the sound of speech in a more convenient form to extract the information contained in the signal. Considering the initial task of processing systems, this work presents a detailed study of three classic methods for features extracting, namely: the Mel - Frequency Cepstrum Coefficients (MFCC), the Ensemble Interval Histogram (EIH), and the Zero Crossing with Peak amplitudes (ZCPA). Still in the literature review step, a study of the human peripheral auditory system was accomplished, since the EIH and ZCPA methods are based on models of human hearing. Moreover, a new extraction method based on detection of level crossings was developed, called here as Elementary Acoustic Events (EAE). In order to compare the methods, both reviewed and developed, two different experiments were applied in this work. At first, experiments with additive noise and channel effects for robustness analysis methods were performed. Finally, experiments related to the task of isolated word recognition were applied using alignment Dynamic Time Warping (DTW). The results suggest that the proposed method is more robust than the classical methods implemented, for the proposed experiments. / Extrair informações de sinais acústicos é uma tarefa bastante comum dentro das áreas de processamento de sinais e reconhecimento de padrões. De uma maneira geral, os sistemas de processamento têm como tarefa inicial obter uma representação de baixa dimensão do sinal acústico, obtida a partir de métodos computacionais denominados extratores de características. Tal representação propõe apresentar o som da fala de uma forma mais conveniente à tarefa de extração e utilização da informação contida no sinal. Dentro deste contexto, nesta dissertação foi realizado um estudo detalhado de três métodos clássicos para extração de características de sinais acústicos existentes na literatura, a saber: os Mel-Frequency Cepstrum Coefficients (MFCC); o modelo Ensemble Interval Histogram (EIH); e o modelo Zero-Crossing with Peak Amplitudes (ZCPA). Sendo que, ainda para revisão bibliográfica, um estudo do sistema auditivo periférico humano foi realizado, visto que os métodos EIH e ZCPA são baseados em modelos do ouvido humano. Em seguida, um novo método de extração baseado em detecção de cruzamentos de nível foi desenvolvido ao longo do trabalho, denominado Eventos Acústicos Elementares (EAE). Diversos experimentos foram realizados a fim de comparar os métodos clássicos e o método desenvolvido nessa dissertação. Na primeira etapa, foram realizados experimentos com ruídos aditivos e com efeitos convolutivos de canal, para análise de robustez dos métodos. Por fim, referente à segunda etapa da análise comparativa dos métodos, foram realizados experimentos relativos à tarefa de reconhecimento de palavras isoladas, utilizando o método de alinhamento temporal Dynamic Time Warping (DTW). Os resultados obtidos indicam que o método proposto possui maior robustez quando comparado aos métodos clássicos implementados.

Extracting information from acoustic signals is a common task in signal processing and pattern recognition. Broadly speaking, the processing system has, as initial task, to obtain a low-dimensional representation of the acoustic signal, extracted trough computational methods called feature extractors. This representation aims to present the sound of speech in a more convenient form to extract the information contained in the signal. Considering the initial task of processing systems, this work presents a detailed study of three classic methods for features extracting, namely: the Mel - Frequency Cepstrum Coefficients (MFCC), the Ensemble Interval Histogram (EIH), and the Zero Crossing with Peak amplitudes (ZCPA). Still in the literature review step, a study of the human peripheral auditory system was accomplished, since the EIH and ZCPA methods are based on models of human hearing. Moreover, a new extraction method based on detection of level crossings was developed, called here as Elementary Acoustic Events (EAE). In order to compare the methods, both reviewed and developed, two different experiments were applied in this work. At first, experiments with additive noise and channel effects for robustness analysis methods were performed. Finally, experiments related to the task of isolated word recognition were applied using alignment Dynamic Time Warping (DTW). The results suggest that the proposed method is more robust than the classical methods implemented, for the proposed experiments. / Extrair informações de sinais acústicos é uma tarefa bastante comum dentro das áreas de processamento de sinais e reconhecimento de padrões. De uma maneira geral, os sistemas de processamento têm como tarefa inicial obter uma representação de baixa dimensão do sinal acústico, obtida a partir de métodos computacionais denominados extratores de características. Tal representação propõe apresentar o som da fala de uma forma mais conveniente à tarefa de extração e utilização da informação contida no sinal. Dentro deste contexto, nesta dissertação foi realizado um estudo detalhado de três métodos clássicos para extração de características de sinais acústicos existentes na literatura, a saber: os Mel-Frequency Cepstrum Coefficients (MFCC); o modelo Ensemble Interval Histogram (EIH); e o modelo Zero-Crossing with Peak Amplitudes (ZCPA). Sendo que, ainda para revisão bibliográfica, um estudo do sistema auditivo periférico humano foi realizado, visto que os métodos EIH e ZCPA são baseados em modelos do ouvido humano. Em seguida, um novo método de extração baseado em detecção de cruzamentos de nível foi desenvolvido ao longo do trabalho, denominado Eventos Acústicos Elementares (EAE). Diversos experimentos foram realizados a fim de comparar os métodos clássicos e o método desenvolvido nessa dissertação. Na primeira etapa, foram realizados experimentos com ruídos aditivos e com efeitos convolutivos de canal, para análise de robustez dos métodos. Por fim, referente à segunda etapa da análise comparativa dos métodos, foram realizados experimentos relativos à tarefa de reconhecimento de palavras isoladas, utilizando o método de alinhamento temporal Dynamic Time Warping (DTW). Os resultados obtidos indicam que o método proposto possui maior robustez quando comparado aos métodos clássicos implementados.

The thesis deals with aplication of data mining methods for the analysis of two Ni3Si supercells, one with a stable grain boundary and the second one with unstable grain boundary. DOS and COHP curves are examined using selected curve matching methods. The surroundings of the individual atoms are examined by the Voronoi diagram. This information was used to reveal the differences in binding between stable and unstable supercell.

Fusion is a fundamental human process that occurs in some form at all levels of sense organs such as visual and sound information received from eyes and ears respectively, to the highest levels of decision making such as our brain fuses visual and sound information to make decisions. Multi-sensor data fusion is concerned with gaining information from multiple sensors by fusing across raw data, features or decisions. The traditional frameworks for multi-sensor data fusion only concern fusion at specific points in time. However, many real world situations change over time. When the multi-sensor system is used for situation awareness, it is useful not only to know the state or event of the situation at a point in time, but also more importantly, to understand the causalities of those states or events changing over time. / Hence, we proposed a multi-agent framework for temporal fusion, which emphasises the time dimension of the fusion process, that is, fusion of the multi-sensor data or events derived over a period of time. The proposed multi-agent framework has three major layers: hardware, agents, and users. There are three different fusion architectures: centralized, hierarchical, and distributed, for organising the group of agents. The temporal fusion process of the proposed framework is elaborated by using the information graph. Finally, the core of the proposed temporal fusion framework – Dynamic Time Warping (DTW) temporal fusion agent is described in detail. / Fusing multisensory data over a period of time is a challenging task, since the data to be fused consists of complex sequences that are multi–dimensional, multimodal, interacting, and time–varying in nature. Additionally, performing temporal fusion efficiently in real–time is another challenge due to the large amount of data to be fused. To address these issues, we proposed the DTW temporal fusion agent that includes four major modules: data pre-processing, DTW recogniser, class templates, and decision making. The DTW recogniser is extended in various ways to deal with the variability of multimodal sequences acquired from multiple heterogeneous sensors, the problems of unknown start and end points, multimodal sequences of the same class that hence has different lengths locally and/or globally, and the challenges of online temporal fusion. / We evaluate the performance of the proposed DTW temporal fusion agent on two real world datasets: 1) accelerometer data acquired from performing two hand gestures, and 2) a benchmark dataset acquired from carrying a mobile device and performing pre-defined user scenarios. Performance results of the DTW based system are compared with those of a Hidden Markov Model (HMM) based system. The experimental results from both datasets demonstrate that the proposed DTW temporal fusion agent outperforms HMM based systems, and has the capability to perform online temporal fusion efficiently and accurately in real–time.

Qu'il tente de prévenir la chute d'une personne âgée, de traduire la langue des signes ou de contrôler un humain virtuel, l'analyse de gestes est un vaste domaine de recherche qui s'attelle à reconnaître, classifier, segmenter, indexer ou encore évaluer différents types de mouvements. Cependant, peu de travaux se concentrent sur cette dernière approche d'évaluation. Ce travail de thèse propose de mettre en place un outil d’évaluation automatique et générique d’un geste sportif, reposant sur l’utilisation d’une base de données de gestes experts acquis via un système de capture de mouvements. Afin d’extraire un mouvement de référence, l’algorithme de déformation temporelle dynamique (DTW) est considéré pour aligner puis moyenner les gestes. Les méthodes d’alignements et de moyennage de séries temporelles se confrontant aux conséquences néfastes de chemins de déformation du DTW pathologiques, des contraintes locales sont ajoutées et donnent lieu à un nouvel algorithme appelé CDBA. La qualité d’un geste est estimée spatialement et temporellement à chaque instant et pour chaque membre par comparaison avec le geste de référence et pondérée par la dispersion des données expertes autour de ce geste moyen. Le processus ainsi mis en place est validé à partir de gestes de karaté et de tennis annotés par des entraîneurs. Un premier prototype d’outil d’entraînement en ligne est finalement proposé et laisse entrevoir les potentialités d’usage qui pourraient être menées à la suite de ce travail. / Either to reduce falling risks in elderly people, to translate the sign language or to control a virtual human being, gesture analysis is thriving research field that aims at recognizing, classifying, segmenting, indexing and evaluating different types of motions. As few studies tackle the evaluation process, this PhD focuses on the design of an autonomous system for the generic evaluation of sport-related gestures. The tool is trained on the basis of experts’ motions recorded with a motion capture system. Dynamic Time Warping (DTW) is deployed to obtain a reference gesture thanks to data alignment and averaging. Nevertheless, this standard method suffers from pathological paths issues that reduce its effectiveness. For this reason, local constraints are added to the new DTW-based algorithm, called CDBA (Constrained DTW Barycenter Averaging). At each time step and for each limb, the quality of a gesture is spatially and temporally assessed. Each new motion is compared to the reference gesture and weighted in terms of data dispersion around the reference.The process is validated on annotated karate and tennis databases. A first online training prototype is given in order to prompt further research on this subject.

This thesis deals with detection of speech disorders. One of the aims of this thesis is choosing suitable parameterization: short-time energy, zero-crossing rate, linear predictive analysis, perceptual linear predictive analysis, RASTA method, cepstral analysis and mel-frequency cepstral coefficient can be chosed for detections. Next aim is construction of detector of speech disorders based on DTW (Dynamic Time Warping) and artificial neuron network. Single detection proceeds on the base of collected tokens from chosen analysis and phonetic transcription of speech. Analyses, detector and phonetic transcription of Czech language are implemented in simulation environment of MATLAB.

This master thesis deals with the use of gestures in user interfaces. The goal of this thesis is to create library for hand tracking and gesture recognition in real time. For hand tracking was choosen algorithm Flock of Features. Classification of gestures is done by using algorithm DTW. This thesis also contains stage design, design and implementation of a system that uses this library. Within the tests was tested control of various application using this library.