Orientador: Maria de Lourdes Bueno Trindade Galo / Resumo: Atualmente o volume de dados produzidos tem atingido patamares nunca imaginados, sobretudo em decorrência da multiplicação do número de sensores e da popularização da internet, com a web 2.0 e as redes sociais. Dentre os diversos tipos de sensores existentes, os de imageamento, transportados principalmente por satélites, produzem vastos conjuntos de observações da superfície da Terra. A observação contínua da Terra por satélites possibilita o monitoramento de mudanças no uso e cobertura da terra. Contudo, em diversas pesquisas relacionadas a mudanças no planeta, são utilizados apenas pequenos fragmentos do imenso conjunto de dados existente, essencialmente devido a ainda haver uma lacuna científicatecnológica relacionada aos procedimentos de organização, armazenamento, análise e representação de grandes conjuntos de dados. Portanto, nessa pesquisa foi definida uma estrutura para organização, armazenamento e recuperação de dados espaço-temporais, com o propósito de fornecer suporte a detecção de mudanças na cobertura da terra. Para tanto, foi definida como aplicação a análise de séries temporais de Normalized Difference Vegetation Index (NDVI) derivadas de imagens adquiridas desde 1984 até 2017, pelos sensores Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) e Operational Land Imager (OLI) para a região de Porto Velho, Rondônia. Foi construída uma série temporal de NDVI para a posição de cada pixel presente na área de estudo. Regiões de referência foram definidas par... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Nowadays the size of datasets has been reaching levels never seen before, mainly due to new sensors and the widespread of the internet, with web 2.0 and social media. Among the various types of sensors, the imaging sensors, mainly carried by satellites, have produced big Earth observations datasets. The regular Earth observation by satellites enable to monitor Land Use/Cover Change (LUCC). However, in many researches related to LUCC, only small parts of the big Earth Observation datasets are normally used, because there is still a scientifictechnological gap related to the organization, storage, analysis and representation of big Earth Observations data. Therefore, in this research was defined a database for the organization, storage and retrieval of spatio-temporal data, to support a LUCC task. Therefore, the time series analysis of Normalized Difference Vegetation Index (NDVI) of images acquired from 1984 to 2017 by Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) for the region of Porto Velho, Rondônia was defined as the application. To the position each of the pixel in the study area was built a NDVI time series. Reference areas were defined to retrieve reference time series that describe the land cover types and the change classes (anthropic and natural). The Fast Dynamic Time Warping (FastDTW) algorithm was used to measure the similarity between the time series, to be classified and reference ones. To find the time series clas... (Complete abstract click electronic access below) / Mestre

Temporal data analysis and mining has attracted substantial interest due to theproliferation and ubiquity of time series in many fields. Time series clustering isone of the most popular mining methods, and many time series clustering algorithmsprimarily focus on detecting the clusters in a batch fashion that will use alot of memory space and thus limit the scalability and capability for large timeseries.The BIRCH algorithm has been proven to scale well to large datasets,which is characterized by an incrementally clustering data objects using a singlescan. However the Euclidean distance metric employed in BIRCH has beenproven to not be accurate for time series and will degrade the accuracy performance.To overcome this drawback, this work proposes an extended BIRCH algorithmfor large time series. The BIRCH clustering algorithm is extended bychanging the cluster feature vector to the proposed modified cluster feature, replacingthe original Euclidean distance measure with dynamic time warping andemploying DTW barycenter averaging method as the centroid computation approach,which is more suitable for time-series clustering than any other averagingmethods. To demonstrate the effectiveness of the proposed algorithm, weconducted an extensive evaluation of our algorithm against BIRCH, k-meansand their variants with combinations of competitive distance measures. Experimentalresults show that the extended BIRCH algorithm improves the accuracysignificantly compared to the BIRCH algorithm and its variants, and achievescompetitive and similar accuracy as k-means and its variant, k-DBA. However,unlike k-means and k-DBA, the extended BIRCH algorithm maintains the abilityof incrementally handling continuous incoming data objects, which is thekey to cluster large time-series datasets. Finally the extended BIRCH-based algorithmis applied to solve a subsequence time-series clustering task of a simulationmulti-variate time-series dataset with the help of a sliding window.

Even though speaker verification is a broad subject, the commercial and personal use implementations are rare. There are several problems that need to be solved before speaker verification can become more useful. The amount of pattern matching and feature extraction techniques is large and the decision on which ones to use is debatable. One of the main problems of speaker verification in general is the impact of noise. The very popular feature extraction technique MFCC is inherently sensitive to mismatch between training and verification conditions. MFCC is used in many speech recognition applications and is not only useful in text-dependent speaker verification. However the most reliable verification techniques are text-dependent. One of the most popular pattern matching techniques in text-dependent speaker verification is DTW. Although having limitations outside the text-dependent applications it is a reliable way of matching templates even with limited amount of training material. The signal processing techniques, MFCC and DTW are explained and discussed in detail along with a Matlab program where these techniques have been implemented. The choices made in signal processing, feature extraction and pattern matching  are determined by discussions of available studies on these topics. The results indicate that it is possible to program text-dependent speaker verification systems that are functional in clean conditions with tools like Matlab.

Cílem této práce je vylepšit implementaci modulu pro mobilní aplikace pro výuku výslovnosti, najít místa vhodná pro optimalizaci a provést optimalizaci s cílem zvýšit přesnost, snížit čas zpracování a snížit paměťovou náročnost zpracování.

My diploma theses deals with the problem of the speaker recognition. The basic theory of this problem is described in the text as well as model and implementation of the system for speaker recognition. The scope of the system is to recognize up to three speakers. The theory is based on calculation parameters for speaker recognition and processing of voice. Program is made in Matlab as a independent application and it has got Czech and English interface.

The essay presents the basic characteristics of human speech recognition, describes systems for the detection of key words and further deals with the proposal of each decoder blocks divided into three chapters. The first one describes the operations that are performed before the signal distribution of the framework and the segmentation. The second chapter describes the calculation of short-term energy, the number of zero passes and self-correlative, prediction and Mel-frequency cepstral coefficients. The third chapter, which describes the design of the block decoder, describes the method of dynamic time destruction and the method based on hidden Markov model. The final part of the essay describes decoders working with a speech and a proposal for a simple decoder working with isolated words, which was based issued and tested based on the preceding chapters.

Modern classification of organisms is performed on molecular data. These methods rely on multiple alignment of sequences of characters which make them computationally demanding. Only small parts of genomes can be compared in reasonable time. In this paper, the novel algorithm based on conversion of the whole genome sequences to cumulative phase signals is presented. Dyadic wavelet transform is used for lossy compression of signals by redundant frequency bands elimination. Signal classification is then performed as a cluster analysis using Euclidian metrics where multiple alignment is replaced by dynamic time warping.

This work deals with the classification of cardiac cycles, which uses a method of dynamic time warping and cluster analysis. Method of dynamic time warping is among the elderly, but for its simplicity compared to others is still very much used, and also achieved good results in practice. Cluster analysis is used in many fields such as marketing or just for biological signals. The aim of this work is a general introduction to the ECG signal and the method and implementation of dynamic time warping algorithm. Subsequently, cluster analysis and finally the creation of the user interface for the algorithms.

Main goal of this work is to get acquainted with the plagiarism problem and propose the methods that will lead to detection of plagiarism in program codes. In the first part of this paper different types of plagiarism and some methods of detection are introduced. In the next part the preprocessing and attributes detection is described. Than the new method of detection and adaptive weights usage is proposed. Last part summarizes the results of the detector testing on the student projects database

In the new context of major public emergencies, this paper will mainly study the measurement and influencing factors of systemic risks in Chinese financial institutions based on three dimensions: overall situation, industries, and institutions. First, it uses the DTW-MST network model to describe the dependence structure between financial institutions and between industries. It explores important institutional nodes of risk dependence from a network perspective. Then, it uses the time-varying Copula-CoVaR model to measure financial institutions' and industries' risk spillover effect on the whole financial system and analyze the characteristics and differences of risk spillover. Last, it uses the panel regression model to study the influencing factors of the risk spillover effect of financial institutions and explore the sources of systemic risks. The results show that: (1) Industrial Bank (CIB), Changjiang Securities (CJSC), and China Pacific Insurance (CPIC) are the central nodes of the banking, securities, and insurance industries, respectively. (2) The risk spillover effect is characterized by a significant asymmetry and thick tail, and negative news has a greater impact on the risk spillover effect. (3) The value at risk (VaR) and volatility of financial institutions have a significant positive correlation with the risk spillover effect, while the size of financial institutions has a significant negative correlation with the risk spillover effect. / Business Administration/Finance