Submitted by Diego Roberto Cintra da Silva null (diegocntr@hotmail.com) on 2017-01-22T14:31:57Z No. of bitstreams: 1 Diego Cintra - Dissertação - Mestrado.pdf: 3332491 bytes, checksum: cb4ba5462d138f7b73df3323acd794c3 (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2017-01-25T16:43:04Z (GMT) No. of bitstreams: 1 silva_drc_me_bauru.pdf: 3332491 bytes, checksum: cb4ba5462d138f7b73df3323acd794c3 (MD5) / Made available in DSpace on 2017-01-25T16:43:04Z (GMT). No. of bitstreams: 1 silva_drc_me_bauru.pdf: 3332491 bytes, checksum: cb4ba5462d138f7b73df3323acd794c3 (MD5) Previous issue date: 2016-12-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / As crises que ocorrem no mercado de ações são prejudiciais não só à parte monetária da economia de um país, mas ao desenvolvimento do país como um todo. A crise do subprime em 2008, que se iniciou nos Estados Unidos da América, atingiu o mundo todo, muitos países tiveram quedas significativas do PIB e vários entraram em recessão. Existe, então, o interesse em se compreender a dinâmica das séries temporais de variáveis como retorno e volatilidade das ações negociadas nesse mercado, a fim de compreender as diferenças de seu comportamento em momentos de crise econômica. Com o objetivo de analisar o espectro de correlação da volatilidade de ações no período da crise de 2008 e em suas vizinhanças, foram verificadas 31 ações de empresas pertencentes a diversos setores da economia brasileira, que compuseram entre 2007 e 2011 o Índice Bovespa. Para tal foram utilizados os métodos do Detrended Fluctuation Analisys – DFA e do Detrended Cross-Correlation Analisys – DCCA. Ambos métodos evidenciaram uma significativa mudança na função de probabilidade no período de crise comparativamente aos períodos de sua vizinhança. / Crises occurring in the stock market are harmful not only to the monetary part of a country's economy, but to the development of the country as a whole. The subprime crisis in 2008, which began in the United States of America, hit the whole world, many countries had significant declines in GDP and several went into recession. There is, therefore, an interest in understanding the dynamics of time series of variables such as return and volatility of the shares traded in this market, in order to understand the differences in their behavior in times of economic crisis. With the objective of analyzing the correlation spectrum of stock volatility in the period of the 2008 crisis and its neighborhoods, 31 stocks of companies belonging to various sectors of the Brazilian economy were verified, which made up the Bovespa Index between 2007 and 2011. The methods of Detrended Fluctuation Analyzes - DFA and Detrended Cross-Correlation Analyzes - DCCA were used for this. Both methods evidenced a significant change in the probability function in the period of crisis compared to the periods of its neighborhood.

O processamento de sinais EEG permite interpretar, analisar, estudar, pesquisar e experimentar a atividade elétrica do cérebro como resposta para diferentes processos cognitivos, efeitos de drogas ou fármacos, estudo de doenças psiquiátricas ou neurológicas, entre outras. Esta dissertação é orientada ao reconhecimento de padrões cerebrais que permitam classificar estados cognitivos mediante os sinais de EEG registrados em sujeitos realizando tarefas programadas. Ademais espera-se obter a maior quantidade de padrões para cada estado cognitivo e procurar os parâmetros que oferecem maior informação, analisando as principais bandas cerebrais e todos os eletrodos disponíveis na base de dados. A metodologia usada compreende o registro de cinco tarefas cognitivas analisadas com três abordagens diferentes: análises de longe-range tenporal correlations com o algoritmo de Detrended Fluctuations Analysis (DFA), análise da potência dos sinais cerebrais utilizando a Transformada Ondeleta e finalmente o estudo da sincronia cerebral usando a Transformada de Hilbert. Conclui-se que as abordagens utilizadas nesta dissertação reportam alentadores resultados para diferenciar as tarefas cognitivas estudadas, demonstrando que a utilização da informação de todos os eletrodos e de suas principais bandas cerebrais contribuem de forma positiva. Também se consegue reconhecer e identificar quais parâmetros produzem maior informação para esta análise. / EEG signal processing allows interpreting, analyzing, studying, researching and experiencing the brain electrical activity in response to different cognitive processes, effects of drugs or drugs, the study of neurological or psychiatric diseases, among others. This thesis is oriented to the recognition of brain patterns to classify cognitive states using the EEG signals recorded from subjects performing mental tasks. Also, we expect to collect as many patterns as possible for each cognitive status and to seek parameters that provide more information, examine the major bands and all brain electrodes available in the database. The methodology used includes the registration of five cognitive tasks analyzed with three different approaches: analysis of long-range temporal-correlations with the Detrended Fluctuations Analysis (DFA) algorithm, the power analysis of brain signals using the Wavelet Transform and finally the study of phased looked brain using the Hilbert transform. The approaches used for this research report excellent results for differentiating the cognitive tasks studied, showing that the use of information from all the electrodes and their main brain bands contribute positively. Also, one can recognize and identify which parameters produce more information for this analysis.

O processamento de sinais EEG permite interpretar, analisar, estudar, pesquisar e experimentar a atividade elétrica do cérebro como resposta para diferentes processos cognitivos, efeitos de drogas ou fármacos, estudo de doenças psiquiátricas ou neurológicas, entre outras. Esta dissertação é orientada ao reconhecimento de padrões cerebrais que permitam classificar estados cognitivos mediante os sinais de EEG registrados em sujeitos realizando tarefas programadas. Ademais espera-se obter a maior quantidade de padrões para cada estado cognitivo e procurar os parâmetros que oferecem maior informação, analisando as principais bandas cerebrais e todos os eletrodos disponíveis na base de dados. A metodologia usada compreende o registro de cinco tarefas cognitivas analisadas com três abordagens diferentes: análises de longe-range tenporal correlations com o algoritmo de Detrended Fluctuations Analysis (DFA), análise da potência dos sinais cerebrais utilizando a Transformada Ondeleta e finalmente o estudo da sincronia cerebral usando a Transformada de Hilbert. Conclui-se que as abordagens utilizadas nesta dissertação reportam alentadores resultados para diferenciar as tarefas cognitivas estudadas, demonstrando que a utilização da informação de todos os eletrodos e de suas principais bandas cerebrais contribuem de forma positiva. Também se consegue reconhecer e identificar quais parâmetros produzem maior informação para esta análise. / EEG signal processing allows interpreting, analyzing, studying, researching and experiencing the brain electrical activity in response to different cognitive processes, effects of drugs or drugs, the study of neurological or psychiatric diseases, among others. This thesis is oriented to the recognition of brain patterns to classify cognitive states using the EEG signals recorded from subjects performing mental tasks. Also, we expect to collect as many patterns as possible for each cognitive status and to seek parameters that provide more information, examine the major bands and all brain electrodes available in the database. The methodology used includes the registration of five cognitive tasks analyzed with three different approaches: analysis of long-range temporal-correlations with the Detrended Fluctuations Analysis (DFA) algorithm, the power analysis of brain signals using the Wavelet Transform and finally the study of phased looked brain using the Hilbert transform. The approaches used for this research report excellent results for differentiating the cognitive tasks studied, showing that the use of information from all the electrodes and their main brain bands contribute positively. Also, one can recognize and identify which parameters produce more information for this analysis.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-05T15:38:42Z No. of bitstreams: 1 Erinaldo Leite Batista Almeida.pdf: 3620819 bytes, checksum: b2532ef7524f47d5417d01445fec797b (MD5) / Made available in DSpace on 2016-07-05T15:38:42Z (GMT). No. of bitstreams: 1 Erinaldo Leite Batista Almeida.pdf: 3620819 bytes, checksum: b2532ef7524f47d5417d01445fec797b (MD5) Previous issue date: 2009-08-10 / Financial markets are complex systems that contain large numbers of interacting units, including interactions among various units in the same market and interactions between units in different markets. Various methods of economics, statistics and econophysics have been developed to analyze financial temporal series (such as price returns, share volume, number of transactions), and serve to establish theoretical models for underlying stochastic processes. The availability of financial data on the internet and increasing computational power have enabled researchers to conduct a large number of empirical studies on financial markets. These studies have shown some universal properties: the risk function of price returns is scale invariant, with power-law behavior and similar value of exponent for different markets; the absolute values of returns (volatility) exhibit long-range power-law correlations. In this work, we use methods if econophysics to study the statistical properties of Brazilian financial markets. We analyze and compare scale properties of risk functions and correlations in temporal series of price returns of agricultural commodities and stocks of various companies traded at Bovespa. We analyze the daily prices of five commodities and twenty stocks traded in the period 2000-2008. For both commodities and stocks, the risk function of daily price returns shows powerlaw behavior with the exponent outside the Levy stable region. The values of exponents are higher for stocks than for commodities. We use Detrended Fluctuation Analysis (DFA) to study correlations in daily time series of absolute values of returns (volatility). This method was developed to quantify long range correlations in non-stationary temporal series.All analyzed series show persistent behavior, meaning that large (small) values are more likely to be followed with large (small) values. The value of the DFA exponent is higher for commodities than for stocks. We also use Detrended Cross Correlation Analysis (DCCA) to study cross-correlations between two series. The values of DCCA exponents are above 0.5 for all series, indicating the existence of long range cross-correlations. This means that each stock or commodity has long memory of its own previous values and of previous values of other stocks or commodities studied. These results are in agreement with results obtained for American financial markets. / Mercados financeiros são caracterizados por um grande número de unidades e interações complexas, incluindo as interações internas (entre diferentes elementos de um mercado) e fatores externos (influência de outros mercados). Vários métodos de economia, estatística e recentemente econofísica foram desenvolvidos para analisar as séries temporais de variáveis financeiras (retorno de preços de ações, mercadorias e taxas de cambio, índice de mercado, volume de negociação, etc.), com objetivo de estabelecer os modelos teóricos para processos estocásticos que estão em base desses fenômenos. A disponibilidade de dados financeiros de vários mercados e crescente poder computacional resultaram em um grande número de estudos empíricos cujos resultados mostraram algumas propriedades universais: a função risco de retornos de preços segue uma lei de potência com o valor de expoente similar para os vários mercados; os valores absolutos de retornos possuem correlações de longo alcance. Neste trabalho foram usados os métodos de econofísica para estudar as propriedades estatísticas do mercado financeiro brasileiro. Foram analisadas e comparadas as propriedades de escala de função risco e de correlações em séries temporais de retornos de preços de mercadorias agrícolas e preços de ações de várias empresas negociadas na Bolsa de Valores de São Paulo (BOVESPA). Foram analisados os preços diários de cinco mercadorias: açúcar, algodão, café, soja e boi, registrados em período 2000-2008. Para ações, analisamos as características seguintes: preços de abertura, fechamento, valores máximo e mínimo, volume e montante. Todas as séries são diárias, registradas no período de 2000-2008. São estudadas 20 empresas divididas em 4 grupos: bancos, energia, telecomunicações e siderurgia (5 empresas de cada grupo). Para todas as séries estudadas a função risco de retornos de preços segue uma lei de potência com os valores de expoente maiores para ações do que para mercadorias. As correlações são analisadas para os valores absolutos de retornos de preços (volatilidade). Foi usado o método Detrended Fluctuation Analysis (DFA), desenvolvido para quantificar as correlações de longo alcance em séries temporais não estacionárias. Todas as séries mostraram um comportamento persistente, significando que os valores grandes (pequenos) tem maior probabilidade de serem seguidos por valores grandes (pequenos). Os valores de expoente DFA são maiores para mercadorias do que para as ações. Foi utilizada uma generalização de DFA, Detrended Cross Correlation Analysis (DCCA) para analisar as correlações cruzadas entre duas séries. Os valores de expoente DCCA para todas as séries estudadas indicam a existência de correlações cruzadas de longo alcance significando que os valores de cada série possuem memória de longo alcance de seus valores anteriores e também de valores anteriores de outras série. Os resultados estão em acordo com os resultados obtidos para mercado americano.

Big data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.

The stride interval of typical human gait is correlated over thousands of strides. This statistical persistence diminishes with age, disease, and pace-constrained walking. Considering the widespread use of treadmills in rehabilitation and research, it is important to understand the effect of this speed-constrained locomotor modality on stride interval dynamics. To this end, and given that the dynamics of children have been largely unexplored, this study investigated the impact of treadmill walking, both with and without handrail use, on paediatric stride interval dynamics. An initial stationarity analysis of stride interval time series identified both non-stationary and stationary signals during all walking conditions. Subsequent scaling analysis revealed diminished stride interval persistence during unsupported treadmill walking compared to overground walking. Finally, while the correlation between stride interval dynamics and gross energy expenditure was investigated in an effort to elucidate the clinical meaning of persistence, no simple linear correlation was found.

The stride interval of typical human gait is correlated over thousands of strides. This statistical persistence diminishes with age, disease, and pace-constrained walking. Considering the widespread use of treadmills in rehabilitation and research, it is important to understand the effect of this speed-constrained locomotor modality on stride interval dynamics. To this end, and given that the dynamics of children have been largely unexplored, this study investigated the impact of treadmill walking, both with and without handrail use, on paediatric stride interval dynamics. An initial stationarity analysis of stride interval time series identified both non-stationary and stationary signals during all walking conditions. Subsequent scaling analysis revealed diminished stride interval persistence during unsupported treadmill walking compared to overground walking. Finally, while the correlation between stride interval dynamics and gross energy expenditure was investigated in an effort to elucidate the clinical meaning of persistence, no simple linear correlation was found.

Human activities, such as agricultural activity, housing construction, forest logging, etc., play an important role in vegetation changes. Any disturbance to the ecosystem by a severe change in landscape patterns may reduce the survival capacity of certain plant species. In recent years, many studies have used a geographic information system to establish spatial data on vegetation changes; this information includes both the plant species and their spatial structure. Therefore, the aim of this study is to investigate the trends and patterns of vegetation changes and the degree of correlation with the particular environmental features at Wangan Island and Chimei Island in the Penghu Islands, from 1979 to 2009. Both islands have very similar natural features, including geographic location, natural environment, and economic development, but the spatial structures of the land use type are different. The study makes use of geographic information systems, detrended correspondence analysis, canonical correspondence analysis and landscape ecological indicators as study tools. The results are expected to promote our understanding of the spatial distribution patterns of vegetation types and plant species. The results can be divided into three areas. First, both islands follow the same trend with regard to changes in land cover, but the rates of change are different. And the spatial structure of land use types affects the location of land cover types. Example, the centralized and decentralized villages both impact the distribution of the woodland. The mesh and ring road both impact the location of agricultural land. Second, the grassland plant species are correlated with the environmental factors, but the forest plant species are not. The major woodland specie is Leucaena leucocephala on both islands, whose physiological habits may reduce the degree of the correlation. Third, compared to the two islands, Chimei Island is more significant about that the vegetation types are correlated with the neighboring land use types. Finally, the plant species of the local vegetation types could be predicted by changes in the type of land use. In addition, this study has built a trend scheme of the spatial structure changes on Wangan Island and on Chimei Island, which can be used in the island¡¦s future environmental planning.

Crack initiation and propagation in the bladed disks of aero-engines caused by high-cycle fatigue under cyclic loads could result in the breakdown of the engines if not detected at an early stage. Although a number of fault detection methods have been reported in the literature, it still remains very challenging to develop a reliable online technique to accurately diagnose defects in bladed disks. One of the main challenges is to characterize signals contaminated by noises. These noises caused by very dynamic engine operation environment. This work presents a new technique for engine bladed disk crack detection, which utilizes advanced analysis of clearance and time-of-arrival signals acquired from blade tip sensors. This technique involves two stages of signal processing: 1) signal pre-processing for noise elimination from predetermined causes; and 2) signal post-processing for characterizing crack initiation and location. Experimental results from the spin rig test were used to validate technique predictions.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-06T20:20:42Z No. of bitstreams: 1 Jose Gomes Cabral Neto.pdf: 2927907 bytes, checksum: 48ae7e81516be4443fde4aa11b9564b9 (MD5) / Made available in DSpace on 2016-07-06T20:20:42Z (GMT). No. of bitstreams: 1 Jose Gomes Cabral Neto.pdf: 2927907 bytes, checksum: 48ae7e81516be4443fde4aa11b9564b9 (MD5) Previous issue date: 2013-02-26 / The lack of a better knowledge to further the proper management of water systems and soil of Brazilian semiarid contributes to maintain the social inequalities which are subject to local populations. The adjust of hydrologic data to probability density functions, and the application of Detrended Fluctuation Analysis method to quantify the long-range correlations in non-stationary time series hydrological contribute to a better use of water resources in the environment semiarid and reduction of the risk of economic loss. This way, the information of hydrological variables of blade height and flow of the Stream catchment Jacu in the semiarid and region of Pernambuco were used and it was found that the maximum and minimum blade height and flow of semiarid watershed Jacu best adjusted to Weibull distributions, Gumbel, Log-Normal and Gamma. The Detrended Fluctuation Analysis method showed the existence of persistent long-range correlations, which represents an important property of stochastic processes generating this phenomenon. The series of blade heights showed persistence stronger than the series of flows. In smaller scales fluctuation softer, represented by exponents , and larger scales showed persistent fluctuations, represented by exponents. / A falta do conhecimento científico mais aprofundado para o manejo adequado dos sistemas hídricos e dos solos do semiárido nordestino contribui para manutenção da desigualdade social ao qual estão submetidas às populações locais. O ajuste de dados hidrológicos às distribuições estatísticas e a aplicação do método Detrended Fluctuation Analysis (DFA) para quantificar as correlações de longo alcance nas séries temporais hidrológicas não estacionárias, contribuem para um melhor uso dos recursos hídricos no semiárido e para redução do risco de ocorrência de perdas econômicas. Dessa forma, foram utilizadas informações das variáveis hidrológicas de altura da lâmina de água e vazão da Bacia hidrográfica semiárida do Riacho Jacu de Pernambuco constatando-se que os valores máximos e mínimos de altura da lâmina e vazão da bacia hidrográfica do referido riacho, se ajustaram melhor as distribuições Weibull, Gumbel, Log-Normal e Gama. O método Detrended Fluctuation Analysis indicou a existência de correlações persistentes de longo alcance, que representa uma propriedade importante dos processos estocásticos geradores desse fenômeno. As séries das alturas da lâmina apresentaram persistência mais forte do que as séries das vazões. Nas escalas menores apresentam flutuações mais suaves, representadas pelos expoentes , e para escalas maiores apresentaram flutuações persistentes, representadas pelos expoentes.