Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-05-23T12:23:58Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) SOME NEW FAMILIES OF CONTINUOUS DISTRIBUTIONS.pdf: 5612905 bytes, checksum: 3fd32464f68606705a4b23070897a8e2 (MD5) / Made available in DSpace on 2017-05-23T12:23:58Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) SOME NEW FAMILIES OF CONTINUOUS DISTRIBUTIONS.pdf: 5612905 bytes, checksum: 3fd32464f68606705a4b23070897a8e2 (MD5) Previous issue date: 2016-06-27 / FACEPE / The area of survival analysis is important in Statistics and it is commonly applied in biological sciences, engineering, social sciences, among others. Typically, the time of life or failure can have different interpretations depending on the area of application. For example, the lifetime may mean the life itself of a person, the operating time of equipment until its failure, the time of survival of a patient with a severe disease from the diagnosis, the duration of a social event as a marriage, among other meanings. The time of life or survival time is a positive continuous random variable, which can have constant, monotonic increasing, monotonic decreasing or non-monotonic (for example, in the form of a U) hazard function. In the last decades, several families of probabilistic models have been proposed. These models can be constructed based on some transformation of a parent distribution, commonly already known in the literature. A given linear combination or mixture of G models usually defines a class of probabilistic models having G as a special case. This thesis is composed of independent chapters. The first and last chapters are short chapters that include the introduction and conclusions of the study developed. Two families of distributions, namely the exponentiated logarithmic generated (ELG) class and the geometric Nadarajah-Haghighi (NHG) class are studied. The last one is a composition of the Nadarajah-Haghighi and geometric distributions. Further, we develop a statistical library for the R programming language called the AdequacyModel. This is an improvement of the package that was available on CRAN (Comprehensive R Archive Network) and it is currently in version 2.0.0. The two main functions of the library are the goodness.fit and pso functions. The first function allows to obtain the maximum likelihood estimates (MLEs) of the model parameters and some goodness-of-fit of the fitted probabilistic models. It is possible to choose the method of optimization for maximizing the log-likelihood function. The second function presents the method meta-heuristics global search known as particle swarm optimization (PSO) proposed by Eberhart and Kennedy (1995). Such methodology can be used for obtaining the MLEs necessary for the calculation of some measures of adequacy of the probabilistic models. / A área de análise de sobrevivência é importante na Estatística e é comumente aplicada às ciências biológicas, engenharias, ciências sociais, entre outras. Tipicamente, o tempo de vida ou falha pode ter diferentes interpretações dependendo da área de aplicação. Por exemplo, o tempo de vida pode significar a própria vida de uma pessoa, o tempo de funcionamento de um equipamento até sua falha, o tempo de sobrevivência de um paciente com uma doença grave desde o diagnóstico, a duração de um evento social como um casamento, entre outros significados. O tempo de vida é uma variável aleatória não negativa, que pode ter a função de risco na forma constante, monótona crescente, monótona decrescente ou não monótona (por exemplo, em forma de U). Nas últimas décadas, várias famílias de modelos probabilísticos têm sido propostas. Esses modelos podem ser construídos com base em alguma transformação de uma distribuição padrão, geralmente já conhecida na literatura. Uma dada combinação linear ou mistura de modelos G normalmente define uma classe de modelos probabilísticos tendo G como caso especial. Esta tese é composta de capítulos independentes. O primeiro e último são curtos capítulos que incluem a introdução e as conclusões do estudo desenvolvido. Duas famílias de distribuições, denominadas de classe “exponentiated logarithmic generated” (ELG) e a classe “geometric Nadarajah-Haghighi” (NHG) s˜ao estudadas. A ´ultima ´e uma composi¸c˜ao das distribuições de Nadarajah-Haghighi e geométrica. Além disso, desenvolvemos uma biblioteca estatística para a linguagem de programação R chamada AdequacyModel. Esta é uma melhoria do pacote que foi disponibilizado no CRAN (Comprehensive R Archive Network) e está atualmente na versão 2.0.0. As duas principais funções da biblioteca são as funções goodness.fit e pso. A primeira função permite obter as estimativas de máxima verossimilhança (EMVs) dos parâmetros de um modelo e algumas medidas de bondade de ajuste dos modelos probabilísticos ajustados. E possível escolher o método de otimização para maximizar a função de log-verossimilhan¸ca. A segunda função apresenta o método meta-heurístico de busca global conhecido como Particle Swarm Optimization (PSO) proposto por Eberhart e Kennedy (1995). Algumas metodologias podem ser utilizadas para obtenção das EMVs necessárias para o cálculo de algumas medidas de adequação dos modelos probablísticos ajustados.

This dissertation develops new approaches for hyperspectral image visualization. Double and multiple layers are proposed to effectively convey the abundant information contained in the original high-dimensional data for practical decision-making support. The contributions of this dissertation are as follows. 1.Development of new visualization algorithms for hyperspectral imagery. Double-layer technique can display mixed pixel composition and global material distribution simultaneously. The pie-chart layer, taking advantage of the properties of non-negativity and sum-to-one abundances from linear mixture analysis of hyperspectral pixels, can be fully integrated with the background layer. Such a synergy enhances the presentation at both macro and micro scales. 2.Design of an effective visual exploration tool. The developed visualization techniques are implemented in a visualization system, which can automatically preprocess and visualize hyperspectral imagery. The interactive tool with a userriendly interface will enable viewers to display an image with any desired level of details. 3.Design of effective user studies to validate and improve visualization methods. The double-layer technique is evaluated by well designed user studies. The traditional approaches, including gray-scale side-by-side classification maps, color hard classification maps, and color soft classification maps, are compared with the proposed double-layer technique. The results of the user studies indicate that the double-layer algorithm provides the best performance in displaying mixed pixel composition in several aspects and that it has the competitive capability of displaying the global material distribution. Based on these results, a multi-layer algorithm is proposed to improve global information display.

Nesta dissertação está proposta uma metodologia para fins de detecção de mudanças do uso do solo em imagens multitemporais de sensoriamento remoto. Em lugar de classificar os pixels de imagens que cobrem uma cena, em duas classes exaustivas e mutuamente excludentes (mudança, não-mudança), propõe-se adotar uma abordagem do tipo fuzzy, na qual são estimados os graus de pertinência às classes mudança e não-mudança. Com este objetivo adota-se aqui uma abordagem em nível de sub-pixel na estimação dos graus de pertinência para cada pixel. Esta abordagem se mostra mais adequada para fins de modelagem do que ocorre em cenas naturais, onde as alterações que acontecem ao longo de um período de tempo tendem a apresentar uma variação contínua em lugar de discreta. Em uma segunda etapa, os graus de pertinência estimados recebem um ajustamento adicional por meio da introdução de informações de contexto espacial. A metodologia proposta foi testada por meio de três experimentos, um empregando uma imagem sintética e dois utilizando imagens reais. A partir da análise quantitativa dos resultados e comparação com estudos semelhantes, comprova-se a adequação da metodologia proposta. / In this dissertation it is proposed a new methodology to land use change detection in remote sensing multitemporal image data. Rather than applying a rigid labeling of the pixels in the image data into two classes (change, no-change), we propose estimating the degrees of membership to classes change and no-change in a fuzzy-like fashion. To this end, a sub-pixel approach is implemented to detect the degree of change in every pixel. This methodology aims at modeling natural scenes in a more realistic way, since changes in natural scenes tend to occur in a continuum rather than in a sharp distinctive way. In a second step, the estimated values for the degrees of membership are further refined by means of spatial context information. Three experiments were performed to test the proposed methodology, one employing synthetic data and two using real image data. From the quantitative analysis of the results and from similar studies we can prove the adequacy of the proposed methodology.

Nesta dissertação está proposta uma metodologia para fins de detecção de mudanças do uso do solo em imagens multitemporais de sensoriamento remoto. Em lugar de classificar os pixels de imagens que cobrem uma cena, em duas classes exaustivas e mutuamente excludentes (mudança, não-mudança), propõe-se adotar uma abordagem do tipo fuzzy, na qual são estimados os graus de pertinência às classes mudança e não-mudança. Com este objetivo adota-se aqui uma abordagem em nível de sub-pixel na estimação dos graus de pertinência para cada pixel. Esta abordagem se mostra mais adequada para fins de modelagem do que ocorre em cenas naturais, onde as alterações que acontecem ao longo de um período de tempo tendem a apresentar uma variação contínua em lugar de discreta. Em uma segunda etapa, os graus de pertinência estimados recebem um ajustamento adicional por meio da introdução de informações de contexto espacial. A metodologia proposta foi testada por meio de três experimentos, um empregando uma imagem sintética e dois utilizando imagens reais. A partir da análise quantitativa dos resultados e comparação com estudos semelhantes, comprova-se a adequação da metodologia proposta. / In this dissertation it is proposed a new methodology to land use change detection in remote sensing multitemporal image data. Rather than applying a rigid labeling of the pixels in the image data into two classes (change, no-change), we propose estimating the degrees of membership to classes change and no-change in a fuzzy-like fashion. To this end, a sub-pixel approach is implemented to detect the degree of change in every pixel. This methodology aims at modeling natural scenes in a more realistic way, since changes in natural scenes tend to occur in a continuum rather than in a sharp distinctive way. In a second step, the estimated values for the degrees of membership are further refined by means of spatial context information. Three experiments were performed to test the proposed methodology, one employing synthetic data and two using real image data. From the quantitative analysis of the results and from similar studies we can prove the adequacy of the proposed methodology.

Nesta dissertação está proposta uma metodologia para fins de detecção de mudanças do uso do solo em imagens multitemporais de sensoriamento remoto. Em lugar de classificar os pixels de imagens que cobrem uma cena, em duas classes exaustivas e mutuamente excludentes (mudança, não-mudança), propõe-se adotar uma abordagem do tipo fuzzy, na qual são estimados os graus de pertinência às classes mudança e não-mudança. Com este objetivo adota-se aqui uma abordagem em nível de sub-pixel na estimação dos graus de pertinência para cada pixel. Esta abordagem se mostra mais adequada para fins de modelagem do que ocorre em cenas naturais, onde as alterações que acontecem ao longo de um período de tempo tendem a apresentar uma variação contínua em lugar de discreta. Em uma segunda etapa, os graus de pertinência estimados recebem um ajustamento adicional por meio da introdução de informações de contexto espacial. A metodologia proposta foi testada por meio de três experimentos, um empregando uma imagem sintética e dois utilizando imagens reais. A partir da análise quantitativa dos resultados e comparação com estudos semelhantes, comprova-se a adequação da metodologia proposta. / In this dissertation it is proposed a new methodology to land use change detection in remote sensing multitemporal image data. Rather than applying a rigid labeling of the pixels in the image data into two classes (change, no-change), we propose estimating the degrees of membership to classes change and no-change in a fuzzy-like fashion. To this end, a sub-pixel approach is implemented to detect the degree of change in every pixel. This methodology aims at modeling natural scenes in a more realistic way, since changes in natural scenes tend to occur in a continuum rather than in a sharp distinctive way. In a second step, the estimated values for the degrees of membership are further refined by means of spatial context information. Three experiments were performed to test the proposed methodology, one employing synthetic data and two using real image data. From the quantitative analysis of the results and from similar studies we can prove the adequacy of the proposed methodology.

Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, it has investigated the problems of (i) the Instantaneous Frequency (IF) estimation of Linear Frequency-Modulated (LFM) signals corrupted in complex-valued zero-mean Multiplicative Noise (MN), and (ii) the Underdetermined Blind Source Separation (UBSS) of LFM signals, while focusing onto the fast-growing area of Wireless Communications (WCom). A common problem in the issue of signal estimation is the estimation of the frequency of Frequency-Modulated signals which are seen in many engineering and real-life applications. Accurate frequency estimation leads to accurate recovery of the true information. In some applications, the random amplitude modulation shows up when the medium is dispersive and/or when the assumption of point target is not valid; the original signal is considered to be corrupted by an MN process thus seriously aecting the recovery of the information-bearing frequency. The IF estimation of nonstationary signals corrupted by complex-valued zero-mean MN was investigated in this research. We have proposed a Second-Order Statistics approach, rather than a Higher-Order Statistics approach, for IF estimation using Time-Frequency Distributions (TFDs). The main assumption was that the autocorrelation function of the MN is real-valued but not necessarily positive (i.e. the spectrum of the MN is symmetric but does not necessary has the highest peak at zero frequency). The estimation performance was analyzed in terms of bias and variance, and compared between four dierent TFDs: Wigner-Ville Distribution, Spectrogram, Choi-Williams Distribution and Modified B Distribution. To further improve the estimation, we proposed to use the Multiple Signal Classification algorithm and showed its better performance. It was shown that the Modified B Distribution performance was the best for Signal-to-Noise Ratio less than 10dB. In the issue of signal separation, a new research direction called Blind Source Separation (BSS) has emerged over the last decade. BSS is a fundamental technique in array signal processing aiming at recovering unobserved signals or sources from observed mixtures exploiting only the assumption of mutual independence between the signals. The term "blind" indicates that neither the structure of the mixtures nor the source signals are known to the receivers. Applications of BSS are seen in, for example, radar and sonar, communications, speech processing, biomedical signal processing. In the case of nonstationary signals, a TF structure forcing approach was introduced by Belouchrani and Amin by defining the Spatial Time- Frequency Distribution (STFD), which combines both TF diversity and spatial diversity. The benefit of STFD in an environment of nonstationary signals is the direct exploitation of the information brought by the nonstationarity of the signals. A drawback of most BSS algorithms is that they fail to separate sources in situations where there are more sources than sensors, referred to as UBSS. The UBSS of nonstationary signals was investigated in this research. We have presented a new approach for blind separation of nonstationary sources using their TFDs. The separation algorithm is based on a vector clustering procedure that estimates the source TFDs by grouping together the TF points corresponding to "closely spaced" spatial directions. Simulations illustrate the performances of the proposed method for the underdetermined blind separation of FM signals. The method developed in this research represents a new research direction for solving the UBSS problem. The successful results obtained in the research development of the above two problems has led to a conclusion that TFSP is useful for WCom. Future research directions were also proposed.