Flathead catfish stocks in the Pascagoula River were decimated by the passage of Hurricane Katrina. Age-0 fish survived the storm, producing a strong 2005 year-class. Reproduction by the remaining adults and/or downstream movement from tributaries produced an additional strong cohort in 2006. The strong 2005 year-class resulted in the capture of a high proportion of two-year-old fish in 2007. In 2008, a high proportion of two- and three-year-old fish were captured, illustrating the high rate of survival of the 2005 year-class, and the presence of a strong 2006 year-class. The flathead catfish population of the Pascagoula River was dominated by immature fish that should begin to reproduce in 2009, and most of these fish should reach sexual maturity by 2011. Density estimates are low when compared to other populations, indicating that a management option of a minimum length limit of 610 mm could prove useful in protecting these future spawners.

Algoritmos de detecção de linhas são usados em muitos campos de aplicação, tais como visão computacional e automação como base para análises mais complexas. Por exemplo, a informação de linha pode ser usada como dado de entrada para algoritmos de detecção de objetos ou mesmo para a estimativa da orientação espacial de robôs aéreos. Uma das formas de detectar linhas é através do uso de um processo de filtragem não linear chamado deWide Line Detector (WLD). Esse algoritmo é eficaz na marcação de pixels de linha em imagens em tons de cinza, separando linhas claras ou linhas escuras. Contudo, os algoritmos de detecção de linha não estão normalmente preocupados com a estimativa de largura local individual associada a um pixel. Se disponível, tal informação poderia ser explorada por algoritmos de visão computacional. Além do mais, a informação de cor também é extensivamente usada em visão computacional como um discriminante de objetos, mas o WLD não a usa. Neste Trabalho, nós propusemos a extensão do WLD para imagens em cores. Nós também desenvolvemos um novo kernel monotonicamente crescente que é mais eficiente e mais robusto para detectar linhas do que que os kernels monotonicamente decrescentes usados pelo WLD. Por fim, desenvolvemos uma maneira de obter uma estimativa de largura de linha partindo da densidade local associada a similaridade entre pixels, revertendo o processo usado pelo WLD para estimar qual kernel deve ser usado. Diversos experimentos foram realizados com o método proposto considerando diferentes parâmetros, além da comparação com o WLD tradicional, para analizar a eficácia do método. / Line detection algorithms are used by many application fields, such as computer vision and automation, as a basis for more complex analysis. For instance, line information can be used as input to object detection algorithms or even attitude estimation in flying robots. One way to detect lines is to use an isotropic nonlinear filtering procedure called the Wide Line Detector (WLD). This algorithm is effective to highlight the line pixels in gray scale images, separating dark or bright lines. However, line detection algorithms are not normally concerned with the pixel-wise estimation of thickness. If available, such information could be further explored by computer vision algorithms. Furthermore, color is extensively used in computer vision as an object discriminant, but not by the WLD. In this work, we propose the extension of the WLD to color images. We also develop a method that allows the estimation of the line width locally using only the density information and no border or center line information. Finally, we develop a new monotonically increasing kernel that is more efficient and yet effective to detect lines than the monotonically decreasing kernels used by the WLD. Finally, we devise a way ro obtain the wideline thickness from the density estimate obtained from the similarity between pixels, reverting the process used by the WLD to determine which kernel should be used. We perform several experiments with the proposed method, considering different parameters, and comparing it to the traditional WLD algorithm to assess the effectiveness of the method.

During the last decade, the transit method has evolved to one of the most promising techniques in the search for extrasolar planets and the quest to find other earth-like worlds. In theory, the transit method is straight forward being based on the detection of an apparent dimming of the host star’s light due to an orbiting planet traversing in front of the observer. However, in practice, the detection of such light curve dips and their confident ascription to a planetary transit is heavily burdened by the presence of different sources of noise, the most prominent of which is probably the so called intrinsic stellar variability. Filtering out potential transit signals from background noise requires a well adjusted high-pass filter. In order to optimize such a filter, i.e. to achieve best separation between signal and noise, one typically requires access to benchmark datasets that exhibit the same light curve with and without obstructing noise. Several methods for simulating stellar variability have been proposed for the construction of such benchmark datasets. However, while such methods have been widely used in testing transit method detection algorithms in the past, it is not very well known how such simulations compare to real recorded light curves - a fact that might be contributed to the lack of large databases of stellar light curves for comparisons at that time. With the increasing amount of light curve data now available due to missions such as Kepler, I have here undertaken such a comparison of synthetic and real light curves for one particular method that simulates stellar variability based on scaled power spectra of the Sun’s flux variations. Conducting the respective comparison also in terms of estimated power spectra of real and simulated light curves, I have revealed that the two datasets exhibit substantial differences in average power, with the synthetic power spectra having generally a lower power and also lacking certain distinct power peaks present in the real light curves. The results of this study suggest that scaled power spectra of solar variability alone might be insufficient for light curve simulations and that more work will be required to understand the origin and relevance of the observed power peaks in order to improve on such light curve models.

Algoritmos de detecção de linhas são usados em muitos campos de aplicação, tais como visão computacional e automação como base para análises mais complexas. Por exemplo, a informação de linha pode ser usada como dado de entrada para algoritmos de detecção de objetos ou mesmo para a estimativa da orientação espacial de robôs aéreos. Uma das formas de detectar linhas é através do uso de um processo de filtragem não linear chamado deWide Line Detector (WLD). Esse algoritmo é eficaz na marcação de pixels de linha em imagens em tons de cinza, separando linhas claras ou linhas escuras. Contudo, os algoritmos de detecção de linha não estão normalmente preocupados com a estimativa de largura local individual associada a um pixel. Se disponível, tal informação poderia ser explorada por algoritmos de visão computacional. Além do mais, a informação de cor também é extensivamente usada em visão computacional como um discriminante de objetos, mas o WLD não a usa. Neste Trabalho, nós propusemos a extensão do WLD para imagens em cores. Nós também desenvolvemos um novo kernel monotonicamente crescente que é mais eficiente e mais robusto para detectar linhas do que que os kernels monotonicamente decrescentes usados pelo WLD. Por fim, desenvolvemos uma maneira de obter uma estimativa de largura de linha partindo da densidade local associada a similaridade entre pixels, revertendo o processo usado pelo WLD para estimar qual kernel deve ser usado. Diversos experimentos foram realizados com o método proposto considerando diferentes parâmetros, além da comparação com o WLD tradicional, para analizar a eficácia do método. / Line detection algorithms are used by many application fields, such as computer vision and automation, as a basis for more complex analysis. For instance, line information can be used as input to object detection algorithms or even attitude estimation in flying robots. One way to detect lines is to use an isotropic nonlinear filtering procedure called the Wide Line Detector (WLD). This algorithm is effective to highlight the line pixels in gray scale images, separating dark or bright lines. However, line detection algorithms are not normally concerned with the pixel-wise estimation of thickness. If available, such information could be further explored by computer vision algorithms. Furthermore, color is extensively used in computer vision as an object discriminant, but not by the WLD. In this work, we propose the extension of the WLD to color images. We also develop a method that allows the estimation of the line width locally using only the density information and no border or center line information. Finally, we develop a new monotonically increasing kernel that is more efficient and yet effective to detect lines than the monotonically decreasing kernels used by the WLD. Finally, we devise a way ro obtain the wideline thickness from the density estimate obtained from the similarity between pixels, reverting the process used by the WLD to determine which kernel should be used. We perform several experiments with the proposed method, considering different parameters, and comparing it to the traditional WLD algorithm to assess the effectiveness of the method.

Algoritmos de detecção de linhas são usados em muitos campos de aplicação, tais como visão computacional e automação como base para análises mais complexas. Por exemplo, a informação de linha pode ser usada como dado de entrada para algoritmos de detecção de objetos ou mesmo para a estimativa da orientação espacial de robôs aéreos. Uma das formas de detectar linhas é através do uso de um processo de filtragem não linear chamado deWide Line Detector (WLD). Esse algoritmo é eficaz na marcação de pixels de linha em imagens em tons de cinza, separando linhas claras ou linhas escuras. Contudo, os algoritmos de detecção de linha não estão normalmente preocupados com a estimativa de largura local individual associada a um pixel. Se disponível, tal informação poderia ser explorada por algoritmos de visão computacional. Além do mais, a informação de cor também é extensivamente usada em visão computacional como um discriminante de objetos, mas o WLD não a usa. Neste Trabalho, nós propusemos a extensão do WLD para imagens em cores. Nós também desenvolvemos um novo kernel monotonicamente crescente que é mais eficiente e mais robusto para detectar linhas do que que os kernels monotonicamente decrescentes usados pelo WLD. Por fim, desenvolvemos uma maneira de obter uma estimativa de largura de linha partindo da densidade local associada a similaridade entre pixels, revertendo o processo usado pelo WLD para estimar qual kernel deve ser usado. Diversos experimentos foram realizados com o método proposto considerando diferentes parâmetros, além da comparação com o WLD tradicional, para analizar a eficácia do método. / Line detection algorithms are used by many application fields, such as computer vision and automation, as a basis for more complex analysis. For instance, line information can be used as input to object detection algorithms or even attitude estimation in flying robots. One way to detect lines is to use an isotropic nonlinear filtering procedure called the Wide Line Detector (WLD). This algorithm is effective to highlight the line pixels in gray scale images, separating dark or bright lines. However, line detection algorithms are not normally concerned with the pixel-wise estimation of thickness. If available, such information could be further explored by computer vision algorithms. Furthermore, color is extensively used in computer vision as an object discriminant, but not by the WLD. In this work, we propose the extension of the WLD to color images. We also develop a method that allows the estimation of the line width locally using only the density information and no border or center line information. Finally, we develop a new monotonically increasing kernel that is more efficient and yet effective to detect lines than the monotonically decreasing kernels used by the WLD. Finally, we devise a way ro obtain the wideline thickness from the density estimate obtained from the similarity between pixels, reverting the process used by the WLD to determine which kernel should be used. We perform several experiments with the proposed method, considering different parameters, and comparing it to the traditional WLD algorithm to assess the effectiveness of the method.

Like coral reefs worldwide, the Florida Reef Tract has dramatically declined within the past two decades. Monitoring of 40 sites throughout the Florida Keys National Marine Sanctuary has undertaken a multiple-parameter approach to assess spatial and temporal changes in the status of the ecosystem. The objectives of the present study consist of the following: In chapter one, we review past coral reef studies; emphasis is placed on recent studies on the stony corals of reefs in the lower Florida Keys. We also review the economic impact of coral reefs on the state of Florida. In chapter two, we identify the underlying probability distribution function of the stony coral cover proportions and we obtain better estimates of the statistical properties of stony coral cover proportions. Furthermore, we improve present procedures in constructing confidence intervals of the true median and mean for the underlying probability distribution. In chapter three, we investigate the applicability of the normal probability distribution assumption made on the pseudovalues obtained from the jackknife procedure for the Shannon-Wiener diversity index used in previous studies. We investigate a new and more effective approach to estimating the Shannon-Wiener and Simpson's diversity index. In chapter four, we develop the best possible estimate of the probability distribution function of the jackknifing pseudovalues, obtained from the jackknife procedure for the Shannon-Wiener diversity index used in previous studies, using the xi nonparametric kernel density estimate method. This nonparametric procedure gives very effective estimates of the statistical measures for the jackknifing pseudovalues. Lastly, the present study develops a predictive statistical model for stony coral cover. In addition to identifying the attributable variables that influence the stony coral cover data of the lower Florida Keys, we investigate the possible interactions present. The final form of the developed statistical model gives good estimates of the stony coral cover given some information of the attributable variables. Our nonparametric and parametric approach to analyzing coral reef data provides a sound basis for developing efficient ecosystem models that estimate future trends in coral reef diversity. This will give the scientists and managers another tool to help monitor and maintain a healthy ecosystem.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-10-23T17:05:10Z No. of bitstreams: 1 renanmottagoulart.pdf: 506891 bytes, checksum: 01d7b3b82d2bc0af0d295fc75de17b91 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-11-09T13:52:19Z (GMT) No. of bitstreams: 1 renanmottagoulart.pdf: 506891 bytes, checksum: 01d7b3b82d2bc0af0d295fc75de17b91 (MD5) / Made available in DSpace on 2017-11-09T13:52:19Z (GMT). No. of bitstreams: 1 renanmottagoulart.pdf: 506891 bytes, checksum: 01d7b3b82d2bc0af0d295fc75de17b91 (MD5) Previous issue date: 2017-09-01 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Jogos de repetição é um ramo de Teoria dos Jogos, em que um jogo é jogado repetidas vezes pelos jogadores. Neste cenário, assume-se que os jogadores nem sempre jogam de modo ótimo ou podem estar dispostos, se possível, a colaborar. Neste contexto é possível um jogador analisar o comportamento dos oponentes para encontrar padrões. Estes padrões podem ser usados para aumentar o lucro obtido pelo jogador ou detectar se o oponente está disposto a realizar uma colaboração mutualmente benéfica. Nesta dissertação é proposto um novo algoritmo baseado em kernel de similaridade capaz de prever as ações de jogadores em jogos de repetição. A predição não se limita a ação do próximo round, podendo prever as ações de uma sequência finita de rounds consecutivos. O algoritmo consegue se adaptar rapidamente caso os outros jogadores mudem suas estratégias durante o jogo. É mostrado empiricamente que o algoritmo proposto obtém resultados superiores ao estado da arte atual. / Repeated games is a branch of game theory, where a game can be played several times by the players involved. In this setting, it is assumed that the players do not always play the optimal strategy or that they may be willing to collaborate. In this context it is possible for a player to analyze the opponent’s behaviour to find patters. These patterns can be used to maximize the player’s profit or to detect if the opponent is willing to collaborate. On this dissertation it is proposed a new algorithm based on similarity kernel capable of predicting the opponent’s actions on repeated games. The prediction is not limited to the next round’s action, being able to predict actions on a finite sequence of rounds. It is able to adapt rapidly if the opponents change their strategies during the course of a game. It is shown empirically that the proposed algorithm achieves better results than the current state of the art.