Reconhecimento de edifícios utilizando o filtro de gabor, a transformada wavelet e a rede neural perceptron de múltiplas camadas / Buildings recognition using the gabor filter, wavelet transform and multilayer perceptron network

O reconhecimento de edifícios é uma tarefa bastante difícil, pois as imagens utilizadas possuem ângulos e condições de iluminação diferentes, obstruções parciais de árvores, veículos em movimento ou outros edifícios. Devido a todas essas dificuldades, esse trabalho propõe um sistema de reconhecimento que utiliza a representação de wavelet Gabor para a extração de características, a transformada wavelet para a redução de dimensionalidade e para o reconhecimento a rede neural Perceptron de múltiplas camadas. Para verificar a eficiência desse sistema, utilizou-se um banco de dados de imagens de edifícios de prédios novos e antigos. O desempenho do método proposto foi satisfatório em relação a outros métodos encontrados na literatura

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rendezvous with a Non-Cooperating Target

Space robotics has a substantial interest in achieving on-orbit service (OOS) operations autonomously, i.e rendezvous and docking/berthing (RVDB) with failed, stranded or malfunction satellites. Visual navigation system has a wide area of applications. In the context of this thesis we investigate a visual navigation system for on-orbit service (OOS). The space servicing area has a very important appeal in space flight dynamics mainly in these days when the scientific community is deeply concerned with space debris and the risk those objects impose on other space missions and even on the ecosystem not to say risk on human life on ground. Moreover current database indicates regular opportunities for satellite servicing including on-orbit upgrades, repair and rescue of stranded satellites. Those satellites are classified as non-cooperative targets when under the goal of space cleaning or space maintenance by space tugs. A chaser spacecraft would have to have the capability to rendezvous and grasp a dead satellite or other space debris, taking it out of its orbit for the sake of fixing problems, refueling, or just for cleaning purposes, without any cooperation from the target vehicle. In this sense failed or dead satellites become non-cooperative targets that are not able to provide any information on their position and attitude and eventually are not capable of maneuvering to cooperate with the docking operation. Position and attitude here shall be understood as relative orbital position and relative attitude between the chaser and target space vehicles or chaser vehicles and other space objects. This thesis presents an algorithm developed for estimating the pose (position and attitude) and the motion (velocities) of a generic target satellite based on visual navigation. The algorithm gathers the strength of classical attitude estimation methods to obtain real-time applicability conditions when adopting a Kalman filter for sequential state estimation. The visual system is monocular and satellite model-based. Therefore, it does not rely on any marker attached to the target satellite. The navigation solution can be used for a vast category of applications such as space debris removal, servicing for stranded satellites, and interception of hostile objects. The approach is first tested with synthetic image data from a spacecraft object generated in virtual reality. A test-bed is used to simulate the on-orbit optical environment. A scaled satellite model and a CCD camera is employed in the test bed in order to evaluate the algorithm for real-time application. A combination of three dimensional (3D) model-based attitude estimation and filtering time series of images produce similar real time solution for the estimation problem and increases the reliability of the relative attitude and position results

Rendezvous mit einem Nicht-kooperativen Objekt

Space robotics has a substantial interest in achieving on-orbit service (OOS) operations autonomously, i.e rendezvous and docking/berthing (RVDB) with failed, stranded or malfunction satellites. Visual navigation system has a wide area of applications. In the context of this thesis we investigate a visual navigation system for on-orbit service (OOS). The space servicing area has a very important appeal in space flight dynamics mainly in these days when the scientific community is deeply concerned with space debris and the risk those objects impose on other space missions and even on the ecosystem not to say risk on human life on ground. Moreover current database indicates regular opportunities for satellite servicing including on-orbit upgrades, repair and rescue of stranded satellites. Those satellites are classified as non-cooperative targets when under the goal of space cleaning or space maintenance by space tugs. A chaser spacecraft would have to have the capability to rendezvous and grasp a dead satellite or other space debris, taking it out of its orbit for the sake of fixing problems, refueling, or just for cleaning purposes, without any cooperation from the target vehicle. In this sense failed or dead satellites become non-cooperative targets that are not able to provide any information on their position and attitude and eventually are not capable of maneuvering to cooperate with the docking operation. Position and attitude here shall be understood as relative orbital position and relative attitude between the chaser and target space vehicles or chaser vehicles and other space objects. This thesis presents an algorithm developed for estimating the pose (position and attitude) and the motion (velocities) of a generic target satellite based on visual navigation. The algorithm gathers the strength of classical attitude estimation methods to obtain real-time applicability conditions when adopting a Kalman filter for sequential state estimation. The visual system is monocular and satellite model-based. Therefore, it does not rely on any marker attached to the target satellite. The navigation solution can be used for a vast category of applications such as space debris removal, servicing for stranded satellites, and interception of hostile objects. The approach is first tested with synthetic image data from a spacecraft object generated in virtual reality. A test-bed is used to simulate the on-orbit optical environment. A scaled satellite model and a CCD camera is employed in the test bed in order to evaluate the algorithm for real-time application. A combination of three dimensional (3D) model-based attitude estimation and filtering time series of images produce similar real time solution for the estimation problem and increases the reliability of the relative attitude and position results