Prostaglandin treatment at the onset of norgestomet and estradiol-based synchronization protocols did not alter the ovarian follicular dynamics or pregnancy per timed artificial insemination in cyclic Bos indicus heifers

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effect of adding a gonadotropin-releasing-hormone treatment at the beginning and a second prostaglandin F-2 alpha treatment at the end of an estradiol-based protocol for timed artificial insemination in lactating dairy cows during cool or hot seasons of the year

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Study of Stability of Rotational Motion of Spacecraft with Canonical Variables

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparação de métodos de classificação de imagens, visando o gerenciamento de áreas citrícolas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Zoneamento geoambiental da região compreendida pelas folhas topográficas São José dos Campos e Jacareí-SP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Identificação e caracterização de zonas de captura e concentração de águas subterrâneas a partir de produtos de sensoriamento remoto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A transformada wavelet na fusão de imagens de sensoriamento remoto

Pós-graduação em Matematica Aplicada e Computacional - FCT

Classificação de cobertura do solo utilizando árvores de decisão e sensoriamento remoto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Imagens hiperespectrais no monitoramento da nutrição em citros

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

A tradução de termos simples, expressões fixas e semifixas em um corpus paralelo da área de sensoriamento remoto: uma proposta de glossário bilíngue

Pós-graduação em Estudos Linguísticos - IBILCE

Análise das colocações especializadas da área de sensoriamento remoto sob a perspectiva da fraseologia

The aim of this research is to build and analyze a parallel corpus in the field of remote sensing in order to identify, according to its frequency, specialized collocations in English and then search for their equivalents in Portuguese. The research is based on the interdisciplinary approach of Corpus-Based Translation Studies (BAKER, 1995; CAMARGO, 2007), Corpus Linguistics (BERBER SARDINHA, 2004; TOGNINI-BONELLI, 2001), Phraseology (ORENHA-OTTAIANO, 2009; PAVEL, 1993), and some principles of Terminology (BARROS, 2004). For manipulating the corpora, the program WordSmith Tools (SCOTT, 2012) version 6.0 is used. To support this study, two comparable corpora in English and Portuguese were also built from articles published in both national and international journals in remote sensing. The results show that the collocations in Portuguese seem to be still in the process of conventionalization, as the translators made use of greater variation in their translational options, which can be a way to make the text clearer for the reader.

Métodos de classificação de imagens na identificação de áreas cultivadas com citros

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparação de métodos de classificação de imagens na identificação de áreas cultivadas com citros

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.

Análise da incerteza na representação de classes de cobertura do solo urbano resultantes da aplicação de uma rede neural artificial

The great diversity of materials that characterizes the urban environment determines a structure of mixed classes in a classification of multiespectral images. In that sense, it is important to define an appropriate classification system using a non parametric classifier, that allows incorporating non spectral (such as texture) data to the process. They also allow analyzing the uncertainty associated to each class from the output alues of the network calculated in relation to each class. Considering these properties, an experiment was carried out. This experiment consisted in the application of an Artificial Neural Network aiming at the classification of the urban land cover of Presidente Prudente and the analysis of the uncertainty in the representation of the mapped thematic classes. The results showed that it is possible to discriminate the variations in the urban land cover through the application of an Artificial Neural Network. It was also possible to visualize the spatial variation of the uncertainty in the attribution of classes of urban land cover from the generated representations. The class characterized by a defined pattern as intermediary related to the impermeability of the urban soil presented larger ambiguity degree and, therefore, larger mixture.