Comportamento de sistemas de informações geográficas por meio de classificação supervisionada em diferentes bacias hidrográficas

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

Investigação de técnicas para o acompanhamento do desgaste de um par engrenado utilizando tribologia e análise da resposta dinâmica processada via função densidade probabilidade Beta

Pós-graduação em Engenharia Mecânica - FEIS

A aplicação da abordagem do Tecnógeno na identificação e classificação dos terrenos e depósitos tecnogênicos no ambiente urbano de Guarapuava-PR

Pós-graduação em Geografia - FCT

Imagens aéreas multiespectrais na identificação de zonas de manejo em áreas de algodão para aplicação localizada de insumos

Crop yield is influenced by several factors with variability in time and space that are associated with the variations in the plant vigor. This variability allows the identification of management zones and site-specific applications to manage different regions of the field. The purpose of this study was the use of multispectral image for management zones identification and implications of site-specific application in commercial cotton areas. Multispectral airborne images from three years were used to classify a field into three vegetation classes via the Normalized Difference Vegetation Index (NDVI). The NDVI classes were used to verify the potential differences between plant physical measurements and identify management zones. The cotton plant measurements sampled in 8 repetitions of 10 plants at each NDVI class were Stand Count, Plant Height, Total Nodes and Total Bolls. Statistical analysis was performed with treatments arranged in split plot design with Tukey’s Test at 5% of probability. The images were classified into five NDVI classes to evaluate the relationship between cotton plant measurement results and sampling location across the field. The results have demonstrated the possibility of using multispectral image for management zones identification in cotton areas. The image classification into three NDVI classes showed three different zones in the field with similar characteristics for the studied years. Statistical differences were shown for plant height, total nodes and total bolls between low and high NDVI classes for all years. High NDVI classes contained plants with greater height, total nodes and total bolls compared to low NDVI classes. There was no difference in Stand Count between low and high NDVI classes for the three studied years. The final plant stand was the same between all NDVI classes for 2001 and 2003 as it was expected due to the conventional seeding application with the same rate of seeds for the entire field.

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)

Discriminação de classes de cobertura vegetal utilizando técnicas de classificação digital de imagens de sensoriamento remoto

The aim of this work is to discriminate vegetation classes throught remote sensing images from the satellite CBERS-2, related to winter and summer seasons in the Campos Gerais region Paraná State, Brazil. The vegetation cover of the region presents different kinds of vegetations: summer and winter cultures, reforestation areas, natural areas and pasture. Supervised classification techniques like Maximum Likelihood Classifier (MLC) and Decision Tree were evaluated, considering a set of attributes from images, composed by bands of the CCD sensor (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture models (soil, shadow, vegetation) and the two first main components. The evaluation of the classifications accuracy was made using the classification error matrix and the kappa coefficient. It was defined a high discriminatory level during the classes definition, in order to allow separation of different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and the kappa coefficient was 0.9389 for the scene 157/128. For the scene 158/127, the values were 88% and 0.8667, respectively. The classification accuracy by MLC was 84.86% and the kappa coefficient was 0.8099 for the scene 157/128. For the scene 158/127, the values were 77.90% and 0.7476, respectively. The results showed a better performance of the Decision Tree classifier than MLC, especially to the classes related to cultivated crops, indicating the use of the Decision Tree classifier to the vegetation cover mapping including different kinds of crops.

Classificação de imagens em áreas cultivadas com citrus por técnicas de sensoriamento remoto e geoestatística

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Validação de conteúdo das intervenções de enfermagem “Ensino: Pré-operatório” e “Ensino: Procedimento/Tratamento” da Classificação das Intervenções de Enfermagem, para procedimento percutâneo em hemodinâmica

Pós-graduação em Enfermagem (mestrado profissional) - FMB

Análise do descritor de padrões mapeados localmente em multiescala para classificação de textura em imagens digitais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Classificação de potenciais unidades climáticas em Presidente Prudente-SP

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

A utilização de material didático impresso para o ensino de um modelo de classificação do esporte na educação física escolar

Pós-graduação em Desenvolvimento Humano e Tecnologias - IBRC

Classificação de anomalias e redução de falsos positivos em sistemas de detecção de intrusão baseados em rede utilizando métodos de agrupamento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação automatizada do relevo a partir de parâmetros morfométricos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

O Raciocínio lógico-matemático: classificação e uso em concursos públicos

This study offers an analysis of classification of the main issues of logic and logical thinking found in competitive tendering and math tests, according to their concepts and characteristics, whether involving mathematics, or not. Moreover, a research on the evolutionary historic processes of logic according to three major crises of the foundations of mathematics was conducted. This research helped to define Logic as a science that is quite distinctive from Mathematics. In order to relate the logical and the mathematical thinking, three types of knowledge, according to Piaget, were presented, with the logical-mathematical one being among them. The study also includes an insight on the basic concepts of propositional and predicative logic, which aids in the classification of issues of logical thinking, formal logic or related to algebraic, and geometric or arithmetic knowledge, according to the Venn diagrams. Furthermore, the key problems - that are most frequently found in tests are resolved and classified, as it was previously described. As a result, the classification in question was created and exemplified with eighteen logic problems, duly solved and explained