Variabilidade espacial e temporal de fatores edafoclimáticos na Bacia do Rio Pontal, Pernambuco

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise espacial e temporal de atributos do solo e da produtividade das culturas visando manejo específico em áreas agrícolas

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparative analysis of two techniques for detection and segmentation of moving bodies

This paper makes a comparative analysis of results produced by the application of two techniques for the detection and segmentation of bodies in motion captured in images sequence, namely: 1) technique based on the temporal average of the values of each pixel recorded in N consecutive image frames and, 2) technique based on historical values associated with pixels recorded in different frames of an image sequence.

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Ritmo das chuvas na Bacia do Pardo (SP/MG): reflexos na vazão dos rios Pardo e Mogi-Guaçu

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação temporal e espacial do balanço de radiação, de energia e da evapotranspiraçâo no noroeste paulista, face a mudança no uso do solo

Pós-graduação em Agronomia - FEIS

Imagem de tensor de difusão na epilesia de lobo temporal mesial

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

Evolução temporal de fragmentos de vegetação nativa no município de Agudos-SP, utilizando fotografias aéreas.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimação espaço-temporal das perdas não técnicas no sistema de distribuição de energia elétrica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O cultivo da soja no oeste do estado do Paraná: contribuição metodológica para a análise espaço-temporal

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)