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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agroindústria Tropical. Para informações adicionais entre em contato com cnpat.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agroindústria Tropical. |
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Data corrente: |
16/03/2021 |
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Data da última atualização: |
16/03/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CARDOSO, V. H. R.; CALDAS, P.; GIRALDI, M. T. R.; FRAZÃO, O.; CARVALHO, C. J. R. de; COSTA, J. C. W. A.; SANTOS, J. L. |
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Afiliação: |
VICTOR H. R. CARDOSO, Federal University of Pará, Applied Electromagnetism Laboratory; Institute for Systems and Computer Engineering, Technology and Science; PAULO CALDAS, Institute for Systems and Computer Engineering, Technology and Science; Polytechnic Institute of Viana do Castelo; M. THEREZA R. GIRALDI, Military Institute of Engineering, Laboratory of Photonics, Rio de Janeiro; ORLANDO FRAZÃO, Department of Physics and Astronomy, Faculty of Sciences of University of Porto; CLAUDIO JOSE REIS DE CARVALHO, CNPAT; JOÃO C. W. A. COSTA, Federal University of Pará, Applied Electromagnetism Laboratory; JOSÉ L. SANTOS, Department of Physics and Astronomy, Faculty of Sciences of University of Porto; Institute for Systems and Computer Engineering, Technology and Science. |
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Título: |
Experimental investigation of a strain gauge sensor based on Fiber Bragg Grating for diameter measurement. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Optical Fiber Technology, v. 61, 102428, January 2021. |
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DOI: |
https://doi.org/10.1016/j.yofte.2020.102428 |
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Idioma: |
Inglês |
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Palavras-Chave: |
Diameter monitoring; FBG; Medidor de tensão; Monitoramento do diâmetro; Strain gauge. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00814naa a2200253 a 4500
001 2130721
005 2021-03-16
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.yofte.2020.102428$2DOI
100 1 $aCARDOSO, V. H. R.
245 $aExperimental investigation of a strain gauge sensor based on Fiber Bragg Grating for diameter measurement.$h[electronic resource]
260 $c2021
653 $aDiameter monitoring
653 $aFBG
653 $aMedidor de tensão
653 $aMonitoramento do diâmetro
653 $aStrain gauge
700 1 $aCALDAS, P.
700 1 $aGIRALDI, M. T. R.
700 1 $aFRAZÃO, O.
700 1 $aCARVALHO, C. J. R. de
700 1 $aCOSTA, J. C. W. A.
700 1 $aSANTOS, J. L.
773 $tOptical Fiber Technology$gv. 61, 102428, January 2021.
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Registro original: |
Embrapa Agroindústria Tropical (CNPAT) |
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URL |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
01/03/2007 |
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Data da última atualização: |
04/03/2020 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
MENDONÇA-SANTOS, M. de L.; MCBRATNEY, A. B.; MINASNY, B. |
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Afiliação: |
MARIA DE LOURDES M SANTOS BREFIN, CNPS. |
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Título: |
Soil prediction with spatially decomposed environmental factors. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
In: LAGACHERIE, P.; MCBRATNEY, A. B.; VOLTZ, M. (Ed.). Digital soil mapping: an introductory perspective. Amsterdam: Elsevier, 2007. cap. 21, 269-278. |
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Idioma: |
Inglês |
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Conteúdo: |
Prediction of soil attributes and soil classes in digital soil mapping relies on finding relationships between soil and the predictor variables of soil-forming factors and processes. The predictor variables can be remotely or proximally sensed images of soil, landscape, parent material or climatic factors. Till date, most prediction methods are based on performing regression on the predictor variables directly to predict soil attributes or classes. There are problems using data layers from different sources, particularly, multicollinearity, and the fact that the relationships between soil and environmental variables can change with spatial scale. To overcome the problem of correlation between variables, principal component analysis can be performed on the predictor variables. With respect to the spatial dependency, each of these variables can be decomposed into separate spatial components and mapped separately. One of the methods of achieving this is wavelet analysis, which decomposes the variables into separate hierarchical spatial components of decreasing spatial resolution. These components could all be derived and subsequently used as separate layers in predicting soil classes or soil attributes. In this chapter, data are decomposed using the wavelet method and examples of predictions of soil classes and surface-clay content are shown, in order to evaluate the effect of using the decomposed layers in comparison with the original data. |
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Palavras-Chave: |
Atributos do solo. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus NAL: |
Remote sensing. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02099naa a2200181 a 4500
001 1338904
005 2020-03-04
008 2007 bl uuuu u00u1 u #d
100 1 $aMENDONÇA-SANTOS, M. de L.
245 $aSoil prediction with spatially decomposed environmental factors.$h[electronic resource]
260 $c2007
520 $aPrediction of soil attributes and soil classes in digital soil mapping relies on finding relationships between soil and the predictor variables of soil-forming factors and processes. The predictor variables can be remotely or proximally sensed images of soil, landscape, parent material or climatic factors. Till date, most prediction methods are based on performing regression on the predictor variables directly to predict soil attributes or classes. There are problems using data layers from different sources, particularly, multicollinearity, and the fact that the relationships between soil and environmental variables can change with spatial scale. To overcome the problem of correlation between variables, principal component analysis can be performed on the predictor variables. With respect to the spatial dependency, each of these variables can be decomposed into separate spatial components and mapped separately. One of the methods of achieving this is wavelet analysis, which decomposes the variables into separate hierarchical spatial components of decreasing spatial resolution. These components could all be derived and subsequently used as separate layers in predicting soil classes or soil attributes. In this chapter, data are decomposed using the wavelet method and examples of predictions of soil classes and surface-clay content are shown, in order to evaluate the effect of using the decomposed layers in comparison with the original data.
650 $aRemote sensing
650 $aSensoriamento Remoto
653 $aAtributos do solo
700 1 $aMCBRATNEY, A. B.
700 1 $aMINASNY, B.
773 $tIn: LAGACHERIE, P.; MCBRATNEY, A. B.; VOLTZ, M. (Ed.). Digital soil mapping: an introductory perspective. Amsterdam: Elsevier, 2007. cap. 21, 269-278.
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Embrapa Solos (CNPS) |
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