BibliotecaPortal de investigación
es | gl
  • Home
  • Contact us
  • Give feedback
  • Help
    • About Investigo
    • Search and Find
    • Submit
    • Intellectual Property
    • Open Access Policy
  • Links
    • Sherpa / Romeo
    • Dulcinea
    • OpenDOAR
    • Dialnet Plus
    • ORCID
    • Creative Commons
    • UNESCO Nomenclature
    • español
    • English
    • Gallegan
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of InvestigoAuthorsTitles Materias Unesco Research GroupsType of ContentsJournal TitlesThis CollectionAuthorsTitlesUNESCO SubjectsResearch GroupsType of ContentsJournal Titles

Library guides

Self-archivingRequest PermissionRelated guides

Statistics

View Usage Statistics

Using artificial neural networks to scale and infer vegetation media phase functions

Gomez Perez, PaulaAutor UVIGO; Caldeirinha, Rafael F. S.; Fernandes, Telmo Rui; Cuiñas Gómez, ÍñigoAutor UVIGO
DATE: 2018-06
UNIVERSAL IDENTIFIER: http://hdl.handle.net/11093/2833
EDITED VERSION: http://link.springer.com/10.1007/s00521-016-2778-6
UNESCO SUBJECT: 3325 Tecnología de las Telecomunicaciones
DOCUMENT TYPE: article

ABSTRACT

Accurate vegetation models usually rely on experimental data obtained by means of measurement campaigns. Nowadays, RET and dRET models provide a realistic characterization of vegetation volumes, including not only in-excess attenuation, but also scattering, diffraction and depolarization. Nevertheless, both approaches imply the characterization of the forest media by means of a range of parameters, and thus, the construction of a simple parameter extraction method based on propagation measurements is required. Moreover, when dealing with experimental data, two common problems must be usually overcome: the scaling of the vegetation mass parameters into different dimensions, and the scarce number of frequencies available within the experimental data set. This paper proposes the use of Artificial Neural Networks as accurate and reliable tools able to scale vegetation parameters for varying physical dimensions and to predict them for new frequencies. This proposal provides a RMS error lower than 1 dB when compared to unbiased measured data, leading to an accurate parameter extracting method, while being simple enough for not to increase the computational cost of the model.
Show full item record

Files in this item

[PDF]
Name:
2018_gomez_neuralcomput_artifi ...
Size:
1.454Mb
Format:
PDF
View/Open

Send to

MendeleyZoteroRefworks

The Institutional Repository of the University of Vigo Investigo is disseminated in:

University library
Rúa Leonardo da Vinci, s/n
As Lagoas, Marcosende
36310 Vigo

Location

Information
+34 986 813 821
investigo@uvigo.gal

Accessibility | Legal notice | Data protection
Logo UVigo

INFORMACIÓN
+34 986 812 000
informacion@uvigo.gal

CONTACTO

CAMPUS DO MAR

CAMPUS DE OURENSE
+34 988 387 102
Campus da Auga

CAIXA DE QUEIXAS, SUXESTIÓNS E PARABÉNS

TRANSPARENCIA

CAMPUS DE PONTEVEDRA
+34 986 801 949
Campus CREA

OUTRAS WEBS INSTITUCIONAIS

EMERXENCIAS

CAMPUS DE VIGO
+34 986 812 000
Campus Vigo Tecnolóxico

MURO SOCIAL