Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method
Optics Letters, Vol. 29, Issue 9, pp. 1019-1021 (2004)
http://dx.doi.org/10.1364/OL.29.001019
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Abstract
A method for evaluating the size of optically soft spheroidal particles by use of the angular structure of scattered light is proposed. It is based on the use of multilevel neural networks with a linear activation function. The retrieval errors of radius R of the equivolume sphere and aspect ratio e are investigated. The ranges of the size of R , e , and the refractive index are 0.31.51 μm , 0.21, and 1.011.02, respectively. The retrieval errors of the equivolume radius and aspect ratio are 0.004 μm and 0.02, respectively, for a three-level neural network (at a precisely measured angular distribution of scattered light). The retrieval errors of R and e for a one-level neural network are 25 times greater. The errors for a multilevel neural network increase faster than those for a single-level network.
© 2004 Optical Society of America
OCIS Codes
(290.3200) Scattering : Inverse scattering
(290.5850) Scattering : Scattering, particles
Citation
Vladimir V. Berdnik, Robert
D. Mukhamedjarov, and Valery A. Loiko, "Characterization of optically soft spheroidal particles
by multiangle light-scattering data by use of the neural-networks
method," Opt. Lett. 29, 1019-1021 (2004)
http://www.opticsinfobase.org/ol/abstract.cfm?URI=ol-29-9-1019
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