Neural Network Pattern Recognition by Means of Differential Absorption Mueller Matrix Spectroscopy
Applied Optics, Vol. 38, Issue 17, pp. 3759-3766 (1999)
http://dx.doi.org/10.1364/AO.38.003759
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Abstract
Artificial neural network systems were built for detecting amino acids, sugars, and other solid organic matter by pattern recognition of their polarized light scattering signatures in the form of a Mueller matrix. Backward-error propagation and adaptive gradient descent methods perform network training. The product of the training is a weight matrix that, when applied as a filter, discerns the presence of the analytes on the basis of their cued susceptive Mueller matrix difference elements. This filter function can be implemented as a software or a hardware module to a future differential absorption Mueller matrix spectrometer.
© 1999 Optical Society of America
[Optical Society of America ]
OCIS Codes
(200.4260) Optics in computing : Neural networks
(280.3420) Remote sensing and sensors : Laser sensors
(300.1030) Spectroscopy : Absorption
(300.6340) Spectroscopy : Spectroscopy, infrared
Citation
Arthur H. Carrieri, "Neural Network Pattern Recognition by Means of Differential Absorption Mueller Matrix Spectroscopy," Appl. Opt. 38, 3759-3766 (1999)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-38-17-3759
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