Neural network pattern recognition by means of differential absorption Mueller matrix spectroscopy

被引:21
作者
Carrieri, AH [1 ]
机构
[1] USA Soldier Biol Chem Command, Edgewood Chem & Biol Ctr, Res & Technol Directorate, Aberdeen Proving Ground, MD 21010 USA
关键词
D O I
10.1364/AO.38.003759
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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. OCIS codes: 200.4260, 280.3420, 300.1030, 300.6340.
引用
收藏
页码:3759 / 3766
页数:8
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