Peak-hopping stepwise wavelength selection algorithm

1999 ◽  
Author(s):  
Michael J. McShane ◽  
Brent D. Cameron ◽  
Gerard L. Cote ◽  
Clifford H. Spiegelman
Keyword(s):  
Selection Algorithm ◽  
Wavelength Selection

New Near Infrared Wavelength Selection Algorithm Based on Monte-Carlo Method

2010 ◽  
Vol 30 (12) ◽  
pp. 3637-3642 ◽  
Author(s):  
洪明坚 Hong Mingjian ◽  
温泉 Wen Quan ◽  
温志渝 Wen Zhiyu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Near Infrared ◽  
Selection Algorithm ◽  
Wavelength Selection ◽  
Infrared Wavelength

scholarly journals Optimal wavelength selection algorithm of non-spherical particle size distribution based on the light extinction data

2012 ◽  
Vol 16 (5) ◽  
pp. 1353-1357 ◽  
Author(s):  
Hong Tang
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Approximation Method ◽  
Spherical Particles ◽  
Selection Algorithm ◽  
Wavelength Selection ◽  
Anomalous Diffraction ◽  
Optimal Wavelength ◽  
Anomalous Diffraction Approximation

In this paper, the anomalous diffraction approximation method is improved for calculating the extinction efficiency of non-spherical particles. Through this step, the range of the refractive index of particles can be enlarged, and the improved anomalous diffraction approximation method can be applied easily to the calculation of extinction efficiency for the most kinds of non-spherical particles. Meanwhile, an optimal wavelength selection algorithm is proposed for the inversion of non-spherical particle size distribution in the dependent mode. Through the improved anomalous diffraction approximation method, the computation time is substantially reduced compared with the rigorous methods, and a more accurate inversion result of particle size distribution is obtained using the optimal wavelength selection method.

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