Inverse Light Scattering Problem in a Planar Waveguide with Statistical Subwavelength Irregularities: Theory and Computer Simulation

2002 ◽  
Vol 2 (1-2) ◽  
pp. 277-285
Author(s):  
Alexandre A. Yegorov
Keyword(s):  
Computer Simulation ◽  
Light Scattering ◽  
Planar Waveguide ◽  
Scattering Problem

Application of the Solution of the Inverse Light Scattering Problem for Characterization of Mononuclear Cells

2009 ◽  
Vol 4 (2) ◽  
pp. 61-68
Author(s):  
Dmitriy Strokotov ◽  
Yuriy Pichugin ◽  
Maxim Yurkin ◽  
Mariya Gridina ◽  
Oleg Serov ◽  
...  
Keyword(s):  
Stem Cells ◽  
Light Scattering ◽  
Mononuclear Cells ◽  
Human Lymphocytes ◽  
Scattering Problem ◽  
Refractive Indices ◽  
Single Particles ◽  
Differentiation Status

In this manuscript we propose two methods to solve inverse light scattering problem for single particles, which can be described as a coated sphere. The efficiency of the methods is illustrated by characterization of lymphocytes and stem cells using light scattering patterns obtained with scanning flow cytometer. Both methods, spectral and global optimization, were used to obtain diameters and refractive indices of the cytoplasm and the nucleus of mice embryo stem cells and human lymphocytes. These results agree with data obtained from other studies. Determination of these parameters is important for diagnostics of pathological states of lymphocytes and differentiation status of embryo stem cells. Moreover, methods described in this manuscript are applicable to all mononuclear cells. We also considered limitations of these methods and their possible improvements.

Light scattering from anisotropic block copolymer grains in a planar waveguide

2007 ◽  
Vol 24 (6) ◽  
pp. 1291 ◽  
Author(s):  
Zhuangxi Fang ◽  
Maurice C. Newstein ◽  
Bruce A. Garetz ◽  
Jeffrey D. Wilbur ◽  
Nitash P. Balsara
Keyword(s):  
Light Scattering ◽  
Block Copolymer ◽  
Planar Waveguide

Light scattering by irregularly shaped particles: computer simulation by ray-tracing

1990 ◽  
Vol 21 ◽  
pp. S145-S149
Author(s):  
Michael Bottlinger ◽  
-v. -Klitzingstr ◽  
Heinz Umhauer
Keyword(s):  
Computer Simulation ◽  
Light Scattering ◽  
Ray Tracing

scholarly journals Light Scattering by Particles with Arbitrary Shape in the Vicinity of the Backward Scattering Direction within Geometrical Optics Approximation

2020 ◽  
Vol 237 ◽  
pp. 08012
Author(s):  
Victor Shishko ◽  
Alexander Konoshonkin ◽  
Natalia Kustova ◽  
Anatoli Borovoi ◽  
Dmitry Timofeev
Keyword(s):  
Experimental Data ◽  
Refractive Index ◽  
Light Scattering ◽  
Arbitrary Shape ◽  
Specular Reflection ◽  
Scattering Problem ◽  
Geometrical Optics ◽  
Optical Characteristics ◽  
Ice Particles ◽  
Scattering Light

The work presents the solution for the light scattering problem by arbitrarily-shaped particles in the vicinity of the backward scattering direction. The solution was obtained within the framework of the geometrical optics approximation. The refractive index was equal to 1.3116. It was shown that the general contribution of scattering light for arbitrarily-shaped particles in the vicinity of the backscattering direction consists of the specular reflection of the particles and two types of non-specular optical beams. It is shown that the optical characteristics of the ice particles with arbitrary shapes correspond to experimental data.

Particle Identification Using Light Scattering

2009 ◽  
pp. 143-160
Author(s):  
M. C. Bartholomew-Biggs ◽  
Z. Ulanowski ◽  
S. Zakovic
Keyword(s):  
Experimental Data ◽  
Global Optimization ◽  
Light Scattering ◽  
Least Squares ◽  
Scattering Problem ◽  
Data Fitting ◽  
Particle Identification ◽  
Identification Procedure ◽  
Robust Identification ◽  
Actual Particle

We discuss some experience of solving an inverse light scattering problem for single, spherical, homogeneous particles using least squares global optimization. If there is significant noise in the data, the particle corresponding to the “best” solution may not correspond well to the “actual” particle. One way of overcoming this difficulty involves the use of peak positions in the experimental data as a means of distinguishing genuine from spurious solutions. We introduce two composite approaches which combine conventional data fitting with peak-matching and show that they lead to a more robust identification procedure.

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