Characterization of spherical particles using high-order neural networks and scanning flow cytometry

Journal of Quantitative Spectroscopy and Radiative Transfer

Volumn 102, Issue 1, 2006, Pages 62-72

  Berdnik, Vladimir V ^a   Gilev, Konstantin ^b   Shvalov, Alexander ^c,d   Maltsev, Valeri ^c,d   Loiko, Valery A ^e  

^a Institute for Aerospace Technology   (Russian Federation)

^b SOBOLEV INSTITUTE OF MATHEMATICS   (Russian Federation)

^c NOVOSIBIRSK STATE UNIVERSITY   (Russian Federation)

^d INSTITUTE OF CHEMICAL KINETICS AND COMBUSTION   (Russian Federation)

^e B I STEPANOV INSTITUTE OF PHYSICS   (Belarus)

Author keywords

Inverse light scattering problem; Neural networks; Particle sizing

Indexed keywords

BLOOD; CELLS; LEAST SQUARES APPROXIMATIONS; LIGHT SCATTERING; NEURAL NETWORKS; OPTIMIZATION; PARTICLES (PARTICULATE MATTER); POLYSTYRENES; REFRACTIVE INDEX; REGRESSION ANALYSIS;

HIGH-ORDER NEURAL NETWORK; MIE THEORY; POLYSTYRENE BEAD; SCANNING FLOW CYTOMETER; SPHERICAL PARTICLE; SPHERIZED RED BLOOD CELL;

IMAGE ANALYSIS;

ARTIFICIAL NEURAL NETWORK; BLOOD; FLOW CYTOMETRY; LIGHT SCATTERING; MIE THEORY; PARTICLE SIZE; SIZE DISTRIBUTION;

EID: 33646756231     PISSN: 00224073     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jqsrt.2006.03.002     Document Type: Article

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