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Proceedings Paper

Application specific band selection with multivariate methods of analysis for a-Si:H multispectral photodiodes
Author(s): Christian Merfort; Andreas Bablich; Oliver Schwaneberg; Krystian Watty; Markus Boehm
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Paper Abstract

In the recent past the generation and processing of multispectral data have had an immense impact on optical characterization systems. A virtual test environment is used to examine which bands provide a high information density. The photocurrent j = ∫ E(λ)*Sabs(λ)*r(λ) dλ was calculated for different light sources E, spectral response curves Sabs (bands), and the reflectance r of whitish powder samples that were suspected to be dangerous or illegal. The multivariate dataset will have to be determined whether we can gain any knowledge from this. The employed factor analysis is a common method of the group of structure-discovering methods and provides good results in the discovery of connections between parameters. It is particularly used if a variety of parameters must be reduced for some reason. For the verification, a dimension of the external separation is defined. To carry out this an n-dimensional vector P must be assigned to each measurement that is registered in the matrix M to determine the volume V of this dot cloud. The dimension normalized volume is defined as ΔCL, where n is the quantity of employed bands. The reliability of the complete measurement system is made by a membership function μ(P) comparable to the definitions from the area of the fuzzy sets. The parameter μ indicates with which reliability a measured pattern P could be assigned to a sample S from a dataset. The use of such optimized multispectral photodiodes would simplify and accelerate the identification of potentially dangerous substances.

Paper Details

Date Published: 24 May 2012
PDF: 11 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839024 (24 May 2012); doi: 10.1117/12.918456
Show Author Affiliations
Christian Merfort, Univ. Siegen (Germany)
Andreas Bablich, Univ. Siegen (Germany)
Oliver Schwaneberg, Hochschule Bonn-Rhein-Sieg Univ. of Applied Sciences (Germany)
Krystian Watty, Univ. Siegen (Germany)
Markus Boehm, Univ. Siegen (Germany)

Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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