Journal
Article title
Authors
Title variants
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Abstracts
The following article describes research on possibility of using pattern recognition algorithms in the optical measurement system for estimation of the blood chamber volume in the Polish Ventricular Assist Device (POLVAD). The optical system is being developed at the Department of Optoelectronics, Silesian University of Technology, Poland. Data analysis methods include a feature subset selection algorithm involving principal components analysis and objective function as quality criterion. The analysis takes into account 17 patterns reflecting particular volumes. The k-nearest neighbours method is used as pattern classifier. The pattern recognition system was initially designed for an array of gas sensors and this paper describes its further development.
Discipline
- 47.54.-r: Pattern selection; pattern formation(see also 82.40.Ck Pattern formation in reactions with diffusion, flow and heat transfer in Physical chemistry and chemical physics; 87.18.Hf Spatiotemporal pattern formation in cellular populations in Biological and medical physics)
- 42.30.Sy: Pattern recognition
Journal
Year
Volume
Issue
Pages
498-501
Physical description
Dates
published
2013-09
Contributors
author
- Department of Optoelectronics, Silesian University of Technology, Akademicka 2, 44-100 Gliwice, Poland
author
- Department of Optoelectronics, Silesian University of Technology, Akademicka 2, 44-100 Gliwice, Poland
author
- Department of Optoelectronics, Silesian University of Technology, Akademicka 2, 44-100 Gliwice, Poland
References
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- [2] A.H. Gómez, J. Wang, G. Hu, A.G. Pereira, Sensors Actuat. B 113, 347 (2006)
- [3] Y. Yin, X. Tian, Sensors Actuat. B 124, 393 (2007)
- [4] K. Gut, Bull. Pol. Acad. Sci., Techn. Sci. 59, 395 (2011)
- [5] K.Z. Mao, IEEE Trans. Syst., Man Cybernet. Part B, Cybernet. 34, 629 (2004)
- [6] P. Marczyński, A. Szpakowski, C. Tyszkiewicz, T. Pustelny, Acta Phys. Pol. A 122, 847 (2012)
- [7] G. Konieczny, T. Pustelny, Acta Phys. Pol. A 122, 962 (2012)
- [8] M. Scholz, R. Vigário, Artificial Neural Networks, Springer, Brugges 2002
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv124n331kz