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Dispersion-Independent Terahertz Classification Based on Geometric Algebra for Substance Detection

Zhou, S. L.; Valchev, D. G.; Dinovitser, A.; Chappell, J. M.; Iqbal, A.; Ng, B. W-H.; Kee, T. W. and Abbott, D. (2016). Dispersion-Independent Terahertz Classification Based on Geometric Algebra for Substance Detection. In: 41st International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW-THz), 25-30 Sep 2016, Copenhagen, Denmark.

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We demonstrate and validate Geometric Algebra (GA) based terahertz (THz) signal classification of various powders in tablet form of various thicknesses, and compare the results with a conventional Support Vector Machine (SVM) approach. By using geometric algebra we can perform classification independently of dispersion and hence independently of the transmission path length through the sample. In principle, it may be possible to extend the GA coordinate-free transformation to other types of pulsed signals, such as pulsed microwaves or even acoustic signals in such fields as seismology. The classifier is available for download at Github,

Item Type: Conference or Workshop Item
Copyright Holders: 2016 IEEE
ISSN: 2162-2035
Project Funding Details:
Funded Project NameProject IDFunding Body
Not Set016YFC0502301National Key R&D Program of China
Not SetXDJK2014c132Fundamental Research Funds for the Central Universities
Not SetFT120100351The Australian Research Council
2014 European Fellowship SchemeNot SetThe Group of Eight Australia
2015 China Scholarship Council SchemeNot SetChina Scholarship Council
Keywords: classification; geometric algebra; multivectors; spectroscopy; Terahertz
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Engineering and Innovation
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 60118
Depositing User: Dimitar Valchev
Date Deposited: 17 Apr 2019 14:07
Last Modified: 19 Apr 2019 21:17
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