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Presentation
Pre-Processing of Signals Observed from Laser Diode Self-mixing Intereferometries using Neural Networks
Faculty of Informatics - Papers (Archive)
  • L. Wei, University of Wollongong
  • Joe F Chicharo, University of Wollongong
  • Yanguang Yu, University of Wollongong
  • Jiangtao Xi, University of Wollongong
RIS ID
22131
Publication Date
3-10-2007
Publication Details

This conference paper was originally published as Wei, L, Chicharo, J, Yu, Y, Xi, J, Pre-Processing of Signals Observed from Laser Diode Self-mixing Intereferometries using Neural Networks, IEEE International Symposium on Intelligent Signal Processing WISP 2007, 3-5 Oct, 1-5.

Abstract

This paper presents a novel neural network signal interpolation technique in order to eliminate the noise and disturbance associated with the self-mixing signal observed from optical feedback self- mixing interferometry (OFSMI). The proposed technique aims to improve the accuracy for displacement and moving track measurement of a target. The performance of the proposed approach is evaluated by both simulation and experimentation, with simulation revealing a measuring accuracy of A/25 for weak feedback and J20 for moderate feed back.

Citation Information
L. Wei, Joe F Chicharo, Yanguang Yu and Jiangtao Xi. "Pre-Processing of Signals Observed from Laser Diode Self-mixing Intereferometries using Neural Networks" (2007)
Available at: http://works.bepress.com/jxi/32/