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Lookup NU author(s): Dr Andrew PikeORCiD
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© 2001-2012 IEEE.The advancement of artificial intelligence technology has led to the widespread adoption of deep learning techniques within spectral analysis over recent years. In this study, we introduce an advanced demodulation approach utilizing a 1-D convolutional neural network (1D-CNN) for feature extraction and the analysis of spectral signals from surface plasmon resonance (SPR) fiber refractive index (RI) sensors featuring a multimode-no-core-multimode (MNM) structure while simultaneously forecasting changes in RI due to environmental factors. Through segmentation-based predictive training on spectral signals, our approach achieves an average prediction accuracy exceeding 98%, even at low resolutions. Experimental findings demonstrate superior demodulation performance using our intelligent demodulation technique based on 1D-CNN compared to conventional methods. Furthermore, our method is adaptable across diverse and intricate structures enabling observation of parameter correlations spanning their entire range, thereby enhancing measurement capabilities within SPR sensing systems with significant potential applications.
Author(s): Liao X-X, Yang H, Wu Q, Liu J, Hu Y, Zhang Y, Liu W-Q, Fu Y, Pike AR, Liu B
Publication type: Article
Publication status: Published
Journal: IEEE Sensors Journal
Year: 2025
Volume: 25
Issue: 4
Pages: 6371-6379
Print publication date: 15/02/2025
Online publication date: 03/01/2025
Acceptance date: 24/12/2024
ISSN (print): 1530-437X
ISSN (electronic): 1558-1748
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/JSEN.2024.3523272
DOI: 10.1109/JSEN.2024.3523272
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