Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106948
| Title: | Fiber-optic activity monitoring with machine learning | Authors: | Zeng, Q Xu, W Yu, C Zhang, N Yu, C |
Issue Date: | 2018 | Source: | CLEO Pacific Rim Conference 2018, OSA Technical Digest (Optica Publishing Group, 2018), paper W4K.5 | Abstract: | Unobtrusive activity monitoring based on fiber-optic Mach-Zehnder interferometer is proposed, employing deep bi-directional long short-term memory network, realizing three activities recognition with accuracy of 99.2% and resolution of 0.5s. | Publisher: | Optica Publishing Group | ISBN: | 978-1-943580-45-3 | DOI: | 10.1364/cleopr.2018.w4k.5 | Description: | Conference on Lasers and Electro-Optics/Pacific Rim 2018, Hong Kong China, 29 July-3 August 2018 | Rights: | © 2018 The Author(s) The following publication Q. Zeng, W. Xu, C. Yu, N. Zhang, and C. Yu, "Fiber-optic Activity Monitoring with Machine Learning," in CLEO Pacific Rim Conference 2018, OSA Technical Digest (Optica Publishing Group, 2018), paper W4K.5 is available at https://doi.org/10.1364/cleopr.2018.w4k.5. |
| Appears in Collections: | Conference Paper |
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|---|---|---|---|---|
| cleopr-2018-w4k.5.pdf | 660.98 kB | Adobe PDF | View/Open |
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