Efficient reconstruction scheme with deep neural network for highly compressive sensing of fiber Bragg grating spectrum
| Autor: | Yen-Jie Ee, Kok-Sing Lim, Kok Soon Tey, Hangting Yang, Cheong-Weng Ooi, Hangzhou Yang, Harith Ahmad |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Transactions of the Institute of Measurement and Control. 45:1515-1524 |
| ISSN: | 1477-0369 0142-3312 |
| DOI: | 10.1177/01423312221149777 |
| Popis: | In this work, we propose an efficient reconstruction scheme for compressive sensing (CS) of fiber Bragg grating (FBG) spectrum. Taking advantage of the sparse reflection spectrum of the FBG array network, we have demonstrated the use of CS for compressing the spectrum at an excessively high compression factor up to 64. In addition to that, the spectral difference (SD) of the spectra is used to further enhance their sparsity for the CS model. In this investigation, four different configurations have been devised and tested to compare their performance and effectiveness. Configuration IV that is based on SD and deep neural network offers the best recovery performance. The proposed method is a potential tool for efficient data storage and transmission for FBG sensor network. |
| Databáze: | OpenAIRE |
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