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Fiber Optics

Deep learning based force recognition using the specklegrams from multimode fiber

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References

  • Efendioglu, H. S. A Review of Fiber-Optic Modal Modulated Sensors: Specklegram and Modal Power Distribution Sensing. IEEE Sensors J. 2017, 17, 2055–2064. DOI: 10.1109/JSEN.2017.2658683.
  • Wang, T.; Li, Y.; Tao, J.; Wang, X.; Qiu, Y.; Mao, B.; Chen, M.; Meng, Y.; Zhao, C.; Kang, J.; et al. Deep-Learning-Assisted Fiber Bragg Grating Interrogation by Random Speckles. Opt. Lett. 2021, 46, 5711–5714. DOI: 10.1364/OL.445159.
  • Liu, Y.; Li, G.; Qin, Q.; Tan, Z.; Wang, M.; Yan, F. Bending Recognition Based on the Analysis of Fiber Specklegrams Using Deep Learning. Opt. Laser Technol. 2020, 131, 106424. DOI: 10.1016/j.optlastec.2020.106424.
  • Zhong, T.; Yu, Z.; Lai, P. Multimode Fiber Specklegram Twist Sensor. In Asia Communications and Photonics Conference (ACP): 2017, Nov 10–13 2017; Guangzhou, Peoples R China. 2017.
  • Li, H.; Liu, Y.; Liang, H.; Han, S.; Wang, Z. Twist-Related Speckle Variation in Graded-Index Multimode Fiber. IEEE Photon. J. 2018, 10, 1–9. DOI: 10.1109/JPHOT.2018.2868276.
  • Loterie, D.; Psaltis, D.; Moser, C. Bend Translation in Multimode Fiber Imaging. Opt. Express 2017, 25, 6263–6273. DOI: 10.1364/oe.25.006263.
  • Rodriguez-Cobo, L.; Lomer, M.; Lopez-Higuera, J.-M. Fiber Specklegram-Multiplexed Sensor. J. Lightwave Technol. 2015, 33, 2591–2597. DOI: 10.1109/JLT.2014.2364318.
  • Fujiwara, E.; Marques dos Santos, M. F.; Suzuki, C. K. Optical Fiber Specklegram Sensor Analysis by Speckle Pattern Division. Appl. Opt. 2017, 56, 1585–1590. DOI: 10.1364/ao.56.001585.
  • Cuevas, A. R.; Fontana, M.; Rodriguez-Cobo, L.; Lomer, M.; Lopez-Higuera, J. M. Machine Learning for Turning Optical Fiber Specklegram Sensor into a Spatially-Resolved Sensing System. Proof of Concept. J. Lightwave Technol. 2018, 36, 3733–3738. DOI: 10.1109/JLT.2018.2850801.
  • Borhani, N.; Kakkava, E.; Moser, C.; Psaltis, D. Learning to See through Multimode Fibers. Optica 2018, 5, 960–966. DOI: 10.1364/OPTICA.5.000960.
  • Shabairou, N.; Cohen, E.; Wagner, O.; Malka, D.; Zalevsky, Z. Color Image Identification and Reconstruction Using Artificial Neural Networks on Multimode Fiber Images: Towards an All-Optical Design. Opt. Lett. 2018, 43, 5603–5606. DOI: 10.1364/ol.43.005603.
  • Wang, P.; Di, J. Deep Learning-Based Object Classification through Multimode Fiber via a CNN-Architecture SpeckleNet. Appl. Opt. 2018, 57, 8258–8263. DOI: 10.1364/ao.57.008258.
  • Liu, A.; Lin, T.; Han, H.; Zhang, X.; Chen, Z.; Gan, F.; Lv, H.; Liu, X. Analyzing Modal Power in Multi-Mode Waveguide via Machine Learning. Opt. Express 2018, 26, 22100–22109. DOI: 10.1364/OE.26.022100.
  • An, Y.; Li, J.; Huang, L.; Li, L.; Leng, J.; Yang, L.; Zhou, P. Numerical Mode Decomposition for Multimode Fiber: From Multi-Variable Optimization to Deep Learning. Opt. Fiber Technol. 2019, 52, 101960. DOI: 10.1016/j.yofte.2019.101960.
  • An, Y.; Huang, L.; Li, J.; Leng, J.; Yang, L.; Zhou, P. Learning to Decompose the Modes in Few-Mode Fibers with Deep Convolutional Neural Network. Opt. Express 2019, 27, 10127–10137. DOI: 10.1364/oe.27.010127.
  • Lin, T.; Liu, A.; Zhang, X.; Li, H.; Wang, L.; Han, H.; Chen, Z.; Liu, X.; Lu, H. Analyzing OAM Mode Purity in Optical Fibers with CNN-Based Deep Learning. Chin. Opt. Lett. 2019, 17, 100603. DOI: 10.3788/COL201917.100603.
  • Fujiwara, E.; Wu, Y. T.; Santos, M. F. M.; Schenkel, E. A.; Suzuki, C. K. Optical Fiber Specklegram Sensor for Measurement of Force Myography Signals. IEEE Sensors J. 2017, 17, 951–958. DOI: 10.1109/JSEN.2016.2638831.
  • Rodriguez-Cuevas, A.; Real Pena, E.; Rodriguez-Cobo, L.; Lomer, M.; Miguel Lopez Higuera, J. Low-Cost Fiber Specklegram Sensor for Noncontact Continuous Patient Monitoring. J. Biomed. Opt. 2017, 22, 37001. DOI: 10.1117/1.JBO.22.3.037001.
  • Ohayon, S.; Caravaca-Aguirre, A.; Piestun, R.; Dicarlo, J. J. Minimally Invasive Multimode Optical Fiber Microendoscope for Deep Brain Fluorescence Imaging. Biomed. Opt. Express 2018, 9, 1492–1509. DOI: 10.1364/boe.9.001492.
  • Maleki, E.; Maleki, N. Artificial Neural Network Modeling of Pt/C Cathode Degradation in PEM Fuel Cells. J. Electron. Mater. 2016, 45, 3822–3834. DOI: 10.1007/s11664-016-4718-8.
  • Maleki, E.; Unal, O. Fatigue Limit Prediction and Analysis of Nano-Structured AISI 304 Steel by Severe Shot Peening via ANN. Eng. Comput. 2021, 37, 2663–2678. DOI: 10.1007/s00366-020-00964-6.
  • Maleki, E.; Unal, O.; Reza Kashyzadeh, K. Surface Layer Nanocrystallization of Carbon Steels Subjected to Severe Shot Peening: Analysis and Optimization. Mater. Charact. 2019, 157, 109877. DOI: 10.1016/j.matchar.2019.109877.
  • Maleki, N.; Kashanian, S.; Maleki, E.; Nazari, M. A Novel Enzyme Based Biosensor for Catechol Detection in Water Samples Using Artificial Neural Network. Biochem. Eng. J. 2017, 128, 1–11. DOI: 10.1016/j.bej.2017.09.005.
  • Maleki, E.; Mirzaali, M. J.; Guagliano, M.; Bagherifard, S. Analyzing the Mechano-Bactericidal Effect of Nano-Patterned Surfaces on Different Bacteria Species. Surf. Coat. Technol. 2021, 408, 126782. DOI: 10.1016/j.surfcoat.2020.126782.
  • Yu, F. T. S.; Wen, M.; Yin, S.; Uang, C.-M. Submicrometer Displacement Sensing Using Inner-Product Multimode Fiber Speckle Fields. Appl. Opt. 1993, 32, 4685–4689. DOI: 10.1364/AO.32.004685.
  • Smith, D. L.; Nguyen, L. V.; Ottaway, D. J.; Cabral, T. D.; Fujiwara, E.; Cordeiro, C. M. B.; Warren-Smith, S. C. Machine Learning for Sensing with a Multimode Exposed Core Fiber Specklegram Sensor. Opt. Express. 2022, 30, 10443–10455. DOI: 10.1364/OE.443932.
  • Wang, Z.; Bovik, A. C.; Sheikh, H. R.; Simoncelli, E. P. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. Image Process. 2004, 13, 600–612. DOI: 10.1109/tip.2003.819861.
  • Zuo, C.; Qian, J.; Feng, S.; Yin, W.; Li, Y.; Fan, P.; Han, J.; Qian, K.; Chen, Q. Deep Learning in Optical Metrology: A Review. Light Sci. Appl. 2022, 11, 39. DOI: 10.1038/s41377-022-00714-x.
  • Szegedy, C.; Liu, W.; Jia, Y. Q.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. IEEE, Going Deeper with Convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR): Jun 07-12 2015; Boston, MA. New York: IEEE; 2015 : 1–9. DOI: 10.1109/cvpr.2015.7298594.
  • Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z. 2016 Rethinking the Inception Architecture for Computer Vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): Jun 27-30 2016; Seattle, WA. New York: IEEE; 2818-2826. doi: 10.1109/cvpr.2016.308. DOI: 10.1109/CVPR.2016.308.
  • Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM. 2017, 60, 84–90. DOI: 10.1145/3065386.

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