Sensing in the presence of strong noise by deep learning of dynamic multimode fiber interference

作     者：Linh V.Nguyen Cuong C.Nguyen Gustavo Carneiro Heike Ebendorff-Heidepriem Stephen C.Warren-Smith 

作者机构：Institute for Photonics and Advanced Sensing and School of Physical SciencesThe University of AdelaideAdelaideSA 5005Australia Australian Institute for Machine LearningThe University of AdelaideAdelaideSA 5005Australia Australian Research Council Centre of Excellence for Nanoscale BioPhotonicsThe University of AdelaideSA 5005Australia Future Industries InstituteUniversity of South AustraliaMawson LakesSA 5095Australia 

出 版 物：《Photonics Research》 (光子学研究（英文版）)

年 卷 期：2021年第9卷第4期

页      面：I0029-I0038页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080202[工学-机械电子工程] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Australian Research Council(CE140100003 CE140100016 FT200100154) 

主　　题：fiber sapphire noise 

摘      要：A new approach to optical fiber sensing is proposed and demonstrated that allows for specific measurement even in the presence of strong noise from undesired environmental perturbations.A deep neural network model is trained to statistically learn the relation of the complex optical interference output from a multimode optical fiber(MMF)with respect to a measurand of interest while discriminating the *** technique negates the need to carefully shield against,or compensate for,undesired perturbations,as is often the case for traditional optical fiber *** is achieved entirely in software without any fiber postprocessing fabrication steps or specific packaging required,such as fiber Bragg gratings or specialized *** technique is highly generalizable,whereby the model can be trained to identify any measurand of interest within any noisy environment provided the measurand affects the optical path length of the MMF’s guided *** demonstrate the approach using a sapphire crystal optical fiber for temperature sensing under strong noise induced by mechanical vibrations,showing the power of the technique not only to extract sensing information buried in strong noise but to also enable sensing using traditionally challenging exotic materials.