[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122839-en":3,"doc-seo-122839-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122839,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Structured light enhanced machine learning for fiber bend sensing - Research Article","Optical distortions in complex media, including few- or multi-mode optical fibers, often appear random and create fundamental errors in fiber communication and sensing. The work introduces orbital angular momentum (OAM) feature extraction to mitigate phase noise and enable intermodal coupling for fiber sensing. Passive all-optical OAM demultiplexing performs the feature extraction, enabling fiber bend tracking at 94.1% accuracy, while a CNN using output intensity achieves only 14% accuracy. OAM training needs 120x less information than intensity image methods.","Research Article  \nVol. 32, No. 5/26 Feb 2024/Optics Express  \n7882  \nStructured light enhanced machine learning for fiber bend sensing  \nSARA ANGELUCCI , 1 ZHAOZHONG CHEN , 1 L’UBOMÍR Š KVARENINA , 1,2 ALASDAIR W. CLARK , 1  ADAM VALLÉS , 3  AND MARTIN P. J. LAVERY1,*  \n1 James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, UK  \n2 Department of Physics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno 616 00, Czech Republic  \n3ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860,Castelldefels (Barcelona), Spain  \n*  \n[martin.lavery@glasgow.ac.uk](martin.lavery@glasgow.ac.uk)  \nAbstract: The intricate optical distortions that occur when light interacts with complex media, such as few-or multi-mode optical fiber, often appear random in origin and are a fundamental source of error for communication and sensing systems. We propose the use of orbital angular momentum (OAM) feature extraction to mitigate phase-noise and allow for the use of intermodalcoupling as an effective tool for fiber sensing. OAM feature extraction is achieved by passive all-optical OAM demultiplexing, and we demonstrate fiber bend tracking with 94.1% accuracy. Conversely, an accuracy of only 14% was achieved for determining the same bend positions when using a convolutional-neural-network trained with intensity measurements of the output of the fiber. Further, OAM feature extraction used 120 times less information for training compared to intensity image based measurements. This work indicates that structured light enhanced machine learning could be used in a wide range of future sensing technologies.  \nPublished by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.  \n1. Introduction  \nComplex optical interactions with media have captured the imagination of many researchers, becoming a rapidly growing field of cross disciplinary research [1] . When light interacts with complex media, the optical field is distorted by cascaded interactions with spatially distributed scatters or variations in refractive index [2,3] . Specifically within multi-mode fibers, manufacturing errors, bends in the fiber, and material impurities can result in distortions that lead to mode-mixing. Mode-mixing will commonly produce intricate interference between the optical modes supported by the fiber that result in output intensity profiles that resemble random behaviour rather than ordered, predictable interactions [4] . These interactions are commonly referred to as speckles, due to their complex patterning. Monitoring changes in the speckle structure has been demonstrated as a method for sensing applications [5,6] . In addition, changes in temperature and strain in the fiber lead to the intensity profile continually evolving over time, therefore it is difficult to determine explicit external environmental factors that create a particular output intensity or phase profile. This creates challenges for the use of multi-mode fibers in communications, imaging or entangled quantum systems [7] .  \nOvercoming disorder in complex media, including but not exclusive to fiber, has led to many interesting research discoveries in communication [4,8–11], imaging [12–15], quantum optics [16–18], and optical sensors [19–24] . However, the continuous changes in relative phase between the optical modes create phase-noise that limits the accuracy of sensors based on few-mode or multi-mode fibers [24] . Intermodal coupling can be a reliable fiber property for sensing,  \n\\#513829 [https://doi.org/10.1364/OE.513829](https://doi.org/10.1364/OE.513829)  \nJournal © 2024 Received 8 Dec 2023; revised 30 Jan 2024; accepted 7 Feb 2024; published 20 Feb 2024  \nResearch Article  \nVol. 32, No. 5/26 Feb 2024/Optics Express  \n7883  \nhowever a","cbCaii8LlI6K9mzM","https://ap.wps.com/l/cbCaii8LlI6K9mzM","pdf",4574535,1,14,"English","en",105,"# Abstract\n# Introduction\n## Complex optical interactions and speckles\n## Phase noise and intermodal coupling\n## Existing fiber shape sensing methods","[{\"question\":\"Why is fiber bend sensing challenging in few- or multi-mode optical fibers?\",\"answer\":\"Optical interactions in complex fibers produce distorted fields that resemble random speckle patterns. Continuous phase changes from thermal and mechanical effects make it difficult to map specific outputs to explicit external conditions.\"},{\"question\":\"What does the proposed method use to improve sensing accuracy?\",\"answer\":\"The method uses orbital angular momentum (OAM) feature extraction. It is implemented via passive all-optical OAM demultiplexing to mitigate phase noise and leverage intermodal coupling.\"},{\"question\":\"How does the performance compare with a convolutional neural network based on intensity measurements?\",\"answer\":\"OAM feature extraction achieves 94.1% accuracy for bend tracking, whereas a CNN trained on output intensity measurements reaches only 14% for determining the same bend positions.\"}]","Structured light enhanced machine learning for fiber bend sensing - Research Article | PDF",1785813190,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"structured-light-enhanced-machine-learning-for-fiber-bend-sensing-research-article","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/structured-light-enhanced-machine-learning-for-fiber-bend-sensing-research-article/122839/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is fiber bend sensing challenging in few- or multi-mode optical fibers?","Question",{"text":76,"@type":77},"Optical interactions in complex fibers produce distorted fields that resemble random speckle patterns. Continuous phase changes from thermal and mechanical effects make it difficult to map specific outputs to explicit external conditions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What does the proposed method use to improve sensing accuracy?",{"text":81,"@type":77},"The method uses orbital angular momentum (OAM) feature extraction. It is implemented via passive all-optical OAM demultiplexing to mitigate phase noise and leverage intermodal coupling.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the performance compare with a convolutional neural network based on intensity measurements?",{"text":85,"@type":77},"OAM feature extraction achieves 94.1% accuracy for bend tracking, whereas a CNN trained on output intensity measurements reaches only 14% for determining the same bend positions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]