[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125439-en":3,"doc-seo-125439-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125439,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Sparse Sensing and Machine Learning for Rapid Calibration of Defect Detection with rf Atomic Magnetometers - Paper","Machine learning algorithms are applied to accelerate both image acquisition and calibration for defect detection using radio-frequency atomic magnetometers. Sparse sensing is used to reconstruct images from a small fraction of sampled pixels, achieving an average relative error of about 5% compared with a full raster scan. Additional image-processing and calibration-support algorithms verify results, tune experimental parameters, and improve image contrast. The work establishes a first step toward a fast, arbitrary non-destructive defect-detection tool.","Edinburgh Research Explorer  \nSparse Sensing and Machine Learning for Rapid Calibration of Defect Detection with rf Atomic Magnetometers  \nCitation for published version:  \nMunko, M, Rushton, LM, Ellis, LM, Zipfel, JD, Bevington, P, Chalupczak, W & Lopez Dubon, S 2025,'Sparse Sensing and Machine Learning for Rapid Calibration of Defect Detection with rf Atomic Magnetometers', Sensors, vol. 25, no. 22, 6930. [https://doi.org/10.3390/s25226930](https://doi.org/10.3390/s25226930)  \nDigital Object Identifier (DOI):  \n10.3390/s25226930  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nSensors  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 08. Jan. 2026  \nArticle  \nSparse Sensing and Machine Learning for Rapid Calibration of Defect Detection with rf Atomic Magnetometers  \nMarek J. Munko 1, *, Lucas M. Rushton 2, Laura M. Ellis 2, Jake D. Zipfel 2, Patrick Bevington 2, *, Witold Chalupczak 2 and Sergio Lopez Dubon 1  \nAcademic Editors: Yongfang Mao, Kunpeng Wang, Yonghua Jiang and Li Feng  \nReceived: 24 September 2025  \nRevised: 24 October 2025  \nAccepted: 31 October 2025  \nPublished: 13 November 2025  \nCitation: Munko, M.J.; Rushton, L.M.; Ellis, L.M.; Zipfel, J.D.; Bevington, P.; Chalupczak, W.; Lopez Dubon, S. Sparse Sensing and Machine Learning for Rapid Calibration of Defect Detection with rf Atomic Magnetometers. Sensors 2025, 25, 6930. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s25226930  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 School of Engineering, The University of Edinburgh, Mayfield Road, Edinburgh EH9 3JL, UK; [sergio.ldubon@ed.ac.uk](sergio.ldubon@ed.ac.uk)  \n2 National Physical Laboratory, Hampton Road, Teddington TW11 0LW, UK; [lucas.rushton@npl.co.uk](lucas.rushton@npl.co.uk) (L.M.R.); [laura.ellis@npl.co.uk](laura.ellis@npl.co.uk) (L.M.E.); [jake.zipfel@npl.co.uk](jake.zipfel@npl.co.uk) (J.D.Z.); [witold.chalupczak@npl.co.uk](witold.chalupczak@npl.co.uk) (W.C.)  \n* [Correspondence: m.munko@ed.ac.uk](Correspondence: m.munko@ed.ac.uk) (M.J.M.); [patrick.bevington@npl.co.uk](patrick.bevington@npl.co.uk) (P.B.)  \nAbstract  \nMachine learning algorithms are utilised to improve the speed of both image acquisition and calibration processes needed for defect detection using radio-frequency atomic magnetometers. Sparse sensing is employed, and an average relative error of ≈5% is observed fora reconstructed image based on 1.25% of the sampled pixels when compared to a raster scan over the target object. Additional algorithms demonstrate the viability of image processing to qualify results and adjust experimental parameters required for calibration, leading to an enhancement of image contrast. This presents a first step in developing a tool for fast, arbitrary defect detection.  \nKeywords: non-destructive testing; rf atomic magnetometers; sparse sensing; machine learning  \n1. Introduction  \nThere is a growing market need for rapid,","cbCaidWD3Ve1tks7","https://ap.wps.com/l/cbCaidWD3Ve1tks7","pdf",2532880,1,23,"English","en",105,"# Introduction\n## Inductive measurement with rf atomic magnetometers\n## Magnetic induction tomography and penetration depth\n# Methods and approach\n## Sparse sensing for rapid calibration\n## Machine learning for image acquisition and reconstruction\n# Results and discussion\n## Reconstruction accuracy versus raster scanning\n## Image processing for qualification and contrast improvement","[{\"question\":\"How does sparse sensing reduce calibration time in defect detection?\",\"answer\":\"Sparse sensing reconstructs defect images using only a small subset of sampled pixels, significantly reducing the number of measurements needed for calibration while maintaining acceptable accuracy.\"},{\"question\":\"What accuracy is reported for the reconstructed images?\",\"answer\":\"The reported average relative error is approximately 5% when reconstructing an image from about 1.25% of sampled pixels, compared to a raster scan.\"},{\"question\":\"Why are radio-frequency atomic magnetometers suitable for detecting subsurface defects?\",\"answer\":\"Oscillating magnetic fields penetrate with frequency-dependent depth, and inductive coupling reveals signals determined by the target’s electrical conductivity and magnetic permeability, enabling detection of concealed features.\"}]","Sparse Sensing and Machine Learning for Rapid Calibration of Defect Detection with rf Atomic Magnetometers - 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