[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124055-en":3,"doc-seo-124055-105":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":4,"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},124055,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhanced Machine-Learning Flow for Microwave-Sensing Systems to Detect Contaminants in Food","Presence of foreign bodies in packaged food threatens consumer safety and food-manufacturing performance, especially for low-density contaminants such as plastics, glass, and wood that are difficult for conventional imaging systems. The work advances Machine-Learning-based Microwave Sensing by enhancing the ML pipeline to improve classifier accuracy. A multi-class model is trained using scattering parameters at multiple microwave frequencies with dedicated preprocessing, data augmentation, quantization-aware training, and pruning. Results report 94.167% accuracy with 26 µs FPGA latency, and datasets are released via OpenML.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nEnhanced Machine-Learning Flow for Microwave-Sensing Systems to Detect Contaminants in Food  \nOriginal  \nEnhanced Machine-Learning Flow for Microwave-Sensing Systems to Detect Contaminants in Food / Štiti, Bernardita; Urbinati, Luca; Di Guglielmo, Giuseppe; Carloni, Luca; Casu, Mario R.. -ELETTRONICO. - (2023), pp. 40-44. (Intervento presentato al convegno 2023 IEEE Conference on AgriFood Electronics (CAFE) tenutosi a Torino, Italy nel 25-27 September 2023) [10 . 1109/CAFE58535 .2023. 10291198] .  \nAvailability:  \nThis version is available at: 11583/2985083 since: 2024-01-15T16:30:39Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/CAFE58535.2023.10291198  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n18 September 2024  \n© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Full citation of the published work: B. Štitić, L. Urbinati, G. Di Guglielmo, L. Carloni and M. R. Casu, \"Enhanced Machine-Learning Flow for Microwave-Sensing Systems to Detect Contaminants in Food,\" 2023 IEEE Conference on AgriFood Electronics (CAFE), Torino, Italy, 2023, pp. 40-44, doi: 10.1109/CAFE58535.2023.10291198 .  \nEnhanced Machine-Learning Flow for MicrowaveSensing Systems to Detect Contaminants in Food  \nBernardita ˇStiti, Luca Urbinati, Giuseppe Di Guglielmo, Luca Carloni, Mario R. Casu[bastitic@uc.cl](bastitic@uc.cl), luca.urbinati@polito.it, [gdg@fnal.gov](gdg@fnal.gov), [luca@cs.columbia.edu](luca@cs.columbia.edu), mario.casu@polito.it  \nAbstract—The presence of foreign bodies in packaged food is a serious concern for both final consumers (allergies, injuries, choking) and food manufacturers (reputation and economic losses). In particular, low-density plastics, glass and wood splinters are hard to detect even by the most advanced X-ray imagers. One solution is Machine-Learning-based Microwave Sensing (MLMWS): a non-invasive, contactless, and real-time method which uses a machine-learning (ML) classifier to analyze the scattered microwaves from the irradiated target object. In this paper, we want to extend our previous work about contaminant detection in cocoa-hazelnut spread jars by proposing an enhanced ML flow to increase the accuracy of the ML classifier. For the first time in this case study, we use a multi-class classifier, we train it with scattering parameters measured at multiple microwave frequencies, with a new pre-processing scaler, data augmentation, quantization-aware training and a pruning schedule. The results show a contaminant detection multi-class accuracy of 94.167% with a latency of 26µs when targeting an AMD/Xilinx Kria K26 FPGA. Finally, we released our datasets publicly to OpenML.1  \nIndex Terms—foreign body detection in food, machine learning, microwave sensing, neural networks, fpga acceleration  \nI. INTRODUCTION  \nForeign bodies in packaged food pose risks for consumers’health (e.g. injuries and choking) and could damage manufacturers’ reputation and finances. Nowadays, there are many noninvasive techniques to detect foreign bodies in food (primarily metal detectors, X-ray, near-infrared, and teraher","cbCaivotzPqIdq3l","https://ap.wps.com/l/cbCaivotzPqIdq3l","pdf",300601,1,6,"English","en",105,"# Introduction\n## Microwave sensing for foreign body detection\n## Motivation and objectives\n# Enhanced machine-learning flow","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It targets the detection of foreign bodies in packaged food, focusing on low-density contaminants that are hard for many existing inspection systems to identify.\"},{\"question\":\"How does the proposed method detect contaminants?\",\"answer\":\"It uses machine-learning-based microwave sensing, sending low-power microwaves to the object, recording scattered waves, and classifying them with an ML classifier.\"},{\"question\":\"What enhancements are introduced to improve accuracy?\",\"answer\":\"The enhanced flow includes multi-class classification, training with scattering parameters measured at multiple microwave frequencies, a new preprocessing scaler, data augmentation, quantization-aware training, and a pruning schedule.\"}]","Enhanced Machine-Learning Flow for Microwave-Sensing Systems to Detect Contaminants in Food | 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