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To address this, a two-phase training process based on transfer learning is adopted. Initially, a base model is developed using synthetic data generated from a detailed RFID simulator, designed to suit diverse scenarios, establish detailed link budgets, and comprehensively simulate the communication protocols. This model is then refined using a small dataset collected experimentally in the actual scenario. This method was validated in a real testbed with four different package types. The base model was trained using 1000 synthetic samples per package type (4000 in total), whereas the refined model was trained with a dataset consisting of only 25 real interrogation traces (samples) per package type (100 in total). The experimental samples were obtained using a software-defined radio unit, the Ettus B210 Universal Software Radio Peripheral (USRP) platform. This experiment achieved an accuracy of over 92%. In summary, this approach introduces a new feature to existing RFID setups, demonstrating potential for advanced package handling and cost optimization in the logistics sector.<\/jats:p>","DOI":"10.1007\/s10489-024-05412-2","type":"journal-article","created":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T03:27:35Z","timestamp":1714793255000},"page":"6053-6068","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A machine learning approach for package size estimation using UHF RFID interrogation signature"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8761-7235","authenticated-orcid":false,"given":"Javier","family":"Vales-Alonso","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9881-6192","authenticated-orcid":false,"given":"Pablo","family":"L\u00f3pez-Matencio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,4]]},"reference":[{"key":"5412_CR1","unstructured":"ABC (2023) Package V2 dataset. https:\/\/universe.roboflow.com\/abc-d9ezq\/package-v2. 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