{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:23:10Z","timestamp":1780464190028,"version":"3.54.1"},"reference-count":22,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"MCIN\/AEI\/10.13039\/501100011033","doi-asserted-by":"publisher","award":["CPP2022-009904"],"award-info":[{"award-number":["CPP2022-009904"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Efficient real-time isotope identification is a critical challenge in nuclear spectroscopy, with important applications such as radiation monitoring, nuclear waste management, and medical imaging. This work presents a novel approach for isotope classification using a System-on-Chip FPGA, integrating hardware-accelerated principal component analysis (PCA) for feature extraction and a software-based random forest classifier. The system leverages the FPGA\u2019s parallel processing capabilities to implement PCA, reducing the dimensionality of digitized nuclear signals and optimizing computational efficiency. A key feature of the design is its ability to perform real-time classification without storing ADC samples, directly processing nuclear pulse data as it is acquired. The extracted features are classified by a random forest model running on the embedded microprocessor. PCA quantization is applied to minimize power consumption and resource usage without compromising accuracy. The experimental validation was conducted using datasets from high-resolution pulse-shape digitization, including closely matched isotope pairs (12C\/13C, 36Ar\/40Ar, and 80Kr\/84Kr). The results demonstrate that the proposed SoC FPGA system significantly outperforms conventional software-only implementations, reducing latency while maintaining classification accuracy above 98%. This study provides a scalable, precise, and energy-efficient solution for real-time isotope identification.<\/jats:p>","DOI":"10.3390\/s25123758","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T09:51:22Z","timestamp":1750067482000},"page":"3758","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Real-Time Isotope Identification on SoC FPGA"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0871-9901","authenticated-orcid":false,"given":"Katherine","family":"Guerrero-Morej\u00f3n","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, University of Sevilla, 41092 Sevilla, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3071-9900","authenticated-orcid":false,"given":"Jos\u00e9 Mar\u00eda","family":"Hinojo-Montero","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering Computer Systems and Automatics, University of Huelva, 21007 Huelva, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7462-740X","authenticated-orcid":false,"given":"Jorge","family":"Jim\u00e9nez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, University of Sevilla, 41092 Sevilla, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6730-5691","authenticated-orcid":false,"given":"Cristian","family":"Rocha-J\u00e1come","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, University of Sevilla, 41092 Sevilla, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3891-8987","authenticated-orcid":false,"given":"Ram\u00f3n","family":"Gonz\u00e1lez-Carvajal","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, University of Sevilla, 41092 Sevilla, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9406-3779","authenticated-orcid":false,"given":"Fernando","family":"Mu\u00f1oz-Chavero","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, University of Sevilla, 41092 Sevilla, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.nima.2016.05.107","article-title":"Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors","volume":"830","author":"Flores","year":"2016","journal-title":"Nucl. 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