Document Type
Article
Publication Date
2025
DOI
10.3390/electronics14081688
Publication Title
Electronics
Volume
14
Issue
8
Pages
1688 (1-23)
Abstract
Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.
Rights
© 2025 by the authors.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Data Availability
Article states: "Data are contained within the article."
Original Publication Citation
Kwan, C., Ayhan, B., Stavola, A., Islam, K. A., Zhang, H., & Li, J. (2025). A fast framework for generating radioactive mixture spectra and its application to remote high-performance mixture identification. Electronics, 14(8), 1-23, Article 1688. https://doi.org/10.3390/electronics14081688
Repository Citation
Kwan, Chiman; Ayhan, Bulent; Stavola, Adam; Islam, Kazi Aminul; Zhang, Hongfang; and Li, Jiang, "A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification" (2025). Electrical & Computer Engineering Faculty Publications. 538.
https://digitalcommons.odu.edu/ece_fac_pubs/538
ORCID
0009-0006-8924-4192 (Zhang), 0000-0003-0091-6986 (Li)
Included in
Artificial Intelligence and Robotics Commons, Materials Science and Engineering Commons, Radiation Medicine Commons, Theory and Algorithms Commons