Detection of Potentially Anomalous Cosmic Particle Tracks Acquired with CMOS Sensors: Validation of Rough k–Means Clustering with PCA Feature Extraction
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| Pubblicato in: | International Journal of Applied Mathematics and Computer Science vol. 35, no. 1 (2025), p. 7 |
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| Autore principale: | |
| Altri autori: | , |
| Pubblicazione: |
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
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| Accesso online: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.61822/amcs-2025-0001 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20250331 | |
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| 100 | 1 | |a Hachaj, Tomasz |u Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow Al. Mickiewicza 30, 30-059 Krakow, Poland | |
| 245 | 1 | |a Detection of Potentially Anomalous Cosmic Particle Tracks Acquired with CMOS Sensors: Validation of Rough <i>k</i>–Means Clustering with PCA Feature Extraction | |
| 260 | |b De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a We present a method capable of detecting potentially anomalous cosmic particle tracks acquired with complementary metal-oxide-semiconductor (CMOS) sensors. We apply a principal components analysis-based feature extraction method and rough k-means clustering for outlier detection. We evaluated our approach on more than 104 images acquired by the Cosmic Ray Extremely Distributed Observatory (CREDO). The method presented in this work proved to be an effective solution. The analysis of the behavior of the rough k-means clustering-based algorithm presented here and the method of selecting its parameters showed that the algorithm performs as expected and demonstrates efficiency, stability, and repeatability of results for the test data set. The results included in this work are very relevant to the international CREDO project and the broader problem of anomaly analysis in image data sets. We plan to deploy the presented methodology in the image processing pipeline of the large data set we are working on in the CREDO project. The results can be reproduced using our source code, which is published in an open repository. | |
| 653 | |a Particle tracking | ||
| 653 | |a Feature extraction | ||
| 653 | |a Outliers (statistics) | ||
| 653 | |a Data analysis | ||
| 653 | |a Datasets | ||
| 653 | |a Source code | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Sensors | ||
| 653 | |a Clustering | ||
| 653 | |a Cosmic rays | ||
| 653 | |a CMOS | ||
| 653 | |a Algorithms | ||
| 653 | |a Image acquisition | ||
| 653 | |a Image processing | ||
| 653 | |a Vector quantization | ||
| 653 | |a Image processing systems | ||
| 700 | 1 | |a Piekarczyk, Marcin |u Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow Al. Mickiewicza 30, 30-059 Krakow, Poland | |
| 700 | 1 | |a Wąs, Jarosław |u Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow Al. Mickiewicza 30, 30-059 Krakow, Poland | |
| 773 | 0 | |t International Journal of Applied Mathematics and Computer Science |g vol. 35, no. 1 (2025), p. 7 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3184405870/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3184405870/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |