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使用 CMOS 相机探测潜在宇宙射线的实践:硬件和算法。

The Practice of Detecting Potential Cosmic Rays Using CMOS Cameras: Hardware and Algorithms.

机构信息

Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, Poland.

出版信息

Sensors (Basel). 2023 May 18;23(10):4858. doi: 10.3390/s23104858.

DOI:10.3390/s23104858
PMID:37430771
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10220736/
Abstract

In this paper, we discuss a practice of potential cosmic ray detection using off-the-shelves CMOS cameras. We discuss and presents the limitations of up-to-date hardware and software approaches to this task. We also present a hardware solution that we made for long-term testing of algorithms for potential cosmic ray detection. We have also proposed, implemented and tested a novel algorithm that enables real-time processing of image frames acquired by CMOS cameras in order to detect tracks of potential particles. We have compared our results with already published results and obtained acceptable results overcoming some limitation of already existing algorithms. Both source codes and data are available to download.

摘要

本文讨论了使用现成的 CMOS 相机进行潜在宇宙射线探测的实践。我们讨论并介绍了当前硬件和软件方法在这一任务中的局限性。我们还提出了一种硬件解决方案,用于对潜在宇宙射线探测算法进行长期测试。我们还提出、实现并测试了一种新算法,该算法能够实时处理 CMOS 相机获取的图像帧,以检测潜在粒子的轨迹。我们将结果与已发表的结果进行了比较,并在克服已有算法的一些局限性的情况下获得了可接受的结果。源代码和数据都可下载。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/e049b643a91a/sensors-23-04858-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/88860fe82c2d/sensors-23-04858-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/e2accc8e3052/sensors-23-04858-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/f2ef98fcbb0f/sensors-23-04858-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/5e45e0b9c9d9/sensors-23-04858-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/97f2e5695ef0/sensors-23-04858-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/41e007dd2e58/sensors-23-04858-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/aaef4631163c/sensors-23-04858-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/6d30c89a1b44/sensors-23-04858-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/c8d56533e377/sensors-23-04858-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/e049b643a91a/sensors-23-04858-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/88860fe82c2d/sensors-23-04858-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/e2accc8e3052/sensors-23-04858-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/f2ef98fcbb0f/sensors-23-04858-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/5e45e0b9c9d9/sensors-23-04858-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/97f2e5695ef0/sensors-23-04858-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/41e007dd2e58/sensors-23-04858-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/aaef4631163c/sensors-23-04858-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/6d30c89a1b44/sensors-23-04858-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/c8d56533e377/sensors-23-04858-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a59/10220736/e049b643a91a/sensors-23-04858-g010.jpg

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本文引用的文献

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Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors.基于泽尼克矩的互补金属氧化物半导体(CMOS)传感器宇宙射线候选撞击点分类
Sensors (Basel). 2021 Nov 19;21(22):7718. doi: 10.3390/s21227718.
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CNN-Based Classifier as an Offline Trigger for the CREDO Experiment.基于卷积神经网络的分类器作为 CREDO 实验的离线触发器。
Sensors (Basel). 2021 Jul 14;21(14):4804. doi: 10.3390/s21144804.
3
Recognition of Cosmic Ray Images Obtained from CMOS Sensors Used in Mobile Phones by Approximation of Uncertain Class Assignment with Deep Convolutional Neural Network.
利用深度卷积神经网络对不确定类别分配的逼近实现对手机用 CMOS 传感器获取的宇宙射线图像的识别。
Sensors (Basel). 2021 Mar 11;21(6):1963. doi: 10.3390/s21061963.
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Technology: Smartphone science.技术:智能手机科学。
Nature. 2016 Mar 31;531(7596):669-71. doi: 10.1038/nj7596-669a.