Suitability of the Raspberry Pi camera for cosmic ray detection and measurement

Cuciuc, Mihai

doi:10.1109/nssmic.2018.8824441

Cited by 3 publications

(9 citation statements)

References 6 publications

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“…Ref. [ 34 ] designed four Raspberry Pi 2.0 cameras placed in a vertical stack detector at ~1180 h, and registered 78 candidate events across all 4 sensors. That is,

and an estimated

.…”

Section: Discussionmentioning

confidence: 99%

“…We presented a comparison of these results and those obtained by us in Table 1 . The solution registered significantly more events than in [ 34 ]; however, it should be noted that a stack of four detectors was used, only registering simultaneous events. Therefore, it is natural that [ 34 ] recorded fewer events.…”

Section: Discussionmentioning

confidence: 99%

“…We used these cameras interchangeably. Earlier research [ 34 ] showed the suitability of CMOS hardware for cosmic ray detection and measurement.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Published research is also devoted to methods for detecting the fact of particle impact in an off-the-shelf CMOS sensor. The use of smartphones cameras [ 31 , 32 , 33 ] or Raspberry Pi cameras [ 10 , 34 ] is described. The literature also includes papers on the use of CCD sensors with long duration exposures for cosmic ray detection [ 1 ] or the use of CMOS cameras for detection of interstellar meteoroids [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

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

Hachaj

Piekarczyk

2023

Sensors

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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.

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“…Ref. [ 34 ] designed four Raspberry Pi 2.0 cameras placed in a vertical stack detector at ~1180 h, and registered 78 candidate events across all 4 sensors. That is,

and an estimated

.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…We used these cameras interchangeably. Earlier research [ 34 ] showed the suitability of CMOS hardware for cosmic ray detection and measurement.…”

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Hachaj

Piekarczyk

2023

Sensors

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“…To implement this project, it was decided to use a single-board computer Raspberry Pi 4 and a camera from the same manufacturer -Raspberry Pi High-Quality Camera with 12,3 megapixels and Tamron 16mm lens. Due to its low-cost and fairly high-quality image, this camera is widely used in scientific research in various fields [Cuciuc, 2018;Dinesh & Bhaskar, 2020;Albert & Surducan, 2017].…”

Section: Methodsmentioning

confidence: 99%

Geodesy, Cartography, and Aerial Photography

Lopatin¹,

Heger²

2021

ISTCGCAP

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The aim of the work is to develop an automated measuring system in a mechanical gyrocompass with the help of specially developed hardware and software in order to facilitate the operation of the device and minimize observer errors. The developed complex provides automation only for the time method, as for the method of the turning point it is necessary to constantly contact the motion screw of the total station. The project is based on an integrated system, the hardware part of which contains a single-board computer, camera, and lens. The main software is a developed motion recognition algorithm with the help of image processing. This algorithm was created using the Python programming language and the open-source computer vision library OpenCV. With the help of the hardware, a video image of the gyroscope's reference scale is obtained, and with the help of the software, the moving light indicator and its position relative to the scale are identified in this image. The result of the study is a functioning automatic measurement system, which determines the value of the azimuth of the direction with the same accuracy as manual measurements. The system is controlled remotely via a computer and wi-fi network. To test the system, a series of automatic and manual measurements were performed simultaneously at the same point for the same direction. Based on the results obtained, it can be stated that the accuracy of the system is within the limits specified by the manufacturer of the device for manual measurements. The application of computer vision technology, namely the tracking of a moving object in the image for gyroscopic measurements can give a significant impetus to the development of automation systems for a wide range of measuring instruments, which in turn can improve the accuracy of measurement results. The developed system can be used together with the Gyromax AK-2M gyrocompass of GeoMessTechnik for carrying out automated measurements, training of new operators. With the help of the developed model, it is possible to avoid gross errors of the observer, to facilitate the measurement process which will not demand the constant presence of the operator near the device. In some dangerous conditions, this is a significant advantage.

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