Marcin Piekarczyk

455 total citations
32 papers, 203 citations indexed

About

Marcin Piekarczyk is a scholar working on Nuclear and High Energy Physics, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Marcin Piekarczyk has authored 32 papers receiving a total of 203 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Nuclear and High Energy Physics, 11 papers in Computer Vision and Pattern Recognition and 6 papers in Artificial Intelligence. Recurrent topics in Marcin Piekarczyk's work include Particle Detector Development and Performance (11 papers), Astrophysics and Cosmic Phenomena (11 papers) and Dark Matter and Cosmic Phenomena (7 papers). Marcin Piekarczyk is often cited by papers focused on Particle Detector Development and Performance (11 papers), Astrophysics and Cosmic Phenomena (11 papers) and Dark Matter and Cosmic Phenomena (7 papers). Marcin Piekarczyk collaborates with scholars based in Poland, Czechia and Ukraine. Marcin Piekarczyk's co-authors include Tomasz Hachaj, Marek R. Ogiela, Łukasz Bibrzycki, P. Homola, Michał Niedźwiecki, David Alvarez-Castillo, Krzysztof Rzecki, Arman Tursunov, Alan R. Duffy and J. A. Zamora Saa and has published in prestigious journals such as IEEE Access, Sensors and Applied Sciences.

In The Last Decade

Marcin Piekarczyk

28 papers receiving 194 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marcin Piekarczyk Poland 8 77 42 42 39 25 32 203
Krzysztof Rzecki Poland 8 49 0.6× 33 0.8× 76 1.8× 29 0.7× 62 2.5× 25 255
Adrian Muscat Malta 8 221 2.9× 146 3.5× 5 0.1× 20 0.5× 17 0.7× 41 370
Miao Xin China 10 197 2.6× 81 1.9× 17 0.4× 33 1.3× 31 315
Yue Jiang Finland 7 162 2.1× 40 1.0× 67 1.6× 20 0.8× 19 297
Liang Chang China 10 208 2.7× 48 1.1× 61 1.5× 9 0.4× 33 313
Peng Cao China 6 150 1.9× 35 0.8× 16 0.4× 41 1.6× 40 231
Pramit Dutta India 9 67 0.9× 23 0.5× 5 0.1× 9 0.2× 22 0.9× 18 178
Iván Huerta Spain 10 229 3.0× 24 0.6× 22 0.5× 20 0.8× 14 268
Yipeng Qin United Kingdom 9 145 1.9× 54 1.3× 23 0.5× 20 0.8× 34 273
Chen Guo China 8 148 1.9× 54 1.3× 15 0.4× 11 0.4× 24 218

Countries citing papers authored by Marcin Piekarczyk

Since Specialization
Citations

This map shows the geographic impact of Marcin Piekarczyk's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marcin Piekarczyk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcin Piekarczyk more than expected).

Fields of papers citing papers by Marcin Piekarczyk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marcin Piekarczyk. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marcin Piekarczyk. The network helps show where Marcin Piekarczyk may publish in the future.

Co-authorship network of co-authors of Marcin Piekarczyk

This figure shows the co-authorship network connecting the top 25 collaborators of Marcin Piekarczyk. A scholar is included among the top collaborators of Marcin Piekarczyk based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marcin Piekarczyk. Marcin Piekarczyk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hachaj, Tomasz & Marcin Piekarczyk. (2025). On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data. Applied Sciences. 15(2). 538–538. 4 indexed citations
2.
Hachaj, Tomasz, Marcin Piekarczyk, Łukasz Bibrzycki, & Jarosław Wąs. (2024). Determination of spherical coordinates of sampled cosmic ray flux distribution using Principal Components Analysis and deep Encoder-Decoder network. 33(2). 29–45.
3.
4.
Woźniak, K. W., Łukasz Bibrzycki, P. Homola, et al.. (2023). Detection of Extensive Air Showers with small array - measurement and estimations. Proceedings Of Science. 382–382. 1 indexed citations
5.
Hachaj, Tomasz & Marcin Piekarczyk. (2023). The Practice of Detecting Potential Cosmic Rays Using CMOS Cameras: Hardware and Algorithms. Sensors. 23(10). 4858–4858. 2 indexed citations
6.
Hachaj, Tomasz & Marcin Piekarczyk. (2023). High-Level Hessian-Based Image Processing with the Frangi Neuron. Electronics. 12(19). 4159–4159. 3 indexed citations
7.
Homola, P., Marcin Piekarczyk, Kévin Almeida Cheminant, et al.. (2022). A New Method of Simulation of Cosmic-ray Ensembles Initiated by Synchrotron Radiation. Symmetry. 14(10). 1961–1961.
8.
Stanek-Maslouska, Weronika, K. W. Woźniak, P. Homola, et al.. (2022). Analysis of the Capability of Detection of Extensive Air Showers by Simple Scintillator Detectors. Universe. 8(8). 425–425. 2 indexed citations
9.
Bulik, T., N. Dhital, P. Homola, et al.. (2022). Simulation of the Isotropic Ultra-High Energy Photon Flux in the Solar Magnetic Field. Universe. 8(10). 498–498. 1 indexed citations
10.
Wibig, Tadeusz, et al.. (2021). Small shower array for education purposes -the CREDO-Maze Project.. Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021). 219–219. 3 indexed citations
11.
Hachaj, Tomasz, Łukasz Bibrzycki, & Marcin Piekarczyk. (2021). Recognition of Cosmic Ray Images Obtained from CMOS Sensors Used in Mobile Phones by Approximation of Uncertain Class Assignment with Deep Convolutional Neural Network. Sensors. 21(6). 1963–1963. 8 indexed citations
12.
Bibrzycki, Łukasz, David Alvarez-Castillo, D. Góra, et al.. (2021). Machine learning aided noise filtration and signal classification for CREDO experiment. Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021). 227–227. 2 indexed citations
13.
Wibig, Tadeusz, David Alvarez-Castillo, Łukasz Bibrzycki, et al.. (2021). Determination of Zenith Angle Dependence of Incoherent Cosmic Ray Muon Flux Using Smartphones of the CREDO Project. Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021). 199–199. 1 indexed citations
14.
Piekarczyk, Marcin & Marek R. Ogiela. (2020). Hierarchical Graph-Grammar Model for Secure and Efficient Handwritten Signatures Classification. TUGraz OPEN Library (Graz University of Technology).
15.
Bibrzycki, Łukasz, P. Homola, Marcin Piekarczyk, et al.. (2020). Towards A Global Cosmic Ray Sensor Network: CREDO Detector as the First Open-Source Mobile Application Enabling Detection of Penetrating Radiation. Symmetry. 12(11). 1802–1802. 15 indexed citations
16.
Piekarczyk, Marcin, et al.. (2020). Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions. Sensors. 20(1). 314–314. 5 indexed citations
17.
Hachaj, Tomasz, Marcin Piekarczyk, & Marek R. Ogiela. (2017). Human Actions Analysis: Templates Generation, Matching and Visualization Applied to Motion Capture of Highly-Skilled Karate Athletes. Sensors. 17(11). 2590–2590. 44 indexed citations
18.
Piekarczyk, Marcin & Marek R. Ogiela. (2015). On Using Palm and Finger Movements as a Gesture-Based Biometrics. 17. 211–216. 4 indexed citations
19.
Hachaj, Tomasz, Marek R. Ogiela, & Marcin Piekarczyk. (2013). Dependence of Kinect sensors number and position on gestures recognition with Gesture Description Language semantic classifier. Federated Conference on Computer Science and Information Systems. 571–575. 17 indexed citations
20.
Piekarczyk, Marcin. (2010). Hierarchical Random Graph Model for Off-line Handwritten Signatures Recognition. 860–865. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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