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Abstract

This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector) and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the realtime operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPU- and GPU-based solutions.

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Authors and Affiliations

Marek Wodziński
Aleksandra Krzyżanowska
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Abstract

Hybrid pixel radiation detectors with a direct photon-to-charge conversion working in a single photon counting mode have gained increasing attention due to their high dynamic range and noiseless imaging. Since sensors of different materials can be attached to readout electronics, they enable work with a wide range of photon energies. The charge-sharing effect observed in segmented devices, such as hybrid pixel detectors, is a phenomenon that deteriorates both spatial resolution and detection efficiency. Algorithms that allow the detection of a photon irrespective of the charge-sharing effect are proposed to overcome these limitations. However, the spatial resolution of the detector can be further improved beyond the resolution determined by the pixel size if information about the charge proportions collected by neighbouring pixels is used to approximate the interaction position. In the article, an approach to achieve a subpixel resolution in a hybrid pixel detector working in the single photon counting mode is described. Requirements and limitations of digital inter-pixel algorithms which can be implemented on-chip are studied. In the simulations, the factors influencing the detector resolution are evaluated, including size of a charge cloud, number of virtual pixel subdivisions, and detector parameters.
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Bibliography

  1. Ballabriga, et al. Review of hybrid pixel detector readout ASICs for spectroscopic X-ray imaging. J. Instrum. 11, P01007–P01007 (2016). https://doi.org/10.1088/1748-0221/11/01/P01007
  2. Taguchi, K. & Iwanczyk, J. S. Vision 20/20: Single photon counting X-ray detectors in medical imaging. Med. Phys. 40, 100901 (2013). https://doi.org/10.1118/1.4820371
  3. Bahadur, D. et al. Evolution of structure and dynamics of thermo-reversible nanoparticle gels-A combined XPCS and rheology study. J. Chem. Phys. 151, 10 (2019). https://doi.org/10.1063/1.5111521
  4. Sheyfer, et al.Nanoscale critical phenomena in a complex fluid studied by X-ray photon correlation spectroscopy.Phys. Rev. Lett. 125, 125504 (2020). https://doi.org/10.1103/PhysRevLett.125.125504
  5. Szczygiel, R., Grybos, P., Maj, P. & Zoladz, M. PXD18k - Fast Single Photon Counting Chip with Energy Window for Hybrid Pixel Detector. in 2011 IEEE Nuclear Science Symposium Conference Record. 932–937 (IEEE, Valencia, Spain 2011). https://doi.org/10.1109/NSSMIC.2011.6154126
  6. Nilsson, H. E., Dubari, E., Hjelm, M. & Bertilsson, K. Simulation of photon and charge transport in x-ray imaging semiconductor sensors. Nucl. Instrum. Methods Phys. Res. A. 487, 151–162 (2002). https://doi.org/10.1016/S0168- 9002(02)00959-2
  7. Ballabriga, R. et al. The Medipix3RX: a high resolution, zero dead-time pixel detector readout chip allowing spectroscopic imaging. Instrum. 8, C02016–C02016 (2013). https://doi.org/10.1088/1748-0221/8/02/C02016
  8. Krzyzanowska, A. et al. Characterization of the photon counting CHASE Jr., chip built in a 40-nm CMOS process with a charge-sharing correction algorithm using a collimated X-ray beam. IEEE Trans. Nucl. Sci. 64, 2561–2568 (2017). https://doi.org/10.1109/TNS.2017.2734821
  9. Bellazzini, R. et al. PIXIE III: a very large area photon-counting CMOS pixel ASIC for sharp X-ray spectral imaging. J. Instrum. 10, C01032–C01032 (2015). https://doi.org/10.1088/1748-0221/10/01/C01032
  10. Otfinowski, P. et al. Comparison of allocation algorithms for unambiguous registration of hits in presence of charge- sharing in pixel detectors. J. Instrum. 12, C01027–C01027 (2017). http://doi.org/10.1088/1748-0221/12/01/C01027
  11. Otfinowski, P., Deptuch, G. W. & Maj, P. Asynchronous approximation of a center of gravity for pixel detectors’ readout circuits. IEEE Solid-State Circuits 53, 1550–1558 (2018). https://doi.org/10.1109/JSSC.2018.2793530
  12. Cartier, et al. Micron resolution of MÖNCH and GOTTHARD, small pitch charge integrating detectors with single photon sensitivity. J. Instrum. 9, C05027–C05027 (2014). https://doi.org/10.1088/1748-0221/9/05/C05027
  13. Dreier, E. S. et al. Virtual subpixel approach for single-mask phase-contrast imaging using Timepix3. J. Instrum. 14, C01011 (2019). https://doi.org/10.1088/1748-0221/14/01/C01011
  14. Maj, P. et al. Measurements of ultra-fast single photon counting chip with energy window and 75 μm pixel pitch with Si and CdTe J. Instrum. 12, C03064 (2017). https://doi.org/10.1088/1748-0221/12/03/C03064
  15. Krzyzanowska, A., Niedzielska, A. & Szczygieł, R. Charge-sharing simulations for new digital algorithms achieving subpixel resolution in hybrid pixel detectors. J. Instrum. 15, C02047 (2020). https://doi.org/10.1088/1748- 0221/15/02/C02047
  16. Lutz, Semiconductor Radiation Detectors, Device Physics. (Berlin, Heidelberg: Springer Berlin Heidelberg, 2007).
  17. NIST XCOM: Photon Cross Sections Database – Introduction. NIST http://www.physics.nist.gov/PhysRefData/Xcom/Text/intro.html (2017).
  18. Otfinowski, A. et al. Pattern recognition algorithm for charge-sharing compensation in single photon counting pixel detectors. J. Instrum. 14, C01017 (2019). https://doi.org/10.1088/1748-0221/14/01/C01017
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Authors and Affiliations

Aleksandra Krzyżanowska
1
ORCID: ORCID
Robert Szczygieł
1
ORCID: ORCID

  1. AGH University of Science and Technology, 30 A. Mickiewicza Ave., 30-059 Krakow, Poland

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