Student EEICT 2023: Selected Papers

Title: Proceedings II of the 29th Conference STUDENT EEICT 2023
Subtitle: Selected Papers

Publisher: Brno University of Technology, Faculty of Electrical Engineering and Communication
Supervisor: Prof. Vladimír Aubrecht
Editor: Assoc. Prof. Vítezslav Novák
Place and year: Brno, 2023

ISBN: 978-80-214-6154-3
ISSN: 2788-1334
https://www.eeict.cz/

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Recent Submissions

Now showing 1 - 5 of 60
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    A Comparison Particle Filter for Searching a Radiation Source in Real and Simulated World
    (Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2023) Cihlar, Milos; Lazna, Tomas; Zalud, Ludek
    In this paper, we are focusing on comparing solutionsfor localizing an unknown radiation source in both aGazebo simulator and the real world. A proper simulation ofthe environment, sensors, and radiation source can significantlyreduce the development time of robotic algorithms. We proposeda simple sampling importance resampling (SIR) particle filter.To verify its effectiveness and similarities, we first tested thealgorithm’s performance in the real world and then in the Gazebosimulator. In experiment, we used a 2-inch NaI(Tl) radiationdetector and radiation source Cesium 137 with an activity of 330Mbq. We compared the algorithm process using the evolution ofinformation entropy, variance, and Kullback-Leibler divergence.The proposed metrics demonstrated the similarity between thesimulator and the real world, providing valuable insights toimprove and facilitate further development of radiation searchand mapping algorithms.
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    Mobile application for electrophoretic gel image processing
    (Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2023) Mojžišová, Anna
    This paper describes a mobile application thatassists in the analysis of 1D gel electrophoresis images. It providesfunctions for image processing, band detection, lane segmentation,and molecular weight approximation. The designed methods wereextensively tested using a dataset of diverse gel images. Theapplication provides a convenient and portable tool for theanalysis of electrophoresis images on-the-go.
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    System for measurement and control of pressure in vacuum apparatus
    (Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2023) Sedlar, Jan; Jurik, Karel
    This paper summarizes the development and implementationof a device designed for controlling and operatinga vacuum gas chamber. Multiple vacuum gauges, a gas pump anda valve can be connected to the device. Communication betweenthe user and the system occurs via a large touch screen withan organized interface. The article describes the current state ofdevelopment and outlines future improvement possibilities andpotential applications.
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    Influence of High Concentration of Silica Nanoparticles on the Dielectric Spectra
    (Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2023) Soud, Ammar Al; Daradkeh, Samer; Knápek, Alexandr; Liedermann, Karel; Holcman, Vladimír; Sobola, Dinara
    In the presented work, we report the dielectricbehavior of epoxy–silicon oxide composites in the temperaturerange 240– 300 K, over the frequency range 10-2 Hz – 107 Hz. Themeasuring apparatus was based on the Novocontrol AlphaAnalyser and the measured data were analyzed and interpretedusing the Havriliak – Negami equation. The master curves of thereal part of permittivity and the dielectric loss number wereobtained by time–temperature superposition principle, and theresults showed that the nano-composite had a much higher lossfactor. Through the analysis of the origin of the dielectric responsein epoxy/silica composite, the reason for the different dielectricrelaxation behaviors of the nano-composite, and the pure epoxywas discussed.
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    Implementation of a deep learning model for vertebral segmentation in CT data
    (Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2023) Blažkova, Lenka; Nohel, Michal
    This paper deals with the problem of vertebral segmentationin CT data with the use of deep learning approaches.Automatic segmentation of vertebrae is a very complex issueand would simplify the work of radiologists and doctors. Thepaper is focused on one of the models published and submittedto the Large Scale Vertebrae Segmentation Challenge (VerSe) in2020 from C. Payer et al. – Improving Coarse to Fine VertebraeLocalisation and Segmentation with SpatialConfiguration-Netand U-Net and its implementation and modification. The modelis evaluated on the corresponding public and hidden dataset. Itsmodification shows an improvement of the results in comparisonwith the published results, a mean Dice score improved from0.9165 to 0.9302 on the public dataset and from 0.8971 to 0.9264on the hidden dataset.