Student EEICT 2024: Selected Papers
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Title: Proceedings II of the 30th Conference STUDENT EEICT 2024
Subtitle: Selected Papers
Publisher: Brno University of Technology, Faculty of Electrical Engineering and Communication
Editor: Assoc. Prof. Vítezslav Novák
Place and year: Brno, 2024
ISBN: 978-80-214-6230-4
ISSN: 2788-1334
https://www.eeict.cz/
Subtitle: Selected Papers
Publisher: Brno University of Technology, Faculty of Electrical Engineering and Communication
Editor: Assoc. Prof. Vítezslav Novák
Place and year: Brno, 2024
ISBN: 978-80-214-6230-4
ISSN: 2788-1334
https://www.eeict.cz/
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Recent Submissions
- ItemDetection of parking space availability based on video(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2024) Kužela, Miloslav; Frýza, TomášThis paper deals with the use of Machine vision and ML (Machine Learning) for a parking lot occupation detection. It presents and compares an already existing technology that solves such a problem with an AI (Artificial Intelligence) usecase. It introduces tools used to train and create such models and their subsequent results as well as a dataset that was used to verify the trained networks and discusses the future of how such a technology could be used to effectively and more affordably detect occupied parking spaces on parking lots.
- ItemThe Human-machine interface for UAV ground control station(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2024) Klouda, Jan; Marcoň, PetrThis paper describes the design of a Humanmachine interface for UAVs and presents general HMI requirements for the operator (commander). The design was created for a ground control station that is tasked with safely and efficiently controlling several UAVs at once. The goal was to create an environment that provides the operator with an overview of each UAV, mission planning and execution.
- ItemMachine Learning-based Fingerprinting Localization in 5G Cellular Networks(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2024) Dinh Le, Thao; Mašek, PavelThis study explores the viability of employing machine learning (ML)-based fingerprinting localization in 5G heterogeneous cellular networks. We conducted an extensive measurement campaign to collect data and utilized them to train three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). The findings reveal that RF delivers the highest accuracy among the three ML algorithms. Furthermore, the results indicate that 5G New Radio (NR) can benefit the most from this localization method due to the dense deployment of base stations, achieving median localization errors of 17.5 m and 106 m during the validation and testing phases, respectively.
- ItemDevelopment Module for Radar Safety Sensor in Single-Track Vehicles(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2024) Ťavoda, MartinThis article describes the hardware and software design of a development module for the Radar Safety Sensor (RSS). RSS uses a Frequency-Modulated Continuous Wave (FMCW) radar to track moving objects behind single-track vehicles. The current radar configuration and antenna have significant object tracking deficiency when the vehicle makes a turn and the radar Field of view (FOV) tilts. The assumed solution is to add Inertial Measurement Unit (IMU) data to the tracking algorithm. The IMU is implemented in the designed development module, which also provides an efficient development and debugging environment.
- ItemAmplitude measurement of small displacement using video magnification(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2024) Řičánek, DominikVideo magnification algorithms show promising results when used to amplify small vibrations. Measuring these small vibrations is integral in modal analysis of an object and is usually done using specialized vibrometers or accelerometers. Computer vision (CV) systems fall short in this task as the magnitude of the vibrations decreases because of a small SNR (Signal-to-Noise Ratio). In this paper we try to further improve the accuracy of the CV approach by adding video magnification into the image pre-processing stage, allowing the algorithms to measure even imperceptibly small vibrations. For this purpose, we have gather ground truth data with a laser vibrometer in tandem with high speed camera footage of a metal cantilever beam, vibrating in its first mode, and have trained a convolutional neural network for regression.