Student EEICT 2022: Selected Papers

Title: Proceedings II of the 28th Conference STUDENT EEICT 2022
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, 2022

ISBN: 978-80-214-6030-0
ISSN: 2788-1334
https://www.eeict.cz/

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Now showing 1 - 5 of 66

A Separation and Characteristics of Inner Egg Shell Membrane
(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2022) Charvot, Jakub
The aim of this work is to characterize inner egg shell membrane (IESM) and compare different methods of extracting it from an egg. Scanning electron microscopy (SEM) was used in a combination with energy dispersive X-Ray spectroscopy (EDX) to get an idea of membrane structure and chemical composition and impacts of the used extraction method. Significant differences were found between the measured samples, but it has not been verified whether they affect the resulting physical properties yet. The importance of this study lies mainly in the future investigation of several factors, such as piezoelectric properties, so it serves as a fundamental research.
Design of a micromanipulator for repairs of printed circuit boards
(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2022) Kopřiva, Pavel; Otáhal, Alexandr
This work deals with design of micromanipulator based on pantographic mechanism with electronic control. The main purpose of this micromanipulator is repairing printed circuit boards, but it has been designed to be universal for any use case. The main focus is on creating device that will have the highest possible accuracy, while still being able to provide necessary force to carry out mechanical repairs of printed circuit boards. The work contains the design of the pantographic mechanism, stepper motor based electrical drive section and control circuits with power supplies.
Exploring the Possibilities of Automated Annotation of Classical Music with Abrupt Tempo Changes
(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2022) Ištvánek, Matěj; Miklánek, Štěpán
In this paper, we introduce options for automatic measure detection based on synchronization, beat detection, and downbeat detection strategy. We evaluate proposed methods on two motifs from the dataset of Leos Janacek's string quartet music. We use specific user-driven metrics to capture annotation efficiency simulating a scenario where a musicologist has to use the output of an automated system to create ground-truth annotations on given recordings. In the case of the first motif, synchronization outperformed other methods by detecting most of the measure positions correctly. This procedure was also the most suitable for the second motif—it achieved a low number of correct detections, but the vast majority of transferred time positions belonged within the outer tolerance window. Therefore, in most cases, only shifting operations were needed resulting in higher annotation efficiency. Results suggest that the state-of-the-art downbeat tracking is not yet efficient for expressive music.
Comparison of machine learning models in outdoor temperature sensing by commercial microwave link
(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2022) Pospisil, Ondrej; Musil, Petr; Fujdiak, Radek
The main objective of this work is to focus on outdoor temperature prediction using machine learning based on parameters from commercial microwave links. This information can be used to refine the weather information at a given link location. Three machine learning models (random forest, linear regression, and lasso) are used for prediction using a combination of two datasets (ERA5 weather dataset and CML monitoring database dataset). The results were evaluated based on two evaluation metrics (R^2 and mean absolute error (MAE)). In this work, the ERA5 outdoor temperature was found to be correlated with the temperature of the microwave link unit, and results were obtained with an accuracy of 0.87144 based on the MAE metric. Thus, the results can fairly well predict actual outdoor temperatures in the microwave link area based on the microwave link unit temperature.
Unfolded Low-rank + Sparse Reconstruction for MRI
(Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, 2022) Mokrý, Ondřej; Vitouš, Jiří
We apply the methodology of deep unfolding on the problem of reconstruction of DCE-MRI data. The problem is formulated as a convex optimization problem, solvable via the primal–dual splitting algorithm. The unfolding allows for optimal hyperparameter selection for the model. We examine two approaches – with the parameters shared across the layers/iterations, and an adaptive version where the parameters can differ. The results demonstrate that the more complex model can better adapt to the data.

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