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Mapping and analyzing signal coverage in 4G/5G mobile networks
Baránek, Michal ; Jeřábek, Jan (oponent) ; Polák, Ladislav (vedoucí práce)
This thesis deals with the advanced measurement of signal coverage, capacity, and reliability in mobile networks, especially with the widespread adoption of 4G and 5G technologies. As these networks become increasingly integral to daily life, there is a need for cost-effective solutions to assess and optimize their performance. The primary objective of this work is to develop affordable software and hardware solutions capable of extracting fundamental Key Performance Indicators (KPIs) from 4G/5G mobile networks. The proposed system aims to provide users with an accessible tool to assess network performance, signal coverage in specific areas, and predictive models for future network capacity. These functionalities are presented through a user-friendly graphical interface (so-called GUI), allowing for straightforward and cost-effective measurements in both outdoor and indoor settings.
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Explainable Face Liveness Classification
Mičulek, Petr ; Beran, Vítězslav (oponent) ; Špaňhel, Jakub (vedoucí práce)
The goal of this thesis is to explore, develop, and evaluate explainable face presentation attack detection (PAD) systems. PAD systems act as security filters for face recognition, preventing spoofed faces from reaching the identification phase. These systems are a necessary component enabling the recent rise of biometric systems used in smartphones and security cameras. While neural networks are the standard method for this task, they are commonly a black-box method providing no explanation. To provide a better understanding of the detection process, input attribution methods are applied. Their suitability is studied and various variants are compared. Of the seven methods compared, GradCAM using test-time augmentation is evaluated as the best, achieving a deletion metric AUC of 0.658 and an insertion metric AUC of 0.908. Experiments with the explanations show their limited capability at helping understand the model, but provide hints at how the predictive accuracy of the PAD system can be verified, and possibly improved.
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