SPMS 2024

Session

Data Processing

21 Jun 2024, 16:10

Dobřichovice

Conveners

Data Processing

• František Gašpar

20. Application of deconvolution in 4D STEM diffraction analysis

David Rendl

21/06/2024, 16:10

The presentation will cover the new 4D-STEM PNBD (Powder Nano Beam Diffraction) analytical method developed in collaboration with UTIA, UMCH and UPT. In the introduction we will explain the principle of the method itself, its advantages, importance and limitations. One of the major limitations is the low accelerating voltage of scanning electron microscopes, which results in heavily noisy...

19. Dispersion of a Point Set: Enhanced Bounds and Practical Applications

Mr Matěj Trödler

21/06/2024, 16:25

This research delves into the dispersion of point sets within a unit cube
$[0,1]^𝑑$, a measure of how well-distributed points are in a space. Building on the work of Hlawka and Niederreiter, we present improved lower bounds for minimal dispersion by constructing specific sets called restriction sets. Our approach involves analyzing test boxes, uncoverable sets, and cover-free families,...

26. Theory of potentials

Daniel Khol (Department of Mathematics)

21/06/2024, 16:40

This presentation is about applications of potential theory. We delve into the use of quadratic forms on d-dimensional torus, emphasizing boundary conditions and the calculation of their minimal values through rigorous mathematical methods. Then we show the application of Feynman-Kac lemma and formulas within the context of potential theory, showcasing their utility in handling complex...

29. Detection of Fraudulent Financial Transactions Using AI

Lydie Rosenkrancová (Department of Mathematics, FNSPE, Czech Technical University in Prague)

21/06/2024, 16:55

Detecting fraudulent financial transactions is crucial for the financial industry, where traditional methods often fall short. This presentation introduces the complexities of fraud detection. We begin by addressing the challenges in fraud detection, followed by an overview of the dataset used for analysis. Finally, we compare the performance of three AI models - SVM, Random Forest and neural...