National Repository of Grey Literature 107 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Diffusion Models and their Impact on Cybersecurity
Dvorščák, Patrik ; Homoliak, Ivan (referee) ; Lapšanský, Tomáš (advisor)
Táto práca skúma výkonnosť difúznych modelov (DM) a Generative Adversarial Network (GAN) - Generatívna sieť súperiacích komponentov, pri vytváraní vizuálneho obsahu generovaného umelou inteligenciou vo viacerých aplikáciách vrátane syntézy tváre, generovania textu na obraz, umeleckého renderovania, prekladu obrazu na obraz, syntézy videa a superrozlíšenia. Prostredníctvom porovnávacích experimentov sa v tomto výskume hodnotí schopnosť modelov generovať podrobné, realistické a umelecky presvedčivé vizuály z textových a obrazových vstupov. Výsledky ukazujú, že DM vynikajú pri vytváraní vysoko detailných obrazov, ktoré presne nasledujú textové vstupy, pričom sú obzvlášť účinné pri úlohách syntézy tváre a prevodu textu na obraz. Naproti tomu GAN sú zručnejšie pri vykresľovaní realistických scén prostredia, ktoré sú vhodné pre aplikácie vyžadujúce pohlcujúce vizuály. Oba typy modelov sú kompetentné v umeleckom vykresľovaní, hoci sa líšia v prispôsobovaní štýlu a kreativite. V závere práce sú uvedené budúce smery výskumu zamerané na zvýšenie účinnosti modelov a efektívnejšiu integráciu týchto technológií do praktických aplikácií.
Image Inpainting using Deep Learning
Zobaník, Radek ; Kubík, Tibor (referee) ; Šilling, Petr (advisor)
In this thesis, an application was developed for testing and comparing methods for completing missing parts of an image using deep learning, and two methods were trained, pconv with convolutional architecture, and AOT-GAN with GAN architecture. The thesis describes the design of the finished application, its functionality, and important implementation details. A dataset was selected on which the chosen models were optimally trained. Experiments were made on the AOT-GAN model to investigate the impact of the number of AOT blocks in generator on the resulting completed image. All experiments were qualitatively and quantitatively compared. The results showed respectable outcomes when working with natural scenery.
Brute Force Attack on Fingerprint Access System Using Genetic Algorithms
Keszi, Marián ; Rydlo, Štěpán (referee) ; Kanich, Ondřej (advisor)
This work deals with a brute force attack on a fingerprint-based access system using genetic algorithms. It includes the design of a genetic algorithm as a mean to perform the brute force attack. In the study, a generative adversarial network was used as a generator of synthetic fingerprints trained on the SOCOFing dataset. Experiments were performed focusing on the possibility of inserting known information about a fingerprint fragment into the input vector, as well as experiments using advanced methods of the genetic algorithm to modify the input vector with the goal of overcoming the VeriFinger algorithm. The experiments led us to the conclusion that even with the help of a generative adversarial network and genetic algorithm, we were unable to surpass the VeriFinger algorithm.
Comparison of Methods for Image Inpainting based on Deep Learning
Rajsigl, Tomáš ; Herout, Adam (referee) ; Španěl, Michal (advisor)
This bachelor thesis aims to compare deep learning methods and approaches for image inpainting using quantitative metrics like PSNR, SSIM, and LPIPS. Moreover, a user study has also been carried out for further subjective assessment. For the purposes of this comparison, four GAN-based neural networks were used. The first network, AOT-GAN, represents a benchmark against which the proposed architecture and its modifications were compared. In the experiments, a variant of the proposed method achieved a 29% improvement against AOT-GAN in images with small missing regions. This claim is also supported by the results of the user study where this method was ranked as the best. As a result of this thesis, a small dataset specifically for the evaluation of image inpainting in the context of object removal was created. Real-world applications of these methods are demonstrated through a web application.
Anthropometric Evaluation of Generated Face Images
Mikyšek, Jakub ; Rydlo, Štěpán (referee) ; Goldmann, Tomáš (advisor)
This work is focused on comparing artificially generated faces with real images by analyzing key facial landmarks and measuring the proportions between these landmarks. It also explores areas related to artificial neural networks, focusing on GANs that can generate artificially generated facial images. It explores their process, architecture and available models. The aim of the work is to evaluate how artificially generated images differ from real ones, and to find out in which proportions the difference is the largest.
Defect localization and analysis in GaN
Gazdík, Richard ; Šik, Ondřej (referee) ; Bábor, Petr (advisor)
Táto diplomová práca sa zaoberá lokalizáciou a analýzou vláknových dislokácií v epitaxných vrstvách GaN. Práca je rozdelená na dve časti - teoretickú a experimentálnu. V teoretickej časti je vysvetlený pôvod a povaha vláknových dislokácií. Okrem toho kladie základy pre lepšie pochopenie možno menej známych techník, ktoré sa dajú použiť na ich štúdium - zobrazovanie elektrónového kanálovacieho kontrastu a selektívne leptanie defektov. V experimentálnej časti sú popísané postupy vykonané pri realizácii týchto techník, ako aj pre TEM zobrazovanie difrakčného kontrastu a s ním spojenej prípravy vzoriek pomocou FIB. Ukazujeme, že každá z týchto techník sa môže použiť samostatne na charakterizáciu vláknových dislokácií, ale že ich kombináciou je možné získať doplňujúce informácie.
Deposition of GaN nanocrystals with Ag nanoparticles
Michalko, Matej ; Dvořák, Petr (referee) ; Čalkovský, Vojtěch (advisor)
The presented bachelor thesis deals with the preparation of GaN nanocrystals with Ag nanoparticles. In the theoretical part GaN, its properties and applications are introduced. Furthermore, possible substrates for GaN growth and preparation methods are presented, with a main focus on MBE. The thesis also discusses the basic principles of photoluminescence and its enhancement using metal nanoparticles. The experimental part deals with the preparation of GaN nanocrystals with Ag nanoparticles. First, Ga islands are deposited on Si(111) substrate, the second step is Ga nitridation. Subsequently, Ag nanoparticles of different sizes are deposited. The different steps were optimized and measured by analytical techniques such as SEM and photoluminescence. Finally, the dependence of the photoluminescence enhancement on the size of Ag nanoparticles was established.
Photo Livening Application
Bobola, Adrián ; Šalko, Milan (referee) ; Malinka, Kamil (advisor)
The goal of this work is to create a web application for animating static photographs. The application allows users to animate their portraits and group photos. Users can upload their own motion, which they want to use, and the application will use it to animate a selected part of the uploaded photo. The faces in the photos are automatically detected, while users have the option of manually marking faces in a given photo. The application supports recording custom motion from a video file or directly from a webcam. The server-side of the application is implemented in Python using the Django framework. The client-side of the application utilizes JavaScript and the React framework. Communication between the client and the server is facilitated via REST API. The thesis also provides an overview of existing tools of similar types, compares them, and discusses identified shortcomings. Additionally, the thesis explains the techniques and principles used to animate static photographs.
Reconstruction of Damaged Parts of Fingerprint Image Using Neural Nets
Bobocký, Boris ; Dyk, Tomáš (referee) ; Kanich, Ondřej (advisor)
In this paper, I propose a method for reconstructing damaged fingerprints using generative adversarial networks (GANs), implemented with Python and the PyThorch library. I have trained a specific GAN model on a dataset of approximately twenty thousand prints, created with Anguli and other damage simulation tools. This approach produced excellent results and could have wide application in biometric systems. This work highlights the potential of deep learning in the fields of image reconstruction and biometrics.
Preparation and characterization of nanostructured III-V semiconductor materials
Maniš, Jaroslav ; Kostelník,, Petr (referee) ; Hospodková,, Alice (referee) ; Šikola, Tomáš (advisor)
Předkládaná dizertační práce se zabývá výrobou a analýzou gallium nitridových (GaN) nanostruktur ve třech odlišných formách. V prvním případě byl zkoumám trojdimenzionální GaN ve formě nanokrystalů rostených na grafenu. Nanokrystaly byly připraveny s využitím techniky droplet epitaxy, která mimo jiné umožňuje růst nanostruktur za nízké teploty substrátu (T = 200°C). Studium se zaměřovalo jak na charakterizaci kvality připravených nanokrystalů, tak na statistický popis růstu. V dalším kroku byly připravené struktury využity pro výrobu fotodektoru citlivého na ultrafialové světlo. Výroba fotodektoru a jeho úspěšné použití slouží jako základ pro navazující výzkum. Ve druhém případě byly studovány dvoudimenzionální GaN nanostruktury, které byly rovněž připraveny za nízké teploty křemíkového substrátu. Následná analýza se soustředila na popis krystalové struktury a prvkovou analýzu, neboť byly takovéto struktury pozorovány vůbec poprvé. Další rozvoj možností přípravy těchto nanostruktur je předmětem navazujícího výzkumu. Ve třetím případě byly zkoumány jednodimenzionální GaN nanodráty připravené na safírovém substrátu. Účelem tohoto projektu bylo získání datasetu pro ověření teoretického modelu, který popisuje růst horizontálních nanodrátů. Na základě sběru a analýzy dat se podařilo modelovat růstovou dynamiku GaN nanodrátů, která byly v souladu s teoretickým modelem.

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