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Image analysis for correction of electron microscopes
Smital, Petr ; Schwarz, Daniel (referee) ; Kolář, Radim (advisor)
This thesis describes the physical nature of corrections of an electron microscope and mathematical methods of image processing required for their complete automation. The corrections include different types of focusing, astigmatism correction, electron beam centring, and image stabilisation. The mathematical methods described in this thesis include various methods of measuring focus and astigmatism, with and without using the Fourier transform, edge detection, histogram operations, and image registration, i.e. detection of spatial transformations in images. This thesis includes detailed descriptions of the mathematical methods, their evaluation using an “offline” application, descriptions of the algorithms of their implementation into an actual electron microscope and results of their testing on the actual electron microscope, in the form of a video footage grabbed from its control computer’s screen.
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Bandlimited signals, their properties and extrapolation capabilities
Mihálik, Ondrej ; Havránek, Zdeněk (referee) ; Jura, Pavel (advisor)
The work is concerned with the band-limited signal extrapolation using truncated series of prolate spheroidal wave function. Our aim is to investigate the extent to which it is possible to extrapolate signal from its samples taken in a finite interval. It is often believed that this extrapolation method depends on computing definite integrals. We show an alternative approach by using the least squares method and we compare it with the methods of numerical integration. We also consider their performance in the presence of noise and the possibility of using these algorithms for real-time data processing. Finally all proposed algorithms are tested using real data from a microphone array, so that their performance can be compared.
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Vectorized Point Clouds for Mobile Robotics
Jelínek, Aleš ; Mazal, Jan (referee) ; Duchoň,, František (referee) ; Žalud, Luděk (advisor)
Disertační práce se zabývá zpracováním mračenen bodů z laserových skenerů pomocí vektorizace a následnému vyhledávání korespondencí mezi takto získanými aproximacemi pro potřeby současné sebelokalizace a mapování v mobilní robotice. První nová metoda je určena pro segmentaci a filtraci surových dat a realizuje obě operace najednou v jednom algoritmu. Pro vektorizaci je představen optimalizovaný algoritmus založený na úplné metodě nejmenších čtverců, který je v současnosti patrně nejrychlejší ve své třídě a blíží se tak eliminačním metodám, které ovšem produkují výrazně horší aproxi- mace. Inovativní analytické metody jsou představeny i pro účely vyjádření podobnosti mezi dvěma vektorizovanými skeny, pro jejich optimální sesazení a pro vyhledávání korespondencí mezi nimi. Všechny představené algoritmy jsou intezivně testovány a jejich vlastnosti ověřeny množstvím experimentů.
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Computer Identification Based on Packet's Timestamps
Krba, Martin ; Košař, Vlastimil (referee) ; Kaštil, Jan (advisor)
Basic way how to identify a device in computer network is by MAC address and IP address. Main goal of this work is to create an application capable of clear identification of devices in computer network regardless change of their MAC address or IP address. This is done by exploiting tiny deviations in hardware clock known as clock skew. They appear in every clock based on quartz oscillator. Using clock skew is beneficial, because there is no need of any changes in fingerprinted device nor their cooperation. Accessing these values is done by capturing packets with timestamps included. Application of this method is very wide, for example computer forensics, tracking the device using different access points or counting devices behind router with NAT.
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Compression of Device Location Data
Morong, Lukáš ; Mrázek, Vojtěch (referee) ; Vašíček, Zdeněk (advisor)
The goal of this work is to analyze the properties of the data used to locate IoT devices using AoA/AoD based on BLE (Bluetooth low energy) technology. The work aims to identify suitable algorithms for lossless compression, while taking into account the computing and memory resources of the target microcontroller architecture. This involves methods such as VLQ, Rice-Golomb coding or predictive coding. Then implement the selected algorithms in the C language for a platform with an ARM processor and evaluate their parameters in terms of both computational and memory complexity as well as the level of compression.
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Statistical models for prediction of project duration
Oberta, Dušan ; Žák, Libor (referee) ; Hübnerová, Zuzana (advisor)
Cieľom tejto bakalárskej práce je odvodiť štatistické modely vhodné pre analýzu dát a aplikovať ich na analýzu reálnych dát týkajúcich sa časovej náročnosti projektov v závislosti na charakteristikách projektov. V úvodnej kapitole sú študované lineárne regresné modely založené na metóde najmenších štvorcov, vrátane ich vlastností a predikčných intervalov. Nasleduje kapitola zaoberajúca sa problematikou zobecnených lineárnych modelov založených na metóde maximálnej vierohodnosti, ich vlastností a zostavením asymptotických konfidenčných intervalov pre stredné hodnoty. Ďalšia kapitola sa zaoberá problematikou regresných stromov, kde sú znova ukázané metóda najmenších štvrocov a metóda maximálnej vierohodnosti. Boli ukázané základné princípy orezávania regresných stromov a odvodenie konfidenčných intervalov pre stredné hodnoty. Metóda maximálnej vierohodnosti pre regresné stromy a odvodenie konfidenčných intervalov boli z podstatnej časti vlastným odvodením autora. Posledným študovaným modelom sú náhodné lesy, vrátane ich základných vlastností a konfidenčných intervalov pre stredné hodnoty. V týchto kapitolách boli taktiež ukázané metódy posúdenia kvality modelu, výberu optimálneho podmodelu, poprípade určenia optimálnych hodnôt rôznych parametrov. Na záver sú dané modely a algoritmy implementované v jazyku Python a aplikované na reálne dáta.
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Robust Student estimator
Rázek, Stanislav ; Friml, Dominik (referee) ; Dokoupil, Jakub (advisor)
The diploma thesis deals with the formulation of the algorithm for estimating the parameters of the linear ARX model with Student's noise using approximate Bayesian inference. The topics of Student's noise, Approximate Bayesian inference and Student's algorithm are discussed. The formulated parameter estimation algorithm is compared with other model parameter estimation methods and evaluated. At the same time, the Student's filter is derived and its connection with the Kalman filter is discussed.
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Band-Limited Signal Extrapolation Using Least Squares Approximation By Prolate Spheroidalwave Functions
Mihálik, Ondrej
This paper is concerned with the band-limited signal extrapolation using a truncated series of Prolate spheroidal wave functions. Our aim is to investigate the extent to which it is possible to extrapolate signal from its samples taken in a finite interval. It is often believed that this extrapolation method depends on computing definite integrals. We show an alternative approach by using the least squares method. We briefly discuss performance of these two methods in the presence of noise and the possibility of using this algorithm for real-time data processing. Finally the extrapolation algorithm is tested with real data from a microphone array.
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Bandlimited signals, their properties and extrapolation capabilities
Mihálik, Ondrej ; Havránek, Zdeněk (referee) ; Jura, Pavel (advisor)
The work is concerned with the band-limited signal extrapolation using truncated series of prolate spheroidal wave function. Our aim is to investigate the extent to which it is possible to extrapolate signal from its samples taken in a finite interval. It is often believed that this extrapolation method depends on computing definite integrals. We show an alternative approach by using the least squares method and we compare it with the methods of numerical integration. We also consider their performance in the presence of noise and the possibility of using these algorithms for real-time data processing. Finally all proposed algorithms are tested using real data from a microphone array, so that their performance can be compared.
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