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Deep Learning in Historical Geography
Vynikal, Jakub ; Pacina, Jan
In relation to the rapid development of artificial intelligence, the possibilities of automatic processing of spatial data are increasing. Scanned topographical maps are a valued source of historical information. Neural networks allow us to extract information quickly and efficiently from such data, eliminating the difficult and repetitive work that would otherwise have to be done by a human. The article presents two case studies exploring the possibilities of using deep learning in historical geography. The first one is concerned with detecting and extracting swamps from topographic maps, while the second one attempts to automatically vectorize contours from the State Map 1 : 5 000
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Application for Statistical Analysis of ICS Communication
Chimenti, Andrea ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
This work aims to present the design and implementation of an application for statistical analysis of network traffic in ICS (Industrial Control Systems) communication. In the first place, the work presents Industrial Control Systems and some of their most common protocols. The protocol IEC 104 is described in more detail. This is followed by an introduction to the basic methods of descriptive statistics, that can be used to analyze industrial communication. For this purpose, several CSV datasets, that capture fragments of industrial communication, have been used. These datasets are used to show how some of the previously described statistical methods can be used. The work then describes the implementation of an application, which allows to analyze the datasets and obtain various statistics and a visual representation of the data. The main objective of the application is to make it easier for the user to find stable characteristics that can be used for anomaly and attack detection. Finally, the benefits that the application brings are demonstrated on a set of datasets containing different types of attacks.
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Acceleration of Lattice-Boltzmann Algorithms for Bloodflow Modeling
Kompová, Radmila ; Kešner, Filip (referee) ; Jaroš, Jiří (advisor)
This thesis aims to explore possible implementations and optimizations of the lattice-Boltzmann method. This method allows modeling of fluid flow using a simulation of fictive particles. The thesis focuses on possible improvements of the existing tool HemeLB which is designed and optimized for bloodflow modeling. Several vectorization and paralellization approaches that could be included in this tool are explored. An application focused on comparing chosen algorithms including optimizations for the lattice-Boltzmann method was implemented as a part of the thesis. A group of tests focused on comparing this algorithms according to performance, cache usage and overall memory usage was performed. The best performance achieved was 150 millions of lattice site updates per second.
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Visualization of Large Volumetric Data on CPU
Dlabaja, Drahomír ; Milet, Tomáš (referee) ; Španěl, Michal (advisor)
This thesis deals with the problem of displaying volumetric data that exceeds the operating memory capacity of the machine. The work describes the design of a visualization pipeline, which consists of a data structure for large volumetric data and an algorithm that visualizes such data. The proposed hierarchical data structure accelerates sampling and allows the reduction of the total amount of data that needs to be loaded into physical memory during visualization. Visualization of processed data is achieved by the ray casting method with existing optimization techniques, such as empty space skipping and early ray termination. The data structure allows up to 12x faster sampling compared to the sampling of raw large volumetric data serialized by rows. Up to 150x faster visualization of large volumetric data in near-lossless mode has been achieved compared to the fully lossless mode by utilizing the data hierarchy. The display scheme is implemented in the form of a library in C++20 language. The implementation uses acceleration by vectorization and allows easy parallelization by the user. The library provides tools for processing and visualization of large volumetric data on the CPU.
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Intel Integrated Performance Primitives and their use in application development
Machač, Jiří ; Přinosil, Jiří (referee) ; Malý, Jan (advisor)
The aim of the presented work is to demonstrate and evaluate the contribution of computing system SIMD especially units MMX, SSE, SSE2, SSE3, SSSE3 and SSE4 from Intel company, by creation of demostrating applications with using Intel Integrated Performance Primitives library. At first, possibilities of SIMD programming using intrinsic function, vektorization and libraries Intel Integrated Performance Primitives are presented, as next are descibed options of evaluation of particular algorithms. Finally procedure of programing by using Intel Integrated Performance Primitives library are ilustrated.
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High Performance Applications on Intel Xeon Phi Cluster
Kačurik, Tomáš ; Hrbáček, Radek (referee) ; Jaroš, Jiří (advisor)
The main topic of this thesis is the implementation and subsequent optimization of high performance applications on a cluster of Intel Xeon Phi coprocessors. Using two approaches to solve the N-Body problem, the possibilities of the program execution on a cluster of processors, coprocessors or both device types have been demonstrated. Two particular versions of the N-Body problem have been chosen - the naive and Barnes-hut. Both problems have been implemented and optimized. For better comparison of the achieved results, we only considered achieved acceleration against single node runs using processors only. In the case of the naive version a 15-fold increase has been achieved when using combination of processors and coprocessors on 8 computational nodes. The performance in this case was 9 TFLOP/s. Based on the obtained results we concluded the advantages and disadvantages of the program execution in the distributed environments using processors, coprocessors or both.
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Conversion of Raster-Curve into Vector Representation
Král, Jiří ; Sumec, Stanislav (referee) ; Beran, Vítězslav (advisor)
In my process of tracing I deal with converting an input grayscale image into a vector one trying to keep as big similarity with the input image as possible. Tracing is carried out with the help of curve approximation even if the approximation is possible only with line elements, that is to say the curve in raster. Therefore it is necessary to extract the line elements from the input image. We can do it in two different ways according to two different objects in the image. The first group is represented by thin, ablong objects which are substituted by their skeleton. The second group is represented by large objects which are susbstituted by their contour. The found lines are then divided into such parts which can be easily curve approximated. Resulting curves are then only depicted into the output by suitable raster method.
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Acceleration of Photoacoustic Imaging
Nedeljković, Sava ; Bordovský, Gabriel (referee) ; Jaroš, Jiří (advisor)
Hlavním cílem této práce je navrhnout novu metodu rekonstrukce obrazu z dat fotoakustického snímkování. Fotoakustické snímkování je velmi populární neinvazivní metoda snímkování založená na detekování ultrazvukových vln vyvolaných laserovým paprskem. Proces snímkování generuje velké množství dat, a kvůli tomu je proces rekonstrukce obrazu velmi časově náročný. Táto práce demonstruje proces rekonstrukce obrazu pomocí zpětné projekce, algoritmu který je dostatečně jednoduchý na přizpůsobení moderním architekturám procesorů umožňující různé způsoby optimalizovaného výpočtu. Dvě různé variantu algoritmu byly navrženy: z pohledu pixelu a z pohledu senzoru, který detekuje ultrazvukové vlny. Obě varianty byly implementovány třemi různými způsoby: pomocí vektorového paralelismu, vláknového paralelismu a paralelismu na grafické karetě (GPU). Všechny 3 implementace obou variant algoritmu byly testovány a výsledky byly srovnány s výsledkem rekonstrukce algoritmu reverzního času, přesnějšího ale mnohokrát pomalejšího algoritmu. Výsledky ukázaly, že GPU paralelismus nabízí nejrychlejší výpočet, cca. 200 krát rychlejší než u algoritmu reverzního času, a proto se dá použit i v aplikacích pracující v reálném čase.
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Simulation of Fracture Tests in Civil Engineering
Bordovský, Gabriel ; Vaverka, Filip (referee) ; Jaroš, Jiří (advisor)
In this thesis, a program for fracture test in civil engineering has been optimized. The simulation is used for a validation of the fracture characteristics for blocks of construct material used for historic buildings reconstructure. This thesis illustrates the possibilities of an effective usage of the processor’s potential without the loss of the output quality. The individual parts of the simulation are analyzed and this thesis proposes for the critical sections some possible optimizations such as vectorization or parallel processing. The techniques used in this thesis may be used on similar computing problems and help shorten the required runtime. The prototype of the simulation was able to process the simulation in 7.7 hours. Optimized version is capable to process the same simulation in 2.1 hours on one core or 21 minutes on eight cores. The parallel optimized version is 21 times faster than the prototype.
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