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Statistical anomaly detection methods of data communication
Woidig, Eduard ; Mangová, Marie (referee) ; Slavíček, Karel (advisor)
This thesis serves as a theoretical basis for a practical solution to the issue of the use of statistical methods for detecting anomalies in data traffic. The basic focus of anomaly detection data traffic is on the data attacks. Therefore, the main focus is the analysis of data attacks. Within the solving are data attacks sorted by protocols that attackers exploit for their own activities. Each section describes the protocol itself, its usage and behavior. For each protocol is gradually solved description of the attacks, including the methodology leading to the attack and penalties on an already compromised system or station. For the most serious attacks are outlined procedures for the detection and the potential defenses against them. These findings are summarized in the theoretical analysis, which should serve as a starting point for the practical part, which will be the analysis of real data traffic. The practical part is divided into several sections. The first of these describes the procedures for obtaining and preparing the samples to allow them to carry out further analysis. Further described herein are created scripts that are used for obtaining needed data from the recorded samples. These data are were analyzed in detail, using statistical methods such as time series and descriptive statistics. Subsequently acquired properties and monitored behavior is verified using artificial and real attacks, which is the original clean operation modified. Using a new analysis of the modified traffics compared with the original samples and an evaluation of whether it has been some kind of anomaly detected. The results and tracking are collectively summarized and evaluated in a separate chapter with a description of possible further attacks, which were not directly part of the test analysis.
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Modelling of perfusion curves in dynamic magnetic resonance
Ochodnický, Erik ; Mangová, Marie (referee) ; Rajmic, Pavel (advisor)
Perfusion MRI can provide information about perfusion characteristics of the observed tissue, which makes it a widely applicable medical procedure. Measuring process of MRI is very time-consuming, and therefore, using classical reconstruction methods, we are often not able to obtain enough samples to accomplish the needed time and space resolution for perfusion analysis. That is why it is necessary to use compressed sensing, which allows reconstruction from under-sampled data by solving an optimization model. In this work, several models for reconstruction of an image sequence are verified on real and artificial data, along with multiple algorithms capable of solving these models. Among the optimization models used in this work are two L+S models with different regularization of the S component that are solved by Forward-Backward and Chambolle-Pock algorithm. The quality of reconstruction for various models was compared especially by their perfusion curves. In the last section, we explore possible modifications of the SASS model in order to increase quality of reconstruction and resistance to under sampling for the purpose of better adaptation for dynamic data.
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Increasing Resolution in Perfusion Magnetic Resonance Imaging Using Compressed Sensing
Mangová, Marie ; Polec,, Jaroslav (referee) ; Šmídl, Václav (referee) ; Rajmic, Pavel (advisor)
Perfusion magnetic resonance imaging is a medical diagnostic method which requires high spatial and temporal resolution simultaneously to capture dynamics of an intravenous contrast agent which is used to perfusion measurement. However, magnetic resonance imaging has physical limits which do not allow to have this resolution simultaneously. This thesis deals with compressed sensing which enables to reconstruct measured data from relatively few acquired samples (below Nyquist rate) while resolution required to perfusion analysis is increased. This aim could be achieved with suitably proposed apriory information about sensed data and model proposal. The reconstruction is then done as an optimization problem. Doctoral thesis brings several new reconstruction models, further proposes method to debias this estimates and examines influence of compressed sensing onto perfusion parameters. Whole thesis is ended with extension of compressed sensing into three-dimensional data. Here, the influence of reconstruction onto perfusion parameters is also described. In summary, the thesis shows that due to compressed sensing, temporal resolution can be increased with the fixed spatial resolution or spatial resolution can be increased with the fixed temporal resolution.
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Interactive software tools for teaching signal processing
Pacas, Ondrej ; Rajmic, Pavel (referee) ; Mangová, Marie (advisor)
This thesis deals with creation of four interactive applications for educational purposes in the field of digital signal processing. The goal of this work is to create four applications which will visually interpretate each of the methods of signal processing. This involves applications for linear regression and least squares method, interpolation and signal reconstruction from its samples, discrete linear convolution and discrete cross-correlation. Applications are created using JavaScript programming language.
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Implemetation of signal processing problems for teaching purposes
Ševčík, Zdeněk ; Rajmic, Pavel (referee) ; Mangová, Marie (advisor)
This thesis is focused on Fourier series, phasor diagram, signal resampling and amplitude modulation. All of there problems are analyzed in theoretical part of thesis. Here are derived all three form of Fourier series, described signal sampling, decimation and interpolation. Next thesis is focused on signal modulation and closer deal with amplitude modulation of harmonic and inharmonic signal. In practical part is example of calculation of Fourier siries for a specific signal, example of signal resampling and example of amplitude modulation for harmonic and inharmonic signal. Objective of practical part is creates programs focused on these problems. Programs should be interactive and their purpose is demonstrate the problem to students.
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Interactive software tools for teaching signal and image processing
Had, Pavel ; Rajmic, Pavel (referee) ; Mangová, Marie (advisor)
This thesis deals with the development of interactive applets for educational purposes. There are four applets: linear image combinations, least squares method and linear regression, discrete linear convolution in 2D, and interpolation in 1D. Each part of this thesis consists of a theoretical analysis of a given problem and its implementation in JavaScript. Specific applets then illustrate the problem so that it can be easily understood.
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Modern restoration of audio containing missing portions
Skyva, Pavel ; Mangová, Marie (referee) ; Rajmic, Pavel (advisor)
This bachelor thesis deals with modern method of audio signal restoration. The reconstruction is primarily based on sparse signal representations. In thesis is described the way of searching sparse coefficients using proximal Douglas-Rachford algorithm and following computation of reconstructed signal using this coefficients. The algorithm of reconstruction is implemented in the MATLAB software with toolbox LTFAT included. Results of the reconstruction are compared using objective evaluation method Signal-to-Noise ratio (SNR).
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Two video-processing problems by means of nontraditional methods
Kánský, Antonín ; Mangová, Marie (referee) ; Rajmic, Pavel (advisor)
The aim of this work is to solve two problems from the field of video editing by means of sparse representation of signals. The problematics of the traditional realisation of two effects, which are background separation of and separation background from moving foreground, is clarified here, as well as the problematics of sparse signals. The solutions was achieved through the method of Principal component analysis (PCP). The resulting algorithm is implemented and tested by simulated and real data.
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