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Classifiers of power patterns
Zapletal, Ondřej ; Člupek, Vlastimil (referee) ; Martinásek, Zdeněk (advisor)
Over the last several years side-channel analysis has emerged as a major threat to securing sensitive information in cryptographic devices. Several side-channels have been discovered and used to break implementations of all major cryptographic algorithms (AES, DES, RSA). This thesis is focused on power analysis attacks. A variety of power analysis methods has been developed to perform these attacks. These methods include simple power analysis (SPA), differential power analysis (DPA), template attacks, etc. This work provides comprehensive survey of mentioned methods and also investigates the application of a machine learning techniques in power analysis. The considered learning techniques are neural networks and support vector machines. The final part of this thesis is dedicated to implemenation of the attack against protected software AES implementation which is used in the DPA Contest.
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Height Measurement in Digital Image
Olejár, Adam ; Přinosil, Jiří (referee) ; Říha, Kamil (advisor)
The aim of this paper is a summary of the theory necessary for a modification, detection of person and the height calculation of the detected person in the image. These information were then used for implementation of the algoritm. The first half reveals teoretical problems and solutions. Shows the basic methods of image preprocessing and discusses the basic concepts of plane and projective geometry and transformations. Then describes the distortion, that brings into the picture imperfections of optical systems of cameras and the possibilities of removing them. Explains HOG algorithm and the actual method of calculating height of person detected in the image. The second half describes algoritm structure and statistical evaluation.
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Social Network Analysis using methods of pattern recognition
Križan, Viliam ; Burget, Radim (referee) ; Atassi, Hicham (advisor)
Diplomová práca sa zaoberá rozpoznávaním emócií z textu v sociálnych sieťach. Práca popisuje súčasné metódy extrakcie príznakov, používané lexikóny, korpusy a klasifikátory. Emócie boli rozpoznávané na základe klasifikátoru, netrénovaného na anotovaných dátach z mikroblogovacej siete Twitter. Výhodou použitia služby Twitter, bolo geografické vymedzenie dát, ktoré umožňuje sledovanie zmien emócií populácie v rôznych mestách. Prvým prístupom klasifikácie bolo vytvorenie Baseline algoritmu, ktorý používal jednoduchý lexikón. Pre zlepšenie klasifikácie sme v druhom bode použili komplexnejší SVM klasifikátor. SVM klasifikátory, extrakcie a selekcie príznakov boli použité z dostupnej Python knižnice Scikit. Dáta pre natrénovanie klasifikátoru boli zhromažďované z oblasti USA, a to s pomocou vytvorenej aplikácie. Klasifikátor bol natrénovaný na dátach, označených pri ich zhromažďovaní - bez manuálnej anotácie. Boli použité dve rôzne implantácie SVM klasifikátorov. Výsledné klasifikované emócie, v rôznych mestách a dňoch, boli zobrazené v podobe farebných značiek na mape.
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Segmetation of tomographic data in 3D Slicer
Korčuška, Robert ; Dvořák, Pavel (referee) ; Mikulka, Jan (advisor)
This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.
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Emotional States of Humans and their Determination using Speech Record Analysis
Lněnička, Jakub ; Míča, Ivan (referee) ; Smékal, Zdeněk (advisor)
The aim of the diploma project is to find a method through which it will be possibleto classify the selected emotion from speech. At the beginning of the work deals with the description of the human body and their voice-generating operation. Furthermore, the text deals with the problem of the human voice into digital form.Great attention is paid to the parameters of the speech signal with an emphasis on describing the symptoms to help the selected emotion. The work deals with therecognition of emotions and a description of some of them. The main part is finding the best methods to reduce symptoms of segmental and suprasegmental speech utterances. The results of success was achieved by comparing the classification of selected emotions when using multiple methods and compare their results. The most important criterion in assessing the results ofthe reduction parameters of the speech signal, based on previous research in this area.
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Recognition of emotions in Czech texts
Červenec, Radek ; Smékal, Zdeněk (referee) ; Burget, Radim (advisor)
With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.
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Simulation of Electromechanical System Control Structures
Petruška, Ľubomír ; Blaha, Petr (referee) ; Václavek, Pavel (advisor)
Construction of motor models is the main topic of this project. Mathematical characterization of AC machine, permanent magnet synchronous motor, separately-excited DC motor, series-wound DC motor, permanent magnet DC motor, switched reluctance motor is also described. Design of models is based on mathematical description of particular motors. Models are created in Matlab Simulink. Each model is implemented in continuous and also in discrete time variant. Selected models are implemented also on processor from Freescale 56F800E Hybrid Controller family. Each model has individual graphic user interface. Besides motor models, there is description and easy algorithm of Space Vector Modulation. Model of this method is also created. Models are build-up into a library, which can be used for simulations and tests of control structures. Results of models simulations are presented at the end of this project. Simulation of models that are implemented on processor is also made in Matlab Simulink environment and is compared to simulation of models that are implemented directly in Matlab Simulink.
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Using data mining methods in the analysis of credit risk data
Tvaroh, Tomáš ; Witzany, Jiří (advisor) ; Matejašák, Milan (referee)
This thesis focuses on comparison of selected data mining methods for solving classification tasks with the method of logistic regression. First part of the thesis briefly introduces data mining as a scientific discipline and classification task is shown in the context of knowledge data discovery. Next part explains the principle of particular methods amongst which, along with logistic regression, artificial neural networks, classification decision trees and Support Vector Machine method were selected. Together with mathematical background of each algorithm, demonstration of how the classification functions for new examples is mentioned. Analytical part of this thesis tests decribed methods on real-world data from the Lending Club company and they are compared based on classification accuracy. Towards the end, an evaluation of logistic regression is made in terms of whether its majority position is due to historical reasons or for its high classification accuracy compared to other methods.
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Automatic recognition of the electrometer status from picture
HANZLÍK, Ondřej
This thesis deals with problems of recognition of an electrometer´s state from sensing image. It is tangibly about electrometer´s scanning by a mobile phone´s camera. There is a surface with an electrometer´s dial which is detected and on this surface the particular numbers are detected consequently. The numbers are recognized via neural network. For more information from this image there are used some techniques of image segmentation to check the status. For the classification of the segmentation´s outputs are used classification tools, especially a support vector machine (SVM) and neural networks. Problems of image segmentations are solved by using OpenCV library. OpenCV is used for the implementation of the vector machine either. Application is on Android platform. Part of the thesis is concerned in a creation of a desktop application which is instrumental towards testing of neural network. The thesis also describes how to save the necessary data gathering in the course of the recognition which are used for working with neural network. The part of the thesis also deals with running web which will be evolved for the opportunity to participate in the further development of the system. There is available a public repository with source codes created during implementation.
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Automatizace generování stopslov
Krupník, Jiří
This diploma thesis focuses its point on automatization of stopwords generation as one method of pre-processing a textual documents. It analyses an influence of stopwords removal to a result of data mining tasks (classification and clustering). First the text mining techniques and frequently used algorithms are described. Methods of creating domain specific lists of stopwords are described to detail. In the end the results of large collections of text files testing and implementation methods are presented and discussed.
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