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QRS complex detection in multilead ECG signals
Šlancar, Matěj ; Smital, Lukáš (referee) ; Kozumplík, Jiří (advisor)
The aim of this thesis is to introduce the principles of software QRS detection, which is based on different combinations of orthogonal (pseudoorthogonal) leads. The thesis describes the main components of the ECG signal, a selection of methods that can be used for QRS detection in orthogonal leads and finally the evaluation of the effectiveness of the chosen methods and a comparing the approaches with the results of other authors. Functionality of detection algorithm has been tested on signals of CSE standard library.
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Sleep stage classification based on Hjorth descriptors of EEG signals
Kupková, Kristýna ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This bachelor thesis is focused on the distinction between sleep stages from EEG signals. In its first part the classical method of visual classification of sleep stages is introduced, the second part introduces an automated method for sleep stage scoring. It is a method that uses the three parameters of Hjorth to create a vector space, in which, on the basis of similarity of formed shapes, different stages of sleep could be distinguished. Parameters of Hjorth are calculated from the whole EEG signal, and also from its bands. In the next section of this thesis a principle component analysis is performed. The principle components are placed into a vector space analogously with parameters of Hjorth and the character of formed objects is observed.
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Study of influence of toxical and nutritional elements on cell metabolism using combination of Raman spectroscopy and Laser-Induced breakdown spectroscopy
Mazura, Martin ; Hrdlička, Aleš (referee) ; Prochazka, David (advisor)
In this work on-line discrimination of six bacteria strains by means of the Multivariate discrimination analysis (MVDA) is presented. Principal components analysis (PCA) was selected as most suitable technique. Two analytical methods – Laser-Induced breakdown spectroscopy (LIBS) and Raman spectroscopy were equipped for chemical analysis of bacteria strains. In order to obtain the best possible bacteria strains differentiation, the data from both methods was analyzed in two ways separately and together. The data from both measurements was normalized separately and afterwards combined in one data frame for simultaneous analysis. This data frame contained information from both analytical methods. Moreover an influence of cultivation period for each bacteria strain was studied. It was determined that Raman spectroscopy is able to discriminate two bacteria strains and LIBS even four bacteria strains. Using combination of both methods the complete discrimination was achieved. From information of first principal component it was determined that most valuable information in LIBS data is not included in different elemental composition but rather in influence of matrix effect. Solely the LIBS was utilized for studying the effect of cultivation period. It was possible to observe transformation of four bacteria strains within 24 hours. Based on results of this work it is possible to assume that combination of Raman spectroscopy and LIBS, because of the complementary information, is suitable for fast discrimination of different bacteria species and strains. Moreover it was determined that LIBS is able to observe the transformation caused by cultivation period.
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Creating a 3D Face Model Using Kinect Device
Nesvadba, Tomáš ; Malinka, Kamil (referee) ; Mráček, Štěpán (advisor)
The main aim of this bachelor's thesis is to suggest a method for increasing the resolution of 3D face models captured by Kinect. The document describes different methods of capturing 3D models and compares the Kinect sensor with more acurate scanners. The main part of this document is dedicated to statistic model based on the PCA and its usage in the project. The last section of the paper describes testing of various models and discuss the results and another options of aplication.
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Human Face Recognition
Burián, Pavel ; Ševcovic, Jiří (referee) ; Herout, Adam (advisor)
The bachelor thesis deals with the problematic of the human face recognition in an image. It describes the face detection principles, methods used in recognition problematic and existing face data sets. Thesis is aimed on analysis, comparison and testing of the eigenface, fisherface and KDDA (Kernel Direct Discriminant Analysis) algorithms.
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Network Anomaly Detection Based on PCA
Krobot, Pavel ; Kováčik, Michal (referee) ; Bartoš, Václav (advisor)
This thesis deals with subject of network anomaly detection. The method, which will be described in this thesis, is based on principal component analysis. Within the scope of this thesis original design of this method was studied. Another two extensions of this basic method was studied too. Basic version and last extension was implemented with one little additional extension. This one was designed in this thesis. There were series of tests made above this implementation, which provided two findings. First, it shows that principal component analysis could be used for network anomaly detection. Second, even though the proposed method seems to be functional for network anomaly detection, it is still not perfect and additional research is needed to improve this method.
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Face Identification
Macenauer, Oto ; Drozd, Michal (referee) ; Chmelař, Petr (advisor)
This document introduces the reader to area of face recognition. Miscellaneous methods are mentioned and categorized to be able to understand the process of face recognition. Main focus of this document is on issues of current face recognition and possibilities do solve these inconveniences in order to be able to massively spread face recognition. The second part of this work is focused on implementation of selected methods, which are Linear Discriminant Analysis and Principal Component Analysis. Those methods are compared to each other and results are given at the end of work.
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Video Retrieval
Černý, Petr ; Mlích, Jozef (referee) ; Chmelař, Petr (advisor)
This thesis summarizes the information retrieval theory, the relational model basic and focuses on the data indexing in relational database systems. The thesis focuses on multimedia data searching. It includes description of automatic multimedia data content extraction and multimedia data indexing. Practical part discusses design and solution implementation for improving query effectivity for multidimensional vector similarity which describes multimedia data. Thesis final part discusses experiments with this solution.
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Handwritten Character Recognition Using Artificial Neural Networks
Horký, Vladimír ; Janda, Miloš (referee) ; Plchot, Oldřich (advisor)
Neural networks with algorithm back-propagation will be presented in this work. Theoretical background of the algorithm will be explained. The problems with training neural nets will be solving there. The work discuss some techniques of image preprocessing and image extraction features, which is one of main part in classification. Some part of work discuss few experiments with neural nets with chosen image features.
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Video Feature for Classification
Behúň, Kamil ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
This thesis compares hand-designed features with features learned by feature learning methods in video classification. The features learned by Principal Component Analysis whitening, Independent subspace analysis and Sparse Autoencoders were tested in a standard Bag of Visual Word classification paradigm replacing hand-designed features (e.g. SIFT, HOG, HOF). The classification performance was measured on Human Motion DataBase and YouTube Action Data Set. Learned features showed better performance than the hand-desined features. The combination of hand-designed features and learned features by Multiple Kernel Learning method showed even better performance, including cases when hand-designed features and learned features achieved not so good performance separately.
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