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ECG signal quality annotation
Waloszek, Vojtěch ; Smíšek, Radovan (referee) ; Vítek, Martin (advisor)
This thesis gives basic information summary about electrophysiology of heart and electrocardiography and overview of several signal quality assessment methods. It also presents a new method for evaluating ECG quality, shows how signal quality indices are extracted and how the quality annotation is performed. It also gives test results of how the signal quality indices reflect the presence of corresponding noise and whether the quality annotation is correct.
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Multicameras Biometric Gateway to Identify People
Kosík, Dominik ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
This thesis is about creating biometric gate to identify people. The Identification is achieved with 5 RGB cameras and one thermal camera. Thermal camera is used for detection of person. Then, from images acquired from RGB cameras, is created 3D model of photographed person. This model is then used for the identification. However due to inaccuracies in created model, identification isn't precise enough. Because of that, it's necessary to modify used algorithms processing 3D model, so better precision is achieved.
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Automatic detection of stress using biological signals
Votýpka, Tomáš ; Kozumplík, Jiří (referee) ; Smíšek, Radovan (advisor)
Bachelor's thesis is focused on stress detection. This thesis defines the concept of stress, analyzes the appropriate biological signals for stress detection, presents databases of biological signals, that were used for stress detection and mentions methods of automatic stress detection. Then, a stress detection program was implemented in the MATLAB software environment. A freely available database of non-EEG signals was used to implement the program. Models classifying stress were created using 4 machine learning methods for binary classification and 3 machine learning methods for classifying 4 psychical states. Efficiency of the classification was summarized in the conclusion of this thesis.
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Multimedia Document Type Diff
Lang, Jozef ; Hlosta, Martin (referee) ; Chmelař, Petr (advisor)
Development of Internet and its massive spread resulted in increased volume of multimedia data. The increase in the amount of multimedia data raises the need for efficient similarity detection between multimedia files for the purpose of preventing and detecting violations of copyright licenses or for detection of similar or duplicate files. This thesis discusses the current options in the field of the content-based image and video comparison and focuses on the feature extraction techniques, distance metrics, design and implementation of the mediaDiff application module for the content-based comparison of video files.
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Music Style Recognizer from MP3
Deutscher, Michael ; Szőke, Igor (referee) ; Grézl, František (advisor)
This document describes the concept of music style recognition. It gives a quick reference to the digitalization of music data and storing music data in computers. It also mentions features used for music style recognition and their extraction. The main part of this document compares the successfulness of music genre recognition using features extracted directly from audio data in mp3 format and features extracted by usual analysis.
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Keyword Spotting Implementation to Mobil Phone (Symbian 60)
Cipr, Tomáš ; Schwarz, Petr (referee) ; Szőke, Igor (advisor)
Keyword spotting is one of the many applications of automatic speech recognition. Its purpose is determining spots in given utterance in which some of the specified words were spoken. Keyword spotting has a great potential to enhance performance of new applications as well as the existing ones. An example could be a mobile phone voice control. Due to OS Symbian's coming to the market it is even possible for end user to implement a keyword spotting for a mobile phone on his or her own. The thesis describes theoretical prerequisites for keyword spotting and its implementation. Firstly the OS Symbian is presented with respect to the given task. Secondly each step of keyword spotting process is described. Finally the object design of keyword spotter is presented followed by implementation description. The thesis concludes with results review and notes on possible improvements.
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Trully Smart Smart Socket
Valušek, Ondřej ; Zemčík, Pavel (referee) ; Materna, Zdeněk (advisor)
There is a large selection of so called smart sockets available on the market today. The possibilities of these sockets are sadly very limited. Typically, they can measure power consumption, be turned off and on remotely by mobile application and timer. This thesis deals with this problem by showing how a smart relay can be used to create a truly smart smart socket that can classify currently connected appliances using just short time window for up to three devices combined. The power consumption is measured using Shelly 1PM together for three plugs. Using time series feature extraction, unknown device detection with SVM and neural network classification, the accuracy was over 99%. on a dataset containing combinations of smart TV, lamp and a laptop consumption. Information about currently connected devices is displayed on a webpage and written to a database to be viewed later. The information about connecting and disconnecting a device can be further sent to a system for smart home management.
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Interactive 3D CT Data Segmentation Based on Deep Learning
Trávníčková, Kateřina ; Hradiš, Michal (referee) ; Kodym, Oldřich (advisor)
This thesis deals with CT data segmentation using convolutional neural nets and describes the problem of training with limited training sets. User interaction is suggested as means of improving segmentation quality for the models trained on small training sets and the possibility of using transfer learning is also considered. All of the chosen methods help improve the segmentation quality in comparison with the baseline method, which is the use of automatic data specific segmentation model. The segmentation has improved by tens of percents in Dice score when trained with very small datasets. These methods can be used, for example, to simplify the creation of a new segmentation dataset.
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Vektorová segmentace objemových medicínských dat založená na Delaunay triangulaci
Španěl, Michal ; Martišek, Dalibor (referee) ; Sochor, Jiří (referee) ; Kršek, Přemysl (advisor)
Image segmentation plays an important role in medical image analysis. Many segmentation algorithms exist. Most of them produce data which are more or less not suitable for further surface extraction and anatomical modeling of human tissues. In this thesis, a novel segmentation technique based on the 3D Delaunay triangulation is proposed. A modified variational tetrahedral meshing approach is used to adapt a tetrahedral mesh to the underlying CT volumetric data, so that image edges are well approximated in the mesh. In order to classify tetrahedra into regions/tissues whose characteristics are similar, three different clustering schemes are presented. Finally, several methods for improving quality of the mesh and its adaptation to the image structure are also discussed.
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Identification of persons via voice imprint
Mekyska, Jiří ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
This work deals with the text-dependent speaker recognition in systems, where just a few training samples exist. For the purpose of this recognition, the voice imprint based on different features (e.g. MFCC, PLP, ACW etc.) is proposed. At the beginning, there is described the way, how the speech signal is produced. Some speech characteristics important for speaker recognition are also mentioned. The next part of work deals with the speech signal analysis. There is mentioned the preprocessing and also the feature extraction methods. The following part describes the process of speaker recognition and mentions the evaluation of the used methods: speaker identification and verification. Last theoretically based part of work deals with the classifiers which are suitable for the text-dependent recognition. The classifiers based on fractional distances, dynamic time warping, dispersion matching and vector quantization are mentioned. This work continues by design and realization of system, which evaluates all described classifiers for voice imprint based on different features.
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