Thesis focuses on analysis of contemporary machine learning methods used for text classification based on emotion and testing several deep neural nework architectures. Outcome of this thesis is a neural network architecture, which is tuned for using with text data and which had the best result of 79,94 percent. Proposed method is language independent and it doesn’t require as precisely classified training datasets as current methods. Training and testing datasets were consisted of short amateur movie reviews in Czech and in English. Thesis contains also overview of theoretical basics for convolutional neural networks and history of neural networks and language processing Scripts were written in Python, neural networks were simulated using Keras library and Theano framework. We used CUDA for better performance.

This master‘s thesis deals with using surface texture parameters for conformity assessment and process control. The aim of the thesis was to create an overview of surface texture parameters, an overview of procedures for conformity assessment and process control using surface texture parameters, practical application on an industrial product and practical recommendations. The thesis includes an overview of profile and areal surface texture parameters, including an original translation of terms of the areal method. Methods of conformity assessment and process control in connection with the surface texture parameters are also described. Statistical analysis was performed based on 7200 values of surface parameters and 1843200 values of profile parameters which were measured on parts from serial production. The last chapter includes practical recommendations.

This master thesis focuses on filtering and forwarding packets in high speed networks. Firstly the DPDK framework is introduced, which is used for fast packet processing. This project also introduces a design of application for high-speed packet filtering and design of tools for making it easier to work with that application. Subsequently, the implementation of this design is introduced and testing with comparison of results with a standard firewall

This paper discusses design and implementation of classifying system for recognition of musical instruments from audio records with use of Musical Information Retrieval techniques. In the first part, paper describes parameters used for instrument classification, calculation of said parameters from records and reduction of feature vector. Next part is devoted to tuning and implementation of various classifiers with focus on neural networks. These classifiers ar further tested on records from IRMAS dataset wchich contain 11 musical instruments playing solo or with other instruments. Results of classifiers tested on different parameters and different numbers of instruments are discussed in the last part.

This diploma thesis is focused on the statistical analysis of chamber and orchestral classical music recordings composed by Czech authors. One of the chapters is dedicated to the description of a feature extraction process that precedes the statistical analysis. Techniques of Music Information Retrieval are used during several stages of this thesis. Databases used for analysis are described and pre-processing steps are proposed. A tool for synchronization of the recordings was implemented in MATLAB. Finally the system used for classification of recordings based on their geographical origin is proposed. The recordings are sorted by a binary classifier into two categories of Czech and world recordings. The first part of the statistical analysis is focused on individual analysis of features. The features are evaluated based on their discrimination strength. The second part of the statistical analysis is focused on feature selection, which can improve the overall accuracy of the binary classifier compared to the individual analysis of the features.

This work deals with the golf ball flight simulation based on video recordings. The way how to determine the trajectory of a golf ball using several initial points is described here. For the ball recognition there are used computer vision techniques, particulary pattern recognition. The work covers physics of the golf ball, it deals with physical influences that occurs during the ball flight and it tries to describe the trajectory approximation.

This thesis deals with the design and implementation of a tool for generating drums triggers from a downmix record. The work describes the preprocessing of the input audio signal and methods for the classification of strokes. The drum classification is based on the similarity of the signals in the frequency domain. Principal component analysis (PCA) was used to reduce the number of dimensions and to find the characteristic properties of the input data. The method support vector machine (SVM) was used to classify the data into individual classes representing parts of the drum kit. The software was programmed in Matlab. The classification model was trained on a set of 728 drum samples for seven categories (kick, snare, hi-hat, crash, ride, kick + hi-hat, snare + hi-hat). The success of the system in the classification is 75 %.

Pokrok při zdokonalování klasifikačních algoritmů je zpomalován nedostatkem dat potřebných pro testování. Reálná data je obtížné získat z důvodu bezpečnosti a ochrany citlivých informací. Existují však generátory syntetických sad pravidel, jako například ClassBench-ng. K vyhodnocení správného fungování, propustnosti, spotřeby energie a dalších vlastností klasifikačních algoritmů je zapotřebí také vhodný síťový provoz. Tématem této práce je tvorba takového generátoru síťového provozu, který by umožnil testování těchto vlastností v kombinaci s IPv4, IPv6 a OpenFlow1.0 pravidly vygenerovanými ClassBench-ng. Práce se zabývá různými způsoby, jak toho dosáhnout, které vedly k vytvoření několika verzí generátoru. Vlastnosti jednotlivých verzí byly zkoumány řadou experimentů. Implementace byla provedena pomocí jazyku Python. Nejvýznamnějším výsledkem je generátor, který využívá principů několika zkoumaných přístupů k dosažení co nejlepších vlastností. Dalším přínosem je nástroj, který bylo nutné vytvořit pro analýzu užitých sad klasifikačních pravidel a vyhodnocení vlastností vygenerovaného síťového provozu.

This thesis describes recording, processing and classifying brain activity which is being captured by a brain-computer interface (BCI) device manufactured by OpenBCI company. Possibility of use of such a device for controlling an application with brain activity, specifically with thinking of left or right hand movement, is discussed. To solve this task methods of signal processing and machine learning are used. As a result a program that is capable of recording, processing and classifying brain activity using an artificial neural network is created. An average accuracy of classification of synthetic data is 99.156%. An average accuracy of classification of real data is 73.71%.

The master thesis focuses on the selection of a suitable algorithm for the classification of selected network traffic services and its implementation. The theoretical part describes the available classification approaches together with commonly used algorithms and selected network services. The practical part focuses on the preparation and preprocessing of the dataset, selection and optimization of the classification algorithm and verifying the classification capabilities of the algorithm in the various scenarios of the dataset.