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Smart distance measurement module for football robot
Hrbáček, Jan ; Honzík, Petr (referee) ; Havránek, Zdeněk (advisor)
Diplomová práce se zabývá vývojem dálkoměrného modulu určeného pro rozšíření senzorické výbavy fotbalového robotu kategorie MiroSot. Tento modul na vstupu přijímá data ze senzorické jednotky vyvinuté na Ústavu automatizace a měřicí techniky a z těchto dat extrahuje polohu míčku. Je srovnáno využití neuronové sítě a zjednodušené Houghovy transformace pro získání polohy těžiště míčku. V práci je popsána pomocná implementace funkcionality v prostředích MATLAB a C#.NET i hlavní implementace pro signálový mikrokontrolér Freescale MC56F8013. Výsledný modul splňuje nároky zadání a je plně funkční.
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Neural networks in audio signal watermarking algorithms
Kaňa, Ondřej ; Smékal, Zdeněk (referee) ; Zezula, Radek (advisor)
Digital watermarking is a technique for digital multimedia copyright protection. The robustness and the imperceptibility are the main requirements of the watermark. This thesis deals with watermarking audio signals using artificial neural networks. There is described audio watermarking method in the DCT domain. Method is based on human psychoacoustic model and the techniques of neural networks.
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Emotional State Recognition and Classification Based on Speech Signal Analysis
Černý, Lukáš ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
The diploma thesis focuses on classification of emotions. Thesis deals about parameterization of sounds files by suprasegment and segment methods with regard for next used of these methods. Berlin database is used. This database includes many of sounds records with emotions. Parameterization creates files, which are divided to two parts. First part is used for training and second part is used for testing. Point of interest is self-organization network. Thesis includes Matlab´s program which can be used for parameterization of any database. Data are classified by self-organization network after parameterization. Results of hits rates are presented at the end of this diploma thesis.
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Neural networks in audio signal watermarking algorithms
Kaňa, Ondřej ; Smékal, Zdeněk (referee) ; Zezula, Radek (advisor)
Digital watermarking is a technique for digital multimedia copyright protection. The robustness and the imperceptibility are the main requirements of the watermark. This thesis deals with watermarking audio signals using artificial neural networks. There is described audio watermarking method in the DCT domain. Method is based on human psychoacoustic model and the techniques of neural networks.
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Optimisation of a Voice Transmission in Communication Networks
Novák, David ; Polívka, Michal (referee) ; Škorpil, Vladislav (advisor)
This master’s thesis deals abou the transmission of voice in communications networks. The theoretical part describes criteria for optimizing voice, such as quality of service, type of service, level of service, service type, and mean opinion score. Next I describe the Internet Protocol, comparing IPv4 and IPv6, VoIP, including security, protocols and parameters necessary for transmission. Other part is about neural networks. There are basically described the neural network, Hopfield neural network and Kohenen neural network. The research is based on a comparison of the network without ensuring the quality of service and with ensuring quality of service. Then, there are compared two types of switches. Classical switch-controlled sequentially, and switch controlled by neural networks. The overall simulation program is implemented in Opnet Modeler. The conclusion deals with the creation of laboratory tasks in this program to compare the different systems of ensuring quality of service.
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Adaptive optimal controllers with principles of artificial intelligence
Samek, Martin ; Malounek, Petr (referee) ; Pivoňka, Petr (advisor)
Master’s thesis describes adaptive optimal controller design and it’s settings. Identification with principles of artificial intelligence and recursive least squares identification with exponential and directional forgetting are compared separately and as part of controller. Adaptive optimal controller is tested on physical model and compared with solidly adjusted PSD controller. Possibilities of implementation of adaptive optimal controller into programmable logic controller B&R are show and tested.
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Emotional State Recognition Based on Speech Signal Analysis
Čermák, Jan ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
The thesis is focused on the emotional states classification in the Matlab program, using neural networks and the classifier which is based on a combination of Gaussian density functions. It deals with the speech signal processing; the prosodic and spectral signs and the MFCC coefficients were extracted from the signal. The work also deals with the quality evaluation of individual signs of which the most suitable were chosen in order to provide the correct classification of emotional states. In order to identify the emotional states, two different methods were used. The first method of classification was the use of neural networks with differently selected parameters, and the second method was the use of the Gaussian mixture model (GMM). In both methods, a database of emotional utterances was divided into the training group and the test group. The testing was based on a method independent of the speaker. The work also includes the comparison of individual analyzed methods as well as the representation and comparison of the results. The conclusion comprises a proposition for the best parameters and the best classifier for the recognition of the speaker’s emotional state.
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Neural networks for EMC modeling of small airplanes
Koudelka, Vlastimil ; Goňa,, Stanislav (referee) ; Raida, Zbyněk (advisor)
This thesis deals with neural modeling of electromagnetic field inside small aircrafts, witch can contain composite materials in their construction. Introduction to neural networks and its application in EMC of small airplanes is discussed in the first part of the text. In the second part of this thesis we design a simple EM model of small airplane. The airplane is simulated by two parallel dielectric layers (the left-hand side wall and the right hand side wall of the airplane). The layers are put into a rectangular metallic waveguide terminated by the absorber in order to simulate the illumination of the airplane by the external wave (both of the harmonic nature and pulse one). Numerical analyses are performed to search the relations between the distribution of an electromagnetic field inside the aircraft and electric parameters of model walls. The results of numerical analyses are used to train two types of neural network. In this way we can obtain accurate continuous model of electromagnetic field inside the aircraft. For the comparison with neural networks a multi-dimensional cubic spline interpolation is provided also. Neural classifiers are also investigated. We use them for classification of imaginary composite materials in terms of EMC. The nearest neighbour algorithm is applied as a classic approach to problem of classification.
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Neural networks in speaker classification
Svoboda, Libor ; Atassi, Hicham (referee) ; Míča, Ivan (advisor)
The content of this work is focused on the neural network per speaker recognition. The work deals with problems of processing speech signal and there are introduction some types of neural network. The part of work was made database of records from speakers with have various sex and ages. The train and test group was made from the database. For classifier were suggested afterwards. One of them was nominated on base Gaussian mixture model and three of them were nominated on neural. This system was tested and analyzed on the basis of age, gender and both criterions each other at the end. Attention is focused on choice suitable feature in each mission of classification at the same time. At the end of work are introduced results of analysis for individual groups and features. The most suitable features are diagnosed from given mission of classification and the most prosperous classifier.
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