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Prediction of Raining from Meteoradar
Vlček, Michael ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
This thesis deals with rain prediction using information from meteoradar images and some other relevant factors through the computational model of a neural network. It focuses on exploring different prediction possibilities using this model and defining the most successful model configuration to fulfill the chosen task.
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Algorithmic Trading Using Artificial Neural Networks
Šeda, Jan ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
The capability to be able to determine the future progression on the worlds stock exchange is an important issue, which has become discernible in the last decades. An important role of this progression lies within the fast advancements in computerized technology.Aforementioned document describes a mechanism used for prediction of the future price of a certain stock. The strategy of trading is build upon this mechanism, and the core of this prediction system is an artificial neural network. Inputs used in this network are indicators derived from technical analysis. This trading system was implemented into historical trades and successfully tested.
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Language Identification of Text Document
Cakl, Jan ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
The thesis deals with a language identification of a text document. The final program includes three different implementation methods of language identification. The first method is based on a frequency statistics of N-gram. The second one represents Markov chains and the last one uses the simulated neural net for the identification purposes. The result is implemented in the Python language.
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Algorithmic Trading Using Artificial Neural Networks
Chlud, Michal ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
This diploma thesis delas with algoritmic trading using neural networks. In the first part, some basic information about stock trading, algorithmic trading and neural networks are given. In the second part, data sets of historical market data are used in trading simulation and also as training input of neural networks. Neural networks are used by automated strategy for predicting future stock price. Couple of automated strategies with different variants of neural networks are evaluated in the last part of this work.
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Speaker Recognition in the VoIP Environment
Remeš, Jan ; Pešán, Jan (referee) ; Plchot, Oldřich (advisor)
Tato práce popisuje použití systémů pro rozpoznávání mluvčího v~prostředí VoIP, úspěšnost systému a přístupy k jejímu zlepšení. Popisuje architekturu těchto systémů, metriky pro vyhodnocení jejich úspěšnosti a klíčové komponenty VoIP z hlediska rozpoznávání mluvčího. Je zde popsáno vytvoření simulace VoIP prostředí, úspěšnost systému je vyhodnocena na datech pocházejících z různých druhů VoIP prostředí a výsledky jsou demostrovány. Adaptace a kalibrace systému je provedena a jejich přínosy zhodnoceny.
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Web Based Audio Editor
Myler, Jan ; Pešán, Jan (referee) ; Schwarz, Petr (advisor)
This thesis deals with the creation of simple web-based audio editor using JavaScript, HTML5 and new Web APIs for audio processing (especially the Web Audio API). The thesis describes the current state of development and implementation of APIs for audio processing in browsers. It also contains a description of the resulting application design and its implementation. In the conclusion is a summary of findings of the applications development and proposal of possible future use and expansion.
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Adhoc VoIP for Maemo or Meego
Klapal, Tomáš ; Pešán, Jan (referee) ; Mlích, Jozef (advisor)
This bachelor thesis deals with sound capture and transfer through computer network. It also looks into design of application for voice communication on Maemo platform. First, the platform is introduced. Then, sound processing is described. Later, computer networks and their communication is presented. Existing applications are described also. Second half of this document introduces design and implementation of own application from modular point of view. Last, restrictions and enhancements are mentioned.
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Visualization of User Pronunciations for Electronic Dictionarties
Pešán, Jan ; Chalupníček, Kamil (referee) ; Černocký, Jan (advisor)
The aim of this bachelor's work is to try to find a new way for development in learning capabilities of electronic dictionaries. There is an introduction of the main concept of learning pronunciations with visualization of phonemes in the first part. It is followed by chapter, which does a global review of methods for speech processing used in this project, e.g. HMM or Viterbi algorithm. In the third chapter, there is description of tools that we have used for implementation of the whole system. Next chapter explains more in detail technology of neural networks, used here as probability estimator. There is also a description of problem with compatibility of the used phoneme sets and in addition, it describes used phoneme models. Chapter 5 is whole about implementation of the system. There are also described scripts and tools applied for the preparation of the source data. In the next chapter, there is a user testing with screenshots. Moreover, in the last chapter I wrote a short conclusion and possible future ways for further developing of this system.
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