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QRS detection using zero crossing counting
Hylmar, Petr ; Janoušek, Oto (referee) ; Vítek, Martin (advisor)
This master's thesis describes basics principles of QRS complex detection. It is focused on QRS detection using zero crossing counts method. There are described princips and program realization of this method. The other part is focused on genetic optimalization algorithm. There are presented obtained optimalization results on standard CSE and MIT-BIH database. The quality of the detector is compared with other authors. The optimalized QRS detector achieves comparable results with other authors. The part of the thesis is graphical user interface which supply view on modified ECG signal and detection results.
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Production planning under uncertainty
Grulich, Martin ; Popela, Pavel (referee) ; Dvořák, Jiří (advisor)
This diploma work deals with a dynamic multi-level multi-item lot sizing problem in a general production-assembly structure represented by a directed acyclic network, where each node may have several predecessors and successors. We assume stochastic demand, finite planning horizon consisting of discrete time periods, dynamic lot sizes, multiple constrained resources and time-varying cost parameters. The objective is to minimize the total costs over the planning horizon. This thesis includes overview of models with stochastic demand and also general description of genetic algorithm. Using different modifications of genetic algorithm I have proposed and implemented methods for solving a chosen model. Then I have made an experimental comparison of these method on selected problems.
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Intelligent System for Generating and Analysis of Trading Recommendations on Financial Markets
Martinský, Ondrej ; Hrubý, Martin (referee) ; Zbořil, František (advisor)
This master thesis deals with the price prediction on financial markets. It describes automated trading systems based on technical analysis and discusses a soft computing approach to construction of such systems. Also, this thesis combines conventional trading strategies with the fuzzy logic. The practical part of this thesis contains also a framework for composing, simulation and analysis of the automated trading strategies. The simulator contained in this framework is implemented in the Java language and based on DEVS formalism. Because of this, there is a possibility to embed real-time components into the trading model. This work contains also a database of historical financial data and tools for their automatic actualization.
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Autonomous Locomotive Robot Path Planning on the Basis of Machine Learning
Krček, Petr ; Bělohoubek, Pavel (referee) ; Štefek, Alexandr (referee) ; Žalud, Luděk (referee) ; Dvořák, Jiří (advisor)
As already clear from the title, this dissertation deals with autonomous locomotive robot path planning, based on machine learning. Robot path planning task is to find a path from initial to target position without collision with obstacles so that the cost of the path is minimized. Autonomous robot is such a machine which is able to perform tasks completely independently even in environments with dynamic changes. Path planning in dynamic partially known environment is a difficult problem. Autonomous robot ability to adapt its behavior to changes in the environment can be ensured by using machine learning methods. In the field of path planning the mostly used methods of machine learning are case based reasoning, neural networks, reinforcement learning, swarm intelligence and genetic algorithms. The first part of this thesis introduces the current state of research in the field of path planning. Overview of methods is focused on basic omnidirectional robots and robots with differential constraints. In the thesis, several methods of path planning for omnidirectional robot and robot with differential constraints are proposed. These methods are mainly based on case-based reasoning and genetic algorithms. All proposed methods were implemented in simulation applications. Results of experiments carried out in these applications are part of this work. For each experiment, the results are analyzed. The experiments show that the proposed methods are able to compete with commonly used methods, because they perform better in most cases.
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Diagnosing Parkinson's disease from analysis of speech recording
Vymlátil, Petr ; Trzos, Michal (referee) ; Lněnička, Jakub (advisor)
This thesis is focused on diagnosing Parkinson’s disease from analysis of speech recording. Introduction of this work deals with description of voice production mechanism, it’s basic qualities and influence of hypokinetic dysarthria on speech. In next chapter, there is described voice signal and some methods of it’s preprocessing. Next part continues dealing with description of chosen individual symptoms, which are needed for PD diagnosing, followed by definition of chosen reduction methods and classifiers. There is a comparison of classify succes of naive bayes classifier, depending on chosen reduction method in last chapter of this work.
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Intelligent System for Generating and Analysis of Trading Recommendations on Financial Markets
Martinský, Ondrej ; Hrubý, Martin (referee) ; Zbořil, František (advisor)
This master thesis deals with the price prediction on financial markets. It describes automated trading systems based on technical analysis and discusses a soft computing approach to construction of such systems. Also, this thesis combines conventional trading strategies with the fuzzy logic. The practical part of this thesis contains also a framework for composing, simulation and analysis of the automated trading strategies. The simulator contained in this framework is implemented in the Java language and based on DEVS formalism. Because of this, there is a possibility to embed real-time components into the trading model. This work contains also a database of historical financial data and tools for their automatic actualization.
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Diagnosing Parkinson's disease from analysis of speech recording
Vymlátil, Petr ; Trzos, Michal (referee) ; Lněnička, Jakub (advisor)
This thesis is focused on diagnosing Parkinson’s disease from analysis of speech recording. Introduction of this work deals with description of voice production mechanism, it’s basic qualities and influence of hypokinetic dysarthria on speech. In next chapter, there is described voice signal and some methods of it’s preprocessing. Next part continues dealing with description of chosen individual symptoms, which are needed for PD diagnosing, followed by definition of chosen reduction methods and classifiers. There is a comparison of classify succes of naive bayes classifier, depending on chosen reduction method in last chapter of this work.
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