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TRECVid Search Information Retrieval
Čeloud, David ; Mlích, Jozef (referee) ; Chmelař, Petr (advisor)
The master's thesis deals with Information Retrieval. It summarizes the knowledge in the field of Information Retrieval theory. Furthermore, the work gives an overview of models used in Information Retrieval, the data and the actual issues and their possible solutions. The practical part of the master's thesis is focused on the implementation of methods of information retrieval in textual data. The last part is dedicated to experiments validating the implementation and its possible improvements.
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Mobile Robot Localization Using Camera
Vaverka, Filip ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This thesis describes design and implementation of an approach to the mobile robot localization. The proposed method is based purely on images taken by a monocular camera. The described solution handles localization as an association problem and, therefore, falls in the category of topological localization methods. The method is based on a generative probabilistic model of the environment appearance. The proposed solution is capable to eliminate some of the difficulties which are common in traditional localization approaches.
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Probabilistic model for textile concrete reinforcement and comparison with experiments
Lomič, Jiří ; Rypl, Rostislav (referee) ; Vořechovský, Miroslav (advisor)
The scope of the presented bachelor’s thesis was the establishment of a probabilistic model for material strength of textile reinforcement used for textile reinforced concrete. This reinforcement is composed of AR-glass multi-filament yarns. The goal of this thesis was to determine the potential weak spot of the textile yarn and evaluate its strength in overall. The weak spot could have been a lateral cross-connection, which narrowed the textile yarn at several locations. Another thing of interest was the observation of statistical size effect with the increasing length of textile yarn. In order to properly fit the numerical model to real behavior of multi-filament yarns, five series of experimental tensile testing has been executed in laboratory. Each series consisted of 8-10 specimens and had a different yarn length. Maximum tensile force and maximum deformation have been measured to obtain L-D diagrams for each specimen. Measured data were statistically analyzed and gave the information necessary for the identification of probabilistic model parameters. This parameter estimation has been carried out with the help of numerical and optimization methods included in Python programming algorithms. The problem statement resulted in a combination of model parameters describing the textile yarn behavior. The statistical size effect was observed corresponding to the Weibull theory. The performed study showed that the failure of the textile yarn depends on material strength of its filaments. There are no load concentrators at the location of lateral cross-connections affecting the yarn failure.
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Conversion of Piano Recording from WAV to MIDI
Bednařík, Jan ; Černocký, Jan (referee) ; Szőke, Igor (advisor)
The aim of the thesis is to propose a system capable of automatic conversion of polyphonic piano recordings from the audio format WAV to MIDI. The thesis describes problems related to single tone recognition in music recordings and proposes a solution based on a probabilistic model that uses the Probabilistic Latent Component Analysis method. Recordings of isolated digital piano tones were used to train the system. The proposed system was tested on classical recordings of the Classical Piano MIDI database and on recordings of a Korg SP-250 piano and evaluated using a variety of metrics. The conclusion part contains the results of recognition success rate and their comparison with other existing systems.
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Tempo detector based on a neural network
Suchánek, Tomáš ; Smékal, Zdeněk (referee) ; Ištvánek, Matěj (advisor)
This Master’s thesis deals with beat tracking systems, whose functionality is based on neural networks. It describes the structure of these systems and how the signal is processed in their individual blocks. Emphasis is then placed on recurrent and temporal convolutional networks, which by they nature can effectively detect tempo and beats in audio recordings. The selected methods, network architectures and their modifications are then implemented within a comprehensive detection system, which is further tested and evaluated through a cross-validation process on a genre-diverse data-set. The results show that the system, with proposed temporal convolutional network architecture, produces comparable results with foreign publications. For example, within the SMC dataset, it proved to be the most successful, on the contrary, in the case of other datasets it was slightly below the accuracy of state-of-the-art systems. In addition,the proposed network retains low computational complexity despite increased number of internal parameters.
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Tempo detector based on a neural network
Suchánek, Tomáš ; Smékal, Zdeněk (referee) ; Ištvánek, Matěj (advisor)
This Master’s thesis deals with beat tracking systems, whose functionality is based on neural networks. It describes the structure of these systems and how the signal is processed in their individual blocks. Emphasis is then placed on recurrent and temporal convolutional networks, which by they nature can effectively detect tempo and beats in audio recordings. The selected methods, network architectures and their modifications are then implemented within a comprehensive detection system, which is further tested and evaluated through a cross-validation process on a genre-diverse data-set. The results show that the system, with proposed temporal convolutional network architecture, produces comparable results with foreign publications. For example, within the SMC dataset, it proved to be the most successful, on the contrary, in the case of other datasets it was slightly below the accuracy of state-of-the-art systems. In addition,the proposed network retains low computational complexity despite increased number of internal parameters.
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Integrating Probabilistic Model for Detecting Opponent Strategies Into a Starcraft Bot
Šmejkal, Pavel ; Černý, Martin (advisor) ; Bída, Michal (referee)
Recent research in artificial intelligence (AI) for real time strategies (RTS) has shown a great need for a computer controlled agent (bot) to be able to adapt its strategy in response to opponent's actions. While some progress has been made in detecting opponent's strategies offline, there has not been much success in using this information to guide in-game decisions. We present a version of UAlbertaBot enhanced by existing probabilistic algorithm for supervised learning from replays and strategy prediction. Bot that adapts its strategies has proved to be superior to a random bot as we show in simulated StarCraft: Brood War AI tournament. Our work exposes the importance of scouting and strategy adaptation. By further improvement of strategies, a bot capable of competing with human players may be created.
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Conversion of Piano Recording from WAV to MIDI
Bednařík, Jan ; Černocký, Jan (referee) ; Szőke, Igor (advisor)
The aim of the thesis is to propose a system capable of automatic conversion of polyphonic piano recordings from the audio format WAV to MIDI. The thesis describes problems related to single tone recognition in music recordings and proposes a solution based on a probabilistic model that uses the Probabilistic Latent Component Analysis method. Recordings of isolated digital piano tones were used to train the system. The proposed system was tested on classical recordings of the Classical Piano MIDI database and on recordings of a Korg SP-250 piano and evaluated using a variety of metrics. The conclusion part contains the results of recognition success rate and their comparison with other existing systems.
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TRECVid Search Information Retrieval
Čeloud, David ; Mlích, Jozef (referee) ; Chmelař, Petr (advisor)
The master's thesis deals with Information Retrieval. It summarizes the knowledge in the field of Information Retrieval theory. Furthermore, the work gives an overview of models used in Information Retrieval, the data and the actual issues and their possible solutions. The practical part of the master's thesis is focused on the implementation of methods of information retrieval in textual data. The last part is dedicated to experiments validating the implementation and its possible improvements.
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Mobile Robot Localization Using Camera
Vaverka, Filip ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This thesis describes design and implementation of an approach to the mobile robot localization. The proposed method is based purely on images taken by a monocular camera. The described solution handles localization as an association problem and, therefore, falls in the category of topological localization methods. The method is based on a generative probabilistic model of the environment appearance. The proposed solution is capable to eliminate some of the difficulties which are common in traditional localization approaches.
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