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A Library for Binary Decision Diagrams
Paulovčák, Martin ; Holík, Lukáš (referee) ; Lengál, Ondřej (advisor)
The aim of this thesis is to create an easy-to-use library that will provide the basic means for Boolean function manipulation based on six different variants of Binary Decision Diagrams - BDD, ZDD, CBDD, CZDD, TBDD, and ESRBDD. The library is implemented in the ISO C programming language, uses closed hashing, index-based node referencing, mark and sweep based garbage collector and diagram construction is based on classical depth-first traversal. The implemented variants of these diagrams were compared on benchmarks and although the optimal choice of decision diagram variant depends on given problem, in general TBDD proved to be the best choice in terms of the resulting graph size and also CPU time.
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Deep Learning for Object Detection
Pitoňák, Radoslav ; Dobeš, Petr (referee) ; Teuer, Lukáš (advisor)
This thesis analyzes different object detection methods which are based on deep neural networks. In the beginning, the convolutional neural networks are described and commonly used object detection methods are compared. In the following parts, the proposal and implementation of the object detection model trained on the specific dataset are described. In conclusion, the achieved results of this model are discussed and compared with the results of other methods.
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A Library for Binary Decision Diagrams
Paulovčák, Martin ; Holík, Lukáš (referee) ; Lengál, Ondřej (advisor)
The aim of this thesis is to create an easy-to-use library that will provide the basic means for Boolean function manipulation based on six different variants of Binary Decision Diagrams - BDD, ZDD, CBDD, CZDD, TBDD, and ESRBDD. The library is implemented in the ISO C programming language, uses closed hashing, index-based node referencing, mark and sweep based garbage collector and diagram construction is based on classical depth-first traversal. The implemented variants of these diagrams were compared on benchmarks and although the optimal choice of decision diagram variant depends on given problem, in general TBDD proved to be the best choice in terms of the resulting graph size and also CPU time.
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Deep Learning for Object Detection
Pitoňák, Radoslav ; Dobeš, Petr (referee) ; Teuer, Lukáš (advisor)
This thesis analyzes different object detection methods which are based on deep neural networks. In the beginning, the convolutional neural networks are described and commonly used object detection methods are compared. In the following parts, the proposal and implementation of the object detection model trained on the specific dataset are described. In conclusion, the achieved results of this model are discussed and compared with the results of other methods.
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Behaviour driven development
Vodička, Petr ; Buchalcevová, Alena (advisor) ; Pecinovský, Rudolf (referee)
This bachelor's thesis discusses the topic of an agile approach to software development -- Be-haviour Driven Development. The aim of this thesis is to acquaint the reader with the de-scribed methodology. Firstly from a theoretical perspective with emphasis on applying it in software projects and in collaboration with customers, secondly from a more practical view, where the thesis presents some of the tools that are used in this style of software development, namely the framework Cucumber JVM and specification by example. For this purpose, the thesis presents an example of a little software project, where the principles of Behaviour Driven Development were applied. As the Behaviour Driven Development is one of the agile approaches, the introductory part of this thesis briefly describes such approach and compares it with the traditional approach to create a context for the following theoretical and practical part.
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