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Experimental studies of Müllerian mimetic complexes
Jůnová, Lenka ; Exnerová, Alice (advisor) ; Hotová Svádová, Kateřina (referee)
Mimicry is one of strategies which provides protection for the prey from predator attack. There are several kinds of mimetic relationships, one of them is Müllerian mimicry, i.e. mutualistic relationship between two unpalatable prey species. If there are more animal species in this relationship, it is called Müllerian mimetic complex. Relationships in mimetic complexes are affected by many factors like defence level and aposematic signals resemblance of particular species, their different frequencies, alternative prey, prey community structure or predator species and their experience level. These factors determine whether the relationship of the species involved is mutualistic or parasitic. They also affect predators behaviour and their learning rate, memory and generalisation between mimetic species. Powered by TCPDF (www.tcpdf.org)
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Plant-pollinator relationships in isolated ecosystems and ecosystems with extreme fluctuations in climatic conditions
Pinc, Jan ; Vlasáková, Blanka (advisor) ; Vazačová, Kristýna (referee)
In extreme and isolated ecosystems, organisms are often specifically adapted to local abiotic conditions due to relatively fast-moving diversification and strong selection pressure. Their mutualistic relationships are very often quite unusual and unique. Plants grow in populations with a limited number of individuals. They are exposed to strong selection pressure for outcrossing to minimize the loss of genetic variability due to inbreeding, which is common in small populations. Maximizing outcrossing usually results in various modifications of pollination systems. These modifications are affected by the insufficient number of potential pollinators and by their irregular occurrence. The aim of this paper is to summarize the typical features of pollination systems in extreme and isolated ecosystems and to clarify the conditions that led to their creation.
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Simplification the Digital Terrain Model based on TIN representation
Pancová, Iveta ; Bayer, Tomáš (advisor) ; Potůčková, Markéta (referee)
The Generalization of the Digital Terrain Model Based on the TIN Abstract This diploma thesis deals with the up to now way and the possibilities of the digital terrain model generalization based on the TIN (the triangulate irregular network). New suitable way of the generalization of the digital terrain model procured from laser scanning data is proposed on the base of the existing generalization methods designated for digital models. Laser scanning data is characterized by a high areal density so the basic requirement is computing speed, maintaining the terrain features, such as a ridge, valley, steep hill, saddle, depression … and so on. The proposed algorithm is compared with the results of suggested algorithms and results from the generalization by the geographic software, such as Atlas DMT and ArcGIS.
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Commented translation: Guide de manoeuvre (Éric Tabarly, Luçon: Éditions Le Télégramme, 2008)
Kofroňová, Zuzana ; Belisová, Šárka (advisor) ; Šotolová, Jovanka (referee)
The thesis consists of two main parts- translation and its commentary. It is a translation of 3 chapters of a French yachtsmen guide Guide de manoeuvre (Wharf mooring or double moor to another boat, Anchoring and Man overboard maneuver). In my translation, I tried to preserve the major function of the original text - to inform and instruct the reader. The second part of the work is a commentary of the translation which is focused on translation analysis of the original text, typology of translation problems and the methods of the translation. In the translation analysis I describe the original text with the factors affecting the text. Then I focus on the translation problems I dealt with and how they were solved. In the conclusion, I describe the translations methods I used.
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Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems
Somol, Petr ; Grim, Jiří
The paper addresses the problem of making dependency-aware feature selection feasible in pattern recognition problems of very high dimensionality. The idea of individually best ranking is generalized to evaluate the contextual quality of each feature in a series of randomly generated feature subsets. Each random subset is evaluated by a criterion function of arbitrary choice (permitting functions of high complexity). Eventually, the novel dependency-aware feature rank is computed, expressing the average benefit of including a feature into feature subsets. The method is efficient and generalizes well especially in very-high-dimensional problems, where traditional context-aware feature selection methods fail due to prohibitive computational complexity or to over-fitting. The method is shown well capable of over-performing the commonly applied individual ranking which ignores important contextual information contained in data.
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Zobecněný váleček
Haindl, Michal ; Hatka, Martin
This paper describes a generalization of our previously published simple roller method for seamless enlargement of colour textures such as natural bidirectional texture functions (BTF) that realistically represent appearance of given material surfaces. The generalized roller allows automatic detection of major texture periodicity directions which do not need to be aligned with coordinate axes. The roller texture synthesis method is based on the overlapping tiling and subsequent minimum error boundary cut. One or several optimal double toroidal BTF patches are seamlessly repeated during the synthesis step. While the method allows only moderate texture compression it is extremely fast due to complete separation of the analytical step of the algorithm from the texture synthesis part. The method is universal and easily implementable in a graphical hardware for purpose of real-time rendering of any type of static or dynamic textures.
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