National Repository of Grey Literature 28 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
Scalable addressing and routing protocol for ad-hoc networks
Drozdík, Tomáš ; Kratochvíl, Miroslav (advisor) ; Kliber, Filip (referee)
Ad hoc networks are dynamic networks with no pre-existing structure or centralized administration, where all the devices participate equally in the routing of packets. The lack of established structure complicates the effi- ciency of routing in such networks, and makes many address allocation meth- ods unsuitable. The thesis proposes a new routing and addressing protocol SARP, which works as a distance-vector routing protocol, but optimizes the sizes of the routing tables on the individual nodes by automatically approxi- mating the routes into groups where possible. Most importantly, SARP does not require any pre-established network structure nor unique router identi- fiers, and works only by exchanging the entries in routing tables. We show that SARP is a scalable routing protocol on networks where the addresses can be grouped well. Additionally, we show that SARP can, to some extent, use the reduced information for successful address assignment. However, a fully working address assignment in general settings will require further re- search in methods to globally detect address collisions without unique router identification. 1
GPU-accelerated Mahalanobis-average hierarchical clustering
Šmelko, Adam ; Kratochvíl, Miroslav (advisor) ; Hric, Jan (referee)
Hierarchical clustering algorithms are common tools for simplifying, exploring and analyzing datasets in many areas of research. For flow cytometry, a specific variant of agglomerative clustering has been proposed, that uses cluster linkage based on Mahalanobis distance to produce results better suited for the domain. Applicability of this clustering algorithm is currently limited by its relatively high computational complexity, which does not allow it to scale to common cytometry datasets. This thesis describes a specialized, GPU-accelerated version of the Mahalanobis-average linked hierarchical clustering, which improves the algorithm performance by several orders of magnitude, thus allowing it to scale to much larger datasets. The thesis provides an overview of current hierarchical clustering algorithms, and details the construction of the variant used on GPU. The result is benchmarked on publicly available high-dimensional data from mass cytometry.
Firmware for CzechLight optical measurement and calibration device
Oboňová, Ivona ; Kratochvíl, Miroslav (advisor) ; Aschenbrenner, Vojtěch (referee)
The goal of this thesis is to implement firmware for the Optical Measurement and Calibration Device, which was designed and constructed in CESNET. The purpose of the device is to simplify the calibration of various fibre-optical networking devices, used in CESNET infrastructure. The thesis includes an overview of the internal structure and communication interfaces within the device, which is than used for designing and implementing the firmware. The results are demonstrated on realistic hardware, by running the measurement on an existing optical component. The produced firmware will serve as a basis for the development of more advanced devices in CESNET.
Predicting novel drug-target interactions via deep learning techniques
Frey, Adam ; Peška, Ladislav (advisor) ; Kratochvíl, Miroslav (referee)
Adam Frey Aim of this work was to develop a machine-learning model for a prediction of drug-target interactions. Inspired by previous state-of-the-art approaches, the work focuses on collaborative filtering methods and deep learning neural network models. The goal of improving upon the previous work was achieved using a series of improvements of a basic latent matrix factorization algorithm on the relevant dataset. The small amount of data currently seems like the bottleneck for utilizing more sophisticated deep learning methods. As such hybrid approaches for recommendation systems can prove to be interesting next step due to their effective utilization of multiple data sources.
Heuristics for paths in maps
Kudláčková, Lada ; Mareš, Martin (advisor) ; Kratochvíl, Miroslav (referee)
The content of the thesis is a description of heuristic procedures, which are used to find the shortest paths in the graphs and verify their effec- tiveness on the actual data. It deals with heuristics for Dijkstra's algorithm, especially the A* algorithm, which uses a lower distance-to-target estimate. Heuristics are implemented and tested on the road network of the Czech Re- public. 1
Procedural Content Generation for Video Games using Open Data
Tuncel, Merve ; Gemrot, Jakub (advisor) ; Kratochvíl, Miroslav (referee)
Games get boring when they start repeating themselves and do not offer players new content. Procedural content generation (PCG) is increasingly used to generate this content. PCG-based game design decreases the need to have a human designer or a writer to generate the content. Algorithmic creation of game content can augment the creativity of human designers and this makes it possible small so-called indie teams to create the content for their game without the big resources. In this work, the field of PCG is introduced. Application of PCG is shown through a mobile game implementation. The implementation details of the mobile game Rush Hour will be presented that makes use of Foursquare, Twitter and Mapbox APIs, which eases the content creation using open data as the input of PCG.
Implementation of a tone mapping operator for scotopic viewing conditions
Safko, Martin ; Wilkie, Alexander (advisor) ; Kratochvíl, Miroslav (referee)
Creating night-time images and movies that look plausible has been a problem in the industry since the creation of camera. To capture an image we need enough light to create a measurable quantity on a camera sensor. For this reason, shooting at night was not possible until sensors sensitive enough were developed and even then the captured images do not look realistic. Movie industry circumvent these issues by manually color correcting the footage in post-production. We implement an algorithm presented in a 2011 SIGGRAPH paper capable of solving this problem in a psycho-physically plausible and consistent way for spectral images and also augment it by a technique taken from a paper by INRIA. 1
High-performance inverted index database
Javorský, Dávid ; Kratochvíl, Miroslav (advisor) ; Peška, Ladislav (referee)
The goal of this thesis is to implement an inverted-index database software that provides improvements in handling raw non-textual data, which is beneficial for several areas of research. The main internal structures of the library are designed to be cache-oblivious, also aiming to reduce the size of stored data. This thesis includes an overview of common inverted index implementation methods and describes retaled structures in a suitable cache-based model. This resulted in improvements of compression ratio, and performance similar to currently available highly optimized databases. The benchmark conducted on cheminformatic data has shown that the resulting software is applicable as an immediate, efficient replacement of the storage back-ends of specialized molecule databases.
iOS emulator for Windows
Joneš, Jan ; Kratochvíl, Miroslav (advisor) ; Kofroň, Jan (referee)
The goal of this thesis is to create a program for Windows that takes a compiled iOS application and emulates it. However, only the application's machine code is emulated, whereas system functionality originally provided by iOS is translated to an equivalent functionality available on Windows. Hence, the emulated application employs a user interface and behavior that feel native on the target platform. At compile time, custom machine code is generated that supports the translation at runtime. The thesis also describes iOS's internals that the emulator needs to imitate and discusses different approaches to cross-platform development. 1
Traffic sign classification by deep learning
Harmanec, Adam ; Blažek, Jan (advisor) ; Kratochvíl, Miroslav (referee)
Classification of road signs has been studied for many years and very promising results have been achieved. We present the analysis of used data sets as very limited for real case classification. In this thesis we analyse publicly available data sets and by merging and extending them, we create a wider and more comprehensive data set applicable in the Czech Republic. Finally, we propose a new convolutional neural network architecture and test it along with several preprocessing techniques on the new data set reaching accuracy of over 99%.

National Repository of Grey Literature : 28 records found   1 - 10nextend  jump to record:
See also: similar author names
1 Kratochvíl, M.
2 Kratochvíl, Marek
16 Kratochvíl, Martin
1 Kratochvíl, Martin Dominik
3 Kratochvíl, Matouš
10 Kratochvíl, Matěj
1 Kratochvíl, Maxim
13 Kratochvíl, Michal
1 Kratochvíl, Milan
1 Kratochvíl, Miloslav
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