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Demonstrational Program for IZU Course
Míšová, Miroslava ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This bachelor's thesis deals with development of new study aplications for course Fundamentals of Artificial Intelligence. These aplications are based on the older version of JavaApplet, which use features, that are no longer supported. Each applicatoin was made acording to an object-oriented paradigm and than implemented. Special care was taken in order for the UI to be intuitive and easy to use and also for the aplication to be able to be further developed.
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Systems for remote measurement in power engineering
Hudec, Lukáš ; Mlýnek, Petr (referee) ; Mišurec, Jiří (advisor)
The work deals with the measurement and management in power. Provides an introduction to the problems of remote meter reading, management, and describes the current situation in the field of modern technologies Smart metering and Smart grids. It also analyzed the issue of collection of networks and data collection from a large number of meters over a wide area. For the purpose of data transmission are described GPRS, PLC, DSL, ... Further, there are given options to streamline communication. This area is used hierarchical aggregation. Using k-means algorithm is a program designed to calculate the number of concentrators and their location in the group of meters. The finished program is written in Java. It has a graphical interface and shows how the calculation is conducted. To verify the results of the optimization program is given simulation model in OPNET Modeler tool. Audited results are described in the conclusion and can deduce that using the optimization program is to streamline communications.
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Knowledge Discovery in Multimedia Databases
Málik, Peter ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.
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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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A Classification Methods for Retinal Nerve Fibre Layer Analysis
Zapletal, Petr ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
This thesis is deal with classification for retinal nerve fibre layer. Texture features from six texture analysis methods are used for classification. All methods calculate feature vector from inputs images. This feature vector is characterized for every cluster (class). Classification is realized by three supervised learning algorithms and one unsupervised learning algorithm. The first testing algorithm is called Ho-Kashyap. The next is Bayess classifier NDDF (Normal Density Discriminant Function). The third is the Nearest Neighbor algorithm k-NN and the last tested classifier is algorithm K-means, which belongs to clustering. For better compactness of this thesis, three methods for selection of training patterns in supervised learning algorithms are implemented. The methods are based on Repeated Random Subsampling Cross Validation, K-Fold Cross Validation and Leave One Out Cross Validation algorithms. All algorithms are quantitatively compared in the sense of classication error evaluation.
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Statistical characteristics of the traffic flow microstructure
Apeltauer, Jiří ; Nagy,, Ivan (referee) ; Kumpošt,, Petr (referee) ; Holcner, Petr (advisor)
The actual traffic flow theory assumes interactions only between neighbouring vehicles within the traffic. This assumption is reasonable, but it is based on the possibilities of science and technology available decades ago, which are currently overcome. Obviously, in general, there is an interaction between vehicles at greater distances (or between multiple vehicles), but at the time, no procedure has been put forward to quantify the distance of this interaction. This work introdukce a method, which use mathematical statistics and precise measurement of time distances of individual vehicles, which allows to determine these interacting distances (between several vehicles) and its validation for narrow densities of traffic flow. It has been revealed that at high traffic flow densities there is an interaction between at least three consecutive vehicles and four and five vehicles at lower densities. Results could be applied in the development of new traffic flow models and its verification.
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Automatic Photography Categorization
Matuszek, Martin ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis deal with choosing methods, design and implementation of application, which is able of automatic categorization photos based on its content into predetermined groups. Main steps of categorization are described in greater detail. Finding and description of interesting points in image is implemented using SURF, creation of visual dictionary by k-means, mapping on the words through kd-tree structure. Own evaluation is made for categorization. It is described, how the selected steps were implemented with OpenCV and Qt libraries. And the results of runs of application with different settings are shown. And efforts to improve outcome, when the application can categorize right, but success is variable.
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Clustering of Protein Sequences Based on Primary Structure of Proteins
Jurásek, Petr ; Stryka, Lukáš (referee) ; Burgetová, Ivana (advisor)
This master's thesis consider clustering of protein sequences based on primary structure of proteins. Studies the protein sequences from they primary structure. Describes methods for similarities in the amino acid sequences of proteins, cluster analysis and clustering algorithms. This thesis presents concept of distance function based on similarity of protein sequences and implements clustering algorithms ANGES, k-means, k-medoids in Python programming language.
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Automatic Photography Categorization
Veľas, Martin ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis deals with content based automatic photo categorization. The aim of the work is to create an application, which is would be able to achieve sufficient precision and computation speed of categorization. Basic solution involves detection of interesting points, extraction of feature vectors, creation of visual codebook by clustering, using k-means algorithm and representing visual codebook by k-dimensional tree. Photography is represented by bag of words - histogram of presence of visual words in a particular photo. Support vector machines (SVM) was used in role of classifier. Afterwards the basic solution is enhanced by dividing picture into cells, which are processed separately, computing color correlograms for advanced image description, extraction of feature vectors in opponent color space and soft assignment of visual words to extracted feature vectors. The end of this thesis concerns to experiments of of above mentioned techniques and evaluation of the results of image categorization on their usage.
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Clustering of ECG cycles
Ředina, Richard ; Smíšek, Radovan (referee) ; Ronzhina, Marina (advisor)
The bachelor thesis explores the aplication of cluster analysis on diferent ECGs in order to create a reliable algorithm for detecting different QRS complexes. Algorithm comprises filtration, R-wave positions adjustment, model cycle creation and comparasion based on mean square error and correlation. Both, correlation and mean square error, become data for k-means clustering. The number of clusters is derived from silhouette values for diferent numbers of clusters.
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