National Repository of Grey Literature 6 records found  Search took 0.01 seconds. 
Classification of organisms using nucleotides frequencies
Kremličková, Lenka ; Maděránková, Denisa (referee) ; Škutková, Helena (advisor)
This thesis deals with the classification of organisms based on the nucleotide frequency. Goal is to get acquainted with the problems of evaluating similarity of organisms on the basis of similarity of DNA sequences to design and implement in Matlab algorithm to classify organisms based on classical phylogenetic methods, basic and advanced numerical methods, and these compare methods with each other.
Evaluation of viability of cardiomyocytes
Kremličková, Lenka ; Čmiel, Vratislav (referee) ; Odstrčilík, Jan (advisor)
The aim of this diploma thesis is to get acquainted with the properties of image data and the principle of their capture. Literary research on methods of image segmentation in the area of cardiac tissue imaging and, last but not least, efforts to find methods for classification of dead cardiomyocytes and analysis of their viability. Dead cardiomyocytes were analyzed for their shape and similarity to the template created as a mean of dead cells. Another approach was the application of the method based on local binary characters and the computation of symptoms from a simple and associated histogram.
Classification of ECG signals using Cluster Analysis
Kremličková, Lenka ; Janoušek, Oto (referee) ; Klimek, Martin (advisor)
This thesis deals with methods of cluster analysis and their applications to short-term recording of the electrocardiogram (ECG). The work describes the physiological properties of the heart and cardiac muscle, as well as its electrical behavior and the formation and ECG. Part of this work is to describe the issue of cluster analysis of biosignals and distribution of clustering methods for hierarchical and non-hierarchical. Are described in detail agglomerative hierarchical method, which is outlined the procedure for the solution. Further described is the use of cluster analysis for classification of ECG and its application to the signals from the CSE. This work assesses which of clustering methods is applied to ECG signals appropriate.
Evaluation of viability of cardiomyocytes
Kremličková, Lenka ; Čmiel, Vratislav (referee) ; Odstrčilík, Jan (advisor)
The aim of this diploma thesis is to get acquainted with the properties of image data and the principle of their capture. Literary research on methods of image segmentation in the area of cardiac tissue imaging and, last but not least, efforts to find methods for classification of dead cardiomyocytes and analysis of their viability. Dead cardiomyocytes were analyzed for their shape and similarity to the template created as a mean of dead cells. Another approach was the application of the method based on local binary characters and the computation of symptoms from a simple and associated histogram.
Classification of organisms using nucleotides frequencies
Kremličková, Lenka ; Maděránková, Denisa (referee) ; Škutková, Helena (advisor)
This thesis deals with the classification of organisms based on the nucleotide frequency. Goal is to get acquainted with the problems of evaluating similarity of organisms on the basis of similarity of DNA sequences to design and implement in Matlab algorithm to classify organisms based on classical phylogenetic methods, basic and advanced numerical methods, and these compare methods with each other.
Classification of ECG signals using Cluster Analysis
Kremličková, Lenka ; Janoušek, Oto (referee) ; Klimek, Martin (advisor)
This thesis deals with methods of cluster analysis and their applications to short-term recording of the electrocardiogram (ECG). The work describes the physiological properties of the heart and cardiac muscle, as well as its electrical behavior and the formation and ECG. Part of this work is to describe the issue of cluster analysis of biosignals and distribution of clustering methods for hierarchical and non-hierarchical. Are described in detail agglomerative hierarchical method, which is outlined the procedure for the solution. Further described is the use of cluster analysis for classification of ECG and its application to the signals from the CSE. This work assesses which of clustering methods is applied to ECG signals appropriate.

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