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Application of neural networks for classification of T-wave alternations
Procházka, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with analysis of T-wave Alternans (TWA), periodical changes of T wave in ECG signal. Presence of these alternans may predict higher risk of sudden cardiac death. From the several possible ways of TWA classification, the training algorithms of self organizing maps are used in this thesis. Result of the thesis is a program, which in the first step detects QRS complexes in the signal. Then, in the next step, gained reference points are used for T-waves detection. Detected waves are represented by a vector of significant points, which is used as an input for artificial neural network. Final output of the program is a decision about presence of TWA in the signal and its rate.
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ECG signal classification
Smělý, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with classification of different types of time courses of ECG signals. Main objective was to recognize the normal cycles and several forms of arrhythmia and to classify the exact types of them. Classification has been done with usage of algorithms of Neural Networks in Matlab program, with its add-on (Neural Network Toolbox). The result of this thesis is application, which makes possible to load an ECG signal, pre-process it and classify its each cycle into five classes. Percentage results of this classification are in the conclusion of this thesis.
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Technical and economic comparison of electrical system designs of the hotel complex
Šedivý, Tomáš ; Jurák, Viktor (referee) ; Ptáček, Michal (advisor)
The aim of the bachelor thesis is to implement the designs of electrical power supply of a selected hotel complex and related technical and non-technical comparison. The subject is specifically to compare the power supply solution options through a centralized power centre, separate transformer stations located within the complex, for example in terms of the evaluation of active power losses on the line, investment and operating costs or in terms of space requirements, etc.
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Basic fingerprint liveness detection
Horák, Tomáš ; Smital, Lukáš (referee) ; Kašpar, Jakub (advisor)
This work focuses on recognizing fingerprints liveness based purely on software-methods evaluating symptoms from just one fingerprint image. At first in this work was described the issue of biometry as such, comparing the advantages and disadvantages of such systems. Next part deal with more detailed process of fingerprint biometry including papillary lines and overall fingerprints as such. In the next phase, the problems and utilization of both software and hardware methods are discussed, including principles of individual approaches. This part is followed by a selection of used fingertip symptoms. This is followed by the practical part and the LivDet 2011 database, which was used for finger recognition. In the practical part is also described the used neural network capturing minor differences in fingerprints according to 13 symptoms.
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Improving Consistency in Text Recognition Datasets
Tvarožný, Matúš ; Hradiš, Michal (referee) ; Kišš, Martin (advisor)
This work is concerned with increasing the consistency of datasets for text recognition. This paper describes the problems that cause the inconsistency and then presents solutions to eliminate it. The effect of the properties of the polygons defining the text line boundaries and hence how the modified version of the dataset, which is composed of ideal text line variants, affected the accuracy of the model is investigated. Further, the work focuses on detecting and then removing or modifying text lines whose ground truth transcription does not match the actual text they contain. Experimentation showed that removing the visual inconsistency on the training set did not have a significant effect on the trained model, but modifying the test set improved the OCR accuracy of the model by 1.1\% CER. By modifying the dataset so that it did not contain mutually inconsistent pairs of recognized text and the corresponding ground truth, the model improved by a maximum of only 0.2\% CER after re-training. The main finding of this work is, above all, the proven beneficial effect of removing inconsistencies on test suites, thanks to which it is possible to determine a more realistic error rate of the OCR model.
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OCR module for recognition of letters and numbers
Kapusta, Ján ; Přinosil, Jiří (referee) ; Sigmund, Milan (advisor)
This paper describes basic methods used for optical character recognition. It explains all procedures of recognition from adjustment of picture, processing, feature extracting to matching algorithms. It compares methods and algorithms for character recognition obtained graphically distorted or else modified image so-called „captcha“, used in present. Further it compares method based on invariant moments and neural network as final classifier and method based on correlation between normals and recognized characters.
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Explainable Convolutional Neural Networks
Kamenický, Daniel ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The aim of this work was to compare several methods for visualizing the features of each class on the input pixel layer of the CNN. Each method uses a different algorithm, based on gradients, to compute the resulting values. Using the implementation of each method, the resultant values of the methods are obtained by using the equation of energy concentration. The resultant values are presented in tables and graphs from which the success rate of the result of the work can be read. The difference between the methods and comparison of their results can be read from the work. This makes it possible to get an overview of gradient based visualization methods.
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Automatic Delivery Note Transcription
Necpál, Dávid ; Kišš, Martin (referee) ; Hradiš, Michal (advisor)
This bachelor thesis aims to create a system for automatic transcription of delivery notes - documents with a fixed structure. The solution is divided into two parts. The first part is table lines detection and subsequent detection and extraction of cells, that contain required data. The second part is handwritten numeric characters recognition in the images of the cutted cells. The resulting system can detect cells with the required data with 100 % accuracy with well-scanned delivery notes, while the success rate of numerical character recognition is more than 95 % for individual characters and more than 92 % for entire character sequences. The benefit of this work is a system for automatic transcription of delivery notes, which provides faster and easier otherwise lengthy rewriting of the contents of delivery notes to the information system in the retail. By using this system, the employee saves more than 50 % of the time on each delivery note.
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Improving Consistency in Text Recognition Datasets
Tvarožný, Matúš ; Hradiš, Michal (referee) ; Kišš, Martin (advisor)
This work is concerned with increasing the consistency of datasets for text recognition. This paper describes the problems that cause the inconsistency and then presents solutions to eliminate it. The effect of the properties of the polygons defining the text line boundaries and hence how the modified version of the dataset, which is composed of ideal text line variants, affected the accuracy of the model is investigated. Further, the work focuses on detecting and then removing or modifying text lines whose ground truth transcription does not match the actual text they contain. Experimentation showed that removing the visual inconsistency on the training set did not have a significant effect on the trained model, but modifying the test set improved the OCR accuracy of the model by 1.1\% CER. By modifying the dataset so that it did not contain mutually inconsistent pairs of recognized text and the corresponding ground truth, the model improved by a maximum of only 0.2\% CER after re-training. The main finding of this work is, above all, the proven beneficial effect of removing inconsistencies on test suites, thanks to which it is possible to determine a more realistic error rate of the OCR model.
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Technical and economic comparison of electrical system designs of the hotel complex
Šedivý, Tomáš ; Jurák, Viktor (referee) ; Ptáček, Michal (advisor)
The aim of the bachelor thesis is to implement the designs of electrical power supply of a selected hotel complex and related technical and non-technical comparison. The subject is specifically to compare the power supply solution options through a centralized power centre, separate transformer stations located within the complex, for example in terms of the evaluation of active power losses on the line, investment and operating costs or in terms of space requirements, etc.
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