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Automatic acquisition of values from measurement devices without communication interface
Dohnálek, Martin ; Čala, Martin (referee) ; Kunz, Jan (advisor)
This bachelor thesis deals with the matter of optical character recognition from displays of measurement devices without communication interface. This would allow carrying out automated experiments using cheaper or older gear, which is not endowed with means for direct connection to a computer. Input image necessary for the character recognition is acquired using a camera pointed at a display of the device. The recognition is afterwards performed on periodically captured image based on an already existing dataset for particular apparatus. The output of the algorithm is a file containing recognized values, units, and timestamps of the recognition. The tool for creating datasets was designed as well. The achieved speed of recognition (as fast as 34 ms per iteration) during practical testing confirmed the sufficient optimalization of OCR algorithm. On the other hand, the determined hit rate of recognition abiding specified conditions was nearly 100 %. Lastly, the resistance to misalignment of display and sensor plane was monitored. The OCR algorithm is resilient to horizontal tilt up to +/- 5° and vertical tilt up to +/- 20°.
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Extension of User Profiles for Targeted Advertising Purposes
Hadač, Filip ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis is devoted to designation and realisation of the extension of user profiles for improvement targeted advertising purposes. Web scraping is used for acquirement of new data information. Extracted data comes from two servers, ČSFD and Recepty. Data from ČSFD are film genres. Data from Recepty are categories of recepies. Streaming applications are used for processing of data and saving them to databases of user profiles. Preprocessing and machine learning classification algorithms are used for benefit evaluation of new informations for profiles in advertising campaigns. Evaluation of experiments shows that new informations have slight benefit in improvement advertising campaigns.
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Cluster analysis in the field of pathological speech signal processing
Čapek, Karel ; Mžourek, Zdeněk (referee) ; Galáž, Zoltán (advisor)
The bachelor thesis deals with the calculation of speech features that quantifies the degradation of speech production caused by the presence of certain speech pathology and the subsequent clasification of considered speech pathologies into several groups using the k-means algorithm. The purpose was to find the groups of pathologies that in spite of possible differences in the origin do affect phonation and articulation skills of the speakers and damage the quality of speech. The work uses the phonation of vowels "a" speech task as the most commonly used speech task in the field of pathological speech processing, because of its resistance to demographic and linguistic characteristics of the speakers. Furthermore, the preliminary analysis was applied to the featuresin order to select the features to best characterize the degradation of speech production. Finally, the selected features were used to find the resulting groups of pathologies using k-means algorithm.
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Detection of changes in the image
Čech, Jan ; Zemčík, Tomáš (referee) ; Richter, Miloslav (advisor)
The aim of this thesis is finding changes in two similar pictures made in different periods of time. This task belongs to the field of computer vision. To solve this problem different methods of computer vision are used. First the angle of scanning has to be found. Then pictures go through the proces of preprocessing, afterwards the segmentation and the determination of descriptors are carried out. Finally, the pictures are compared and the differences are pointed out.
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Preprocessing of ECG signals for detection of significant points
Kuběna, Zdeněk ; Kozumplík, Jiří (referee) ; Vítek, Martin (advisor)
Bachelor thesis Preprocessing of ECG signal for the detection of significant points is about the primary preprocessing of ECG signal, to allow the subsequent signal diagnostic. The main task of preprocessing is to suppress the artifacts of the ECG signal, that makes the further interpretation impossible. Because there is typically an additive mixture of useful signal and noise, the simplest preprocessing way is linear filtering using digital filters. This work describes the most common types of interference, which occurs during the ECG signal measuring. Then there is the issue of repression dismantled, and design of filters with their bank.
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Identification of emotional state using speech signal analysis
Navrátil, Michal ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
The diploma thesis deals with the analysis of human emotional states speaker by the help of analyse speech signals. The thesis has two parts. In the first part, the process of speech generating is described in addition to the description of the commonly used pre-processing methods such as denoising or preemphasis. The first part also deals with the major and minor prosody features, these features are: the fundamental frequency, energy, spectral features and time domain features such as the speech rate. The second part of this thesis deals with a task of emotion recognition from the speech signal. When we accumulate sufficient of the number of recordings emotive state will be able to rekognize emotive state with high probability. All project is prepared for use in real time. The last part of this thesis thesis contains description and results of the experiments made on a large number of speech records.
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Visualization of 3D data in biomedical applications
Karzel, Michal ; Harabiš, Vratislav (referee) ; Štohanzlová, Petra (advisor)
This thesis describes the basic principle of optical coherence tomography and prepro- cessing of the measured raw data. Preprocessing is focused mainly on noise filtration, removing artifacts, normalization, conversion and compression of raw data. In this way preprocessed data is saved in a *. PFRG file as ”preprocessed fringe data”. Those pre- processed data will be visualised by simply software, which support three methods of visualisation. Volume data represented by voxels. Reconstruction of volume by marching cube algorithm. Cuts through volume along X, Y and Z axis.
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Paralinguistic signals recognition in spoken dialogs
Mašek, Jan ; Míča, Ivan (referee) ; Atassi, Hicham (advisor)
This document describes the three methods for the detection and classification of paralinguistic expressions such as laughing and crying from usual speech by analysis of the audio signal. The database of records was originally designed for this purpose. When analyzing everyday dialogs, music might be included, so the database was extended by four new classes as speech, music, singing with music and usual speech with background music. Feature extraction, feature reduction and classification are common steps in recognizing for all three methods. Difference of the methods is given by classification process in detail. One classification of all six classes at once is proposed in the first method called straight approach. In the second method called decision tree oriented approach we are using five intuitive sub classifiers in the tree structure and the final method uses for classification emotion coupling approach. The best features were reduced by feature evaluation using F-ratio and GMM classifiers were used for the each classification part.
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Machine Learning Text Classifier for Short Texts Category Prediction
Drápela, Karel ; Křena, Bohuslav (referee) ; Šimková, Hana (advisor)
This thesis deals with categorization of short spam texts from SMS messages. First part summarizes current methods for text classification and~it's followed by description of several commonly used classifiers. In following chapters test data analysis, program implementation and results are described. The program is able to predict text categories based on predefined set of classes and also estimate classification accuracy on training data. For the two category types, that I designed, classifier reached accuracy of 82% and 92% . Both preprocessing and feature selection had a positive impact on resulting accuracy. It is possible to improve this accuracy further by removing portion of samples, which are difficult to classify. With 80\% recall it is possible to increase accuracy by 8-10%.
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