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Potential calculation of mutual information from a time series
Hubr, Ivo ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
Mutual information is one of the factors used in traffic analysis and preparation phase space. Begin of this work deal with information theory, focusing on the calculation of mutual information. To calculate this parameter has been available for many algorithms which are analyzing in this final work. Two of the algorithms (Fraser-Swinney and calculation of mutual information using adaptive XY subdivision) are applied to the input data Rössler’ attractor, as shown in the output tables and graphs. The third consideration method is the computational Dinh-Tuan-Pham algorithm. The main goal of this work is a comparison of efficiency, speed and accuracy of the calculation of these algorithms.
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Calculation of speech rate
Galáž, Zoltán ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
his semestral thesis deals with a design of system for calculating the rate of speech. The sys-tem consists of several block, such as signal pre-processing block and its segmentation into smaller parts, block of the feature calculation, block of the feature vector quantization and finally block calculating the actual rate. The first step is a change of the input speech signal into a form suitable for the feature extraction. In next step these features are assigned to the calculated centroids. The change of centroid means change of phonemes. The system will record the following boundaries of fonems contained in speech and calculates its rate.
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Speech Enhancement Methods
Kukučka, Peter ; Mekyska, Jiří (referee) ; Hudec, Antonín (advisor)
Aim of this work is summarize some single-channel methods of speech enhancement. These methods are explained in this work: Basic Spectral Subtraction Method, Modified Spectral Subtraction, Multi-band Spectral subtraction, spectral subtraction MMSE and Wiener filtering. All methods are implemented. Preprocessing, voice activity detector and speech scores are explained in this paper, too.
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Analysis of Speech Signals for the Purpose of Neurological Disorders IT Diagnosis
Mekyska, Jiří ; Dostál, Otto (referee) ; Přibilová, Anna (referee) ; Smékal, Zdeněk (advisor)
This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Next, features that significantly correlate with subjective tests are found. These features can be used to estimate scores of different scales like Unified Parkinson’s Disease Rating Scale (UPDRS) or Mini–Mental State Examination (MMSE). A protocol of dysarthric speech acquisition is introduced in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, features based on modulation spectrum, inferior colliculus coefficients, bicepstrum, approximate and sample entropy, empirical mode decomposition and singular points are originally introduced in this work. All the designed techniques are integrated into the system concept in way that it can be implemented in a hospital and used for a research on Parkinson’s disease or its evaluation.
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Analysis of Parkinson's disease using segmental speech parameters
Mračko, Peter ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.
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Analysis of hand-written text of patients with neurological disorders
Galáž, Zoltán ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
The master‘s thesis deals with the analysis of the hand-written text. There is a design and a realization of a system for the purpose of diagnosing a Parkinson’s desease based on the analysis of hand-written text. The system consists from several modules and it is programmed in the programming environment of MATLAB. The first module provides pre-processing of the records to adjust records to the form suitable for the segmentation. Afterwards, the records are divided into those with signals onto the surface of the tablet and those with the signals above the surface of the tablet. In the next module the records are segmented by the two-phase metod of automatic segmentation.High-level featuresare calculated from the extracted features. The results of the statistical analysis are exported in the form suitable for the classification process. The classification is performed by the proposed model made in the programming environment of RapidMiner. The output of designed system is the trained model capable of automatic classification of the Parkinson’s disease by the analysis of the hand-written text.
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Analysis of hand-written text of patients with neurological disorders
Galáž, Zoltán ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
The master‘s thesis deals with the analysis of the hand-written text. There is a design and a realization of a system for the purpose of diagnosing a Parkinson’s desease based on the analysis of hand-written text. The system consists from several modules and it is programmed in the programming environment of MATLAB. The first module provides pre-processing of the records to adjust records to the form suitable for the segmentation. Afterwards, the records are divided into those with signals onto the surface of the tablet and those with the signals above the surface of the tablet. In the next module the records are segmented by the two-phase metod of automatic segmentation.High-level featuresare calculated from the extracted features. The results of the statistical analysis are exported in the form suitable for the classification process. The classification is performed by the proposed model made in the programming environment of RapidMiner. The output of designed system is the trained model capable of automatic classification of the Parkinson’s disease by the analysis of the hand-written text.
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Statistic Characteristic Function and its Usage for Digital Signal Processing
Mžourek, Zdeněk ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
Aim of this thesis is provide basic information about characteristic function used in statistic and compare its properties with the Fourier transform used in engineering applications. First part of this thesis is theoretical, there are discussed basic concepts, their properties and mutual relations. The second part is devoted to some possible applications, for example normality testing of data or utilization of the characteristic function in independent component analysis. The first chapter describes the introduction to probability theory for the unification of terminology and mentioned concepts will be used to demonstrate the interesting properties of characteristic function. The second chapter describes the Fourier transform, definition of characteristic function and their comparison. The second part of this text is devoted to applications the empirical characteristic function is analyzed as an estimate of the characteristic function of examined data. As an example of application is describe a simple test of normality. The last part deals with more advanced applications of characteristic function for methods such as independent component analysis.
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Analysis of the seismic velocity field
Kratochvíl, Pavel ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
Seismic velocities are an important prerequisite for seismic processing as a method for hydrocarbons accumulations detection. Seismic velocities are often displayed for mutual comparing, improvement checking, they are filtrated and recalculated for its different characteristic determination. This work deals with basic seismic propagation laws, the meaning of velocities in different stages of seismic processing and this theoretical background is followed by a proposition of method for calculating and displaying of stack velocities.
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Thermal image processing using the superresolution technique
Petrásek, Daniel ; Číka, Petr (referee) ; Mekyska, Jiří (advisor)
Thesis deals with problematic of raising digital image spacial resolution, mainly thermal image. There are mentioned methods of interpolation, panorama and super-resolution. Main topic of this thesis is super-resolution which is detailly described during the thesis. Finally there is a description of algorithm implementation and problems that may occur during the implemetation.
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