National Repository of Grey Literature 19 records found  1 - 10next  jump to record: Search took 0.00 seconds. 
Design of a system for detecting devices connected to the electrical network
Homola, Michal ; Kováč, Daniel (referee) ; Musil, Petr (advisor)
This master's thesis deals with the design of a system for detecting devices connected to power line network using the measurement of high-frequency noise through BPL (Broadband over Power Line) modems. The theoretical part involved familiarization with Power Line Communication (PLC), electromagnetic compatibility (EMC), impedance issues in PLC, and characteristics of noise in PLC. In the practical part, the suitability of the chosen PLC modems for the actual measurement was verified, followed by the measurement of temporal and spatial variability of network noise characteristics using these modems.For temporal variability, an experiment involving long-term measurement of refrigerator activity was conducted. For spatial variability, measurements were taken at multiple locations, with some locations serving as a training set and the remaining ones as a testing set. After selecting an appropriate machine learning model, the input data were feature engineered accordingly, followed by their evaluation.
Digital Biomarkers for Assessing Respiratory Disorders in Parkinson’s Disease
Kováč, Daniel ; Cvetler, Dominik
Respiratory disorders are a significant part of hypokineticdysarthria (HD) that affects patients with Parkinson’sdisease (PD). Still, their potential role in the objective assessmentof HD has not yet been fully explored, which is the primary goalof this study. Several respiratory features were designed andextracted from acoustic signals recorded during text reading.Based on these features, the XGBoost model was able to predictclinical test scores of phonorespiration with an estimated errorrate of 12.54%. Statistical analysis revealed that measuring respirationrate and quantifying signal fluctuations during inspirationhave great potential in the objective assessment of respiratorydisorders in patients with PD.
Remote and passive speech monitoring application
Klimeš, Jiří ; Mikulec, Marek (referee) ; Kováč, Daniel (advisor)
Motor speech disorders in patients with Parkinson’s disease collectively referred to as hypokinetic dysarthria, occur in the early phase of the disease. Language plays an essential role in classifying speakers into healthy and those with dysarthria. Author explains which aspects of speech are most often affected. Then explains how mobile applications work on the Android operating system, and if it is possible to use them in passive and distant speech monitoring. Then the topic of voice call recording is described and how is it possible to implement this solution. Such application is then designed and partially developed.
Security system for detecting devices connected to the electrical network
Macho, Radim ; Kováč, Daniel (referee) ; Musil, Petr (advisor)
This semester thesis defines the basic principles of detection of devices connected to the power grid with regard to their potential threat to the operation of critical systems. The detection will be performed using data obtained from PLC modems connected to the monitored electrical network. The data will then be processed using machine learning-based code.
Analysis of speech disorders in patients with a high risk of developing Lewy body diseases
Novotný, Kryštof ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
Lewy bodies diseases (one of the most common neurodegenerative disorders) have the same pathological basis, but the individual representatives differ in their clinical manifestations. Different diseases affect the mental or physical side of the patient to a greater or lesser extent. This work assumes that thanks to the acoustic analysis of speech, it is possible to distinguish individual diseases from one another, because the disorders of the cognitive and motor aspects of a patient reflect in speech in specific ways. The thesis aims to describe the clinical features of the main representatives of the Lewy bodies diseases, to investigate their impact on speech, to propose characterizing acoustic parameters and then to compare their discriminative power. Speech recordings from the CoBeN and preLBD databases are used as input data for the proposed algorithm. Descriptive statistics, Mann-Whitney U test, FDR correction and XGBoost machine learning model using stratified cross-validation and balanced accuracy are used for subsequent evaluation. The result are scripts for the automated calculation of speech parameters from the database and their evaluation. The results of the analysis prove that the selected diseases can really be distinguished from each other and from a healthy control based on the manifestations in speech, already in the prodromal stages.
Development of features quantifying respiratory dysfunctions in Parkinson’s disease patients
Cvetler, Dominik ; Mekyska, Jiří (referee) ; Kováč, Daniel (advisor)
In the beginning of the thesis, Parkinson's disease and hypokinetic dysarthria are briefly described, which have a negative effect on speech production and cause breathing problems during speech in sick patients. The aim of the thesis is to create an algorithm for automated detection of breaths and the design of parameters for the quantification of respiratory disorders in patients with Parkinson's disease. In the MATLAB environment, the recordings of the researched subjects were processed and an algorithm was created for the detection of breaths, which used the logistic regression method. Based on the predicted breaths, proposed parameters were extracted from the recordings, which were then statistically analyzed and compared in healthy controls and patients with Parkinson's disease. By using a machine learning model, it was possible to predict the clinical data of patients from the proposed parameters to a certain extent. The average accuracy of the model for predicting puffs was 0.85. Of the 14 proposed parameters, 6 were suitable for quantifying respiratory disorders associated with hypokinetic dysarthria. The result of the work is a functional algorithm for the automated detection of breaths in the speech signal and proposed parameters that could be useful for the quantification of respiratory disorders in patients with Parkinson's disease.
Sub-types of hypokinetic dysarthria in patients with moderete Parkinson's disease
Adamják, Adam ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
This final thesis deals with the research of Parkinson's disease, hypokinetic dysarthria, and acoustic and statistical analyses. Hypokinetic dysarthria is a speech disorder that is a typical manifestation of Parkinson's disease, a neurodegenerative disease that affects approximately 2% of the population over the age of 65. The aim of this work is to reveal the subtypes of hypokinetic dysarthria, based on clinical parameters, acoustic analysis, and statistical analysis. In the acoustic analysis, parameters that examine the area of phonation, prosody, articulation, and speech tempo have been implemented. Subsequently, a statistical analysis was processed, thanks to which it was possible to reveal the subtypes of hypokinetic dysarthria.
Design of a system for detecting devices connected to the electrical network
Homola, Michal ; Kováč, Daniel (referee) ; Musil, Petr (advisor)
This master thesis deals with the creation of a system for detecting devices connected to the power network using the measurement of high-frequency noise obtained via BPL modems. In the theoretical part, there was an introduction to the issue of PLC, electromagnetic compatibility of EMC, the issue of impedance in PLC and noise characteristics in PLC. In the practical part, measurement of noise characteristics for individual devices and the creation of a dataset took place. The dataset, which was then tested on five machine learning models selected for this task based on their properties. Finally, the suitability of each model for our application was evaluated.
The Drug problems and road safety in the České Budějovice region
KOVÁČ, Daniel
This bachelor thesis focuses on the issue of driving under the influence of drugs in the South of Bohemia. First chapter focuses on the history of drug use and basic concepts of drug issues which are related with bachelor thesis. Second chapter focuses on the individual drugs and their definition as addictive substances and the most common drugs in the Czech Republic. To understand the issue of driving under the influence it is very important to mention law and legislative measures which are connected with drug issues and also defined terms such as offence and crime. The last chaptor of the theoretical part of this bachelor thesis focuses on the Police of the Czech republic and their resources used to secure the intoxicated driver. The empirical chapters are oriented on issues of intoxicated drivers in the South of Bohemia and the issues of violating road safety. This part also contain research and evaluation. The aim of this work is a mapping road safety issues related to drugs and to find out if there are any patterns concerning the amount of incident involving drug using issues
Analysis of impact of noise in recordings on the automated detection of hypokinetic dysarthria
Havelková, Nikola ; Galáž, Zoltán (referee) ; Kováč, Daniel (advisor)
This thesis deals with the automated detection of hypokinetic dysarthria by analysing the influence of noise present in recordings. Appropriate single-channel methods, specifically the spectral subtraction and Kalman filter, are selected and implemented in the MATLAB R2022a to enhance speech. These methods are also used for noise-free recordings, to which additive white noise was added. Afterwards, the effectiveness of these methods is objectively evaluated by using signal-to-noise ratio values. After enhancing of speech, interferences are extracted from the recordings. The effect of the presence of noise, as well as its subsequent suppression by individual methods, is then evaluated by statistical analysis, specifically using the Kruskal-Wallis test and the post hoc Dunn’s test. The probability of distributing parameters of clean, noisy and enhanced recordings, for which the effect of noise is significant, according to statistical tests, are plotted using violin and box graphs. Finally, the classification was done by logistic regression with the help of machine learning, where the effect of the presence of noise and subsequent speech enhancement on automated detection of hypokinetic dysarthria was described according to the area values under the ROC curve.

National Repository of Grey Literature : 19 records found   1 - 10next  jump to record:
See also: similar author names
9 KOVÁČ, Daniel
5 KOVÁČ, David
1 Kovač, D.
1 Kovač, Dejan
1 Kováč, Dan
2 Kováč, Dominik
5 Kováč, Dávid
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