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The relation of emotions and intonation curves
Gavlasová, Radka ; Smékal, Zdeněk (referee) ; Tučková,, Jana (advisor)
This thesis deals with intonation curves and their relation to human emotions. Besides the theoretical part where you can learn about speech production, signal processing and psychological distribution of emotions, there is also a unique database recorded with the help of two professional actors. The main goal of this thesis is to classify created data using artificial neural networks into four classes. Those classes are anger, joy, boredom and sadness. The practical part was implemented in a programming platform called Matlab using Classification Learner app. Features used for this method were variations of fundamental frequency and MFCC. The results were compared with a listening survey so that it could be determined whether the results provided by neural network are relevant to some kind of a human factor. Success rate of the trained models reached 82 %, new data testing reached 75 %. Listening survey confirmed that the results correspond to the assumption of human perception. Better success rate would be accomplished by using a bigger set of higher quality data.
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Classification of sleep phases using polysomnographic data
Králík, Martin ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
Aim of this thesis is the classification of polysomnographic data. The first part of the thesis is a review of mentioned topic and also the statistical analysis of classification features calculated from real EEG, EOG and EMG for evaluating of the features suitability for sleep stages scoring. The second part is focused on the automatic classification of the data using artificial neural networks. All the results are presented and discussed.
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The Use of Means of Artificial Intelligence for the Decision Making Support on Stock Market
Vaško, Jan ; Kříž, Jiří (referee) ; Dostál, Petr (advisor)
Diploma thesis deals with analyzing the possibility of using artificial intelligence, specifically artificial neural networks and fuzzy logic, on the capital markets as a tool to support decision making in business. The Matlab software is used for this purpose. The work is divided into three parts. The first part deals with theoretical knowledge, brief description of the current situationin is covered in a second part and the theoretical solutions are applied to the system in the third section.
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State of the art speech features used during the Parkinson disease diagnosis
Bílý, Ondřej ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.
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Object Detection and Recognition in Image
Muzikářová, Michaela ; Hradiš, Michal (referee) ; Zemčík, Pavel (advisor)
This bachelor's thesis deals with design and implementation of client-server application for object recognition with the use of existing mobile application. Theoretical part describes the differences between human and computer vision, followed by information about object detection and recognition with selected methods. The next section provides a detailed overview of artificial neural networks, which were used for this work, with their qualities for object recognition. Following part examines selected mobile applications for object recognition, followed by existing frameworks and libraries with focus on artificial neural networks. Among these, Caffe Framework was selected for the work. The next section illustrates the progress of design and implementation and describes the system, along with experiments and dataset used to prove its functionality.
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Movement Prediction of Wireless Nodes in Mobile Ad Hoc Networks (MANETS)
Makhlouf, Nermin ; Šimák, Boris (referee) ; Slavíček, Karel (referee) ; Koton, Jaroslav (advisor)
Rychlý vývoj v oblasti mobilní informatiky vyústil v nový, alternativní způsob mobilní komunikace, v němž mobilní uzly tvoří samoorganizující se bezdrátovou síť, jíž se říká mobilní síť ad hoc (Mobile Ad hoc Network, MANET). Specifické vlastnosti sítí MANET stavějí návrh síťového protokolu před řadu problémů na všech vrstvách protokolové sady . Příčinou jsou nepředvídatelné změny topologie a mobilní povaha těchto sítí. Nástrojem, který řeší problémy plynoucí z mobility uzlů, je predikce budoucích změn v topologii sítě. To má zásadní význam pro různé úlohy jako přesměrování. Tato disertační práce se zabývá dvěma metodami predikce mobility pro sítě MANET. První metoda se nazývá „predikce mobility s využitím virtuální mapy“ (mobility prediction using virtual map) a předpokládá, že každý uzel si dokáže vybudovat svou virtuální mapu v závislosti na svém umístění v průběhu času. Vyvinutý predikční algoritmus byl implementován do síťového simulátoru NS-2, aby jej bylo možné vyhodnotit. V této práci zkoumám stávající modely mobility a způsob, jakým v nich lze aplikovat tuto metodu predikce. Simulace sledují zlepšení výkonnosti, co se týče průměrného zpoždění na bázi end-to-end, poměru doručených paketů a propustnosti sítě. Navržený koncept predikce byl implementován pomocí směrovacího protokolu AODV(Ad Hoc On-Demand Distance Vector). Pro druhou metodu jsem vyvinula umělou neuronovou síť pro predikci pohybů v sítích MANET. Model pro predikci mobility vznikl na základě dat shromážděných ze vzorců umístění. K učení či trénování ANN byl využit bayesovský přístup. Ten byl implementován v softwaru pro trénování bayesovských neuronových sítí s názvem Model Manager. Nejlepším způsobem hodnocení závěrečného modelu je provedení predikcí a jejich srovnání s cílovými daty. Predikce vznikají na základě 50 vzorců jako vstupních proměnných. Dosažené výsledky prezentované s diskutované v práci se vyznačují zlepšením zásadních parametrů komunikační sítě, jako jsou propustnost, zpoždění, Poměr doručených paketů, až o 30% v porovnání s klasickým směrovacím protokolem AODV, kde není implementován predikční model.
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Classification of ECG by artificial neural networks
Loviška, David ; Vítek, Martin (referee) ; Hrubeš, Jan (advisor)
The aim of project with name Classification ECG by artificial neural networks is simplify and speed up working a doctor. That reaches created program that the is capable simply and almost at once classify EKG signal using artificial neuronal nets. Created program will give to the doctor basic information about used electrocardiogram, as are time period and amplitude signal in single surveyed sections. Subsequently will program warn doctor about abnormalities from normal. Part of program is also graphic window with painted signal and on him in color points and partitions marked by program behind special. In next phase program alone classifies gained data and designating without doctor diagnose that doctor can evaluate and in case agreeable it sign and place for true diagnose patient. This program is also fit for data reading from bigger of the number of hours as far as days. It is concerned primarily Holter ECG monitoring.
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