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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: Brain-Computer Interface (BCI) monitors brain activity by using signals such as EEG, EcOG, and MEG, and attempts to bridge the gap between thoughts and actions by providing control to physical devices that range from wheelchairs to computers. A crucial process for a BCI system is feature extraction, and many studies have been undertaken to find relevant information from a set of input signals. This thesis investigated feature extraction from EEG signals using two different approaches. Wavelet packet decomposition was used to extract information from the signals in their frequency domain, and cepstral analysis was used to search for relevant information in the cepstral domain. A BCI was implemented to evaluate the two approaches, and three classification techniques contributed to finding the effectiveness of each feature type. Data containing two-class motor imagery was used for testing, and the BCI was compared to some of the other systems currently available. Results indicate that both approaches investigated were effective in producing separable features, and, with further work, can be used for the classification of trials based on a paradigm exploiting motor imagery as a means of control. / AFRIKAANSE OPSOMMING: ’n Brein-Rekenaar Koppelvlak (BRK) monitor brein aktiwiteit deur gebruik te maak van seine soos EEG, EcOG, en MEG. Dit poog om die gaping tussen gedagtes en fisiese aksies te oorbrug deur beheer aan toestelle soos rolstoele en rekenaars te verskaf. ’n Noodsaaklike proses vir ’n BRK is die ontginning van toepaslike inligting uit inset-seine, wat kan help om tussen verskillende gedagtes te onderskei. Vele studies is al onderneem oor hoe om sulke inligting te vind. Hierdie tesis ondersoek die ontginning van kenmerk-vektore in EEG-seine deur twee verskillende benaderings. Die eerste hiervan is golfies pakkie ontleding, ’n metode wat gebruik word om die sein in die frekwensie gebied voor te stel. Die tweede benadering gebruik kepstrale analise en soek vir toepaslike inligting in die kepstrale domein. ’n BRK is geïmplementeer om beide metodes te evalueer. Die toetsdata wat gebruik is, het bestaan uit twee-klas motoriese verbeelde bewegings, en drie klassifikasie-tegnieke was gebruik om die doeltreffendheid van die twee metodes te evalueer. Die BRK is vergelyk met ander stelsels wat tans beskikbaar is, en resultate dui daarop dat beide metodes doeltreffend was. Met verdere navorsing besit hulle dus die potensiaal om gebruik te word in stelsels wat gebruik maak van motoriese verbeelde bewegings om fisiese toestelle te beheer.

QC 20100601

The aim of this master thesis is the comparison of selected methods and parameters for roller bearings diagnostics. Selected statistical parameters are kurtosis, crest factor, and parameter K(t). The other selected methods are envelope analysis, cepstral analysis, and ACEP method. These methods are implemented in LabVIEW software and compared based on noise resistance, computation speed, and overall capability of identifying roller bearing faults.

This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.