[pt] Independente da metodologia adotada para realizar análise de fácies sísmicas, a segmentação temporal e espacial da região do reservatório deve ser realizada cuidadosamente. A confiança no resultado da interpretação depende da complexidade do sistema geológico, da qualidade dos dados sísmicos, e da experiência do intérprete. Portanto, qualquer erro de interpretação pode levar a resultados incoerentes. Especialmente, a análise de fácies sísmicas utilizando formas de onda do sinal na região do reservatório é bastante sensível a ruídos de interpretação. Sabe-se que variações no conteúdo de freqüência dos traços sísmicos podem estar associadas às informações de refletividade da sub-superfície. Conseqüentemente, análises conjuntas em tempo - freqüência podem levar a formas não convencionais para a caracterização de reservatórios. Especificamente, esta tese propõe o uso das propriedades em tempo - freqüência, obtidas através do algoritmo de matching pursuit, e das singularidades detectadas e caracterizadas via transformada wavelet, como ferramenta para detecção de eventos sísmicos e para análise não supervisionada de fácies sísmicas quando associadas ao agrupamento dos mapas auto organizáveis de Kohonen. / [en] Independent of the adopted methodology to perform the seismic facies analysis, the geological oriented spatial and temporal segmentation of the reservoir region should be carefully done. Depending on the complexity of the reservoir system, seismic data quality, and the experience of the interpreter, the level of confidence in an interpretation can vary from very high to very low. Therefore, any interpretation error could lead to wrong or noisy results. Specially, when using seismic trace shapes, defined by the values of the seismic samples along each segmented trace, as the seismic input attributes to the chosen seismic facies algorithm. These facies analysis artifacts are introduced because seismic waveform in the reservoir delimited area changes quickly as a function of the interpretation, then waveforms with almost the same shape could be assigned to different classes due only to their different phases. It is known that variations of the frequency content of a seismic trace with time carry information about the properties of the subsurface reflectivity sequence. Consequently, seismic trace time- frequency analyses could provide an unconventional way to reservoir characterization. Specifically, in this work we propose to use the time-frequency properties of the atoms obtained after the matching pursuit signal representation and the singularities identified by wavelet transform, jointly with Self Organizing Maps as an unsupervised seismic facies analyses system.

The radar transmitter identification problem involves the identification of a specific radar transmitter based on a received pulse. The radar transmitters are of identical make and model. This makes the problem challenging since the differences between radars of identical make and model will be solely due to component tolerances and variation. Radar pulses also vary in time and frequency which means that the problem is non-stationary. Because of this fact, time-frequency representations such as shift-invariant quadratic time-frequency representations (Cohen’s class) and wavelets were used. A model for a radar transmitter was developed. This consisted of an analytical solution to a pulse-forming network and a linear model of an oscillator. Three signal classification algorithms were developed. A signal classifier was developed that used a radially Gaussian Cohen’s class transform. This time-frequency representation was refined to increase the classification accuracy. The classification was performed with a support vector machine classifier. The second signal classifier used a wavelet packet transform to calculate the feature values. The classification was performed using a support vector machine. The third signal classifier also used the wavelet packet transform to calculate the feature values but used a Universum type classifier for classification. This classifier uses signals from the same domain to increase the classification accuracy. The classifiers were compared against each other on a cubic and exponential chirp test problem and the radar transmitter model. The classifier based on the Cohen’s class transform achieved the best classification accuracy. The classifier based on the wavelet packet transform achieved excellent results on an Electroencephalography (EEG) test dataset. The complexity of the wavelet packet classifier is significantly lower than the Cohen’s class classifier. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

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Most drill wear monitoring research found in the literature is based on conventional vibration technologies. However, these conventional approaches still have not attracted real interest from manufacturers for multiples of reasons: some of these techniques are not practical and use complicated Tool Condition Monitoring (TCM) systems with less value in industry. In addition, they are also prone to give spurious drill deterioration warnings in industrial environments. Therefore, drills are normally replaced at estimated preset intervals, sometimes long before they are worn or by expertise judgment. Two of the great problems in the implementation of these systems in drilling are: the poor signal-to-noise ratio and the lack of system-made sensors for drilling, as is prevalent in machining operations with straight edge cutters. In order to overcome the noise problems, many researchers recommend advanced and sophisticated signal processing while the work of Rehorn et al. (2005) advises the following possibilities to deal with the lack of commercial system-made sensors:  Some research should be directed towards developing some form of instrumented tool for drill operations.  Since the use of custom-made sensors is being ignored in drilling operations, effort should be focused on intelligent or innovative use of available sensor technology. It is expected that the latter could minimize implementation problems and allows an optimal drill utilization rate by means of modern and smart sensors. In addition to the accelerometer sensor commonly used in conventional methods, this work has considered two other sensor-based methods to monitor the drill wear indirectly. These methods entail the use of an instrumented drill with strain gauges to measure the torque and the use of an encoder to measure the Instantaneous Angular Speed (IAS). The signals from these sensors were analyzed using signal processing techniques such as, statistical parameters, Fast Fourier Transform (FFT), and a ii preliminary Time-Frequency (TF) analysis. A preliminary investigation has revealed that the use of a Regression Analysis (RA) based on a higher order polynomial function can very well follow and give prognosis of the development of the monitored parameters. The experimental investigation has revealed that all the above monitoring systems are sensitive to the deterioration of the drill condition. This work is however particularly concerned with the use of IAS on the spindle of the drill, compared to conventional monitoring systems for drill condition monitoring. This comparison reveals that the IAS approach can generate diagnostic information similar to vibration and torque measurements, without some of the instrumentation complications. This similitude seems to be logical, as it is well known that the increase of friction between the drill and workpiece due to wear increase the torque and consequently it should reduce or at least affect the spindle rotational speed. However, the use of a drill instrumented with a strain gauge is not practical, because of the inconvenience it causes on production machines. By contrast, the IAS could be measured quite easily by means of an encoder, a tachometer or some other smart rotational speed sensors. Thus, one could take advantage of advanced techniques in digital time interval analysis applied to a carrier signal from a multiple pulse per revolution encoder on the rotating shaft, to improve the analysis of chain pulses. As it will be shown in this dissertation, the encoder resolution does not sensibly affect the analysis. Therefore, one can easily replace encoders by any smart transducers that have become more popular in rotating machinery. Consequently, a non-contact transducer for example could effectively be used in on-line drill condition monitoring such as the use of lasers or time passage encoder-based systems. This work has gained from previous research performed in Tool Condition Monitoring TCM, and presents a sensor that is already available in the arsenal of sensors and could be an open door for a practical and reliable sensor in automated drilling. iii In conclusion, this dissertation strives to answer the following question: Which one of these methods could challenge the need from manufacturers by monitoring and diagnosing drill condition in a practical and reliable manner? Past research has sufficiently proved the weakness of conventional technologies in industry despite good results in the laboratory. In addition, delayed diagnosis due to time-consuming data processing is not beneficial for automated drilling, especially when the drill wears rapidly at the end of its life. No advanced signal processing is required for the proposed technique, as satisfactory results are obtained using common time domain signal processing methods. The recommended monitoring choice will definitely depend on the sensor that is practical and reliable in industry. / Dissertation (MEng)--University of Pretoria, 2012. / gm2013 / Mechanical and Aeronautical Engineering / MEng / Unrestricted

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We experimentally demonstrate time-frequency entangled photons with comb-like spectra via both bulk optical crystals and on-chip microring resonators and explore their characterization in both time and frequency domain using quantum state manipulation techniques. Our characterization of these quantum frequency combs involves the use of unbalanced Mach-Zehnder interferometers and electro-optic modulators for manipulation in time- and frequency-domain, respectively. By creating indistinguishable superposition states using these techniques, we are able to interfere states from various time- and frequency-bins, consequently proving time- and frequency-bin en-tanglement. Furthermore, our time-domain manipulations reveal pair-wise continuous time-energy entanglement that spans multiple frequency bins, while our utilization of electro-optic modulators to verify high-dimensional frequency-bin entanglement constitutes the proof of this phenomenon for a spontaneous four-wave mixing pro-cess. By doing so, we show the potential of these quantum frequency combs for high-dimensional quantum computing with frequency-encoded quantum states, as well as fully secure quantum communications via quantum key distribution by per-forming a nonlocal dispersion cancellation experiment. To show the potential of our entangled photons source for encoding quantum information in the frequency domain, we carry out a frequency-domain Hong-Ou-Mandel interference experiment by implementing a frequency beam splitter. Lastly, we use the high-dimensionality of our time-frequency entangled source in both time and frequency domain to implement deterministic high-dimensional controlled quantum gates, with the quantum information encoded in both the time and frequency degrees of freedom of a single photon. This novel demonstration of deterministic high-dimensional quantum gates paves the way for scalable optical quantum computation, as quantum circuits can be implemented with fewer resources and high success probability using this scheme.<div><br></div><div> </div>

This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.

Elektroencefalografický záznamový systém slouží k vyšetření mozkové aktivity. Na základě tohoto vyšetření lze stanovit diagnózu některých nemocí, například epilepsie. Účelem této práce bylo zpracování signálu z toho systému a vytvoření modelového signálu, který bude s reálným signálem porovnán. Uměle vytvořený signál vychází z Jansenova matematického modelu, který byl dále implementován v prostředí MATLAB a rozšířen ze základního modelu na komplexnější zahrnující nelinearity a model rozhraní elektroda – elektrolyt. Dále bylo provedeno měření signálů na EEG fantomu a následná identifikace parametrů naměřených signálu. V první fázi byly testovány jednoduché signály. Identifikace parametrů těchto signálů sloužila k validaci daného EEG fantomu. V druhé fázi bylo přistoupeno k testování EEG signálů navržených podle matematického Jansenova modelu. Analýza veškerých signálů zahrnuje mimo jiné časově frekvenční analýzu či ověření platnosti principu superpozice.

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The Cardiac Rhythm Management Division of St. Jude Medical specializes in the development of implantable cardioverter defibrillators that improve the quality of life for patients diagnosed with a variety of cardiac arrhythmias, especially for patients prone to sudden cardiac death. With the goal to improve detection of cardiac arrhythmias, this study explored the value in time-frequency analysis of intracardiac electrogram in four steps. The first two steps characterized, in the frequency domain, the waveforms that construct the cardiac cycle. The third step developed a new algorithm that putatively provides the least computationally expensive way to identifying cardiac waveforms in the frequency domain. Lastly, this novel approach to analyzing intracardiac electrogram was compared to a threshold crossing algorithm that strictly operates in the time domain and that is currently utilized by St. Jude Medical. The new algorithm demonstrated an equally effective method in identifying the QRS complex on the ventricular channel. The next steps in pursing time-frequency analysis of intracardiac electrogram include implementing the new algorithm on a testing platform that emulates the latest implantable cardioverter defibrillator manufactured by St. Jude Medical and pursuing a similar algorithm that can be employed on the atrial channel.