Um dos atributos fundamentais associados aos escoamentos multifásicos é a existência de estruturas características segundo as quais as diferentes fases do líquido escoam. O surgimento de uma dessas estruturas, conhecidas como configurações ou regimes de escoamento, é determinado pelas vazões e propriedades físicas dos componentes, além de parâmetros geométricos como diâmetro e inclinação do conduto. O desenvolvimento de metodologias de caracterização de regimes, bem como a caracterização e o diagnóstico da transição destes regimes de escoamento são de fundamental importância. Este trabalho utiliza a análise tempo-frequência da transformada de Gabor para caracterizar os regimes de escoamento horizontais gás-líquido intermitentes. Mais especificamente, o principal objetivo está em investigar a existência de sub-regimes dentro do regime intermitente, para tanto recorremos à covariância tempo-frequência da transformada de Gabor, que é capaz de detectar transições através da não-estacionaridade associada com as correspondentes transições. Testes experimentais foram conduzidos no circuito TALC em CEA-Grenoble e uma extensiva base de dados foi obtida, cobrindo diversos tipos de escoamento intermitente. Uma sonda de condutividade elétrica, consistindo de dois anéis de eletrodos montados junto à tubulação, produziu sinais dos quais a covariância tempo-frequência foi calculada através da correspondente transformada de Gabor. / One of the main features associated to multiphase flows is the existence of characteristic dynamic structures according to which the different phases of a mixture of immiscible fluids can flow. The manifestation of one of these structures, known a flow pattern or regime, is determined by the flow rates as well as by physical and geometrical properties of the fluids and piping. The development of flow pattern characterization and diagnostic methods, and the associated transitions in between, is of crucial importance for an efficient engineering of such phenomena. Time-frequency analysis based on the Gabor transform is used in this work to characterize horizontal air-water intermittent flow regimes. More specifically, our main objective is to reveal the existence of sub-regimes inside the intermittent regimes region with the help of the corresponding time-frequency covariance based on the Gabor transform, which is capable of detecting transitions by assessing the unstationarity associated with the corresponding transitions. Experimental tests were conducted at the TALC facility at CEA-Grenoble and an extensive database was obtained, covering several types of intermittent flow. A conductivity probe, consisting in two ring electrodes flush mounted to the pipe, delivered signals from which the time-frequency covariance were calculated from the corresponding Gabor transform.

In mobile communications, using multiple transmit and receive antennas has shown considerable improvement over single antenna systems. The performance increase can be characterized by more reliable throughput obtained through diversity and the higher achievable data rate through spatial multiplexing. The combination of multiple-input multiple-output (MIMO) wireless technology with the IEEE 802.16e-2005 (WiMAX) standard has been recognized as one of the most promising technologies with the advent of next generation broadband wireless communications. The dissertation introduces a performance evaluation of modern multi-antenna coding techniques on a MIMO-WiMAX platform developed to be capable of simulating space-selective, time-selective and frequency-selective fading conditions, which are known as triply-selective fading conditions. A new concatenated space-time-frequency low-density parity check (LDPC) code is proposed for high performance MIMO systems, where it is shown that the newly defined coding technique outperforms a more conventional approach by concatenating space-time blocks with LDPC codes. The analysis of the coding techniques in realistic mobile environments, as well as the proposed STFC-LDPC code, can form a set of newly defined codes, complementing the current coding schemes defined in the WiMAX standard. / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induced impulses are further modulated by various low frequency signal contents. The compounded effects of interferences, background noise and the combined modulation effects make it difficult to detect bearing faults. This is further complicated by the nonstationary nature of vibration signals due to speed variations in some cases, such as the bearings in a wind turbine. As such, the main challenges in the vibration-based bearing monitoring are how to address the modulation, noise, interference, and nonstationarity matters. Over the past few decades, considerable research activities have been carried out to deal with the first three issues. Recently, the nonstationarity matter has also attracted strong interests from both industry and academic community. Nevertheless, the existing techniques still have problems (deficiencies) as listed below: (1) The existing enveloping methods for bearing fault feature extraction are often adversely affected by multiple interferences. To eliminate the effect of interferences, the prefiltering is required, which is often parameter-dependent and knowledge-demanding. The selection of proper filter parameters is challenging and even more so in a time-varying environment. (2) Even though filters are properly designed, they are of little use in handling in-band noise and interferences which are also barriers for bearing fault detection, particularly for incipient bearing faults with weak signatures. (3) Conventional approaches for bearing fault detection under constant speed are no longer applicable to the variable speed case because such speed fluctuations may cause “smearing” of the discrete frequencies in the frequency representation. Most current methods for rotating machinery condition monitoring under time-varying speed require signal resampling based on the shaft rotating frequency. For the bearing case, the shaft rotating frequency is, however, often unavailable as it is coupled with the instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be determined without knowing the fault type. Additionally, the effectiveness of resampling-based methods is largely dependent on the accuracy of resampling procedure which, even if reliable, can complicate the entire fault detection process substantially. (4) Time-frequency analysis (TFA) has proved to be a powerful tool in analyzing nonstationary signal and moreover does not require resampling for bearing fault identification. However, the diffusion of time-frequency representation (TFR) along time and frequency axes caused by lack of energy concentration would handicap the application of the TFA. In fact, the reported TFA applications in bearing fault diagnosis are still very limited. To address the first two aforementioned problems, i.e., (1) and (2), for constant speed cases, two morphology-based methods are proposed to extract bearing fault feature without prefiltering. Another two methods are developed to specifically handle the remaining problems for the bearing fault detection under time-varying speed conditions. These methods are itemized as follows: (1) An efficient enveloping method based on signal Fractal Dimension (FD) for bearing fault feature extraction without prefiltering, (2) A signal decomposition technique based on oscillatory behaviors for noise reduction and interferences removal (including in-band ones), (3) A prefiltering-free and resampling-free approach for bearing fault diagnosis under variable speed condition via the joint application of FD-based envelope demodulation and generalized demodulation (GD), and (4) A combined dual-demodulation transform (DDT) and synchrosqueezing approach for TFR energy concentration level enhancement and bearing fault identification. With respect to constant speed cases, the FD-based enveloping method, where a short time Fractal dimension (STFD) transform is proposed, can suppress interferences and highlight the fault-induced impulsive signature by transforming the vibration signal into a STFD representation. Its effectiveness, however, deteriorates with the increased complexity of the interference frequencies, particularly for multiple interferences with high frequencies. As such, the second method, which isolates fault-induced transients from interferences and noise via oscillatory behavior analysis, is then developed to complement the FD-based enveloping approach. Both methods are independent of frequency information and free from prefiltering, hence eliminating the tedious process for filter parameter specification. The in-band vibration interferences can also be suppressed mainly by the second approach. For the nonstationary cases, a prefiltering-free and resampling-free strategy is developed via the joint application of STFD and GD, from which a resampling-free order spectrum can be derived. This order spectrum can effectively reveal not only the existence of a fault but also its location. However, the success of this method relies largely on an effective enveloping technique. To address this matter and at the same time to exploit the advantages of TFA in nonstationary signal analysis, a TFA technique, involving dual demodulations and an iterative process, is developed and innovatively applied to bearing fault identification. The proposed methods have been validated using both simulation and experimental data collected in our lab. The test results have shown that the first two methods can effectively extract fault signatures, remove the interferences (including in-band ones) without prefiltering, and detect fault types from vibration signals for constant speed cases. The last two have shown to be effective in detecting faults and discern fault types from vibration data collected under variable speed conditions without resampling and prefiltering.

The task of detecting and separating multiple radio-frequency signals in a wideband scenario has attracted much interest recently, especially from the cognitive radio community. Many successful approaches in this field have been based on machine learning and computer vision methods using the wideband signal spectrogram as an input feature. YOLO and R-CNN are deep learning-based object detection algorithms that have been used to obtain state-of-the-art results on several computer vision benchmark tests. In this work, YOLOv2 and Faster R-CNN are implemented, trained and tested, to solve the signal separation task. Previous signal separation research does not consider representations other than the spectrogram. Here, specific focus is placed on investigating different time-frequency representations based on the short-time Fourier transform. Results are presented in terms of traditional object detection metrics, with Faster R-CNN and YOLOv2 achieving mean average precision scores of up to 89.3% and 88.8% respectively. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2017. / Saab Grintek Defence / University of Pretoria / Electrical, Electronic and Computer Engineering / MEng (Computer Engineering) / Unrestricted

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The influx of unmanned aerial systems over the last decade has increased need for airspace awareness. Monitoring solutions such as drone detection, tracking, and classification become increasingly important to maintain compliance for regulatory and security purposes, as well as for recognizing aircraft that may not be so. Vision systems offer significant size, weight, power, and cost (SWaP-C) advantages, which motivates exploration of algorithms to further aid with monitoring performance. A method to classify aircraft using vision systems to measure their motion characteristics is explored. It builds on the assumption that at least continuous visual detection or at most visual tracking of an object of interest is already accomplished. Monocular vision is in part limited by range/scale ambiguity, where range and scale information of an object projected onto the image plane of a camera using a pin- hole model is generally lost. In an indirect effort to attempt to recover scale information via identity, classification of aircraft can aid in improvement of. These measured motion characteristics can then be used to classify the perceived object based on its unique motion profile over time, using signal classification techniques. The study is not limited to just unmanned aircraft, but includes full scale aircraft in the simulated dataset used to provide a representative set of aircraft scale and motion. / Doctor of Philosophy / The influx of small drones over the last decade has increased need for airspace awareness to ensure they do not become a nuisance when operated by unqualified or ill-intentioned personnel. Monitoring airspace around locations where drone usage would be unwanted or a security issue is increasingly necessary, especially for more range and endurance capable fixed wing (airplane) drones. This work presents a solution utilizing a single camera to address the classification part of fixed wing drone monitoring, as cameras are extremely common, generally cheap, information rich sensors. Once an aircraft of interest is detected, classifying it can provide additional information regarding its intentions. It can also help improve visual detection and tracking performance since classification can help change expectations of where and how the aircraft may continue to travel. Most existing visual classification works rely on features visible on the aircraft itself or its silhouette shape. This work discusses an approach to classification by characterizing visually perceived motion of an aircraft as it flies through the air. The study is not limited to just drones, but includes full scale aircraft in the simulated dataset used. Video of an airplane is used to extract motion from each frame. This motion is condensed to and expressed as a single time signal, that is then classified using a neural network trained to recognize audio samples using a time-frequency representation called a spectrogram. This transfer learning approach with Resnet based spectrogram classification is able to achieve 90.9% precision on the simulated test set used.

No / Extensive research has been performed to investigate effective techniques, including advanced sensors and new monitoring methods, to develop reliable condition monitoring systems for industrial applications. One promising approach to develop effective monitoring methods is the application of time-frequency analysis techniques to extract the crucial characteristics of the sensor signals. This paper investigates the effectiveness of a new time-frequency analysis method based on Empirical Model Decomposition and Hilbert transform for analyzing the nonstationary cutting force signal of the machining process. The advantage of EMD is its ability to adaptively decompose an arbitrary complicated time series into a set of components, called intrinsic mode functions (IMFs), which has particular physical meaning. By decomposing the time series into IMFs, it is flexible to perform the Hilbert transform to calculate the instantaneous frequencies and to generate effective time-frequency distributions called Hilbert spectra. Two effective approaches have been proposed in this paper for the effective detection of tool breakage. One approach is to identify the tool breakage in the Hilbert spectrum, and the other is to detect the tool breakage by means of the energies of the characteristic IMFs associated with characteristic frequencies of the milling process. The effectiveness of the proposed methods has been demonstrated by considerable experimental results. Experimental results show that (1) the relative significance of the energies associated with the characteristic frequencies of milling process in the Hilbert spectra indicates effectively the occurrence of tool breakage; (2) the IMFs are able to adaptively separate the characteristic frequencies. When tool breakage occurs the energies of the associated characteristic IMFs change in opposite directions, which is different from the effect of changes of the cutting conditions e.g. the depth of cut and spindle speed. Consequently, the proposed approach is not only able to effectively capture the significant information reflecting the tool condition, but also reduces the sensitivity to the effect of various uncertainties, and thus has good potential for industrial applications.

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Oscillatory states in the Electroencephalogram (EEG) reflect the rhythmic synchronous activation in large networks of neurons. Time-frequency methods quantify the spectral content of the EEG as a function of time. As such, they are well suited as tools for the study of spontaneous and induced changes in oscillatory states. We have used time-frequency techniques to analyze the flow of activity patterns between two strongly connected brain structures: the entorhinal cortex and the hippocampus, which are believed to be involved in information storage. EEG was recorded simultaneously from the entorhinal cortex and the hippocampus of behaving rats. During the recording, low-intensity trains of electrical pulses at frequencies between 1 and 40 Hz were applied to the olfactory (piriform) cortex. The piriform cortex projects to the entorhinal cortex, which then passes the signal on to the hippocampus. Several time-frequency methods, including the short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and multiple window (MW) time-frequency analysis (TFA), were used to analyse EEG signals. To monitor the signal transmission between the entorhinal cortex and hippocampus, the time-frequency coherence functions were used. The analysed results showed that stimulation-related power in both sites peaked near 15 Hz, but the coherence between the EEG signals recorded from these two sites increased monotonically with stimulation frequency. Among the time-frequency methods used, the STFT provided time-frequency distributions not only without cross-terms which were present in the WVD, but also with higher resolutions in both time and frequency than the MW-TFA. The STFT seems to be the most suitable time-frequency method to study the stimulation-induced signals presented in this thesis. The MW-TFA, which gives low bias and low variance estimations of the time-frequency distribution when only one realization of data is given, is suitable for stochastic and nonstationary signals such as spontaneous EEG. We also compared the performance of the MW-TFA using two different window functions: Slepian sequences and Hermite functions. By carefully matching the two window functions, we found no noticeable difference in time-frequency plane between them. / Thesis / Master of Engineering (ME)