Contributions à l'étude des communications numériques sur le réseau électrique à l'intérieur des bâtiments : modélisation du canal et optimisation du débit

In recent years, the electrical network has become an essential candidate for high-speed data transmission inside buildings. Many solutions are currently underway in order to optimize these technologies known under the name of in-home Power-Line Communications (PLC). Multiple-Input Multiple-Output (MIMO) technique has recently been transposed into power-line networks for which different signal feeding possibilities can be considered between phase, neutral and earth wires. In this thesis, we propose two original contributions to indoor broadband PLC. The first contribution concerns the MIMO-PLC channel modeling. Based on a Single-Input Single-Output (SISO) parametric channel model presented in the literature, we propose a MIMO one by considering a new parameter which characterizes the spatial correlation. The proposed model enables an accurate description of the spatial correlation of European MIMO PLC field measurements. The second contribution is related to the impulsive noise present in power-line networks which constitutes a major problem in communications systems. We propose an outage capacity approach in order to optimize the average data rate in Orthogonal Frequency Division Multiplexing (OFDM) systems affected by impulsive noise. First, we study the channel capacity as a function of a noise margin provided to the transmitted symbols. Then we determine the analytical expression of the outage probability of an OFDM symbol in terms of the noise margin, by studying in detail the interaction between the noise impulse and the symbol. Based on the two aforementioned relations, we deduce the outage capacity. Then we propose an approach that enables to maximize the average system data rate. Finally, we present the results in the particular case of indoor broadband PLC in the presence of impulsive noise.Au cours de ces dernières années, le réseau électrique est devenu un candidat incontournable pour la transmission de données à haut débit à l’intérieur des bâtiments. De nombreuses solutions sont actuellement à l’étude afin d’optimiser ces technologies connues sous le nom Courants Porteurs en Ligne (CPL) ou PLC (Power-Line Communications). La technique MIMO (Multiple-Input Multiple-Output) a été tout récemment transposée au réseau filaire électrique pour lequel différents modes d’alimentation peuvent être envisagés entre la phase, le neutre et la terre. Dans le cadre de cette thèse, nous proposons deux contributions originales à l’étude des communications numériques sur le réseau électrique à l’intérieur des bâtiments. La première contribution concerne la modélisation du canal MIMO-PLC. En repartant d’un modèle du canal paramétrique SISO (Single-Input Single-Output) connu dans la littérature, nous proposons un modèle du canal MIMO en considérant un nouveau paramètre caractérisant la corrélation spatiale. Le modèle proposé permet de représenter fidèlement la corrélation spatiale des mesures effectuées à l’échelle européenne. La deuxième contribution concerne le bruit impulsif présent sur le réseau électrique domestique qui constitue un problème majeur dans les systèmes de communications. Nous proposons une méthode basée sur la notion de capacité de coupure afin d’optimiser le débit moyen dans les systèmes OFDM (Orthogonal Frequency Division Multiplexing) soumis aux bruits impulsifs. D’abord, nous étudions la capacité du système en fonction d’une marge de bruit fournie aux symboles transmis. Ensuite, nous déterminons l’expression analytique de la probabilité de coupure (outage) d’un symbole OFDM en fonction de cette marge, en étudiant de manière détaillée l’interaction entre l’impulsion de bruit et le symbole. A partir de ces deux calculs, nous déduisons la capacité de coupure. Puis, nous proposons une approche qui maximise l’espérance mathématique du débit reçu. Finalement, nous présentons les résultats obtenus dans le cas particulier d’une transmission à haut débit sur PLC en présence de bruits impulsifs

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

Robust wireless sensor network for smart grid communication : modeling and performance evaluation

Our planet is gradually heading towards an energy famine due to growing population and industrialization. Hence, increasing electricity consumption and prices, diminishing fossil fuels and lack significantly in environment-friendliness due to their emission of greenhouse gasses, and inefficient usage of existing energy supplies have caused serious network congestion problems in many countries in recent years. In addition to this overstressed situation, nowadays, the electric power system is facing many challenges, such as high maintenance cost, aging equipment, lack of effective fault diagnostics, power supply reliability, etc., which further increase the possibility of system breakdown. Furthermore, the adaptation of the new renewable energy sources with the existing power plants to provide an alternative way for electricity production transformed it in a very large and complex scale, which increases new issues. To address these challenges, a new concept of next generation electric power system, called the "smart grid", has emerged in which Information and Communication Technologies (ICTs) are playing the key role. For a reliable smart grid, monitoring and control of power system parameters in the transmission and distribution segments are crucial. This necessitates the deployment of a robust communication network within the power grid. Traditionally, power grid communications are realized through wired communications, including power line communication (PLC). However, the cost of its installation might be expensive especially for remote control and monitoring applications. More recently, plenty of research interests have been drawn to the wireless communications for smart grid applications. In this regard, the most promising methods of smart grid monitoring explored in the literature is based on wireless sensor network (WSN). Indeed, the collaborative nature of WSN brings significant advantages over the traditional wireless networks, including low-cost, wider coverage, self-organization, and rapid deployment. Unfortunately, harsh and hostile electric power system environments pose great challenges in the reliability of sensor node communications because of strong RF interference and noise called impulsive noise. On account of the fundamental of WSN-based smart grid communications and the possible impacts of impulsive noise on the reliability of sensor node communications, this dissertation is supposed to further fill the lacking of the existing research outcomes. To be specific, the contributions of this dissertation can be summarized as three fold: (i) investigation and performance analysis of impulsive noise mitigation techniques for point-to-point single-carrier communication systems impaired by bursty impulsive noise; (ii) design and performance analysis of collaborative WSN for smart grid communication by considering the RF noise model in the designing process, a particular intension is given to how the time-correlation among the noise samples can be taken into account; (iii) optimal minimum mean square error (MMSE)estimation of physical phenomenon like temperature, current, voltage, etc., typically modeled by a Gaussian source in the presence of impulsive noise. In the first part, we compare and analyze the widely used non-linear methods such as clipping, blanking, and combined clipping-blanking to mitigate the noxious effects of bursty impulsive noise for point-to-point communication systems with low-density parity-check (LDPC) coded single-carrier transmission. While, the performance of these mitigation techniques are widely investigated for multi-carrier communication systems using orthogonal frequency division multiplexing (OFDM) transmission under the effect of memoryless impulsive noise, we note that OFDM is outperformed by its single-carrier counterpart when the impulses are very strong and/or they occur frequently, which likely exists in contemporary communication systems including smart grid communications. Likewise, the assumption of memoryless noise model is not valid for many communication scenarios. Moreover, we propose log-likelihood ratio (LLR)-based impulsive noise mitigation for the considered scenario. We show that the memory property of the noise can be exploited in the LLR calculation through maximum a posteriori (MAP) detection. In this context, provided simulation results highlight the superiority of the LLR-based mitigation scheme over the simple clipping/blanking schemes. The second contribution can be divided into two aspects: (i) we consider the performance analysis of a single-relay decode-and-forward (DF) cooperative relaying scheme over channels impaired by bursty impulsive noise. For this channel, the bit error rate (BER) performances of direct transmission and a DF relaying scheme using M-PSK modulation in the presence of Rayleigh fading with a MAP receiver are derived; (ii) as a continuation of single-relay collaborative WSN scheme, we propose a novel relay selection protocol for a multi-relay DF collaborative WSN taking into account the bursty impulsive noise. The proposed protocol chooses the N’th best relay considering both the channel gains and the states of the impulsive noise of the source-relay and relay-destination links. To analyze the performance of the proposed protocol, we first derive closed-form expressions for the probability density function (PDF) of the received SNR. Then, these PDFs are used to derive closed-form expressions for the BER and the outage probability. Finally, we also derive the asymptotic BER and outage expressions to quantify the diversity benefits. From the obtained results, it is seen that the proposed receivers based on the MAP detection criterion is the most suitable one for bursty impulsive noise environments as it has been designed according to the statistical behavior of the noise. Different from the aforementioned contributions, talked about the reliable detection of finite alphabets in the presence of bursty impulsive noise, in the thrid part, we investigate the optimal MMSE estimation for a scalar Gaussian source impaired by impulsive noise. In Chapter 5, the MMSE optimal Bayesian estimation for a scalar Gaussian source, in the presence of bursty impulsive noise is considered. On the other hand, in Chapter 6, we investigate the distributed estimation of a scalar Gaussian source in WSNs in the presence of Middleton class-A noise. From the obtained results we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian channel

Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Probabilistic Modelling of Classical and Quantum Systems

While probabilistic modelling has been widely used in the last decades, the quantitative prediction in stochastic modelling of real physical problems remains a great challenge and requires sophisticated mathematical models and advanced numerical algorithms. In this study, we developed the mathematical tools for solving three long-standing problems in Polymer Science and Quantum Measurement theory. The question, “Why kinetic models cannot reproduce experimental observations in Controlled Radical Polymerization (CRP)?” has been answered by introducing in the kinetic model a delay and treating CRP as a non-Markovian process. The efficient stochastic simulation (SS) approach allowing for an accurate description of CRP has been formulated, theoretically grounded and tested using experimental data and the less advanced SS algorithms. An accurate prediction of a morphology development in multi-phase polymers is vital for synthesis of new materials but still not feasible due to its complexity. We proposed a Population Balance Equations (PBE)-based model and derived a conceptually new and computationally tractable numerical approach for its solution in order to provide a systematic tool for a morphology prediction in composite polymers. Finally, we designed a stochastic simulation framework for continuous measurements performed on quantum systems of theoretical and experimental interest, which helped us to re-examine the “fuzzy continuous measurements” theory by Audretsch and Mensky (1997) and expose some of its deficiencies, while making amendments where necessary. All developed modelling approaches are general enough to be applied to the broad range of physical applications and thus ultimately to contribute to the understanding and prediction of complex chemical and physical processes.BES-2014-06864, MTM2013-46553-C3-1-

Probabilistic modelling of classical and quantum systems

147 p.While probabilistic modelling has been widely used in the last decades, the quantitative prediction in stochastic modelling of real physical problems remains a great challenge and requires sophisticated mathematical models and advanced numerical algoritms. In this study, we developed the mathematical tools for the quantitative prediction of three applications in Polymer Science and Quantum Measurements theory. In particular, we addressed a stochastic approach for the quantitative modelling of Controlled Radical Polymerization. Then, a Population Balance Equations based framework was derived for the on-the-fly prediction of Multi-phase Polymers Morphology. Finally, we designed a stochastic simulation framework for measurements performed on quantum systems, which helped us to re-examine the "continuous fuzzy measurements" theory by Audretsch and Mensky (1997)

Time domain classification of transient RFI

Since the emergence of radio astronomy as a field, it has been afflicted by radio frequency interference (RFI). RFI continues to present a problem despite increasingly sophisticated countermeasures developed over the decades. Due to technological improvements, radio telescopes have become more sensitive (for example, MeerKAT’s L-band receiver). Existing RFI has become more prominent as a result. At the same time, the prevalence of RFI-generating devices has increased as new technologies have been adopted by society. Many approaches have been developed for mitigating RFI, which are typically used in concert. New telescope arrays are often built far from human habitation in radio-quiet reserves. In South Africa, a radio-quiet reserve has been established in which several world class instruments are under construction. Despite the remote location of the reserve, careful attention is paid to the possibility of RFI. For example, some instruments will begin observations while others are still under construction. The infrastructure and equipment related to the construction work may increase the risk of RFI, especially transient RFI. A number of mitigation strategies have been employed, including the use of fixed and mobile RFI monitoring stations. Such stations operate independently of the main telescope arrays and continuously monitor a wide bandwidth in all directions. They are capable of recording spectra and high resolution time domain captures of transient RFI. Once detected, and if identified, an RFI source can be found and dealt with. The ability to identify the sources of detected RFI would be highly beneficial. Continuous wave intentional transmissions (telecommunication signals for example) are easily identified as they are required to adhere to allocated frequency bands. Transient RFI signals, however, are significantly more challenging to identify since they are generally broadband and highly intermittent. Transient RFI can be generated as a by-product of the normal operation of devices such as relays, AC machines and fluorescent lights, for example. Such devices may be present near radio telescope arrays as part of the infrastructure or equipment involved in the construction of new instruments. Other than contaminating observation data, transient RFI can also appear to have genuine astronomical origins. In one case, transient signals received from a microwave oven exhibited dispersion, suggesting a distant source. Therefore, the ability to identify transient RFI by source would be enormously valuable. Once identified, such sources may be removed or replaced where possible. Despite this need, there is a paucity of work on classifying transient RFI in the literature. This thesis focusses on the problem of identifying transient RFI by source in time domain data of the type captured by remote monitoring stations. Several novel approaches are explored in this thesis. If used with independent RFI monitoring stations, these approaches may aid in tracking down nearby RFI sources at a radio telescope array. They may also be useful for improving RFI flagging in data from radio telescopes themselves. Distinguishing between transient RFI and natural astronomical signals is likely to be an easier prospect than classifying transient RFI by source. Furthermore, these approaches may be better able to avoid excising genuine astronomical transients that nevertheless share some characteristics with RFI signals. The radio telescopes themselves are significantly more sensitive than RFI monitoring stations, and would thus be able to detect RFI sources more easily. However, terrestrial RFI would likely enter via sidelobes, tempering this advantage somewhat. In this thesis, transient RFI is first characterised, prior to classification by source. Labelled time-domain recordings of a number of transient RFI sources are acquired and statistically examined. Second, components analysis techniques are considered for feature selection. Cluster separation is analysed for principal components analysis (PCA) and kernel PCA, the latter proving most suitable. The effect of the supply voltage of certain RFI sources on cluster separation in the principal components domain is also explored. Several na¨ıve classification algorithms are tested, using kernel PCA for feature selection A more sophisticated dictionary-based approach is developed next. While there are variations in repeated recordings of the same RFI source, the signals tend to adhere to a common overarching structure. Full RFI signals are observed to consist of sequences of individual transients. An algorithm is presented to extract individual transients from full recordings, after which they are labelled using unsupervised clustering methods. This procedure results in a dictionary of archetypal transients, from which any full RFI sequence may be represented. Some approaches in Automated Speech Recognition (ASR) are similar: spoken words are divided into individual labelled phonemes. Representing RFI signals as sequences enables the use of hidden Markov models (HMMs) for identification. HMMs are well suited to sequence identification problems, and are known for their robustness to variation. For example, in ASR, HMMs are able to handle the variations in repeated utterances of the same word. When classifying the recorded RFI signals, good accuracy is achieved, improving on the results obtained using the more na¨ıve methods. Finally, a strategy involving deep learning techniques is explored. Recurrent neural networks and convolutional neural networks (CNNs) have shown great promise in a wide variety of classification tasks. Here, a model is developed that includes a pre-trained CNN layer followed by a bidirectional long short-term memory (BLSTM) layer. Special attention is paid to mitigating class imbalance when the model is used with individual transients extracted from full recordings. High classification accuracy is achieved, improving on the dictionary-based approach and the other na¨ıve methods. Recommendations are made for future work on developing these approaches further for practical use with remote monitoring stations. Other possibilities for future research are also discussed, including testing the robustness of the proposed approaches. They may also prove useful for RFI excision in observation data from radio telescopes