Tremor is a neuro degenerative disease causing involuntary musclemovements in human limbs. There are many types of tremor that arecaused due to the damage of nerve cells that surrounds thalamus of thefront brain chamber. It is hard to distinguish or classify the tremors asthere are many reasons behind the formation of specific category, soevery tremor type is named behind its frequency type. Propermedication for the cure by physician is possible only when the disease isidentified.Because of the argument given in the above paragraph, there is a needof a device or a technique to analyze the tremor and for extracting theparameters associated with the signal. These extracted parameters canbe used to classify the tremor for onward identification of the disease.There are various diagnostic and treatment monitoring equipment areavailable for many neuromuscular diseases. This thesis is concernedwith the tremor analysis for the purpose of recognizing certain otherneurological disorders. A recording and analysis system for human’stremor is developed.The analysis was performed based on frequency and amplitudeparameters of the tremor. The Fast Fourier Transform (FFT) and higherorderspectra were used to extract frequency parameters (e.g., peakamplitude, fundamental frequency of tremor, etc). In order to diagnosesubjects’ condition, classification was implemented by statisticalsignificant tests (t‐test).

This work presents a robust algorithm for voice activity detection (VAD) and noise reduction mechanism using combined properties of higher-order statistics (HOS) and an efficient algorithm to estimate the instantaneous Signal-to-Noise Ratio (SNR) of speech signal in a background of acoustic noise. The flat spectral feature of Linear Prediction Coding (LPC) residual results in distinct characteristics for the cumulants in terms of phase, periodicity and harmonic content and yields closed-form expressions for the skewness and kurtosis. The HOS of speech is immune to Gaussian noise and this makes them particularly useful in algorithms designed for low SNR environments. The proposed algorithm uses HOS and smooth power estimate metrics with second-order measures, such as SNR and LPC prediction error, to identify speech and noise frames. A voicing condition for speech frames is derived based on the relation between the skewness, kurtosis of voiced speech and estimate of smooth noise power. The algorithm presented and its performance is compared to HOS-only based VAD algorithm. The results show that the proposed algorithm has an overall better performance, with noticeable improvement in Gaussian-like noises, such as street and garage, and high to low SNR, especially for probability of correctly detecting speech. The proposed algorithm is replicated on DSK C6713.

This research investigates signal processing involving a single electromagnetic vector-sensor, with an emphasis on the problem regarding signal-selective narrowband direction-of-arrival (DOA) estimation in the presence of interference. The approach in this thesis relies on a high-resolution ESPRIT-based algorithm. Unlike spatially displaced arrays, the sensor cannot estimate the DOA of sources using phase differences between the array elements, as the elements are spatially co-located. However, the sensor measures the full electromagnetic field vectors, so the DOA can be estimated through the Poynting vector. Limited information is available in the open literature regarding signal-selective DOA estimation for a single electromagnetic vector-sensor. In this thesis, it is shown how the Uni-Vector-Sensor-ESPRIT (UVS-ESPRIT) algorithm that relies on a time-series invariance and was originally devised for deterministic harmonic sources can be applied to non-deterministic sources. Additionally, two algorithms, one based on cyclostationarity and the other based on fourth-order cumulants, are formulated based on the UVS-ESPRIT algorithm and are capable of selectively estimating the source DOA in the presence of interference based on the statistical properties of the sources. The cyclostationarity-based UVS-ESPRIT algorithm is capable of selectively estimating the signal-of-interest DOA when the sources have the same carrier frequency, and thus overlap in frequency. The cumulant-based UVS-ESPRIT algorithm devised for this sensor relies on the independent component analysis algorithm JADE and is capable of selectively estimating the signal-of-interest DOA through the fourth-order cumulants only, is robust to spatially colored noise, and is capable of estimating the DOA of more sources than sensor elements. / Master of Science / Electromagnetic vector-sensors are specialized sensors capable of capturing the full electromagnetic field vectors at a single point in space. Direction-of-arrival (DOA) estimation is the problem of estimating the spatial-angular parameters of one or more wavefronts impinging on an array. For a single electromagnetic vector-sensor, the array elements are not spatially displaced, but it is still possible to estimate the direction-of-arrival through the Poynting vector, which relates the electric and magnetic field vectors to the direction of propagation of an electromagnetic wave. Although direction-of-arrival estimation is a well-established area of research, there is limited discussion in the open literature regarding signal-selective DOA estimation in the presence of interference for a single electromagnetic vector-sensor. This research investigates this problem and discusses how the high-resolution Uni-Vector-Sensor-ESPRIT (UVS-ESPRIT) algorithm may be applied to non-deterministic sources. ESPRIT based algorithms capable of selectively estimating the source DOA are formulated based on the cyclostationarity and higher-order statistics of the sources, which are approaches known to be robust to interference. The approach based on higher-order statistics is also robust to spatially colored noise and is capable of estimating the DOA of more sources than sensor elements. The formulation of the UVS-ESPRIT for higher-order statistics relies on the application of the independent component analysis algorithm JADE, an unsupervised learning technique. Overall, this research investigates signal-selective direction-of-arrival estimation using an ESPRIT-based algorithm for a single electromagnetic vector-sensor.

"Blind" inversion of the effects of a given operator on a signal is an extremely difficult task that has no easy solutions. However,. Dr. Hany Farid has published several works that each individua:lly appear to achieve exactly this seemingly impossible result. In this work, we contribute a comprehensive overview of the published applications of blind process inversion, as well as provide the generalized form of the algorithms and requirements that are found in each of these applications, thereby formulating and explaining a general framework for blind process inversion using Farid's Algorithm. Additionally, we explain the knowledge required to derive the ROSA-based cost function on which Farid's Algorithm depends. As our primary contribution, we analyze the algorithmic complexity of this cost function based on the way it is currently, naively calculated, and derive a new algorithm to compute this cost function that has greatly reduced algorithmic complexity. Finally, we suggest an additional application of Farid's Algorithm to the problem of blindly estimating true camera response functions from a single image.

Over the past thirty years, extensive research has been conducted with the objective of reducing wind damage to structures. Wind tunnel simulations of wind loads have been the major source of building codes. However, a simple comparison of pressure coefficients measured in wind tunnel simulations with full-scale measurements show that the simulations, in general, underpredict extreme negative pressure coefficients. One obvious reason is the lack of consensus on wind tunnel simulation parameters. The wind in the atmospheric surface layer is highly turbulent. In simulating wind loads on structures, one needs to simulate the turbulent character besides satisfying geometric and dynamic similitudes. Some turbulence parameters that have been considered in many simulations include, turbulence intensities, integral length scales, surface roughness, and frequency spectrum. One problem with these parameters is that they are time varying in the atmospheric boundary layer and their averaged value, usually considered in the wind tunnel simulations, cannot be used to simulate pressure peaks. In this work, we show how wavelet analysis and time-scale representation can be used to establish an intermittency factor that characterizes energetic turbulence events in the atmospheric flows. Moreover, we relate these events to the occurrence of extreme negative peak pressures. / Ph. D.

A parametric identification procedure is proposed that combines the method of multiple scales and higher-order statistics to efficiently and accurately model nonlinear systems. A theoretical background for the method of multiple scales and higher-order statistics is given. Validation of the procedure is performed through applying it to numerical simulations of two nonlinear systems. The results show how the procedure can successfully characterize the system damping and nonlinearities and determine the corresponding parameters. The procedure is then applied to experimental measurements from two structural systems, a cantilevered beam and a three-beam frame. The results show that quadratic damping should be accounted for in both systems. Moreover, for the three-beam frame, the parametric excitation is much more important than the direct excitation. To show the flexibility of the procedure, numerical simulations of ship motion under parametric excitation are used to determine nonlinear parameters govening the relation between pitch, heave, and roll motions. The results show a high level of agreement between the numerical simulation and the mathematical model with the identified parameters. / Master of Science

Neste trabalho, é apresentado e discutido um novo modelo para proteção de Linhas de Transmissão. O sistema proposto executa, individualmente, as etapas tradicionais da filosofia de proteção de distância: detecção, classificação e localização. Este modelo emprega Estatísticas de Ordem Superior (EOS) como ferramenta de extração de características, para posterior aplicação das Redes Neurais Artificiais (RNAs). As RNAs são responsáveis pelas tomadas de decisões do sistema, no sentido de identificar a ocorrência da falta e o tipo da mesma, além de localizar a falta no que tange às zonas de proteção consideradas. O processamento com tais estatísticas é responsável pela transformação dos dados para um domínio onde as diferentes faltas são evidenciadas através de agrupamentos de dados (padrões). O banco de dados disponível com sinais elétricos de LTs em condições de falta é utilizado para cálculo das estatísticas e o posterior treinamento supervisionado (e validação) das redes. A junção das etapas de proteção em um único modelo permitiu o desenvolvimento de um protótipo de relé, sendo executada uma bateria extensiva de testes, com as mais diversas condições de faltas possíveis. Apesar de operar apenas com sinais de corrente, o método proposto alcançou resultados que, em comparação com a técnica tradicional de proteção de distância, baseada na impedância aparente, aumenta consideravelmente o desempenho da proteção de LTs. Especialmente para as faltas monofásicas, de ocorrência mais comum, o desempenho obtido com o algoritmo proposto é largamente superior ao obtido com um relé de distância tradicional normalmente empregado em proteção de LTs, evidenciando a relevância da técnica empregada em aplicações de proteção. / A novel method of Transmission Lines (TLs) protection is presented and discussed in this work. The proposed algorithm performs the traditional steps of distance relaying, such as: fault detection, classification and location. The new method applies the Higher Order Statistics (HOS), also known as cumulants, as a tool for feature extraction in order to apply Artificial Neural Networks (ANN) for pattern classification. These networks are responsible for the processing of information, identifying a possible fault condition, the type of fault and, finally, its location in terms of fault zones considered for the problem. The application of HOS in a protection scheme is responsible for the transformation of electrical data, such as current signals, to a different domain where the different types of faults are highlighted by different classes of samples. The available database was obtained by simulating an Electric Power System and it is used for computing the statistics and training/validating the distinct neural networks of each step of the distance protection. A relay prototype is obtained by combining these steps in a synchronized operation. This prototype allowed the execution of extensive tests, simulating the operation of a protective system in real-time. Despite the use of currents signals only, the proposed method provided efficient protection for the EPS under study. In fact, comparing the results with a traditional method applied to distance protection, based on apparent impedance, an improvement of the protection performance was demonstrated. Especially for faults involving one phase and the ground, the most common in power systems, the results of the new methodology was significantly superior to that of the conventional relay. It can be concluded that the technique presents a high relevance for applications in transmission line protection.

Neste trabalho, é apresentado e discutido um novo modelo para proteção de Linhas de Transmissão. O sistema proposto executa, individualmente, as etapas tradicionais da filosofia de proteção de distância: detecção, classificação e localização. Este modelo emprega Estatísticas de Ordem Superior (EOS) como ferramenta de extração de características, para posterior aplicação das Redes Neurais Artificiais (RNAs). As RNAs são responsáveis pelas tomadas de decisões do sistema, no sentido de identificar a ocorrência da falta e o tipo da mesma, além de localizar a falta no que tange às zonas de proteção consideradas. O processamento com tais estatísticas é responsável pela transformação dos dados para um domínio onde as diferentes faltas são evidenciadas através de agrupamentos de dados (padrões). O banco de dados disponível com sinais elétricos de LTs em condições de falta é utilizado para cálculo das estatísticas e o posterior treinamento supervisionado (e validação) das redes. A junção das etapas de proteção em um único modelo permitiu o desenvolvimento de um protótipo de relé, sendo executada uma bateria extensiva de testes, com as mais diversas condições de faltas possíveis. Apesar de operar apenas com sinais de corrente, o método proposto alcançou resultados que, em comparação com a técnica tradicional de proteção de distância, baseada na impedância aparente, aumenta consideravelmente o desempenho da proteção de LTs. Especialmente para as faltas monofásicas, de ocorrência mais comum, o desempenho obtido com o algoritmo proposto é largamente superior ao obtido com um relé de distância tradicional normalmente empregado em proteção de LTs, evidenciando a relevância da técnica empregada em aplicações de proteção. / A novel method of Transmission Lines (TLs) protection is presented and discussed in this work. The proposed algorithm performs the traditional steps of distance relaying, such as: fault detection, classification and location. The new method applies the Higher Order Statistics (HOS), also known as cumulants, as a tool for feature extraction in order to apply Artificial Neural Networks (ANN) for pattern classification. These networks are responsible for the processing of information, identifying a possible fault condition, the type of fault and, finally, its location in terms of fault zones considered for the problem. The application of HOS in a protection scheme is responsible for the transformation of electrical data, such as current signals, to a different domain where the different types of faults are highlighted by different classes of samples. The available database was obtained by simulating an Electric Power System and it is used for computing the statistics and training/validating the distinct neural networks of each step of the distance protection. A relay prototype is obtained by combining these steps in a synchronized operation. This prototype allowed the execution of extensive tests, simulating the operation of a protective system in real-time. Despite the use of currents signals only, the proposed method provided efficient protection for the EPS under study. In fact, comparing the results with a traditional method applied to distance protection, based on apparent impedance, an improvement of the protection performance was demonstrated. Especially for faults involving one phase and the ground, the most common in power systems, the results of the new methodology was significantly superior to that of the conventional relay. It can be concluded that the technique presents a high relevance for applications in transmission line protection.

Contexte & Motivation:Les installations électriques des bâtiments se détériorent au fil du temps et leur gravité et leur taux de détérioration dépendent de facteurs environnementaux (chaleur, humidité, réactions chimiques corrosives et vieillies isolations) ou d'actions externes indésirables telles que la manipulation humaine erronée, qui conduit à des charges ou des câbles/réseaux endommagés.L'European Fire Academy (EFA) et de nombreuses compagnies d'assurance indiquent que 25% des incendies de bâtiments sont d'origine électrique. Ces incendies peuvent être déclenchés par des circuits surchargés, des court-circuits, des courants de fuite à la terre, des surtensions et / ou des défauts d'arc électrique dans les connexions et les câbles.Les protections électriques classiques telles que les disjoncteurs et les disjoncteurs différentiels offrent une protection insuffisante. Par exemple, en cas de défauts d'arcs en série, la valeur du courant de défaut d'arc reste inférieure à la valeur du courant nominal, car elle est limitée par la résistance du carbone généré par le défaut d'arc. Dans ce cas, toute protection existante peut détecter ce type de faute.Détection de défaut d'arc : approche de traitement du signalDans le cadre de ce travail, l'objectif a été de détecter chaque instant d'arc, ce qui, pour un réseau alternatif, permettrait d'identifier correctement chaque arc dans chaque demi-cycle de réseau où il se produit.En fonction de la caractéristique numérique utilisée à des fins de détection, nous avons introduit différentes classes de méthodes:• Caractéristiques énergétiques (bandes étroite et large bande)• Caractéristiques statistiques (moments statistiques, analyse de la corrélation etc.)• Caractéristiques basées sur un model (ex. modelés AR, ARMA etc.)• Caractéristiques data-driven (utiliser Phase Space Embedding pour les séries temporelles)Chaque approche a été testée et évaluée sur une base de données de signaux construite avec soin, capable de fournir la variabilité du monde réel, dans un cadre d'évaluation statistique qui permet de trouver des seuils appropriés et leurs plages associées. Il donne également des performances relatives, d'une fonctionnalité à l'autre, en fonction de la façon dont les plages de seuils couvrent tout l'espace des caractéristiques.Une approche prometteuse est montrée avec un résultat intermédiaire sur la figure 8. La configuration est plutôt courante, avec une charge résistive (R-Load) en fonctionnement normal, avec un gradateur allumé et ajouté dans la configuration et un arc persistant apparaissant dans le circuit.Il suffit d'analyser simplement la forme d'onde du courant 50 Hz, car même lors d'une simple inspection visuelle, il est difficile d'identifier l'origine du défaut d'arc et s'il est stable ou s'il s'éteint après (ou où). En mesurant correctement le bruit de défaut d'arc haute fréquence et en sélectionnant correctement la bande passante, nous parvenons à obtenir un signal beaucoup plus facile à traiter. L'arc est difficile à détecter en raison de la variation de l'intensité énergétique d'un réseau à l'autre (encore plus: pour un même réseau, ajouter / enlever des charges ou des rallonges modifie la distribution d'amplitude et de fréquence de l'arc). Par conséquent, nous exploitons le caractère aléatoire intrinsèque de l'arc, ce qui permet une variabilité suffisante d'une réalisation d'arc à une autre.En conclusion, nous proposons une nouvelle méthodologie de traitement du signal pour la détection des défauts d'arc, à mettre en œuvre dans un algorithme de produit AFDD. En outre, une autre approche est présentée, basée sur l'analyse de diagramme de phase, qui permet la séparation entre les arcs et les signaux de communication, ce qui est également un grand défi dans ce domaine. / Context & Motivation:Electrical installations in buildings deteriorate, over time and the severity and rate of deterioration depend on environmental factors (such as heat, humidity, corrosive chemical reactions and aging insulations) and unwanted external actions (such as human mishandling, that leads to damaged devices or cables/network).Caution is mandatory when handling electrical installations, seeing that potential hazards include electric shocks, burns, explosions and fire, if proper safety precautions are ignored or neglected. The European Fire Academy (EFA) and many property and casualty insurance companies report that 25% of building fires are electrical in origin. These fires can be triggered by overloaded circuits, short-circuits, earth leakage currents, overvoltage and/or electrical arc faults in connections and cables.Classical electrical protection such as circuit breakers and RCDs offer insufficient protection. For example, in case of series arc faults, the arc fault current value remains below the rated current value, since it is limited by the resistance of the carbon generated by the arc fault and by the load itself. In this case, no existing protection can detect such kind of fault.Arc Fault Detection: Signal Processing ApproachIn the context of this work, the objective has been to detect each instant of arcing, which for an AC network, would mean correctly identifying each arcing in each network half-cycle where it occurs.Depending on the numerical feature used for detection purposes, we introduced different classes of methods:• Energy-related features (narrow and wideband)• Statistical features (statistical moments, correlation analysis etc.)• Model-based features (using numerical models, such as AR, for example)• Data-driven features (using Phase Space Embedding for time series)Each approach has been tested & evaluated on a carefully constructed signal database, capable of supplying real-world variability, within a statistical evaluation framework which enables finding suitable thresholds and their appropriate ranges. It also gives relative performances, from one feature to another, based on how threshold ranges cover the entire feature space.A promising approach is shown with an intermediary result in Figure 9. The configuration is rather common, with a resistive load (R – Load) in normal operation, with a dimmer being turned on and added in the configuration and a persistent arc appearing in the circuit.Figure 9 Resistive load, dimmer and persistent arcing – processing result (example).Simply analyzing the 50Hz line current waveform is insufficient, as even at a simple visual inspection there is difficulty in identifying where the arc fault ignites and if it is a stable one, or if it extinguishes afterwards (or where). By correctly measuring the high frequency arc fault noise and with correct selection of the bandwidth, we manage to obtain a signal much easier to process further on. Arcing is inherently difficult to detect, due to high frequency energy intensity variation from one network to another (even more: for the same network, adding/removing loads or extension cords will change the amplitude and frequency distribution of the arc fault energy). Therefore, we exploit the intrinsic randomness of arcing, which enables sufficient variability from one arcing realization to another.To conclude, we propose a new signal processing methodology for arc fault detection, to be implemented in an AFDD product algorithm. Also, another approach is presented, based on phase diagram analysis, that allows the separation between the arcs and communication signals, which is also a great challenge in this field.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-26T13:23:08Z No. of bitstreams: 1 marianagenymoreira.pdf: 8374988 bytes, checksum: 38a7b2c61f3df104e6c9f034df1386ae (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-26T13:53:43Z (GMT) No. of bitstreams: 1 marianagenymoreira.pdf: 8374988 bytes, checksum: 38a7b2c61f3df104e6c9f034df1386ae (MD5) / Made available in DSpace on 2017-04-26T13:53:43Z (GMT). No. of bitstreams: 1 marianagenymoreira.pdf: 8374988 bytes, checksum: 38a7b2c61f3df104e6c9f034df1386ae (MD5) Previous issue date: 2016-05-31 / Nas últimas décadas a qualidade da energia elétrica disponibilizada na rede elétrica tornou-se foco. Este fato é justificado pela combinação dos aumentos da poluição dos sinais na rede e do número de dispositivos eletrônicos sensíveis às variações do sinal de alimentação, especialmente tendo em vista o novo cenário de redes inteligentes. Um dos desafios das redes inteligentes é a redução de eventos ou falhas devido à baixa qualidade de energia elétrica, principalmente envolvendo distúrbios harmônicos e inter-harmônicos, provocados pela utilização de cargas e sistemas eletrônicos. Os impactos e prejuízos causados por distúrbios harmônicos e inter-harmônicos são importantes o suficiente para torná-los foco de inúmeros estudos, posto que não há, até a presente data, um método de análise desses componentes eficiente o bastante para atuar no controle e proteção de sistemas de potência. Este trabalho busca contribuir para a melhoria da análise desses componentes de frequência e, nesse sentido, propõe um sistema de detecção, classificação e identificação de distúrbios de qualidade de energia, especialmente, harmônicos, sub-harmônicos e inter harmônicos, baseado em estatística de ordem superior. São contemplados ainda distúrbios de amplitude de curta duração: sags e swells. A estrutura proposta contempla ainda um contador de componentes inter-harmônicos e sub-harmônicos, capaz de apontar a existência e quantificar esses componentes mesmo quando combinados a outros distúrbios harmônicos. As metodologias propostas pelo trabalho exibem resultados bastante expressivos tanto em relação à eficiência das aplicações em qualidade de energia quanto em relação ao desempenho computacional. / The power quality available on the electrical power system became focus in the last decades. This fact is explained by the combination of signal pollutions increasing and the number of electronic devices sensitive to power signal variations, especially in the smart grids scenario. One of the challenges of smart grids is the reduction of events or failures due to the low power quality, mainly involving harmonics and inter-harmonics disturbances, caused by the use of fillers and electronic systems. Impacts and losses caused by these disturbances are of great concern in numerous studies, since there is not an efficient analysis method capable of dealing with this kind of disturbances. This thesis seeks to contribute in the sense of harmonics and inter-harmonics analysis by proposing a power quality disturbances detection, classification and identification system, based on higher order statistics. The proposed structure also includes a inter-harmonics and sub-harmonics components counter, able to point out the existence and to quantify these components even when they are combined with other harmonic disturbances. The proposed methodologies show suitable results both in terms of efficiency of investments in power quality as compared to the computational performance.