CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / In applications related to power converters, such as inverters, rectifiers and the use of active filters, the synchronization method represent a very important element in the performance of the control strategy of this equipment. The estimated values of the synchronism angle, frequency and amplitude determined by the synchronization algorithms present, facing strongly distorted signals with the presence of sub and interharmonics, high errors. This study presents two algorithms: one applied on single-phase electrical systems and one applied on three-phase electrical systems, with high immunity to interharmonics and subharmonics. First are presented the main synchronization systems that are used in the electrical power systems. In addition, will be presented the main causes and consequences of the presence of subharmonics and interharmnics in the system, as well as the mathematical modeling of the two algorithms with high rejection to these disturbances. Will be presented the simulation and the experimental results of the proposed algorithms and the comparison between these synchronization methods with particular methods present in the literature. As a result of the study, it can be seen that the proposed structures present a higher response time, but the error of the estimated signal with respect the fundamental component of the input signal is lower when compared to structures such as EPLL and structures based on SOGI. It was observed that the proposed synchronization methods are enabled to estimate the synchronism angle, the frequency and the fundamental component of the input signal adequately and can be used in control strategies of power converters. / Em aplicaÃÃes relacionadas à EletrÃnica de PotÃncia, como inversores, retificadores e a utilizaÃÃo de filtros ativos, o mÃtodo de sincronizaÃÃo representa um elemento chave no desempenho da estratÃgia de controle destes equipamentos. Os valores do Ãngulo de sincronismo, frequÃncia e amplitude estimados com determinados algoritmos de sincronizaÃÃo apresentam, diante de sinais fortemente distorcidos com a presenÃa de sub e inter-harmÃnicos, erros elevados. Neste trabalho sÃo apresentados dois algoritmos: um aplicado a sistemas elÃtricos monofÃsicos e outro aplicado a sistemas elÃtricos trifÃsicos, com elevada imunidade a inter-harmÃnicos e sub-harmÃnicos. Primeiramente sÃo apresentados os principais sistemas de sincronizaÃÃo utilizados em sistemas elÃtricos de potÃncia. AlÃm disso, sÃo apresentadas as principais causas e consequÃncias da presenÃa de sub-harmÃnicos e inter-harmÃnicos no sistema, bem como a modelagem matemÃtica dos dois algoritmos com elevada rejeiÃÃo a estes distÃrbios. SÃo apresentados os resultados de simulaÃÃo e experimentais dos algoritmos propostos e a comparaÃÃo entre estes mÃtodos de sincronizaÃÃo com determinados mÃtodos presentes na literatura. Como resultado do estudo, pode-se observar que as estruturas de sincronizaÃÃo propostas apresentam um tempo de resposta mais elevado, porÃm o erro do sinal estimado em relaÃÃo a componente fundamental do sinal de entrada à inferior quando comparado a estruturas como o EPLL e estruturas baseadas no SOGI. Com isso, tem-se que as mesmas estÃo habilitadas para estimar o Ãngulo de sincronismo, a frequÃncia e a componente fundamental do sinal de entrada adequadamente e podem serem utilizadas eficientemente em estratÃgias de controle de conversores de potÃncia.

The doctoral thesis focuses on a Vold-Kalman filter (VKF). Theoretical part describes properties of VKF and other order tracking methods, namely computed order tracking (COT) and Gabor order tracking (GOT). It also characterizes requirements for rotational speed measurements as one of the key elements for correct functionality of VKF. Practical part depicts own filter implementation and its properties. Main stress is put on computational efficiency, that is in result better than in available codes. Thesis also points out possible issues with numerical instabilities within calculation caused by limited dynamic range of double data type. This is solved by restricting the inputs to prevent the instabilities. Restriction is applied also to cases where the result is numerically correct but unusable. Following part extends the comparison with methods STFT, COT and GOT, where benefits of VKF for nonstationary conditions are shown. The last section shows given information used on simulated signals. This is then applied to show mentioned techniques on experimental data, for instance from turbo engine or electric motor, where the ability of VKF in checking the accordance between speed profile and vibration data is illustrated.

SAAB has developed an autonomous underwater vehicle that can mimic a conventional submarine for military fleets to exercise anti-submarine warfare. The AUV actively emits amplified versions of received sonar pulses to create the illusion of being a larger object. To prevent acoustic feedback, the AUV must distinguish between the sound to be actively responded to and its emitted signal. This master thesis has examined techniques aimed at preventing the AUV from responding to previously emitted signals to avoid acoustical feedback, without relying on prior knowledge of either the received signal or the signal emitted by the AUV. The two primary types of algorithms explored for this problem include blind source separation and adaptive filtering. The adaptive filters based on Leaky Least Mean Square and Kalman have shown promising results in attenuating the active response from the received signal. The adaptive filters utilize the fact that a certain hydrophone primarily receives the active response. This hydrophone serves as an estimate of the active response since the signal it captures is considered unknown and is to be removed. The techniques based on blind source separation have utilized the recordings of three hydrophones placed at various locations of the AUV to separate and estimate the received signal from the one emitted by the AUV. The results have demonstrated that neither of the reviewed methods is suitable for implementation on the AUV. The hydrophones are situated at a considerable distance from each other, resulting in distinct time delays between the reception of the two signals. This is usually referred to as a convolutive mixture. This is commonly solved using the frequency domain to transform the convolutive mixture to an instantaneous mixture. However, the fact that the signals share the same frequency spectrum and are adjacent in time has proven highly challenging.

This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevation of the T wave of an ECG signal in the MRI. The first step is to simulate the entire situation and obtain the magnetic field dependent T wave elevation. This is achieved by building a model of the aorta and simulating the blood flow in it. This model is then subjected to a static magnetic field and the surface potential on the thorax is measured to observe the T wave elevation. The various parameters on which the T wave elevation is dependent are then analyzed. Different approaches are used to reduce this T wave elevation problem. The direct approach aims at computing the magnitude of T wave elevation using magneto-hydro-dynamic equations. The indirect approach uses digital signal processing tools like the least mean square adaptive filter to remove the T wave elevation and obtain artifact free ECG signal in the MRI. Excellent results are obtained from the simulation model. The model perfectly simulates the ECG signal in the MRI at all the 12 leads of the ECG. These results are compared with ECG signals measured in the MRI. A simulation package is developed in MATLAB based on the simulation model. This package is a graphical user interface allowing the user to change the strength of magnetic field, the radius of the aorta and the orientation of the aorta with respect to the heart and observe the ECG signals with the elevation at the 12 leads of the ECG. Also the artifacts introduced due to the magnetic field can be removed by the least mean square adaptive filter. The filter adapts the ECG signal in the MRI to the ECG signal of the patient outside the MRI. Before the adaptation, the heart rate of the ECG outside the MRI is matched to the ECG in the MRI by interpolation or decimation. The adaptive filter works excellently to remove the T wave artifacts. When the cardiac output of the patient changes, the simulation model is used along with the adaptive filter to obtain the artifact free ECG signal.

Dans ce mémoire de thèse, nous présentons de nouvelles approches dans le domaine de la détection et de la localisation des défauts non-francs dans les réseaux filaires. Dans le domaine de la détection, l’idée est d’adapter le signal de test au réseau à tester, donc celui-là dépendra de la configuration du système sans devoir être prédéfini, comme c’est le cas des méthodes standard de réflectométrie. Nous prouvons que cette approche MP est plus bénéfique lorsque le système est plus complexe, c’est-à-dire lorsque sa réponse est plus riche en échos, ce qui est contraire aux méthodes existantes. L’étude de la MP est menée à travers une étude mathématique, et les résultats de simulation et d’expérimentation valident l’approche proposée. Dans le domaine de la localisation des défauts, et en se basant sur les propriétés de la DORT, nous développons une méthode distributive non-itérative capable de synthétiser des signaux de test se focalisant directement sur la position du défaut. Une étude statistique nous permet d’analyser quelques-uns des paramètres les plus influents sur la performance de la méthode, puis les résultats de simulation et expérimentaux montrent la capacité de la méthode à synthétiser des signaux se focalisant directement sur la position du défaut non-franc, sans avoir besoin d’algorithmes tératifs. / In this thesis we present new approaches in the domains of soft fault detection and location in complex wire networks, based on the properties of time reversal. When addressing the detection of soft faults, the idea is to adapt the testing signal to the network under test, instead of being predefined for all the tested networks, as opposed to standard reflectometry techniques. We prove that this approach, which we name the Matched Pulse approach (MP), is beneficial whenever the system is more complex, i.e., its response is richer in echoes, which is opposed to common understanding. The MP analysis is conducted via a formal mathematical analysis, followed by simulation and experimental results validating the proposed approach. In the domain of soft fault location, and based on the DORT (Décomposition de l’Opérateur de Retournement Temporel) properties, we derive a distributive non-iterative method able to synthesize signals that focus on the fault position. Through a statistical study we analyze some of the influencing parameters on the performance of the method, and then simulation and experimental results show that the method is able to synthesize signals directly focalizing on the soft fault position, without the need for iterations.

<div> <div> <div> <p>Speech quality is a demand in voice commanded systems and in telephony. The voice communication system in real time often suffers from audible echoes. In order to cancel echoes, an acoustic echo cancellation system is designed and applied to increase speech quality both subjectively and objectively. </p> <p>In this research we develop various nonlinear adaptive filters wielding the new channel sparsity-aware recursive least squares (RLS) algorithms using a sequential update. The developed nonlinear adaptive filters using the sparse sequential RLS (S-SEQ-RLS) algorithm apply a discard function to disregard the coefficients which are not significant or close to zero in the weight vector for each channel in order to reduce the computational load and improve the algorithm convergence rate. The channel sparsity-aware algorithm is first derived for nonlinear system modeling or system identification, and then modified for application of echo cancellation. Simulation results demonstrate that by selecting a proper threshold value in the discard function, the proposed nonlinear adaptive filters using the RLS (S-SEQ-RLS) algorithm can achieve the similar performance as the nonlinear filters using the sequential RLS (SEQ-RLS) algorithm in which the channel weight vectors are sequentially updated. Furthermore, the proposed channel sparsity-aware RLS algorithms require a lower computational load in comparison with the non-sequential and non-sparsity algorithms. The computational load for the sparse algorithms can further be reduced by using data-selective strategies. </p> </div> </div> </div>

In general, PN code synchronization consists of two steps: PN code acquisition (coarse alignment) and PN code tracking (fine alignment), to estimate the delay offset between received and locally generated codes. Recently, the schemes with a joint adaptive process of PN code acquisition and the weight coefficients of smart antenna have been proposed for improving the received signal-to-interference-plus-noise ratio (SINR) and simultaneously achieving better mean-acquisition-time (MAT) performance in direct-sequence code-division multiple access (DS-CDMA) systems. In which, the setting of the threshold plays an important role on the MAT performance. Often, the received SINR is varying, using the fixed threshold acquisition algorithms may result in undesirable performance. To improve the above problem, in this thesis, a new adaptive threshold scheme is devised in a joint adaptive code acquisition and beam-forming DS-CDMA receiver for code acquisition under a fading multipath and additive white Gaussian-noise (AWGN) channels. The basic idea of this new adaptive threshold scheme is to estimate the averaged output power of smart antenna to scale a reference threshold for each observation interval, such that it can approximately achieve a constant false alarm rate (CFAR) criteria. The system probabilities of the proposed scheme are derived for evaluating MAT under a slowly fading two-paths channels. Numerical analyses and simulation results demonstrate that the proposed adaptive threshold scheme does achieve better performance, in terms of the output SINR, the detection probability and the MAT, compared to a fixed threshold method.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

In system identification scenario, the linear approximation of the system modelled by its impulse response, is estimated in real time by gradient type Least Mean Square (LMS) or Recursive Least Squares (RLS) algorithms. In recent applications like acoustic echo cancellation, the order of the impulse response to be estimated is very high, and these traditional approaches are inefficient and real time implementation becomes difficult. Alternatively, the system is modelled by a set of shorter adaptive filters operating in parallel on subsampled signals. This approach, referred to as subband adaptive filtering, is expected to reduce not only the computational complexity but also to improve the convergence rate of the adaptive algorithm. But in practice, different subband adaptive algorithms have to be used to enhance the performance with respect to complexity, convergence rate and processing delay. A single subband adaptive filtering algorithm which outperforms the full band scheme in all applications is yet to be realized. This thesis is intended to study the subband adaptive filtering techniques and explore the possibilities of better algorithms for performance improvement. Three different subband adaptive algorithms have been proposed and their performance have been verified through simulations. These algorithms have been applied to acoustic echo cancellation and EEG artefact minimization problems. Details of the work To start with, the fast FIR filtering scheme introduced by Mou and Duhamel has been generalized. The Perfect Reconstruction Filter Bank (PRFB) is used to model the linear FIR system. The structure offers efficient implementation with reduced arithmetic complexity. By using a PRFB with non adjacent filters non overlapping, many channel filters can be eliminated from the structure. This helps in reducing the complexity of the structure further, but introduces approximation in the model. The modelling error depends on the stop band attenuation of the filters of the PRFB. The error introduced due to approximation is tolerable for applications like acoustic echo cancellation. The filtered output of the modified generalized fast filtering structure is given by (formula) where, Pk(z) is the main channel output, Pk,, k+1 (z) is the output of auxiliary channel filters at the reduced rate, Gk (z) is the kth synthesis filter and M the number of channels in the PRFB. An adaptation scheme is developed for adapting the main channel filters. Auxiliary channel filters are derived from main channel filters. Secondly, the aliasing problem of the classical structure is reduced without using the cross filters. Aliasing components in the estimated signal results in very poor steady state performance in the classical structure. Attempts to eliminate the aliasing have reduced the computation gain margin and the convergence rate. Any attempt to estimate the subband reference signals from the aliased subband input signals results in aliasing. The analysis filter Hk(z) having the following antialiasing property (formula) can avoid aliasing in the input subband signal. The asymmetry of the frequency response prevents the use of real analysis filters. In the investigation presented in this thesis, complex analysis filters and real'synthesis filters are used in the classical structure, to reduce the aliasing errors and to achieve superior convergence rate. PRFB is traditionally used in implementing Interpolated FIR (IFIR) structure. These filters may not be ideal for processing an input signal for an adaptive algorithm. As third contribution, the IFIR structure is modified using discrete finite frames. The model of an FIR filter s is given by Fc, with c = Hs. The columns of the matrix F forms a frame with rows of H as its dual frame. The matrix elements can be arbitrary except that the transformation should be implementable as a filter bank. This freedom is used to optimize the filter bank, with the knowledge of the input statistics, for initial convergence rate enhancement . Next, the proposed subband adaptive algorithms are applied to acoustic echo cancellation problem with realistic parameters. Speech input and sufficiently long Room Impulse Response (RIR) are used in the simulations. The Echo Return Loss Enhancement (ERLE)and the steady state error spectrum are used as performance measures to compare these algorithms with the full band scheme and other representative subband implementations. Finally, Subband adaptive algorithm is used in minimization of EOG (Electrooculogram) artefacts from measured EEG (Electroencephalogram) signal. An IIR filterbank providing sufficient isolation between the frequency bands is used in the modified IFIR structure and this structure has been employed in the artefact minimization scheme. The estimation error in the high frequency range has been reduced and the output signal to noise ratio has been increased by a couple of dB over that of the fullband scheme. Conclusions Efforts to find elegant Subband adaptive filtering algorithms will continue in the future. However, in this thesis, the generalized filtering algorithm could offer gain in filtering complexity of the order of M/2 and reduced misadjustment . The complex classical scheme offered improved convergence rate, reduced misadjustment and computational gains of the order of M/4 . The modifications of the IFIR structure using discrete finite frames made it possible to eliminate the processing delay and enhance the convergence rate. Typical performance of the complex classical case for speech input in a realistic scenario (8 channel case), offers ERLE of more than 45dB. The subband approach to EOG artefact minimization in EEG signal was found to be superior to their fullband counterpart. (Refer PDF file for Formulas)