Orientador: Elnatan Chagas Ferreira / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-24T07:34:18Z (GMT). No. of bitstreams: 1 Spengler_AndersonWedderhoff_D.pdf: 6948072 bytes, checksum: 5751258b2120091b7b942e5664da4fb2 (MD5) Previous issue date: 2014 / Resumo: São apresentadas neste trabalho novas técnicas de condicionamento e processamento de sinais para girômetros interferométricos a fibra óptica (IFOG) em malha fechada óptica para sistemas de navegação inercial. Uma nova técnica de condicionamento do sinal do girômetro foi desenvolvida com o objetivo de minimização do offset da medida. As técnicas de modulações mais usadas na configuração de malha fechada óptica foram comparadas através de simulações e implementações físicas indicando algumas contribuições para melhoria delas. Uma nova técnica de modulação foi desenvolvida, com quatro níveis e seis fases. Os detalhes da montagem final são mostrados, enfatizando os pontos críticos da implementação, como os filtros utilizados no condicionamento do sinal e os componentes eletrônicos utilizados na geração do sinal de realimentação. Um girômetro foi desenvolvido utilizando a eletrônica apresentada neste trabalho e a parte óptica feita pela Optsensys. Ele apresentou deriva de aproximadamente a 0,01°/h e estabilidade do fator de escala na ordem de 10 ppm, valores compatíveis com girômetros da classe inercial. O angle random walk deste girômetro foi 0,0022°??hr, superior ao indicado para a classe inercial / Abstract: In this work are presented new techniques for signal conditioning and processing for inter-ferometric fiber optic gyrometers (IFOG) with optical closed-loop for inertial navigation systems. A new conditioning technique for the gyro signal was developed in order to reduce the offset. The most commonly used modulation techniques were compared through simulations and im-plementations indicating some improvements. A new modulation technique was developed, with four levels and six phases. Details of the final assembly are shown, emphasizing the critical points of implementation, such as the filters used in signal conditioning and electronic components used in the generation of the feedback signal. A gyro was developed using shown electronics and an optical made by Optsensys. It achieved a bias instability of approximately 0.01 °/ h and scale factor stability on the order of 10 ppm, consistent with the inertial class specifications. The gyro¿s angle random walk was 0.0022 °/?hr, this value is higher than that indicated for the inertial class / Doutorado / Eletrônica, Microeletrônica e Optoeletrônica / Doutor em Engenharia Elétrica

The objective of this thesis is to study the demodulation of narrowband radio frequency signals by aliasing sampling in order to reduce the sampling rate. The spectrum can be recreated at the lower frequency position by aliasing sampling. However, if the sampling rate is deviated from the desired one, error will occur. The sensitivity to the frequency error of aliasing sampling is studied. One main reason of the deviation of the sampling rate is the frequency drifting of the local oscillator. Being able to compensate the oscillator drifting errors inexpensively, automatic frequency control (AFC) loops are important at receivers. Two major digital AFC algorithms are studied. One is the Phase method AFC, and the other is the Magnitude method AFC. Study indicates that both methods perform almost equally well. One adaptive AFC algorithm is also proposed. The scheme of the adaptive AFC algorithm is to use Upper-bound and Lower-bound techniques to squeeze the frequency errors. It is shown that the adaptive AFC algorithm can achieve up to 20 dB average signal-to-noise power ratio over the Magnitude method AFC under a noisy environment.

Electromagnetic disturbance can be detrimental to the performance of a radio communication system, and in today’s society where more and more electronic devices are present in our everyday life it is increasingly vital to consider man-made interference. A communication system can take into consideration the noise characteristics and by doing so will excel in such areas, however, this follows that the algorithms utilized in such systems are more computationally complex and are therefore slow. This master thesis aims to explore the possibility of a neural network-based solution that reaches the same accuracy, as existing methods, but more quickly. Numerous different existing model alternatives have been explored and a plethora of different improvement techniques have been outlined. Two models, Hannet and Lannet, have been designed and improved to enable adaptive demodulation both including or excluding decoding at the receiver in an end-to-end communication system. The evaluation results demonstrate that the proposed models are comparable and in some cases even more accurate than current standardized methods. However, the models are unable to fully learn the decoding algorithms present in the experiments. Thus even though demodulation by itself thrives, performing decoding in conjunction with demodulation is out of reach for these models.

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<p>Soft multiple-input multiple-output (MIMO) demodulators are a core component of iterative receivers for MIMO communication systems that employ bit-interleaved coded modulation (BICM). The role of these demodulators is to extract a good approximation of the posterior likelihood of each bit transmitted at each channel use. The main challenge in designing a soft MIMO demodulator is to achieve the desired level of performance at a reasonable computational cost. This is important because in the case of a memoryless MIMO channel, the computational cost of the exact soft demodulator increases exponentially with the number of bits transmitted per channel use, and the cost grows faster in the case of the channels with memory.</p> <p>Several approximate low-complexity soft demodulators for memoryless channels have been proposed in the literature. In this thesis, we develop a low-complexity soft MIMO demodulator that is based on semidefinite relaxation (SDR) and uses the max-log approximation to reduce the cost of the demodulation. In particular, we develop a customized dual-scaling algorithm to solve the semidefinite program that constitutes the core computational task of the SDR-based soft demodulator. The computational cost per iteration of the customized dual algorithm is about half that of the existing customized primal-dual algorithm, and this leads to a reduction in the overall computational cost. We apply the customized dual-scaling algorithm to two different list-based soft demodulators, the list-SDR and single-SDR demodulators, and compare the performance, computational cost, and EXIT chart characteristics of these demodulators with other existing methods. This comparison shows that the developed demodulator provides a desirable trade-off between performance and complexity.</p> / Master of Applied Science (MASc)

M.S.

Optical fiber magnetometers are extremely sensitive devices which are capable of competing with magnetometers implemented using other technologies. Demodulation of the signals detected using these magnetometers can be performed using either homodyne or heterodyne techniques. Higher sensitivities have been achieved using homodyne rather than heterodyne techniques, but with homodyne demodulation there exists the inherent problem of quadrature-point stabilization. Presented here is a review of existing quadrature-point stabilization methods and experimental results concerning the application of one of them which uses a piezoelectric transducer in the reference arm of an all-fiber Mach-Zehnder interferometer. / Master of Science

Advances in fiber optic communications and the convergence of the optical-wireless network will dramatically increase the network heterogeneity and complexity. The goal of our research is to create smart receivers that can autonomously identify and demodulate, without prior knowledge, nearly any signal emerging from the next-generation of high-speed optical communication networks.

Speech is a non-stationary signal and contains modulations in both spectral and temporal domains. Based on the type of modulations studied, most speech processing algorithms can be classified into short-time analysis algorithms, narrow-band analysis algorithms, or joint spectro-temporal analysis algorithms. While traditional methods of speech analysis study the modulation along either time (Short-time analysis algorithms) or frequency (Narrowband analysis) at a time. A new class of algorithms that work simultaneously along both temporal as well as spectral dimensions, called the spectro-temporal analysis algorithms, have become prominent over the past decade. Joint spectro-temporal analysis (also referred to as 2-D speech analysis) has shown promise in applications such as formant estimation, pitch estimation, speech recognition, etc. Over the past decade, 2-D speech analysis has been independently motivated from several directions. Broadly these motivations for 2-D speech models can be grouped into speech-production motivated, source-separation/machine- learning motivated and neurophysiology motivated. In this thesis, we develop 2-D speech model based on the speech production motivation. The overall organization of the thesis is as follows: We first develop the context of 2-D speech processing in Chapter one, we then proceed to develop a 2-D multicomponent AM-FM model for narrowband spectrogram patch of voiced speech and experiment with the perceptual significance of number of components needed to represent a spectrogram patch in Chapter two. In Chapter three we develop a demodulation algorithm called the inphase and the quadrature phase demodulation (IQ), compared to the state-of-the art sinusoidal demodulation, the AM obtained using this method is more robust to carrier estimation errors. The demodulation algorithm was verified on call voiced sentences taken from the TIMIT database. In chapter four we develop a demodulation algorithm based on Riesz transform, a natural extension of the Hilbert transform to higher dimensions, unlike the sinusoidal and the IQ demodulation techniques, Riesz-transform-based demodulation does not require explicit carrier estimation and is also robust to pitch discontinuous in patches. The algorithm was validated on all voiced sentences from the TIMIT database. Both IQ and Riesz-transform-based methods were found to give more accurate estimates of the 2-D AM (relates to vocal tract) and 2-D carrier (relates to source) compared with the sinusoidal modulation. In Chapter five we show application of the demodulated AM and carrier to pitch estimation and for creation of hybrid sounds. The hybrid sounds created were found to have better perceptual quality compared with their counterparts created using the linear prediction analysis. In Chapter six we summarize the work and present with possible directions of future research.