In this thesis, the problem of multiple access interference (MAI) suppression for the multi-carrier (MC) code division multiple access (CDMA) system, based on the multi-carrier modulation with modified Gaussian wavelet, associated with the combining process is investigated for Rayleigh fading channel. The main concern of this thesis is to derive a new scheme, based on the linearly constrained constant modulus (LCCM) criterion with the robust inverse QR decomposition (IQRD) recursive least squares (RLS) algorithm to improve the performance of the wavelet-based MC-CDMA system with combining process. To verify the merits of the new algorithm, the effect due to imperfect channel parameters estimation and near-far effect are investigated. We show that the proposed robust LCCM IQRD-RLS algorithm outperforms the conventional LCCM-gradient algorithm, in terms of output SINR, for MAI suppression under channel mismatch environment. Also, the performance of the modified Gaussian wavelet-based MC-CDMA system is superior to the one with wavelet-based MC-CDMA system. It is more robust to the channel mismatch and near-far effect. Moreover, the modified Gaussian wavelet-based MC-CDMA system with robust LCCM IQRD-RLS algorithm does have better performance over other conventional approaches, such as the LCCM-gradient algorithm, maximum ratio combining (MRC), and blind adaptation algorithm, in terms of the capability of MAI suppression and bit error rate (BER).

<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>

Submitted by PPG Engenharia El?trica (engenharia.pg.eletrica@pucrs.br) on 2018-10-29T13:30:23Z No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2018-10-30T17:21:55Z (GMT) No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5) / Made available in DSpace on 2018-10-30T17:27:25Z (GMT). No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5) Previous issue date: 2018-08-31 / Heart rate monitoring using Photoplethysmography (PPG) signals acquired from the individuals pulse has become popular due to emergence of numerous low cost wearable devices. However, monitoring during physical activities has obstacles because of the influence of motion artifacts in PPG signals. The objective of this work is to introduce a new algorithm capable of removing motion artifacts and estimating heart rate from pulse PPG signals. Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) algorithms are proposed for an adaptive filtering structure that uses acceleration signals as reference to remove motion artifacts. The algorithm uses the Periodogram of the filtered signals to extract their heart rates, which will be used together with a PPG Signal Quality Index to feed the input of a Kalman Filter. Specific heuristics and the Quality Index collaborate so that the Kalman filter provides a heart rate estimate with high accuracy and robustness to measurement uncertainties. The algorithm was validated from the heart rate obtained from Electrocardiography signals and the proposed method with the RLS algorithm presented the best results with an absolute mean error of 1.54 beats per minute (bpm) and standard deviation of 0.62 bpm, recorded for 12 individuals performing a running activity on a treadmill with varying speeds. The results make the performance of the algorithm comparable and even better than several recently developed methods in this field. In addition, the algorithm presented a low computational cost and suitable to the time interval in which the heart rate estimate is performed. Thus, it is expected that this algorithm will improve the obtaining of heart rate in currently available wearable devices. / O monitoramento da frequ?ncia card?aca utilizando sinais de Fotopletismografia ou PPG (do ingl?s, Photopletismography) adquiridos do pulso de indiv?duos tem se popularizado devido ao surgimento de in?meros dispositivos wearable de baixo custo. No entanto, o monitoramento durante atividades f?sicas tem dificuldades em raz?o da influ?ncia de artefatos de movimento nos sinais de PPG. O objetivo deste trabalho ? introduzir um novo algoritmo capaz de remover artefatos de movimento e estimar a frequ?ncia card?aca de sinais de PPG de pulso. Os algoritmos do M?nimo Quadrado M?dio Normalizado ou NLMS (do ingl?s, Normalized Least Mean Square) e de M?nimos Quadrados Recursivos ou RLS (do ingl?s, Recursive Least Squares) s?o propostos para uma estrutura de filtragem adaptativa que utiliza sinais de acelera??o como refer?ncia para remover os artefatos de movimento. O algoritmo utiliza o Periodograma dos sinais filtrados para extrair suas frequ?ncias card?acas, que ser?o utilizadas juntamente com um ?ndice de Qualidade do Sinal de PPG para alimentar a entrada de um Filtro de Kalman. Heur?sticas espec?ficas e o ?ndice de Qualidade colaboram para que filtro de Kalman forne?a uma estimativa da frequ?ncia card?aca com alta acur?cia e robustez a incertezas de medi??o. O algoritmo foi validado a partir da frequ?ncia card?aca obtida de sinais de Eletrocardiografia e o m?todo proposto com o algoritmo RLS apresentou os melhores resultados com um erro m?dio absoluto de 1,54 batimentos por minuto (bpm) e desvio padr?o de 0,62 bpm, registrados para 12 indiv?duos realizando uma atividade de corrida em uma esteira com velocidades variadas. Os resultados tornam o desempenho do algoritmo compar?vel e at? mesmo melhor que v?rios m?todos desenvolvidos recentemente neste campo. Al?m disso, o algoritmo apresentou um custo computacional baixo e adequado ao intervalo de tempo em que a estimativa da frequ?ncia card?aca ? realizada. Dessa forma, espera-se que este algoritmo melhore a obten??o da frequ?ncia card?aca em dispositivos wearable atualmente dispon?veis.