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<p> In this thesis, we present three algorithms for blind estimation of the finite impulse response (FIR) channels in the presence of unknown noise. The algorithms are developed considering different available system resources: 1) If only one receiving antenna is available, based on the single-input-single-output (SISO) system model, with the output being up-sampled, we develop the maximum a posteriori (MAP) algorithm for Gaussian distributed noise. With large enough samples being collected, during which the channel keeps invariant, an efficient implementation of the MAP algorithm is also obtained; 2) If two receiving antennae can be affordable, based on the singleinput-multiple-output (SIMO) system model and up-sampling both the outputs, we develop a subspace based algorithm utilizing Canonical Correlation Decomposition (CCD) to obtain the subspaces, and a maximum likelihood (ML) based algorithm which starts from the Gaussian distributed projection error from the noise subspace onto the COD-estimated signal subspace. The developed channel estimators achieve superior performance measured by the normalized root mean square error (NRMSE), compared with some existing second-order-statistics (SOS) based methods while keeping the computation complexity comparable. When more than two receiving antennae are available, by treating them as one group and applying the MAP algorithm or separating them into two groups and applying the CCD based algorithms, the channels can still be blindly estimated with or without up-sampling the outputs. </p> / Thesis / Master of Applied Science (MASc)

This thesis presents a new approach to blind deconvolution algorithms. The proposed method is a combination of a classical blind deconvolution subspace method and a marginalized particle filter. It is shown that the new method provides better performance than just a marginalized particle filter, and better robustness than the classical subspace method. The properties of the new method make it a candidate for further exploration of its potential application in acoustic blind dereverberation. / Thesis / Master of Applied Science (MASc)

The fetal electrocardiogram contains within it, information regarding the health of the fetus. Currently, fetal ECG is recorded directly from the scalp of the baby during labour. However, it has been shown that fetal ECG can also be measured using surface electrodes attached to a pregnant mother's abdomen. The advantage of this method lies in the fact that fetal ECG can be measured noninvasively before the onset of labour. The difficulty lies in isolating the fetal ECG from extraneous signals that are simultaneously recorded with it. Several signal processing methodologies have been put forth in order to extract the fetal ECG component from a mixture of signals. Two recent techniques that have been put forth include a scheme that has previously been used to nonlinearly reduce noise in deterministically chaotic noise and the other uses a blind source separation technique called independent component analysis. In this thesis, we describe the significance of the fetal electrocardiogram as a diagnostic tool in medicine, a brief overview of the theory behind the nonlinear noise reduction technique and blind source separation, and results from having processed synthetic and real data using both techniques. We find that although the noise reduction technique performs adequately, the blind source separation process performs faster and more robustly against similar data. The two techniques can be used in tandem to arrive at an approximate fetal ECG signal, which can be further analyzed by calculating, for example, the fetal heart rate. / Thesis / Master of Engineering (ME)

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Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1981 / Includes bibliographical references. / by John Harvey Serocki. / M.S. / M.S. Massachusetts Institute of Technology, Department of Mechanical Engineering