This thesis describes the design of a device for generating a reproduceable noise signals. The noise signal is generated by adding three periodic waveforms having non-multiple periods. Pulse techniques are used in the generation of the member functions so that the output may be reproduced exactly. Theoretical and experimental determinations of the amplitude probability distribution and of the autocorrelation function of the signal were made. On the basis of tests and observations made, it is concluded that the signal generated may be considered a noise signal having a near-Gaussian amplitude probability distribution, very little correlation for time-shifts greater than 30 seconds, and a bandwidth of about 60 cps. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

An Electrocardiogram (ECG) records electrical signals from the heart to detect abnormal heart rhythms or cardiac arrhythmias. Atrial Fibrillation (AF) is the most common arrhythmia which leads to a large number of deaths annually. The diagnosis of heart disease is skill-dependent and time-consuming, therefore using an intelligent system is a time- and cost-effective approach which can also enhance diagnostic accuracy. This study uses several types of Neural Networks (NNs) including the Deep Neural Network (DNN) GoogLeNet, Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Long Short-Term Memory (LSTM) to identify arrhythmias in AF signals. The results obtained are compared in order to identify the most effective and accurate system for AF diagnosis. The proposed system has two main steps, preprocessing and postprocessing. In the preprocessing step, different approaches based on the classifier network are used. More specifically, for MLP, ANFIS, and LSTM the 1-D Daubechies wavelet is used, and the extracted wavelet coefficients and statistical features are used as input data to the network. For GoogLeNet, the Continuous Wavelet Transform (CWT) is used to create a time-frequency representation of the signal (scalogram) and extract key signal features. In the postprocessing step, the data obtained (extracted features) are used as the input data to classify the signals. Also, the train and test accuracies and the running times are compared. The results obtained indicate that GoogLeNet provides the best accuracy, but its running time is long. Further, although the ANFIS and MLP networks are much faster than LSTM and GoogLeNet, their accuracy is much lower. / Graduate

Thesis PlanB (M.S.)--University of Wisconsin--Stout, 2009. / Includes bibliographical references.

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 73-76). Also available on the World Wide Web.

The position of a sound source can be accurately determined in both azimuth and elevation through the use of localisation cues extracted from the incident audio signals. Compared to lateral localisation, 2-D hardware localisation is novel and requires the extraction of spectral cues in addition to time delay cues. The objective of this work is to develop an analogue VLSI system which extracts these cues from audio signals arriving at the left and right channels of the system, and then map these cues to the source position. The use of analogue hardware, which is broadly adapted from the biological auditory system, enables fast and low power computation. To obtain accurate 2-D localisation from the hardware-extracted cues a novel algorithm for the mapping process has been developed. The performance of this algorithm is evaluated via simulation under different environmental conditions. The effects of hardware non-idealities on the localisation accuracy, including mismatches and noise are also assessed. The analogue hardware implementation is divided into three main sections: a front-end for splitting the input signal into different frequency bands and extraction of spectral cues, an onset detector for distinguishing between the incident portion and the echo portion of the acoustic signal, and a correlator for determination of time delay cues. Novel building blocks have been designed using standard CMOS in order to enable low voltage low power operation of the differential architecture essential for the accuracy of the extracted cues. A novel feedback technique enables accurately controlled Class AB operation of a low voltage switched-current memory cell. A novel cross-coupling technique ensures correct Class AB operation of a log-domain bandpass filter. The five chips developed here operate at ± 0.9 V supply. The system has been tested by applying audio signals convolved with a position-dependent transfer function at the input, and then processing the resulting hardware-generated cues. Measurement results show that 2-D localisation within 5° accuracy is achievable using hardware extracted cues. Key words: sound localisation, analogue VLSI, silicon cochlea, log domain, switched capacitor, switched current, current mode, analogue processing.

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