Erdol, Nurgun

One of the limiting factors restricting aircraft landings at maJor airports is the
minimum spacing requirements due to vortex wake avoidance. If it can be shown that the
separation requirements are too conservative, then it may be possible to increase the rate
of landings on a given runway. During August/September 2003, NASA and the (United
States Department of Transportation) USDOT sponsored a wake acoustics test at the
Denver International Airport. The central instrument of the test was a large microphone
phased array. Different types of aircrafts were recorded during landing and the acoustic
data obtained was stored. From acoustic data the spectrograms were generated using the
technique of AutoRegressive (AR) spectral estimation from multitaper autocorrelation
estimates.
Several sources of sound that are recorded in the audio files can be observed in the
spectrograms. Some these signals, such as the noise generated from the aircraft engine can be identified easily because of their strength and the Doppler shift they undergo. In
contrast to this, the wake vortex signal is weaker and does not exhibit a Doppler shift
because it's stationary in space. Therefore it may not be identified easily because of the
existence of stronger signals. The motive in our research is to develop methods to
determine these strong signals that appear as spectral lines in the spectrogram. In the
future, the results obtained in this work can be used to eliminate these strong signals from
the spectrogram thus allowing us to see and identify wake vortex signal which is more
important to us.

Cardiac auscultation, an important part of the physical examination, is difficult for
many primary care providers. As a result, diagnoses are missed or auscultatory signs
misinterpreted. A reliable, automated means of interpreting cardiac auscultation should
be of benefit to both the primary care provider and to patients. This paper explores a
novel approach to this problem and develops an algorithm that can be expanded to
include all the necessary electronics and programming to develop such a device. The
algorithm is explained and its shortcomings exposed. The potential for further
development is also expounded.

This research presents a novel approach to categorize dolphin whistles into various types. Most accurate methods to identify dolphin whistles are tedious and not robust, especially in the presence of ocean noise. One of the biggest challenges of dolphin whistle extraction is the coexistence of short-time duration wide-band echo clicks with the whistles. In this research, a subspace of select orientation parameters of the 2D Gabor wavelet frames is utilized to enhance or suppress signals by their orientation. The result is a Gabor image that contains a noise free grayscale representation of the fundamental dolphin whistle which is resampled and fed into the Sparse Representation Classifier. The classifier uses the l1 norm to select a match. Experimental studies conducted demonstrate: a a robust technique based on the Gabor wavelet filters in extracting reliable call patterns, and b the superior performance of Sparse Representation Classifier for identifying dolphin whistles by their call type.

Society's increased demand for communications requires searching for techniques that preserve bandwidth. It has been observed that much of the time spent during telephone communications is actually idle time with no voice activity present. Detecting these idle periods and preventing transmission during these idle periods can aid in reducing bandwidth requirements during high traffic periods. While techniques exist to perform this detection, certain types of noise can prove difficult at best for signal detection. The use of wavelets with multi-resolution subspaces can aid detection by providing noise whitening and signal matching. This thesis explores its use and proposes a technique for detection.

In this thesis, we develop a set of programs in the MATLAB RTM Graphical User Interface environment, for use as an Interactive Digital Signal Processing Laboratory. The software toolbox consists of programs on selected topics covered in undergraduate Digital Signal Processing courses. Care is taken to give the user sufficient degrees of freedom to illustrate the effect of various parameter changes. Program code is left open and well documented to allow expansion.

This study deals with applying the wavelet transform to mainly two different areas of signal processing: adaptive signal processing, and signal detection. It starts with background information on the theory of wavelets with an emphasis on the multiresolution representation of signals by the wavelet transform in Chapter 1. Chapter 2 begins with an overview of adaptive filtering in general and extends it to transform domain adaptive filtering. Later in the chapter, a novel adaptive filtering architecture using the wavelet transform is introduced. The performance of this new structure is evaluated by using the LMS algorithm with variations in step size. As a result of this study, the wavelet transform based adaptive filter is shown to reduce the eigenvalue ratio, or condition number, of the input signal. As a result, the new structure is shown to have faster convergence, implying an improvement in the ability to track rapidly changing signals. Chapter 3 deals with signal detection with the help of the wavelet transform. One scheme studies signal detection by projecting the input signal onto different scales. The relationship between this approach and that of matched filtering is established. Then the effect of different factors on signal detection with the wavelet transform is examined. It is found that the method is robust in the presence of white noise. Also, the wavelets are analyzed as eigenfunctions of a certain random process, and how this gives way to optimal receiver design is shown. It is further demonstrated that the design of an optimum receiver leads to the wavelet transform based adaptive filter structure described in Chapter 2.

Techniques for shaping component mismatch error in the real and imaginary paths of a complex (quadrature) delta-sigma are studied, simulated and compared to existing techniques. Complex bandpass delta-sigma modulators provide improved performance over a pair of real bandpass delta-sigma modulators of the same order in the conversion of narrow-band quadrature IF (intermediate frequency) signals. This is possible due to elimination of redundant conjugate pairs of poles and zeros in its noise and signal transfer function. Mismatches in the modulators real and imaginary paths, however, results in spectral leakage of image band interference and image band quantization noise to the passband of the converted output. Strategic pole-zero placement, as well as adaptive techniques have been studied in earlier works. This thesis will take advantage of the inherent dual paths of the complex bandpass delta-sigma modulator to reduce component mismatch effects using a switched capacitor error shaping technique.

Glottal pulse models provide vocal tract excitation signals which are used in producing high quality speech. Most of the currently used glottal pulse models are obtained by concatenating a small number of parametric functions over the pitch period. In this thesis, a new glottal pulse model is proposed. It is an alternative approach, which is based on the projection of glottal volume velocity over multiresolution subspaces spanned by wavelets and scaling functions. A detailed multiresolution analysis of the glottal models is performed using the compactly supported orthogonal Daubechies wavelets. The wavelet representation has been tested for optimality in terms of the reconstruction error and the energy compactness of the coefficients. It is demonstrated that by choosing proper parameters of the wavelet representation, high compression ratios and low rms error can be achieved.

This thesis presents an image coding system using binomial QMF based subband decomposition and vector quantisation. An attempt was made to compress a still image of size 256 x 256 represented at a resolution of 8 bits/pixel to a bit rate of 0.5 bits/pixel using 16 channel subband decomposition with binomial QMFs and coding the subbands using a full search LBG Vector Quantizer (VQ). Simulations were done on SUN work station and the quality of the image was evaluated by computing the Signal to Noise Ratio (SNR) between the original image and the reconstructed image.

The problems encountered in development and implementation of orthonormal two dimensional wavelet bases and their filter banks in polar coordinates are addressed. These wavelets and filter banks have possible applications in processing signals that are collected by sensors working in the polar coordinate system, such as biomedical and radar generated signals. The relationship between the space of measurable, square-integrable functions on the punctured polar coordinate system L^2(P) and space of measurable, square-integrable functions on the rectangular plane L^2(R^2) is developed. This allows us to develop complete wavelet bases in a more convenient and familiar surrounding of L^2(R^2) and to transport this theory to L^2(P). Corresponding filter banks are also developed. The implementation of wavelet analysis of punctured polar plane is discussed. An example of wavelet bases, filter banks, and implementation is provided.