This dissertation presents different methods for the deconvolution of time domain signals. The techniques developed in this work are frequency domain filtering techniques. and are suitable for the type of deconvolution problems encountered in time domain reflectometry (TOR). They include a smoothing technique that is a variant of the well known lowpass filter. This technique is parameter dependent in order to allow for adequate choice of cutoff frequency. Another more powerful method developed is an adaptive smoothing (regularization) technique, which is both frequency dependent and input-signal dependent as well. Thus, it is an adaptive technique whose performance depends on a parameter associated with its smoothing constraint. These frequency domain techniques and their variants are parameter dependent; hence a parameter optimization criterion must be included. However, in deriving an optimization criterion, great importance must be given to its adequacy in the determination of the appropriate parameter value as well its time efficiency. A parameter optimization method that fulfills those two reqUirements is also developed. The method is fully implemented in the frequency domain in which the filtering techniques are used. The techniques developed are derived with a magnitude component only. i.e., non-causal. The limited derivation is due to the fact that we are usually interested in reducing only the noise level from the magnitude point of view. However, If we consider time domain measurements as an example, physical pulses and transients are causal functions of time, i.e., their values are zero before t = 0, the time at which they begin. Their measured waveform data are also causal. When deconvolution processing is applied to remove instrumentation errors and/or suppress the effects of noise, non-causal deconvolution methods, that were mentioned previously, may introduce unacceptable errors. The conventional deconvolution is modified to ensure that causality is maintained in the deconvolution result. The impulse response of an unknown system is recovered from time domain reflectometry data by implementing a method based on the homomorphic deconvolution technique. In time domain reflectometry, the reflected waveform by a line with several discontinuities is represented as the convolution of the reflection coefficient of the line and the input excitation of the line source. The reflection coefficient is generally a train of spikes (delta functions) when the discontinuities are resistive. However, this is not the case when the discontinuities are capacitive in nature. In this work, we will attempt to show that the conventional frequency domain deconvolution techniques fail to provide good estimates when the waveform contains certain amounts of noise. Since it has been shown that homomorphic systems are useful in separating signals which have combined through convolution, homomorphic filtering can then be applied to recover either the input excitation or the impulse response (reflection coeffiCient) of the network. / Ph. D.

no abstract provided by author / Master of Science

In many applications of time series analysis, the scientist estimates the spectral density function of the process. One type of spectral density estimator is obtained by using the periodogram representation of the spectral density function. However, if the estimator is to be consistent, a weight function which satisfies certain conditions must be added to the periodogram estimator. If the weight function is a truncated function, the estimator of the spectral density function is labeled a truncated estimator. For this type of estimator, a truncation point can be chosen which yields a minimum mean square estimator among all truncated estimators. In this study we are concerned with two problems; i) finding consistent estimators of the spectral density function, and ii) determining the best truncation point. Two different types of weight functions, both of which give consistent estimators of the spectral density function, are presented in this dissertation. Both of the weight functions have related truncation functions. For the problem of determining the best truncation function one particular weight function is considered. Using this weight function, the asymptotic forms of the variance and the bias of the estimator are found. It is discovered that the best truncation function is dependent on one term of the bias of the estimator; this term is denoted by C(m(T)). There are three different forms of C(m(T)); each of these three forms can be used to obtain the best truncation function. There are several different types of covariance functions for a time series. The asymptotic relative efficiency of the spectral density estimator is dependent on the covariance function of the process. Using the best truncation point for a given weight function the asymptotic relative efficiency is derived for two covariance functions. The proposed method of finding the best truncation point is applied to other types of weight functions. Finally, the problem of estimating the best truncation function when the covariance function is unknown is discussed. / Ph. D.

No description available.

Autofluorescence spectroscopy can improve the early detections of oral cancer. Biochemical and structural changes associated with dysplastic progression alter the optical properties of oral mucosa and cause diagnostically significant differences in spectra from normal and neoplastic sites. This dissertation describes experimental and modeling studies aimed at revealing biological reasons for the diagnostically significant differences observed in depth-resolved fluorescence spectra from normal and neoplastic oral mucosa. An experimental approach, based on high-resolution fluorescence imaging, is used to study the autofluorescence patterns of oral tissue. At UV excitation, most of the epithelial autofluorescence originates from cells occupying the basal and intermediate layers, while stromal signal originates from collagen and elastin crosslinks. With dysplasia, epithelial autofluorescence increases, while autofluorescence from subepithelial stroma drops significantly. Benign lesions also display a drop in autofluorescence from subepithelial stroma, but have different epithelium fluorescence patterns compared to dysplasia. Optical probes that measure mostly stromal fluorescence, may reveal a similar loss of fluorescence intensity and thus fail to distinguish benign inflammation from dysplasia. These results emphasize the importance of using probes with enhanced detection of epithelial fluorescence for improved diagnosis of different types of oral lesions. The second part of this work presents a Monte Carlo model that predicts fluorescence spectra of oral mucosa obtained using a depth-selective probe as a function of tissue optical properties. A model sensitivity analysis determines how variations in optical parameters associated with neoplastic development influence the intensity and shape of spectra, and elucidates the biological basis for differences in spectra from normal and premalignant oral mucosa. Spectra of oral mucosa collected with the depthselective probe, are affected by variations in epithelial optical properties and to a lesser extent by changes in superficial stromal parameters, but not by changes in the optical properties of deeper stroma. Changes in parameters associated with dysplastic progression lead to a decreased fluorescence intensity and a shift of the spectra to longer emission wavelengths. Decreased fluorescence is due to a drop in detected stromal photons, whereas the shift of spectral shape is attributed to an increased fraction of detected photons arising in the epithelium.

LD2668 .T4 1954 S38 / Master of Science

Call number: LD2668 .T4 1965 E13 / Master of Science

Direct liquid fuel cells represent an interesting alternative to conventional hydrogen fuel cell technology. A novel analytical method for the monitoring of direct liquid fuel cells is presented. Employing a combination of chronoamperometric, gravimetric and Raman spectroscopic measurements this method allows a straightforward determination of the Faradaic efficiency of a fuel cell. This method was applied in a proof of concept study analysing the operational behaviour of a direct methanol fuel cell. A very low Faradaic efficiency was found for the fuel cell under study. This was attributed to loss of methanol from the fuel mixture due to methanol crossover, stripping of methanol by carbon dioxide as well as evaporation. It is known from the literature that a fuel change from methanol towards higher alcohols and other hydrocarbons can help to mitigate the effects of these loss processes. However, the behaviour of such alternative fuels and their mixtures in an operating fuel cell and hence the performance of the fuel cell depends at least in part on the intermolecular interactions present in those fuel mixtures. Therefore, the intermolecular interactions in binary and ternary mixtures of potential candidates for alternative fuels were investigated in the main part of this thesis. Studies on the intermolecular interactions in binary mixtures of acetone with ethanol and 1-butanol showed a tendency for self association of both compounds albeit in different concentration ranges. It was further found that the alkyl chain length of the alcohols did not have a significant effect on the intermolecular interactions in the binary and ternary mixtures. Further, the behaviour of the ternary mixture was found to closely resemble the effects found in the individual binary mixtures. Finally, binary mixtures of dimethyl sulfoxide (DMSO) and different alcohols did not show self association. It was rather found that alcohol molecules inserted into chains of DMSO molecules eventually leading to the formation of alcohol-DMSO dimers.

published_or_final_version / Physics / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / toc / Physics / Master / Master of Philosophy