Doctor of Philosophy, The Ohio State University, 2007, Speech and Hearing Science

Spectral and temporal processing have an extensive history of research for the discrimination and integration of tones. The integration of both dimensions simultaneously, however, has received little attention in psychoacoustics. This dual integration is vital to our daily processing of sounds around us, and has also not been effectively addressed in the ecological acoustics research. For this reason, we still have essentially no understanding of how the auditory system processes sounds that are changing in both frequency and time domains at the same time. This study was designed to begin the process of measuring the basic detection of signals that vary in both spectral and temporal dimensions. Baseline measures of detection for 10 msec pure tones were taken and the levels adjusted so that all the frequencies could be detected at the same level of attenuation. The thresholds were then obtained for spectral integration of the signals and for temporal integration, so that these results could be compared with prior research. The signals were then varied on both dimensions simultaneously in several ways: with equal spectral and temporal step sizes, different spectral and temporal step sizes, random presentation, and with doubled spectral or temporal information. The data were also analyzed along several differences: spectral step size, temporal step size, frequency range, direction, slope, and predictability. The spectral and temporal integration conditions showed a good match with the results of prior research, showing that the current procedures and signals could be used to reliably compare to existing results. The spectrotemporal integration conditions showed the threshold for overall detection of the signals to be limited by the ability to integrate spectral information, while the temporal integration was much better. Additionally, very little influence could be seen by most of the differences in signals. Surprisingly, random presentation of frequencies did not nega (open full item for complete abstract)

Committee: Lawrence Feth (Advisor) Subjects:

Doctor of Philosophy (PhD), Wright State University, 2010, Engineering PhD

We integrated a sensor hardware test-bed using scientific grade, commercial off-the-shelf (COTS) technology and developed supporting software to enable rapid prototyping. The validity of this test-bed and associated software was demonstrated through the delivery of a ground-based multispectral visual surveillance prototype for improvised explosive devices (IED) detection using electro-optical (EO) and short-wave infrared (SWIR) cameras. Software developed to support the test-bed included modules for image acquisition, preconditioning, segmentation, feature extraction, data regularization and pattern recognition. To provide spatially co-aligned data, we optimized a mutual information-based image registration algorithm to improve its convergence rate and benchmarked it against the established simplex method. For four different multimodal test image sets, our algorithm convergence success improved by 15 to 40% as compared to the downhill simplex method, albeit with an approximately four times higher computational cost. Additional strategies, such as bit-depth reduction, image down-sampling and gradient-based regions of interest (ROI) selection, were systematically evaluated and led to the registration of high resolution images at nearly 60 times faster than the standard approach. To automatically identify IED in the acquired multi-spectral imagery, four different pattern classifiers were tested; Bayes, k-nearest neighbor (knn-NN), support vector machines (SVM) and our novel piece-wise linear convex-hull classifier. Initial tests with the convex-hull classifier using simulated data indicated significant reduction in error rates of up to 89% (p=3e-6) when compared to the Bayes classifier. Subsequently, each of the four classifiers was tested using the IED data set that consisted of 154 different intensity-based and content-based features extracted from the EO and SWIR imagery. Salient features were selected using receiver operating characteristic (ROC) curve analysis an (open full item for complete abstract)

Committee: Julie A. Skipper PhD (Advisor); Thomas N. Hangartner PhD (Committee Member); Lang Hong PhD (Committee Member); S. Narayanan PhD (Committee Member); Mark E. Oxley PhD (Committee Member) Subjects: Engineering; Remote Sensing; Scientific Imaging; Systems Design