Using Synchronized Audio Mapping to Predict Velar and Pharyngeal Wall Locations during Dynamic MRI Sequences

Automatic tongue, velum (i.e., soft palate), and pharyngeal movement tracking systems provide a significant benefit for the analysis of dynamic speech movements. Studies have been conducted using ultrasound, x-ray, and Magnetic Resonance Images (MRI) to examine the dynamic nature of the articulators during speech. Simulating the movement of the tongue, velum, and pharynx is often limited by image segmentation obstacles, where, movements of the velar structures are segmented through manual tracking. These methods are extremely time-consuming, coupled with inherent noise, motion artifacts, air interfaces, and refractions often complicate the process of computer-based automatic tracking. Furthermore, image segmentation and processing techniques of velopharyngeal structures often suffer from leakage issues related to the poor image quality of the MRI and the lack of recognizable boundaries between the velum and pharynx during contact moments. Computer-based tracking algorithms are developed to overcome these disadvantages by utilizing machine learning techniques and corresponding speech signals that may be considered prior information. The purpose of this study is to illustrate a methodology to track the velum and pharynx from a MRI sequence using the Hidden Markov Model (HMM) and Mel-Frequency Cepstral Coefficients (MFCC) by analyzing the corresponding audio signals. Auditory models such as MFCC have been widely used in Automatic Speech Recognition (ASR) systems. Our method uses customized version of the traditional approach for audio feature extraction in order to extract visual feature from the outer boundaries of the velum and the pharynx marked (selected pixel) by a novel method, The reduced audio features helps to shrink the search space of HMM and improve the system performance.   Three hundred consecutive images were tagged by the researcher. Two hundred of these images and the corresponding audio features (5 seconds) were used to train the HMM and a 2.5 second long audio file was used to test the model. The error rate was measured by calculating minimum distance between predicted and actual markers. Our model was able to track and animate dynamic articulators during the speech process in real-time with an overall accuracy of 81% considering one pixel threshold. The predicted markers (pixels) indicated the segmented structures, even though the contours of contacted areas were fuzzy and unrecognizable.  M.S

Research Reports: 1997 NASA/ASEE Summer Faculty Fellowship Program

For the 33rd consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama in Huntsville and MSFC during the period June 2, 1997 through August 8, 1997. Operated under the auspices of the American Society for Engineering Education, the MSFC program was sponsored by the Higher Education Branch, Education Division, NASA Headquarters, Washington, D.C. The basic objectives of the program, which are in the 34th year of operation nationally, are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers. The Faculty Fellows spent 10 weeks at MSFC engaged in a research project compatible with their interests and background and worked in collaboration with a NASA/MSFC colleague. This document is a compilation of Fellows' reports on their research during the summer of 1997. The University of Alabama in Huntsville presents the Co-Directors' report on the administrative operations of the program. Further information can be obtained by contacting any of the editors

Multiscale Analysis for Characterization of Remotely Sensed Images.

In this study we addressed fundamental characteristics of image analysis in remote sensing, enumerated unavoidable problems in spectral analysis, and highlighted the spatial structure and features that increase information amount and measurement accuracy. We addressed the relationship between scale and spatial structure and the difficulties in characterizing them in complex remotely sensed images. We suggested that it is necessary to employ multiscale analysis techniques for analyzing and extracting information from remotely sensed images. We developed a multiscale characterization software system based on an existing software called ICAMS (Image Characterization And Modeling System), and applied the system to various test data sets including both simulated and real remote sensing data in order to evaluate the performance of these methods. In particular, we analyzed the fractal and wavelet methods. For the fractal methods, the results from using a set of simulated surfaces suggested that the triangular prism surface area method was the best technique for estimating the fractal dimension of remote sensing images. Through examining Landsat TM images of four different land covers, we found that fractal dimension and energy signatures derived from wavelets can measure some interesting aspects of the spatial content of remote sensing data, such as spatial complexity, spatial frequency, and textural orientation. Forest areas displayed the highest fractal dimension values, followed by coastal, urban, and agriculture respectively. However, fractal dimension by itself is insufficient for accurate classification of TM images. Wavelet analysis is more accurate for characterizing spatial structures. A longer wavelet was shown to be more accurate in the representation and discrimination of land-cover classes than a similar function of shorter length, and the combination of energy signatures from multiple decomposition levels and multispectral bands led to better characterization results than a single resolution and single band decomposition. Significant improvements in classification accuracy were achieved by using fractal dimensions in conjunction with the energy signature. This study has shown that multiscale analysis techniques are very useful to complement spectral classification techniques to extract information from remotely sensed images

A living lump of appetites\u27: the reinvention of the primitive in naturalist and modernist literature

When we consider the historical and cultural events that mark the latenineteenth through early twentieth centuries, we discover a growing fear about the vanishing Anglo-Saxon. This response to the non-Anglo-Saxon or primitive differs significantly, however, from earlier, more romantic definitions and philosophies wherein the primitive is considered a positive alternative to civilization. While earlier eras conceive of the primitive as positive, these eras\u27 judgments change as one considers turn of the twentieth-century American literature. It is this reassessment of the primitive that is the focus of my study. am particularly interested in how these ruminations about the primitive are codified scientifically and developed in the late nineteenth and early twentieth centuries. In this dissertation, I examine selected literature written during the naturalist and modernist movements in order to determine the degree to which these texts advance theories about the primitive that participate in an agenda of fear and loathing. Pivotal to my study is a trajectory of the primitive that shows its many forms, its changes and contradictions, and the degree to which naturalist and modernist texts draw upon earlier romantic images of the primitive and transmogrify them

Suffolk University Undergraduate Academic Catalog, College of Arts and Sciences, 2017-2018

This catalog contains information for the undergraduate programs in the College of Arts and Sciences. The catalog is a PDF version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as a separate PDF file listing all CAS course offerings. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1175/thumbnail.jp

Suffolk University Undergraduate Academic Catalog, College of Arts and Sciences and Sawyer Business School, 2013-2014

This catalog contains information for the undergraduate programs in the College of Arts and Sciences and the Sawyer Business School. The catalog is a pdf version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as separate pdf files with lists of CAS and SBS courses. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1166/thumbnail.jp

Suffolk University Undergraduate Academic Catalog, College of Arts and Sciences and Sawyer Business School, 2015-2016

This catalog contains information for the undergraduate programs in the College of Arts and Sciences and the Sawyer Business School. The catalog is a PDF version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as separate PDF files with lists of CAS and SBS courses. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1170/thumbnail.jp