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QC 351 A7 no. 78 / Observers responded to abstract forms (quadrigons) in six experiments, under a signal detection paradigm. Duration of stimulus exposure was shown to have strong effects upon detection accuracy (two studies); immediate feedback of accuracy information to observers affected performance chiefly by influencing guessing bias, not sensitivity (two studies); images that had been blurred and then deblurred by means of an analog device were compared with unblurred originals, and the effects of the retrieval process (deblurring) were characterized quantitatively by a signal detection index (one study); and electroencephalographic correlates of signal detection responses were found to vary with performance accuracy and observer confidence (one study). Discussions of the theory of signal detectability and of electroencephalography, as tools in the study of image quality and of observer sensitivity, are included in the report.

Prematurity, especially if extreme, is one of the leading causes of problems and/or death after delivery. Among all the problems encountered by premature infants, feeding difficulties are very common. Many premature infants are fed intravenously at first, and they progress to milk feedings provided by a tube passed into the stomach. At around 34 weeks of gestation, premature infants should be able to breastfeed or take a bottle. At the same time such premature infants are usually faced with difficulty making the transition from tube-feeding to full oral feeding. In this study three physiological measurements of premature infants including sucking, swallowing and breathing were measured. The objective of this work was to detect, identify and classify these three signals independently and in relation to each other. The goal was to look at the specification of sucking, swallowing and breathing signals to extract the ratio of suck swallow-breath coordination. The results of this study were used to predict the readiness of a premature infant for introduction to oral feeding.To accomplish this, three different methods were examined. In the first method, the integration of the wavelet packet transform and a neural network was investigated. According to results of the first approach, integration of the wavelet packet transform and the neural network failed due to the inefficiency of the feature extraction method. Thus, the wavelet packet energy nodes did not provide a good feature extraction tool in this specific application.In the second approach, the frequency content of each signal was investigated to study the relationship between the shape of each waveform and the frequency content of that specific signal. Spectral analysis for suck, swallow and breathing signals showed that the shape of the signal was not tightly related to the frequency content of that specific waveform. Therefore, the frequency content could not be used as a method of feature extraction in this specific application.In the third method, the integration of correlation and matched filtering techniques was investigated and demonstrated promising result for the detection of suck and breathing signal but not for the swallowing waveform. Based on the results for sucking and breathing signals, this method should also work for good quality swallowing signal. To understand the relationship between the suck, swallow and breathing signals a matrix containing information on the time of occurrence of each event was developed.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Lori F. Lamel. / M.S.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by John A. Cartelli. / M.S.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERO. / Includes bibliographical references. / Ph.D.

This thesis presents the unified bias analysis of subspace-based DOA estimation algorithms in terms of physical parameters such as source separation, signal coherence, number of senors and snapshots. The analysis reveals the direct relationship between the performance of the DOA algorithms and signal measurement conditions. Insights into different algorithms are provided. Based upon previous first-order subspace perturbations, second-order subspace perturbations are developed which provide basis for bias analysis and unification. Simulations verifying the theoretical bias analysis are presented.

There has been an increasing interest in uncovering smuggled nuclear materials associated with the War on Terror. Detection of special nuclear materials hidden in cargo containers is a major challenge in national and international security. We propose a new physics-based method to determine the presence of the spectral signature of one or more nuclides from a poorly resolved spectra with weak signatures. The method is different from traditional methods that rely primarily on peak finding algorithms. The new approach considers each of the signatures in the library to be a linear combination of subspectra. These subspectra are obtained by assuming a signature consisting of just one of the unique gamma rays emitted by the nuclei. We propose a Poisson regression model for deducing which nuclei are present in the observed spectrum. In recognition that a radiation source generally comprises few nuclear materials, the underlying Poisson model is sparse, i.e. most of the regression coefficients are zero (positive coefficients correspond to the presence of nuclear materials). We develop an iterative algorithm for a penalized likelihood estimation that prompts sparsity. We illustrate the efficacy of the proposed method by simulations using a variety of poorly resolved, low signal-to-noise ratio (SNR) situations, which show that the proposed approach enjoys excellent empirical performance even with SNR as low as to -15db. The proposed method is shown to be variable-selection consistent, in the framework of increasing detection time and under mild regularity conditions. We study the problem of testing for shielding, i.e. the presence of intervening materials that attenuate the gamma ray signal. We show that, as detection time increases to infinity, the Lagrange multiplier test, the likelihood ratio test and Wald test are asymptotically equivalent, under the null hypothesis, and their asymptotic null distribution is Chi-square. We also derived the local power of these tests. We also develop a nonparametric approach for detecting spectra indicative of the presence of SNM. This approach characterizes the shape change in a spectrum from background radiation. We do this by proposing a dissimilarity function that characterizes the complete shape change of a spectrum from the background, over all energy channels. We derive the null asymptotic test distributions in terms of functionals of the Brownian bridge. Simulation results show that the proposed approach is very powerful and promising for detecting weak signals. It is able to accurately detect weak signals with SNR as low as -37db.