The evolution of technology and gadgets has led to a significant increase in the number of transmitted signals, making RF sensing more complex than ever. Challenges such as signal interference and the lack of prior information about all signal parameters further complicate the task. To address this challenge, researchers have explored machine learning and deep learning approaches to generalize solutions for real-world sensing problems. In this thesis, we focus on two key issues in RF signal detection using deep learning. Firstly, we tackle the problem of increasing signal detection coverage by utilizing multiple resolution eigengram images derived from a bank of channelizers. These channelizers, varying in size, are adept at sensing different types of signals, such as low duration or low bandwidth signals. Channelizer deconfliction is a known challenge in RFML. We use YOLO, a deep learning algorithm, to deconflict the outputs from different channelizers to avoid overreporting. YOLO's ability to handle three channels makes it ideal for our study as we also use three channelizers. While our approach is not dependent on YOLO, it provides a good testing ground for this study. To address signal overlap, we utilize an eigengram image capturing the overlap region between signals. By overlaying this eigengram onto the original, we create a composite image highlighting the overlap. We train another YOLO model using two channels, one for each eigengram, enabling detection even with over 50 percent overlap. This work is versatile and promising, extending to other signal visualizations. It has significant potential for wireless industry applications and sets the stage for further RFML research. / Master of Science / Due to the exponential growth in Radio Frequency (RF) signals over the last few decades, brought about by the proliferation of gadgets, signal detection has become more complex than ever. To address these complexities in signal sensing, adopting a dynamic approach that is not reliant on specific parameters or thresholds is essential. RF approaches using deep learning show great promise in tackling these challenges. Deep learning is the branch of machine learning based on artificial neural networks. An artificial neural network uses layers of interconnected nodes called neurons that work together to process and learn from the input data. The first part of this thesis addresses increasing signal coverage by leveraging different signal perspectives, each capturing unique characteristics. By combining these perspectives into a dataset, we train a deep learning model that incorporates the strengths of each view, resulting in maximum detection coverage. The novelty lies in innovative data preprocessing techniques and using YOLO to deconflict signal views with up to three channelizers. In the second part, we focus on detecting overlapped or occluded signals. We utilize a new dimension of information describing interference regions between signals. By integrating this overlap perspective, we enhance the dataset to identify instances of extensive signal overlap and determine their regions of coverage. This enhancement enables the deep learning network to identify patterns and effectively detect highly overlapped or completely occluded signals.

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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.