The emphasis of this thesis is to use differencing approaches to develop data-based acoustic and seismoacoustic sensing methods (actively and passively) with minimal a priori information and modeling; such as extracting seismic information without using seismometers, performing an active localization in a complex medium without an accurate propagation model, and performing an active probing without active sources. The differencing methods are explored to extract information from ocean and laboratory data. Chapter two provides a simple vector sensor emulation analysis by differencing the acoustic pressure fields. We test this concept by processing hydroacoustic data from Comprehensive Nuclear-Test-Ban Treaty Organization's (CTBTO) International Monitoring System (IMS) stations in the Indian Ocean, and comparing the obtained simulated seismograms to velocity records from a nearby island seismic station. We demonstrate the procedure with simple analyses of records of the Great Sumatra-Andaman Earthquake of 2004, and show that water column data can be used to emulate seismometer measurements. The demonstrated vector sensor emulation method provides a potential opportunity to employ ocean remote sensing methodologies in deep water regions, therefore providing supplemental seismic measurements. Chapter three demonstrates a target localization using a data-based sensitivity kernel (SK), a perturbation approach, without using a complicated model. Experimental confirmation of the method is obtained using a cylindrical tank and an aggregate of ping-pong balls as targets surrounded by acoustic sources and receivers in a multistatic configuration. The differencing approach is implemented as amplitude change between target-free and target-present fields. The experimental observations show that target localization is successful using only the direct path arrivals, and improves by including later arrivals from the tank wall and the bottom/surface reverberation. As a follow up study, ambient noise resident in the tank is analyzed with a motivation of active probing without active sources. Using the same laboratory-set up as in Chapter three, we show that it is possible to extract the acoustic response of the tank using only ambient noise recorded in the tank. The work presented here is a validation of early work in the ocean environment

Novel approaches are needed to accurately classify and monitor sleep patterns in older adults, particularly those with cognitive impairment and non-normative sleep. Traditional methods ignore underlying sleep architecture in these patient populations, and other modern approaches tend to focus on healthy, normative patient populations. In this paper, we developed a model using a long-short-term memory neural network (LSTM) and trained it on a sample of older, non-normative patients. The 22 nights of data collected were trained on gold-standard polysomnography (PSG) as ground truth and were compared against the clinical standard threshold-based method for sleep detection. The LSTM more than doubled the traditional method's ability to detect clinically-relevant wakefulness during sleep (37.7% vs. 15%) without significantly sacrificing accuracy (67.7% vs. 75%) or precision (90.7% vs. 94%) of sleep classification.