The aging population and increase of ambulatory surgeries have greatly increased strain on surgical and post-surgical staff that decreases the safety of care. Our overall goal is to find ways to decrease the time of anesthetic recovery to allow for more efficient post-surgical treatment. The specific aims of this study were to assess the correlations between neurocognitive recovery measures of attention and vigilance to brain dynamics. We analyzed reaction time via auditory psychomotor vigilance testing (aPVT) testing and the Richmond agitation-sedation scale (RASS) scores in 145 patients prior to and preceding surgery. Intraoperative electroencephalogram was also recorded for 115 of those patients. Data was analyzed to associate aPVT performance to recovery time and intraoperative brain dynamics. We found an association coefficient between reaction time and RASS recovery of 0.022 (p-value = 0.0001) showing a significant association. Further, we found age to be a significant confounding variable (p=0.04421) and included this in our association model. Lastly, there was no significant association found between intraoperative burst suppression and reaction time values (p=0.497). Overall, aPVT was found to be a robust test to assess recovery timeline in peri-operative anesthesia care unit patients. These results highlighted the potential use of an objective metric to track neurocognitive recovery after anesthesia, especially in elderly patients undergoing surgery.

The wireless spectrum is limited and the demand for its use is increasing due to technological advancements in wireless communication, resulting in persistent interference issues. Despite progress in addressing interference, it remains a challenge for effective spectrum usage, particularly in the use of license-free and managed shared bands and other opportunistic spectrum access solutions. Therefore, efficient and interference-resistant spectrum usage schemes are critical. In the past, most interference solutions have relied on avoidance techniques and expert system-based mitigation approaches. Recently, researchers have utilized artificial intelligence/machine learning techniques at the physical (PHY) layer, particularly deep learning, which suppress or compensate for the interfering signal rather than simply avoiding it. In addition, deep learning has been utilized by researchers in recent years to address various difficult problems in wireless communications such as, transmitter classification, interference classification and modulation recognition, amongst others. To this end, this dissertation presents a thorough analysis of deep learning techniques for interference classification and suppression, and it thoroughly examines complexity (sample and model) issues that arise from using deep learning. First, we address the knowledge gap in the literature with respect to the state-of-the-art in deep learning-based interference suppression. To account for the limitations of deep learning-based interference suppression techniques, we discuss several challenges, including lack of interpretability, the stochastic nature of the wireless channel, issues with open set recognition (OSR) and challenges with implementation. We also provide a technical discussion of the prominent deep learning algorithms proposed in the literature and also offer guidelines for their successful implementation. Next, we investigate convolutional neural network (CNN) architectures for interference and transmitter classification tasks. In particular, we utilize a CNN architecture to classify interference, investigate model complexity of CNN architectures for classifying homogeneous and heterogeneous devices and then examine their impact on test accuracy. Next, we explore the issues with sample size and sample quality with regards to the training data in deep learning. In doing this, we also propose a rule-of-thumb for transmitter classification using CNN based on the findings from our sample complexity study. Finally, in cases where interference cannot be avoided, it is important to suppress such interference. To achieve this, we build upon autoencoder work from other fields to design a convolutional neural network (CNN)-based autoencoder model to suppress interference thereby ensuring coexistence of different wireless technologies in both licensed and unlicensed bands. / Doctor of Philosophy / Wireless communication has advanced a lot in recent years, but it is still hard to use the limited amount of available spectrum without interference from other devices. In the past, researchers tried to avoid interference using expert systems. Now, researchers are using artificial intelligence and machine learning, particularly deep learning, to mitigate interference in a different way. Deep learning has also been used to solve other tough problems in wireless communication, such as classifying the type of device transmitting a signal, classifying the signal itself or avoiding it. This dissertation presents a comprehensive review of deep learning techniques for reducing interference in wireless communication. It also leverages a deep learning model called convolutional neural network (CNN) to classify interference and investigates how the complexity of the CNN effects its performance. It also looks at the relationship between model performance and dataset size (i.e., sample complexity) in wireless communication. Finally, it discusses a CNN-based autoencoder technique to suppress interference in digital amplitude-phase modulation system. All of these techniques are important for making sure different wireless technologies can work together in both licensed and unlicensed bands.

In historically fire-dependent upland oak forests of the eastern U.S., anthropogenic fire exclusion is likely causing a hypothesized feedback loop between an increase in fire-sensitive species and self-promoting, fireree conditions at the detriment of oak regeneration. This study determined how shifts from oaks (Quercus stellata and Q. falcata) to fire-sensitive non-oaks (Carya spp., Liquidambar styraciflua, and Ulmus alata) affected flammability and related processes that consequently determine species composition. Using treatments of increasing non-oak leaf litter, experimental burns were conducted and flammability measured under field conditions, and a laboratory litter moisture desorption experiment was conducted. As litter composition shifted from oak-dominated to non-oak-dominated, flammability decreased (R2 = 0.59, P < 0.001) and moisture-holding capacity increased (R2=0.88, P<0.001). To prevent further shifts toward fireree conditions and loss of economically and ecologically valuable oaks, prescribed fire should be reintroduced soon while oak maintains overstory dominance and controls forest flammability.

This work presents a solution for a real-time fire suppression control system. It also serves as a support tool that allows creation of virtual ship models and testing them against a range of representative fire scenarios. Model testing includes generating predictions faster than real time, using the simulation network model developed by Hughes Associates, Inc., their visualization, as well as interactive modification of the model settings through the user interface. In the example, the ship geometry represents ex-USS Shadwell, test area 688, imitating a submarine. Applying the designed visualization techniques to the example model revealed the ability of the system to process, store and render data much faster than the real time (in average, 40 times faster).

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