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.

This thesis documents the tasks in support of the design and instrumentation of a miniature tensile load frame.

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The extraction and recovery of trace organic material from semi-solid and solid matrices is often the slowest and most error-prone step of an analytical method. The conventional liquid extraction techniques for solids and semi-solids materials (Soxhlet) have two main disadvantages. The first, large volumes of organic solvent are required, which can lead to sample contamination and "losses" due to volatilization during concentration steps. The second, to achieve an exhaustive extraction can require several hours to days. With the development of sophisticated instrumentation with detection limits in the picogram and femtogram levels, pressure is finally felt within the analytical community to develop and validate sample preparation procedures which can be used to rapidly isolate trace level organics from complex matrices.Because of its applicability to solid, semi-solid, and liquid matrices microwave-assisted (MAE) extraction has emerged as a powerful sample preparation technique. The objective of this research was to evaluate directly focused microwave energy for the isolation of systemic fungicide residues from woody plant tissues.The hallmark of microwave extraction (MAE) is accelerated dissolution kinetics as a consequence of the rapid heating processes that occur when a microwave field is applied to a sample. The current popularity of MAE resides mainly on its applicability to a wide range of sample types because the selectivity can be easily manipulated by altering solvent polarities.Propiconazole is a systemic fungicide, used to combat the fungal pathogen Ophiostoma ulmi, the casual agent of Dutch elm disease (DED). It was successfully extracted from treated Ulmus americana (elm tree) using MAE with a percent recovery of 395% in 15 minutes. Until now, techniques for rapid and efficient extraction of polar material from wood were non-existent. This work produces results much quicker than Supercritical Fluid Extraction (SFE). The influence of pH, microwave power, and time on extraction efficiency was also investigated. The extraction methodology was optimized and statistically validated.This MAE method combined with GC-MS was used to study the diffusion patterns and degradation of propiconazole in tree bark over extended time periods. Because of the complex nature of woody plant systems, it was realized that a more theoretical means must be used to determine the degradation rate of propiconazole in water systems. As a result, propiconazole was reacted with water under controlled temperature and pH conditions; to measure the degradation rate of propiconazole.The internal pH of elm sap is about 6.0; the slightly acidic environment and natural enzymes within the xylem vessels are known to catalyze the degradation of propiconazole (1). Novartis Inc. has marketed propiconazole as having fungicidal effects in injected elms for nearly two years. Our degradation studies have indicated much shorter lifetimes. To confirm our fate studies, the activation energy for the degradation reaction of propiconazole was calculated. This information provided valuable insight into revising dosage and treatment frequency for maximum protection of the elm against Dutch elm disease. Anti-fungal activity among metabolites was also explored. This is the first reported use of MAE to monitor the degradation of systemic pesticides in woody plant material. / Ph. D.

Dielectrophoresis (DEP), an electrokinetic force, is the motion of a polarizable particle in a non-uniform electric field. Contactless DEP (cDEP) is a recently developed cell sorting and isolation technique that uses the DEP force by capacitavely coupling the electrodes across the channel. The cDEP platform sorts cells based on intrinsic biophysical properties, is inexpensive, maintains a sterile environment by using disposable chips, is a rapid process with minimal sample preparation, and allows for immediate downstream recovery. This platform is highly competitive compared to other cell sorting techniques and is one of the only platforms to sort cells based on phenotype, allowing for the isolation of unique cell populations not possible in other systems. The original purpose of this work was to determine differences in the bioelectrical fingerprint between several critical cancer types. Results demonstrate a difference between Tumor Initiating Cells, Multiple Drug Resistant Cells, and their bulk populations for experiments conducted on three prostate cancer cell lines and treated and untreated MOSE cells. However, three significant issues confounded these experiments and challenged the use of the cDEP platform. The purpose of this work then became the development of solutions to these barriers and presenting a more commercializable cDEP platform. An improved analysis script was first developed that performs whole cell detection and cell tracking with an accuracy of 93.5%. Second, a loading system for doing smart sample handling, specifically cell focusing, was developed using a new in-house system and validated. Experimental results validated the model and showed that cells were successfully focused into a tight band in the middle of the channel. Finally, a proof of concept for an on-chip micropump is presented and achieved 4.5% in-plane deformation. When bonded over a microchannel, fluid flow was induced and measured. These solutions present a stronger, more versatile cDEP platform and make for a more competitive commercial product. However, these solutions are not just limited to the cDEP platform and may be applicable to multitudes of other microfluidic devices and applications. / Master of Science

International service learning (ISL) has been integrated into engineering education and has become increasingly more popular in co-curricular experiences. While prior research investigates each of these avenues of ISL, we have not investigated how these experiences compare to one another in terms of student learning outcomes or understood these experiences from a national perspective. The purpose of this thesis is to address these gaps in existing literature and to provide a comprehensive, holistic perspective of ISL experiences ability to impact student learning on a national scale. To better understand student learning outcomes in engineering ethics, agency and identity and draw comparisons in student career choices, several survey instruments were used within a nationally-representative survey distributed to engineering seniors (n=1911) at four-year universities within the United States. Descriptive statistics were used to categorize he responses by type of ISL experience: capstone, work, or co-curricular. The survey instruments were used to measure the individual learning outcomes: engineering ethics contained 5 items, engineering identity contained 14 items, and engineering agency contained 12 original items. Each survey instrument was validated using an exploratory factor analysis (EFA) to determine the relevant factor groups for each construct. An ANOVA test or Kruskal Wallis, the non-parametric equivalent test, was used for each dataset depending on normal distribution of the data. Responses in engineering ethics showed a significantly higher score in students’ ethics understanding with ISL capstone (p< 0.001) and work experience (p<0.0001) and a medium effect size for both (Cohen’s d=0.3). Responses in engineering agency scores showed a significant difference with ISL capstone (p<0.05) and co-curricular experience (p<0.05) with a medium effect size (Cohen’s d=0.3). Additionally, responses to predicted career choice post-graduation showed a lower percentage of students anticipating leaving engineering from the 9% population rate to 6% for those with ISL capstone experience and 5% for those with ISL co-curricular experience. These results give reason to consider more frequent incorporation of ISL projects into engineering courses such as senior capstone design. / Master of Science / International service learning (ISL) is a way of learning that allows students to use their engineering skills to help others, while gaining experience in a global context. ISL projects allow students to interact with people around the world, gaining experience with cultural and social diversity while using and developing their engineering skills. ISL projects take many forms and have been used within engineering education in many ways. The three most common ISL experiences are integration into capstone courses, independent ISL work experience, and co-curricular programs such as Engineers Without Borders. Prior research has focused mainly on capstone and co-curricular ISL experiences. Research found ISL experiences beneficial for student learning, however prior research has not investigated how these experiences compare. Additionally, many studies are conducted within courses or programs which provides a limited general understanding. This study aims to provide more clarity between student learning by ISL experiences and provide a national perspective on the impacts of ISL experiences. The purpose of this study was to determine how effective ISL experiences are in improving student learning in engineering ethics, agency, identity, and retaining engineering students in the field after graduation. A survey with a total of 31 questions related to these topics (5 for engineering ethics, 12 for engineering agency and 14 for engineering identity) was nationally distributed to senior engineering students enrolled at four-year universities in the United States. The responses were categorized by student’s ISL experience (capstone, work, and co-curricular) and compared for each engineering topic. The data was analyzed statistically, and the survey questions were analyzed to ensure that they were measuring student learning as expected. The results showed that ISL capstone projects improved students’ understanding of engineering ethics, increased their sense of engineering agency, and led to a lower percentage of students who planned to leave the field of engineering after graduation. ISL work experiences improved ethics understanding for students but had little to no impact on engineering agency. Finally, ISL co-curricular experiences had little impact on engineering ethics understanding, but improved student’s engineering agency beliefs and led to a less students leaving the field. These results give reasons to consider integrating ISL experiences into engineering education more frequently to provide benefits to students.