Establishing the Effectiveness of the OpenBCI EEG System in Identifying Physiological Markers of Healthy Brains

Parkinson disease (PD) is the second most common neurodegenerative disorder in the United States, affecting 1.1 million people. However, ~20% of PD patients are misdiagnosed because diagnosis often relies on subjective motor assessments by doctors. Electroencephalography (EEG) is a non-invasive, accessible tool that records neural activity using electrodes placed on the scalp, and can be used to improve diagnostics with objective neural biomarkers of PD. The cognitive neuromodulation lab bought the OpenBCI EEG system last spring, and this project sought to identify the reliability of the system. Three experiments were conducted with healthy volunteers. The data for each experiment was preprocessed using EEGLAB to isolate neural data from electrical noise. Then, MATLAB’s signal processing toolbox was used to extract the neural features. The first two experiments looked for robust neurological biomarkers of health data identified with more advanced EEG systems. With subjects alternating between eyes open and eyes closed states in the first experiment and performing thirty trials of finger tapping in the second experiment, two nonmotor biomarkers and one motor biomarker were successfully identified. The third experiment had patients perform finger tapping and spiral drawing, bilaterally, replicating motor tasks in PD assessments. With this data, three biomarkers known to differ between PD and healthy subjects were identified in this healthy cohort, consistent with the data in the literature. Having identified these biomarkers, the reliability of the OpenBCI system is verified and a comparative study between healthy subjects and PD patients will be conducted to identify novel PD biomarkers.

Comparing Functional and Anatomical Division of the Subthalamic Nucleus as a Predictor of Clinical Outcomes in Deep Brain Stimulation

The subthalamic nucleus (STN) is one of the most common targets for deep brain stimulation (DBS), a treatment for alleviating the motor symptoms of Parkinson disease (PD). DBS involves implanting electrodes into the STN to deliver electrical stimulation, with the goal of reducing motor symptoms and improving quality of life for patients with PD. However, its success strongly depends on where within the STN the stimulation occurs. This study aims to determine which segmentation method more closely correlates with motor symptom improvement in DBS.

40 PD patients who previously underwent bilateral STN DBS were included for analysis. Outcomes were measured as percentage improvement across rigidity, tremor, bradykinesia, and overall motor symptom. Anatomical segmentation was performed by dividing each STN into six regions using its center of mass as a reference point. Functional segmentation was derived using the Accolla atlas, which labels the STN into motor, associative, and limbic zones. The atlas was registered to each STN and then the volume of tissue activation relative to the total volume of the STN was calculated to quantify stimulation.

Results showed that functional motor zone activation weakly correlated with rigidity improvement, while other functional zones showed no significant associations. In contrast, anatomical dorsal STN stimulation significantly correlated with rigidity and distinguished responder groups. The dorsal anatomical region demonstrated stronger clinical relevance.

Development of a Laboratory Methodology to Manufacture Tailings
Recent failures of tailings storage facilities have demanded a greater understanding of the behavior of tailings. However, characterization of tailings properties can be challenging due to limited access to representative samples. Hence, this study aimed to develop a methodology to recreate tailings in the laboratory that reproduce field characteristics. To accomplish this goal, we performed self-learning on the topic, laboratory investigation, and analysis of the samples created. Various geotechnical and mechanical properties of soils were studied to gain an understanding of the properties that would be tested and examined. Material characterization, sample preparation, and sample testing standards were reviewed and then applied to Ottawa sand and bentonite clay. These standard materials are accessible and readily available for laboratory use, which would allow for easy replication of the methodology created. Additionally, these materials have characteristics, for example, plasticity and shear strength, that, when mixed, would resemble field tailings. As a result of the work, a preliminary observation is that Ottawa sand has a large grain size and may not be the appropriate material for the final tailings sample, as tailings usually have a very fine grain size. Further research showed that it is possible to obtain silt-sized soils by crushing Ottawa sand, which would allow us to make a sample that is similar to field tailings and still maintains other tailings properties. Future research includes investigation of the silt-sized soil and bentonite clay mixtures to find the proper ratio of these materials to recreate tailings and then their strength properties.

Nutrient Loading Reduction Capability in Aged Green Roofs

As climate change accelerates, intense rain events have been persistent, damaging communities in unprecedented ways. Green roofs are a type of sustainable infrastructure that has been implemented on top of modern buildings to mitigate the effects of climate change. However, the presence of nutrients important to plant life, such as nitrogen and phosphorus, in stormwater runoff can deeply damage nearby waterways through eutrophication. This research focuses on the capabilities of the four green roof test plots atop Academic East to reduce stormwater volume, how the green roof may affect nutrient loading in stormwater runoff, and how changing plant cover may affect these parameters. To do this, the green roof testing lab found in Academic East was restored and redesigned for side-by-side analysis of the test plots. Although the project could not be fully completed in one summer, it was found that the restored green roof, with no additional changes, considerably reduced stormwater volume, peak flow rate, and ammonium mass loading for one large storm event. For this same storm event, nitrate and phosphate mass loading in the runoff appeared to increase. In the future, more research focusing on the effects of changing plant cover plans needs to be done.

Exploring 2D and 3D facial recognition systems and its security

Two-dimensional (2D) face recognition is widely deployed for authentication and security, yet it remains vulnerable to spoofing attacks using printed photos, replay videos, and deepfakes. Its reliability also degrades when real-world conditions drift from the enrollment image, especially under changing lighting, facial
expressions, and occlusions such as masks or glasses, creating ongoing concerns about both security and robustness. Three-dimensional (3D) facial recognition is often presented as a stronger alternative because it can exploit depth and facial geometry, but its practical resilience and attack surface under adversarial conditions still need clearer, reproducible evidence.

This study evaluated and compared the accuracy, robustness, and security of 3D facial recognition systems against 2D baselines, benchmarking open-source pipelines across public datasets and controlled laboratory experiments. Testing systematically varies illumination (direction and intensity), expression changes, and occlusions, measuring performance in verification and identification tasks. To assess security, the systems were exposed to spoofing attempts using printed media and 3D-printed facial models, recording attack success rates and characteristic failure patterns.

We revealed where 3D methods provide meaningful gains (for example, reduced sensitivity to harsh lighting and certain occlusions) and where weaknesses persisted (for example, vulnerability to high-fidelity 3D replicas or sensor-specific artifacts). By linking failure modes to specific conditions, the work aims to propose targeted upgrades, such as depth-consistency checks, temporal liveness cues, and multi-modal fusion, to harden 3D recognition. Overall, the research clarified trade-offs between 2D and 3D facial recognition and supports the development of more robust and secure 3D authentication in realistic environments.

Control System Optimization for a Smart Residential Microgrid

This thesis began with a simple objective: to restore and optimize the control system of a smart residential microgrid so that it can respond intelligently to electricity prices while remaining safe and reliable on low-cost embedded hardware. The central research question is whether artificial intelligence can improve economic dispatch decisions without compromising system stability.

The work required restoring a legacy microgrid commissioned in 2015. Data acquisition was rebuilt using a Raspberry Pi 4 and an AcuRev smart meter to log voltage, current, power, power factor, and frequency at five-minute intervals. A stable data pipeline was achieved, with over 99 percent local logging uptime and 93 percent cloud upload reliability. This phase transformed the project from a control study into a grounded cyber-physical systems investigation.

A two-layer artificial neural network framework was developed. Layer 1 predicts next-hour PJM Real-Time Locational Marginal Prices using historical and time-based features, explaining about 92 percent of price variation. Layer 2 integrates predicted prices with real-time electrical measurements to guide operational modes such as load management and islanded operation. Although the controller achieved measurable economic improvement over a rule-based baseline, its performance was limited by constrained historical and seasonal data. Nevertheless, the implementation validated the complete data-to-decision pipeline and established a practical foundation for refinement.

The key finding is that economic optimization alone is insufficient. Safety must be explicitly enforced through hybrid control, combining AI prediction with rule-based protections.

Detecting Landcover from satellite images using AI and Ellmer

Discharge Process Optimization and Evaluation of Waste Streams from Spent Lithium-Ion Battery

Lithium-based batteries are increasingly replacing traditional battery technologies, leading to a growing accumulation of spent lithium batteries. These used batteries contain valuable heavy metals that can be recovered through various recycling methods. Due to their residual voltage, they must first be safely discharged before dismantling can occur. This research investigates optimal solvents for discharging lithium-based batteries to a safe voltage level for handling and recycling. Additionally, the study evaluates the environmental impact of the discharging process, with a focus on potential pollution risks if the resulting waste enters the environment without further treatment. Analytical techniques were employed for waste liquid analysis and waste solid characterization. Three solvents were tested: sodium chloride (NaCl), sodium sulfate (Na₂SO₄), and iron sulfate (FeSO₄). NaCl had the highest discharge efficiency, nearly 40%. At this voltage, the batteries can be safely dismantled. Preliminary results show that NaCl produced the highest heavy metal ion concentrations, with nickel, lithium, and magnesium being the most abundant. Na₂SO₄ produced lower concentrations overall but still showed elevated levels of nickel, copper, and zinc. GC-MS analysis confirmed that none of the solvents caused leakage of volatile organic compounds. Waste solid characterization revealed that sodium chloride use leads to precipitates of iron oxide (Fe₂O₃) and some unreacted NaCl. Sodium sulfate use results in the formation of solid copper sulfate (CuSO₄). Although no volatile organic compounds were detected, the high heavy metal concentrations and precipitate formation indicate that the waste could cause significant environmental pollution if not treated further.

Co-Liquefaction of Food Waste and Waste Cooking Oil: Reaction Mechanisms, Product Characterization and Ozonation of the Aqueous Phase
Food waste (FW) and waste cooking oil (WCO) make up a large portion of organic waste and are disposed of in ways that create environmental challenges. However, this waste can serve as valuable sources of sustainable energy and materials. This study investigates the hydrothermal liquefaction (HTL) of carbohydrate-rich FW and WCO to evaluate potential to produce biocrude as a sustainable aviation fuel (SAF) precursor, while examining HTL byproducts and post-treatment strategies. HTL experiments were conducted at temperatures between 280 and 320 °C and reaction times of 20–60 minutes with varying ratios of WCO and FW. Biocrude was extracted using diethyl-ether and acetone. The study results indicate reaction temperature strongly influenced product yields and conversion pathways. Increasing the temperature from 280 to 300 °C during FW liquefaction increased biocrude yield from 55.7 to 62.7 wt.%, while increasing the temperature to 320 °C reduced yield to 50.5 wt.%. Extending the reaction time to 60 minutes reduced byproduct formation while maintaining moderate biocrude yields.
Co-liquefaction of FW and WCO at a 1:1 ratio and 320 °C for 60 minutes produced the highest biocrude yield (77.1 wt.%). Biocrude composition varied with HTL severity and feedstock, with lower temperatures favoring oxygenated compounds and higher temperatures increasing hydrocarbons and aromatics. The addition of WCO altered the aqueous phase composition, and subsequent ozone treatment further oxidized and simplified organic species, improving suitability for downstream treatment.
Overall, this study demonstrates a promising pathway for food waste valorization with environmental and resource recovery benefits.

A Conceptual Approach to Estimate the Environment Impact of Electric Aircraft in the U.S. NAS
Electric aircraft are becoming more prevalent in our skies, and they are currently being developed with short ranges and small payloads. Electric aircraft offer a solution for reducing the environmental impact of the NAS, and, as with advances in aircraft fuel efficiency, battery technology has continued to improve, advancing electric aircraft over time and reducing NAS emissions. Similar to integrating electric cars into the U.S. National Transportation System, an important question is: What is the environmental impact of integrating electric aircraft into the U.S. National Airspace System (NAS)? This research proposes a methodology to quantify the environmental impact of the NAS by optimizing electric aircraft for inclusion in the NAS and then creating a “mixed” NAS comprising petrol and electric aircraft. This mixture then changes as battery technology improves. Results of this research show that for one day of aircraft operations in the U.S., with battery technologies of 250, 350, 500, 1000, and 2000 Wh/kg, 2.25%, 4.82%, 8%, 13.8%, and 14.81% of all petrol aircraft in the NAS can be replaced by electric aircraft, respectively. This mixed NAS leads to a total reduction in NAS emissions of 0.26% (161 mt of CO2), 0.688% (386 MT CO2), 1.14% (746 mt of CO2), 4.55% (2,017 mt of CO2), and 6.92% (2,744 mt CO2) for 250, 350, 500, 1000, and 2000 Wh/kg battery capabilities, respectively.