Engineering Commons^™

E-Bike Improvements, Reuben M. Eye, Cole Huddleston, Jeffery Scott, Cade Lewis, Ethan Ridenour

ATU Research Symposium

No abstract provided.

Ieee Robotics Competition, Emily Webb, Arath Sanchez, Genesis V. Garay, Caleb Bynum, Brandon Bunton

ATU Research Symposium

No abstract provided.

Breast Cancer Classification With Machine Learning, Rahanuma Tarannum

ATU Research Symposium

Breast cancer is one of the foremost causes of death amongst women worldwide. Breast tumours are characteristically classified as either benign (non-cancerous) or malignant (cancerous). Benign tumours do not spread external side of the breast and are not fatal, whereas malignant tumours can metastasize and be incurable if untreated. Rapidly and accurate diagnosis of malignant tumours is significant for efficient treatment and advanced outcomes. In 2022, breast cancer claimed 670 000 lives worldwide. Women without any particular risk factors other than age and sex account for half of all cases of breast cancer. In 157 out of 185 nations, breast …

Arkansas Tech University Modular Robotics Training System, Andrew L. Hilsdon, Jacob L. Crawford, Luke C. Hartman, Samuel E. Allen, Anthony W. Knudson, Kyle B. Mcmillan

ATU Research Symposium

Arkansas Tech University Modular Robotic Training System (ATUM RTS) project aims to combine two existing systems: The Georgia Tech Robotarium and the Micromouse maze-solving competition. The goal of ATUM RTS is to incorporate the challenge and excitement of maze-solving robots with the cloud-based learning system of the Robotarium. We intend to develop an automatic maze table with future remote capabilities to further student learning and engagement. By being able to create custom mazes with ATUM RTS, students will have access to a resource that will allow them to practically apply what they are learning in the classroom. The ATUM RTS …

Applications Of Supercapacitors In Robotic Systems, Charles Davis, Zachary Giese, Joseph Gober, Samuel Grisham, Avery Mahan

ATU Research Symposium

This project explores the utilization of a bespoke supercapacitor system to energize and propel a robot across various challenging courses. The custom supercapacitor setup serves as the primary power source, providing rapid charging capabilities and high energy density. The research investigates the integration of this innovative power solution into the robot's design, aiming to optimize its performance and endurance in competitive environments.

Advisync: A Dynamic Academic Course Scheduler, Spencer M. Anderson, Wilson Escobar, Devin Scott Sandlin, Nathan Patrick Doyle

ATU Research Symposium

Academic advising at universities can be a tedious and disorganized process for both students and advisors. Each advisor may have several dozen advisees to manage each semester, and each individual student has unique sets of classes they need to take to graduate. This might lead to scheduling errors. These errors can put the student behind in their degree, thus extending the time it takes for them to graduate past financial aid periods and delay their entry into the workforce. To address this issue, we create AdviSync. It is a tool for both students and advisors that aims to provide a …

Pyroscan: Wildfire Behavior Prediction System, Derek H. Thompson, Parker A. Padgett, Timothy C. Johnson

ATU Research Symposium

During a wildfire, it is of the utmost importance to be updated about all information of the wildfire. Wind speed, wind direction and dry grass often works as fuel for the fire allowing it to spread in multiple directions. These different factors are often issues for any firefighting organization that is trying to help fight the fire. An uncontrolled wildfire is often a threat to wildlife, property, and worse, human and animal lives. In our paper, we propose an artificial intelligence (AI) powered fire tracking and prediction application utilizing Unmanned Aerial Vehicles (UAV) to inform fire fighters regarding the probability …

Promoting Electric Propulsion (Pep) For Small Craft, Robert Manatt, Brad Fletcher, Hector Campos, Johnathon Poindexter, Quinn Reynolds

ATU Research Symposium

The Promoting Electric Propulsion (PEP) For Small Craft event is a competition held every year by the American Society of Naval Engineers (ASNE) and the United States Coast Guard (USCG), and this coming year’s event is being held in Virginia Beach. It is aimed at inspiring students from universities all over the country and allowing them to design innovative fully electrically propelled boats, present their designs and builds, and race them against each other to show off their finished projects. Using fully electric propulsion systems for marine use can be challenging, but if done right they can provide a clean, …

Ambulatory Electrocardiographic Monitoring, Genesis V. Garay, Arath Sanchez

ATU Research Symposium

•Cardiovascular disease (CVD) is the world's leading cause of death, especially prevalent in low- and middle-income countries. Up to 80% of premature heart attacks and strokes are preventable. •Current ambulatory electrocardiographic (ECG) monitors, while effective in diagnosing heart rhythm issues, are often cost-prohibitive and have limitations like short monitoring periods, user-initiated recording, and offline data analysis. •This poster summarizes the foundation of the creation of a monitoring system that attempts to address these issues.

League Of Learning: Deep Learning For Soccer Action Video Classification, Musfikur Rahaman

ATU Research Symposium

The field of sports video analysis using deep learning is rapidly advancing. Proper classification and analysis of sports videos are essential to manage the growing sports media content. It offers numerous benefits for the media, advertising, analytics, and education sectors. Soccer, also known as football, worldwide, is among the most popular sports. This research study used a deep learning-based approach for soccer action detection. Deep learning has become a popular machine learning technique, especially for image and video classification. We have used the SoccerAct dataset, which consists of ten soccer actions like corner, foul, freekick, goal kick, long pass, on …

Analyzing The Impact Of Socioeconomic Factors On Cancer Clinical Trials Accessibility In The U.S. Using Machine Learning, Krysta L. Ray, Hiromi Honda

ATU Research Symposium

While cancer impacts all segments of the United States population, specific groups experience a disproportionate burden of the disease due to social, environmental, and economic disadvantages. This research examines the correlation between socioeconomic factors and the accessibility of cancer clinical trials across U.S. counties, employing a comprehensive dataset, County-Level Socioeconomic and Cancer Clinical Trial Data from Noah Ripper, and advanced machine-learning techniques. Our findings, derived from regression analysis and machine learning models like gradient boosting, highlight significant disparities in trial availability linked to socioeconomic indicators, including poverty rates, population estimates, median income, incidence rates, and mortality rates. Many regression models …

Hybrid Pv-Teg System, Anna-Marie Pesaresi, Paige Woolheater, Chance Eoff, Nicholas Colburn

ATU Research Symposium

This research is focused on an innovative approach to improving the efficiency of a well- established renewable energy source. Solar cells are becoming more prominent as the power industry is shifting towards using more clean energy sources. Photovoltaic (PV) solar cells can only absorb a portion of the irradiance spectrum. The portion that is not absorbed raises the temperature of the system. The efficiency of PV cells drastically decreases as the temperature of the module rises and more energy is lost in the form of heat waste. Thermoelectric generator (TEG), when combined with PV cell, thrives off of the PV …

Multi-Rotor Hexicopter, Corbin G. Beard, Payton Riddle, John Washburn, Nate Burckhartzmeyer, Alejandro Gamez

ATU Research Symposium

The Multi-Rotor Hexicopter is an original design for an eighteen motor, hydrogen fuel cell powered drone. After initial success with the hydrogen fuel cell powered drone created by previous students, our goal was take their success and create a much larger drone that could be controlled both from the ground and in the air. Our design was based on a drone created by the company Lift Aircraft. The drone is separated into four components that were designed in Autodesk Inventor. The bottom, known as the sled, is used for the storage of the two hydrogen fuel tanks. The second section, …

Enhancing Disease Detection In South Asian Freshwater Fish Aquaculture Through Convolutional Neural Networks, Hayin Tamut, Musfikur Rahaman, Dr. Robin Ghosh

ATU Research Symposium

Aquaculture expansion necessitates innovative disease detection methods for sustainable production. This study investigates the efficacy of Convolutional Neural Networks (CNNs) in classifying diseases affecting South Asian freshwater fish species. The dataset comprises 1747 images representing 7 class, healthy specimens and various diseases: bacterial, fungal, parasitic, and viral. The CNN architecture includes convolutional layers for feature extraction, max-pooling layers for down sampling, dense layers for classification, and dropout layers for regularization. Training employs categorical cross-entropy loss and the Adam optimizer over 30 epochs, monitoring both training and validation performance. Results indicate promising accuracy levels, with the model achieving 92.14% and test …

Anomaly Detection With Spiking Neural Networks (Snn), Shruti Bhandari, Vyshnavi Gogineni

ATU Research Symposium

Abstract:

Anomaly detection, the identification of rare or unusual patterns that deviate from normal behavior, is a fundamental task with wide-ranging applications across various domains. Traditional machine learning techniques often struggle to effectively capture the complex temporal dynamics present in real-world data streams. Spiking Neural Networks (SNNs), inspired by the spiking nature of biological neurons, offer a promising approach by inherently modeling temporal information through precise spike timing. In this study, we investigate the use of Spiking Neural Networks (SNNs) for detecting anomalies or unusual patterns in data. We propose an SNN model that can learn what constitutes normal …