2026 EPS Participating Projects

Emergency response teams often rely on roads that may not exist after storms, landslides, conflict or in remote areas. Air delivery could bridge that gap, but conventional quadrotors do not scale well. As the vehicle grows, RPM based thrust control becomes slow, inefficient, and harder to stabilize, limiting payload and safety. We designed and built a large variable‑pitch quadrotor that commands lift by changing blade pitch, delivering rapid thrust control and better authority at scale. The engineering challenge is the mechanical design, structural analysis, and robust control laws to keep a heavy‑lift platform safe, stable and reliable.

The team was responsible for designing a scaled-down, remote controlled aircraft derived from requirements for a firefighting, water drop capable aircraft. As the frequency and area of wildfires in mountainous terrain increases, the need for specially designed aircraft that can drop water on fires is also higher. Current solutions usually use older, converted aircraft, rather than a design like ours, which is specifically designed for one purpose: to fight fires. This problem requires engineering in all areas, including structures, weight and balance, stability and control, aircraft performance and the payload drop system. Our solution provides a design explicitly designed for the challenge.

In the new space age, as companies like SpaceX, Amazon, and OneWeb launch an unprecedented number of satellites into orbit, the need for in-space servicing grows at a dramatic rate. The Spacecraft Platform for Autonomous Repair Refuel and Orbital Work (SPARROW) is designed to fulfill part of the In-Space Servicing, Assembly, and Manufacturing needs of the United States. The SPARROW is a fully autonomous, modular spacecraft capable of servicing client satellites while in orbit. SPARROW's critical servicing functions include diagnostics and repair, client refueling and maneuvering, and data transfer. This can help the problem of growing space objects by allowing satellites to last longer.

UAVs are creating opportunities to increase the safety of military personnel on the battlefield. ChemHAWK is a UAV detection platform that leverages DetectaChem’s sensor technology to remotely determine and relay the existence of airborne threats back to the operator's ground station. Chemical Welfare Agents and other gas phase threats can be more readily assessed from afar before moving into a given area with this capability. This capstone developed solution is specifically engineered to meet the requirements of both DetectaChem and the Department of War in a way that other UAV manufacturers currently cannot.

Deployment of multiple aircraft requires more time, equipment, and manpower than deploying a single aircraft. To gain the benefits of multiple aircraft deployment with lower resource expenditure a solution is to develop a multi-agent unmanned aircraft system capable of taking off as a single combined vehicle and autonomously separating mid-flight into multiple independent aircraft. With many missions requiring intelligence, surveillance, reconnaissance, and payload delivery, a system that launches as one and later splits into two independent aircraft would reduce mission logistics and increase mission adaptability for the Army Research Laboratory (ARL).

SpaceRedi has tasked the team with designing a theoretical satellite, which will be used to develop a Directed Acyclic Graph (DAG) for long-term risk analysis of satellites and other unmanned space missions. Mission controllers must analyze large amounts of data in a short time span to make critical decisions for various space missions. The DAG is intended for use by mission controllers to streamline risk analysis and reduce their mental load. The performance of the DAG built by the team will be compared to SpaceRedi’s current algorithms to see the strengths and weaknesses of each method.

Hypersonic systems are critical to national security but remain expensive to develop and operate. This project addresses the need for low-cost, high-production hypersonic vehicles by developing a design optimization framework for both powered and unpowered concepts. The core engineering challenge lies in the strong coupling between subsystems, increasing design complexity. Hypersonic systems also require exotic materials to survive extreme aerothermal environments, further increasing cost. Our solution optimizes vehicle design as a function of cost and manufacturability, helping sponsors evaluate scalable systems that reduce development costs and accelerate hypersonic system deployment.

The problem we are solving is the in-space-manufacturing (ISM) of carbon fiber tubes. The main challenge to overcome with this task is thermal management as most epoxy-curing reactions are exothermic, or require high-temperature heat curing furnaces; either of which would be detrimental to a spacecraft's limited thermal rejection capability. Our mechanism will be capable of fabricating 1" diameter carbon fiber tubes in microgravity inside a rarefied atmosphere (20 mbar, argon) environment. Our solution will directly benifit our sponsor, Arkisys, by being a simple testbed experiment for their Bosuns Locker station concept.

The main objective of the Molten, Oxygen, and Lunar Lander In-Situ Electrolysis (MOLLIE) system is to provide future lunar stations with liquid oxygen to support future space exploration missions. Molten Regolith Electrolysis (MRE) is a material-refining process that uses electric currents to separate the oxygen from the metals in lunar soil. Developing a liquid oxygen supply on the Moon will reduce vehicle mass because the vehicles will not need to carry the oxidizer required for return or more distant missions. In the future of space exploration, this could help the moon act as a refueling station, especially for missions to Mars.

Morphing aircraft offer unique yet often difficult to validate stability and control benefits. While flight testing poses higher costs and risks, static wind tunnel testing still proves insufficient for comparison to dynamic computational models. Previous capstone work introduced pitch and plunge free flight capable wind tunnel testing to Texas A&M, from which this work adds modular reconfigurability, wing morphing capabilities, and extensive software improvements. Notably, flight controller integration provides autonomous flight training and improved analytical possibilities for in flight morphing. This platform aims to lower testing barriers for researching unconventional aircraft designs.

Solar sails are a unique form of space propulsion that can provide unlimited, propellantless propulsion when exposed to sunlight. However, the thrust that they produce is very small, so a major challenge is to minimize the mass and volume of the spacecraft so that the resulting acceleration is significant. At the same time, the sail must be designed so that it can stow compactly and deploy to as large an area as possible. Our novel solution addresses these problems by using shape memory alloy actuators as a lighter, more compact method of deploying sails - which our sponsor, the TAMU SEDS Solar Sail project, aims to demonstrate in space aboard a student-led CubeSat mission.

The Regolith Excavation Vehicle for Lunar Exploration (REV-LE) is a lunar rover designed to identify, level, and compact regolith to create stable surfaces for future lunar landings and construction. REV-LE autonomously performs site assessment operations by scanning the lunar surface to locate viable compaction areas. The prototype integrates autonomous mobility, terrain sensing, and site selection, while the compaction mechanism and post-compaction surface validation are validated through modeling and simulation. REV-LE aims to demonstrate practical and scalable site preparation methods that support the establishment of long-term lunar infrastructure.

TAMU-SPIRIT Capstone Team 1 is developing three ISS-bound experiments: a Chitosan biopolymer materials study, the ASCEND biological payload, and an X-ray magnetosphere observatory. These projects address the challenge of designing diverse spaceflight experiments that survive launch loads and operate within strict constraints such as mass, power, and the complex LEO environment, while collecting meaningful data. Our project provides Aegis Aerospace and TAMU-SPIRIT with nearly flight-ready, standards-compliant payloads that advance materials, biomedical, and space science research while demonstrating Texas A&M's student-led engineering capabilities.

TRITON is an Unmanned Underwater Vehicle, equipped with high-thrust propulsion and a custom robotic arm. Sponsored by Los Alamos National Laboratory (LANL), TRITON is designed to locate, collect, and return samples up to 5 lbs to the surface following a nuclear incident, minimizing the need for personnel to enter hazardous areas. TRITON operates at depths up to 45 ft, where pressures reach about 2.2 atm, while maintaining watertight sealing to protect onboard electronics. The 6-DoF distributed propulsion system ensures stable and responsive control in dynamic water conditions for adaptable sample collection. TRITON provides LANL with a scalable prototype for future field deployment.

Our project addresses the poor health and wellbeing of the Al Zaatari camp refugees by developing a sustainable food production system to grow nutritious foods by utilizing alternative sources of water and renewable energy. Key engineering challenges that needed to be overcome were a lack of access to reliable sources of water and energy, restrictions on building and spacing within the camp, a climate not conductive for crop growth, and cost. Our solution provides a sustainable, cost-effective food production system that will improve the wellbeing of the refugees without utilizing already strained resources.

As Burton’s school district and community grew, the aging, mostly gravity-fed wastewater system, installed in the 1980s, faced increasing risks of backups, overflows, and costly failures. EcoFlow Engineers consolidated records, collected new field data, and created a comprehensive digital map of the network. The team analyzed capacity and pipe condition, with special focus on high-impact lines, and recommended phased, cost-effective improvements. With these enhancements, Burton is now poised for sustainable growth and improved quality of life. City officials now have reliable information to confidently plan for the future, while Burton sets a strong example for other rural Texas communities.

Cottonseed fires are a costly and dangerous problem for seed storage facilities, as fires begin within the material and can quickly spread out of control. Carbon dioxide, a precursor to cottonseed ignition, can be measured to predict such fires, but the gas is hard for workers to sample given cottonseed's unstable consistency. Cotton Crawler Engineering was tasked by the USDA ARS to design a lightweight robotic platform with the ability to traverse cottonseed of varying angles and densities, enabling gas sampling across cottonseed piles. The platform needs only one operator and can provide real-time carbon dioxide warnings in order to predict and therefore prevent devastating seed fires.

Laboratory-scale cotton gin stands are widely used in breeding and engineering research but are typically hand-fed, introducing variability that reduces experimental accuracy. This project, conducted in collaboration with the USDA Agricultural Research Service, developed a compact mechanical feeding system that delivers seedcotton to a benchtop gin stand at a controlled, uniform rate. The design eliminates manual handling, minimizes fiber and seed damage, and remains compatible with multiple benchtop gin stand models. Improved feed consistency enhances data reliability for fiber quality evaluation, ginning efficiency studies, and cotton variety development.

Our project focuses on mitigating chronic flooding in west Seguin by redesigning undersized and aging storm drain, water, and wastewater systems. The engineering challenge centers on performing detailed hydrologic and hydraulic modeling to size conveyance structures, while navigating tight rights‑of‑way, utility conflicts, and multi‑agency permitting requirements. Our solution will reduce flood risk, enhance public safety, and modernize essential utilities, providing the City of Seguin and Pape‑Dawson with resilient, cost‑effective infrastructure improvements.

Progressive erosion along the Brazos River threatens the stability of FM 159 in Brazos County, risking embankment failure, roadway closure, and regional economic disruption. The engineering challenge was to design a resilient, cost-effective stabilization strategy that meets hydraulic performance, constructability, environmental, and TxDOT regulatory requirements. Our team developed a calibrated hydraulic model and produced detailed engineering drawings for a stone revetment (riprap) system that protects the riverbank, preserves the existing corridor, and reduces long-term maintenance and capital risk exposure for TxDOT.

The Hydrating Dog Pod addresses canine hydration issues during outdoor activities, when bowls are impractical and dogs may resist drinking due to dynamic environments. The engineering challenge centers on developing a safe, durable, and palatable edible membrane that prevents leakage, meets shelf-life targets, and withstands general handling. Through material evaluation and laboratory validation, the team will deliver a proof-of-concept prototype. The solution provides the sponsor with a validated casing formulation and testing framework to advance future development and commercialization.

Our project addresses the challenge of developing a financially viable and environmentally sustainable nursery production plan for our sponsor’s property. The engineering challenge involves integrating site constraints, hydrologic analysis, infrastructure design, plant production cycles, and compliance with local regulations into a cohesive, data-driven plan. Our solution optimizes land use, water management, and phased harvesting to maximize efficiency and long-term profitability. This approach provides the sponsor with a practical, regulation-compliant design that supports sustainable operations and strengthens economic return.

The community of San Gregorio Picacho, Nicaragua lacks reliable access to safe drinking water and depends on distant, low-capacity sources of uncertain quality. Our project evaluates local water sources and designs a gravity-fed conveyance, storage, and distribution system that is affordable and sustainable. The primary engineering challenge is developing a low-cost solution with limited elevation data and minimal electricity access. Our design is intentionally scalable and replicable, creating a model that can be adapted for other rural communities while providing our sponsor with a practical, fundable solution.

Freshwater scarcity across the Mediterranean is placing increasing pressure on farmers, as limited water availability threatens agricultural productivity and food security. Desalination offers a viable pathway by improving the quality of brackish water for irrigation. This project evaluates a prototype desalination system in Lebanon and its broader agricultural potential. Brack Water Consulting aims to develop a solar-powered reverse osmosis unit that is portable, energy-efficient, cost-effective, and compatible with existing irrigation systems. The design is adaptable to farms of varying sizes and suitable for settings such as avocado and olive production.

Our team is partnering with AWTY International School to expand and improve its vegetable garden to meet the growing demand for herbs, vegetables, and native plants. The school needs a dedicated, automated greenhouse and nursery to effectively produce and house a diverse range of seedlings at scale. The engineering challenge is to design a durable, climate-responsive structure that integrates engineered foundations, proper drainage, raised growing tables, efficient irrigation, shading, and environmental controls. Our solution will increase reliable food production for the cafeteria, reduce long-term costs, and create hands-on STEM learning opportunities for students.

Precision pesticide application depends on accurate spray-nozzle performance, yet USDA ARS currently relies on slow, manual bucket-and-scale testing that limits throughput and repeatability. Our team is developing an automated, closed-loop nozzle flow-rate test stand that delivers stable pressure (15–90 psi), measures flow from 0.1–4.0 GPM within ±2% accuracy, and logs data in real time through a Python-based interface. The key engineering challenge is integrating precise hydraulic control, sensor feedback, and automation into a safe, compact, and affordable system. Our solution reduces labor, increases data reliability, and accelerates research in precision application technologies.

12th Man BioFrontier’s Astro-Ring addresses astronaut musculoskeletal loss during long-duration missions, where confined space and low motivation limit or threaten consistent exercise habits among crew members/astronauts. The engineering challenge is designing a compact, durable, sensor-integrated resistive exercise device that provides meaningful loading, real-time feedback, and engaging gamification in microgravity. Our solution promotes consistent and effective exercise, helping astronauts maintain strength, muscle density, and mission readiness while reducing health risks on extended flights and long-term missions.

Brachytherapy radiation treatment is a procedure for treating gynecological cancer. However, it poses a risk of time and error. The project aims to develop an AI-driven software platform for automated applicator and needle digitization on CT image datasets of gynecological brachytherapy patients with implanted applicators and interstitial needles, thereby reducing procedure time and improving patient care. It will be a software platform that leverages extensive patient data sets, high patient volume, and innovative AI and image processing techniques to improve the precision and accessibility of brachytherapy treatment. The platform will also integrate with the clinical workflow.

As human spaceflight extends deeper into space, strict mass and volume constraints limit what can be launched to support astronaut health. Large exercise systems are often impractical, increasing the risk of cardiovascular deconditioning and orthostatic intolerance upon return to Earth due to reduced venous return and cardiac output. The challenge is to design a compact, lightweight exercise device that effectively stimulates cardiac activity and circulation in microgravity. Our solution adapts the VersaClimber concept to meet these constraints, enabling NASA to better protect astronaut cardiovascular health while minimizing launch mass and volume costs.

Patients with urinary voiding disorders rely on clean intermittent catheterization (CIC), and physicians depend on 3–5 day diaries to guide bladder management. Current paper-based diaries are burdensome, often incomplete, and frequently lost, leaving patients frustrated and providers with limited data for medical conclusions. This leads to suboptimal treatment decisions and potential harm to the bladder and kidneys. Our team has aimed to create an automated system to record urine output digitally to improve adherence, data accuracy, and clinical outcomes.

Millar’s current BP-22 test system is outdated and requires updates to enhance the automation and user friendliness of the system, while adhering to the ANSI/AAMI BP-22:2016 standard in order to ensure efficient, reliable, and accurate evaluation of pressure transducers for critical care use. Since many patients undergoing invasive blood pressure monitoring are in critical care, test accuracy is essential. Ensuring efficient and reliable testing helps verify the integrity of transducers prior to use. The BP-22 standard requires 13 tests, and this project aims to modernize Millar’s test equipment for the internal testing department.

Accurate measurements of core body temperature are essential for proper diagnosis and determining appropriate interventions. In a study, approximately 21.5% of patients admitted had abnormal temperatures. Existing methods of non-invasive core body temperature measurement are marred by inaccuracy, while invasive technologies present greater risks and patient discomfort. This device measures core body temperature by assessing blood temperature through a blood tube. It utilizes thermistors and an insulated, handheld design that can easily integrate into clinical workflows, which will save time and reduce the need for inaccurate and invasive methods.

Multi-cat households, especially those with pets on prescriptive diets, currently lack effective ways to feed each cat separately while ensuring accurate dispensing, store food hygienically, monitor and track each cat's intake by weight, and prevent food-stealing. For this project, we aim to create a smart, automatic feeder that can identify multiple cats via RFID, hygienically store a 14-day supply of dry food, monitor the intake of each cat while accurately following prescriptive diets, and inform the owner of each cat’s feeding habits through an app or website. The solution benefits our sponsor by providing a competitive cat feeder option in the market that is more consumer-friendly.

Central venous catheters provide essential access for long-term treatment and critical care, but are prone to central line-associated bloodstream infections(CLABSI), causing an estimated 250,000 cases annually and contributing to billions in healthcare costs. Current manual disinfection methods such as alcohol or CHG hub scrubs are inconsistently followed and error-prone. Our team designed a wireless, rechargeable UV-C decontamination device that attaches to the catheter hub to deliver automated, uniform, germicidal light. This solution ensures consistent, hands-free decontamination, reducing infection rates, improving staff compliance, and lowering costs for healthcare providers.

This maintenance framework will assist patients with subcutaneous insulin resistance and Type 1 diabetes, clinicians, and clinical engineers in identifying when the Medtronic MiniMed Implantable Insulin Pump System (MMIIPS) requires unscheduled maintenance. The core engineering challenge involves developing robust mathematical models to detect pump catheter clogs without altering the existing sensor hardware in the device. By having high-fidelity monitoring within these constraints, the solution ensures strict adherence to safety requirements. This model and framework will benefit Medtronic MiniMed by mitigating device risks, ensuring regulatory compliance, and improving brand reputation.

Around 48.6 million women in the US alone rely on contraceptives, resulting in an average $6 billion spent annually on birth control methods. Current methods are unreliable, highly invasive, and disrupt hormone cycles. Using a specific frequency, we inhibit sperm motility through the vaginal canal. With this device design, considerations include isolation from patient tissue, long term efficacy, and remote activation. As a gynecologist in a pediatric hospital, our sponsor is invested in creating a novel contraceptive that does not affect menstrual cycles and does not subject pediatric patients to invasive procedures.

Biomolecular solutions to genetic diseases require advanced delivery vehicles. Peptide nanoparticles can deliver gene editors, but there are few high-throughput methods for testing. This gradient generator chip was designed for this testing. The engineering challenges include the size constraint of microfluidics, compatibility of the peptide nanoparticles with chip and buffer materials, and integration of the chip and pH gradient with the analysis methods. The chip minimizes the amount of sample needed to test functionality and integrity across a pH range that simulates different environments within the body, ultimately decreasing the cost and resources needed to ensure product quality.

Current treatment of hypersplenism through partial splenic artery embolization lacks continuous, controlled occlusion, resulting in excessive obstruction, pain, secondary necrosis, and repeat surgeries. The goal of this project is to develop a novel embolization device with the gradual release of embolic particles for occlusion. With this device, patients with hypersplenism can reduce the risk of abscess formation, decrease the likelihood of post embolization syndrome, and allow patients to receive earlier treatment. Challenges with our solution include controlling the rate of embolization and fabricating to the scale of the splenic artery.

Our team is developing a mobile heart rate variability (HRV) biofeedback app that enables users to safely initiate therapy during high-stress or anxiety events. The engineering challenge lies in delivering accurate real-time physiological monitoring, intuitive single-action activation, and reliable signal filtering within a user-friendly interface. By integrating validated HRV analysis with human-factors-driven design, our solution improves accessibility and usability. This benefits our sponsor by providing a scalable, clinically informed digital tool that enhances stress management outcomes and expands market reach.

Intraoperative photo and video documentation is essential for clinical records, education, and patient communication, yet current methods disrupt workflow or compromise sterility. Existing solutions depend on non-sterile staff, obstructed overhead cameras, or bulky head-mounted systems with limited surgeon control. The engineering challenge is to develop a lightweight, sterile, surgeon-controlled imaging system that provides stable, high-quality point-of-view documentation while integrating seamlessly into the operating room. Our solution enables consistent documentation during open surgeries, supporting education and postoperative communication without interrupting surgical workflow.

The KazooView addresses the challenge of obtaining clear pediatric oral images when fear and limited cooperation reduce diagnostic accuracy, especially in telemedicine. Traditional tools are intimidating and ineffective for at-home use. The engineering challenge is integrating a high-resolution camera and gentle LED into a safe device that is small, hygienic, interactive, and easy for caregivers to use with a smartphone. This solution benefits the project sponsor by improving remote assessments, reducing unnecessary visits, increasing patient satisfaction, and positioning them as a leader in innovative, family-centered pediatric care.

Medication nonadherence is a widespread public health concern affecting 50% of patients prescribed chronic medications and resulting in 125,000 deaths annually. At Take Your Meds Innovation, we have created MedBuddy: a website/mobile app interface that allows patients to track their medications, consult physicians outside the doctors office, and remain motivated to finish medication cycles and take their medications on time. Using a physical RFID chip uniquely paired to a given medication, patients can file their entire medication regimen and set reminders to take their meds to move toward a healthier lifestyle and eliminate the consequences of unfinished medications.

Medical Personnel within the U.S. Army currently lack the ability to easily perform maintenance on medical devices currently deployed outside of the reach of specialized military teams and commercial service entities. Due to the varying complexity of medical devices, repairs can be difficult to complete as specific tools and/or software are withheld by manufacturers, citing security or intellectual property concerns. The MARS utilizes an assistant algorithm to aid military personnel in the troubleshooting and repair of medical equipment in locations where more specialized support is not available. The system utilizes information from preloaded data which is customizable for a deployment.

MSKInsight addresses the gap in effective rehabilitation for musculoskeletal (MSK) injuries, which impact 1.7 billion people worldwide and disproportionately affect athletes. Current treatments lack real-time feedback and measurable results. The engineering challenge is developing a device that integrates accurate biomechanical sensing with responsive therapeutic support in a single platform. By delivering actionable data and active recovery assistance, MSKInsight enables faster, evidence-based rehabilitation and provides our sponsor with a scalable, high-impact solution in the sports health market.

Inside the cardiac catherization laboratory, Electrocardiogram electrodes have a lack of reusability and sustainability within their design. The goal of this project is to create an alternative ECG electrode system that is reusable, sustainable, and easier to use on patients compared to the normal standard. Our project would benefit our sponsor by designing a 3 Lead ECG system with novel electrodes that can operate on the peripheral limbs of a patient during an interventional cardiology procedure. This would eliminate the need for disposable electrodes while providing an environmentally sustainable alternative that is not only portable but also wirelessly connected to nearby devices.

Non-invasive positive pressure ventilation (NPPV) is commonly employed to support breathing in infants and children within neonatal and/or pediatric intensive care units. A significant challenge in administering NIPPV effectively is ensuring proper mask fit, particularly in patients with abnormal craniofacial anatomy. Improper mask fit frequently leads to significant issues, including air leaks and pressure-related skin injuries. The project's primary objective is to develop a novel NIPPV system that significantly improves mask fit and usability for infants and small children. The solution should reduce pressure injuries, enhance patient comfort, and allow for widespread clinical adoption.

Approximately 150,000 transurethral resections of the prostate (TURP) procedures are performed in the U.S. every year. Complications increase linearly with operative time, and urologists have complained of the tedious, time-consuming process inherent to current loops. The challenge is to cut more tissue faster while fitting within 8 mm diameter resectoscopes. The Resect2scope loop is designed to increase resection rate for TURP procedures through the use of a two loop system instead of a single loop. Our entrepreneurial sponsors will be better able to market a new design that can reduce surgery duration and complication risk while seamlessly integrating into current processes and equipment.

The goal of the overall project is to reduce the size of the given Implantable Cardioverter Defibrillator (ICD), so that it fits more comfortably and is more compatible with pediatric patients who require an ICD but are too small for an adult device. We are working alongside a team at St. Thomas to achieve this goal. The St. Thomas team is focusing on the shield portion of the ICD, while our team is working on the connector portion to ensure compatibility with the new shield and to help reduce the device’s overall volume. Successful completion of this project could lead to our sponsor advancing the concept and eventually producing it for patient use, helping those who need it.

Brachytherapy is essential for definitive management of gynecologic cancers, specifically cervical cancer. Yet, standard intracavitary and interstitial applicators often fail to conform to anatomic variation and complex tumor geometry, which limits dose coverage and risks excess organs-at-risk (OAR) exposure. We developed an automated framework for generating personalized 3D-printed ovoid applicators that are compatible with commercially available tandem–ovoid systems. The automated process eliminates manual applicator design steps and enables rapid patient-specific customization. The 3D model is then sent to a FormLab printer for in-house fabrication.

Our team developed a torso-based testing bench to evaluate wireless communication reliability between an implantable insulin pump and a mobile device. Communication failures can occur when RF signals travel through human tissue, creating safety and performance risks. The engineering challenge is accurately simulating anatomical conditions while collecting repeatable, quantitative data on RF signal strength and communication stability. Our modular phantom system enables controlled testing of tissue thickness and composition, providing our sponsor with data to identify failure modes and improve system performance.

Pediatric Thoracostomy (chest) tubes are essential for draining fluid or air from the pleural space after trauma or surgery. Current securement relies on complex sutures, adhesive tapes, or other adhesive devices, which can be inconsistent, hard to place, and prone to failure. This is exacerbated in pediatric patients, as they have thinner chest walls and smaller anatomy. Movement or faulty securement may cause tubes to slip, leading to air/fluid leaks and open channels from the body to the environment. There is a need for a simple and streamlined securement device that prioritizes comfort, maintains a reliable seal, and preserves skin integrity.

Our project addresses the lack of personalized treatment for Type 1 Diabetes by integrating continuous glucose monitoring with automated insulin delivery to create a data-driven, comprehensive system. Unlike conventional solutions that rely on manual dose calculations, our platform tailors insulin timing and quantity to each patient in real time. Key engineering challenges include designing intuitive, stakeholder-specific interfaces while ensuring full FDA and HIPAA compliance. The system centralizes critical data to accelerate future research and development and supports internal tools for technicians and engineers.

Current tissue engineering methods rely on manual culture methods that are labor intensive, inconsistent, and difficult to scale, while chronic wounds and tissue loss affect millions of patients. Regenerative therapies require large amounts of tissue sheets, which are difficult to produce with existing solutions. The engineering challenge is maintaining uniform nutrient delivery, gas exchange, and environmental control without damaging cells. This project develops a semiautomated bioreactor with integrated sensing and novel geometry to enable scalable, reproducible culture, providing our sponsor with a reliable platform to produce high quality tissue sheets for regenerative medicine.

The design problem being addressed is centered around central venous catheters (CVCs), particularly their vulnerability to catheter related infections and the lack of early infection detection. Current techniques are passive, relying on visual inspection from nurses or site indicators and often undergo scheduled dressing removals and change without any reason. This results in nurses recognizing issues only after the underlying risk has already increased. The engineering challenge is to develop a smart catheter dressing system that continuously monitors local temperature change at the insertion site to shift CVC dressing care from reactive to proactive.

Postoperative weight-bearing noncompliance following lower limb surgery increases the risk of implant failure, delayed healing, and revision surgery. Current monitoring tools, such as force plates, are expensive and confined to clinical settings, while commercial insoles prioritize athletic metrics over clinical accuracy. Our engineering challenge is to develop a cost-effective, sensorized insole that accurately measures plantar pressures and reliably monitors compliance outside the clinic. This system would provide Memorial Hermann physicians with objective recovery data, enabling earlier intervention, improved outcomes, and enhanced patient care.

Current methods to conduct routine syringe changeovers for short half-life medications (e.g. inotropes, vasopressors) in intensive care units are unstandardized, error prone, and increase nursing burden. These methods often introduce variation to drug delivery, causing hemodynamic instability that may lead to arrhythmias, cardiac arrest, and death. The engineering challenge is enabling continuous, consistent flow during changeovers while minimizing air entry and maintaining sterility. Our solution is a modified stopcock with in-line priming for faster and safer setup and minimized dead space to maintain flow. This standardizes changeovers, reduces interruptions, and lowers nursing burden.

Hemifacial microsomia (HFM), affecting about 1 in 3,000–5,000 births, often causes unilateral mandibular hypoplasia that increases pediatric risk for obstructive sleep apnea when improper jaw placement allows the tongue to fall backward and block the airway. Current treatments are invasive, costly, or poorly tolerated by children. The engineering challenge is creating a noninvasive device that safely repositions the mandible while accommodating growth, asymmetry, and comfort of patients. Our orthopedic mandibular brace offers a safer, lower-cost solution that improves patient compliance and provides our sponsor a practical, patient-centered alternative.

Gastrointestinal cancers remain difficult to study and treat due to the lack of physiologically relevant in vitro models which replicate the colon’s tubular structure and dynamic peristaltic motion. Current flat and scaffold-based systems fail to reproduce realistic mechanical forces, limiting disease modeling and pre-clinical testing. Our team developed a tubular bioreactor which mimics peristaltic contractions through a screw driven linear actuator and microfluidic PDMS membrane. The main engineering challenges were integrating controlled shear and strain in a scalable, low-cost device. This solution is an accessible, customizable platform for accurate cancer research and drug evaluation.

There is currently a discrepancy between training and real-life scenarios for Army basic training in the realm of uncontrolled hemorrhaging. It is simplified to tourniquets and basic wound packing that doesn’t account for external stress factors or noncompressible wounds. This leads to fewer positive outcomes in battlefield situations when soldiers are required to render aid. Realistic devices are also heavy, difficult to transport, and may cost up to $85,000, which is more expensive than typical budgets can adequately cover. There is a need for a more realistic wound training device that provides accurate feedback to training personnel to improve patient outcomes.

Overhand pitching and throwing athletes face immense risk for chronic shoulder and elbow overuse injuries. Current pitch monitoring does not quantify joint kinetics or fatigue, limiting their immediate effectiveness in injury prevention. The key challenge is capturing accurate kinetic data during high-velocity, sweat-heavy motion without sacrificing comfort, durability, and ease of use. Our wearable pitching sleeve tracks real-time pitching kinetics to help professional and collegiate programs optimize injury monitoring, rehabilitation, and recovery.

This project aims to design a minimally intrusive electroencephalography (EEG) device capable of quantifying alertness. The device is intended to be a head-mounted wearable to be used alongside a neural stimulator being developed by Architex Labs. To analyze the effectiveness of this stimulation, Architex Labs aims to integrate real-time EEG monitoring. Architex Labs requests assistance to develop a compact, plug-and-play EEG system capable of quantifying alertness through brainwave analysis. The intended users include researchers, physicians, and their patients, and potentially military personnel for applications in cognitive state monitoring and performance optimization.

Bryan Research and Engineering, LLC has tasked our team with designing a natural gas processing plant for a private equity firm known as Good Bull Energy LLC. This plant is located in the Permian Basin and should be designed to achieve the highest possible net present value and return on investment while meeting all necessary product specifications and safety requirements. BRE, LLC is one of the world's leading process simulation companies and they often work with midstream companies for opimtization purposes. Our work on this project will provide valuable research on optimizing midstream systems.

Our team is proposing the design of a 200 MMSCFD midstream natural gas processing plant in the Permian Basin for Good Bull Energy LLC. The project focuses on converting raw natural gas into high-value residue gas and NGL products while meeting strict environmental and product specifications. The core engineering challenges lie in selecting the optimal feed stream, contract structure, and cryogenic technology to balance capital cost, recovery efficiency, and market volatility. By integrating process design with contract-driven economics, our solution will work to maximize value, ensure regulatory compliance, and deliver long-term value for the supplier and the processor.

In this project, the team was given 5 NGL well options, each producing a 200 MMSCFD feed with different compositions. The team was tasked with assessing the wells and completing FEL-1, FEL-2, and FEL-3 for a processing plant that draws its feed from one of the wells. These stages are used in industry and include many problems, such as rigorous economic analysis, process flow diagrams, a plant simulation, piping and instrumentation diagrams, equipment sizing, and much more. As students with little to no experience in the processing industry, we may benefit the sponsor by having a different way of thinking than most professionals, and we may find out-of-the-box solutions to these problems.

Natural gas demand continues to rise while production in the Delaware Basin increases, both pointing to an increasing need for midstream infrastructure that can efficiently process large volumes of raw hydrocarbon gas. Efficient plant design improves product recovery, resource use, and operational sustainability. This project involves the design of a 200MMSCFD cryogenic midstream processing plant that produces residue gas and natural gas liquids. The engineering challenge is optimizing cryogenic operating temperatures and heat integration to maximize product separation and recovery, while minimizing energy consumption, capital cost, and environmental impact.

Our team has developed a comprehensive natural gas processing facility that transforms raw feed gas into pipeline‑quality residue gas and high‑value NGL products. The project tackles integrating gas treating, dehydration, cryogenic recovery, and compression systems into a cohesive design that remains safe, energy‑efficient, and environmentally compliant. We evaluate technology choices, emissions controls, process optimization strategies, and economic trade‑offs to maximize long‑term value. The resulting design is a reliable, cost‑effective midstream solution that improves product recovery, enhances operational flexibility, and significantly increases the overall return on investment.

The Permian Basin has become the leading area for US shale gas production, making up 29% of U.S. natural gas (NG). To accompany production, NG processing capacity, comprising removing contaminants and separating the hydrocarbons into sales gas and natural gas liquids, must be modernized. Processing must be done with low cost to provide affordable energy and low emissions to meet greenhouse gas emissions targets. We have designed a net-zero NG processing plant using renewable energy and carbon capture. This project provides our sponsor valuable insight into how process simulation software handles large-scale NG processing and how carbon capture can be integrated into simulation and design.

For this semester’s project, Bryan Research and Engineering LLC has sponsored and tasked chemical engineering students with creating and optimizing a midstream natural gas liquid (NGL) processing plant in West Texas in the Permian Basin. The project is bid by the private equity firm “Good Bull Energy LLC”, who has provided students with 5 possible feed streams of natural gas with different producers, each with varying chemical compositions and distances from the NGL plant. Teams are tasked with selecting 1 of 5 feed streams and optimizing the economic analysis of our project in terms of profitability, feasibility, environmental impacts, and the chemical analysis of NGL processing.

Our team is designing a 200 MMSCFD natural gas processing plant in the Permian Basin for Good Bull Energy LLC. Our aim is to design the most economical plant through selection of the most profitable gas source and contract structure along with process optimization. The biggest challenge in this project is balancing high ethane/propane recoveries with operational costs and contaminant removal. Overall, our solution will benefit our sponsor by providing an efficient design that optimizes ROI through energy savings and reliable production of NGLs and residue gas.

Project PIVOT (Process Integration & Value Optimization for Treating) develops and optimizes a 200 MMSCFD natural gas processing facility to capitalize on the value and volume of natural gas in the Permian Basin. The design upgrades variable sour/wet field gas into pipeline-spec residue gas and Y-grade NGLs while meeting environmental requirements and defining a feasible acid-gas disposition strategy. PIVOT selects the most economically viable pathway by simulating and optimizing integrated unit operations, screening technologies, and capturing utility impacts. Advanced process design and techno-economic analysis delivers a safe, optimized concept for Bryan Research and Engineering, LLC.

Our capstone project designs a 200 MMSCFD natural gas processing facility in West Texas utilizing cryogenic separation to recover NGLs and deliver pipeline-quality methane with minimal flaring. The challenge was integrating thermodynamic modeling, heat integration, hydraulics, and regulatory constraints into a safe, economically viable design. On top of the engineering an in depth contract structure analysis was done to ensure the plant is profitable for both the producer and processed. Our solution optimizes recovery and energy efficiency while reducing emissions and water impact, delivering increased product value and regulatory compliance for the sponsor.

The problem to be solved is to design and construct a natural gas processing plant in West Texas that captures value presented as residue gas, NGLs, and byproducts. The main objective of our project is to propose a design under a contract structure that achieves the highest NPV and ROI. The engineering challenge lies in designing the plant and safety facilities to meet environmental specifications while maintaining high ethane recovery. Our solution of designing our plant using the Blocker feed gas source under the Fixed Recovery contract structure will benefit our sponsor by allowing greater revenue potential from NGL recovery and reducing capital investment.

We are a midstream natural gas processing company that has the opportunity to process natural gas from one of five available gas fields in the Permian Basin. We are tasked with selecting a field and designing a contract structure that returns the highest net present value (NPV) and return on investment (ROI) for the upstream company. The challenge is designing a processing plant, simulating and optimizing the plant, staying within product constraints, and following EPA and TCEQ guidelines for air emissions limits. Our solution should demonstrate to our product sponsor we are capable of considering both engineering and business aspects of plant design to maximize efficiency and profitability.

Multiple well sites contain different feed gas compositions that need to be processed. We are tasked with analyzing which of these well sites have the highest profitability based on their gas composition, distance, and cost to build a process facility for the gas streams. The challenge becomes assessing which of the five sites should be chosen to process via the economic viability of each facility and the cost to physically design a plant that is efficient and cost effective. The sponsor receives a high-level technological analysis of the chosen site and a complete simulation for each unit used in the process. The sponsor assesses if the chosen wellsite will be profitable to invest in.

Given the volatile nature of the natural gas market, this project aims to design, optimize, and commercially structure an operationally flexible natural gas processing plant for Permian Basin feed gas. The facility will produce pipeline-quality residue gas and NGLs meeting downstream specifications. Its configuration integrates acid gas removal, dehydration, cryogenic separation, and compression into a dynamic system with multiple operating modes, enabling the plant to adapt to price fluctuations. Combined with a strategically aligned contract structure, the project mitigates risk to deliver an economically optimized solution that provides stable returns to both producer and processor.

This project designs a 200 MMSCFD natural gas processing plant in the Permian Basin for a producer called Good Bull LLC. Engineering challenges includes selecting an optimal feed, selecting a proper contract structure, and determining the correct cryogenic technology to maximize Net Profit Value while meeting product, emission, and safety specifications. The integrated designs contain gas sweetening, dehydration, efficient NGL recovery, and acid gas management. These designs maximize recoveries, minimize flares, and provide strong returns to both sponsor and producer.

The MMI Gas Processing Facility is a comprehensive design for treating upstream gas for Goodbull Energy (GBE). Our team developed an optimized processing plant for West Texas feed gas to maximize the value of NGLs and residue gas. We assessed multiple gas stream compositions and applied an advanced cryogenic recovery scheme to enhance economic performance and product flexibility. The key engineering challenge is selecting and integrating equipment to balance cost, efficiency, reliability, and long-term operability. Our solution provides GBE LLC with a high-return, scalable midstream asset.

Our team designed a natural gas midstream processing plant for the Permian Basin to help Good Bull Energy LLC, a theoretical private equity firm, to maximize value from abundant West Texas gas resources. The challenge is to select the best gas composition available to GBE LLC, and propose a design capable of achieving the highest possible net present value (NPV) and return on investment (ROI) for your stakeholders. Using advanced process simulation, we optimized separation and recovery systems to deliver a technically robust, economically attractive midstream solution for our sponsor.

Our team addressed the challenge posed by Bryan Research & Engineering to extract the most value from a specified set of natural gas streams from the perspective of a West Texas refiner. We analyzed the value of the various streams, selected the highest value stream, and designed a Natural Gas plant with the units necessary to create products that abide by the Environmental Protection Agency requirements in the most economical way. We also crafted a contract scheme that responsibly compensated both our prospective company and the natural gas stream producer, illustrating a high level understanding of the economics involved in the Oil and Gas Industry.

This project evaluates and develops a proposed 200 MMSCFD natural gas processing facility. The problems include optimal feed gas selection, processing configuration, and contract structure under technical, economic, and regulatory uncertainty. The engineering challenge lies in balancing capital intensity, energy consumption, recovery performance, and environmental compliance while meeting strict product specifications. Via preliminary screening, techno-economic analysis, and detailed design and optimization, the project aims to deliver a robust processing solution that maximizes long-term value, manages risk, and supports the sponsor's development objectives.

The group was tasked with designing a safe, economic, and environmentally-friendly plant to process natural gas into refined natural gas liquids (NGLs). This involved using process simulation software to design the plant; estimating costs and profits based on global market trends, scientific literature, and data from currently-operating plants; and optimizing the final design to ensure safe operation and regulatory compliance. This project was sponsored by Bryan Research and Engineering, LLC, which develops and provides the process simulation software ProMax that was used in this project.

Consider an IoT device running a machine learning model deployed in a hostile environment as part of an operational monitoring system. Because communication between the device and centralized infrastructure may be delayed or unavailable, the machine learning models on the device must balance predictive accuracy with aggressive resource optimization to sustain long-term deployment operation capability. The AFTAC Edge AI project seeks to develop a process that can enable and support real-time probabilistic inference capabilities of audio, visual, and sensor health monitoring, while processing simultaneous multimodal inputs without exceeding memory or latency budgets.

We are solving AFTAC’s inability to reliably display HYSPLIT nuclear plume model outputs due to limitations and layering issues in current GIS platforms. The engineering challenge is ensuring accurate ingestion, normalization, and time-aware visualization of KMZ/Shapefile data while maintaining performance, security, and geospatial fidelity. Our solution provides a purpose-built, maintainable GIS viewer that ensures consistent plume visualization, reduces analyst workload, improves decision confidence, and eliminates reliance on deprecated or poorly supported platforms.

In a business setting, it is common to have a substantial number of interactions. This makes it difficult to remember who we’ve already spoken to and what the conversation is about. Our solution is AR glasses equipped with an agent that will remember previous conversations and provide real-time context using facial cognition models and LLMs. This project uses facial recognition to identify interlocutors & speech recognition models to transcribe the conversion. Their data is passed onto LLMs to parse data and extract meaningful context which gets displayed on the AR platform. The user can view a more detailed conversation summary and edit the information captured.

Learning ARM binary exploitation is challenging for beginners because many resources are paywalled, scattered, or assume prior experience. Our project addresses this by creating a fully web-based course that guides learners from ARM architecture basics to hands-on binary exploitation in a structured environment. The main engineering challenge is building a secure, scalable platform that supports interactive labs, progress tracking, and performance assessment while remaining user-friendly. This solution benefits the sponsor by providing a dedicated training platform that supports cybersecurity education and helps students develop practical ARM exploitation skills.

Our project addresses inefficient coordination and tracking within volunteer organizations and structured groups. Many systems lack group-based goal tracking, controlled event finalization, and intelligent opportunity matching. The engineering challenge involves building a secure, scalable platform with role-based permissions, persistent relational data, safe lifecycle state transitions, and an optimized recommendation algorithm. Our solution benefits the sponsor by improving transparency, engagement, and data-driven oversight of volunteer participation.

DevOps teams struggle to keep test cases aligned with rapidly changing requirements. QA engineers spend significant time writing and updating tests, and important edge cases are often missed as work items evolve. Our project proposes an AI-powered, context-aware test generation extension integrated directly into Azure DevOps. By combining LLM-based generation with conversational refinement, we embed automation into existing QA workflows. This reduces manual effort, improves test quality, and increases efficiency for our project sponsor’s engineering teams.

Early stage startups often fail because founders draw premature conclusions from incomplete customer discovery interviews, overlooking key assumptions and underexplored themes. While lean startup methods emphasize discovery, current tools focus on transcription rather than evaluating interview quality. We propose DiscoveryIQ, a interview analysis system that structures topics, assumptions, and gaps, highlights underexplored areas, and generates follow up questions. We evaluate it through extraction accuracy and qualitative feedback from founders and students to assess usefulness and support reflective learning.

Our project is a remote driving system where a human operator controls a vehicle from a stationary cockpit using real-time video, audio, and steering wheel/pedals. Technical challenges include ultra‑low “glass‑to‑glass” latency, handling packet loss and jitter on 4G/5G/LTE, designing a UI that preserves situational awareness with multiple camera views, bandwidth‑efficient video compression, and optional security hardening of the control link. Our project helps ENDEAVR by providing a prototype that can be expanded to develop more advanced solutions for remote taxi services and remote delivery services. These services would be very useful in small towns without accessible public transport.

In today’s cybersecurity landscape, AI is both a powerful defense and a growing threat. As the machine learning models used in intrusion detection grow more sophisticated, they become increasingly difficult to interpret and trust. Our capstone project addresses this problem by developing an intrusion detection system that reduces the opacity of model decisions. By making results clear and explainable, our system builds trust between analysts and technology while also serving as a training tool for the next generation of cybersecurity professionals.

The Knowledge Navigator is a web app that translates linear text documents into interactive knowledge graphs by levying LLMs to produce associations between the documents and their related concepts. Users can upload academic documents such as research papers, datasets, etc, and use the outputted Knowledge Graph as a brainstorming tool or learning aid. As such, the tool is useful for researchers and other academics, students learning new concepts, and business owners looking for new ideas. The generated graphs are downloadable so that users can access them offline.

Our project develops an AI-driven cybersecurity remediation system that automatically identifies and fixes security vulnerabilities in enterprise Linux environments. The engineering challenge lies in integrating automated compliance scanning, secure remote command execution, and multi-agent decision logic into a reliable, fault-tolerant remediation pipeline. Our solution reduces manual security workload, improves compliance with industry standards, and provides our sponsor with a scalable, intelligent, and cost-effective security hardening platform.

This project aims to develop a centralized, Python-based data acquisition and experiment-control interface for Los Alamos National Laboratory gas gun experiments. These experiments rely on precisely synchronized operation of multiple instruments. The proposed system provides a unified graphical user interface that discovers instruments, applies validated configuration profiles, coordinates timing and triggering, acquires waveform data, and exports results in required scientific formats. The system emphasizes reliability, extensibility, and operator usability in experimental environments. Success will be demonstrated through stable multi-device operation and reduced configuration errors.

The field of Cybersecurity is lacking in free, open-source educational materials. Educational and public communities require a platform for hosting, evaluating, and scoring adversarial machine learning artifacts to host MLSEC-style competitions. Our team's solution, MLSEC 2.0, is a containerized, web-hosted platform for this very purpose. MLSEC 2.0 offers a user portal where competitors can submit security artifacts to be automatically validated, evaluated, and scored against their peers. This solution benefits our project sponsor by providing a customizable, easy-to-use platform for competitions that encourage cybersecurity education.

The Maritime Digitization team has developed an application that digitizes and standardizes Bills of Lading (B/Ls) in maritime shipping by converting paper scans, PDFs, and electronic Bill of Lading exports into one canonical digital record. The engineering challenge is extracting reliable data from inconsistent document formats, then validating authenticity using OCR/LLM field extraction, hashing, and rule-based checks. Our solution reduces manual errors, speeds document authentication and customs clearance, and gives shippers, forwarders, and port stakeholders a secure, shareable workflow that bypasses physical paper transfers and lowers fraud risk.

Modern streaming platforms have made sharing individual songs effortless, yet the deeply personal art of the curated mixtape has been lost. Mixd is a smartphone app that revives intentional music sharing by letting users craft and exchange playlists designed to be experienced sequentially, like receiving a handmade mixtape. The engineering challenge involves real-time digital signal processing for precise editing and a backend optimized for efficient audio synchronization. By prioritizing curation over algorithms, Mixd fosters authentic connections through music, addressing a growing desire for more intentional digital experiences.

Neuromorphic event-based cameras have significant advantages compared to traditional frame-based cameras, including improved FPS, higher dynamic range, and better energy efficiency. In order to visualize data from neuromorphic cameras, several visualization platforms have been created, yet robust support for current generation neuromorphic cameras, 3D point cloud reconstruction from streamed data, and a comprehensive API to access visualization features are lacking. We propose an extension to NOVA, an existing neuromorphic visualization platform, to add these features.

Analysts using BRL-CAD often lack an interactive, ray-traced viewer for native .g models; workflows are typically command-line driven and hard to integrate into modern tools. Our capstone builds a Qt-based application/widget that loads BRL-CAD .g geometry, translates the scene into Intel OSPRay, and provides real-time pan/rotate/zoom with progressive ray-traced rendering. The engineering challenge is bridging BRL-CAD’s CSG data and materials to OSPRay efficiently while maintaining responsiveness, correctness, and testability. The result gives our sponsor a reusable, maintainable visualization component that speeds analysis, demos, and future feature development.

Our project develops a software-based posture monitoring system that helps computer users maintain healthy sitting habits during long work or study sessions. Poor posture contributes to musculoskeletal strain, fatigue, and reduced productivity. The engineering challenge involves real-time pose detection using a webcam, accurate posture classification, and low-latency feedback without storing video data to preserve privacy. Our solution provides live alerts, posture analytics, and break reminders to encourage healthier behavior. This benefits our project sponsor by promoting user wellness, improving productivity, and demonstrating an accessible, privacy-conscious health technology solution.

ProxSy is a gesture controlled, peer-to-peer clipboard sharing system that enables secure, cross platform data transfer over Bluetooth Low Energy (BLE). It addresses the friction of cloud based sharing and ecosystem locked tools like AirDrop by enabling direct, local device-to-device communication without internet dependency. By integrating BLE discovery, secure TCP transmission, and real-time computer vision gesture recognition, ProxSy creates an intuitive, low latency interface for nearby collaboration. The project demonstrates secure networking, cross platform interoperability, and innovative human-computer interaction in a unified system.

Our project is developing a mobile application to support sea turtle conservation by allowing field researchers to collect and manage patrol, nest, and poaching data in real time. Currently, conservation teams rely on a web system that is difficult to use in remote areas with limited connectivity. The engineering challenge is creating an offline-first mobile app that can store data locally and synchronize with cloud systems when connectivity is restored. This solution benefits our sponsor by improving data accuracy, efficiency, and response to threats. This will help protect endangered sea turtles and support long-term conservation efforts.

Our project is solving the problem of limited access to sports equipment due to high cost and short-term usage needs. There is currently no dedicated platform focused on peer-to-peer sports equipment rentals that integrates modern AI tools to improve pricing, trust, and user experience. The engineering challenge lies in designing and deploying a secure, scalable, full-stack web application that integrates multiple complex components into one platform. This solution targets a clear consumer frustration and proposes a sustainable, community-driven solution. In addition, this solution promotes equipment reuse, reduces unnecessary purchases, and supports local engagement.

Energy systems generate growing volumes of market, generation, and weather data, yet many organizations struggle to access, integrate, and analyze these datasets at scale. Existing platforms are fragmented, lack unified analytics, and support for forecasting or reproducible research. SunSight offers a scalable, cloud-based platform that ingests multi-source data, provides standardized APIs, and a secure dashboard for analysis and modeling. It will be evaluated on reliability, performance, and user feedback to improve data accessibility and analytical readiness.

Our project strengthens legacy space communication systems by integrating post-quantum cryptography (PQC) to defend against emerging quantum computing threats. Current satellite systems were designed around classical encryption methods that may become vulnerable in the future. The engineering challenge is implementing advanced lattice-based algorithms on constrained hardware with strict limits on power, memory, and processing latency. We design modular, lightweight PQC adapters that maintain operational performance while significantly improving long-term security for our sponsor’s critical space assets.

The United States Space Force has tasked us with taking a prototype that transmits basic post-quantum cryptographic data, and asked for us to extend it by providing adequate testing against hazardous conditions the system would encounter in orbit. Our group has implemented a testing harness that automates and orchestrates different tools and components providing a detailed analysis of said conditions for our sponsor to determine future steps. Post-quantum threats are imminent, and providing a proactive solution with testing for the Space Force ensures a better security posture for their communications.

Materials and manufacturing defects can severely impact performance, especially in mission-critical components. By classifying defects and damages in detonators, it reduces the amount of human reliance on identifying these damages. Utilizing Convolutional Neural Networks, we aim to measure and identify damages, creating a more robust and reliable pipeline for these damages. In addition to the classification, we aim to create a comprehensive and easy-to-use application housing all of this information. Enabling easy identification of these damages reduces the amount of manpower required, and allows for non-trained individuals to contribute.

Team IndigoAffect is developing a Voxel-Accelerated Hybrid Global Illumination system to achieve realistic lighting in real time for VR and interactive scenes. Traditional ray tracing is too slow, so our engineering challenge is optimizing voxelization and cone tracing across CPU-GPU pipelines while maintaining accuracy, stability, and testability. By improving lighting quality at interactive speeds, our solution helps our sponsor create more immersive environments, reduce rendering costs, and accelerate content development for next-generation graphics applications.

Our project is a computer vision powered workout optimization app that improves exercise safety and effectiveness. Many gym-goers struggle with improper form and training plans, increasing injury risk and limiting progress. The engineering challenge lies in accurately detecting body pose in real time, generating meaningful feedback overlays and skeleton views, and integrating this data into adaptive workout optimization algorithms. Our solution delivers personalized form correction and goal-based planning, helping our sponsor provide smarter, data-driven fitness experiences that boost user outcomes and engagement. It also helps out sponsor by furthering computer science education.

ZeroPoint is a mobile system designed to quickly identify wheelchair models and retrieve accurate specifications and maintenance procedures for airport ramp agents. Currently, agents rely on manual searches through scattered documentation, which can cause delays, misidentification, and safety risks. The engineering challenge involves integrating barcode/OCR scanning, structured database queries, and retrieval-augmented AI to deliver precise, conversational responses. Our solution improves operational efficiency, reduces equipment handling errors, and provides our sponsor with a scalable, intelligent support tool for wheelchair management.

This project is centered around open-source chip design. The issue is that chip design is a costly process in both time and money using the traditional design flow. The question we are faced with is how to design a custom 16-bit processor, based on RISC-V. The objective is to create a methodology that utilizes open-source tools (OpenROAD flows) to reduce cost and maximize efficiency for chip-design flow that can be used in academic or hobbyist settings. The benefit of this is producing a chip in a low-cost manner that can be taped-out and utilized in an academic setting.

Gas leaks possess a great monetary and safety cost. Both are solved by quickly alerting personnel and providing location of the leak. Our solution is a fully autonomous, differential drive rover that listens for ultrasonic sound waves that gas leaks produce, maneuvers to the leak's location, and provides leak alerts, location, and other information to a companion computer application. The rover listens for leaks using an array of 4 microphones, and their signals are filtered to tune for leak frequencies. This is fed to a localization algorithm that determines if a leak is detected, and its direction, which is fed to the motors for navigation.

This project is to design and build a three-phase Variable Frequency Drive (VFD) for controlling an AC induction motor. Many existing motor systems operate at a constant speed, which can waste energy and shorten equipment life. A VFD addresses this by adjusting the frequency of the supplied power so the motor can run at the speed and torque required for the application. The system will take in an AC power supply, convert it to DC, and then produce an AC output at the desired frequency. Our control board will set and monitor the output through an analog and app based interface. Built-in protections will prevent the motor from exceeding its rated specifications.

This project addresses the challenge of optimally sizing community-scale rooftop photovoltaic (PV) systems on distribution feeders with growing electrification and limited hosting capacity. The engineering challenge lies in integrating high-resolution rooftop data, feeder topology, time-varying load, and solar variability into a scalable optimization framework. The solution determines cost-optimal PV deployment that maximizes energy value while minimizing grid impacts. This approach helps utilities and planners identify high-value PV opportunities, improve hosting capacity, and support reliable, data-driven decarbonization strategies.

Practical implementations of Software-defined radios (SDRs) for military responders are often difficult in complexity due to the expert knowledge needed to translate mission requirements from operational terminology into signal processing language. This entails the bridging of multiple technical domains such as tuning analog parameters and digital signal processing, which slows down development time and leads to missed mission-critical timelines. To solve this issue our team has developed an end to end waveform generation tool. We are able to create a waveform from taking in user data from our user interface and using a intelligent feedback (LLM powered) to create flexible waveform code.

AgriPollinate tackles the challenge of providing farmers with real-time, accurate insights into pollinator activity, something current labor-intensive and reactive methods fail to deliver. Our engineering solution integrates LiDAR technology and machine learning detection to continuously monitor and map pollinator presence in the field. Through an intuitive web dashboard, farmers can visualize pollination heatmaps, rapidly identify under-serviced zones, make data-driven hive placement decisions, and ultimately boost crop yield and quality for their farm.

AnalogMind is a circuit design tool. The system aims to streamline the circuit design process in industry and be used by students to gain familiarity with circuits. The problem includes three engineering challenges. The front end is a robust system that is accessible to all designers regardless of their use case. The optimization system is a complex pipeline that orchestrates tools and algorithms to design a circuit. The hardware integration is a physical implementation that validates the circuit results in real time. This project can benefit the TAMU Engineering Department as an educational tool for students, and can benefit industry by saving time and resources on manual simulations.

Navigating large airports can be exhausting and stressful, especially for travelers with disabilities, as existing assistance services are not always timely or available. With millions of passengers traveling annually and rising disability-related complaints, having accessible mobility in crowded areas is critical. Our Autonomous Luggage Carrier and Tracker is a rover that follows a user within six feet and navigates around obstacles while connecting to a mobile app which has pinpoint location tracking and live rover metrics. Overall our rover aims to reduce physical strain, improve independence, and provide our sponsor with a scalable, assistive mobility platform.

This project addresses the challenge of providing scalable, hands-on assistance in university laboratories and research environments where instructor availability and expertise may be limited. We developed a battery-powered, WiFi autonomous robotic assistant that integrates AI, computer vision, speech interaction, and document-based reasoning. The primary engineering challenge was enabling reliable autonomous navigation, real-time hardware identification, and context-aware assistance from uploaded technical documents. This solution benefits our sponsor by reducing instructional overhead, improving lab efficiency, and enabling consistent, on-demand support across diverse research topics.

Rising energy costs are adding to the already high cost of living across America. Our home power monitor aims to give customers insight into their electricity usage and provide suggestions on how to improve their energy habits. Our power monitor takes the form of a sensor box inside a breaker panel, with current sensors reading from the panel inputs. Through machine learning and data processing in the cloud, the unit distinguishes individual appliances from a noisy power output. Along with a mobile application and persistent backend, our system considers energy costs, usage habits, and other data to notify the user of actions that can reduce their energy consumption and carbon footprint.

Arbitrary Waveform Generators are often expensive and difficult to use. Our goal is to close this gap in difficulty and price, while still providing a reliable and efficient Arbitrary Waveform Generator. Our solution will yield an arbitrary waveform generator system that takes user input of various waveforms through an interactive web application with access to a user manual and be able to produce and output these waveforms via BNC jacks. It will be able to display two +/- 5 V waveforms at a time and have a maximum waveform frequency of around 100 kHz. It will be easily movable and will function in any lab setting as long as it has access to a wall outlet power supply.

We are building a low-cost, dual-channel arbitrary waveform generator with an integrated mobile app to replace bulky, expensive lab AWGs that are inaccessible to many students and educators. Our project’s engineering challenge is delivering flexible, user‑drawn or preprogrammed waveforms up to 100 kHz with 16‑bit resolution, on two independent channels. Output options include sine, square, sinc, triangle, DC, and hand drawn functions. Our project involves coordinating an app, Wi‑Fi microcontroller, FPGA, DACs, and low‑noise analog outputs under tight cost and power constraints. ​

Our project is to create an intuitive wireless DC motor controller that drives a 24V brushless DC motor. This controller will implement both speed and position control and send real time feedback to the user through an easy to use mobile application. This app will be the user interface that will connect to the motor via Bluetooth. This project solves the problem of motor controllers being difficult to use and dependent on the user being present. This project can benefit our sponsor as a building block to many applications because of the wide use of motors.

Robo-Picasso addresses the challenge of accurately converting digital drawings into precise physical artwork. The core engineering challenge lies in reliably translating user-generated sketches into coordinated motor commands while maintaining accuracy, repeatability, and smooth motion across mechanical, electrical, and software systems. Our solution integrates a mobile application, Bluetooth communication, and an embedded motor control platform to drive a robotic drawing arm. This system benefits our project sponsor by providing a platform for automated drawing, prototyping, and future applications in precision motion control and human-machine interaction.

In this project our team is tasked with fabricating a portable and inexpensive yet highly functional device which can be used to complete all ECEN 215 lab assignments. This device will include at minimum the following components in order to complete these assignments: a digital voltmeter, an oscilloscope, a signal generator, and a DC variable power supply. These components will be accessible through a web interface which will allow for an easy Wi-Fi connection to mobile devices. This will allow for all ECEN 215 labs to be completed at home, and will allow for a cheaper alternative to equipment currently used to complete the labs.

We are designing a low cost and portable lab equipment replacement for students to use for remote lab work. This is designed around the class ECEN 215 that most non-electrical engineering students are required to take. Students taking the course over the summer or remotely for any other reason will have access to a limited multimeter, oscilloscope, power supply, and signal generator. This benefits our sponsor John Lusher by saving him from having to record videos of himself completing the labs for the students to view as well as give students a much better lab experience since currently the labs are impossible to complete remotely.

Remote ECEN 215 students lack hands-on laboratory experience because tools like the Analog Discovery 2 are too expensive for single-semester use. Our team is developing a portable, low-cost electrical measurement device integrating an oscilloscope, waveform generator, and multimeter tailored to ECEN 215 labs. The system is controlled through a mobile app using BLE for provisioning and WiFi for communication. The challenge lies in achieving accurate measurements, reliable wireless connectivity, and low-cost hardware integration. Our solution enables our sponsor to improve student engagement, expand lab access, and scale hands-on learning for future remote classes.

The EE-Assistant is a stationary robotic kiosk for Zachry ECEN teaching labs that reduces student downtime waiting for help and parts. It answers common lab questions via speech/keyboard using an on-device LLM, identifies components with camera-based vision, and dispenses stocked parts through an ESP32-controlled motor carousel tied to an inventory database. The engineering challenge is reliable end-to-end integration: safe multi-rail power, robust audio/LLM intent routing, accurate vision on a constrained dataset, and repeatable dispensing with inventory feedback. It benefits Dr. Byung-Jun Yoon as a real testbed for ML-driven decision-making and automation in hands-on education.

Parking in garage spaces can be difficult for inexperienced drivers, those with slow or impaired perception, and owners of older vehicles that lack modern parking technologies. Limited visibility, slower reaction times, or reduced spatial awareness can increase stress and the risk of vehicle damage during parking. We present a vehicle-independent parking guidance system designed to help less confident drivers and vehicles without built-in sensors park safely and confidently. Using an external sensor and audiovisual feedback, the system provides real-time steering and braking cues without requiring any modification to the vehicle.

In ECEN circuit labs students are often waiting for the TA to come over and look at their breadboarded circuit in order to find an issue with how they wired their circuit. This wastes time and strains TA’s who have to get to potentially 20 groups in order to explain where they went wrong. Our solution is to build a bot that can debug a real world breadboarded circuit by taking in the schematic, simulation data, and real world data. The bot can then determine the most likely error scenario based on the expected values and what the real world values actually are.

GuardianSense is designed to monitor an elderly individual in assisted living environments and provide quick notification when a fall is detected. The system generates a real-time 3D point cloud using LiDAR to determine position and classify posture and movement. Engineering challenges include accurate spatial reconstruction, reliable power integration for the LiDAR-based system, and effective data representation. Posture data is stored in a database, and when a fall occurs, text notifications and phone alerts are sent to caregivers to improve response time and reduce injury severity.

Our project addresses the lack of real-time, circuit-level visibility in residential power systems, which limits homeowners’ ability to manage energy use, reduce costs, and detect unsafe conditions. The engineering challenge is accurately measuring, processing, and classifying electrical loads in real time, then securely transmitting and presenting this data through an intuitive mobile application. Our solution integrates hardware sensing, and a user-focused app to deliver actionable insights. This benefits our sponsor by demonstrating a scalable, consumer-ready energy monitoring platform that supports improved reliability, safety, and data-driven energy management.

Our project develops a low-cost, real-time home power monitoring system that measures voltage and current to calculate electrical power and energy usage. The problem addressed is the lack of accessible, detailed insight into residential power consumption, which limits efficiency improvements and fault detection. The primary engineering challenge lies in accurately sensing and conditioning analog signals, synchronizing measurements, and performing embedded signal processing under hardware constraints. Our solution benefits the sponsor by enabling scalable, data-driven energy monitoring that supports efficiency analysis, diagnostics, and future smart-grid integration.

Households often face high utility costs because device-level energy usage is invisible with traditional whole-home monitors. The Home Power Monitoring Outlet (HOP-MO) addresses this by measuring voltage and current at each outlet and processing the data using embedded signal processing and machine learning to identify connected appliances. The engineering challenge is achieving accurate sensing, appliance classification, and reliable wireless communication in a compact, low-cost device. HOP-MO provides a scalable platform for detailed energy insights, remote device control, and actionable cost breakdowns that help users reduce wasted power, lower expenses, and improve sustainability.

1) We are solving the problem of falls in assisted living facilities by utilizing high frequency radar. Using this approach gives a solution that is more passive and avoids the privacy concerns associated with a camera. 2) The engineering challenge in this problem is in taking in data from a radar and using machine learning to output "fall" or "no fall." With this there is a lot of issue for false positives, and for falls that aren't registered or that aren't alerted quickly. 3) The solution benefits the sponsor because it demonstrates proof of concept: that high frequency radar can be used for human activity detection in place of other forms of detection.

Industrial systems widely use 4 to 20 mA current loops for reliable sensing and control, but many low cost solutions lack integrated monitoring and remote visibility. This project develops a dual channel industrial controller that measures two 4 to 20 mA inputs, drives relay outputs, and streams real time data to a secure cloud database. The primary engineering challenges include precise current measurement, signal conditioning, electrical noise immunity, and dependable wireless communication. The result is a scalable, low cost platform that improves monitoring capability, remote diagnostics, and overall operational efficiency for industrial environments.

Power electronic circuits are essential in modern energy/industrial systems. As these systems operate under high stress, they require continuous monitoring to ensure safe operation & to maintain performance. This project presents the Intelligent Data Acquisition (iDAQ) system, a platform that combines real-time sensing and AI-assisted analysis to support condition monitoring & fault detection. The iDAQ system collects electrical/thermal measurements from a target circuit and processes the signals in real time, which are analyzed by an AI inference engine that can identify abnormal operating behavior, support predictive fault classification, and provide insights for optimization.

Autonomous warehouse robots generally experience instability when handling unbalanced loads or making sudden maneuvers. These movements can cause tilting, making balance a key challenge. Our project develops a four-wheeled inverted pendulum robot that autonomously balances a dynamically unstable payload while navigating through constrained spaces. By integrating mechanical design, sensing, power management, and embedded control into a unified microcontroller-based system, we create a reliable and safety-focused platform. The resulting design insights and performance data can inform future development of safer, more reliable warehouse and mobile robotic platforms.

Our project tackles the mobility limits of current planetary rovers, which struggle to safely traverse slopes steeper than ~45 degrees, restricting access to valuable scientific regions like crater walls and cliffs. The core engineering challenge is maintaining balance, traction, and control on steep and uneven terrain while carrying a payload. Our solution is a 1-meter-tall bi-wheeled balancing rover that actively adjusts its center of mass to stay directly over the wheels at any slope angle. This approach enables stable operation on extreme terrain and benefits our project sponsor by expanding exploration range, improving data collection, and accessing previously unreachable environments.

The goal of our project is to build a two-wheeled robot that balances autonomously while carrying a 1.5kg weight above its center of gravity. The most significant challenge we are faced with is designing a control system that will interact correctly with our hardware to keep the robot perfectly balanced while it travels between locations. This project demonstrates how more advanced control systems can pave the way for more stable and precise transportation solutions, especially in dynamic environments. It also contributes valuable data to our sponsor's research in robotics and control systems.

As digital systems grow in complexity, the risk of designing bugs and inefficiencies increases. However, current EDA tools provide limited automation and lack of natural language-to-HDL capabilities that could accelerate design flow Our Solution is to develop a LLM-assisted digital design framework that takes natural language and automates majority of chip design process. As a final design implements a DDR2 memory controller, which is a non-trivial and widely used integrated chip that serves as a realistic benchmark for evaluating the effectiveness of our project.

Large language models (LLMs) struggle to generate functionally correct HDL designs because they lack integration with simulation and synthesis tools that are needed for verification. The engineering challenge is bridging natural language design intent with automated simulation, synthesis, and validation, while maintaining accuracy and reliability. Our solution is to create a tool that can be prompted with human language and automate most of the design process by connecting to various external tools. This benefits our sponsor by accelerating chip design workflows, reducing manual effort, and supporting academic and research applications.

The retail industry faces persistent labor shortages, with self-checkout systems failing to fully address the challenge. Our project proposes an innovative solution: instead of automating the checkout process, we aim to automate inventory management and customer assistance. By leveraging autonomous robots equipped with LiDAR, our system can navigate grocery stores, monitor stock levels, and interact with customers to help them locate products. This addresses the dual challenge of efficient inventory tracking and superior customer service, delivering increased productivity and improved satisfaction for retailers.

We are designing a fast, feeder-aware method to site public EV charging stations without overloading distribution networks. The engineering challenge is coupling equity-driven demand (multi-family housing hotspots) with electrical feasibility: we enforce Kirchhoff’s Current Law on a radial feeder and cap power flows by line hosting capacity, while an L1 “activation” penalty keeps the chosen sites sparse and interpretable. Implemented as a convex linear program, our tool screens many feeders quickly and highlights bottleneck segments, helping the sponsor place chargers where demand is highest and upgrades deliver the most value.

The Autonomous Luggage Rover is a self-navigating robotic carrier designed to follow a user and transport personal belongings hands-free across pedestrian environments. The core engineering challenge is achieving reliable, real-time navigation and obstacle avoidance using low-cost sensors while maintaining stable power delivery and motor control in a compact mobile platform. Our solution integrates custom power electronics, sensing, and embedded control to create a robust assistive mobility device, reducing user burden and demonstrating a platform for automated material transport for our sponsor.

The Luggage Carrier is designed to autonomously transport a user’s luggage through large, crowded environments while maintaining a continuous connection to the user. The system integrates reliable user-tracking software, real-time obstacle avoidance, motor control, and load verification. It connects to a mobile device to monitor the user’s location, while a mobile app provides alerts if separation occurs or if the luggage is removed. Sensor data is processed to detect obstacles and enable safe navigation using coordinated motor control and obstacle-avoidance algorithms.

The Luggage Rover is an autonomous cargo carrier designed to aid disabled and elderly people in airport settings. The rover follows its user, avoids obstacles and features an attractive Android app user interface, which provides the user with basic rover controls, status and cargo alerts. Technical challenges for this project include developing a time-of-flight tracking system with data processing algorithms, intelligent obstacle avoidance using ultrasonic sensors, smooth autonomous control accomplished through PID controllers and connecting each subsystem through the app.

Traditional RTL-to-GDSII chip design is expensive and there are major cost associated from licensing tools and having specialized expertise, creating a high barrier for System-on-Chip (SoC) development. Our engineering challenge is to create a chip within a timely manner and a manufacturable layout, which will meet power, area, performance, and routing constraints. Our project will implement a 16-bit RISC-V processor that will utilize the open source OpenROAD/OpenLane design flow to demonstrate the ability to produce a full RTL-to-GDSII design flow. By utilizing these tools, our solution will show a lower cost and reduced time for SoC developments.

The United States Patent and Trademark Office using a keyword search to parse its database. This fails to capture contextual meaning, leading to inaccuracy of search results and expensive billing rates for litigation professionals. Patent Miner optimizes the patent search process by retrieving contextually relevant patents faster and more accurately compared to a traditional database. The challenge lies in embedding and indexing a large scale database, while also maintaining accuracy and low latency. By delivering more precise search results, Patent Miner reduces research time and strengthens the defense of intellectual property.

The Patent Mining Tool solves the inefficiency of searching the USPTO database for infringement evidence or existing technologies. Legal professionals and developers currently rely on limited keyword searches that miss crucial semantic meaning. The core engineering challenge involves building a robust Retrieval-Augmented Generation pipeline to convert complex patent information into vector embeddings for accurate semantic similarity searches. By delivering an application with summarized generative AI responses, this solution benefits the project sponsor by accelerating prior art discovery, vastly improving search, and reducing reliance on costly technical expertise.

The project is a radar that conducts vitals monitoring operations. A radar module will collect physiological data regarding a subject's heart rate and respiratory patterns, which will be processed by a Raspberry Pi 5 hosting an onboard AI model. An external mobile application will provide a user with alerts and access to vitals data. Respiration and heart rate are metrics that are traditionally measured via equipment that physically makes contact with the body, which can cause discomfort. Our radar offers a contactless alternative, leveraging mmWave technology to obtain this data in a comparatively non-intrusive manner. This product offers a simple and accessible way to monitor vitals data.

Coordinating unmanned vehicles for missions in complex environments is critical for improving efficiency and safety. Our goal is to provide a fully collaborative system that integrates aerial and ground autonomy. The UAV-UGV cooperative system will address this challenge by having the UAV identify targets and communicate their locations to the UGV, which can then deliver payloads or navigate terrain precisely. Our approach enhances situational awareness and allows unmanned missions to be conducted safely and efficiently in applications such as disaster response, defense logistics, and remote exploration.

Our project develops an unmanned arial vehicle (UAV) and ground vehicle (UGV) for the ‘2526 Raytheon Autonomous Vehicle Competition. We are required to do challenges of escalating difficulty culminating in an UAV taking off from the UGV, scanning a field for obstacles and the objective, landing on the UGV, and finally the UAV and UGV must arrive at the destination together. The main engineering challenges are implementing computer vision, using a shared ROS 2 framework for both vehicles, and maintaining reliable communication. Our solution models real-world remote mission scenarios that benefits our sponsor by providing a framework for remote missions that keep our troops safe.

Our project aims to address the challenges of operating in GPS-denied environments by using a fully autonomous UAV–UGV system. The UAV deploys from the UGV, conducts forward reconnaissance using image processing algorithms to detect obstacles, and generate safe navigation paths. It transmits this data directly to the UGV, eliminating the need for a ground station. The key challenge is developing a system by which a precise landing on top of the UGV can be executed safely. Our solution advances resilient navigation and coordinated autonomy for hazardous operations where GPS is unavailable or compromised.

Command-and-Control (C2) modules are becoming increasingly responsible for holistic regulation for many automated systems (e.g., Industrial Systems, submarines). Consequently, disruptions to the system can incur huge costs and pose risks to safety-critical functions. Numerous practices and tools are available for hardening computer systems and networks; however, these methods fall short of mitigating the impact of attacks on safety-critical C2 systems due to increased diversity of functions. Thus, this project aims to develop fundamental new methods for C2 cyber threat detection, providing real-time alerting systems and detecting zero-day threats at the network and embedded system levels.

The use of Kubernetes Clusters as a means of network management has exponentially grown in a short period of time. With this rapid growth in use, the cybersecurity sector has fallen behind. To meet this need, our team has been tasked with creating a real-time cyber threat detection tool for use on a Kubernetes Cluster. This entails monitoring the network PCAP data from the cluster of its internal and external communication, analyzing the telemetry data of the robot controlled by the cluster, and alerting the user of any attacks on the system. This is crucial for ensuring the integrity of the system, proper execution of the robot's missions, and allows the user to take appropriate action.

The Global Cyber Research Institute is researching cybersecurity in Command-and-Control modules, due to the increasing integration of these in large industries. They have requested that we come up with a solution for the cybersecurity of their Kubernetes cluster. The solution was expected to be a real-time operating system, provide network intrusion detection, provide peripheral intrusion detection, and allow visualization of the threat. A combination of software, network, and embedded systems engineering is employed to achieve a solution that satisfies all requirements. The system provides the GCRI's research with a higher level of safety.

MRI systems operate under tight capacity constraints, making patient throughput a critical challenge. A major bottleneck is cardiac gating, which traditionally requires ECG leads attached to the patient to synchronize imaging with cardiac motion. This process can be time-consuming and susceptible to setup variability. Our team has developed a contactless cardiac gating system using a millimeter-wave radar system to detect chest motion and extract heart and respiration rates in real time. The generated cardiogram enables accurate synchronization of MRI data without electrodes, improving workflow efficiency, patient comfort, and overall scanner utilization.

S.N.A.C.K. streamlines large ECEN 214 labs where students lose time hunting parts, miswiring breadboards, and wait in long TA lines for basic help. Our engineering challenge is integrating a reliable kit dispenser, an onboard multimeter probe debug mode that feeds measurements and lab manual context to a tailored LLM, and voice plus web interfaces with secure student TA dashboards and session logging. For the sponsor, S.N.A.C.K. standardizes kits, reduces routine TA workload, improves lab throughput, and gives students faster, consistent troubleshooting and mentoring focused lab time.

Our project, the SLAM Robot, addresses the limitations of camera-based navigation in low-light or obstructed environments, such as search and rescue or convoy scenarios. The primary engineering challenge lies in developing a sound-based alternative that uses a microphone array and ESP32 microcontrollers to accurately detect auditory cues and autonomously navigate toward a sound source. By implementing Time Delay of Arrival processing and vector mapping, the robot provides a reliable navigation solution where visual data is unavailable. This benefits our sponsor by offering a robust, sound-localized mapping system that maintains operational reliability in visually compromised settings.

The SOLARIS project will provide an illumination system that runs off stored solar energy collected by a single axis panel tracking system. This system will power foyer lighting (indoor) and porch lighting (outdoor), using cameras and sensors to operate in dark environments when light is necessary, and avoid false triggers by pets, leaves, or any other non-human movement to conserve energy whenever possible. The system will provide operation feedback to the user through a companion app, where the user can see the system’s status, view the system’s security footage, set preferences, manually control lights, and schedule operation hours.

Our sponsor provided us with a simple problem: create a physical security sensor that is configurable for a variety of environments and that is resistant to common defeat methods (such as hiding in shadows or utilizing heat shielding). Our solution is to use indirect time-of-flight (ToF) LiDAR. Our sensor actively illuminates the environment, not requiring ambient light, and provides a full 3D depth map of the scene. Additionally, since we took a more bare-metal approach to the design, our sensor is extremely configurable with settings such as modulation frequency, integration time, and more to tailor it for many applications.

Traditional patent discovery is inefficient, often failing to find relevant results due to rigid keyword mismatches. To solve this, we engineered a multimodal search tool that allows users to query the US patent database using natural language, capturing semantic nuances that standard tools miss. The central engineering challenge was optimizing the system to index millions of records for real time retrieval on constrained hardware. This solution transforms legal research for our sponsor, drastically reducing the time needed to identify prior art and mitigating the risk of overlooking critical intellectual property.

Our project addresses a critical gap in smart-home reliability; current systems rely on grid power and cloud connectivity, leaving homes without lighting or monitoring during outages. The engineering challenge is designing an integrated platform that maintains secure, real-time communication and control even when external power or internet access is disrupted. Our solution combines security, lighting, and user interaction into a unified system with both local and remote functionality. By ensuring continuous operation during grid fluctuations, our platform increases resilience, improves user trust, and delivers a more dependable smart-home solution.

The Solar Home Lighting System solves the problem of unreliable grid-dependent lighting by providing a cost-effective, solar-powered solution with battery storage and smart controls that improve energy efficiency, safety, and user flexibility during nighttime. The main engineering challenge is designing a reliable, low-cost system that efficiently converts and manages solar energy while integrating sensors, automation, and user-friendly control without increasing power consumption. Our sponsor would benefit by having access to a system that powers lights on the porch and foyer at night while also allowing him to charge his phone, power a lamp, and operate a security camera outside his home.

The Solar Home Efficient Lighting System (S.H.E.L.S.) addresses wasted household energy caused by lights left on in low-occupancy areas such as foyers and porches. The engineering challenge is to design a fully solar-powered, battery-backed system that reliably distinguishes humans from pets, manages 12 V power distribution safely, and integrates a custom PCB with a real-time monitoring application. The result is a cost-effective, renewable lighting solution that reduces grid dependence, lowers operating costs, and improves residential safety and security.

Current home lighting systems waste energy and lack intelligent, user-friendly control. Our project addresses this by designing a solar-powered smart lighting system that combines motion detection, ambient light sensing, and app-based control. The main engineering challenge is integrating low-power embedded electronics, LED drivers, sensors, and a solar power system into a reliable and efficient platform. Our solution enables scheduling, dimming, and adaptive lighting based on occupancy and light levels, reducing energy consumption while improving user convenience. This benefits the project sponsor by demonstrating a scalable, energy-efficient lighting solution for modern smart homes.

Testing solar power electronics is hindered by the difficulty of performing repeatable experiments under uncontrollable environmental variables. This project addresses this by developing a Solar PV Emulator that replicates the I-V characteristics of physical panels. The challenge lies in the real-time integration of Single Diode Model algorithms with hardware control. Our solution provides a system where a web interface manages PV profiles and a dual-microcontroller hardware stage delivers the ideal electrical response. With integrated sensing, Wi-Fi telemetry, and a local display, this system offers a safe, repeatable platform for validating MPPT algorithms and accelerating research.

We address the need for sustainable, smart infrastructure in public spaces with a solar-operated playground system. Essential park services are powered via a solar panel and dual-battery system, utilizing custom PCB-based charge switching and buck converters for stable operation. Charging stations will be provided as a user amenity. We are addressing modern safety concerns with machine-learning-based automated lighting and surveillance systems, enabled by remote operator control with web and mobile access. Our design offers a reliable, off-grid solution that enhances safety for communities and encourages park attendance.

Our group is developing a solution to underutilized and unsafe public playgrounds due to lack of security and lighting. Our team is trying to reduce the electrical footprint most playgrounds currently exert by implementing solar panel(s) in the playground paired with a battery and an Mobile Application and Human detection Machine Learning. It can turn on and off lights automatically by sensing daylight and motion detection and manually. Users can charge their phones and operators can watch in real time and rewatch the videos recorded based upon human activities through an app. This project will improve community wellness by fostering a safe, and well-lit area to be used by the public.

Our project addresses the lack of safe, reliable, and energy-efficient infrastructure in public playgrounds after dark. Many playgrounds rely on grid-powered lighting with limited security, leading to wasted energy, higher costs, and safety concerns. The engineering challenge is designing a fully solar-powered system that integrates smart lighting, motion detection, video monitoring, and cloud connectivity while operating reliably under changing weather and power constraints. Our solution benefits the project sponsor by reducing operating costs, improving community safety, and delivering a scalable, low-maintenance, and sustainable playground lighting and security platform.

Public playgrounds often lack sufficient lighting and security, creating safety concerns for families and increasing the risk of vandalism. Our project addresses this issue by developing a solar-powered playground monitoring system. The engineering challenge involved designing and integrating two custom PCBs to regulate and distribute power from a solar panel and battery to a Raspberry Pi, sensors, lighting, cameras, and a device charging station. We also created a mobile application that enables operators to remotely monitor activity and control lighting. This solution provides our sponsor with a solution to enhance safety and support sustainable infrastructure in public spaces.

Our project is a sound localization robot which will autonomously navigate towards a sound source and live stream the sound source via an app to the user. The problem we are solving is that baby monitors only view a narrow angle and only can rotate given the caretaker's manual input, our robot will do everything autonomously and notify the user when it was found the source of the baby cry. The engineering challenge is to make it find the sound source with obstacle avoidance without using a camera and solely rely on sound to navigate and locate the sound. The solution benefits our project sponsor as the sound localization can be used in sonar for an autonomous vehicle.

First Responders lack reliable methods for navigating dangerous environments, where visibility is limited, putting both their safety and the victims at risk. Our solution is an autonomous Sound Localization Robot that can hear distress calls, pinpoint their direction, and move toward them autonomously, allowing first responders to focus on saving lives instead of risking theirs. The primary engineering challenge lies in implementing a Time Difference of Arrival system that can detect the location of a specific sound, and avoid obstacles. Our project provides our sponsor a proof of concept for a search and rescue technology that does not rely on visibility.

The TI Parking Assist project takes on the challenge of older vehicles lacking modern reverse safety features. Our team developed a user-friendly installable kit that provides real time video and obstacle detection along with a classic backup camera UI without intense vehicle modification. The system uses a custom PCB powered from the 12V auxiliary outlet, a Raspberry Pi4, four HC-SR04 ultrasonic sensors, and two cameras to detect steering wheel angle and stream real-time video wirelessly to the iOS smartphone app. Designed for reliability, ease of installation, and scalability, our kit serves to reduce the chances of low-speed collisions and extend the modern usability of older vehicles.

Our project addresses the need for a flexible, user friendly audio system that will allow for real-time sound customization without a physical interface that can also be done from a users mobile device. This will be done by developing a high-performance Bluetooth audio amplifier. The challenge lies in building a low noise and reliable wireless communication while allowing the audio to propagate smoothly. Our solution will benefit TI as it will demonstrate TI's analog and embedded technology in a simple and practical design, also demonstrating the scalability for smart audio applications.

Arbitrary Waveform Generators produce custom electrical signals essential for electronics testing, but commercial units cost thousands and lack wireless control. Our project is a dual-channel AWG controlled through a web browser over WiFi. The core engineering challenge is coordinating four custom subsystems, a web application, wireless microcontroller, FPGA, and two 16-bit digital-to-analog converters, to output precise analog waveforms at frequencies up to 100 kHz. Each subsystem sits on a custom-designed PCB, housed together in a 3D-printed enclosure. The result is a portable, low-cost instrument providing accessible signal generation without the expense of traditional lab equipment.

The purpose of this project is to create a system that can help mitigate the negative impact of falls for elderly people, especially in care facilities. The described system utilizes a mmWave radar device to continuously monitor a room and send processed data to a compute unit. The compute unit uses a trained Artificial Intelligence (AI) model to identify falls and send a notification to a connected device when one is detected. The scope of this project is to create a minimum viable product for the system using commercially available components. This solution benefits our sponsors by showcasing a real-world medical use case for radar systems in the medical field.

In emergency and rescue situations, people who need help may not be visible due to smoke, debris, or low lighting, but they can often call out for assistance. Most robots rely on cameras, which are ineffective when the person cannot be seen so our project addresses this gap. The main engineering challenge is accurately determining the direction of a sound source in real time and navigating toward it while avoiding obstacles. Our solution is a mobile robot that uses a microphone array and an MCU for sound direction processing and LiDAR for obstacle detection. This system benefits the project sponsor by showcasing a reliable, non-visual localization method improves search effectiveness.

Our project addresses the challenge of inconsistent and inaccessible laboratory equipment for undergraduate electrical engineering courses. Traditional lab setups rely on shared, high-cost instruments that limit flexibility, scalability, and remote learning. The engineering challenge is designing a low-cost, portable lab kit integrated with a mobile application that reliably controls and monitors multiple instruments through Bluetooth communication. Our solution benefits the project sponsor by reducing equipment costs, increasing student accessibility, and enabling a standardized, guided lab experience across courses and learning environments.

Falls are the leading cause of serious injury in elderly individuals. Many solutions are present in the market today, including wearables such as the Life Alert and Apple Watch, as well as cameras to determine if a fall has occurred. Unfortunately, many options require a physical interaction or could invade privacy. The RADIAN group has developed the RADIAN (RADar Intelligent Activity moNitoring) system to help solve these concerns. Using mmWave radar, the RADIAN group has designed a sensing system to accurately predict a fall using machine learning and relay it back to the caregiver in real time. Using the RADIAN system will give caregivers a sense of safety for their loved ones.

Traditional arbitrary waveform generators (AWGs) are bulky, expensive, and difficult to use, limiting accessibility for students, educators, and rapid prototyping teams. Our project addresses this by developing a compact, dual-channel AWG controlled through a simple mobile app. The primary engineering challenge is generating precise, low-noise waveforms while maintaining portability, affordability, and reliable wireless control. Our solution outputs standard signals (sine, square, triangle) as well as custom user-defined waveforms. This system reduces cost and complexity while expanding usability, directly benefiting our sponsor through a modern, accessible test and measurement platform.

A variable frequency drive (VFD) enables precise motor control by converting a fixed 60 Hz AC input into an AC output with adjustable frequency. Because motor speed is directly proportional to supply frequency, regulating the output allows accurate speed control and improved energy efficiency, particularly at partial loads. Our design uses two PCBs: a high-power stage and a low-power control board. The control system includes an STM32 for PWM generation and sensor readings, and an ESP32 for communication between the user interface, consisting of an app and a touchscreen, and the STM32.

A variable frequency drive (VFD), commonly used in pumps, fans, and conveyors, controls an AC induction motor by regulating output voltage and frequency through PWM signals to enable precise speed and direction control. Traditional motor control systems run at full speed regardless of demand, causing unnecessary energy use, increased wear, and higher operating costs. Our project addresses this inefficiency through an open‑loop design to achieve smooth speed and bidirectional operation. Furthermore, our system is supported by current sensing, DC bus voltage monitoring, and temperature feedback for protection.

We are improving the performance of XycLOps by enabling parallel execution of circuit simulations during curve fitting. The core problem is that optimization currently runs Xyce simulations sequentially, which leads to long runtimes as design complexity increases. The engineering challenge is safely coordinating multiple concurrent simulations while avoiding file conflicts and shared state errors. By introducing multithreading, we significantly reduce optimization time, allowing our sponsor to explore more design iterations faster and improve overall productivity.

Current shooting targets provide no real-time feedback, forcing shooters to walk downrange to assess accuracy, slowing training and limiting measurable performance improvement. The engineering challenge is integrating durable 3/8-inch AR500 steel with multi-zone impact sensing, reliable hit detection, motorized protraction/retraction, and wireless data transmission in a safe, rugged system. Our solution delivers instant scoring and shot tracking through a mobile app, improving training efficiency, data-driven evaluation, and long-term cost effectiveness for our sponsor.

The Automated Trading Card Sorter addresses the labor-intensive and error-prone process of manual inventory management, which leads to lost revenue and backlogs for card shops. Employees spend hours identifying and valuing cards, where it could be better spent on customer service. The main engineering challenge lies in integrating a robust, accurate image recognition system and efficient sorting mechanism that avoids card damage. This solution streamlines operations for the sponsor, reduces errors, and boosts efficiency in their card sorting process.

RV travelers in remote areas often lack real-time weather updates due to cellular coverage gaps and the limitations of county-based NOAA alerts. The StormSeek ASTRA solves this with a portable, satellite-based acquisition system that provides live weather imagery independent of all terrestrial networks. The system utilizes a KrakenRF Discovery Dish, SDR hardware, and onboard computing to autonomously lock on to geosynchronous satellites to receive and process weather data. By integrating GPS, accelerometer, and magnetometer data, the device employs motorized azimuth and elevation controls to maintain a precise signal lock, even when challenged by wind or other environmental shifts.

Coastal environments accumulate litter and small debris which can be difficult to remove using traditional manual methods. To solve this problem, we have designed and built a robot that is able to pick up small to medium sized objects while also not disturbing the environment. The robot we have designed is smaller in comparison to the commercial grade products currently on the market while also being more cost effective and consumer friendly. The current products on the market are rake type objects either pushed by a human or pulled by a tractor. This project benefits our sponsor by providing a solution to beach clean up efforts for those unable to afford commercial methods.

For our project, we will be designing an automatic scoring system for competitive axe-throwing. Our Solution will be able to automatically project targets, track the locations of axes thrown at the target, and automatically score each player's points based on the location of the axe. This will be achieved by using LiDAR, Camera, Projectors, and a mini PC for computing. There will also be safety features implemented through our cameras to detect people in the axe-throwing lanes, and cancel projection to increase safety. Our final goal is to have a fully integrated system that can be deployed in the axe-throwing lanes at The Cut in College Station.

Strike Sense is developing the E-Shido Smart Scoring Suit to automate a new weapon-based combat sport called "E-Shido." The main issue is the lack of automated equipment to track hits and manage scores while minimizing human error. The engineering challenge is integrating pressure sensors and LEDs into 2 modular, wearable suits that accurately detect a hit's force. From there, the suits wirelessly transmit the data to a real-time scoring app via Bluetooth. This solution benefits sponsors by delivering a reliable, fully automated system that ensures safety, discourages using brute force through pressure restrictions, and makes the sport accessible and engaging for all ages.

The EcoTrace project addresses the challenge of collecting accurate movement and behavior data from small urban wildlife, specifically squirrels, without causing harm or behavioral disruption. Existing tracking solutions are often too heavy, bulky, or power-hungry for small animals. The primary engineering challenge is designing a lightweight, low-power tracking system that integrates GNSS location sensing, BLE communication, and efficient power management within size and weight limits. This solution benefits the project sponsor by enabling long-term data collection on squirrel movement patterns, habitat use, and activity cycles to support ecological research and conservation planning.

Boeing is seeking to develop a data acquisition system capable of sampling Acoustic Emission (AE) signals at high speeds. These AE signals arise from impacts and stresses on structures and can be analyzed to assess structural integrity. Low cost, low power, light weight, remotely deployable, untended solutions are required. The proposed solution is a modular system that samples three AE sensors at 5 million samples per second, processes and stores AE data locally, and transmits it to a central coordinator for storage and export .

Fluid Robotics aims to address the issues of identifying and locating invasive fish species in Texas by developing a submersible ROV. The ROV is propelled by a magneto-hydrodynamic (MHD) drive, an eco-friendly drive system that uses magnets and electrodes to silently generate thrust in salt water. It uses computer vision to identify invasive fish species and has an operator-controlled GUI to present a live feed of the fish. Our team aims to implement the MHD drive system in an ROV so it can be used to aid divers in scouting for the invasive species, saving divers time and preventing them from taking unnecessary risks.

Every heartbeat tells a story, but too often, the tiniest ones go unheard. Worldwide, hundreds of thousands of babies lose their lives due to oxygen deprivation at birth that could have been prevented with early and accurate monitoring. Our team developed a maternal and fetal heart monitoring system to improve real time health tracking during pregnancy. The challenge was designing a device that accurately distinguishes between maternal and fetal heart signals in a reliable, and user-friendly way. By integrating signal processing with wearable sensor technology, our solution provides monitoring, enabling earlier detection of complications, and enhancing safety for mothers and babies.

ICS Defenders is developing a non‑invasive system to detect fruit fly infestations on produce. Invasive fruit flies threaten billions of dollars in crops each year by slipping through busy ports of entry. Our engineering challenges include integrating Doppler data from a radar and gas sensors with a trained machine learning model to recognize micro‑motion wing beats within a few millimeters and chemical signatures. This system will be among the first to detect multiple fruit fly life stages, enabling much earlier infestation detection, while presenting real‑time results on a GUI, giving USDA inspectors a fast, scalable tool to stop invasive species at the border and protect U.S. agriculture.

Cybersecurity in Operational Technology is critical due to the scale of potential consequences of a cyber attack on power grids, water treatment plants, and other civil infrastructure. Neo-Electronic Solutions developed an open-source software tool that can provide insight into the security of operational technology (OT). Using ethernet as the physical interface, the user will be able to plug into a device such as a power protection relay and run an adjustable attack module that includes Denial of Service, Man in the Middle, Brute Force Password Guessing, and Protocol Fuzzing. After running the sweep, a vulnerability report will be generated and displayed on the graphical user interface.

Manual ventilation via Bag Valve Mask is a vital skill, yet cynical studies show 98% of providers deliver inconsistent breaths during emergencies, potentially leading to barotrauma or hypoxia. FLO2 addresses this gap with P.U.L.S.E., a universal, Bluetooth-enabled monitoring attachment. The engineering challenge involves integrating high-precision pressure and temperature sensors into a portable housing to calculate real-time tidal volume and detect spontaneous breathing. By providing audio-visual alerts and graphical data via an Android app, P.U.L.S.E serves as an intelligent tool to stabilize patient care during high-stress situations.

Pediatric Sleep Apnea is a significant issue alone, but ultimately is a major warning "beacon" linked to Sudden Infant Death Syndrome (SIDS). Within the scope of this project, our solution will collect crucial data to detect warning signs early, including a real-time mitigation by waking the user . Our system is comprised of 3 wireless, non-invasive subsystems: a "belly band" wearable (measures heart/respiration rate), a foot wearable (measures pulse oximetry), and a main display unit (performs calculations and stores data). The engineering challenge for our project is handling integration of numerous subsystems while maintaining integrity of wireless communications.

The Pill Dispenser can identify and dispense up to five medications according to a selected patient profile. Each pill input references different sizes of over-the-counter medications, ranging from small to large medications. It accurately counts pills using a photoelectric sensor and identifies the dispensed pills using an imaging device for error checking before dispensing the final assortment to the user. The Pill Dispenser is primarily designed to assist with the medication administration process in a nursing home/long-term care facility setting, but is also applicable in doctor’s offices and rural care facilities.

Celanese manufactures Ultra-High Molecular Weight Polyethylene, also known as GUR. One test required for the quality assurance of GUR includes a thermal elongation test, which measures the elongated stress property. The existing test stands that perform this test contain a PCB prone to failure as it reaches the wear-out stage of its life. The goal of this project is to manufacture a new test stand with a redesigned PCB. This PCB includes a transmitter circuit, which will provide a "live zero" analog output. This benefits Celanese because it expands testing capacity, is more maintenance-friendly, and provides a PCB design that can be implemented in existing test stands in the future.

Porta Mail was created to help solve how inefficient manually delivering large volumes of office mail is by automating indoor package distribution. The main engineering challenge is creating a reliable, autonomous navigation system for a dynamic indoor environment. It is not just the hardware or software alone that makes this difficult, but the integration of a multi-layer control stack operating under uncertainty, including localization drift, obstacle avoidance, and real-time motor control. Our solution would benefit our project sponsor by reducing labor time, increasing delivery efficiency, and demonstrating a scalable automation platform that can be adapted for larger facilities.

Project Delta is a portable, hand-held pulmonary function device that brings clinic-quality spirometry beyond traditional healthcare settings. Many patients with asthma and Chronic Obstructive Pulmonary Disease (COPD) lack convenient, repeatable lung monitoring, delaying detection of respiratory decline. Our engineering challenge is integrating a replaceable mouthpiece, precision differential pressure sensing, embedded processing, and BLE transmission into a compact, low-cost system while maintaining accuracy. The result is a spirometer that enables real-time lung data tracking for patients, supporting earlier intervention and improved respiratory care for our sponsor’s target population.

R.I.P.P.L.E is a low-cost roadside flood detection and warning system designed to prevent vehicle-related fatalities during flash flooding, particularly in rural Texas. Although flood risk has been well understood for decades, high costs and limited outreach leave many communities without real-time warnings or protection. R.I.P.P.L.E. addresses this gap by combining pole-mounted water-level sensors, immediate roadside alerts, and live data delivered to both a public mobile app and an emergency services dashboard. By transforming known flood dangers into timely, actionable warnings, this project helps protect drivers, support first responders, and prevent avoidable loss of life.

Public safety operations utilizing autonomous robotic networks are highly targeted devices that have many vulnerabilities. There has been a 66% rise in attacks on mission-critical systems in public safety, such as mobile radio, computer-aided dispatch, and 9-1-1 call handling systems in 2024. This problem deals with a lot of networking, cybersecurity, and Linux, and tying all this together to defend a network of autonomous vehicles from a variety of cyber attacks. The solution of creating not only a defensive and resilient network, but also a real-time monitoring and detection system that adapts to different offensive scenarios.

STAR is an autonomous indoor mapping robot solving the $25k-$150k cost barrier of commercial SLAM systems, enabling real-time floor plans for emergency response and facility management. We built a distributed safety-critical architecture with custom motor drivers, error-corrected SPI/USB stacks, and ROS2 Nav2, all Protocol Buffer-connected across 4 languages. Challenges: 100 Hz motor control under NASA Power of 10 (zero malloc), multi-sensor fusion (lidar, IMU, encoders), and loop closure detection. Impact: 96% cost reduction vs. commercial systems, centimeter-accurate autonomous mapping, and safety-compliant hazardous environment navigation.

The Theremin was an electronic musical instrument invented in the 1920s which was played without contact using two metal antennas which attached to the central box at a 90 degree angle to each other. Moving your hand closer to one antenna increased the pitch of the instrument, and the other its volume. Popular for a period due to its distinct, haunting sound and unique playing method, the instrument broadly fell out of style with the rise of the more versatile synthesizer. With this project, we endeavor to improve the Theremin using digital processing, adding a touchscreen interface with configurable sound, akin to an analogue synthesizer, creating a new approach to this classic instrument.

Dealerships typically verify a new trailer hookup by only checking that the lights turn on, leaving hidden 7-pin wiring faults undetected until customers are on the road, eventually leading to safety risks, warranty disputes, and costly module repairs. Our engineering challenge is to build a portable, battery-powered tester and tablet workflow that applies controlled loads, measures voltage/current on each circuit, detects shorts/overloads, and generates a clear compatibility report in minutes. This system helps our sponsor, Razorbill Technologies, deliver a dealer-friendly product that reduces post-sale failures, protects reputation, and documents electrical health at the point of sale.

The Tri-Wing Interceptor Drone project focuses on designing and constructing a flight-ready UAV platform using a tri-wing configuration with three independently controlled shrouded EDF propulsion units. Our team is responsible only for the airframe, propulsion integration, thrust vectoring, and closed-loop flight control to achieve stable VTOL, hover, transition, and forward flight. We are not developing interception or targeting capabilities; those systems will be completed by a future team as the next phase of the overall project. The platform emphasizes modular design, structural integrity, and reliable control system validation.

The Tritone Audio-Machine Unit (TAMU) is a 2.1 High Fidelity, vacuum tube audio system that combines the best of the analog and digital worlds. The analog vacuum tube pre-amplifiers create the beautiful, resonant sound that enthusiasts crave. The digital interface and digital equalizer allow users to adjust the sound to their heart's content. The TAMU combines the DAC, pre-amps, and power-amps into a streamlined enclosure. The challenge lies in integrating analog tube warmth and digital precision without noise/interference. We are providing our sponsor with a distinct, market-ready product that pairs high-end audio performance with expanded commercial appeal.

Lockheed Martin’s supplier proposal review process is slow, manual, and prone to delays that create material shortages, increase expediting costs, and threaten production schedules. The engineering challenge is designing an AI-powered solution capable of accurately interpreting and comparing complex RFP documents with supplier proposals. Our team is developing an automated comparison tool that reduces review cycle time, improves decision speed, and increases operational efficiency while supporting Lockheed Martin’s objective of solving complex problems through innovation.

IMS global is a consultancy that focuses on safety management and risk analysis. Our project is to compare and simplify 3 core standards from the IT security and Cybersecurity sectors, in order for clients to have a better understanding of the standards. The 3 standards given are split into 111 topics, and using those topics, we are to create a presentation for each topic to simplify the standard. Our goal is to also create stakeholder value for IMS and their clients, and create a procedure that can be used to accomplish this goal on future iterations and/or other standards. The challenge would be finding the best way for non-technical stakeholders to understand the standards.

This project focuses on enhancing maritime safety through advanced sensor fusion and machine learning. We are developing a synthetic data generation pipeline designed to simulate complex underwater environments, providing high-fidelity training data for sensor integration. Using this data, we are architecting a lightweight neural network optimized for anomaly detection. This model serves as a proof of concept for anomaly detection, utilizing inputs to identify irregularities or hazards in aquatic settings. By merging synthetic data efficiency with a streamlined architecture, we aim to deliver a robust, scalable solution for autonomous maritime monitoring and threat assessment.

This project addresses a critical operational challenge at Applied Materials by reducing packaging cycle time for high-value semiconductor equipment. The current process is highly manual and varies across configurations, creating inconsistent packaging durations that limit throughput and technician availability. The engineering challenge is to minimize variability while maintaining strict cleanroom, quality, and protection requirements. Through process mapping, time studies, and simulation modeling, our team is developing data-driven recommendations to streamline packaging operations and support scalable manufacturing performance.

Baker Hughes struggles with fragmented supplier data across multiple systems, creating duplicates that obscure procurement visibility. We are engineering a scalable, automated cleaning framework to resolve this. The technical challenge involves developing a modular Python/SQL pipeline using weighted matching and survivor-victim logic to detect duplicates with high accuracy. Our solution provides Baker Hughes with a reproducible, auditable system that consolidates records and reconstructs hierarchies, significantly reducing manual effort and streamlining future data migrations.

Baker Hughes suffers from duplicate and inconsistent supplier records that hinder sourcing, compliance, and financial operations across multiple ERP systems as a result of mergers, acquisitions, and organizational growth. This project develops a structured data‑processing pipeline that standardizes supplier information, identifies duplicate supplier records through deterministic and similarity‑based methods, flags the most complete record for use, and accurately maps parent companies to their subsidiaries. The solution enhances data quality, reduces manual reconciliation, and supports more reliable supplier consolidation decisions for the sponsor.

C.H. Guenther relies on an automated system to wash and route trays to bun loading workstations, but inconsistent tray delivery slows production and forces two additional employees to keep trays moving at a cost of thousands each year. Our team will conduct a root cause analysis, build an accurate model of the current system, and design improved control logic that optimizes tray flow by regulating queue size and length to reduce stoppages. We will also propose a practical reconfiguration of the existing layout to maximize tray line capacity. The result will be a more reliable tray supply to the workstations, reducing wasted labor and saving our sponsor thousands annually.

The goal of our project is to help our sponsor, Oil City Iron Works, identify and implement a process to create a beneficial use for spent foundry sand. Oil City Iron Works is a metal foundry in Corsicana, Texas that uses large amounts of specialized foundry sand to create molds for their casted products. After a certain amount of uses, the foundry sand becomes "spent" and can no longer be used in production. To find a solution we had to tackle many engineering challenges including identifying the material properties of the sand, investigating ideal reuse purposes, and creating a process path capable of sustainably purifying the large amount of sand OCIW has on hand.

This project addresses a throughput-waste tradeoff on a CH Guenther bun production line at the Bryan, TX facility. The team is analyzing bottlenecks, capacity constraints, process parameters, non-value-added time, and quality requirements to support increasing line speed from 110 to 130 cuts per minute while keeping in-process waste below 5% and maintaining customer specifications and food safety requirements. Using production data, time studies, and simulation, the team will develop low-CapEx recommendations and standardized performance metrics to improve sustained throughput, reduce runtime per order, and increase production capacity.

The purpose of the project is to perform an analysis of C.H. Guenther’s muffin production line to increase its throughput. The challenge is identifying bottlenecks and implementing improvements to enhance supplier operations for customers like McDonald’s and Tim Horton. In alignment with the demand for muffin production, improvements made in the factory will result in increased throughput, leading to a financial impact exceeding one million dollars per year. This project supports C.H. Guenther’s commitment to provide best-in-class, safe food solutions and develop business relations with customers.

This project is meant to provide additional services outside of the standard scope of traffic research on a local level, including providing services outside of the standard area that is normally worked with. The engineering challenge would be the project cooperation with the districts to minimize the traffic of the area, including overlapping of other infrastructure projects by other companies. Other considerations include public safety, private property, large public spaces, and environmental conditions. This project gives our project sponsor the necessary information and assessment of the provided evidence to carry out project funded by grants given by the local government.

Quality data for aerosol packaging at QuestSpecialty is currently captured on paper and later scanned into legacy systems, limiting traceability and trend analysis across production runs. The engineering challenge is to design a practical, compliant data capture approach that integrates with existing manufacturing constraints while supporting regulatory documentation and quality control requirements. Improving how quality data is recorded and accessed enables faster audits, better process insight, and reduced manual labor time, supporting operational efficiency, compliance, and product quality.

Using SONAR-LiDAR system scans gathered from an uncrewed service vessel, interpret terrain and identify the presence of unknown, human, or human made shapes in a marine environment. Our project is a research oriented project focused on creating an autonomous detection system that works in near real time. Detecting these anomalies affects the safety, security, and sustainability of the marine domain. Our project is a small part of our sponsor’s current project, ARGUS, and one of MerLion's multiple current capstone projects. Our solution will help our sponsor continue their goal of creating a standardized GIS model of the maritime domain.

Houston Methodist developed the Cardiac Arrest Prediction Model (CAP-M) to identify high-risk patients before cardiac arrest; however, there is a lack of streamlined and efficient communication that limits its effectiveness. The engineering challenge is to design a sustainable, scalable workflow that ensures CAP-M alerts are properly received, prioritized, documented, and acted upon by vICU and bedside teams. This project will create a standardized integration plan that improves communication, enables earlier intervention, and supports Houston Methodist’s mission to enhance patient safety and outcomes through data-driven innovation.

Samsung is launching a new fabrication facility in Taylor, TX, while maintaining steady operations at its existing Austin SAS fab. The challenge is determining how to balance workload between sites under uncertainty, differing capabilities, and high-stakes capacity risks. We need to develop an optimization model to balance machine utilization, incorporate site-specific constraints, and deliver quick solutions that are visually modeled. Our model enables efficient data-driven capacity planning, reducing the risk of missed deliveries and supporting production at the new Taylor facility.

Our mission is to understand the challenges faced by TAMU faculty seeking recognition for their work and to develop an AI solution that synthesizes large amounts of faculty and award data into numeric scores. By exemplifying values of transparency, diligence, accountability, and curiosity, we will empower TAMU faculty and attract more recognition to the Aggie community. This was done by creating and implementing a software system that automatically ingests faculty credentials and award requirement information, then synthesizes the data to create a knowledge base. The knowledge base will be used by interested parties to query which faculty are qualified for a given award and vice versa.

This project focuses on the ISEN nominations committee. The current process is to manually filter a spreadsheet of candidates by award qualification. Nominees are chosen based on basic qualifications and committee member’s knowledge of their experience. While committee members aim to have vast knowledge of faculty affairs, this still involves the risk of missed opportunities for lesser-known or newer faculty. This projects' goal is to implement a system that ingests faculty credential and award requirement information, and a system to organize and continuously update the data. The database will be used by a 2nd DAEN Capstone group to recommend nominees through machine learning selection.

Deep water well production requires low-particle chemicals to prevent equipment wear and irreversible formation damage under extreme subsea conditions. Currently, our sponsor relies on a generalized filter that is often not the most cost-effective or efficient for their complex product mixes. The engineering challenge is to evaluate and identify alternative filtration systems that handle specialized chemical formulations without compromising product performance. By optimizing filter selection based on the specific product mix, our solution will improve operational efficiency, enhance fluid cleanliness, and significantly reduce operational costs for our sponsor.

ErgoGenesis produces highly customized ergonomic chairs through a manual job-shop process, where final inspection decisions currently rely on individual judgment. The engineering challenge is to reduce subjectivity in evaluation while preserving the flexibility required for custom manufacturing. Our team is developing a standardized inspection framework that integrates measurable criteria, decision tools, and process guidance to improve consistency across inspectors. This solution strengthens quality control, reduces rework risk, and supports ErgoGenesis in reliably delivering high-performance ergonomic products that meet customer expectations worldwide.

Our project addresses the environmental and operational footprint of Houston Methodist's intensive care unit (ICU) by targeting inefficiencies in clinical workflows, diagnostic practices, and waste handling that drive unnecessary resource use and greenhouse gas emissions. The engineering challenge is to analyze ICU processes, quantify their environmental and cost impacts, and design data‑driven process improvements and intuitive visual aids that integrate seamlessly into a high‑stakes, highly regulated environment. Our solution aims to reduce waste, lower costs and emissions, and streamline care processes, creating a practical, evidence‑based sustainability model for Houston Methodist.

Our team researched the different possibilities for incorporating AI/ML into the engineering analysis process for a civilian branch of the Army called NSIN. We have looked into many possible routes including data processing, model creation, and incremental analysis using AI. We achieved this by looking into model capability and application, model validation and trust, possible workflow and human factors implications, and practicality. Our research is focused on a general approach to integrating AI into their process, not a specific problem or project.

Sepsis is a leading cause of ICU mortality, and Houston Methodist lacks a standardized way to use point‑of‑care ultrasound (POCUS) early in sepsis care to aid in treatment decisions. Our project applies workflow design and human‑factors principles to map the current workflow, identify gaps, and design an improved, realistic process that specifies when POCUS should be used, what should be documented, and how results should guide fluid and medication decisions. The solution aims to reduce variability, support timely, individualized resuscitation, and strengthen data capture for ongoing quality improvement for our clinical sponsors.

Project LiAISon develops a pipeline for underwater infrastructure inspection for MerLion Advisory Group. Current diver-based inspections are costly, hazardous, and often produce inconsistent data due to poor visibility and environmental conditions. The engineering challenge lies in generating realistic synthetic fused sonar-LiDAR point clouds, preprocessing 3D datasets, and accurately detecting stationary anomalies using validated performance metrics. This solution enables safer, more reliable, and scalable monitoring for MerLion's maritime operations.

Ranch Hand customers are receiving bumpers with missing or incorrect bolt packs (the hardware used to attach the bumper to the truck), resulting in rework, delays, and dissatisfaction. In the current process, bolt packs are attached during end-of-line packaging. As Ranch Hand transitions to a new, larger production facility, this creates a one-time opportunity to redesign the packaging and warehouse operations so bolt packs are stored separately and only matched at shipping using robust error-proofing. We will develop an improved process, rapid packaging attachment method, and poke-yoke controls that reduce mis picks, labor, and waste while improving order accuracy and customer experience.

Upstream oilfield chemicals move through too many facilities which increases cost, inventory, and delays. Consolidation and network optimization could save Baker Hughes $3M in third-party logistics costs. Our goal is to determine whether a hub and spoke model would be more applicable to the distributions in the North America Land Region. By successfully implementing this distribution model, the need for a 3rd party freight could be reduced or even outright eliminated. After starting in the Permian Basin region, we will look towards potential further implementation in other regions as well.

There is a significant amount of wasted movement within the shop that contributes to lost productivity. Time is frequently lost looking for parts or tools, or during the transfer of parts between departments. Over the years, we’ve expanded and made the best use of the space available, and while there is a general logical flow, floor space is limited and materials are often scattered. We’d like to evaluate our processes and operations to see if we can improve efficiency by reducing wasted time and movement. This may involve reorganizing the layout or adding new facilities.

Sabre Industries’ Alvarado facility produces highly customized power transmission poles in a labor-intensive fabrication process, yet constrained staffing and product variability lead to inconsistent throughput and missed quotas. Our engineering challenge is to model this complex, variable job-shop system and define optimal labor allocations without compromising safety or quality. By developing and validating a data-driven simulation, we will recommend strategies that increase throughput, reduce downtime, and help Sabre meet growing grid infrastructure demand.

Our project addresses excessive production downtime on the aerosol line caused by frequent changeovers to support low minimum order quantities. Each changeover includes cleaning, relabeling, repackaging, and mechanical adjustments for multiple can sizes, with the lack of standardized procedures contributing to time variability. The engineering challenge is to quantify performance, identify root causes through process mapping and time studies, and develop a standardized, repeatable changeover method without major capital purchases. By optimizing procedures and standardizing operations, our solution reduces non-value-added time and improves throughput for our sponsor.

Firefighters are 9% more likely to be diagnosed with cancer, and with around 4,000 firefighters, the Houston Fire Department wants to combat this rising risk. Their desired solution is a gear exchange program that promotes advanced cleaning after significant exposure to contaminants. This calls for a system that quickly delivers clean equipment anytime and anywhere in Houston, while efficiently utilizing limited resources. Our solution will help prevent long-term health complications, making firefighters safer on the job and reducing the number of lives lost to occupational cancer.

DoubleDave’s current buffet system results in a high cognitive load for a workforce with diverse cognitive, learning, and life backgrounds, leading to inconsistent freshness, availability, and waste, particularly during peak lunch and dinner periods. By applying industrial engineering, optimization, and human-factors principles, this project aims to simplify decision-making, standardize buffet operations, and enable consistent execution. This will allow our project sponsor, Alex to more seamlessly expand his franchise to more locations in the Greater Houston Area and continue to support his goals of buffet and operational excellence.

Team Purrlion is developing a Marine Mapping Validation Framework to address Merlion Advisory’s lack of a repeatable, cost-effective method for validating LiDAR–Sonar fusion accuracy . Physical sea trials are expensive, hazardous, and rarely provide reliable ground truth. Our engineering challenge is building a deterministic synthetic pipeline that generates paired sensor point clouds, injects realistic maritime noise, and quantitatively evaluates fusion performance using RMSE and statistical validation. This solution enables objective pre-deployment certification, reduces operational risk, and accelerates fusion algorithm development without reliance on costly field testing.

Applied Materials brought outsourced lamphead assembly in-house to reduce 12-week lead times, support 20+ product configurations with greater flexibility, and enable delayed configurability. The challenge is designing a new workcenter that balances robotic lamp and reflector installation, labor, material flow, buffers, and crane movement to meet throughput targets of 12 lampheads per week. Our team developed an Excel-linked parameterized FlexSim digital twin to model the layout, identify cycle times and bottlenecks, and make staffing recommendations. This enabled data-driven decisions prior to construction and will continuously ensure the workcenter can meet demand efficiently.

The Houston metropolitan area currently lacks a centralized resource to identify and prioritize railroad crossings that cause frequent issues. The challenge is to develop standardized criteria for a grade railroad crossing catalog that consistently evaluates risk levels, infrastructure needs, and project readiness at crossings in seven counties. The centralized database will compile all relevant crossing data and incorporate a ranking system, enabling the East End District to make data-driven decisions and strategically invest capital in the highest-risk locations to improve public safety.

Our project optimizes chemical consumption for QuestSpecialty’s aerosol production lines. Initial systems analysis identified the flush process as a primary driver of chemical "cost-off" waste. However, the facility lacks the granular tracking infrastructure required to quantify these waste events or identify recurring causes. The core engineering challenge lies in developing a language-agnostic data collection framework that ensures high data integrity across a linguistically diverse workforce. Our solution provides the visibility needed to minimize waste, improve resource allocation, and drive long-term cost savings across manufacturing operations.

Uncrewed service vessels (USVs) patrol coastlines efficiently, yet most detect surface and underwater features independently. This leaves the water-to-surface seam vulnerable. To bridge this gap, MerLion is developing Argus, a USV system designed to integrate LiDAR and SONAR for a comprehensive, real-time tactical view. The project aims to fuse these sensors into a single 3D file, allowing operators to locate contacts across the entire operating environment. As multi-medium threats evolve, the goal for Argus is to provide a Common Operating Picture (COP) vital to the "Identify – Track – Assess – Classify – Respond" chain, ensuring no threat remains hidden.

Sabre Industries, a critical electricity infrastructure manufacturer, has asked our team to reoptimize their Kennedale facility to increase their throughput while reducing downtime, rework, and handling time. Their current system of manufacturing relies on large cranes to move the 180 foot plus pylons across the facility to be fitted with different parts before shipping. The challenge for our team is to use system simulation to find a more efficient production method. Our team chose to mimic a model, battle tested, and used in American industry today. We chose a company whose system currently propels America to the global forefront of defense technology and industry: Lockheed Martin.

Texas Instruments is transitioning from outdated wafer fabs to more technologically advanced factories. Older wafer fabs consist of outdated equipment, which doesn't allow for the newer semiconductors to be made. The new fabs use less human labor, cutting down costs significantly. The purpose of this project is to evaluate a legacy fab to determine which products should be discontinued and which ones should be transitioned. When a factory gets shut down, the build ahead for items that are being discontinued should be planned to satisfy the customer needs. Combining operational data as well as financial data enables the project to make ROI-centered decisions.

The Capstone Project completed by Team Powerhouse addresses vendor consolidation challenges within the Baker Hughes supplier database. The core engineering challenge is to design a system that establishes a single, reliable source of truth for supplier data by identifying duplicate records and clearly mapping parent-child relationships between related supplier entities. By improving data accuracy, the solution enables Baker Hughes to maintain a unified view of suppliers across systems. This enhanced transparency supports more informed reporting, stronger sourcing strategies, and better enterprise-wide decision-making while preventing duplicates in the future.

The Sabre Industries Conroe facility faces inefficiencies in manufacturing pole arms for electricity transmission. Currently, subprocesses are located in separate bays, requiring cranes to move these heavy poles. This slows down production and also poses a safety risk. The engineering challenge is to redesign the facility layout to streamline material flow and maintain safety and productivity. Our team is developing and simulating the current layout and an optimized facility layout to eliminate non-value-added time. By improving workflow and throughput, Sabre's manufacturing capacity and operations safety will increase.

Dessert Holdings is experiencing lengthy and inconsistent washdown changeover times on Production Line B, often exceeding the four-hour target. This extended downtime reduces overall production efficiency and limits available production capacity. The engineering challenge lies in optimizing this complex, multi-step cleaning process while maintaining strict food safety standards. By streamlining the process, Dessert Holdings can reduce downtime, leading to improved throughput, lower costs, and can more effectively achieve its operational and strategic goals.

Our project develops a data-acquisition sleeve and behavior model to interpret human movement and convert it into usable input for machine control. Our sponsor, SymbioLabs, initially sought a method to control a robotic prosthetic arm, but the scope has expanded to enable control of a myriad of devices, from robotic arms to drones. The system is designed to be cost-effective, comfortable for regular use, and accurate for consistent control. The project is definitively interdisciplinary, merging electrical, mechanical, and biomedical engineering with computer science. This work serves as a prototype and foundation for SymbioLabs to build upon in developing an intuitive controller.

Our project focuses on developing a compact, low-cost solar tracking platform to support research and teaching in Fresnel lens-based concentrated solar power (CSP). Fixed systems lose focal accuracy as the sun moves, reducing thermal output and experimental reliability, so we were tasked with achieving precise, continuous sun alignment to maintain a stable focal point while keeping the system affordable, durable, and classroom-friendly. Our dual-axis, ESP32-controlled tracker enables repeatable lab experiments and scalable CSP prototyping, helping our sponsor advance solar thermal research and provide hands-on learning in renewable energy courses.

One million Americans fall victim to strokes each year; as a result, many develop impaired gait. Existing correction systems often range from $5,000 to $100,000+ and require patient 3D scanning and custom fabrication, which is detrimental to a patient’s rehabilitation progress as manufacturing can take months. Our team developed a low-cost and modular waist, upper and lower leg braces that accommodate all body types without any custom manufacturing. Color coded resistance bands provide tunable corrective forces while preserving full range of motion, rapid donning and real time adjustments improve therapist efficiency and patient comfort all for under $500.

The problem we are solving is making a machine that would clean O-rings more quickly and more effectively than Applied Materials currently does. They currently take 2-3 minutes to clean O-rings with IPA wipes. We are using a conveyor belt and DI water spraying system to clean the O-rings automatically. The process is as simple as placing an O-ring on the conveyor belt by hand and pressing the start button. Then, DI water sprays from above and below to clean the O-rings; the conveyor belt moves the O-rings to the second chamber, where they are dried with a DI air knife. Finally, the O-rings fall into a basket at the end. The process can be stopped at any time with an emergency stop button.

Traditional broach manufacturers have discontinued production, and existing designs compact bone rather than cut it while failing to match modern hip stem geometries. This mismatch can compromise implant fit and surgical efficiency. The engineering challenge is to design and validate a broach with optimized cutting teeth that precisely conforms to current implant shapes while remaining durable, sterilizable, and manufacturable at scale. Our solution will enhance surgical precision, reduce operative variability, and provide our sponsor with a scalable, clinically aligned instrument design ready for future production.

The project develops a custom attachment system for the Brokk 70 demolition robot to support the removal of radioactive waste from underground storage tanks at the Hanford Site. The engineering challenge involves designing compact, durable, and maintenance-free attachments capable of operating in highly confined, abrasive, and radioactive environments while maintaining robotic stability and mobility. The solution integrates a hydraulic thumb, excavator bucket, and vacuum hose bracket system that improves waste removal efficiency, reduces human exposure to hazardous materials, and supports Los Alamos National Laboratory’s environmental cleanup mission.

As the Manufacturing Team for the Formula SAE Internal Combustion car, we bridge the gap between design and reality by transforming complex CAD models into high-performance, race-ready components. We solve the challenge of producing lightweight, precise, and reliable parts within strict budget, safety, and timeline constraints. The engineering difficulty lies in optimizing manufacturability, material selection, and process planning while preserving structural integrity and performance. By improving production efficiency and part quality, we deliver a competitive, durable vehicle that maximizes sponsor investment and demonstrates real-world engineering capability.

Our project reduces variability in drill bit cutter inspection by implementing an automated, image based evaluation system. Using a Raspberry Pi and an AI camera, the system captures and analyzes cutter images to determine cutter reusability and eliminate human subjectivity. The primary challenge is controlling ambient lighting to prevent glare on the diamond face of the cutter, which can interfere with image accuracy. This solution increases inspection throughput, improves consistency, and reduces false positives and negatives, improving the overall reliability and operational efficiency for our sponsor.

Our team is developing a handheld helium/nitrogen leak-check gun for semiconductor manufacturing equipment, specifically vapor deposition chambers. Current leak detection methods are slow, imprecise, and ergonomically inefficient, increasing downtime and leading to uncertainty in the process. The engineering challenge is to design a compact, lightweight device that delivers controlled gas flow with high precision while remaining durable for cleanroom use. Our solution improves the accuracy of a leak check, reduces tool downtime, and enhances technician efficiency, directly benefiting our sponsor by providing greater accuracy and control to equipment inspection.

Our project is to design an excavator arm that will go on a quadruped robot to scoop nuclear waste. The environmental design challenges for this project are that we are dealing with a radioactive material that is very viscus, so the materials of the arm must be strong and resistant to the waste. Our bucket must also be able to effectively scoop the waste. The structural design challenges for this project are the arm has to be compact enough to fit on the robot and not weigh it down into the waste, while also having enough range that allows for high digging efficiency. Our sponsor will gain an optimized design of the arm that has increased durability and efficiency.

Los Alamos National Laboratory has tasked our capstone team with the design and development of a hydraulically actuated delta robot system intended for vehicle-mounted operation at the Hanford site. The system will be used to position a cleaning end-effector around nuclear waste storage tanks to remove adhered waste from the interior surfaces. The primary engineering objective of this project is to evaluate the feasibility of using hydraulic actuation to effectively drive a delta robot. The proposed system will serve as a proof-of-concept to determine whether hydraulics is a viable actuation method that is capable of performing large-area surface cleaning.

Our project directly addresses the challenge of safely retrieving sludge and salt-cake nuclear waste from aging Hanford storage tanks. Traditional wet methods increase leakage risks, creating the need for a dry retrieval solution. The engineering challenge involves designing a remotely operated robot that can be lowered through a 42-inch pipe, traverse unstable clay-like waste, and excavate material. Our screw-propelled, hydraulically powered dozer improves material removal efficiency while reducing environmental and operational risk for our LANL sponsor.

This project improves worker safety in oil-rig red-zone environments by demonstrating a portable safety-monitoring system. The engineering challenge was designing a stable, durable stand to securely mount LiDAR and camera hardware in harsh industrial conditions. The final system is a fully engineered physical platform that powers and displays real-time LiDAR and camera operation, demonstrating how activity can be detected in hazardous areas. This validates sensor placement and integration for the sponsor while providing a foundation for future system development.

Our team is participating in the NASA Human Exploration Rover Challenge. Within this challenge, a team must design, build, and pilot a rover over a half-mile course where they must complete three tasks and maneuver over ten different obstacles within an 8-minute time constraint. Some engineering challenges for this problem include fabricating all wheel components from scratch, a weight restraint of 130lbs, collapsible to a 5 foot box, and completing the course. The project benefits the faculty sponsor by promoting the ETID department and by inspiring the next generation of engineers to employ creative techniques and new technologies towards the next phase of human space exploration.

Our team designed a proof-of-concept, 42'' center-articulated vehicle to support radioactive waste retrieval at the Hanford Site. The site contains underground tanks filled with hazardous material that must be removed before vitrification. The challenge was fitting a reliable drivetrain, hydraulic steering system, and structural frame within strict size and power limits while carrying heavy loads across uneven terrain. This prototype shows the concept is feasible and helps reduce human exposure during cleanup. As a first-iteration design, it provides our sponsor with a starting point for future refinements and long-term development.

The NFPA Fluid Power Vehicle Challenge project focuses on designing and building a hydraulically powered vehicle that meets competition performance and safety requirements while expanding its capabilities for off-road operation. The engineering challenge lies in optimizing fluid power systems, energy efficiency, durability, and control while maintaining reliability in both standard and rough-terrain conditions. This solution benefits our project sponsor by demonstrating innovative, real-world fluid power applications while increasing student interest and awareness of the fluid power industry.

Halliburton’s fixed-cutter drill bits rely on tungsten-carbide hardfacing to resist abrasion and erosion, but current application methods introduce variability in coating thickness, geometry, and performance. This project addresses the challenge of achieving consistent, high-quality hardfacing through automation while maintaining required mechanical properties through compositional investigation. Laser cladding offers a method to develop this automated, repeatable, and high performing hardfacing process. The solution can benefit Halliburton by reducing human variability, improving product consistency, lowering rework and cost, and enabling scalable, data-driven manufacturing.

Current parylene coatings on alumina substrates show inconsistent adhesion and defect formation. More specifically, the defects that are found in parylene coatings are present in the form of scratches, pitting, and voids. Additionally, adhesion issues are suspected to stem from surface roughness and contamination of the alumina substrates on a microscopic scale. The combination of the adhesion and defect issues can reduce the coating integrity or cause delamination under more extreme conditions. To address these issues, our team will develop a repeatable deposition process for alumina substrates which minimizes surface defects and maximizes adhesion integrity.

For barbers and clients, dealing with loose hairs is a constant struggle that our sponsor deals with by using a lint-roller, but the adhesives used are less effective on skin and create discomfort for the client. Our sponsor wants to address this with an adhesive combined with a cooling agent like an astringent, that is soothing to the skin while sticking to hair. The challenge is that adhesives are mostly incompatible with astringents which weaken adhesive properties necessary to stick to hair. To address this, we’ve designed an adhesive that captures hair on skin and delivers a soothing effect, that allows our sponsor to pick up loose hairs in a way that is comfortable for clients as well.

Our team is developing an exhaust sampling device to help AZZ accurately characterize particulate and gaseous emissions from the galvanization process. The problem lies in obtaining repeatable, representative emissions data without modifying existing plant infrastructure or disrupting operations. The key engineering challenge is designing a compact, thermally robust system that integrates filtration and sensing while maintaining sample integrity under high temperature exhaust conditions. Our solution enables reliable particulate capture and gas analysis, providing AZZ with higher quality emissions data to support regulatory compliance and informed decision making.

Metal Oxide Semiconductor Field-Effect Transistors (MOSFETs) are a vital component of modern automotive systems, serving as high-speed power switches in automotive engines. However, MOSFETs experience significant thermal loads and non-uniform temperature distributions, leading to thermomechanical stress and die warpage that can cause premature failure. We mitigate these issues by redesigning the geometry and aspect ratio of the die. We report both the optimized design parameters for the die and the finite element analysis (FEA) model that informed our parameters. As a result, we can increase the lifespan and reduce the in-operation failure rate of Diodes Inc.’s automotive MOSFETs.

We are developing a reliable copper thin-film deposition process for polyimide-based conductive tapes used in EMI shielding of electric bridge wire detonators at Los Alamos National Laboratory. Current tapes suffer from inconsistent adhesion, resistivity variation, and stress-induced cracking caused by thermal mismatch, creating serious reliability risks. The engineering challenge is optimizing sputtering parameters and interface design to achieve stable electrical performance and mechanical durability. Our solution enables safer, more predictable components and improves manufacturing yield and process consistency for the sponsor.

High-temperature tensile testing is essential for qualifying structural metals used in aerospace and national security applications. While automated systems exist, no commercial solution enables reliable, hands-free testing at elevated temperatures up to 600 °C. The engineering challenge lies in integrating precision mechanics, thermal control, sensing, and software into a robust automated platform. Our system will provide Los Alamos National Labs with a cost-effective tool to generate accurate elevated temperature mechanical data with greater throughput, consistency, and reduced operator workload.

The objective is to evaluate the rapid corrosion of modern plain carbon steel blades that forces small-scale manufacturers to rely on costly coatings and alloying alternatives. The engineering challenge is to design and validate a heat-treatment process that improves corrosion resistance without reducing the hardness below 58 HRC built from austenitizing, quenching, and tempering condition parameters. By developing a reproducible process that achieves high corrosion resistance without increasing material costs or manufacturing complexity, this solution will allow the sponsor to produce higher-quality, longer-lasting blades, and gain a competitive advantage in artisan bladesmithing.

Rare earth permanent magnets, based on NdFeB, are used in a diverse set of applications, including electric vehicles, offshore wind turbines, and mobile phones. There is a need to improve performance (maximum energy product & coercivity) of these magnets to enhance the efficiency of these important technologies and reduce their reliance on expensive and critical elements. A machine-learning approach was implemented to model processing/structure-property relationships in these magnets and to predict new compositions and processing parameters for improved performance. By identifying new, high-performance NdFeB magnets, our sponsor can reduce their dependence on critical supply chains.

The growth of aluminum whisker defects in the semiconductor bond pad results in conductivity issues and yield loss. Whisker formation has been linked to thermal stress between the Al film and the substrate. However, there is currently no validated model that correlates stress and whisker reduction. This relationship can be analyzed by varying processing parameters such as Physical Vapor Deposition (PVD) temperature and layer thickness. These parameters were tested in the Aggie Nanofabrication Facility using a simplified, planar wafer structure. This would benefit Samsung by providing an outline to reduce whisker formation and decreasing the current yield loss of 0.1-0.5%.

Aluminum whisker growth in Samsung Austin Semiconductor's (SAS) 14 nm bond pad manufacturing leads to short circuits and yield loss due to high compressive stresses in the bond pad thin films. The engineering challenge is optimizing deposition pressure, deposition rate, and cooling steps to reduce residual stress and whisker density. The team developed a computational stress modeling framework to map process conditions to stress distributions and identify operating conditions achieving at least a 5% stress reduction relative to SAS' baseline. This approach provides SAS with actionable process recommendations to lower whisker density for improved device reliability and product yield.

Single-use plastic water bottles are used globally yet contribute greatly to plastic waste due to their multi-material construction and their multi-component design. By simplifying the design to use a single material, along with reducing the parts used for the design through an integrated cap, the manufacturing cost and overall environmental impact of each single-use water bottle will be reduced, while the recyclability will be improved. Overall, Project Dolphin aims to support a more circular product lifecycle, reduce environmental impact, and align with industry and regulatory shifts toward more sustainable packaging solutions.

This project addresses inconsistent results in Floating Roller Peel testing when applied to modern aerospace materials and PFAS-free adhesives. Current fixtures at CFAN produce skipping behavior and unclear failure modes, limiting reliable bond qualification. The student team must redesign the fixture and test procedure to control peel geometry, reduce mechanical artifacts, and remain compliant with ASTM D3167 while accommodating varying material stiffnesses. The redesign improves repeatability and failure-mode clarity, enabling CFAN to qualify new adhesive systems with greater confidence, reduced rework, and faster adoption of advanced materials.

Conventionally made metallic valve seats in high-pressure butterfly valves often distort during manufacturing, resulting in uneven sealing, leakage, and increased maintenance costs. The engineering challenge is redesigning the valve seat that maintains dimensional stability and uniform contact pressure under thermal and pressure loading while remaining fully compatible with existing assemblies. This project evaluates an additively manufactured, variable-thickness design to reduce distortion and improve sealing reliability, providing BP with a more consistent, manufacturable, and performance-driven solution for their seat equipment and processes.

We designed a materials selection framework to find a replacement for Grade 29 Ti in Tapered Stress Joints (TSJ). Grade 29 Ti has exceptional corrosive and mechanical properties due to the addition of Ruthenium, but in less harsh environments, its full capabilities and high cost is not needed. The finalized framework focuses on highlighting alternative materials based on their cost, fatigue strength, and corrosion resistance while keeping the TSJ's original geometry. This framework aims to reduce the TSJ cost by 10% and limit the weight increase to a maximum of 5%. This will allow Hunting to bid their products more competitively without sacrificing integrity in various subsea environments.

Wind tunnel testing of parts is a common approach to simulating aerodynamics in real use conditions. When a part is being wind tunnel tested, it is typical for it to be tested in multiple configurations to model the way it would be affected at different angles. However, this process has been difficult as it has required the fabrication and testing of many fixed parts to test each different angle. Remote control actuation is a new innovation which allows the configuration of a part in the wind tunnel to be adjusted externally via remote control. The team has optimized a shape memory alloy to be used as a remote control actuator to make wind tunnel testing more efficient and cost effective.

Molybdenum alloys utilized in applications near recrystallization temperature (850℃ - 1250℃) suffer embrittlement due to grain growth and impurity content, yet no standardized methodology links high operating temperatures to microstructural evolution and mechanical degradation. This creates uncertainty in defining embrittlement onset and its severity, as well as safe operating limits. Our project develops a reproducible framework correlating processing history, thermal exposure, grain size, and mechanical properties to establish quantitative embrittlement thresholds and provide Los Alamos National Laboratory with data-driven guidance for safer, more reliable component design.

This project evaluates flexible graphite (NeoGraf HT-C3200) as a thermal interface material for high-power GaN RF amplifier pallets used in Los Alamos National Laboratory’s LINAC upgrade. The core challenge is accurately characterizing and modeling the thermo-mechanical behavior and contact pressure distribution of a compressible, viscoelastic material under realistic assembly loads. Our team is developing a validated ANSYS thermomechanical model informed by DMA testing and pressure film measurements, alongside a physical testbed. The resulting framework will enable LANL to optimize pallet stack-ups, improve heat dissipation, and increase reliability of high-power RF hardware.

This project aims to capture materials transmissibility measurements using a Scanning Electron Microscope (SEM). Conventionally, materials transmissibility measurements have been conducted in billion-dollar synchrotron facilities, but long lead times, and high cost create significant barriers. By creating a lower-cost design inside of a SEM, similar transmissibility measurements can be made much faster and are more widely available for laboratories across the world. This project includes the design of the sample mount, operating procedures on collecting data, and the acquisition of transmissibility results using aluminum foils.

Texas A&M's Hypervelocity Impact Lab operates a Two-Stage Light Gas Gun requiring a compression piston to be inserted for each test. Currently, pistons are manually hammered in using subjective force assessment, limiting experimental repeatability. We designed a system with a 450 lb actuator, 2 kN load cell, and lightweight aluminum frame. The challenge was creating a barrel-mounted device providing quantitative force measurement and precise alignment while attaching non-intrusively. Our solution enables correlation between piston fit and projectile velocity, reducing test variability and maintaining fast turnaround time.

Chemical injection is a development that has played a crucial role in optimizing oil and gas operations, as it helps maintain proper flow conditions and combats against corrosive fluids, system clogging, and complex fluid mixtures. The SLB Capstone Team was tasked with designing a field-deployable mixing device, which requires no additional energy input, to rapidly mix and dilute chemicals before they are injected into an oil and gas production line. The team’s final product was achieved and validated via detailed optimization and testing to ensure all needs were met. The designed device will create a new service for SLB and ultimately, provide a solution for greater operational efficiency.

This team is participating in the annual American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) 2026 Design Competition. This team is tasked with calculating all relative heating and cooling loads for a 90,000 sq ft multi-use university building in Denver, Colorado. Using these results, this team has designed and optimized a Heating, Ventilation, and Air Conditioning (HVAC) system that best suits the campus building's needs and mechanical budget of $8,000,000. This team uses this project to further the goals of energy efficiency and understanding of the HVAC industry.

According to research, adding geometry to the underside of a supersonic aircraft changes the way the sound comes off the aircraft, reducing the perceived noise on the ground. With our sponsor, we will be creating a prototype for an adaptive structure that will be able to cause sound reduction if attached to the underside of a plane. This adaptive structure will transition from a flat disk to a dome-like shape and will be constructed using an elastomer skin with a supporting metal structure. With this, our sponsor will be able to further test the noise reduction of supersonic travel, with the hope of reintroducing it in the future, since it was banned in 1973.

This project develops an automated, hands-free system for remotely capturing, locking, and tensioning large diameter chain under high load. Current tensioning systems require significant manual oversight and specialized equipment, increasing cost, risk, and time to tension. The engineering challenge involves designing a robust mechanism that maintains controlled load transfer while preventing unintended release. The proposed solution improves safety, repeatability, and operational efficiency through reliable alignment, secure locking, and remote actuation.

The Texas A&M 3D Printed Aircraft Competition team is tasked with developing a fully additive manufacturing fixed-wing aircraft. The goals of this project are to have the longest aircraft flight time within strict thrust, boundary, and manufacturing constraints set by the competition coordinators. The primary engineering challenge is to achieve high aerodynamic efficiency and structural strength while minimizing weight using 3D printed polymers. Through iterative design, research, analysis, prototyping, and testing, the project advances aerospace design for lightweight 3D-printed flight, providing Boeing with insight into high-performance aerospace concepts at a smaller scale.

The team has modeled and built a prototype pressure-independent control valve (PIC Valve) and tested various types of springs to evaluate their behavior and performance in the valve. Additionally, the team has modeled both the valve and the springs and compared them to data gathered from the physical prototype. The engineering challenge is designing, modeling, building, and testing the valves and springs to be used while applying engineering principles to ensure the valve meets all parameters, is safe, and works effectively. This solution will help the team's sponsor improve the efficiency of their increasingly used PIC Valve in large HVAC systems.

The purpose of this project is to select and design a power plant with improved thermal efficiency relative to a simple cycle gas turbine. The student team researched improved designs during the fall semester before selecting the combined cycle power plant for further evaluation in the spring semester. The final design includes a 3D model of the plant, an equipment list, and data sheets for major components. The engineering challenge stems from thermal calculations related to the machinery in the plant. For the sponsor, FLUOR, this project simulates the early phases of the plant construction process, namely the Feasibility phase and the Front-End Engineering Design (FEED) phase.

This project focuses on developing a thermal interface material (TIM) curing system for AI servers, sponsored by Dell. Current systems rely on uneven heating during initial operation, leading to inconsistent thermal performance and potential overheating. The engineering challenge is to design a system that can uniformly heat and melt TIM through a liquid cooling loop while maintaining safe temperatures, proper flow, and integration with existing hardware. Our solution improves reliability, ensures consistent heat transfer, and supports more efficient server performance.

The Texas A&M Solar Racing Team currently lacks a cost-effective method to validate suspension designs prior to full vehicle assembly, resulting in delayed feedback and increased development risk. This project delivers a modular suspension testing system capable of simulating 3G bump, 2G braking, and 2G steering loads while capturing precise force, displacement, acceleration, and strain data for various geometry suspension systems. The primary engineering challenge is designing a safe, rigid, and repeatable platform that replicates real-world loading without full-vehicle integration. This system reduces iteration time, lowers costs, and improves vehicle reliability and competitive edge.

High-frequency chest wall oscillation (HFCWO) devices are essential for airway clearance in patients with conditions such as cystic fibrosis, yet current systems are often bulky, loud, and heavy. Our project addresses the need for a more compact, quieter, and lightweight design. The engineering challenge involves optimizing oscillation performance, airflow delivery, structural integrity, and system reliability while meeting medical safety standards. Our solution benefits the sponsor by delivering a more portable, user-friendly device that improves patient comfort, mobility, and overall satisfaction.

Many of the current issues plaguing assistive exoskeletons stem from user interactions. These problems are often attributed to the methods used to exert control over the system. A viable method relies on direct user input, the focus of this project. Using a dynamic spring-plunger design, our team has created a controller that provides seamless user feedback, continuing the previous DUCK-E project (Direct User Control of Knee Exoskeletons), with a focus on improved comfort, quality, and user input accuracy. This is especially useful when building an accurate user-based torque profile, the focus of the next research study our sponsor aims to conduct.

The Kerr County floods on July 4th, 2025, killed over 130 people, with alerts sent out as late as 8 hours after flooding began. This was caused by cell network congestion and the high cost of installing a new, capable system. Cognascents Consulting sponsored us to address the issues of existing systems. We have developed a scalable flood warning and detection system intended to address problems such as unreliable power or network connectivity, high reliability as an emergency system, and ease of deployment and maintenance while remaining cost-effective. Our system differs from existing systems by having a lower cost, high modularity, and vertically integrated communication and sensing.

Our team has been tasked with curating design and manufacturing recommendations for novel axial flux motor windings. The team is producing a forming die to stamp copper blanks into shape, an automated linear actuator dipping system to coat the windings with enamel, and an extensive validation process to ensure quality control. The largest engineering challenge is creating a scalable and modular stamping die. Completion of this project will allow the sponsor to conduct small-scale prototyping and manufacturing, greatly increasing the efficiency and consistency compared to the current solution.

Tenaris Bay City’s forming crane vertical rack-and-pinion drive is failing early due to rapid rack wear, frequent bearing replacements, and column misalignment, causing costly unplanned downtime. Our challenge is a compact guidance/drive upgrade that fits existing geometry while maintaining greater than 7 kN per arm and reducing contact stress, vibration, and alignment sensitivity. We propose a self-centering triangular rail with four high-capacity rolling bearings and a redesigned mounting bracket to balance loads, limit pendular motion, extend component life, and cut maintenance.

Design, analysis, manufacturing, and implementation of various components for Texas A&M Formula Electric's AME26 race car. Individual design scopes range across several subteams, including Aerodynamics, Battery, Chassis, Electronics, Powertrain, and Suspension. Particular focus is given to the methods used to optimize performance metrics - such as weight, stiffness, reliability, driver experience, tunability, and thermal performance, among others - making the car as fast as possible in preparation for an upcoming competition at FSAE Michigan 2026.

The Formula SAE IC Capstone team designed and built a competition ready internal combustion race car to maximize dynamic performance within strict FSAE rules. The challenge lies in balancing weight, performance, manufacturability, cost, and reliability under severe endurance loads. Our solution integrates a decoupled suspension system, high-downforce aero package, and a 4-cylinder engine into a stiffness-to-weight optimized chassis using validated manufacturing processes to improve lap-time consistency and durability. This approach delivers a high-performance, test-validated platform that demonstrates SAE excellence in engineering rigor, innovation, and real-world manufacturability.

The HAND Project team is working in collaboration with the Human Empowering Robotics and Control (HERC) Lab at Texas A&M University under the direction of Dr. Gray Thomas. The HAND Project team was presented with the problem of developing a robotic hand capable of measurable strength amplification while maintaining fine control and dexterity. The challenges presented involved the absence of teleoperation and the novel integration of rolling contact joints and Bowden cables. While this iteration of the project is a V0 proof of concept, Dr. Thomas envisions this hand assisting physically impaired individuals in performing daily tasks, or for industrial workers to better assemble heavy parts.

The Hanford nuclear waste site, located near the Columbia River, contains millions of gallons of nuclear waste. The full cleanup process was projected to take decades; LANL is working to accelerate it. They have robot rigs/excavators; those can get jammed due to the prevalence of sludge and salt cakes on-site. A maintenance robot to send out for a quick repair keeps LANL's cleanup process moving along. This maintenance robot is to mimic humanoid hand size and motion, be remotely operated, and be fit for heavy-duty tasks. The engineering challenge is to design a hydraulically actuated robotic hand with suitable robustness, strength, and range of motion for assisting LANL's mission.

The hydraulically actuated robotic arm is a humanoid maintenance arm developed to support Los Alamos National Laboratory’s nuclear waste management efforts at the Hanford Site, where 212 million liters of hazardous radioactive waste are at risk of leaking from aging storage tanks. The engineering challenge lies in achieving human-like dexterity and reliable 10-lb load capacity while maintaining structural strength, corrosion resistance, and hydraulic control. This proof-of-concept prototype will facilitate remote radioactive waste management, establishing a scalable foundation for safe, high-strength robotic intervention in hazardous environments.

Hydrogel 3D Printing addresses the lack of research-grade control in Direct Ink Writing (DIW) systems for soft materials. Existing printers cannot precisely synchronize pressure-driven extrusion with motion, limiting the study of time-dependent gelation and filament stability. We developed a modified Ender-3 platform integrated with a Fluigent pressure pump and PID-based control to achieve sub 100 ms motion-pressure synchronization. Our system enables real-time meniscus visualization, +/- 0.1 mm print accuracy, and repeatable double-network hydrogel fabrication, providing our sponsor a controllable platform for studying viscoelastic flow, gelation kinetics, and programmable soft materials.

Our project develops a Phase-2 in-situ hydrogen embrittlement testing chamber for Los Alamos National Laboratory (LANL). Their current design of the chamber, Phase 1, was a concept and never became a prototype, and it lacked precise control of pressure, temperature, and hydrogen charging while mechanically loading a material sample. The engineering challenge is to design a sealed, thermally controlled, mechanically loaded and instrumented system capable of operating up to 5 atm and 200°C without leakage or material degradation. Our solution demonstrates a proof-of-concept closed-loop system that validates pressure, temperature, and fluid control under operating conditions.

Innovex International requires a cost-effective slip design for its expandable liner hanger system. These slips grip the host casing and prevent pressure from pushing the casing up-hole. The main engineering challenge is to increase the loading capacity while maintaining the system's structural integrity. Our solution incorporates asymmetric ridges on the backs of the slips, which engage with the liner hanger body. As these ridges provide greater friction, each slip can withstand a higher hanging load. Consequently, the sponsor can achieve the same capacity with fewer slips in the liner hanger system, resulting in lower costs while maintaining the system's reliability.

To maintain strict ISO 7 standards within Los Alamos National Laboratory’s detonator production clean rooms, our team is developing an autonomous robotic vacuum to replace manual floor cleaning. The primary engineering challenge involves integrating a HEPA 13 filtration system onto a mobile platform while overcoming significant static pressure. This requires a secondary high-performance fan to ensure sufficient volumetric flow and a dual-stage filtration process capable of capturing 99.97 percent of particulates as small as 0.3 microns. By adhering to strict security protocols and excluding cameras and wireless communication, our solution provides a secure, compliant system.

Roadside litter poses a persistent safety, environmental, and economic challenge on Texas highways, with millions of pieces of debris accumulating each year. The fatal injury rate of collection efforts is reported to be 5 times the national average, and the Texas Department of Transportation spends approximately $50 million each year on litter removal. To address these issues, this senior design team has developed MARTIN (the Mobile Autonomous Remover of Trash in the Environment): a robotic system engineered to detect, collect, and store roadside litter with minimal human intervention in a cost-effective manner.

The Material Transfer Tether Hauling Powertrain team is developing a compact electric drivetrain to reposition a 500 lb material transfer tether inside a nuclear waste tank at the Hanford Site for Los Alamos National Laboratory. The primary challenge is delivering high torque within a strict 42 inch diameter access constraint while ensuring safe, reliable operation. Our validated rear chassis prototype integrates an electric motor, differential, and braking system to demonstrate towing capability, reduce cost and complexity, and provide a jumping point for future LANL electric vehicle development.

The NOVA Liquid Jet project develops a laboratory-scale system that generates a stable, high-velocity liquid jet for use in Laser Produced Plasma x-ray systems for semiconductor wafer analysis. The primary engineering challenge is achieving precise dimensional and positional stability at pressures up to 5 MPa while maintaining uniform flow velocity and controllable droplet breakup. This requires careful hydrodynamic design, precision mechanical alignment, and high-resolution imaging to verify jet diameter and velocity. Our solution delivers a repeatable, experimentally validated prototype that reduces technical risk and supports NOVA’s continued development of advanced LPP x-ray technology.

NOVA has a testing chamber for a precise metrology tool, but due to external temperature fluctuations and thermal cycles of expansion and contraction, inaccurate data has been recorded by the measuring device. A control system with a controller and temperature regulating devices was needed to be implemented on a benchtop model scale to maintain the internal testing chamber walls at 27 degrees Celsius. This model will allow NOVA to evaluate potential courses of action to follow in implementing a thermal control system for their full scale measuring device.

Aging, disabled, and rural populations face challenges accessing affordable autonomous vehicle technology, limiting their mobility. This project aims to redesign a retrofittable steering actuator for the ENDEAVR Institute. The key engineering challenge is to develop a robust, vibration-resistant system with precise torque control and safe manual override. Our solution will feature enhanced actuation, durable components, and flexible mounting for reliable installation. A driving simulation with autonomous control will demonstrate the prototype's functionality, supporting ENDEAVR's mission to promote safe, low-cost autonomous steering for underserved communities.

In the past 40 years, three billion birds have disappeared from North America. This is an unprecedented rate of loss of birds and a critical conservation issue, as birds are vital for economic growth and ecological functions. A key step in conserving bird species is understanding their migration patterns, which can be achieved by identifying specific bird species by their unique nocturnal flight calls. We designed a high-performing, durable, autonomous, and simple Nocturnal Flight Call Monitoring Station that can be deployed in remote and urban locations to record, amplify, and analyze nocturnal flight calls, and transmit relevant data to the end-user to aid in conservation efforts.

The On-Cow Methane Compression System is a feasibility study regarding a novel technology being developed by PLANET.TECH involving the compression of methane in an agricultural application. By combining thermodynamic modeling and professional industry consultation, a series of technical analyses reveals the current state of technology in the field and provides recommendations to PLANET.TECH about how to guide development. Finally, the feasibility study grants insights into both the physical and economic constraints and possibilities of developing new technologies in this field.

The objective of this project is to design a system that automates the imaging process, allowing the High Velocity Impact Laboratory (HVIL) to independently scan damaged samples. The system must be capable of scanning an area up to 9″ × 9″ with a target resolution of 100 µm, completing a full scan in approximately 7 minutes (2”x2”). In addition to creating stitched 2D surface images, the system must generate XYZ depth estimations in the form of heat maps to characterize surface deformation caused by hypervelocity impacts. Doing this will speed up the analysis and provide more data for the HVIL.

The mechanical flushing of pipes is an important component of construction for data centers, hospitals, and other uptime-critical facilities. To deploy and maintain clean fluid loops, large and powerful pumps are required to effectively remove dirt and debris from pipes. This project aims to reduce the head losses on our sponsor’s pump platform by optimizing the geometry of the inlet and outlet pump headers. This was achieved through computational fluid dynamics (CFD) simulations and physical-scale modeling. By reducing energy losses in the header, the sponsor will be able to run fewer pumps with greater fuel efficiency for each application.

This project focuses on developing a robust attachment system for a piezoelectric sensor designed to assess structural and environmental integrity in orphan oil and gas wells. The system enables the sensor to capture vibrational and structural data that can indicate leakage, subsurface activity, or well-integrity issues. Our design focuses on durability, ease of installation, and compatibility with harsh outdoor environments common to abandoned well sites. By enabling continuous, low-power monitoring, this solution supports safer environmental conditions and offers a deployable, field-ready approach to identifying risks associated with orphan wells.

We are solving the lack of real-time temperature monitoring of the drawworks brake drum, which currently prevents operators from being able to detect overheating during high-load braking events. The major engineering challenge is accurately measuring the dynamic drum temperature in a harsh environment while ensuring reliability, calibration accuracy, and seamless integration with the existing rig systems. Our solution is very beneficial to the sponsor's HSE goals. It will inform the operator when the brakes are overheating, preventing any operator and equipment safety concerns associated with the inability to stop movement of the drill string.

Rotary flex testing is a critical qualification requirement for mechanically attached fittings (MAFs), yet current methods are expensive, time-consuming, and externally dependent. Our sponsor, Parker, relies on external laboratories charging up to $60–70K per sample, limiting testing flexibility and autonomy. The engineering challenge is to design, simulate, and build a rotary flex testing machine capable of maintaining 500 psi internal pressure, applying a bending stress equivalent to 35% of the tubing’s ultimate tensile strength, and rotating at 1750 rpm for high-cycle fatigue testing. This solution provides in-house testing capability, faster design validation, and cost reduction.

This showcase focuses on the development, testing, and validation of autonomous vehicle systems for Texas A&M University’s entry in the Autodrive Challenge Competition. This challenge, primarily sponsored by General Motors (GM) and the Society of Automotive Engineers (SAE), aims to equip students with technical skills by requiring them to navigate a vehicle in an urban environment using a Level 4 automated driving mode. This showcase will present our work on the testing, simulation, design, and validation of our planning, control, and sensing concepts, as well as experiments conducted at the Texas A&M RELLIS Campus.

Our team is designing and building a high-efficiency electric vehicle to compete in the Shell Eco-marathon, where performance is evaluated on energy efficiency rather than lap time. The vehicle has been engineered to maximize miles per kilowatt-hour by reducing weight, rolling resistance, and aerodynamic drag, while preserving driver safety, structural integrity, reliability, and adhering to competition rules. This project advances ultra-efficient vehicle design methodologies and generates data-driven insights that can support Shell’s efforts to develop lower-emission technologies and reduce the environmental impact of future road vehicles.

Offshore drilling rigs can cost up to $500,000 per day, and installing the Lockdown Sleeve and Seat Protector requires two separate downhole trips, adding nearly a full day of rig time per trip while increasing costs and operational risk. Our challenge was to engineer a single-trip running tool that securely deploys and installs both components simultaneously in a harsh subsea environment without electronics. We designed a hydraulically actuated system integrating Stem, Actuating, and Connection Modules to install both components in one trip. By simplifying the operation and integrating mechanical fail-safes, our solution cuts rig time, reduces risk, and delivers cost savings for Innovex.

A sit-ski is an adaptive ski method for individuals with limited lower mobility. These users often cannot return to an upright position after a fall on the slopes. Falls are frequent, and recovery requires immense upper body strength, or a paid assistant. The challenge is to create a lightweight, durable, assistive system that works in cold, wet, and high-impact conditions without affecting ski performance. Our solution integrates a compressed-air lift into the sit-ski outriggers, helping users upright safely. This design improves user independence, safety, and accessibility while reducing reliance on ski escorts, directly benefiting the project sponsor and adaptive skiing community.

Our team is developing a solar-powered portable cold storage system to reduce post-harvest food loss in off-grid farming communities. Many smallholder farmers lack reliable electricity, causing perishable produce to spoil before reaching market. The engineering challenge is creating a lightweight, affordable refrigeration unit that maintains safe storage temperatures using only solar energy and battery storage. Our design uses an efficient DC compressor, high-performance insulation, and a modular aluminum frame. The system benefits our sponsor by demonstrating a practical, scalable solution that improves food security and supports sustainable agricultural practices.

Southwest Research Institute (SwRI) has tasked our capstone team with designing a Mechanical Ground Support Equipment (MGSE) lifting fixture that is capable of maneuvering spacecraft, regardless of initial orientation, during integration and testing. Due to the sensitive satellite equipment and the positions it will end up in, there will be circumstances where the satellite can only be supported from the sides. The team will circumvent this challenge by utilizing an adjustable frame that can attach to the current satellite, as well as future spacecraft, while avoiding critical areas through the use of linear motion methods.

The Texas A&M Solar Car Racing (SCR) team is developing a motor dynamometer to optimize vehicle efficiency for the upcoming American Solar Challenge. The team aims to simulate race conditions on the motor to support strategy and vehicle optimization. The team will create an Eddy-Current Brake dynamometer to test the motor under varying loads expected during race conditions. The motor dynamometer must withstand the motor's peak torque and support dynamic testing. With the dynamometer, the team can create efficiency maps, test under different environmental factors, and quantify losses found in the motor.

This project develops a modular cryogenic fixture for Resonant Ultrasound Spectroscopy (RUS) testing of small TEM samples under high vacuum and liquid nitrogen temperatures. The engineering challenge is isolating the sample’s true vibrational response while minimizing fixture interference and optimizing excitation geometry as oppose to the Charpy test's which limits our ability to gather data without sacrificing the sample and is not repeatable. Our solution enables controlled comparison of multiple PZT configurations to maximize detectable resonance peaks, providing LANL with a reliable platform to refine cryogenic material characterization methods.

During detonation tube experiments at the Texas A&M Fluid Mixing at Extreme Conditions Lab, the lab faced issues protecting a precision droplet dispenser because the installed valve had a near-1-second closing time. The team’s engineering challenge was to design a compact isolation device capable of fully sealing a 2.5-inch flow path in 10 milliseconds while withstanding pressures up to 12 MPa. With an improved, rapidly closing solution, the laboratory can better protect this costly equipment and improve data accuracy with faster response times.

Our project is an Ultra-High Vacuum Motion Feedthrough. Semiconductor metrology requires moving instruments inside an ultra-high vacuum without breaking the vacuum. Current feedthroughs are bulky, costly, and lack the needed degrees of freedom, forcing repeated venting and pump-down cycles. The engineering challenge is to transmit precise x, y, z translation and tip-tilt motion across a UHV boundary while preserving sealing, cleanliness, and stiffness at low cost. Our compact, customizable feedthrough enables five-axis monochromator positioning entirely in-vacuum, reducing downtime, contamination risk, and system cost for the sponsor.

This project challenges students to design a mechanical and measurement system capable of leveling small samples, so that their top surfaces lie in the same plane with a maximum allowable tilt and roll of just 0.02 degrees. The core problem is achieving and verifying ultra-precise coplanarity across multiple samples. Students must develop a mechanical system that allows for independent adjustment of each sample’s orientation, and a measurement system with sufficient resolution and repeatability to confirm that the leveling requirement is met. This project offers a unique opportunity to work at the intersection of precision mechanics, metrology, and design innovation.

Our capstone team designed, fabricated, and tested a 3D-printed composite hull for a compact underwater drone/ROV. Currently, metal housings are expensive, and printed composites can have anisotropy, weak layer bonding, and micro-voids that may fail or absorb water at deep depths. The engineering challenge is to create a lightweight geometry that can survive 4000 psi while minimizing water ingress. We built parametric CAD models, ran FEA, fabricated prototypes, and validated the design with saline pressure chamber testing. The results have turned into design guidelines that will help the ONR rapidly prototype reliable underwater composite drones.

The problem with current float valve packages is that they require proprietary thread geometries to be installed on casing strings. These thread geometries are widely used throughout the industry and incur immediate costs and critical lead time. The challenge is to identify a universal anchoring and sealing technique that can withstand conditions up to 7,500 psi and 350°F. Our barbed compression fittings and inflatable elastomer bladders convert axial setting force into radial grip and seal the package from unwanted leaks. For Halliburton, this system can cut down on custom packages, simplify logistics, and speed the deployment of float equipment across many wells.

The sponsor seeks a cost-effective method for tracking worker locations across large outdoor properties. The engineering challenge lies in designing a wearable device that delivers 10-meter location accuracy and operates for a full 8-10-hour shift outdoors, withstanding temperature variation, moisture, vibration, and dust without recurring subscription fees. Our solution integrates GPS, local data logging, and a durable enclosure design to reliably record location data in a lightweight, comfortable form. From this, the sponsor can review daily or weekly movement patterns and optimize routing strategies.

Our project develops a wheelchair-mounted exercise attachment that enables users to perform safe, adjustable resistance training from their chair. Many wheelchair users lack accessible, strength-focused exercise options that integrate with daily mobility. The engineering challenge involves designing a compact, durable, and ergonomically adaptable system that mounts securely to various wheelchair frames without compromising stability or safety. Our solution provides an inclusive, manufacturable product that expands rehabilitation and fitness opportunities for any and all wheelchair users.

Our project develops an automated, closed-loop liquid transfer system that precisely fills, drains, and recirculates small containers moving on a conveyor. The engineering challenge is to achieve accurate, spill-free filling system using real-time sensing, PLC control, and coordinated motion between conveyors, valves, and sensors. By integrating the ultrasonic level detection, flow measurement, and safety interlocks, our solution improves reliability, reduces waste, and provides our sponsor with a scalable, low-cost prototype for safety and more efficient automated filling operations.

Autonomous Navigation Systems Capstone (ANS) is one of two teams on the GCRI Interference-Resilient Robot project. ANS tackles the problem of keeping a robot navigating safely when positioning or sensor data are disrupted by interference. The engineering challenge is robust ROS2/Nav2 autonomy: fusing GPS, IMU, and LiDAR mapping, then adapting localization and path tracking based on interference alerts. Secure Navigation Systems (SNS) provides real time indicators of whether interference is present, enabling ANS to maintain reliability and reduce field downtime for the sponsor.

Dust accumulation on solar panels reduces power output and poses a critical challenge in space and remote environments where manual cleaning and inspection are impractical. The engineering challenge lies in detecting and quantifying dust buildup accurately while maintaining extremely low power consumption and system simplicity. Our solution uses a low-power capacitive sensing approach with interdigitated electrodes to monitor subtle changes associated with performance loss in real time. This capability provides our project sponsor with actionable health data for solar arrays, enabling autonomous monitoring, improved reliability, and informed maintenance strategies for long-duration missions.

Team EasyMedRx presents MedBot, an automated medication dispensing system designed to improve medication adherence among elderly patients, nearly 50% of whom do not take their prescriptions correctly. The engineering challenge involves integrating secure patient identification through RFID tag integration, precise mechanical dispensing, and real-time caregiver communication via phone or desktop into a reliable embedded system. Sponsored by Texas Instruments, our solution demonstrates scalable PCB design, motor control, modular product design, and cloud connectivity that can translate into impactful healthcare IoT applications.

MobiBath addresses the challenge of providing safe, temperature-controlled, and water-efficient bathing for mobility challenged individuals. Traditional systems are often too expensive or impractical for most people. Our solution integrates precise flow control, thermal regulation, and electrical protection into a reliable, user-friendly system that is both accessible and affordable. This solution benefits our sponsor by delivering a compact and efficient product that enhances user safety, reduces water consumption, and provides a clean and comfortable environment.

PeekIR addresses the need for fast, non-invasive security screening at venue entrances and lesser developed areas where inconsistent checks, and missed threats can put people at risk. The core challenge is building a compact system that combines mmWave radar, thermal imaging, and metal detection, then fuses those signals with on-device AI to generate a trustworthy real-time risk score. This requires reliable sensing, robust calibration, and filtering to reduce false alarms. For our project sponsor, PeekIR provides clearer, actionable alerts, lowers screening labor and training burden, shortens wait times, and improves overall detection confidence with a scalable deploy-anywhere checkpoint.

Our project addresses the lack of accessible, objective tools for monitoring hand rehabilitation outside clinical settings. Patients and therapists often rely on subjective assessments, and this makes it difficult to quantify recovery over time. The engineering challenge is integrating sensors that measure grip strength, joint angles, and physiological data into a compact, wearable glove with reliable data acquisition, wireless communication, and clear visualization. This low-cost, portable solution enables quantitative progress tracking, supports data-driven clinical decisions, and allows remote patient monitoring.

The Sensor Smoker is a smart meat smoker that allows users to see information about the internal cooking conditions and semi-automate parts of the process of smoking meat. The purpose of our project is to allow users to see the internal conditions without altering them, as measurement can cause changes in the environment (such as lifting the door to check the temperature, thereby letting heat out). This project involves integrating several different sensors (temperature, oxygen, air particulate, humidity) to control various systems to keep the temperature, humidity, and smoke level within acceptable ranges. This project serves to make the experience of smoking meat more user-friendly.

FlowX is developing a proof-of-concept thruster-driven space drone to access steep, permanently shadowed lunar regions that rovers cannot reach, where water ice essential for fuel, oxygen, and life support may exist. The engineering challenge is achieving precise attitude control using cold-gas thrusters, custom Raspberry Pi–based control algorithms, and sensor feedback. A 3-DOF gyroscopic test rig validates stability and control. This project gives our sponsor a low-cost platform to research thruster mobility and advance technologies for future lunar exploration and resource missions.

This project aims to address the shortage of medical isotopes using a novel, compact, and cheaper method compared to industry standard approaches. Using a deuterium beam from an accelerator (with a power range of 10 - 40MeV), neutrons are produced when the beam interacts with circulating FLiBe. The lithium and beryllium in the FLiBe interacts with the deuterium beam to produce reactor levels of neutron flux; which are used to produce medical isotopes that require neutrons for their production reactions. The system uses a completely autonomous and modular design which could change target samples out in minutes; which far outperforms all other methods to date.