The Promoting Electric Propulsion (PEP) program is a national workforce development initiative sponsored by the Office of Naval Research and administered by the American Society of Naval Engineers. Student teams design, analyze, fabricate, integrate, test, and race an electric-powered crewed or uncrewed surface craft while applying mechanical, electrical, manufacturing, controls, naval architecture, and systems engineering principles. The project follows a complete engineering lifecycle, including concept development, computer modeling, component selection, fabrication, systems integration, validation testing, technical documentation, and public demonstration at a national competition. UIUC is building off of many years of successful iteration and improvement.
Students also interact with Navy engineers and industry mentors while presenting their engineering decisions through white papers and technical discussions. The program is specifically designed to prepare graduates for careers supporting the maritime and defense industrial base.
Students will design, build, and race a boat for a competition in or near Bremerton, Washington in April 2027.

***This project is conducting remote interviews. Please see remote interview sheet on BrightSpace (D2L) to schedule a time to speak to the sponsor.***

ASML uses barcode scanners that use transmissive line scanning to read 1D barcodes on a 6mm thick quartz photomask. Mask has a coating that determines % light transmission. Dark regions in barcode made of this coating, Light region is simply plain quartz. Scanners perform well with ‘high-contrast’ coating : coating is optically dense which allows for clear contrast between dark and light.
Problem : Scanners cannot deal with ‘low contrast’ coating that is semi transparent (transmission > 35%). SNR is degraded
Deliverables :
- Define optical scheme (lens, diffusers, aperture stop, etc.) to collimate the source and achieve at least 50% contrast (defined as high – low / high + low) for low contrast targets
- Device should be passive (static lens)
- Subject to constraints in the table (see PDF attachment). Full design freedom otherwise
- Validate the solution in simulation and experiment. Experiment will need a test barcode sample (thickness 6mm with barcode engraved on absorber material that has transmission > 55% @ 470nm)
- Document findings in investigative report

***This project is conducting remote interviews. Please see remote interview sheet on BrightSpace (D2L) to schedule a time to speak to the sponsor.***

Design, build, and validate a compact dark-field optical inspection module capable of detecting particles and contamination on opaque objective surfaces. The project will develop a common illumination and detection architecture using off-the-shelf optical and mechanical components while minimizing bright-field reflections through dark-field illumination techniques. Students will design the optical layout, mechanical packaging, image acquisition system, and image-processing algorithms required to detect approximately 100 µm particles with high reliability. The final system should satisfy packaging constraints, achieve processing times below one second per image, and demonstrate robust contamination detection performance using representative test samples.

***This project is conducting remote interviews. Please see remote interview sheet on BrightSpace (D2L) to schedule a time to speak to the sponsor.***

The TRACE project (Target Recognition via Adaptive Contrast Enhancement) challenges the student team to develop a prototype system that improves the visibility of a man‑sized target through adaptive contrast‑enhancement algorithms, man-sized assumed to be 1.5 meters square at 250 meters or about 2.7 mrad angular resolution. Using standard commercial off‑the‑shelf (COTS) hardware, the team will collect test imagery across varying illumination conditions (full sun, overcast, dusk/twilight) and diverse target–background combinations such as white clothing, camouflage, urban environments, and desert terrain. Core project objectives include defining a quantitative visibility metric, performing research into contrast‑enhancement techniques, and developing a post‑processing algorithm that measurably improves target visibility. The final system should operate in real time at 30 Hz with 100 ms processing latency.

As a stretch goal, the team can integrate live target marking with real‑time enhancements onto the scene in a wide-field-of-view lens.

Visibility metric:
This is intended to be a scalar score that is computed by the software based on the contrast of the target to its background. It can be per pixel or given to the target as a whole. Then after the post-processing enhancement algorithms, there should be able to an increase in the metric. This will require research, thought, and perhaps some experimentation by the team to land on a suitable method for calculating "visibility".

Hardware:
COTS monochromatic visible spectrum digital camera and lenses. Team can run video processing discrete embedded electronics, but running all software on a computer with accompanying display is acceptable.

Testing:
At least 3 light levels, e. g. full sun, overcast, dusk, twilight, etc.
At least 2 target contrast levels including light or reflective colored clothing and camouflage
At least 2 different environments, e. g. urban, sandy, rocky, forest, etc.
Stretch goal to capture and enhance images of Bigfoot.

***This project is conducting remote interviews. Please see remote interview sheet on BrightSpace (D2L) to schedule a time to speak to the sponsor.***

Project Goal/Summary: Advance last year’s CAMDAR prototype into an integrated, working system that uses two video cameras to identify and localize an at-risk individual in three-dimensional space, directs an existing alt-azimuth radar mount toward that person, measures non-contact vital signs with updated millimeter-wave radar, and displays the person, posture, location, vital signs and alert status for a responder.

Project Background: Last year’s team established the core CAMDAR architecture, built the camera/computer-vision pipeline and a functional aimable altitude–azimuth radar platform. The major unfinished tasks were reliable two-camera stereovision/3D localization, robust radar vital-sign acquisition, and complete subsystem integration. Their final report specifically recommends improved stereovision calibration/depth estimation, radar processing, mount stability/pointing accuracy, and earlier incremental integration. This year is an ADVANCE + INTEGRATE project—not a restart.

Requirements:
• Step 1 – Inherit + baseline. Begin with last year’s hardware, code, camera subsystem, Jetson/main console and alt-azimuth mount. Reproduce known functions and define quantitative baseline tests before modifying the system.
• Step 2 – Build true 3D vision. Integrate two synchronized/calibrated cameras with overlapping fields of view. Detect/track multiple people, assess posture (lean/slump/fall/collapse), and generate stable real-time X-Y-Z coordinates for the selected person of interest.
• Step 3 – Coordinate transform + aiming. Convert camera-derived 3D coordinates into radar azimuth/elevation commands. Reuse the existing turret design; refine rigidity, calibration, repeatability and pointing accuracy rather than redesigning from scratch.
• Step 4 – Upgrade mmWave radar. Integrate the identified newer millimeter-wave radar board(s) and establish reliable non-contact respiratory-rate and heart-rate measurement. Temperature may be added if supported by a separate validated sensor; do not make it a core radar requirement.
• Step 5 – Close the autonomous loop. VISION → PERSON-OF-INTEREST → 3D LOCATION → AIM → RADAR ACQUISITION → VITAL-SIGN ANALYSIS. Demonstrate repeatable handoff from vision to radar without manual aiming.
• Step 6 – Build the CAMDAR display. Create a clear real-time GUI/heads-up display showing the monitored scene, highlighted person of interest, posture/risk state, 3D position, radar targeting status, respiratory rate, heart rate, confidence/quality indicators and system state.
• Step 7 – Alert + validate. Generate a responder alert when predefined visual + physiologic criteria are met. Validate with staged non-clinical scenarios involving multiple people; quantify localization error, aiming error, vital-sign accuracy versus reference devices, detection/alert latency, false alerts and end-to-end reliability.

Project Goal/Summary: Design and build CELLBOMB, a benchtop laboratory instrument exposing blood or blood constituents to independently controlled rotational/propulsive shear and positive or negative pressure (vacuum) representative of continuous-flow mechanical circulatory support (MCS). The platform will enable controlled study of red-cell hemolysis and platelet activation and provide a basis for standardized blood-pump testing.

Project Background: Temporary and permanently implantable MCS devices commonly use high-speed rotary impellers. Blood passing through these pumps experiences substantial shear together with regions of positive pressure and negative pressure/vacuum. Shear-induced blood damage is well studied; the independent and combined effects of pressure/vacuum + shear are less well characterized. CELLBOMB will provide a reproducible platform for separating and systematically varying these stresses.
Requirements:
• Step 1 – Define the MCS operating space. Establish representative ranges of impeller speed, fluid shear, positive pressure, negative pressure/vacuum and exposure time. Define target test conditions before fabrication.
• Step 2 – Engineer the CELLBOMB pressure vessel. Design a transparent cylindrical chamber with removable sealed end caps, gaskets, sampling/access ports and axial bearings. Perform pressure/vacuum and structural safety analysis before testing; use containment and approved laboratory safety procedures.
• Step 3 – Build three CELLBOMB platforms. A) Demonstration Bomb: ~2–3 in diameter × ~12 in, visualization/engineering demonstration only – NO BIOLOGIC FLUIDS. B) Experimental Bomb: ~25 mL with a custom 3D-printed impeller. C) MCS Bomb: ~10–12 mL incorporating an actual Abiomed/J&J blood-pump impeller.
• Step 4 – Build the magnetic impeller drive. Develop an external electromagnetic/inductive drive acting on embedded impeller magnets, avoiding a rotating shaft penetration. Measure/control RPM while preserving chamber sealing.
• Step 5 – Characterize shear + pressure/vacuum. Measure pressure/vacuum, impeller speed and, where feasible, flow and temperature. Use analytical methods and/or CFD to define flow patterns, shear-stress distribution and exposure.
• Step 6 – Integrate controller + GUI. Develop one controller/interface for all three chambers. Set/display RPM, pressure/vacuum and exposure time; log sensor outputs and experimental conditions; include safety limits, alarms and emergency shutdown.
• Step 7 – Validate + perform biologic testing. Validate first with water/saline or appropriate non-biologic fluids. If time, approvals and resources permit, test blood/blood constituents across defined shear × pressure × time conditions. Primary biologic endpoints: hemolysis/red-cell damage and platelet activation.

1. Project description: The United States Coast Guard (USCG) faces a persistent and growing capability gap in detecting semi-submersible and low-profile vessels (LPVs) used by transnational criminal organizations for narcotics trafficking. These vessels are deliberately engineered to minimize radar and visual signatures, rendering conventional maritime surveillance methods largely ineffective. Distributed underwater acoustic sensor networks — commonly known as hydrophone arrays — offer a compelling solution by exploiting the unavoidable acoustic signatures generated by vessel propulsion systems. This capstone project challenges students to design, build, and demonstrate a scaled proof-of-concept (PoC) hydrophone array system that integrates distributed sensing, real-time signal processing, machine learning-based classification, and a command-and-control (C2) dashboard interface.

At full operational scale, such a system would consist of networks of seafloor-mounted hydrophone nodes spanning hundreds of nautical miles, feeding acoustic data into AI-powered fusion centers that cue intercept assets in near-real time. For this capstone effort, students will demonstrate the same fundamental technical concepts at small scale — using a network of 2 to 5 prototype sensor nodes deployed in pool and/or open-water lake environments — targeting small, motorized watercraft as acoustic sources in place of full-sized vessels. The core technical challenge is identical to the operational problem: detect, classify, and localize an acoustic source of interest using a distributed sensor network, with results presented to an operator through an intuitive command interface.

2. Students will gain hands-on, portfolio-quality experience across a broad, in-demand skill set:
• Underwater Acoustics & Signal Processing:
- Fundamentals of underwater acoustic propagation and the acoustic environment
- Digital signal processing techniques: sampling, filtering, FFT, spectrogram generation
- Time Difference of Arrival (TDOA) and GCC-PHAT cross-correlation algorithms
• Machine Learning & Data Science:
- End-to-end ML development lifecycle: data collection, labeling, augmentation, training, evaluation
- Convolutional Neural Network (CNN) architecture design and training using PyTorch or TensorFlow
- Audio classification using spectrogram-based deep learning approaches
- Deploying a trained model for real-time inference in an embedded/edge environment
• Embedded Systems & Hardware Engineering:
- Embedded Linux operation on single-board computers (Raspberry Pi / NVIDIA Jetson)
- Audio interface integration and audio capture programming
- Waterproof enclosure design and fabrication
- Sensor node power management and power system design
- Wireless communication module integration (WiFi / LoRa)
• Software Engineering & Full-Stack Development
- Python application development for sensor data acquisition and processing
- Real-time web application development
- Interactive map integration

3. Deliverables:
Project deliverables are phased through the two-semester sequence and include:
- Weekly verbal or written progress reports on work completed and problems encountered
- Trade study report with technology recommendations
- Preliminary Design Review
- Testing and validation report
- Final implementation of the system
- Comprehensive project documentation and user manual
- Project presentation and demonstration

4. Required background: Background information required to successfully execute the project includes the following. Note that not all students are expected to possess all skills — the team should collectively cover these areas, and the faculty advisor and industry mentor will provide supplemental guidance
- Programming Proficiency (Python) —Python programming skills; the majority of signal processing, machine learning, and backend development will be implemented in Python. Familiarity with NumPy, SciPy, and Pandas is highly desirable.
- Machine Learning Fundamentals — Prior coursework or experience in machine learning, including familiarity with neural network concepts, training procedures, and model evaluation metrics. Experience with PyTorch or TensorFlow is preferred but not required.
- Digital Signal Processing (DSP) — Coursework or familiarity with DSP concepts including sampling theory (Nyquist), Fourier transforms (FFT), filtering, and spectral analysis. Prior audio or communications signal processing experience is a plus.
- Electrical Engineering Fundamentals — Basic understanding of electronics, circuit design, power systems, and sensor interfaces sufficient to assemble and troubleshoot prototype hardware nodes.
- Web Development (Basic) — Familiarity with web application development concepts; experience with React.js, JavaScript, or Python web frameworks (Flask, FastAPI, Django) is desirable for the C2 dashboard development workstream

5. Supplies/Equipment Required: The following materials and equipment are required for project execution: underwater microphone elements, edge compute, audio capture interfaces, enclosures, power source, communications equipment, target acoustic sources, GPS modules, waterproofing hardware, and other miscellaneous electronics and hardware dependent on student design.

Category Estimated Cost
Sensor Node Hardware (×3 nodes) $1350
Target Vessels & Acoustic Sources $325
Compute and Communications Equipment (x3 nodes) $750
Miscellaneous electronics $75
Miscellaneous hardware $150
Documentation & Miscellaneous $175
Subtotal $2,825
2 Additional nodes $1400
TOTAL ESTIMATED BUDGET $2,825-$4,225

PROJECT INTENT. Build an undergraduate-engineering prototype that receives actual spent hemodialysate collected from dialysis treatments, selectively removes undesirable solutes, measures the residual chemistry, adds back missing constituents, mixes, re-measures, and either RELEASES or REJECTS the regenerated dialysate. Fresh commercial dialysate will serve as the reference control. The project is a bench engineering system only and is not intended for human use.

Project Background: This project builds directly on two years of UA work. Chemical Engineering students first defined the chemistry of fresh versus spent dialysate; last year's Biomedical Engineering team then used actual spent dialysate to build a working WATER ECONOMY system that removed urea/ions, filtered and disinfected the fluid, and recovered purified water. This year the goal is to regenerate the dialysate itself rather than strip it completely to water: preserve useful constituents where practical, remove harmful/excess components, determine what is missing, and reconstitute the fluid toward a defined fresh-dialysate composition.

Project Goal/Summary: Develop a closed-loop system that recaptures actual spent hemodialysate, selectively removes accumulated wastes/excess solutes, measures the remaining useful constituents, adds back what is missing, and verifies regenerated dialysate against fresh dialysate for reuse. SELECTIVELY CLEAN → MEASURE → RECONSTITUTE → VERIFY → REUSE. Long-term goal: reduce water/dialysate waste and enable smaller home and ultimately portable hemodialysis systems.

This invention relates to a novel, real-time blood loss quantification device for use in surgical and obstetric settings. The system estimates blood volume absorbed by surgical gauze or sponges by extracting hemoglobin into a fixed-volume reagent (e.g., Drabkin’s reagent), forming cyanmethemoglobin, and then quantifying absorbance at 540 nm using an optical detection module based on Beer–Lambert Law.

The device includes a disposable gauze cartridge with a sieve, a reagent chamber, and an optical cuvette aligned with dual-wavelength LEDs (540 nm and 850–880 nm) and a photodetector (e.g., OPT101). A microcontroller calculates hemoglobin concentration, compensates for turbidity via secondary IR LED measurement, and estimates total blood loss in mL using patient-entered baseline hemoglobin.

The solution improves accuracy over gravimetric and camera-based methods (e.g., Gauss Triton), minimizes interference from non-blood fluids, and is suitable for clinical, low-resource, and emergency settings.

Pelvic organ prolapse affects millions of women, yet the primary non-surgical treatment option, the vaginal pessary, remains a passive device that offers no information about vaginal health or device performance. This Phase I project will develop the Smart Pessary, a soft intravaginal device made of medical-grade silicone that contains miniaturized pH and temperature sensors, a low-power microcontroller, and wireless data transmission. The goal is to demonstrate the technical feasibility of continuous monitoring of the vaginal environment to detect early signs of infection or device-related complications. The objectives include fabricating a functional prototype, validating sensor accuracy and stability in simulated vaginal conditions, confirming data transmission and onboard signal processing, and assessing the device’s performance during extended laboratory use. This work will establish feasibility for a future pathway toward real-world deployment.