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This project focuses on developing the software 鈥渂rain鈥� for an F1/10 autonomous racing car. The student will work on algorithms for computer vision, perception, and decision-making, enabling the car to sense its environment and navigate a track at speed. Tasks may include implementing camera-based lane detection, fusing sensor data for localization, and optimizing control strategies for racing performance.

The project is highly hands-on, combining AI, robotics, and real-time programming in a multidisciplinary setting. By joining this effort, the student will gain experience in cutting-edge autonomy technologies, contribute to Villanova鈥檚 first F1/10 racing platform, and help prepare the team for participation in the international F1/10 racing competition at ICRA.

This project focuses on the development of an iOS application designed to support experience sampling methodology (ESM), a widely used approach for capturing real-time data on human behavior, cognition, and emotional states. The app will allow researchers to design and deploy flexible surveys and prompts that can be delivered to participants at random or scheduled intervals throughout the day. Participants will be able to respond directly on their smartphones, enabling the collection of ecologically valid, time-sensitive data in natural environments. Key features of the app will include customizable survey templates, integration of branching logic, and support for multimedia questions (e.g., image or audio responses). The platform will also incorporate secure data storage and user-friendly dashboards to facilitate real-time monitoring of participation and response rates. Additionally, the app will be designed with scalability in mind, allowing it to adapt across multiple research domains such as psychology, transportation, and public health. By developing this tool, the project aims to reduce reliance on paper-based or delayed self-report methods and provide a more accurate, efficient, and user-centered system for collecting in-the-moment experiences. The outcome will be a flexible and extensible iOS app that enhances research capabilities across diverse fields of study.

Rapid quantification of multi-per per- and polyfluoroalkyl substances (PFAS) at sub-ng路L鈦宦� concentrations in water remains a critical technological challenge. PFAS, nicknamed "forever chemicals" because they don't break down naturally, can build up in our bodies and the environment over time. They're found in everything from non-stick pans to firefighting foam, and they're increasingly showing up in our drinking water. This project aims to develop a portable and highly adaptable sensor platform for rapid electrochemical detection of multiple PFAS at sub-ng L-1 concentrations. Four tasks are proposed to achieve this goal: (1) synthesize conductive and redox-active MIPs for individual PFAS, (2) select the best-performing MIP for individual PFAS with the highest selectivity and sensitivity, (3) assemble an MIP-microelectrode array for simultaneous quantification of multiple PFAS, (4) assess sensor performance and perform techno-economic analysis. This proposed work leverages the novel PFAS sensing mechanism discovered by PI Xu for direct electrochemical quantification, which enables the miniaturization of the developed sensor platform and opens up the possibility of continuous monitoring. 

In extremely crowded environments with thousands of wireless devices, we have often observed wireless links become unreliable: Wi-Fi operates slowly, intermittently, or can't connect entirely. We assume the wireless channel is clogged with transmissions, and there is no hope of communication. However, we have recorded a large set of wireless spectrum data in such a crowded environment and found that much of the 2.4 GHz spectrum is empty, even when Wi-Fi is unusable! This indicates we can design intelligent algorithms to identify narrow slices of bandwidth and time during which communication can be successful, despite the apparent clogging of Wi-Fi communication. Furthermore, we have recordings of other environments we can analyze for comparison, including a convention presentation hall with thousands of people in attendance, a room with approximately 10 virtual reality and other gaming systems causing wireless interference, and a traditional office environment with approximately 10 occupants using ordinary Wi-Fi-enabled laptops.

In this project, the student will first analyze existing data, looking for gaps in the spectrum that present narrowband communication opportunities. The goal will be to confirm the hypothesis that ample spectrum exists for low-latency wireless communication of approximately 5 MHz bandwidth at most, or any time during the spectral recordings. We will also examine salient differences in the spectrum usage between recordings. 

We will then propose and test algorithms to determine which wireless channel to communicate on at what time, and which channel to switch to in case of a collision, to maximize throughput and minimize packet transmission retries. Near the conclusion of the project, we will write up the results for publication at a suitable wireless communications conference or peer-reviewed journal. 

Advanced students will have the opportunity to use new observation hardware platforms we will purchase as needed. This project is intended to grow and, in the future, implement developed algorithms on a real radio system and test it in a high-interference environment.

Post-quantum cryptography (PQC) is a new type of cryptosystem that is resistant to the attacks launched by mature quantum computers. The 2022 U.S. National Security Memorandum emphasized that the U.S. must transition to PQC by 2035. Meanwhile, several PQC algorithms have been proposed for potential standardization under the National Institute of Standards and Technology (NIST) standardization process. More importantly, the recent advancement in the field involves determining the implementation performance of a PQC algorithm on a hardware platform.

In this project, we follow this trend to conduct research on efficient hardware acceleration of a PQC algorithm that is already selected for standardization by NIST, the lattice-based cryptography Kyber (also named as ML-KEM). We have identified that one of the most complicated arithmetic operations for ML-KEM is the polynomial multiplication over a ring. Hence, in this project, we will: (i) train and equip the supported undergraduate research student with the required hardware skills for implementing this polynomial multiplication (Project Mentor and the student will study the mathematical background together); (ii) learn the hardware design language coding skills and hardware design techniques to design the targeted operations into efficient hardware architectures, along with testing and evaluation; (iii) explore possible optimization techniques to improve the hardware accelerator鈥檚 final implementation performance.

This project will be led by Dr. Jiafeng Harvest Xie from the Electrical and Computer Engineering Department and the whole project will last for 10 weeks, with 10 hours per week. The Project Mentor will advise the supported student to conduct the research proposed above. A complete version of the polynomial multiplication accelerator for ML-KEM is expected to be achieved by the end of the project. The results obtained from this project will be documented and compiled into a paper for possible publication in an IEEE Conference or even a Journal.

This project centers on the design and development of smart educational technologies in the form of bio-inspired robots. These robots take inspiration from nature, such as the mechanics of a snake鈥檚 jaw, a goldfish鈥檚 swimming motion, and a starfish鈥檚 locomotion, and translate them into engineered systems that demonstrate real scientific and mathematical principles. These robots integrate biology and engineering and support integrative STEM learning - highlighting how nature and engineering connect in the real world. 

The research advances two complementary goals. First, it is the technical design of bio-inspired robots that are safe, robust, and engaging, while being practical for use in K鈥�12 classrooms and informal learning environments like museums. Second, it investigates their educational impact, examining how these tools foster cross-disciplinary connections and increase student engagement with STEM concepts. This work is highly collaborative, bringing together teachers, museum educators, and students in iterative cycles of design, testing, feedback, and refinement.

This work highlights how technology interfaces with society. Students gain experience not only in mechanical and robotic system design but also in human-centered problem solving, developing technologies that directly serve learners and educators.

Many additive manufacturing materials are metal materials, while the 3D printers for undergraduate experiments and research are based on polymer/plastic materials. The 3D metal printers are often expensive when purchased commercially, making low-cost 3D metal printers is vital to enable many undergraduate research and experiments. This project will give the senior design group an exciting experience of the production and testing of an electrochemical 3D metal printer. The 3D printer can create metallic components based depositing adherent layers of metal ions onto the surface of a conductive substrate. The printing head consists of a sharp-tipped electrode submerged in an electrolyte close to a conductive substrate. Under a positive potential between anode and cathode, metal ions deposit on the conductive substrate as the metal ions are reduced. Meniscus confined electrochemical (MCE) 3D printing is another approach to building metallic structures through the formation of a stabilized liquid meniscus between the dispensing nozzle. The design considerations for a meniscus confined printing approach will have superior print speeds to equivalent works of classical 3D printers, which will require the meniscus diameter of the printing nozzle to be submillimeter size and the integration of a porous sponge into the print head to balance the hydraulic head of the electrolyte. Other piston-based methods of controlling the electrolyte meniscus will be tested, too, for better control. The carbon paste working electrode will be tested in potassium ferricyanide and other metallic solutions for both variable scan rate and concentration responses. In the end, the goal of this low-cost 3D metal printer should demonstrate it can be used in a variety of electrochemical experiments for metal parts printing.

The goal of this project is to create soft, skin-conformal pressure sensors for next-generation wearable health monitoring. Current commercial patches for electrocardiography (ECG) or pulse tracking are often bulky, stiff, and require strong contact pressure to maintain signal quality, which can cause discomfort or red marks after extended use. These properties limit their potential for long-term and continuous physiological monitoring. Our group has developed a new ultrathin sensor with a thickness down to a few nanometers. This ultrathin dielectric has a low elastic modulus, closely matching the softness of human epidermis and enabling sub-pascal pressure detection. These features allow the sensor to detect subtle pressure changes while remaining gentle and non-invasive on the skin. In this project, we will explore how fabrication conditions such as surface cleanliness, annealing temperature, and dielectric thickness influence signal stability, noise level, and long-term performance. Our goal is to reduce the noise level while preserving the high sensitivity and mechanical compliance of the device. This is a new system for our group and will provide insight into how nanoscale dielectric structures influence signal behavior in soft capacitive electronics. The outcome may also support our ongoing intellectual property development and contribute to broader efforts in soft and wearable electronics for health applications. We hope this project will inspire students to explore the fields of nanomaterials, bio-integrated sensors, and biomedical device engineering.

The goal of this project is to advance the development of smart and sustainable sensing technologies for environmental monitoring by creating a novel Raman platform for the detection of per- and polyfluoroalkyl substances (PFAS). PFAS, often referred to as 鈥渇orever chemicals,鈥� are highly persistent, bioaccumulative, and toxic pollutants that pose serious risks to both human health and ecological systems. Their long-term accumulation in water resources requires highly sensitive and accessible detection methods to ensure safety and sustainability. Currently, liquid chromatography鈥搈ass spectrometry (LC-MS/MS) is considered the gold standard for PFAS detection, but its reliance on costly instrumentation and labor-intensive protocols makes it impractical for widespread, routine use. Our team has recently demonstrated that MPRS provides an innovative solution by employing molecular probes that are uniformly distributed on polymer substrates, enabling highly sensitive detection through enhanced molecular interactions. This platform has already achieved a detection limit exceeding the capabilities of LC-MS/MS by four orders of magnitude. In this proposed project, we will build on this success by incorporating novel nanomaterials to further advance the sensitivity and selectivity. We will further investigate sensor fabrication methods, test performance in realistic water samples, and establish scalable procedures for broader application. By enabling rapid, ultratrace, and cost-effective detection of PFAS, this project will contribute directly to environmental protection, public health, and long-term sustainability, while also providing training opportunities that inspire students in the fields of materials science, spectroscopy, and environmental engineering.

Traumatic brain injury (TBI) is a critical public health issue that affects individuals across all demographics, with particularly high prevalence in sports-related activities. Concussions, the most common form of mild TBI, remain difficult to study due to the complex anatomy of the head. The brain is suspended in cerebrospinal fluid, supported by porous subarachnoid trabeculae, and encased within the opaque skull. This makes direct observation of impact mechanics nearly impossible using conventional imaging or experimental approaches.

To overcome this challenge, our lab has developed a patented biomimetic head surrogate known as Smart Brain Technology. This model replicates the structure of the human head with a hydrogel-based brain and a transparent skull, providing a unique platform to visualize internal responses to external forces. By outfitting the surrogate with pressure and acceleration sensors, we can quantify how forces propagate through brain tissue and identify localized regions of stress or strain during impact.

In this project, we will subject the surrogate to controlled impact experiments designed to mimic real-world collision scenarios common in sports. The data collected will allow us to observe the onset and progression of concussion mechanisms that cannot be directly measured in living subjects.

The insights gained from this study have three major implications: first, improving scientific understanding of how and where brain injuries occur; second, supporting the development of more effective diagnostic and treatment strategies; and third, informing the design of advanced protective equipment, such as helmets, to better safeguard athletes and reduce the risk of long-term neurological damage.

By combining innovative experimental design with cutting-edge biomimetic modeling, this project has the potential to bridge critical gaps in TBI research and contribute to both clinical and preventative advances in brain health.