The Student Faculty Research Awards are used to advance the scholarship of graduate students and faculty working in partnership. Grants up to $5,000 are awarded to the selected student/faculty pairs and are intended to help support student research/scholarship/creative activity; give students experience writing grants; and foster the mentoring relationship between faculty and graduate students. This fall, 25 student/faculty pairs were selected to receive the awards. Read some of the winning proposal summaries below that include topics such as AI and robot design, computers, communication, and technology.

Input Shaping for Smooth Control of Soft Robots

Joshua Gaston—PhD student, mechanical engineering

Daniel Rucker—associate professor, Department of Mechanical, Aeronautic, and Biomedical Engineering

Robots can come in a wide variety of different shapes and sizes and can be suited for many different tasks ranging from moving heavy loads to performing surgery. Some robots known as soft robots or continuum robots are made from flexible material, allowing the robot to bend anywhere along its arm. When a robot makes a sudden change in its speed, residual vibrations are created along the robot, like a diving board that vibrates after being released. One technique used to reduce these vibrations in industrial robots is known as input shaping. By creating an additional vibration that has a waveform equal and opposite to the waveform of the residual vibration, the two vibrations will cancel each other. This allows the robot to continue to perform at the same speed without severe residual vibrations. Joshua Gaston, mechanical engineering PhD student, and Daniel Rucker, associate professor in the Department of Mechanical, Aerospace and Biomedical Engineering, intend to investigate input shaping techniques by developing an open-source platform that can be used to investigate optimal control techniques for dynamic behavior in continuum robots. By developing better control techniques, they hope to not only advance continuum robotics, but also open the door to other research opportunities.

YouTube in the Classroom

Scott Greves—PhD student, communication and information

Mustafa Oz—assistant professor, College of Communication and Information

Although YouTube was not designed as an educational resource, the social media platform has sought to foster its educational utility, in part through a recent $20 million funding program for educational channels. Increasingly, YouTube has been integrated into the classroom by educational professionals, and is commonly used to introduce new topics, provide supplementary information, and break up traditional lecture-style information delivery. Despite their overwhelming adoption in the classroom, most educational videos on the site are unregulated and published without editorial oversight or any form of peer review. Very few studies seek to investigate how an educator can navigate YouTube’s over four billion videos to find a source that is most suited to their class’s educational requirements. Focusing on the college classroom, Scott Greves, communication and information PhD student, and Mustafa Oz, assistant professor in the College of Communication and Information, propose to understand how professors use YouTube’s search options, how they navigate and filter search results, how they screen possible videos, and which metrics they may use to infer content validity, reliability, and aptitude for knowledge transfer. A better understanding of these concepts may inform a more rigorous and successful application of YouTube in the classroom.

A Robotic Platform to Detect Lost Moment in People with Dementia

Fengpei Yuan—PhD student, mechanical engineering

Xiaopeng Zhao—professor, Department of Mechanical, Aerospace, and Biomedical Engineering

More than 6 million Americans live with Alzheimer’s disease and related dementias (ADRD), which are irreversible, progressive brain disorders that slowly destroy memory, thinking, and other neuro-cognitive skills. Due to short-term memory loss, people with ADRD have difficulty accomplishing daily activities. For example, they may forget to start a routine task or perform it in a proper sequence, a phenomenon called forgetful or lost moment. The emerging technologies of artificial intelligence (AI) and AI-empowered social robots have the potential to help people with ADRD live independently and safely. A social robot would learn to provide effective reminders, guidance, coaching and assistance to help sufferers out of lost moments, ensure their safety, and improve their autonomy. However, no data exists about the performance of daily tasks by people with ADRD. Fengpei Yuan, a mechanical engineering PhD student, and Xiaopeng Zhao, professor in the Department of Mechanical, Aerospace, and Biomedical Engineering, aim to develop a robotic platform that can help detect lost moments during Performance of Selfcare Skills (PASS) experiments. This will allow researchers to comprehensively evaluate the performance of daily activities and identify which daily tasks are compromised among people with ADRD and the point of their task breakdown, i.e., lost moment.

A Robust Methodology for Creating 3D Implant Models Using a Morphing Algorithm that Utilizes either X-Ray or Fluoroscopic Images

Viet Dung Nguyen—PhD student, biomedical engineering

Michael LaCour—research assistant professor, Department of Mechanical, Aerospace, and Biomedical Engineering

The success of knee replacement surgery, or total knee arthroplasty, can be improved by analyzing the anticipated postoperative movement of the joints using 3D CAD models of implant components. Unfortunately, these 3D models are not always available, may require multiple X-ray or fluoroscopic images, or require expensive databases or training sets. Viet Dung Nguyen, a PhD student in biomedical engineering, and Michael LaCour, research assistant professor in the Department of Mechanical, Aerospace, and Biomedical Engineering, propose to expand and improve a novel morphing algorithm they have developed. This algorithm builds a model using just a few X-ray or fluoroscopic images and a starting model to morph the starting model to fit the silhouette of the images without changing the structure of the knee. The expansion of this algorithm may eliminate the need for CT scans or unnecessary X-ray exposure for patients and will allow for more frequent and detailed evaluations of new and existing total joint replacement systems.

Diversity as a Factor in the Design of Human-Robot Interfaces

Jessica Barfield—PhD student, communication and information

Jianghen He—assistant professor, School of Information Sciences

Robots are becoming more prevalent and involved in social interactions where information is being exchanged with humans. While engaged in these interactions, people often “anthropomorphize” robots, even to the extent of attributing a race or ethnicity to a robot. Jessica Barfield, a PhD student in communication and information, and Jiangen He, assistant professor in the School of Information Sciences, propose to investigate how a robot’s design features, which may signal ethnicity, affect trust in the information provided by the robot. Through a series of studies using a robot that can be programmed with social skills and interface features, the team hopes to bring to light ways in which the interface between humans and robots may increase diversity, inclusivity and equity.

Emulation of a Quantum Qubit System Using Classical Computing Hardware

Nazmul Amin—PhD student, electrical engineering

Ahmedullah Aziz—assistant professor, Department of Electrical Engineering and Computer Science

Endeavoring to understand the physical systems around us led to the development of conventional computers to manage the computation workload involved in that exploration. However, for particular and very specific problems, a quantum computer employing the principles of superposition and entanglement can be the only effective solution. Large companies have invested time, money and effort in the development of quantum computing systems only to be challenged by issues such as scalability and the maintenance of ideal conditions for a true quantum system. Nazmul Amin, working toward a PhD in electrical engineering, and Ahmedullah Aziz, assistant professor in the Department of Electrical Engineering and Computer Science, propose to develop a scalable classical emulator of a quantum system to test quantum algorithms. Their approach is expected to help troubleshoot errors in quantum systems and open new pathways to future research.

Electrochemical Disinfection as an Innovative Technology to Provide Safe Drinking Water

Caitlyn Smugor—MS student, environmental engineering

Qiang He—professor, Department of Civil and Environmental Engineering

Even though access to clean water is a modern necessity, estimates indicate that about 20% of the world’s population does not have access to adequately treated water. In order to reduce serious health risks from pathogens, one of the key steps in the production of drinking water is disinfection, commonly accomplished through chlorination. But chlorination can be challenging to implement in small communities or individual households due to the need for specialized equipment and skilled technicians, high cost, and safety requirements. Caitlyn Smugor, an MS student in environmental engineering, and Qiang He, professor in the Department of Civil and Environmental Engineering, propose to investigate an electrochemical method of disinfection as an alternative to chlorination. Such a method would be more easily implemented in households and communities, can use renewable sources of electric power, and would significantly contribute to the decarbonization of the water industry. Therefore, it would not only reduce climate change impacts, but help to ensure that more people can rely on the safety of their water.

A Remote Sensing Analysis of Barringer Meteorite Impact Crater: Implications for Impact Crater Degradation and Chronology

Cole Nypaver—PhD student, geology

Bradley Thomson—assistant professor, Department of Earth and Planetary Sciences

During a meteor impact, matter is forcibly thrown out of the crater. The rate at which this matter, known as ejecta, breaks down can be a key indicator of the surface age of a planet and its history of change. Observations of ejecta placement and erosion patterns on airless bodies, such as our Moon, have been made using remote sensing data. However, Cole Nypaver, PhD student in geology, and Bradley Thomson, assistant professor in the Department of Earth and Planetary Sciences, propose to study terrestrial impact craters to corroborate those remote sensing observations with measurements on the ground. The team will study the most well-preserved crater on Earth: Meteor Crater, or Barringer Crater, a 50,000-year-old one kilometer crater located in northeast Arizona. Direct measurements like these are currently difficult or impossible to obtain for other planetary bodies. Taking into account the recent formation age of Meteor Crater, these results will help to further our ability to determine the age of planetary bodies by defining the interactions between planetary surface conditions, ejecta patterns, and the breakdown rates of that ejecta.