2025 ASEE Annual Conference & Exposition

This paper presents a multidisciplinary research project to develop an optical sensing system based on hybrid Ultraviolet-Visible (UV-VIS) spectrum image fusion algorithms that can contribute to the technical advances for the automated Ultraviolet Disinfection (UVD) mobile systems. The objectives of the proposed research were twofold: 1) To establish a collaborative project between the Departments of Engineering and Biology to provide training and mentoring opportunities for a diverse group of undergraduate research assistants; and 2) To develop a novel adaptive real-time optical sensing algorithms in near-Ultraviolet (UV) spectrum by combining reflected-UV and UV fluorescence techniques to transform our ability to detect biological surface contaminants, such as saliva, that could potentially contain infectious pathogens. The reflected-UV and UV fluorescence imaging methods are used in various scientific, industrial, and medical optical sensing systems, such as in germicidal irradiation (disinfecting), digital forensics, food/agricultural industries, remote sensing, space science (NASA Perseverance), etc. The recent use of UV light surface disinfection mobile robot platforms and devices has shown promising results in the reduction of harmful microorganisms. These UVD systems have been marketed to reduce the transmission of coronavirus and other pathogens that can live for extended periods on the surfaces of objects and that can subsequently lead to infection. However, for complex environments, e.g. a hospital or office with dozens of rooms and various layouts, questions arise during the operation of UV surface disinfection mobile semi-autonomous or autonomous systems, such as can the robot unknowingly miss certain areas or not expose the surface to UV light long enough leading to incomplete sterilization and the possibility of unexpected contagion? The proposed system was developed and implemented by using a FujiFilm X-T1 mirrorless camera as the optical sensor for UV-VIS image data acquisition, a Raspberry Pi Model 3B+ for dual spectrum image fusion, analysis, presentation, and edge-cloud computing algorithms to provide rapid delivery of output data. This project provided the undergraduate Engineering and Biology students an opportunity to apply their existing technical knowledge, improve their time management, communication skills, and work as a team on a real-world problem.