Nelms Institute Contact: Swarup Bhunia

Face masks provide effective, easy-to-use, and low-cost protection against airborne pathogens or infectious agents, including SARS-CoV-2. Existing masks are all passive in nature, i.e., simply act as air filters for the nasal passage and/or mouth. This article presents a new “active mask” paradigm, in which the wearable device is equipped with smart sensors and actuators to both detect the presence of airborne pathogens in real time and take appropriate action to mitigate the threat. The proposed approach is based on a closed-loop control system that senses airborne particles of different sizes near the mask and then makes intelligent decisions to reduce their concentrations. In the current implementation, an on-board controller determines ambient air quality via a commercial particulate matter sensor, and if necessary, activates a piezoelectric actuator that generates a mist spray to load these particles, thus causing them to fall to the ground. The system communicates with the user via a smart phone application that provides various alerts, including the need to recharge and/or decontaminate the mask prior to reuse. The application also enables a user to override the on-board control system and manually control the mist generator if necessary. Experimental results from a functional prototype demonstrate significant reduction in airborne PM counts near the mask when the active protection system is enabled.

Nelms Institute Contact: Sanjeev Koppal

The light-transport matrix is a rich and complex representation of how light from an illumination source interacts with a scene and reaches the camera. Unfortunately, light transport matrices are huge; the light ray set is typically large and, further, the radiance quality along the rays (high dynamic range, color, etc.) implies a big data footprint. In this project, the researchers consider dynamic light-transport matrices for scenes with motion, where the size and throughput requirements are even higher and have inhibited previous work on capture, analysis and applications. They believe that breaking through these imaging challenges is useful because a dataset of dynamic light-transport matrices will allow the team to intricately unwrap complex interactions of light and objects in time, such as motion, occlusion as line-of sight changes, illumination due to lighting variation, secondary light paths such as specular interreflections and indirect/ global illumination of dynamic scenes. The full light-transport at each moment in time provides a complete picture of these dynamic interactions (allowing scene recovery by tracking and 3D scanning), with secondary light paths that offer robustness in the face of complex visual effects (allowing post-capture image-based relighting). The goal of this project is to fundamentally understand and characterize the properties of light transport for dynamic, moving objects.