Jun 9, 2026 09:19 PM
https://www.eurekalert.org/news-releases/1130994
INTRO: Although self-driving cars and sophisticated robots use advanced cameras, computer algorithms and artificial intelligence (AI) to picture their surroundings, these artificial eyes struggle to remain reliable in mixed lighting conditions. A team of researchers, co-led by an engineer from Penn State, has proposed a solution that mimics the mechanics of the human eye to adapt from bright to dark light in seconds.
They did this by adjusting how one of the main electrical components used in these optical systems are built, employing a new design that swells or desorbs with water depending on the light levels present. The approach, detailed in a paper published in Nature Communications, illuminates a road to building systems that could potentially process light data faster and more adaptively than humans.
The improved components are known as memory resistors, or “memristors” — tiny electrical devices that can store information or data in a system, even if the original power source fueling the application is removed. These devices mimic the complex way neurons process and store data in the brain. Photomemristors are a type of memristor capable of sensing and collecting light information then translating it into an electrical current, a process that could more effectively power advanced cameras and optical systems.
According to Larry Cheng, James L. Henderson Jr. Memorial Associate Professor of Engineering Science and Mechanics at Penn State and co-corresponding author on the paper, traditional photomemristors are calibrated and optimized for consistent lighting conditions. Although this allows the systems to work well in both bright and dark environments, maintaining recognition accuracy in changing or mixed lighting can be challenging.
“Self-driving cars are exposed to a mixture of light levels in use — imagine the contrast of the dark sky with the bright headlights of other cars when driving at night,” Cheng explained. “It can be difficult for an artificial optical system to distinguish details, like the glow of a red light, in these mixed lightning conditions.”
Inside the human eye, a series of rod and cone cells helps adjust vision to different lighting conditions. Specific pigments in the rod cells allow the eye to distinguish details, even in the dark. In bright light, though, these pigments in the rod cells “bleach” before slowly regenerating, while the cone cells remain to allow the eye to discern contrasting details. The team theorized that this process, mimicked in a photomemristor, could offer monitoring more adaptive and accurate than traditional designs.
To achieve this, the team mainly built their photomemristors out of two different materials... (MORE - no ads)
INTRO: Although self-driving cars and sophisticated robots use advanced cameras, computer algorithms and artificial intelligence (AI) to picture their surroundings, these artificial eyes struggle to remain reliable in mixed lighting conditions. A team of researchers, co-led by an engineer from Penn State, has proposed a solution that mimics the mechanics of the human eye to adapt from bright to dark light in seconds.
They did this by adjusting how one of the main electrical components used in these optical systems are built, employing a new design that swells or desorbs with water depending on the light levels present. The approach, detailed in a paper published in Nature Communications, illuminates a road to building systems that could potentially process light data faster and more adaptively than humans.
The improved components are known as memory resistors, or “memristors” — tiny electrical devices that can store information or data in a system, even if the original power source fueling the application is removed. These devices mimic the complex way neurons process and store data in the brain. Photomemristors are a type of memristor capable of sensing and collecting light information then translating it into an electrical current, a process that could more effectively power advanced cameras and optical systems.
According to Larry Cheng, James L. Henderson Jr. Memorial Associate Professor of Engineering Science and Mechanics at Penn State and co-corresponding author on the paper, traditional photomemristors are calibrated and optimized for consistent lighting conditions. Although this allows the systems to work well in both bright and dark environments, maintaining recognition accuracy in changing or mixed lighting can be challenging.
“Self-driving cars are exposed to a mixture of light levels in use — imagine the contrast of the dark sky with the bright headlights of other cars when driving at night,” Cheng explained. “It can be difficult for an artificial optical system to distinguish details, like the glow of a red light, in these mixed lightning conditions.”
Inside the human eye, a series of rod and cone cells helps adjust vision to different lighting conditions. Specific pigments in the rod cells allow the eye to distinguish details, even in the dark. In bright light, though, these pigments in the rod cells “bleach” before slowly regenerating, while the cone cells remain to allow the eye to discern contrasting details. The team theorized that this process, mimicked in a photomemristor, could offer monitoring more adaptive and accurate than traditional designs.
To achieve this, the team mainly built their photomemristors out of two different materials... (MORE - no ads)