Interspecies-chimera machine vision with polarimetry for real-time navigation and anti-glare pattern recognition

Tao Guo, Shasha Li, Y. Norman Zhou, Wei D. Lu, Yong Yan () and Yimin A. Wu ()
Additional contact information
Tao Guo: Henan Normal University
Shasha Li: Henan Normal University
Y. Norman Zhou: University of Waterloo
Wei D. Lu: the University of Michigan
Yong Yan: Henan Normal University
Yimin A. Wu: University of Waterloo

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Cutting-edge humanoid machine vision merely mimics human systems and lacks polarimetric functionalities that convey the information of navigation and authentic images. Interspecies-chimera vision reserving multiple hosts’ capacities will lead to advanced machine vision. However, implementing the visual functions of multiple species (human and non-human) in one optoelectronic device is still elusive. Here, we develop an optically-controlled polarimetry memtransistor based on a van der Waals heterostructure (ReS2/GeSe2). The device provides polarization sensitivity, nonvolatility, and positive/negative photoconductance simultaneously. The polarimetric measurement can identify celestial polarizations for real-time navigation like a honeybee. Meanwhile, cognitive tasks can be completed like a human by sensing, memory, and synaptic functions. Particularly, the anti-glare recognition with polarimetry saves an order of magnitude energy compared to the traditional humanoid counterpart. This technique promotes the concept of interspecies-chimera visual systems that will leverage the advances of autonomous vehicles, medical diagnoses, intelligent robotics, etc.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-51178-z Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51178-z

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-51178-z

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().