Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data.

ABSTRACT: Sparse sensor placement is a central challenge in the efficient characterization of complex systems when the cost of acquiring and processing data is high. Leading sparse sensing methods typically exploit either spatial or temporal correlations, but rarely both. This work introduces a sparse sensor optimization that is designed to leverage the rich spatiotemporal coherence exhibited by many systems. Our approach is inspired by the remarkable performance of flying insects, which use a few embedded strain-sensitive neurons to achieve rapid and robust flight control despite large gust disturbances. Specifically, we identify neural-inspired sensors at a few key locations on a flapping wing that are able to detect body rotation. This task is particularly challenging as the rotational twisting m

SUBMITTER: Mohren TL 

PROVIDER: S-EPMC6196534 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature