Lianlin Li

TL;DR: The proposed reprogrammable hologram may be a key in enabling future intelligent devices with reconfigurable and programmable functionalities that may lead to advances in a variety of applications such as microscopy, display, security, data storage, and information processing.

TL;DR: A real-time digital-metasurface imager that can be trained in-situ to generate the radiation patterns required by machine-learning optimized measurement modes, and is electronically reprogrammed in real time to access the optimized solution for an entire data set.

TL;DR: A smart metasurface that has self-adaptively reprogrammable functionalities without human participation is put forth, capable of sensing ambient environments by integrating an additional sensor(s) and can adaptively adjust its EM operational functionality through an unmanned sensing feedback system.

TL;DR: In this article, the authors exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering, and propose DeepNIS, which consists of a cascade of multilayer complex-valued residual convolutional neural network modules.

TL;DR: This work establishes an analytical connection between the coding pattern of an arbitrary coding metasurface and its far-field pattern, and introduces geometrical entropy and physical entropy to describe the information of the far- field pattern of the metAsurface.