English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep learning, as a highly efficient method for metasurface inverse design,commonly use simulation data to train deep neural networks (DNNs) that can mapdesired functionalities to proper metasurface designs. However, the assumptionsand simplifications made in the simulation model may not reflect the actualbehavior of a complex system, leading to suboptimal performance of the DNNs inpractical scenarios. To address this issue, we propose an experiment-based deeplearning approach for metasurface inverse design and demonstrate itseffectiveness for power allocation in complex environments with obstacles.Enabled by the tunability of a programmable metasurface, large sets ofexperimental data in various configurations can be collected for DNN training.The DNN trained by experimental data can inherently incorporate complex factorsand can adapt to changed environments through its on-site data-collecting andfast-retraining capability. The proposed experiment-based DNN holds thepotential for intelligent and energy-efficient wireless communication incomplex indoor environments.

Experiment-based deep learning approach for power allocation with a programmable metasurface