# Intelligent Metasurface Imager and Recognizer

arXiv: https://arxiv.org/abs/1910.00497 · HTML: https://mlsanity.com/paper/1910.00497/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction from Millimeter-Wave Radar](https://mlsanity.com/paper/2408.01672/) (2024)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [Noise-Adaptive Intelligent Programmable Meta-Imager](https://mlsanity.com/paper/2208.10171/) (2022)
- [Deep-Learning-Empowered Inverse Design for Freeform Reconfigurable Metasurfaces](https://mlsanity.com/paper/2211.08296/) (2022)
- [Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision](https://mlsanity.com/paper/2306.07365/) (2023)

## Abstract (arXiv)

It is ever-increasingly demanded to remotely monitor people in daily life using radio-frequency probing signals. However, conventional systems can hardly be deployed in real-world settings since they typically require objects to either deliberately cooperate or carry a wireless active device or identification tag. To accomplish the complicated successive tasks using a single device in real time, we propose a smart metasurface imager and recognizer simultaneously, empowered by a network of artificial neural networks (ANNs) for adaptively controlling data flow. Here, three ANNs are employed in an integrated hierarchy: transforming measured microwave data into images of whole human body; classifying the specifically designated spots (hand and chest) within the whole image; and recognizing human hand signs instantly at Wi-Fi frequency of 2.4 GHz. Instantaneous in-situ imaging of full scene and adaptive recognition of hand signs and vital signs of multiple non-cooperative people have been experimentally demonstrated. We also show that the proposed intelligent metasurface system work well even when it is passively excited by stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported strategy could open a new avenue for future smart cities, smart homes, human-device interactive interfaces, healthy monitoring, and safety screening free of visual privacy issues.