RIS-based Over-the-air Diffractional Channel Coding

Reconfigurable Intelligent Surfaces (RIS) are programmable metasurfaces utilizing sub-wavelength meta-atoms and a controller for precise electromagnetic wave manipulation. This work introduces an innovative channel coding scheme, termed RIS-based diffractional channel coding (DCC), which capitalizes on diffraction between two RIS layers for signal-level encoding. Contrary to traditional methods, DCC expands signal dimensions through diffraction, presenting a novel countermeasure to channel effects. This paper focuses on the operational principles of DCC, including encoder and decoder designs, and explores its possibilities to construct block and trellis codes, demonstrating its potential as both an alternative and a supplementary conventional coding scheme. Key advantages of DCC include eliminating extra power requirements for encoding, achieving computation at the speed of light, and enabling adjustable code distance, making it a progressive solution for efficient wireless communication, particularly in systems with large-scale data or massive MIMO.

EgoPAT3Dv2: Predicting 3D Action Target from 2D Egocentric Vision for Human-Robot Interaction

A robot's ability to anticipate the 3D action target location of a hand's movement from egocentric videos can greatly improve safety and efficiency in human-robot interaction (HRI). While previous research predominantly focused on semantic action classification or 2D target region prediction, we argue that predicting the action target's 3D coordinate could pave the way for more versatile downstream robotics tasks, especially given the increasing prevalence of headset devices. This study expands EgoPAT3D, the sole dataset dedicated to egocentric 3D action target prediction. We augment both its size and diversity, enhancing its potential for generalization. Moreover, we substantially enhance the baseline algorithm by introducing a large pre-trained model and human prior knowledge. Remarkably, our novel algorithm can now achieve superior prediction outcomes using solely RGB images, eliminating the previous need for 3D point clouds and IMU input. Furthermore, we deploy our enhanced baseline algorithm on a real-world robotic platform to illustrate its practical utility in straightforward HRI tasks. The demonstrations showcase the real-world applicability of our advancements and may inspire more HRI use cases involving egocentric vision. All code and data are open-sourced and can be found on the project website.