MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces

Abstract

Semantic segmentation is a process of partitioning an image into multiple segments for recognizing humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring.To avoid privacy violation, using RF signals instead of an image for human and object recognition has gained increasing attention. However, human and object recognition by using RF signals is usually a passive signal collection and analysis process without changing the radio environment, and the recognition accuracy is restricted significantly by unwanted multi-path fading, and/or the limited number of independent channels between RF transceivers in uncontrollable radio environments. This paper introduces MetaSketch, a novel RF-sensing system that performs semantic recognition and segmentation for humans and objects by making the radio environment reconfigurable. A metamaterial surface is incorporated into MetaSketch and diversifies the information carried by RF signals. Using compressive sensing techniques, MetaSketch reconstructs a point cloud consisting of the reflection coefficients of humans and objects at different spatial points, and recognizes the semantic meaning of the points by using symmetric multilayer perceptron groups. Our evaluation results show that MetaSketch is capable of generating favorable radio environments and extracting exact point clouds, and labeling the semantic meaning of the points with an average error rate of less than 1% in an indoor space.

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

“…Noninva-sive patient sensing is another case in healthcare [116,117]. The RF signals could be utilized as an illuminator of opportunity, combining an ambient signal from a BS with echoes from test subjects to collect information on their movements [118]. Using ambient signals, it can obtain high-resolution full-scene images and accurately detect human body postures and vital signs [116,119].…”

Intelligent meta-imagers: From compressed to learned sensing

“…Similarly, experiments with programmable metasurface reflect-arrays recently optimized the coding pattern sequence to improve the measurement diversity. 82,83 All these works minimize mutual coherence; [84][85][86] that is, they try to flatten the singular value spectrum of the sensing matrix. Usually, they do not achieve perfectly flat singular value spectra, such that some degree of redundancy remains in subsequent measurements.…”

“…More recently, various machine learning and deep learning algorithms have been applied in computational imaging [126][127][128][129][130] and inverse scattering, 131,132 including some cases of compressive metaimagers. 58,82,83,97,[133][134][135] Their potential benefits include fast online inference on tasks that are difficult or impossible to formulate analytically, at the cost of expensive offline training accompanied by a need for a large training dataset. With reference to the sensing pipeline from Fig.…”