Intelligent Metasurface Sensors (Elements in Emerging Theories and Technologies in Metamaterials)

✍ Scribed by Lianlin Li, Hanting Zhao, Tie Jun Cui

• Publisher: Cambridge University Press
• Year: 2024
• Tongue: English
• Leaves: 136
• Category: Library

✦ Synopsis

Intelligent electromagnetic (EM) sensing is a powerful contactless examination tool in science, engineering and military, enabling us to 'see' and 'understand' visually invisible targets. Using intelligence, the sensor can organize by itself the task-oriented sensing pipeline (data acquisition plus processing) without human intervention. Intelligent metasurface sensors, synergizing ultrathin artificial materials (AMs) for flexible wave manipulation and artificial intelligences (AIs) for powerful data manipulation, emerge in response to the proper time and conditions, and have attracted growing interest over the past years. The authors expect that the results in this Element could be utilized to achieve the goal that conventional sensors cannot achieve, and that the developed strategies can be extended over the entire EM spectra and beyond, which will produce important impacts on the society of the robot-human alliance.

✦ Table of Contents

Cover
Title page
Copyright page
Intelligent Metasurface Sensors
Contents
1 Introduction
2 Reprogrammable Coding Metasurfaces
2.1 Historical Background and Fundamental Principles
2.2 Wireless Energy Reallocation of Stray Wi-Fi Signals
2.2.1 Design of a One-Bit Reprogrammable Coding Metasurface
2.2.2 Modified Gerchberg–Saxton (GS) Algorithm
2.2.3 Results and Discussions
2.3 Dynamic Metasurface Holograms
2.3.1 Design of Dynamic Metasurface Hologram
2.3.2 Results
2.4 Reprogrammable OAM-Beam Generator
2.4.1 Design of Two-Bit Reprogrammable Coding Metasurface
2.4.2 Results
3 Information Fundamentals of Reprogrammable Metasurfaces
3.1 Signal Representation of One-Bit Coding Metasurfaces
Case I: SISO
Case II: SIMO
Case III: MIMO
3.2 Information Capacities of One-Bit Coding Metasurfaces
Appendix 1 Derivation of Eq. (3.2)
Appendix 2 Derivation of Eq. (3.10)
Appendix 3 Derivations of Eqs. (3.17) and (3.18)
4 Compressive Metasurface Imager
4.1 Introduction to Compressive Imagers
4.2 Single-Frequency Multiple-Shot Compressive
Metasurface Imager
4.2.1 System Configuration
4.2.2 Imaging Model and Theoretical Guarantee
Proof of Theorem 2
4.2.3 Results
4.3 Wideband Single-Shot Compressive Imager
4.3.1 System Configuration and EM Theory
4.3.2 Results
5 Machine„-Learning„ Metasurface Imager
5.1 Sensing Principle
5.2 System Design
5.3 Some Details about Algorithms
5.3.1 EM Modeling of Meta-atom and Metasurface
5.3.2 Training Methods
5.4 Results
5.4.1 Machine-Learning Guided Imaging
5.4.2 Object Recognition in Compressed Domain
6 Intelligent EM Metasurface Camera
6.1 System Configuration
6.2 Signal Model
6.2.1 Signal Model for Active Sensing Mode
6.2.2 Signal Model for Passive Sensing Mode
6.3 Performance Evaluation
6.3.1 In Situ High-Resolution Imaging
6.3.2 Instant Recognition of Hand Signs and Vital Signs
6.3.3 Imaging and Recognition with Passive Modes
7 Intelligent Integrated Metasurface Sensors
7.1 VAE-Based Integrated Sensing
7.1.1 System Design
7.1.2 VAE Principle
7.1.3 In Situ Imaging of the Human Body
7.2 Free-Energy-Based Intelligent Integrated Sensing
7.2.1 System Design
7.2.2 Principle of Free-Energy Minimization
7.2.3 Through-the-Wall Sensing Results
References
Acknowledgments