Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing
© 2020 The Authors..
Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time in situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition and integrating it into a data-driven learnable data-processing pipeline. Thereby, a measurement strategy can be learned jointly with a matching data post-processing scheme, optimally tailored to the specific sensing hardware, task, and scene, allowing us to perform high-quality imaging and high-accuracy recognition with a remarkably reduced number of measurements. We report the first experimental demonstration of "learned sensing" applied to microwave imaging and gesture recognition. Our results pave the way for learned EM sensing with low latency and computational burden.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:1 |
|---|---|
| Enthalten in: |
Patterns (New York, N.Y.) - 1(2020), 1 vom: 10. Apr., Seite 100006 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Li, Hao-Yang [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial neural network |
|---|
| Anmerkungen: |
Date Revised 30.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1016/j.patter.2020.100006 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM317723189 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM317723189 | ||
| 003 | DE-627 | ||
| 005 | 20240330233828.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.patter.2020.100006 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1356.xml |
| 035 | |a (DE-627)NLM317723189 | ||
| 035 | |a (NLM)33205083 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Li, Hao-Yang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 30.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2020 The Authors. | ||
| 520 | |a Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time in situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition and integrating it into a data-driven learnable data-processing pipeline. Thereby, a measurement strategy can be learned jointly with a matching data post-processing scheme, optimally tailored to the specific sensing hardware, task, and scene, allowing us to perform high-quality imaging and high-accuracy recognition with a remarkably reduced number of measurements. We report the first experimental demonstration of "learned sensing" applied to microwave imaging and gesture recognition. Our results pave the way for learned EM sensing with low latency and computational burden | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a artificial neural network | |
| 650 | 4 | |a intelligent electromagnetic | |
| 650 | 4 | |a programmable metamaterials | |
| 700 | 1 | |a Zhao, Han-Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Meng-Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Ruan, Heng-Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Shuang, Ya |e verfasserin |4 aut | |
| 700 | 1 | |a Cui, Tie Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Del Hougne, Philipp |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Lianlin |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Patterns (New York, N.Y.) |d 2020 |g 1(2020), 1 vom: 10. Apr., Seite 100006 |w (DE-627)NLM311273858 |x 2666-3899 |7 nnns |
| 773 | 1 | 8 | |g volume:1 |g year:2020 |g number:1 |g day:10 |g month:04 |g pages:100006 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.patter.2020.100006 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 1 |j 2020 |e 1 |b 10 |c 04 |h 100006 | ||