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Record W4293868610 · doi:10.1109/ims37962.2022.9865516

Experimental Demonstration of Nonlinear Metasurfaces for High-Performance Low-Cost Near-Field Base Station

2022· article· en· W4293868610 on OpenAlexaff
Jorge Virgilio de Almeida, Xiaoqiang Gu, Marbey M. Mosso, Carlos Sartori, Ke Wu

Bibliographic record

Venue2022 IEEE/MTT-S International Microwave Symposium - IMS 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMetamaterialBandwidth (computing)Electric power transmissionBase stationLens (geology)Nonlinear systemWireless power transferElectronic engineeringTransmission (telecommunications)OpticsElectrical engineeringComputer sciencePower transmissionPower (physics)Topology (electrical circuits)WirelessTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

2D metamaterials (MTMs) or metasurfaces based on periodic arrays of high-Q coils have been long used as artificial magnetic lenses to boost the power transfer efficiency of inductive power transmission systems. In conventional linear topologies, this focalization effect is physically limited to extremely narrow bands due to the high Q of the unit cells. This paper, for the first time, experimentally demonstrates that nonlinear MTM lenses can produce a stable gain over a much wider bandwidth than any linear MTM lens. Such observation is validated with both continuous-wave and modulated excitations. Furthermore, the measurements also show that the group delay inside the gain region is almost linear, indicating that the proposed MTM lens presents very low in-band phase distortion. Such nonlinear MTM lenses are believed to find great potential in developing high- performance low-cost base station for near-field simultaneous wireless information and power transmission for many low-to- medium power applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.228
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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