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Near-Field Sensing Improvement of a Radar AntennaOn-Chip Using a Transmissive Superstrate

2023· article· en· W4408717432 on OpenAlexafffund
Mohammad Omid Bagheri, Omar M. Ramahi, George Shaker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaInfineon TechnologiesAnsysHennepin County Medical Center
KeywordsRadarChipPerformance improvementField (mathematics)Materials scienceComputer scienceElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The design of a planar transmissive superstrate for near-field sensing improvement of the compact short-range radar antenna is presented. The superstrate structure is designed to be placed at a half wavelength above the radar transmitter/receiver antennas while contacting human skin without airgap. The purpose is to increase the power absorbed from the transmitter antenna into the human skin while simultaneously improving the radar SNR. The proposed superstrate with dimensions of 1.54 λ × 1.54 λ × 0.16 λ consists of two metallic layers of a phase-corrected microstrip crossed-dipole array printed on both sides of a substrate. Using full-wave electromagnetic simulations, the nearfield analysis inside the human skin model illustrates that using the planar superstrate enhances the near-field Poynting power into a human skin model from 19,825 W/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 41,585 W/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> which is a 2.1 times improvement, and the transmit-receive coupling analysis demonstrates an improvement of 5.2 dB of the radar received power.

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 categoriesnone
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 score0.161
Threshold uncertainty score0.472

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes2
Has abstractyes

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