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Near-Field Enhancement of Microstrip Antenna Using Engineered Superstrate for Biomedical Applications

2022· article· en· W4296910795 on OpenAlexaff
Mohammad Omid Bagheri, George Shaker

Bibliographic record

Venue2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI) · 2022
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceMicrostripMicrostrip antennaPatch antennaAntenna (radio)DielectricNear and far fieldOptoelectronicsElectric fieldImpedance matchingAcousticsOpticsElectronic engineeringElectrical engineeringElectrical impedanceEngineeringPhysics

Abstract

fetched live from OpenAlex

The design of an engineered superstrate to enhance the near-field of the microstrip antenna at 60 GHz is presented for biomedical applications. The proposed superstrate uses 49-unitcells consisting of two metallic layers printed on both sides of a dielectric layer with a material commonly used in 3D printing technology. The total structure is simulated using a full-wave electromagnetic simulator and the results are compared in the case of a rectangular patch antenna with and without using the engineered superstrate. The near-field analysis inside the model of human body skin shows that using the engineered superstrate leads to 4.25-dB enhancement of the near electric field, from 1613 V/m to 2639 V/m, without disturbing input matching.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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