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Record W2621174687 · doi:10.23919/eucap.2017.7928102

A circuit-driven design methodology for a linear-to-circular polarizer

2017· article· en· W2621174687 on OpenAlexaff
Mehdi Hosseini, Sean V. Hum

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolarizerPlanarPolarization (electrochemistry)Topology (electrical circuits)Circular polarizationPhysicsOpticsComputer scienceMathematicsCombinatoricsComputer graphics (images)

Abstract

fetched live from OpenAlex

The paper presents a fast design methodology to achieve a circular polarizer with a relatively low thickness of one-fifth of a free-space wavelength. A three-layer planar structure is devised to exhibit transparency to both TE and TM polarized fields while imparting a quadrature phase difference between them, as required for the generation of circularly-polarized fields. The structure is designed using a circuit-driven approach whereby an independent equivalent circuit describes the behavior of each constituent polarization. Meanwhile, the small unit cell size endows the structure with a low sensitivity to the angle of incidence of the impinging waves. This polarizer with a unit cell volume of 0.13λ0×θ.13λ0×0.21λ0renders axial ratios of less than 3dB and 1dB over 12.5% and 4.4% fractional bandwidths, respectively. Numerical simulations verify the accuracy of the circuit-driven approach and its applicability as a design tool in the synthesis of this class of polarizer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.215
GPT teacher head0.349
Teacher spread0.134 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2017
Admission routes1
Has abstractyes

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