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Record W2097685897 · doi:10.1109/aps.2009.5171640

Carbon-fiber composite T-match folded bow-tie antenna for RFID applications

2009· article· en· W2097685897 on OpenAlexaff
A. Mehdipour, C.W. Trueman, A. Sebak, Suong V. Hoa

Bibliographic record

VenueDigest - IEEE Antennas and Propagation Society. International Symposium · 2009
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceComposite materialAntenna (radio)EpoxyComposite numberElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, the authors investigate the use of carbon-fiber composite material for the radiating element for an radiofrequency identification antenna, the T-match folded bow-tie antenna. We consider two kinds of composites that have relatively high electrical conductivity for the radiating element: (1) a braided tissue of carbon fibers embedded in epoxy resin, and (2) reinforced long carbon-fibers in an epoxy matrix. All simulations are performed using CST MWS software. Since braided-tissue of carbon-fiber composite shows isotropic behavior, the composite antenna has almost the same resonant frequency of 950 MHz as the metal antenna. However, the radiation efficiency degrades due to the low conductivity of composites compared to metals. By using anisotropic reinforced carbon-fiber composite as a material for the radiating element, the behavior of the antenna changes and the resonant frequency increases to 2.45 GHz. The anisotropic conductivity allows the antenna designer to largely restrict current flow to one direction, and this may lead to novel antenna designs.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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

Citations37
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueDigest - IEEE Antennas and Propagation Society. International SymposiumSame topicRFID technology advancementsFrench-language works237,207