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Record W2953697808 · doi:10.1049/iet-map.2019.0261

Design and packaging of a compact circularly polarised planar antenna for 2.45‐GHz RFID mobile readers

2019· article· en· W2953697808 on OpenAlexaff
A. K. Gautam, Mohd Farhan, Niraj Agrawal, Karumudi Rambabu

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPlanarAntenna (radio)OptoelectronicsElectrical engineeringTelecommunicationsPhysicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

In this study, a novel design and packaging of miniaturised planar microstrip antenna with circular polarisation (CP) that is suitable for 2.45‐GHz radio‐frequency identification (RFID) applications is proposed. The circular polarisation is realised by truncating the corners of the left to right diagonal and 76% of miniaturisation is achieved by incorporating two slots in each of the four directions and one at the centre of the square‐shaped radiator. This type of miniaturisation method is known in the literature but combining with CP radiation is a novel approach. The radiator of the designed antenna has a compact size of 19.6 × 19.6 mm 2 with an overall size of the antenna 30 × 30 × 1.6 mm 3 , and this technique offers 76% miniaturisation in antenna size. Finally, a prototype antenna is fabricated and experimentally characterised to verify the design concept as well as to validate the simulation results. The designed antenna achieves measured return loss bandwidth of around 80 MHz (2.42– 2.5 GHz) and axial ratio bandwidth of 21 MHz (2.447–2.468 GHz). The proposed antenna is a good candidate for RFID mobile reader applications as it shows a good radiation performance, when it is installed in various types of housing.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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