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Record W2929449122 · doi:10.3390/electronics8040400

A Circularly Polarized Antenna Array with Gain Enhancement for Long-Range UHF RFID Systems

2019· article· en· W2929449122 on OpenAlexaff
Wei Hu, Guangjun Wen, Daniele Inserra, Yongjun Huang, Jian Li, Zhizhang Chen

Bibliographic record

VenueElectronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsDalhousie University
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsUltra high frequencyAxial ratioAntenna gainMicrostrip antennaArray gainMicrostripAntenna arrayAntenna (radio)BroadsideRadio-frequency identificationRadiation patternBandwidth (computing)Electrical engineeringOpticsAntenna efficiencyEngineeringPhysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

A 2 × 2 circularly polarized (CP) sequential rotation microstrip patch antenna array with high gain for long-range ultra-high frequency (UHF) radio frequency identification (RFID) communication is proposed in this paper. In order to meet the operational frequency band requirement of 840–960 MHz and, at the same time, achieve enhanced broadside gain, a two-level sequential rotation structure is developed. Series power divider is used as the basic element of the feed network that is implemented with the substrate-integrated coaxial line technology for minimizing radiation losses. The manufactured prototype exhibits a peak gain of 12.5 dBic at 900 MHz and an axial ratio (AR) bandwidth (AR ≤ 3 dB) of 18.2% from 828 to 994 MHz. In comparison with the state-of-the-art, the proposed antenna shows an excellent gain/size trade-off.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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 designSimulation or modeling
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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