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Circularly Polarized Square Loop Shaped Passive RFID Transponder for Thin UHF Applications in Random Mobility Use

2023· article· en· W4388855405 on OpenAlexaff
Boualem Mekimah, Tarek Djerafi, Abderraouf Messai, Abdelkrim Belhedri, Mohammed Boulesbaa, Amira Hamidi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsUltra high frequencyAxial ratioOpticsCircular polarizationImpedance matchingTransponder (aeronautics)PhysicsElectrical impedanceSlot antennaLoop antennaBandwidth (computing)RangingRadiation patternAntenna (radio)OptoelectronicsElectrical engineeringMicrostripCoaxial antennaTelecommunicationsComputer scienceEngineering

Abstract

fetched live from OpenAlex

A Circularly polarized square-loop shaped passive RFID tag antenna is proposed, studied and designed in this paper. The design consists of a metallic square-loop, fed by two strips on the basis of a T-match structure in order to ensure matching in impedance between the chip and the antenna. The loop is excited via two points to have a 90° phase difference ensuring, therefore, circularly polarized radiation. The designed antenna shows an impedance bandwidth (IBW) ranging from 893 to 944 MHz (51 MHz) within an axial ratio bandwidth (ARBW) starts from 892 up to 947 MHz (55 MHz) in bidirectional circularly polarized broadside radiation. The proposed structure shows a high gain of 2.82 dBic with an extremely low axial ratio of 0.15 dB at 915 MHz in a reading range up to 12.83 meters. In addition, the antenna shows a high flexibility and repeatability in the band occupying a very compact area at a height of 0.00005λ. With the performance achieved, the proposed transponder may be considered as a good choice for thin labels in random mobility.

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)0.000
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: none
Teacher disagreement score0.853
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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