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Record W2729693777 · doi:10.3390/s17071576

Multiport Circular Polarized RFID-Tag Antenna for UHF Sensor Applications

2017· article· en· W2729693777 on OpenAlexaff
Jamal Zaid, Abdulhadi Abdulhadi, Arun Kesavan, Yassin Belaizi, Tayeb A. Denidni

Bibliographic record

VenueSensors · 2017
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPatch antennaUltra high frequencyMicrostrip antennaCircular polarizationAntenna (radio)Electrical engineeringResistive touchscreenLoop antennaPhysicsBandwidth (computing)MicrostripComputer scienceAntenna factorTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A circular polarized patch antenna for UHF RFID tag-based sensor applications is presented, with the circular polarization (CP) generated by a new antenna shape, an asymmetric stars shaped slotted microstrip patch antenna (CP-ASSSMP). Four stars etched on the patch allow the antenna's size to be reduced by close to 20%. The proposed antenna is matched with two RFID chips via inductive-loop matching. The first chip is connected to a resistive sensor and acts as a sensor node, and the second is used as a reference node. The proposed antenna is used for two targets, serving as both reference and sensor simultaneously, thereby eliminating the need for a second antenna. Its reader can read the RFID chips at any orientation of the tag due to the CP. The measured reading range is about 25 m with mismatch polarization. The operating frequency band is 902-929 MHz for the two ports, which is covered by the US RFID band, and the axial-ratio bandwidth is about 7 MHz. In addition, the reader can also detect temperature, based on the minimum difference in the power required by the reference and sensor.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.015
GPT teacher head0.264
Teacher spread0.248 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

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