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A New Design of a Spiral UHF RFID Tag Dipole Antenna Mounted on Metallic Objects

2023· article· en· W4391895362 on OpenAlexaff
Errachidi Zakaria, Jamal Zbitou, Mohamed Latrach, Ahmed Lakhssassi, Oukaira Aziz, Noha Chahboun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsUltra high frequencyDipole antennaSpiral (railway)Spiral antennaAntenna (radio)DipoleElectrical engineeringMonopole antennaComputer scienceAcousticsCoaxial antennaPhysicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This paper deals with the design of a new structure of an Ultra High Frequency (UHF) dipole antenna for RFID TAG. To design such antenna, we have to optimize such structure by taking into account the input impedance matching by reaching an input impedance of the antenna which is equal to the conjugate one of the microchips, the implementation of this type of antenna on an object with a metallic surface, influences the efficiency of these parameters. Knowing that the manufacturing cost of an antenna is one of the most important factors that must be taken into consideration during the design and realization phase. To achieve this goal, this paper deals with the design of a new and a simple Ultra High Frequency RFID tag antenna formed from a folded spiral dipole printed on a FR4 substrate having a dielectric constant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon \mathrm{r}=4.4$</tex> , and loss tangent <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tan\delta=0.02$</tex> , the antenna structure is matched to an “ALIEN HIGGS 4” UHF microchip in strap packaging, it can be mounted on a metallic surface, and it should be noted that the structure does not include metal vias or short-circuiting stubs.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.894

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)0.0000.001

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 head0.022
GPT teacher head0.251
Teacher spread0.229 · 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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