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Record W3202441023 · doi:10.1016/j.procs.2021.09.022

UHF RFID Spiral-Loaded Dipole Tag Antenna Conception for Healthcare Applications

2021· article· en· W3202441023 on OpenAlexaff
Ibtissame Bouhassoune, Hasna Chaibi, Abdellah Chehri, Rachid Saadane

Bibliographic record

VenueProcedia Computer Science · 2021
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversité du Québec à Chicoutimi
FundersUniversité Mohammed V de Rabat
KeywordsComputer scienceRobustness (evolution)STRIPSPlanarUltra high frequencyRadio-frequency identificationDipole antennaAntenna (radio)AcousticsTelecommunicationsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper reports the characterization procedure of a radiofrequency identification tag powered by meandered L-matching configuration and placed directly on the planar layered anatomical model of a human arm. The tag antenna component and its matching system interface to the RFID chip are designed with the help of electromagnetic simulators. A new optimal tag structure is combining multiconductor strips and a meandering schema used to achieve the required inductance. The folded configuration of the proposed tag adds stretchability and more reduction of the antenna size, especially when attached to the non-uniform as the human body. It is demonstrated that the tag can communicate with a reader. The simulated performances indicate the robustness of the proposed tag structure and its ability to be deployed in several healthcare sensing applications.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 integrity0.0010.000
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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
GenreMethods

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

Citations6
Published2021
Admission routes1
Has abstractyes

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