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Record W2941753200 · doi:10.14419/ijet.v7i2.28.12944

Behavioral Simulation of ISO 18000-6 Type-C Class 1 Gen2 Protocol for RFID UHF Transponder and its Application as Anti-collision Protocol in Interference Case

2018· article· en· W2941753200 on OpenAlexaff
Hadjer Saadi, Ahmed Rnnane, R. Touhami, M.C.E. Yagoub

Bibliographic record

VenueInternational Journal of Engineering & Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTransponder (aeronautics)Ultra high frequencyProtocol (science)Radio-frequency identificationInterference (communication)Near field communicationComputer scienceCollisionClass (philosophy)Embedded systemIdentification (biology)SoftwareCommunications protocolComputer hardwareComputer networkEngineeringTelecommunicationsChannel (broadcasting)Computer securityOperating system

Abstract

fetched live from OpenAlex

In RFID systems, the Transponder Protocol usually uses the standard ISO 18000-6 Type-C Class 1 Generation 2, originally developed to communicate with the reader. Since a typical RFID system could be used in a myriad of tasks from product identification to environmental sensing, behavioral software functionality and hardware cost constraints are extremely constricted, principally due to their ¶standard’s requirements.¶ Thus, in this paper, an advanced behavioral simulation of the Tag ID layer of ISO 18000-6 Type-C protocol is proposed with all its states, commands and functionality, a crucial step toward effective design and test. The approach was then successfully applied to collision issues in interference case.

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)0.001
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.359
Teacher spread0.338 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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