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Record W2129406872 · doi:10.1109/uksim.2009.104

Simulation of a Radio Frequency Identification System

2009· article· en· W2129406872 on OpenAlexaff
Sara Abou Chakra, Usamah O. Farrukh, Beatriz Amante García

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsBurman University
Fundersnot available
KeywordsUltra high frequencyRadio-frequency identificationComputer sciencePath lossIdentification (biology)Code (set theory)Channel (broadcasting)WirelessRadio frequencySIGNAL (programming language)Software deploymentSimple (philosophy)System deploymentFunction (biology)Real-time computingElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

As supply chains become more complex, RFID deployment is needed for efficient item identification and tracking. A system simulation of RFID is constructed in this paper. RFID systems consist of tags and readers. Tags are components that store the information and are physically attached to each item. The tag transmits information back to the reader by modulating an RF signal in the UHF frequency range. The reader recovers data to identify the tag. In this paper, the system is simulated by electrical models.The tag is represented by a simple model. A wireless channel with path loss and variable environmental factors establishes the reader-tag link. The reader has a mono-static architecture. Performance of the whole system can be studied as function of the operation distance. System modeling can be optimized by modifying the parameters of the building blocks. Finally, simulation results showing code recovery are also included in this paper.

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)0.001
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 score0.013
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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