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Record W1924419635 · doi:10.15439/2015f298

Analysis of Inductively Coupled RFID Marker Localization Methods

2015· article· en· W1924419635 on OpenAlexaff
Peter Vestenický, Tomáš Mravec, Martin Vestenický

Bibliographic record

VenueAnnals of Computer Science and Information Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of Regina
FundersKultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR
KeywordsSIGNAL (programming language)Computer scienceExcitationAmplitudeProcess (computing)Identification (biology)TRACE (psycholinguistics)AcousticsElectronic engineeringPhysicsEngineeringOptics

Abstract

fetched live from OpenAlex

The presented paper is focused on analysis of two methods of marker localization. The markers are passive RFID transponders (without or with identification chip) consisting of tuned LC circuit and being used to mark and trace underground networks such as cables and pipes. Localization of the marker is based on evaluation of signal amplitude received from the excited marker, i. e. it is RSSI based localization method. The excitation of marker can be periodically repeated or continuous. In the first case the localization process consists of two stagesexcitation and receiving of marker damped oscillations, in the second case the amplitude of continuously generated excitation signal is decreased by vicinity of the marker. Both localization methods are mathematically analyzed by modeling of their circuits using differential equations. The results of analysis are used to compare both methods and to evaluate their suitability for practical utilization.

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)0.002
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.330
Teacher spread0.281 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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