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Record W2546569350 · doi:10.1109/lmwc.2016.2623243

Low-Cost Inkjet Printed Passive Booster for Increasing the Magnetic Coupling in Proximity of Metal Object for NFC Systems

2016· article· en· W2546569350 on OpenAlexafffund
Hossein Saghlatoon, Mohammad Mahdi Honari, Pedram Mousavi

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsBooster (rocketry)Coupling (piping)OptoelectronicsObject (grammar)Inductive couplingMaterials scienceElectrical engineeringComputer scienceElectronic engineeringEngineeringAerospace engineeringMechanical engineering

Abstract

fetched live from OpenAlex

A passive fully inkjet-printed circuit on a plastic based flexible substrate for increasing the communication read range of an NFC based system is proposed. Two parallel coils resonating at 13.56 MHz are used for the improvement of coupling between the transmitter and receiver. The proposed system behaves like a lens by concentrating the absorbed magnetic energy from a wide area in a small focal region. Being a passive circuit, fully inkjet-printed and having only one discrete component made this circuit a very low-cost and tempting solution for improving the magnetic link between transmitter and receiver. Another advantage of this circuit is its operability in proximity of metal asset. Functionality of ordinary systems is severely affected by close metallic objects; hence, having a system that can operate in both conditions is favorable. The measured boosted power after the exploitation of the booster in proximity of the metal asset at 13.56 MHz is 15 dB.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 science0.0010.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.219
Teacher spread0.200 · 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 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

Citations16
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Microwave and Wireless Components LettersSame topicModular Robots and Swarm IntelligenceFrench-language works237,207