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Record W2980547240 · doi:10.3390/electronics8101182

Data-Dense and Miniature Chipless Moisture Sensor RFID Tag for Internet of Things

2019· article· en· W2980547240 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElectronics · 2019
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsMcMaster UniversityPolytechnique Montréal
FundersHigher Education Commision, PakistanUniversity of Engineering and Technology, TaxilaUniversity of Engineering and Technology, Lahore
KeywordsChipless RFIDInternet of ThingsMoistureComputer scienceEmbedded systemRadio-frequency identificationComputer securityMaterials scienceComposite material

Abstract

fetched live from OpenAlex

A novel and miniaturized semi-elliptical 20-bit fully passive chipless RFID sensor tag is proposed in this article. The realized sensor tag is made up of semi-elliptical shaped open-end slots within the compact size of 25 mm × 17 mm. The multi-substrate analysis of the proposed tag is examined using non-flexible and flexible materials. The articulated tag configuration is capable of monitoring moisture levels when the largest resonator is covered by a heat-resistant sheet of Kapton HN (DuPontTM). The proposed tag functions in the operational frequency band of 4.1 GHz–16 GHz and possesses the overall bit density of 4.70 bit/cm2. The structure is composed of a thin passive substrate layer topped with an active layer of conductive path and is considered as a potential candidate for low-cost identification of the tagged objects. In addition to that, its moisture sensing property and flexible nature make it a reliable smart sensor for conformal 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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