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Record W1949265717 · doi:10.1109/bsn.2015.7299384

Emissive performance of wearable RF textiles made from multi-material fibers

2015· article· en· W1949265717 on OpenAlexaff
Stepan Gorgutsa, Mazen Khalil, Victor Bélanger-Garnier, Jeff Viens, Younès Messaddeq, Benoit Gosselin, Sophie LaRochelle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWeavingRadio frequencyMaterials scienceTextileFiberISM bandWearable computerBeamwidthClothingRadio spectrumComputer scienceOptoelectronicsTelecommunicationsWirelessComposite materialEmbedded systemAntenna (radio)

Abstract

fetched live from OpenAlex

In this work, we present the emissive performance of wearable radio-frequency (RF) textiles made from multi-material fibers, for both on-body and off-body scenarios, for body area network applications through ISM (2.4 GHz) bands. It is shown that the emissive performance of the RF textiles in terms of return loss (S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> ), radiation pattern, and efficiency (gain) were similar to commercial router antennas, while the center frequency shift and band broadening were reduced due in part to the small form factor of the fiber antennas. The RF textiles were fabricated by integrating unobtrusive polymer-glass-metal fiber composites into a textile host using conventional weaving process. This approach provided good RF emissive performance in compliance with safety regulations while preserving the mechanical and cosmetic properties of the garments.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.483

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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 teacher head, 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

Citations0
Published2015
Admission routes1
Has abstractyes

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