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Record W2405795632 · doi:10.1002/admt.201600032

User‐Interactive and Wireless‐Communicating RF Textiles

2016· article· en· W2405795632 on OpenAlexafffund
Stepan Gorgutsa, Mickaël Blais-Roberge, Jeff Viens, Sophie LaRochelle, Younès Messaddeq

Bibliographic record

VenueAdvanced Materials Technologies · 2016
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsGenia Photonics (Canada)Université Laval
FundersCanada Research Chairs
KeywordsBluetoothWirelessCapacitive sensingRadio frequencyComputer scienceTextileElectrical engineeringMaterials scienceTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This paper presents user‐interactive and wireless‐communicating radio‐frequency (RF) textiles made from glass–metal–polymer composite fibers that interact with the user through touch‐sensing and that relays the touch data through IEEE 802.15.4 and Bluetooth protocols at 2.4 GHz frequency. This work integrates several textile functionalities in fiber‐form including capacitive touch sensing, electrical interconnects, and 2.4 GHz wireless communications using submillimeter glass–metal–polymer composite fibers imbedded into the textile host. Tests conducted with these user‐interactive RF textiles have shown that the emissive performance in terms of radiation pattern, gain, and signal strength adequately address short‐range wireless communications applications in compliance with Bluetooth protocols, and that the user‐interaction is enabled through capacitive finger‐touch sensing with a 1 ms time response. This approach demonstrates that glass‐fiber textiles can be adequately designed for user‐interactive and wireless communications applications addressing 2.4 GHz frequency bands 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 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0030.001

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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