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A Compact CP Wearable Antenna backed by AMC Array for WBAN/WLAN Applications

2022· article· en· W4296911408 on OpenAlexaff
Youcef Braham Chaouche, Mourad Nedil

Bibliographic record

Venue2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI) · 2022
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsBody area networkWidebandElectrical engineeringMicrostripComputer scienceBandwidth (computing)Specific absorption rateWirelessMicrostrip antennaPhysicsAntenna (radio)TelecommunicationsElectronic engineeringTopology (electrical circuits)Engineering

Abstract

fetched live from OpenAlex

In this paper, a simple and compact wide-slot circularly polarized antenna backed by a 2×2 artificial magnetic conductor (AMC) array is presented for 5.8 GHz wireless body area network (WBAN) applications. An inverted T-shaped parasitic strip at the open end of a microstrip line extension and slot modification was applied to attain wideband CP. The total footprint of the proposed antenna is only 30.9 × 30.9 mm2board of semi-flexible Rogers RT-Duroid 5880 substrate. Full-wave EM simulation results have been presented in numerous studies, both in free space and in proximity to the human body. The integrated design provided an impedance bandwidth |S11| ≤ -10 dB of ~9.41-percent (5.57-6.12 GHz) and an AR bandwidth ≤ 3 dB of 9.8-percent (5.24-5.78 GHz). Furthermore, over a gap of 3 mm from the human body, the final design achieved peak gain and total efficiency enhancements of 8.5 dBi and 91.4%, respectively. A very low specific absorption rate, compact size, and high gain make the proposed design a good candidate for most off-body wearable 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.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 integrity0.0010.000
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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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