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Record W3020680672 · doi:10.1038/s41598-020-63780-4

Ultra-Miniature Circularly Polarized CPW-Fed Implantable Antenna Design and its Validation for Biotelemetry Applications

2020· article· en· W3020680672 on OpenAlexaff
Vikrant Kaim, Binod Kumar Kanaujia, Sachin Kumar, Hyun Chul Choi, Kang Wook Kim, Karumudi Rambabu

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Alberta
FundersMinistry of Education, Science and TechnologyNational Research Foundation
KeywordsBiotelemetryAntenna (radio)Computer scienceTelemetryTelecommunications

Abstract

fetched live from OpenAlex

The paper presents a coplanar waveguide (CPW)-fed ultra-miniaturized patch antenna operating in Industrial, Scientific and Medical (ISM) band (2.4-2.5 GHz) for biotelemetry applications. The proposed antenna structure is circular in shape and its ground plane is loaded with a pair of slots for obtaining circular polarization. In the proposed design, asymmetric square slots generate phase condition for right-hand circularly polarized (RHCP) radiation. And, by merely changing the position of the slots, either RHCP or left-hand circularly polarized (LHCP) radiation can be excited. In the proposed design, a meandered central strip is used for miniaturization. The simulations of the proposed antenna are carried out using Ansys HFSS software with a single-layer and multilayer human tissue models. The antenna shows good performance for different tissue properties owing to its wide axial ratio bandwidth and impedance bandwidth. The antenna is fabricated and measurements are carried out in skin mimicking phantom and pork. Simulated and measured performances of the antenna are in close agreement. The power link budget is also calculated using an exterior circularly polarized (CP) receiving antenna.

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 score0.002

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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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

Citations70
Published2020
Admission routes1
Has abstractyes

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