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Record W6901723098 · doi:10.60692/bpje5-wkw16

An Ultra-Miniaturized Antenna With Ultra-Wide Bandwidth Characteristics for Medical Implant Systems

2021· article· en· W6901723098 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2021
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsSpecific absorption rateBandwidth (computing)MiniaturizationAntenna factorAntenna (radio)Coaxial antennaAntenna measurementAntenna noise temperatureMonopole antenna

Abstract

fetched live from OpenAlex

In this study, an ultra–miniaturized implantable antenna based system with ultra-wideband characteristics in the industrial, scientific, and medical band (i.e., 2.4–2.48 GHz) is proposed for biomedical applications. A biocompatible and flexible liquid crystalline polymer material, Rogers ULTRALAM (tan $\delta =0.0025$ and $\varepsilon _{r} =2.9$ ), is employed as both the substrate and superstrate. The proposed antenna with a compact size ( $7\times 7\times0.2$ mm 3 ) and a wide bandwidth (1533 MHz), was primarily designed for overcoming the detuning challenges that may occur owing to the electronic circuitry and irregularity as well as inhomogeneity of the human tissue environment. The miniaturization of this antenna was achieved by introducing a shorting pin and open-ended cuts in the ground plane, as well as in the radiating patch. The proposed antenna also yielded a higher gain and lower specific absorption rate (SAR). Through the link budget analysis, it was observed that 1 Mbps of data could be easily transmitted over a distance of 15 m. The simulated and in vitro measured results confirmed that compared to the recently reported antenna systems, our proposed ultra-wideband antenna based system could work more efficiently in the complex environment of the human body, thus establishing itself as an attractive candidate for biomedical 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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.189
Teacher spread0.178 · 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.

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
Published2021
Admission routes1
Has abstractyes

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