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A Compact Implantable Antenna for Biotelemetry Applications in Arteriovenous Grafts

2025· article· W7136212103 on OpenAlexaff
Manogna Naidu Neerukattu, Anil Kumar Nayak, Kambiz Moez, Amalendu Patnaik

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiotelemetryAntenna (radio)TelemetryHum

Abstract

fetched live from OpenAlex

The increasing demand for real-time health monitoring has driven significant advancements in wireless implantable medical devices, particularly for organ-specific applications such as kidney diagnostics. These systems use implantable antennas to establish biotelemetry communication between the internal device and external monitoring units. However, designing antennas suitable for implantation has several challenges, especially in complex anatomical regions like the abdomen. High signal attenuation, size limitations, restricted gain, and adherence to safety regulations are among the constraints. This work mainly presents the design and characterization of a compact, low-profile, high-gain implantable antenna optimized for kidney applications operating in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.4-2.5 \text{GHz}$</tex> ISM band. Initially, the antenna was designed with a coaxial feed placed at its center. Four lshaped slots are etched to obtain the resonant frequency. Two shorting pins are employed to improve the impedance matching. Four circular slots are used on the ground to further enhance the gain of the antenna. The antenna is simulated in a human tissue phantom model representing realistic abdominal conditions. The measured fractional impedance bandwidth of 31.42 %, simulated gain of -24 dBi, and acceptable SAR have been obtained. The antenna has compact, low profile and is suitable for wireless biotelemetry in arteriovenous grafts.

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: none
Teacher disagreement score0.955
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
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.248
Teacher spread0.240 · 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
Published2025
Admission routes1
Has abstractyes

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