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An Ultraminiaturized Ultrawideband Low Profile Antenna for Skin, Muscle, and Heart Implantable Medical Devices

2025· article· W7136122942 on OpenAlexaff
Amarveer Singh Dhillon

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAntenna (radio)Antenna rotatorNoise (video)

Abstract

fetched live from OpenAlex

We present an ultraminiaturized Ultrawideband low-profile antenna for wireless implantable medical communication systems (WIMCS). The radiating element of the miniaturized antenna is composed of Interconnected Concentric Square Loops (ICSLs) with a full ground plane. The proposed antenna has a small footprint of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6 \times 6 \times 0.71 ~\text{mm} 3$</tex>. The key to achieving the desired bandwidth is the interconnection between the feed point and the inner loop, and the interconnection between the inner and outer loops. Optimizing the location, length, and width of the interconnects yields miniaturization and wide bandwidth characteristics. The ICSL antenna operates in the ISM (0.902 GHz - 0.928 GHz), WMTS (1.395 GHz- 1.495 GHz), and Midfield band (1.45 GHz- 1.6 GHz) with a bandwidth of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt;62 \%$</tex> over the 0.9 to 1.6 GHz frequency range. Initially, the wideband antenna was designed, optimized, and analyzed in a 3-layer phantom consisting of skin, fat, and muscle. A 4-layer phantom, consisting of skin, fat, muscle, and heart tissue, is designed to evaluate performance in heart tissue. The ultrawideband antenna exhibits omnidirectional radiation characteristics. The small footprint and ultrawideband bandwidth features make the proposed antenna a potential candidate for integration in WIMCS, such as skin implants, Arteriovenous Grafts (AVGs), and leadless pacemakers.

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)0.001
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 score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.244
Teacher spread0.239 · 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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