An Ultra-Miniaturized Antenna With Ultra-Wide Bandwidth Characteristics for Medical Implant Systems
Bibliographic record
Abstract
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 ($7\times 7\times0.2$mm3) 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".