Dual-Band 3-D Implantable MIMO Antenna for IoT-Enabled Wireless Capsule Endoscopy
Bibliographic record
Abstract
This article proposes an implantable antenna using a 3-D multiple-input-multiple-output (MIMO) configuration, which operates at 915 and 2450-MHz bands. Both frequency bands are wider enough to cover the important industrial, scientific, and medical (ISM) bands at 915 and 2450 MHz. Using a meandered geometry, introducing capacitive regions between meandered arms and partially slotted ground keeps the antenna dimensions small. Consequently, it consumes an overall volume of$5\times 5\times 4.75{=}118.75$mm3 ($0.0001\lambda _{g} {^{{3}}}$, where$\lambda _{g}$is the guided wavelength at 915 MHz). All elements of the antenna radiate in diversified directions, enabling radiation pattern diversity, resulting in a lower envelope correlation coefficient (ECC) value. Moreover, it provides good gains at both frequency bands (−28.85 dBi at 915 MHz and −20.68 dBi at 2450 MHz). The MIMO channel parameters, and user safety analysis are performed, indicating satisfactory results. Each element of the proposed antenna has a peak SAR value of 306.19 and 252.36 W/Kg at 915 MHz and 2.45 GHz, respectively. The radiation efficiency of the MIMO antenna system is noted to be 0.15 and 0.19% at 915 and 2450 MHz, respectively. When the implant has$B_{r}{=}120$Mb/s (high-speed connectivity), the proposed antenna can cover an area up to 8 and 5.2 m at 915 and 2450 MHz, respectively. The practical validation of the proposed design is performed considering two software-defined radios (SDRs). To the best of the authors’ knowledge, this is the most compact 3-D implantable MIMO antenna that has been designed to date.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".