Design and Performance Measurement of Implantable Differential Integrated Antenna for Wireless Biomedical Instrumentation Applications
Bibliographic record
Abstract
The growing demand of implantable medical devices is crucial for enabling real-time monitoring in biomedical and healthcare fields. This paper presents a differential integrated antenna for biomedical instrumentation applications in the Industrial, Scientific, and Medical (ISM) frequency bands (915 MHz and 2.45 GHz). The size of the proposed cubic flat differential system is (0.103λg × 0.103λg × 0.005λg), where λg is the guided wavelength at 915 MHz. The performance analysis of the differential antenna is carried out within homogeneous, heterogeneous, and realistic body models to design the proposed implantable integrated antenna. To validate the design method, a differential antenna is fabricated and assembled with different circuit components as per the simulation scenario and experimentally verified in the vicinity of skin mimicking phantom and minced pork. The measured -10 dB impedance bandwidth and far-field gain in the phantom are 16.4% and -30.3 dB, respectively, at 915 MHz, and 10.2% and -21.2 dB, respectively, at 2.45 GHz. Also, the communication link is calculated and evaluated based on the specific absorption rate (SAR) of the proposed differential integrated antenna at 1 W input power.
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 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".