Design and Performance Measurement of Worn-on-Body Instrumental Ultra-Miniaturized UWB Wearable Patch for e-Health Monitoring
Bibliographic record
Abstract
A conformal, ultra-miniaturized, circuit integrated ultra-wideband (UWB) coplanar-waveguide (CPW) antenna system for worn-on-body applications is developed in this article. The performance study of the proposed antenna is performed over a four-layer human body tissue model. A shortened ground plane and a pair of L-shaped stubs are included to the umbrella-shaped patch configuration to enhance the impedance bandwidth between 3.15-10.55 GHz, which encompass a variety of applications like WiMAX band (3.3-3.8 GHz), WLAN band (5.150-5.350 GHz), unlicensed ISM band (5.725-5.875 GHz), and X-band (7.250-7.745, and 7.900-8.395 GHz). The realization of wideband behavior for the reported antenna is studied using the characteristic mode theory (CMT). A maximum peak gain of 4.2 dBi is achieved with maximum radiations in broadside with higher front-to-back ratio in both E-plane and H-plane. Furthermore, the robustness of the proposed antenna is evaluated by studying its performances under different conditions, such as using a coaxial feeding system, bending and assessing SAR levels. The antenna is fabricated and assembled with various circuit components to validate its performance with the simulated counterpart. The experiment is carried out by placing the antenna structure over different parts of the human body. Finally, a comparative analysis is carried out and it is found that the proposed antenna exhibits 98.2% compactness than the existing antenna designs available in the literature.
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) | 0.001 | 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".