Energy Autonomous Dual-Band Antenna System for RFID-Based Real-Time Battery Level Monitoring
Bibliographic record
Abstract
This paper presents an energy-autonomous antenna system that combines energy harvesting (EH) with radio frequency identification (RFID) communication to measure the battery charge level in real time. The system employs a dual-band patch antenna, a dual-band matching stub, and a diplexer, which are designed with metamaterial-based electromagnetic bandgap technology (MTM-EBG). Due to the MTM-EBG’s uniplanar and via-less design, it can be directly embedded into the antenna and microstrip feed components, thereby contributing to their compactness, lowering cost, and simplifying fabrication. The RFID and EH portions of the system operate at frequencies of 915 MHz and 2.48 GHz, respectively. This system operates by rectifying RF power in the EH band in order to charge a supercapacitor battery, while a varactor imparts phase to the backscattered RFID signal. The phase of reflection is related to the level of charge in the battery, which is then read by the RFID reader. By acquiring the tag and charging information without power, this structure is energy autonomous, so there is no need for a permanent power source that will need to be replaced over time. Using supercapacitors or rechargeable batteries, the proposed structure can provide power for critical functions while monitoring their charging status remotely. The antenna system and RFID/EH architecture have been fabricated and measured, and battery level monitoring has been demonstrated.
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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 | 0.000 | 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".