An innovative design of a frequency-tunable UHF RFID antenna for identification applications
Bibliographic record
Abstract
This paper introduces the design of a new frequency-reconfigurable ultra-high frequency radio frequency identification (UHF RFID) antenna, demonstrating an innovative approach that enables dynamic adjustment of its resonance frequency. The proposed antenna design features a central dipole structure, enhanced by two hexagonal split-ring resonators (H-SRR) at each end. A T-match network is integrated into the center of the dipole, which is essential for achieving impedance matching between the antenna and the Alien Gen2 H4 RFID microchip. The antenna is designed using a Rogers 4350B substrate, a high-performance dielectric material ideal for RFID applications. With dimensions of 68×32.6×1.524 mm3, the compact antenna maintains full UHF band (860 MHz to 930 MHz) coverage compliant with International Telecommunications Union (ITU) RFID standards. This ensures that the antenna can be used in different regions around the world, offering broad compatibility with various RFID systems. The antenna's frequency reconfigurability is achieved through the integration of localized capacitors with variable values, which plays a key role in enabling precise adjustments to the antenna's center frequency across the entire UHF band. Extensive simulation results validate the effectiveness of this reconfigurable design, demonstrating that the antenna can dynamically adjust its frequency while maintaining excellent performance metrics, including impedance matching, radiation efficiency, and bandwidth. This makes the proposed antenna an ideal choice for modern RFID 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".