Uniform Magnetic Field Characteristics Based UHF RFID Tag for Internet of Things Applications
Bibliographic record
Abstract
This paper presents a novel inkjet-printed near-field ultra-high-frequency (UHF) radio frequency identification (RFID) tag/sensor design with uniform magnetic field characteristics. The proposed tag is designed using the theory of characteristics mode (TCM). Moreover, the uniformity of current and magnetic field performance is achieved by further optimizing the design using particle swarm optimization (PSO). Compared to traditional electrically small near-field tags, this tag uses the logarithmic spiral as the radiating structure. The benefit of the logarithmic spiral structure lies in its magnetic field receiving area that can be extended to reach a higher reading distance. The combination of TCM and PSO is used to get the uniform magnetic field and desired resonant frequency. Moreover, the PSO was exploited to get a uniform magnetic field in the horizontal plane of the normal phase of the UHF RFID near-field reader antenna. As compared with the frequently-used commercial near field tag (Impinj J41), our design can be readable up to a three times greater read distance. Furthermore, the proposed near-field tag design shows great potential for commercial item-level tagging of expensive jewelry products and sensing applications, such as temperature monitoring of the human body.
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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 | 0.000 | 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".