Dielectric Waveguide Filled With Particulate Media for Ultrahigh Frequency (UHF) Radio Frequency Identification (RFID) Applications
Bibliographic record
Abstract
Radio frequency identification (RFID) is a wireless communication technique that has a wide variety of applications in many fields. In some cases, the read range of the RFID transponder is a limiting factor to the application; thus, it is desired that the read range of a given RFID system be maximized. This paper presents a method to extend the read range of UHF RFID signal (860-960 MHz) by using cost-effective dielectric particulate materials (e.g., dry sand) as filler in dielectric waveguides, which is useful for applications that employ UHF RFID as a sensing or communication tool that need long read ranges, e.g., underground pipeline corrosion monitoring or leak detection. Reading tests conducted in both laboratory (air) and underground showed that the transmission of the UHF RFID signal through dielectric particulate media can considerably extend the read range of passive RFID transponders, which makes it a good material choice for the manufacturing of wireless monitoring systems for underground infrastructures. Classic waveguide theories developed by Marcatili and Emslie et al. were modified to analyze the transmission of the UHF RFID signal in the waveguide filled with dielectric particulate media. Reasonably good agreement was observed between the measured and calculated power values.
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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 | 0.000 |
| 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 | 0.001 |
| 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".