Experimental performance evaluation of passive UHF RFID systems under interference
Bibliographic record
Abstract
Performance of passive ultrahigh frequency radio-frequency identification (UHF RFID) systems is determined by the ability of the RFID reader to precisely decode the RF signal backscattered by the RFID tag. For a successful operation, two conditions must be met: (1) the power level of the impinged signal on the RFID tag must be above its threshold to power up its internal circuitry, and (2) the backscattered signal received by the RFID reader must be higher than its sensitivity threshold. The environment in which such systems are deployed has a major effect on these two conditions. In this paper, performance of a passive UHF RFID system is experimentally evaluated for potential deployment in industrial settings where many sources that generate ambient electromagnetic interference (EMI) exist. This interference will inevitably affect the performance of the deployed passive UHF RFID systems. Three different types of EMI are considered in this work: impulsive continuous wave interference and interference with Gaussian and Rayleigh distributions. The performance metric used in this paper is the reading rate per second. The obtained experimental results show that the performance of passive UHF RFID system depends on the nature and the strength of the ambient EMI. The results reported are of particular interest to those who deploy passive UHF RFID systems for industrial applications particularly in situations that already have machines in use that generate unpredictable EMI.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".