Intercepting UHF RFID signals through synchronous detection
Bibliographic record
Abstract
Recently, augmented ultrahigh frequency radio-frequency identification (UHF RFID) systems have been developed, and they contain additional components that can detect a tag’s backscattered response and use this information for the localization of the tag and other applications. The methods currently employed either have poor performance because the detection of the tag’s response is based on envelope detection or are costly because they are based on software-defined radio. The solution proposed in the paper is to use a method called synchronous detection to intercept tag signals. Using synchronous detection, we were able to use a conventional UHF RFID reader integrated circuit for the method, leading to a cost-effective, high-performance solution. We performed an analysis of its read rate and read range performance. The analysis showed that our receiver is capable of receiving tag signals with a read rate of 50% for passive and 66% for semi-passive tags at a 1-m distance between the tag and the receiver and is capable of receiving tag signals at a maximum distance between the tag and the receiver of 3.25 m for passive and 5.5 m for semi-passive tags, with the reader being within 8 m of the receiver. This augmented RFID system has a potential to facilitate localization and prevent the cross-read problem in RFID-based portals. In addition, it can be used as a protocol analyzer as well as a component of future Internet of Things.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".