Multiple tags identification for short id rfid networks
Bibliographic record
Abstract
Utilization of radio frequency identification (RFID) tags to localize objects in real time requires the collection of the identities (IDs) of the tags within a short time. This paper presents a novel orthogonal group identification (Ogrid) method that addresses the above requirement. When queried, multiple tags with mutually orthogonal IDs respond, such that these tag IDs can be separated at the reader without using spread spectrum technique. Ogrid yields a small and fixed identification delays for any given tag ID size B. Ogrid is especially suitable for dense networks where the tag ID sizes are relatively short. In the case of B = 16 bits, which supports at most 65536 tags, Ogrid incurs a fixed identification delay of about 4 seconds regardless of the number of tags present, whereas several existing protocols incur average delays of 20 and 100 seconds to identify 10,000 and 50,000 tags, respectively. Results for a dynamic system with tag arrivals and departures further demonstrate the consistently low identification delay of Ogrid.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".