Optimal distance-based clustering for tag anti-collision in RFID systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tag collisions can impose a major delay in radio frequency identification (RFID) systems. Such collisions are hard to overcome with passive tags due to their limited capabilities. In this paper, we look into the problem of minimizing the time required to read a set of passive tags. We propose a novel approach, the distance-based clustering, in which the interrogation zone of an RFID reader is divided into equal sized clusters (discs), and tags of different clusters are read separately. The novel contributions of this paper are the following. First, we provide a mathematical analysis to the problem and derive a closed-form formula relating delay to the number of tags and clusters. Second, we devise a method to efficiently find the optimal number of clusters. The proposed scheme can be augmented with any tree-based anti-collision scheme, and substantially improve its performance. Simulation results show that our approach makes significant improvements in reducing collisions and delay.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it