A new hybrid frame ALOHA and binary splitting algorithm for anti-collision in RFID systems
Why this work is in the frame
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Bibliographic record
Abstract
Collision is considered as one of the most important issues to be in mind in RFID system designing. Although, there are many algorithms that all of them are aiming - at the end - to decrease collision state numbers or to process such states that help achieve an accurate identification process in an acceptable period of time. In this paper, we intend to propose an algorithm offering balanced performance in general and excellent performance in environments with low or medium tags population. It gives the ability to reduce the number of collisions, in the first stage, and to identify tags that failed to be identified due to the occurred collisions, in the next stage. For illustration, we present simulation showing our algorithm performance against some commonly used algorithms.
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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