TDMA‐SDMA‐based RFID algorithm for fast detection and efficient collision avoidance
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
Summary In radio frequency identification, collisions between tags and readers are among key weaknesses to address for reliable operation. To efficiently tackle the above issues and shorter the identification time, different algorithms have been adopted. However, they are still perfectible. For this aim, an efficient fast detection and collision avoidance (FD‐CA) algorithm is proposed to solve tag to tag collision drawbacks. Design and Methods Based on an innovative combination of TDMA and SDMA algorithms, it takes into account spatial distribution of tags to significantly reduce their identification time. Results and Discussion Besides, to further highlight its efficiency, the proposed algorithm was successfully applied to reader‐to‐reader collision with an increasing throughput and a decreasing average waiting time and average rate collision while compared to existing algorithms. Conclusion Finally, the proposed FD‐CA was demonstrated through practical examples, comparison with existing works, and successful FPGA implementation.
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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 | 0.001 | 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