A Method for Identifying Multiple RFID Tags in High Electromagnetic Interference Environments
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
Radio frequency identification (RFID) plays an important role in warehouse management. Unfortunately, the normal work environment in warehouses has electromagnetic interferences that cause reading failures. Failures can also be caused by other circumstances, such as collision between reading attempts, tag type, and hardware problems. Rather than propose a hardware-based approach, we propose a method based on information processing. We propose the points assignment (PA) method for the identification of multiple RFID tags based on the Hamming distances. The basic idea is to use all the readings, even if they contain errors. The method allows groups of tags to be identified in high-interference environments, where other methods have great difficulty for achieving error-free readings. The performance of the proposed method is compared with two other methods, showing that the PA achieves zero errors with fewer readings, and that it has more consistent performance as interference increases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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