Cubic spline-based tag estimation method in RFID multi-tags identification process
Bibliographic record
Abstract
Radio Frequency Identification (RFID) system is a communication technology used to identify objects using electromagnetic waves. The key advantage of RFID systems stems from their ability to simultaneously identify multiple tagged objects. However, communication of multiple tags with a reader may result in a collision problem, which is both time and energy inefficient, hindering the effectiveness of tag identification process. Presently, several anti-collision algorithms can be applied in order to reduce the collision probability. The reader¿s a priori knowledge of tag quantity significantly affects the overall performance of the system. Since the exact number of tags is not available for the reader, it is essential to develop an accurate tag estimation method to increase the efficiency of tag identification process. This paper presents a novel tag quantity estimation method, whereby, after simulating the tag distribution process, cubic spline interpolation method is employed to approximate the number of tags. According to the simulation results and the evaluation of the previous estimation methods, the new proposed method estimates the number of tags with a higher accuracy yielding an error rate of less than 1%, on average. Moreover, this low error rate is preserved even when the number of tags increases considerably.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".