Bibliographic record
Abstract
In radio frequency identification (RFID) systems, location information is of great importance to provide location-aware services combined with identification. Conventional RFID systems can only provide coarse localization information. In this paper, we propose a novel approach named MDS-RFID to locate active RFID tags based on multidimensional scaling (MDS), an efficient data analysis technique. The approach has the advantage of fully utilizing the distance information in the network simultaneously, and thus can achieve better localization results than previous multilateration-based methods. The MDS-RFID algorithm first infers the tag-to-reader distances from the received signal strength (RSS). To obtain the distance matrix, the inter-tag distances are estimated using a triangular method. Then, classical MDS algorithms can be applied to determine the estimated locations of the tags. An optional refinement step can be added to further improve the accuracy using maximum likelihood estimation at the expense of additional computation costs. Simulation results show that the MDS-RFID algorithm can achieve a significant gain in accuracy over the previous localization schemes based on multilateration using only a few readers.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".