Fingerprint positioning based on piecewise filtering of received signal strength indices and space-scene constraints
Why this work is in the frame
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Bibliographic record
Abstract
This paper aims to reduce the dimensionality in fingerprint algorithm and achieve the optimal positioning accuracy at the minimal cost. For these purposes, the piecewise feature of iBeacon signal transmission was taken as the filtering factor of fingerprint positioning and adopted to filter the received signal strength indices (RSSIs) collected in real time. Then, the related fingerprints were filtered into fragments for subsequent online matching. After that, the indoor space-scene was divided into passage and hall, and the relevant constraint factor and data structure were discussed for fingerprint indexing. On this basis, the author proposed a novel method to optimize fingerprint positioning considering RSSI filtering and space-scene constraints. The experiments on an office space-scene reveal that the proposed method achieved the same result as the traditional one using 88% shorter matching time. This research provides an efficient and accuracy way of fingerprint positioning.
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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 | 0.001 |
| 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