Robust indoor positioning using differential wi-fi access points
Why this work is in the frame
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Bibliographic record
Abstract
Location positioning systems using wireless area local network (WLAN) infrastructure are considered cost effective and practical solutions for indoor location tracking and estimation. However, accuracy deterioration due to environmental factors and the need for manual offline calibration limit the application of these systems. In this paper, a new method based on differential operation access points is proposed to eliminate the adverse effects of environmental factors on location estimation. The proposed method is developed based on the operation of conventional differential amplifiers where noise and interference are eliminated through a differential operation. A pair of properly positioned access points is used as a differential node to eliminate the undesired effects of environmental factors. As a result the strength of received signals, which is used to determine the location of a user, remains relatively stable and supports accurate positioning. To estimate wave propagation in indoor environments, log-distance path loss model has been employed at the system level. Experimental results indicate that the proposed method can effectively reduce the location estimation error and provide accuracy improvement over existing methods.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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