LOS/NLOS Identification Based on Stable Distribution Feature Extraction and SVM Classifier for UWB On-Body Communications
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Bibliographic record
Abstract
This paper presents a technique for identifying between both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) propagation schemes for UWB on-body context. In the last few years, a great attention has been paid to wireless communications for body area networks especially since the IEEE 802.15.6 standard has been released. We focus at first to extract only the pertinent information using Stable Distribution compared with statistical techniques, and secondly to classify it using Support Vector Machine (SVM) with as main goal to identify between the two LOS and NLOS phenomena. We propose a technique to make the classification easy between LOS and NLOS contexts for UWB on-body communications. Our approach gives a good recognition rate of 87.5%, better than other methods in the same context.
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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 | 0.001 | 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