4 - Segmentation de signaux par maxima d'ondelettes : application à la prédiction de zones de couverture radioélectrique
Bibliographic record
Abstract
Within the framework of a research on cellular networks of radio communication, it is essential to be able to predict the area which would be covered by transmitters. To study a transmitter, the standard method consists in applying an electromagnetic wave propagation model to various positions defined according to a constant spatial step. Yet, that method leads to a considerable computation time which might become unexploitable in complex geographical environments. There have already been some researches studying how to reduce that computation time. They consist in the simplification of the propagation model used. The processes in our article is complementary to them. Indeed, our technique is independent of the model. The idea is to reduce the number of calculation points of the model. The method presented here is based on an hypothesis which needs two elements to be confirmed: the segmentation of the signals measured by a mobile receiver ; a software used for the electromagnetic analysis of the geographic studied area. Thus, the purpose is to segment the received signal into intervals corresponding to particular combinations of physical phenomena. To do that, a representation suggested by Mallat and Zhong called “Wavelet Maxima Representation” is studied. That decomposition allows the study of the derivative of a function at different scales. We shall present a method of signal segmentation based on the maxima chaining through the scales of the decomposition. The chaining helps us select the largest discontinuities of the signal and thus segment it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".