Reduction in the Number of Averages Required in BOTDA Sensors Using Wavelet Denoising Techniques
Bibliographic record
Abstract
This paper reports on a new mechanism to decrease the number of averages and, consequently, the measurement time of Brillouin optical time-domain analysis (BOTDA) sensors using wavelet shrinkage techniques. Two different wavelet shrinkage techniques, VisuShrink and SureShrink, are applied to denoise signals acquired from measurements in BOTDA sensors. The conventional method to denoise signals in BOTDA sensors is ensemble averaging. Ensemble averaging is a time consuming technique, as it requires many acquisitions of signals to provide an acceptable SNR. To reduce the number of acquisitions, the setup of the BOTDA sensor is modified to denoise acquired signals using VisuShrink or SureShrink before applying ensemble averaging. Experimental results show a significant reduction in the number of averages required to provide an accurate measurement, and consequently, a substantial saving in the measurement time of the sensor. It has been shown that the combination of ensemble averaging with VisuShrink or SureShrink reduces the measurement time of the sensor up to 90%. This reduction in the measurement time enables the implementation of dynamic and fast measurements with BODTA sensors and opens opportunities to target a new range of applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".