Wavelet Denoising of Coarsely Quantized Signals
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a practical wavelet-based approach to denoising coarsely quantized signals. Such signals can arise from the status data collected within large-scale engineering plants employing traditional limit checking fault detection and identification (FDI). Transitioning such plants to more advanced FDI techniques requires that the coarsely quantized data be accurately denoised. As FDI by its nature is concerned with the analysis of nonstationary signals, wavelets offer an appropriate denoising framework. Existing techniques for optimal wavelet denoising presuppose Gaussian noise contamination and, hence, are suboptimal for coarsely quantized signals. In this paper, a secondary correction stage is added to the standard wavelet-denoising process to improve its denoising performance on coarsely quantized signals. This correction stage exploits a priori knowledge of the known coarsely quantized signal dependencies to "tune" the wavelet thresholds. The effectiveness of the approach is demonstrated through the analysis of real-world data collected from an operational large-scale engineering plant
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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 | 0.000 |
| 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