Parallel weak signal detection algorithm under Gauss noise interference
Bibliographic record
Abstract
At present, weak signal detection algorithm detects parallel weak signals under Gauss noise interference, which has the problems of low denoising performance, inaccurate detection results and low detection efficiency. To this end, a parallel weak signal detection algorithm based on Gauss noise interference is proposed. Wavelet transform is applied to detect weak signals with Gauss noise by wavelet threshold denoising method, and the weak signal is denoised based on the set threshold function and threshold. The EMD decomposition method is used to decompose the weak signal after denoising, and the weak signal is filtered through the imitation Cauchy convergence filter stopping criterion to extract the characteristics of weak signal. The weak signal detection under the interference of Gauss noise is completed based on the Doffing oscillator and the characteristic of the weak signal extracted. The experimental results show that the proposed method has high signal-to-noise ratio, accurate detection of weak signal, and the time of detection is below 8 s. The results show that the proposed method has high denoising performance, high detection accuracy and high detection efficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".