Improving the Signal-to-Noise-Ratio of Free Induction Decay Signals Using a New Multilinear Singular Value Decomposition-Based Filter
Bibliographic record
Abstract
The free induction decay (FID) signal output by a proton precession magnetometer (PPM) is usually only of the microvolt level, and its frequency is proportional to the magnetic field strength. Therefore, obtaining a high signal-to-noise ratio (SNR) FID signal is crucial for improving the measurement accuracy of the magnetometer. The current gold standards for noise reduction in FID signals-singular value decomposition (SVD) and principal component analysis (PCA)-still have limited denoising capabilities, especially in cases with strong noise interference. In this study, a new noise-reduction algorithm for FID based on multilinear SVD (MLSVD) is proposed. First, equal delay-based multichannel data sampling is used to obtain multiple correlated signals, and thus, the obtained multiple signals are constructed as a third-order tensor; second, the MLSVD is employed to calculate and remove the noise singular value of the tensor; and third, canonical polyadic decomposition (CPD) is used to fuse the multichannel FID signal processed by MLSVD and eliminate signal noise, further improving the SNR. Subsequently, a PPM experimental test platform was constructed, and extensive simulation and practical comparison tests were conducted. The results show that, when the SNR is -10 dB, the noise-reduction effect of the MLSVD is about 9.12 dB higher than that of the SVD and about 8.15 dB higher than that of the PCA, with an overall increase of 28%. In an environment with strong noise interference-with an SNR of -30 dB-both PCA and SVD are no longer viable, while MLSVD can still effectively suppress noise, with a signal-to-noise improvement ratio (SNIR) being as high as 50.36 dB.
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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 | 0.000 |
| 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 | 0.001 |
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".