Tuning Frequency Extraction of Free Induction Decay Signal Leveraging Higher Order Singular Value Tensor Decomposition and Fourier Synchrosqueezing Transform
Bibliographic record
Abstract
The frequency of the free induction decay (FID) signal output from an Overhauser magnetometer sensor is proportional to the magnetic field to be measured. Due to the low initial signal-to-noise ratio (SNR), sensor tuning is necessary to suppress the noise and thus improve the frequency estimation accuracy. To improve the tuning performance in complex strong-disturbance environments, this article introduces a novel method using higher order singular value tensor decomposition (HOSVTD) and Fourier synchrosqueezing transform (FSST), namely, SVDFT. First, multiple FID signals are obtained using an equal delay multichannel acquisition strategy to establish a deeper, more intrinsic correlation attribute. Second, matrix segmentation is used to organize the signals into a higher order tensor to compute singular values, and tensor canonical polyadic (CP) decomposition is incorporated to derive a low-noise FID signal. Subsequently, the FSST is used to analyze the low-noise signal to extract the time-frequency ridges to capture the tuning frequency. Finally, the SVDFT is contrasted with three techniques: autocorrelation and fast Fourier transform (AC-FFT), singular value decomposition (SVD) combined with short-time Fourier transform (STFT) (ST-SVD), and the HOSVTD and synchroextracting transform (SET), namely, SVDET. The experimental results demonstrate that under the presence of spike noise and the SNR is less than −20 dB, the frequency tuning deviations of the commonly used methods are up to 100 Hz, while that of the SVDFT is within 5 Hz, which verifies that the SVDFT can significantly enhance the sensor tuning accuracy in complex strong-disturbance conditions and has a strong ability for environmental adaptation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".