All‐Optical Parametric‐Assisted Oversampling and Decimation for Signal Denoising Amplification
Bibliographic record
Abstract
Abstract Decimation is a common process in digital signal processing that involves reducing the sampling rate of an oversampled signal by linearly combining consecutive samples. Among other applications, this process represents a simple means to mitigate noise content in the digital signal. In this work, a novel optical signal processing concept inspired by these operations is proposed, which is called Parametric‐assisted Oversampling and Decimation (POD). By using a simple all‐fiber setup, the POD processor first realizes an ultra‐fast parametric oversampling of the incoming temporal signal (at >100 Gigasamples per second), a process that is followed by a decimation that reduces the sampling rate by any user‐defined factor in a lossless manner. In this way, the POD delivers an amplified sampled copy of the optical signal, where the peak‐to‐peak gain results from the combination of parametric amplification and a “passive” amplification equal to the decimation factor. In this report, joint parametric and passive amplification by a factor ≈50 on GHz‐bandwidth signals is demonstrated. Furthermore, it is shown that the decimation process can effectively mitigate effects of narrowband noise, outperforming traditional optical and digital filtering techniques. By experimentally achieving ultra‐high decimation factors (>750), narrowband (MHz‐bandwidth) optical waveformsthat are lost in a much stronger noise background are recovered.
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 | 0.000 | 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) | 0.001 | 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".