Fully reversible spectral compression of arbitrary pulsed data signals
Bibliographic record
Abstract
Data signals consisting of arbitrarily modulated sequences of pulses are extensively used for processing and communication applications. The spectral extent of such signals is determined by the bandwidth of the individual pulses in the sequence, which imposes a fundamental limit to the maximum amount of information that can be transmitted or processed per time period. In this work, we propose and experimentally demonstrate that the frequency spectrum occupied by a data-modulated pulse sequence can be significantly compressed to well below the individual pulse bandwidth while still maintaining the temporal duration of the pulses in the sequence and without losing any of the information carried by the signal. The proposed method involves fully reversible linear transformations of the data signal along the time and frequency domains. We demonstrate successful pulse-shape-preserving spectral compression and subsequent full waveform and information recovery of a ∼ 10 Gb / s optical pulse sequence, modulated by an arbitrary data pattern, liberating over 60% of its bandwidth. These findings should prove useful for applications in signal processing, communications, and others.
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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 | 0.000 |
| 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 | 0.001 |
| 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".