All-Optical Short-Time Fourier Transform Based on a Temporal Pulse-Shaping System Incorporating an Array of Cascaded Linearly Chirped Fiber Bragg Gratings
Bibliographic record
Abstract
An all-optical approach to implementing short-time Fourier transform (STFT) of a high-speed and broadband electrical signal is proposed and demonstrated for the first time to our knowledge. The STFT is implemented based on a temporal pulse shaping system incorporating an array of cascaded linearly chirped fiber Bragg gratings (LCFBGs). An electrical signal to be analyzed is applied to a Mach-Zehnder modulator (MZM) to modulate the optical spectrum of a time-stretched optical pulse from a mode-locked laser (MLL). Each individual LCFBG in the LCFBG array functions as a bandpass filter to filter a specific range of the spectrum, which is equivalent to applying a window function to the corresponding section of the temporal signal, and at the same time, as a dispersive element to implement real-time Fourier transform. A theoretical analysis is performed which is verified by a numerical simulation and a proof-of-concept experiment. The key feature of this technique is that STFT can be implemented in real time which can find applications in analyzing an electrical signal with a bandwidth up to several hundreds of gigahertz.
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 | 0.001 | 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".