A short cavity fiber laser for microwave and radio-wave generation
Bibliographic record
Abstract
Broadband and low loss capability of photonics has led to increasing interests in its use for generating, processing, controlling and distributing of microwave and radio-wave signals with low phase noise for applications such as Radio over Fiber systems (RoF), broadband wireless access networks, sensor networks and satellite communitarians. In this thesis, I have introduced and demonstrated a short cavity Distributed Bragg Reflector (DBR) laser operating in two stable longitudinal modes to generate microwave and radio-wave frequency signals. In the laser, Er/Yb core doped fiber has been used as the gain medium and two wavelength matching fiber Bragg gratings (FBGs) of 99.9% and 90% reflectivity in C-band were used as end reflectors. The dual mode operation was achieved by reducing the cavity length of the fiber laser to 8 mm. It was pumped with a 980 nm pump laser and the laser output has an optical signal-to-noise ratio (SNR) larger than 65 dB at 1533 nm. Due to the birefringence introduced during FBG fabrication two orthogonal polarization modes were observed for each longitudinal mode. Microwave and radio-wave signals were generated by beating these longitudinal polarization laser modes on a fast photo detector. The generated microwave signals were at 1.2687 GHz, 1.2828 GHz, 14.6962 GHz and 14.7103 GHz with a SNR of 45 dB. The generated radio-wave signals were at 14.1 MHz with a SNR of 30 dB. The 3dB bandwidth of microwave and radio-wave signals were measured to be less than 30 kHz and the Allan variance measurements indicate that the signals are highly stable for temperature variations while the generated microwave signals demonstrated a linear relationship with temperature at 1 MHz/Celsius. Due to high stability of the generated radio-wave signal and low complexity of the overall system this short cavity fiber laser has potential applications such as ultrasonic sensors and optical clocks
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".