Instantaneous Microwave Frequency Measurement With Improved Measurement Range and Resolution Based on Simultaneous Phase Modulation and Intensity Modulation
Bibliographic record
Abstract
A novel approach to implementing instantaneous microwave frequency measurement based on simultaneous optical phase modulation and intensity modulation with improved measurement range and resolution is proposed and experimentally demonstrated. The simultaneous optical phase modulation and intensity modulation are implemented using a polarization modulator (PolM) in conjunction with an optical polarizer. The phase- and intensity-modulated optical signals are then sent to a dispersive element, to introduce chromatic dispersions, which results in two complementary dispersion-induced power penalty functions. The ratio between the two power penalty functions has a unique relationship with the microwave frequency. Therefore, by measuring the microwave powers and calculating the power ratio, the microwave frequency can be estimated. Thanks to the complementary nature of the power penalty functions, a power ratio having a faster change rate versus the input frequency, i.e., a greater first-order derivative, is resulted, which ensures an improved measurement range and resolution. The proposed approach for microwave frequency measurement of a continuous-wave and a pulsed microwave signal is experimentally investigated. A frequency measurement range as large as 17 GHz with a measurement resolution of plusmn 0.2 GHz for a continuous-wave microwave signal and plusmn0.5 GHz for a pulsed microwave signal is achieved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".