Correcting Timebase Errors in Ultra-Wideband Equivalent-Time Sampling Receivers
Bibliographic record
Abstract
Timebase errors in UWB pulsed-radar equivalent-time sampling receivers can be significant, leading to unacceptable time-domain and frequency-domain distortions when picosecond pulses are processed. Here, we report a calibration method which mitigates these errors effectively. The method is demonstrated with an in-house dual-channel receiver with an effective sampling rate of 20 GSa/s. A 200-MSa/s analog-to-digital converter (ADC) is used in conjunction with a programmable delay chip (PDC), which shifts the temporal sampling point from one pulse period to the next. The programmable delays of the PDC are provided by manufacturers. However, the actual delays deviate from the prescribed values. Moreover, the deviation depends on environmental factors, especially temperature. Therefore, a calibration procedure is needed to obtain the true delays provided by the PDC. The calibration’s effectiveness is demonstrated through sinusoidal waveforms and picosecond pulse measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 0.001 |
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 teacher head, 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".