Timing Improvement by Low-Pass Filtering and Linear Interpolation for the LabPET Scanner
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital processing for positron emission tomography (PET) scanners commonly relies on low frequency sampling (MHz) to reduce power consumption. Timestamps must then be interpolated between samples to achieve adequate time resolution for coincidence detection of annihilation radiation. A low-pass filter based interpolation algorithm adding up to 31 samples between original samples was designed to improve timing resolution of the LabPET scanner. A 2-bit refinement in the determination of the pulse maximum amplitude leads to a better estimation of the triggering threshold, which in turn enables a more accurate timestamp generation. Timestamp accuracy was investigated as a function of trigger level (15%-50% of maximum value). With the trigger threshold set at 20%, coincidence time resolution of ns for LYSO-LYSO and ns for LGSO-LGSO are obtained. A real time implementation of the algorithm was achieved in a Xilinx FPGA.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 it