A Real-Time Embedded Digital Processing Architecture for a Modular Time-of-Flight Computed Tomography Readout using a FPGA-based TDC
Bibliographic record
Abstract
Time-of-flight (TOF) in computed tomography (CT) can lead to an increase in image quality with no loss of sensitivity. To implement this approach, a new architecture using a FGPAbased TDC is being developed. Our group has developed a FPGA-based Time-To-Digital converter (TDC) and a new front-end electronics board which allows a more flexible testbench testing for various Silicon Photomultiplier (SiPM) configurations. The readout can explore different data extraction strategies prior to ASIC implementation. To solve this problem, a FPGA-based TDC is used to generate the necessary timestamps. The data is compressed by generating one histogram per channel in a dedicated dual port RAM. Along with front-end electronics board and the FPGA-based TDC, a custom Linux driver running on the CPU allows the transfer between the FPGA and the computer through an Ethernet Link for data post-processing. The system has two separate channels : time and energy channels. Only the time channel has been tested and the results can be improved by implementing TDC calibration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".