An FPGA-Based, 12-Channel TDC And Digital Signal Processing Module For The RatCAP Scanner
Bibliographic record
Abstract
Front end digital signal processing and VME based DAQ electronics for the RatCAP (Rat Conscious Animal PET) is discussed. All digital approach to front end signal processing for the mobile animal PET scanner is presented. Altera Cyclone family FPGA based realization of the 12 channel TDC (time to digital converter), address serial decoder and VME based DAQ system development is discussed in detail. Routing delays between logic array blocks combined with propagation delay of logic cells were used to generate different clock phases, to achieve subclock speed resolution. Altera LogicLock/spl trade/ toolsets were used for replicable and tighter placements of the supporting logic to achieve the required timing performance. TDC realized using controlled placements of the logic elements to specific logic cells within a specific LAB (logic array block) has the maximum DNL of 0.7 ns. VME based custom designed board with FIFO memory constituted the DAQ electronics. Test results with full 12 blocks, RatCAP front end electronics are presented. TDC realization and characterization is discussed in details. Timing spectrum obtained for 12 blocks, 384 channels of full RatCAP scanner is also presented.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".