An adaptive Viterbi algorithm based on strongly connected trellis decoding
Bibliographic record
Abstract
An adaptive Viterbi algorithm using strongly connected trellis decoding of binary convolutional codes is presented. It is known that the strongly connected trellis decoding method can be used to improve the efficiency of hardware utilization and the throughput of the decoding in a systolic array-based Viterbi decoder. However, this method makes the amount of ACS (addition, comparison, and selection) computations in the decoding process much larger than in the conventional trellis decoding. It is shown that the proposed adaptive Viterbi algorithm can reduce the large amount of ACS computations without a degradation in the performance. Further, this algorithm, unlike the adaptive Viterbi algorithm based on low connectivity trellis, does require a sorting operation to determine the most likely survivor paths among all the possible survivor paths. The simulation results show that the proposed adaptive Viterbi algorithm can reduce up to 70% of the average number of ACS computations per strongly connected stage over that using the conventional Viterbi algorithm, while keeping the same error performance as that of the latter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".