Parallel‐computing‐based implementation of fast algorithms for discrete Gabor transform
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Bibliographic record
Abstract
Parallel‐computing‐based implementation of the two recent fast parallel algorithms for the discrete Gabor transform (DGT) is presented in this paper. First of all, the first existing block time‐recursive DGT algorithm with parallel lattice structure is analysed, and then an improved implementation method under a parallel computing environment is presented. Each parallel channel (i.e. process in parallel computing) in the improved method is independent, thereby reducing the interprocess communication by 99.2% on average over the original algorithm. Second, the second existing fast parallel DGT algorithm based on multirate filtering is analysed. Through the use of parallel computing, the communication overhead of the multirate filtering‐based parallel DGT algorithm is optimised and its time efficiency is raised from 31.26 times to 54.52 times faster than the serial fast DGT algorithm in processing of long sequences. Finally, the experimental results are compared and analysed, which indicate that the proposed fast DGT implementation methods are attractive for real‐time signal processing.
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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 | 0.001 | 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 | 0.001 |
| 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