The Discrete Orthogonal Stockwell Transforms for Infinite-Length Signals and Their Real-Time Implementations
Bibliographic record
Abstract
In recent literature, the discrete Stockwell Transform (DST) for infinite length signals has been introduced along with its fast implementation. This method allows for low computational cost and enables processing of an infinite-length or large-size signal segment-by-segment while overcoming the boundary effects produced by conventional DST. The algorithm also preserves the absolute-reference phase, making it suitable for real-time signal processing. In this paper, we propose a new formulation of the discrete Orthogonal Stockwell Transform for infinite length signals. Based on the definition, we implement its fast algorithm using FFT. Our proposed scheme can process an infinite signal segment-by-segment, eliminating boundary effects and preserving the absolute-reference phase. Compared to the DST for infinite length signals, the DOST version significantly reduces computational complexity, making it more practical for real-time signal processing.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".