FFT tutor: A matlab-based instructional tool for FFT parameter exploration
Why this work is in the frame
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Bibliographic record
Abstract
An overview of the way various fast Fourier Transforms (FFT) parameters relate and can be selected in a practical way, is presented. Significant factors associated with spectral leakage, windowing, and zero-padding are also discussed. A MetaLab-based tool is introduced to help in visualizing these concepts. The tool allows the user, to graphically evaluate the influence of the analysis parameters on harmonic signals and a custom dataset, such as a sound recording. It also allows the user the user to experiment with and optimize the FFT analysis parameters, to enhance the resulting FFT spectrum, while enabling visual comparison of the inverse of the spectrum produced with the original time-domain signal. The parameters governing the time and the frequency domain windows also need to be better selected, to use the FFT effectively.
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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 | 0.000 | 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 | 0.000 | 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