Author manuscript, published in "IEEE International Symposium on Industrial Electronics, Montreal: Canada (2006)" DOI: 10.1109/ISIE.2006.295519 Time-Frequency Characterization using Instantaneous Moment Concept:
Bibliographic record
Abstract
Abstract- The instantaneous frequency law (IFL) is a very important element when the physical parameters of the corresponding signal have to be evaluated. Blind equalisation, modulation recognition and mechanical diagnostic are just three domains where the non-stationarity behavior of the signal imposes the IFL estimation. Generally, an IFL is composed by slowly varying time-frequency structures separated by fast transitions which could be considered as phase discontinuities. Digital phase modulations or signals propagated trough a multipath channel are typical examples of IFLs having fast transient parts. The common methods employed for these operations are wavelet transform and Cohen’s class time-frequency representation, respectively. In this paper we propose an alternative based on the instantaneous moments. By an appropriate choice of the moment order and lags it is possible to accurately estimate the both transient and slowly time-frequency parts. While this method uses one dimensional data and, thanks to its recursive structure, it is well suited for real time applications. Therefore, its real-time implementation on TMS320C6x structure is described. On the other hand, few examples on realistic data will show the practical interest for this method. I.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.420 | 0.176 |
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".