Cumulant based phase estimation for 1-D and 2-D nonminimum phase systems by Fourier series based allpass model
Bibliographic record
Abstract
Yang and Chi (se Proc. IEEE Seventh SP Workshop on Statistical Signal and Array Processing, Quebec City, Canada, p.231-34, 1994) proposed a family of 1-D criteria for estimating the phase of a 1-D nonminimum phase linear time-invariant (LTI) system with only non-Gaussian measurements corrupted by additive Gaussian noise. The phase of the LTI system is obtained through an iterative algorithm which processes the given measurements by an ARMA allpass model such that a single absolute Mth-order (M/spl ges/3) cumulant of the allpass model output is maximum. A family of 1-D and 2-D criteria, in which Yang and Chi's 1-D criteria are included, is proposed for phase estimation using a Fourier series based allpass model. The optimum allpass models for 1-D and 2-D LTI systems are obtained by a 1-D and a 2-D iterative algorithms, respectively. The paper concludes with some simulation results followed by some conclusions.
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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 | 0.000 | 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 | 0.001 |
| 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 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".