Matrix filter design using semi-infinite programming with application to DOA estimation
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Bibliographic record
Abstract
We propose using a semi-infinite programming technique to design a matrix filter. The idea is to formulate the design problem into a semi-infinite optimization model where the mean square error between the desired response and the designed filter in the passband and stopband is minimized subject to a set of nonlinear functional inequalities. These inequality constraints are used to ensure that the stopband attenuation and the passband deviation satisfy the prescribed specifications. Simulations showed that the proposed method was better than the conventional matrix filter design techniques. The matrix filters based on the proposed design method was also applied to the direction-of-arrival (DOA) estimation problem. It was shown that the filter greatly improved the estimation accuracy at low signal-to-noise ratios (SNRs).
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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 | 0.001 |
| 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