Projection-based adaptive Am-FM chirp components signal decomposition
Bibliographic record
Abstract
In this paper, the maximum windowed likelihood cost function is utilized to decompose a signal into AM-FM chirp components in presence of the white Gaussian noise. First, the MWL function is deployed in amplitude estimation assuming the frequencies are approximately known. This is equivalent to the projection of the input signal to the signature subspace of the signal. In the frequency and the frequency change rate tracking using this optimum amplitude as a function of the frequency and the frequency change rate, the cost function is optimized. This leads to minimizing the orthogonal projection of the input vector onto the estimated signature subspace of the input signal. A gradient descent adaptive algorithm is deployed to track the frequency and the frequency change rate. Simulations are conducted for both single and two component signals to study the performance of the algorithm. Comparing the results with a similar algorithm, when the estimated amplitude is not considered as a function of the frequency and the frequency change rate in the optimization process of the frequency and the frequency change rate, suggests a faster convergence and a more accurate performance in tracking the crossing frequencies by the proposed algorithm.
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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 | 0.000 |
| 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".