Designing of adaptive bandpass filter with adjustable notch for frequency demodulation
Bibliographic record
Abstract
The state variables of an internal model controller in a feedback loop can provide direct estimate of the instantaneous frequency of a signal. An adaptive algorithm to identify and track the instantaneous frequency was developed in our previous works (L.J. Brown et. al., 2002, June 2003). This approach has as design parameters a fictitious plant and feedback controller. This paper presents a strategy for choosing the transfer functions of the fictitious plant and controller to incorporate a desired filter in the scheme. By choosing and designing the suitable forms and coefficients of transfer functions for the controller and plant in the system, a bandpass filter with an adjustable notch can be achieved. The location of the notch frequency is adjusted in the range of bandwidth by our adaptive algorithm. It is shown that a bandpass filter characteristics enhances the ability of the algorithm to reject noise. However, simulations show that this benefit comes at the expense of slower transient characteristics of the feedback loop which has negative consequence for identification of the instantaneous frequency.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".