A Time-Domain Frequency Analyzer Based on Goertzel Algorithm
Bibliographic record
Abstract
This article presents a novel time-domain implementation of the second-order Goertzel frequency analyzer, which can be extended for use in infinite impulse response (IIR)/finite impulse response (FIR) filters. A set of time-domain arithmetic circuits, including a one-step time register (TR), time amplifier (TA), time adder, and unit delay operator (${z}^{-1}$), are introduced to overcome the limitations of conventional time-domain filters. The working principles and nonidealities of each block are analyzed and compared with the existing methods. The proposed filter is implemented in a 180-nm CMOS process with a 0.9-V supply voltage. The designed frequency analyzer is tunable to extract the amplitude and phase angle of signals up to 400 Hz. Simulation results, targeting a 280-Hz signal at a 19.88-kHz sampling frequency, demonstrate that the filter can detect the amplitude and phase of a voltage signal in the time domain with an error below 5%. The filter achieves a resolution of$76.7~\text {dBV/s}$, consumes less than$24~\mu \text {W}$of power, and the estimated silicon area is almost$0.828~\mathrm {mm}^{2}$.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".