A Statistical Approach to Probe Chaos from Noise in Analog and Mixed Signal Designs
Bibliographic record
Abstract
Chaotic circuits have gained increasing attention in many engineering applications. Qualitative measures such as Lyapunov Exponent (LE) are the most common methods for identifying chaotic behavior. However, the use of these measures is limited due to the short output signal length and its contamination by noise. In this paper, we propose a novel methodology for modeling and detecting chaotic vs stochastic behavior in AMS designs. First, the design is modeled using a system of recurrence equations for analog and digital parts. Second, a surrogate generation method is performed. The obtained surrogates are a typical realization of the circuit output under the hypothesis that the circuits exhibits noise. Next, hypothesis testing with Gaussian Kernel measure as test statistic is conducted over these surrogates and the original circuit output to statistically assess the circuit behavior. The effectiveness of the proposed methodology is illustrated on several AMS circuits such as PLL or Colpitts oscillator. The obtained results show sufficient improvements over the existing methods. For instance, comparing with the LE method, our approach is an order of magnitude faster and provides a more accurate detection of the chaotic circuit behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".