{
  "annotation_id": "0592cc74-0c7a-480c-80e1-89d8a2a29708",
  "date_created": "2026-02-17T05:53:24.169000Z",
  "date_modified": "2026-02-17T05:53:24.169000Z",
  "file_hash": "025ce2dfea60c7f6bb117fb86f9748ab8944d2bbbd0de5eacacca2b72a74909b",
  "private": false,
  "record": {
    "abstract": "This work presents a new coupled array of frequency-adaptive Duffing oscillators. Based on learning rules, the natural frequency of each oscillator changes with the external excitation to achieve the frequency-adaptive capability in the response. The frequency range of vibrational resonance in the response is greatly extended through the frequency-adaptive learning rule. Moreover, the theoretical condition for vibrational resonance is derived and its validity is verified numerically. The coupled self-learning Duffing oscillators can also perform signal denoising in strong noise environment, and its performance in signal denoising has been verified through processing the simulated signal and the wireless radio frequency signal under two scenarios. The superiority of vibrational resonance to the conventional denosing methods such as wavelet transform and Kalman filter has also been illustrated by experimental radio frequency signal processing. The combination of broadband frequency adaptability and strong noise-reduction capability suggests that these oscillators hold considerable potential for engineering applications.",
    "arxiv_id": "2601.09743",
    "authors": [
      "Jianhua Yang",
      "Litai Lou",
      "Shangyuan Li",
      "Zhongqiu Wang",
      "Miguel A. F. Sanju\u00e1n"
    ],
    "categories": [
      "nlin.AO"
    ],
    "doi": "10.1016/j.physleta.2025.131311",
    "journal_ref": "Physics Letters A 571, 131311 (2026)",
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Vibrational resonance in coupled self-learning Duffing oscillators and its application in noisy radio frequency signal processing",
    "url": "https://arxiv.org/abs/2601.09743",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "2c90e93f-3049-4d9a-8e49-329c38e50ccd",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}