@InProceedings{7362764,
  author = {L. Barbini and M. O. T. Cole and A. J. Hillis and J. L. {du Bois}},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Weak signal detection based on two dimensional stochastic resonance},
  year = {2015},
  pages = {2147-2151},
  abstract = {The analysis of vibrations from rotating machines gives information about their faults. From the signal processing perspective a significant problem is the detection of weak signals embedded in strong noise. Stochastic resonance (SR) is a mechanism where noise is not suppressed but exploited to trigger the synchronization of a non-linear system and in its one-dimensional form has been recently applied to vibration analysis. This paper focuses on the use of SR in a two-dimensional system of gradient type for detection of weak signals submerged in Gaussian noise. Comparing the traditional one-dimensional system and the two-dimensional used here, this paper shows that the latter can offer a more sensitive means of detection. An alternative metric is proposed to assess the output signal quality, requiring no a priori knowledge of the signal to be detected, and it is shown to offer similar results to the more conventional signal-to-noise ratio.},
  keywords = {resonance;signal detection;stochastic processes;vibrational signal processing;weak signal detection;two dimensional stochastic resonance;rotating machines;signal processing perspective;nonlinear system;Gaussian noise;output signal quality;a priori knowledge;signal-to-noise ratio;one-dimensional system;Steady-state;Couplings;Synchronization;Stochastic resonance;Signal to noise ratio;Tuning;stochastic resonance;weak signal detection;non linear signal processing},
  doi = {10.1109/EUSIPCO.2015.7362764},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103493.pdf},
}

{"_id":"JZjnKzfunu7vRPfXo","bibbaseid":"barbini-cole-hillis-dubois-weaksignaldetectionbasedontwodimensionalstochasticresonance-2015","authorIDs":[],"author_short":["Barbini, L.","Cole, M. O. T.","Hillis, A. J.","du Bois , J. L."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["L."],"propositions":[],"lastnames":["Barbini"],"suffixes":[]},{"firstnames":["M.","O.","T."],"propositions":[],"lastnames":["Cole"],"suffixes":[]},{"firstnames":["A.","J."],"propositions":[],"lastnames":["Hillis"],"suffixes":[]},{"firstnames":["J.","L."],"propositions":["du Bois"],"lastnames":[],"suffixes":[]}],"booktitle":"2015 23rd European Signal Processing Conference (EUSIPCO)","title":"Weak signal detection based on two dimensional stochastic resonance","year":"2015","pages":"2147-2151","abstract":"The analysis of vibrations from rotating machines gives information about their faults. From the signal processing perspective a significant problem is the detection of weak signals embedded in strong noise. Stochastic resonance (SR) is a mechanism where noise is not suppressed but exploited to trigger the synchronization of a non-linear system and in its one-dimensional form has been recently applied to vibration analysis. This paper focuses on the use of SR in a two-dimensional system of gradient type for detection of weak signals submerged in Gaussian noise. Comparing the traditional one-dimensional system and the two-dimensional used here, this paper shows that the latter can offer a more sensitive means of detection. An alternative metric is proposed to assess the output signal quality, requiring no a priori knowledge of the signal to be detected, and it is shown to offer similar results to the more conventional signal-to-noise ratio.","keywords":"resonance;signal detection;stochastic processes;vibrational signal processing;weak signal detection;two dimensional stochastic resonance;rotating machines;signal processing perspective;nonlinear system;Gaussian noise;output signal quality;a priori knowledge;signal-to-noise ratio;one-dimensional system;Steady-state;Couplings;Synchronization;Stochastic resonance;Signal to noise ratio;Tuning;stochastic resonance;weak signal detection;non linear signal processing","doi":"10.1109/EUSIPCO.2015.7362764","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103493.pdf","bibtex":"@InProceedings{7362764,\n author = {L. Barbini and M. O. T. Cole and A. J. Hillis and J. L. {du Bois}},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Weak signal detection based on two dimensional stochastic resonance},\n year = {2015},\n pages = {2147-2151},\n abstract = {The analysis of vibrations from rotating machines gives information about their faults. From the signal processing perspective a significant problem is the detection of weak signals embedded in strong noise. Stochastic resonance (SR) is a mechanism where noise is not suppressed but exploited to trigger the synchronization of a non-linear system and in its one-dimensional form has been recently applied to vibration analysis. This paper focuses on the use of SR in a two-dimensional system of gradient type for detection of weak signals submerged in Gaussian noise. Comparing the traditional one-dimensional system and the two-dimensional used here, this paper shows that the latter can offer a more sensitive means of detection. An alternative metric is proposed to assess the output signal quality, requiring no a priori knowledge of the signal to be detected, and it is shown to offer similar results to the more conventional signal-to-noise ratio.},\n keywords = {resonance;signal detection;stochastic processes;vibrational signal processing;weak signal detection;two dimensional stochastic resonance;rotating machines;signal processing perspective;nonlinear system;Gaussian noise;output signal quality;a priori knowledge;signal-to-noise ratio;one-dimensional system;Steady-state;Couplings;Synchronization;Stochastic resonance;Signal to noise ratio;Tuning;stochastic resonance;weak signal detection;non linear signal processing},\n doi = {10.1109/EUSIPCO.2015.7362764},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103493.pdf},\n}\n\n","author_short":["Barbini, L.","Cole, M. O. T.","Hillis, A. J.","du Bois , J. L."],"key":"7362764","id":"7362764","bibbaseid":"barbini-cole-hillis-dubois-weaksignaldetectionbasedontwodimensionalstochasticresonance-2015","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103493.pdf"},"keyword":["resonance;signal detection;stochastic processes;vibrational signal processing;weak signal detection;two dimensional stochastic resonance;rotating machines;signal processing perspective;nonlinear system;Gaussian noise;output signal quality;a priori knowledge;signal-to-noise ratio;one-dimensional system;Steady-state;Couplings;Synchronization;Stochastic resonance;Signal to noise ratio;Tuning;stochastic resonance;weak signal detection;non linear signal processing"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2015url.bib","creationDate":"2021-02-13T17:31:52.534Z","downloads":0,"keywords":["resonance;signal detection;stochastic processes;vibrational signal processing;weak signal detection;two dimensional stochastic resonance;rotating machines;signal processing perspective;nonlinear system;gaussian noise;output signal quality;a priori knowledge;signal-to-noise ratio;one-dimensional system;steady-state;couplings;synchronization;stochastic resonance;signal to noise ratio;tuning;stochastic resonance;weak signal detection;non linear signal processing"],"search_terms":["weak","signal","detection","based","two","dimensional","stochastic","resonance","barbini","cole","hillis","du bois "],"title":"Weak signal detection based on two dimensional stochastic resonance","year":2015,"dataSources":["eov4vbT6mnAiTpKji","knrZsDjSNHWtA9WNT"]}