Non‐periodic pulse signal detection based on variable scale coupled Duffing oscillators

Luo, Wenmao; Zhang, Yerong

doi:10.1049/el.2017.3676

Cited by 18 publications

(10 citation statements)

References 4 publications

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“…In the case that the total periodic amplitude in (2) is

γ + a \geq γ_{normalc}

; the Duffing oscillator only goes to periodic‐stable state as shown in Fig. 1 b , when the angular frequency of the reference signal and the periodic signal in the input term is equal to each other (

ω = ω_{0}

) [11–15, 27].…”

Section: Theoretical Basismentioning

confidence: 99%

“…We used Matlab in all applications in this work. We performed a simulation to demonstrate the high sensitivity of the Duffing oscillator for the detection of weak periodic signals with very low SNR [11–15, 23].…”

Section: Theoretical Basismentioning

confidence: 99%

“…The weak periodic signals within the external forcing input term in the Duffing oscillator are detected by observing the transition of the oscillator from a chaotic state to periodic state. When the Duffing oscillator, in the critical edge of chaos, is stimulated by the weak periodic signals embedded in strong background noise, the Duffing oscillator jumps to a periodic state [11–20]. This conventional method of detecting weak periodic signals is based on analysing the change in the state space of the Duffing oscillator manually which is not practical and time‐consuming.…”

Section: Introductionmentioning

confidence: 99%

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Automated system for weak periodic signal detection based on Duffing oscillator

Akıllı

Yılmaz

Akdeniz³

2020

IET signal process.

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The periodic signals that have predictable and deterministic characteristics are used in the analysis and modelling of dynamical systems in diverse fields. These signals can be detected as the weak signals within the time series obtained from the measurable processes of dynamical systems. The Duffing oscillator is effective in detecting weak periodic signals with a very low signal‐to‐noise ratio. In this study, the authors present a method to automate the weak periodic signal detection of the Duffing oscillator using a quantitative index for the classification of the periodic and non‐periodic signals. In this method, the authors use the wavelet scale index as the quantitative index in the classification of signals. Thus, they are able to plot the wavelet scale index spectrum of the Duffing oscillator where the frequency values of the weak periodic signals correspond to near‐zero wavelet scale index parameters. First, the authors perform simulations using the method and detect weak periodic signals embedded in noise. Then, they employ two electroencephalogram signals to demonstrate the feasibility of the proposed method in the empirical data. Lastly, they compare the method to the periodogram power spectral density estimate based on fast Fourier transform.

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“…In the case that the total periodic amplitude in (2) is

γ + a \geq γ_{normalc}

ω = ω_{0}

) [11–15, 27].…”

Section: Theoretical Basismentioning

confidence: 99%

Section: Theoretical Basismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated system for weak periodic signal detection based on Duffing oscillator

Akıllı

Yılmaz

Akdeniz³

2020

IET signal process.

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show abstract

“…The traditional computer vision detection technology has three important characteristics: non-contact, high precision and high automation. It can monitor objects independently and objectively, and measure and analyze their corresponding static and dynamic states [10]- [14]. But the traditional computer vision processing technology mostly depends on the computer hardware equipment and the corresponding digital image transmission and processing scheme.…”

Section: Introductionmentioning

confidence: 99%

Subpixel Computer Vision Detection Based on Wavelet Transform

2020

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Computer vision detection technology is one of the most popular topics in the field of computer vision. With the continuous improvement of the relevant algorithm and the performance-price ratio of the corresponding imaging equipment, the corresponding computer vision detection algorithm is also constantly upgraded and deepened. Computer vision detection technology is mainly used in transportation, public security, national defense and military fields, but the pixel accuracy of traditional computer vision detection technology has been unable to meet today's accuracy requirements. In this paper, firstly, the quantum denoising algorithm based on dual-tree and dual-density wavelet transform is used to realize the combination of quantum image coding expression and wavelet transform, and finally achieve a more detailed and accurate description of the image and realize the noise reduction of the image. In order to further realize sub-pixel image processing, cubic spline interpolation edge detection algorithm will be added to wavelet transform, which mainly calculates the zeros of the second-order function corresponding to the cubic spline function on both sides of the image edge points, so as to realize sub-pixel location of the image edge points. Finally, by comparing with the traditional pixel accuracy detection algorithms, it can be found that the proposed subpixel computer vision detection algorithm based on wavelet transform has good robustness, and its computing time is relatively faster, so it will have better adaptability in practical applications. INDEX TERMS Wavelet transform, vision detection algorithm, quantum denoising algorithm, subpixel vision detection algorithm.

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“…The method is based on the phase-states of the chaotic oscillator that has the property of sensitive dependence on initial conditions. As a consequence, a weak periodic signal, which is a low amplitude signal, is detected via the state transition from a chaotic state to a periodic state of the driven Duffing oscillator [1,2,3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Study of Weak Periodic Signals in the EEG Signals and Their Relationship With Postsynaptic Potentials

Akıllı

Yılmaz

2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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In this paper, weak periodic signals detected in the EEG signals were analyzed for each region of the brain, and its relationships with postsynaptic potentials were investigated. For this, EEG signals were collected from 16 different channels according to the international standard channel 10/20 system in the scalp of two patients, one epileptic and one non-epileptic. In our recent work, we have detected weak periodic signals in the EEG signals using a Duffing oscillator system. In this paper, we have used a compact method by combining the Duffing oscillator system with scale index, which is based on a wavelet analysis in order to make the determination of the weak periodic signals more straightforward in practice. A number of weak periodic signals were detected in the range of frequencies 4.7-16 Hz in all channels, and however, these were less observed in epileptic EEG signals than non-epileptic. This methodology illustrates that weak periodic signals can be used to distinguish epileptic EEG signals from non-epileptic EEG signals. We have revealed some findings that the weak periodic signals are the postsynaptic potentials of the brain. Therefore, we can measure some frequencies values of the postsynaptic potentials within the EEG signals with this methodology.

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Non‐periodic pulse signal detection based on variable scale coupled Duffing oscillators

Cited by 18 publications

References 4 publications

Automated system for weak periodic signal detection based on Duffing oscillator

Automated system for weak periodic signal detection based on Duffing oscillator

Subpixel Computer Vision Detection Based on Wavelet Transform

Study of Weak Periodic Signals in the EEG Signals and Their Relationship With Postsynaptic Potentials

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