The Optimization of Ridge Detection Algorithm for Multi-Component Signals, Entered into System in Time-Frequency Domain
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In this paper, we propose a method for applying the time and frequency domain's representation to multicomponent signals. Our discussion is based on the method of ridge detection extraction taking into account the time and frequency domain by following the demodulation method, and the numerical results obtained by applying this method are evident compared to other methods that do not use demodulation. The simulation carried out on the examine signals indicates that the signal estimation can be accessed by the initial estimation of the information carrier signals. In both noisy and noise-free environments, the frequency-time-based observation method is more accurate than the other methods.
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References
-
I. Daubechies, J. Lu, and H-T. Wu, ?Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool,? Applied and Computational Harmonic Anal-ysis, vol. 30, no. 2, pp. 243?261, 2011.
Google Scholar
1
-
G. Thakur, E. Brevdo, N.S. Fuckar,? and H-T. Wu, ?The synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications,? Signal Processing, 2012.
Google Scholar
2
-
C. Franco, P-Y. Gumery,? N. Vuillerme, A. Fleury, and J. Fontecave-Jallon, ?Synchrosqueezing to investigate cardio-respiratory interactions within simulated volu-metric signals,? in Proceedings of the 20th European Signal Processing Conference (EUSIPCO). IEEE, 2012, 939?943.
Google Scholar
3
-
D. Iatsenko, A. Bernjak, T. Stankovski, Y. Shiogai, P.J. Owen-Lynch, P.B.M. Clarkson, P.V.E. McClintock, and A. Stefanovska, ?Evolution of cardiorespiratory interac-tions with age,? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sci-ences, vol. 371, no. 1997, 2013.
Google Scholar
4
-
G. Thakur and H-T. Wu, ?Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples,? SIAM Journal on Mathematical Analysis, vol. 43, no. 5, pp. 2078?2095, 2011.
Google Scholar
5
-
I. Daubechies, J. Lu, and H.-T. Wu, ?Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool,? Appl. Comput. Harmon. Anal., vol. 30, no. 2, pp. 243?261, 2011.
Google Scholar
6
-
T. Oberlin, S. Meignen, and V. Perrier, ?The Fourier-based synchrosqueezing transform,? in IEEE Int. Conf. Acoust., Speech., Sig. Proc. (ICASSP), 2014, pp. 315?319.
Google Scholar
7
-
H.-T. Wu, ?Adaptive analysis of complex data sets,? PhD, Princeton University, 2012.
Google Scholar
8
-
S. Wang, X. Chen, G. Cai, B. Chen, X. Li, and Z. He, ?Matching demodulation transform and synchrosqueezing transform in time-frequency analysis,? IEEE Trans. Sig. Proc., vol. 62, no. 1, pp. 69?84, 2014.
Google Scholar
9
-
T. Oberlin, S. Meignen, and V. Perrier, ?Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time-frequency representations,? IEEE Trans. Sig. Proc., vol. 63, no. 5, pp. 1335?1344, March 2015.
Google Scholar
10
-
D.-H. Pham and S. Meignen, ?High-order synchrosqueezing transform for multicomponent signals analysiswith an application to gravitational-wave signal,? IEEE Trans. Sig. Proc., vol. 65, no. 12, pp. 3168?3178, 2017.
Google Scholar
11
-
S. Meignen, D.-H. Pham, and S. McLaughlin, ?On demodulation, ridge detection and synchrosqueezing for multicomponent signals,? IEEE Trans. Sig. Proc., vol. 65, no. 8, pp. 2093?2103, 2017.
Google Scholar
12
-
H. Yang, ?Statistical analysis of synchrosqueezed transforms,? Appl. Comput. Harm. Anal., vol. doi.org/10.1016/j.acha.2017.01.001, 2017.
Google Scholar
13
-
J. Xiao and P. Flandrin, ?Multitaper time-frequency reassignment for nonstationary spectrum estimation and chirp enhancement,? IEEE Trans. Sig. Proc., vol. 55, no. 6, pp. 2851?2860, 2007.
Google Scholar
14
-
D. W. Griffin and S. L. Jae, ?Signal estimation from modified short-time Fcourier transform,? IEEE Trans. Acoust., Speech, and Sig. Proc., vol. 32, no. 2, pp. 236?243, 1984.
Google Scholar
15
-
R. Behera, S. Meignen, and T. Oberlin, ?Theoretical analysis of the second-order synchrosqueezing transform,? Appl. Comput. Harm. Anal., vol. , doi:10.1016/j.acha.2016.11.001, 2016.
Google Scholar
16
-
G. Thakur and H.-T. Wu, ?Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples.? SIAM J. Math. Anal., vol. 43, no. 5, pp. 2078?2095, 2011.
Google Scholar
17
-
S. Wang, X. Chen, G. Cai, B. Chen, X. Li, and Z. He, ?Matching demodulation transform and synchrosqueezing in time-frequency analysis,? Signal Processing, IEEE Transactions on, vol. 62, no. 1, pp. 69?84, 2014.
Google Scholar
18
-
movements using multicomponent am?fm decomposition,? IEEE journal of biomedical and health informatics, vol. 19, no. 5, pp. 1672?1681, 2015.
Google Scholar
19
-
R. Sharma, L. Vignolo, G. Schlotthauer, M. A. Colominas, H. L. Rufiner, and S. Prasanna, ?Empirical mode decomposition for adaptive am-fm analysis of speech: a review,? Speech Communication, vol. 88, pp. 39?64, 2017.
Google Scholar
20
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