AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
Nonstationary time series are ubiquitous in almost all natural and engineering systems. Capturing the time-varying signatures from nonstationary time series is still a challenging problem for data mining. Quadratic Time-Frequency Distribution (TFD) provides a powerful tool to analyze these data. However, they suffer from Cross-Term (CT) issues that impair the readability of TFDs. Therefore, to achieve high-resolution and CT-free TFDs, an end-to-end architecture termed Quadratic TF-Net (QTFN) is proposed in this paper. Guided by classic TFD theory, the design of this deep learning architecture is heuristic, which firstly generates various basis functions through data-driven. Thus, more comprehensive TF features can be extracted by these basis functions. Then, to balance the results of various basis functions adaptively, the Efficient Channel Attention (ECA) block is also embedded into QTFN. Moreover, a new structure called Muti-scale Residual Encoder-Decoder (MRED) is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder architecture. Finally, although the model is only trained by synthetic signals, both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.
D.-H. Pham and S. Meignen, High-order synchrosqueezing transform for multicomponent signals analysis—With an application to gravitational-wave signal, IEEE Trans. on Signal Processing, vol. 65, no. 12, pp. 3168–3178, 2017.
N. Liu, Y. Lei, R. Liu, Y. Yang, T. Wei, and J. Gao, Sparse time-frequency analysis of seismic data: Sparse representation to unrolled optimization, IEEE Tran. on Geoscience and Remote Sensing, vol. 61, pp. 1–10, 2023.
V. C. Chen, F. Li, S.-S. Ho, and H. Wechsler, Micro-Doppler effect in radar: phenomenon, model, and simulation study, IEEE Trans. on Aerospace and electronic systems, vol. 42, no. 1, pp. 2–21, 2006.
M. Mercuri, I. R. Lorato, Y.-H. Liu, F. Wieringa, C. V. Hoof, and T. Torfs, Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor, Nature Electronics, vol. 2, no. 6, pp. 252–262, 2019.
S. K. Hadjidimitriou and L. J. Hadjileontiadis, Toward an EEG-based recognition of music liking using time-frequency analysis, IEEE Trans. on Biomedical Engineering, vol. 59, no. 12, pp. 3498–3510, 2012.
T. Kinoshita, K. Fujiwara, M. Kano, K. Ogawa, Y. Sumi, M. Matsuo, and H. Kadotani, Sleep spindle detection using RUSBoost and synchrosqueezed wavelet transform, IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 28, no. 2, pp. 390–398, 2020.
T. Chen, Q. Zheng, L. Xie, and H. Su, Sinusoidal-assisted synchrosqueezing transform: Algorithms and biomedical applications, Biomedical Signal Processing and Control, vol. 85, p. 105043, 2023.
Z. Feng, M. Liang, and F. Chu, Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples, Mechanical Systems and Signal Processing, vol. 38, no. 1, pp. 165–205, 2013.
S. Wang, X. Chen, I. W. Selesnick, Y. Guo, C. Tong, and X. Zhang, Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis, Mechanical Systems and Signal Processing, vol. 100, pp. 242–288, 2018.
S. Meignen and N. Singh, Analysis of reassignment operators used in synchrosqueezing transforms: With an application to instantaneous frequency estimation, IEEE Tran. on Signal Processing, vol. 70, pp. 216–227, 2021.
F. Auger, P. Flandrin, Y.-T. Lin, S. McLaughlin, S. Meignen, T. Oberlin, and H.-T. Wu, Time-frequency reassignment and synchrosqueezing: An overview, IEEE Signal Processing Magazine, vol. 30, no. 6, pp. 32–41, 2013.
I. Daubechies, J. Lu, and H.-T. Wu, Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool, Applied and computational harmonic analysis, vol. 30, no. 2, pp. 243–261, 2011.
G. Yu, M. Yu, and C. Xu, Synchroextracting transform, IEEE Trans. on Industrial Electronics, vol. 64, no. 10, pp. 8042–8054, 2017.
T. Oberlin, S. Meignen, and V. Perrier, Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time-frequency representations, IEEE Trans. on Signal Processing, vol. 63, no. 5, pp. 1335–1344, 2015.
R. Behera, S. Meignen, and T. Oberlin, Theoretical analysis of the second-order synchrosqueezing transform, Applied and Computational Harmonic Analysis, vol. 45, no. 2, pp. 379–404, 2018.
X. Zhu, B. Li, K. Yang, Z. Zhang, and W. Li, Parameter analysis of chirplet transform and high-resolution time-frequency representation via chirplets combination, Signal Processing, vol. 205, p. 108824, 2023.
H.-I. Choi and W. J. Williams, Improved time-frequency representation of multicomponent signals using exponential kernels, IEEE Trans.on Acoustics, Speech, and Signal Processing, vol. 37, no. 6, pp. 862–871, 1989.
R. G. Baraniuk and D. L. Jones, Signal-dependent time-frequency analysis using a radially Gaussian kernel, Signal processing, vol. 32, no. 3, pp. 263–284, 1993.
D. L. Jones and R. G. Baraniuk, An adaptive optimal-kernel time-frequency representation, IEEE Tran. on Signal Processing, vol. 43, no. 10, pp. 2361–2371, 1995.
B. Barkat and B. Boashash, A high-resolution quadratic time-frequency distribution for multicomponent signals analysis, IEEE Tran. on Signal Processing, vol. 49, no. 10, pp. 2232–2239, 2001.
M. Abed, A. Belouchrani, M. Cheriet, and B. Boashash, Time-frequency distributions based on compact support kernels: properties and performance evaluation, IEEE Trans. on Signal Processing, vol. 60, no. 6, pp. 2814–2827, 2012.
N. A. Khan and B. Boashash, Instantaneous frequency estimation of multicomponent nonstationary signals using multiview time-frequency distributions based on the adaptive fractional spectrogram, IEEE Signal Processing Letters, vol. 20, no. 2, pp. 157–160, 2012.
B. Boashash, G. Azemi, and J. M. O’Toole, Time-frequency processing of nonstationary signals: Advanced TFD design to aid diagnosis with highlights from medical applications, IEEE signal processing magazine, vol. 30, no. 6, pp. 108–119, 2013.
B. Boashash and S. Ouelha, An improved design of high-resolution quadratic time-frequency distributions for the analysis of nonstationary multicomponent signals using directional compact kernels, IEEE Trans. on Signal Processing, vol. 65, no. 10, pp. 2701–2713, 2017.
M. Mohammadi, A. A. Pouyan, N. A. Khan, and V. Abolghasemi, Locally optimized adaptive directional time-frequency distributions, Circuits, Systems, and Signal Processing, vol. 37, pp. 3154–3174, 2018.
M. Al-Sad, B. Boashash, and M. Gabbouj, Design of an optimal piece-wise spline WignerVille distribution for TFD performance evaluation and comparison, IEEE Trans. on Signal Processing, vol. 69, pp. 3963–3976, 2021.
S. Ziani, Y. Farhaoui, and M. Moutaib, Extraction of fetal electrocardiogram by combining deep learning and SVD-ICA-NMF methods, Big Data Mining and Analytics, vol. 6, no. 3, pp. 301–310, 2023.
R. Pang, Y. Yang, A. Huang, Y. Liu, P. Zhang, and G. Tang, Multi-scale feature fusion model for bridge appearance defects detection, Big Data Mining and Analytics, vol. 7, no. 1, pp. 1–11, 2023.
X. Liu and M. Yang, Research on conversational machine reading comprehension based on dynamic graph neural network, Journal of Integration Technology, vol. 11, no. 2, pp. 67–78, 2022.
S. Zhang, M. S. R. Pavel, and Y. D. Zhang, Crossterm-free time-frequency representation exploiting deep convolutional neural network, Signal Processing, vol. 192, p. 108372, 2022.
S. Mann and S. Haykin, The chirplet transform: Physical considerations, IEEE Trans. on Signal Processing, vol. 43, no. 11, pp. 2745–2761, 1995.
P. Pan, Y. Zhang, Z. Deng, and W. Qi, Deep learning-based 2D frequency estimation of multiple sinusoidals, IEEE Trans. on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5429–5440, 2021.
T. Chen, Q. Chen, Q. Zheng, Z. Li, Z. Zhang, L. Xie, and H. Su, Adaptive multi-scale TF-net for high-resolution time-frequency representations, Signal Processing, vol. 214, p. 109247, 2024.
P. Flandrin and P. Borgnat, Time-frequency energy distributions meet compressed sensing, IEEE Trans. on Signal Processing, vol. 58, no. 6, pp. 2974–2982, 2010
L. Stankovic, A measure of some time-frequency distributions concentration, Signal Processing, vol. 81, no. 3, pp. 621–631, 2001.
Z. Chen and H.-T. Wu, Disentangling modes with crossover instantaneous frequencies by synchrosqueezed chirplet transforms, from theory to application, Applied and Computational Harmonic Analysis, vol. 62, pp. 84–122, 2023.
X. Xiong, H. Liu, Z. Deng, M. Fu, W. Qi, and Y. Zhang, Micro-Doppler ambiguity resolution with variable shrinkage ratio based on time-delayed cross correlation processing for wideband radar, IEEE Trans. on Geoscience and Remote Sensing, vol. 57, no. 4, pp. 1906–1917, 2018.
225
Views
23
Downloads
1
Crossref
0
Web of Science
0
Scopus
0
CSCD
Altmetrics
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
京公网安备11010802044758号