參考文獻 |
[1] N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A, Vol 454, No 1971, pp. 903-995, March 1998.
[2] Z. Wu, and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Adv. Adap. Data Anal., vol. 1, no. 01, pp. 1-41, 2009.
[3] J.-R. Yeh, J.-S. Shieh, and N. E. Huang, “Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method,” Advances in adaptive data analysis, vol. 2, no. 02, pp. 135-156, 2010.
[4] Wang, Yung-Hung, Kun Hu, and Men-Tzung Lo. "Uniform phase empirical mode decomposition: An optimal hybridization of masking signal and ensemble approaches." IEEE Access 6 (2018): 34819-34833.
[5] X. Y. Hu, S. L. Peng, and W. L. Hwang, “EMD Revisited: A New Understanding of the Envelope and Resolving the Mode-Mixing Problem in AM-FM Signals,” IEEE Trans. Signal Process., vol. 60, no. 3, pp. 1075-1086, Mar, 2012.
[6] R. Deering, and J. F. Kaiser, "The use of a masking signal to improve empirical mode decomposition." in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Philadelphia, PA, Mar, 2005, pp. iv/485-iv/488 Vol. 4.
[7] M. E. Torres, M. Colominas, G. Schlotthauer, and P. Flandrin, "A complete ensemble empirical mode decomposition with adaptive noise." in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Prague, Czech Republic, May, 2011, pp. 4144-4147.
[8] Y. H. Wang, C. H. Yeh, H. W. V. Young, K. Hu, and M. T. Lo, “On the computational complexity of the empirical mode decomposition algorithm,” Physica A, vol. 400, pp. 159-167, Apr 15, 2014.
[9] C. A. Fletcher, Computational techniques for fluid dynamics 2: Specific techniques for different flow categories: Springer Science & Business Media, 2012.
[10] Huang, Norden E., Zheng Shen, and Steven R. Long. "A new view of nonlinear water waves: the Hilbert spectrum." Annual review of fluid mechanics 31.1 (1999): 417-457.
[11] Wang, Yung-Hung, and Shao-Ho Cheng. "Boundary Effects for EMD-Based Algorithms." IEEE Signal Processing Letters (2022).
[12] P.-Y. Chen, Y.-C. Lai, and J.-Y. Zheng, “Hardware design and implementation for empirical mode decomposition,” IEEE Transactions on Industrial Electronics, vol. 63, no. 6, pp. 3686-3694, 2016.
[13] [R34]G. Rilling, P. Flandrin, and P. Goncalves, "On empirical mode decomposition and its algorithms." IEEE-EURASIP workshop on nonlinear signal and image processing, IEEER Grado, pp. 8-11, 2003 .
[14] [R35]R. Faltermeier, A. Zeiler, I. R. Keck et al., "Sliding empirical mode decomposition." The 2010 international joint conference on neural networks (IJCNN), IEEE, pp. 1-8, 2010
[15] [R36]W.-C. Shen, H.-I. Jen, and A.-Y. Wu, “New Ping-Pong scheduling for low-latency EMD engine design in Hilbert–Huang Transform,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 60, no. 8, pp. 532-536, 2013.
[16] R. Fontugne, P. Borgnat, and P. Flandrin, "Online empirical mode decomposition." 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 4306-4310, 2017.
[17] Y. -H. Wang, I. -Y. Chen, H. Chiueh and S. -F. Liang, "A Low-Cost Implementation of Sample Entropy in Wearable Embedded Systems: An Example of Online Analysis for Sleep EEG," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-12, 2021, Art no. 4002412, doi: 10.1109/TIM.2020.3047488.
[18] Joseph, Chacko N., et al. "Slow breathing improves arterial baroreflex sensitivity and decreases blood pressure in essential hypertension." hypertension 46.4 (2005): 714-718.
[19] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
[20] Pan, Jiapu, and Willis J. Tompkins. "A real-time QRS detection algorithm." IEEE transactions on biomedical engineering 3 (1985): 230-236.
[21] T.-T. Tran, V.-T. Pham, C. Lin et al., “Empirical mode decomposition and monogenic signal-based approach for quantification of myocardial infarction from mr images,” IEEE journal of biomedical and health informatics, vol. 23, no. 2, pp. 731-743, 2018.
[22] S. Asano, T. Maruyama, and Y. Yamaguchi, "Performance comparison of FPGA, GPU and CPU in image processing." 2009 international conference on field programmable logic and applications, IEEE, pp. 126-131, 2009.
[23] T. Mujahid, A. U. Rahman, and M. M. Khan, “GPU-accelerated multivariate empirical mode decomposition for massive neural data processing,” IEEE Access, vol. 5, pp. 8691-8701, 2017.
[24] D. Chen, D. Li, M. Xiong et al., “GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 6, pp. 1417-1427, 2010. |