[[1] Boudraa, A. O., Cexus, J. C. (2007), EMD-Based Signal Filtering, IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 6, pp. 2196-2202.10.1109/TIM.2007.907967]Search in Google Scholar
[[2] Bastiaans, M.J., Alieva, T., Stankovic, L. (2002), On rotated time-frequency kernels, IEEE Signal Processing. Letters, vol. 9 (11), pp.378-381.10.1109/LSP.2002.805118]Search in Google Scholar
[[3] Boashash B. (2016), Time-Frequency Signal Analysis and Processing: A Comprehensive Review, 2nd ed., Eurasip and Academic Press Series in Signal and Image Processing, Academic Press.]Search in Google Scholar
[[4] Rilling G., Flandrin P. and Goncalves P. (2003), On Empirical Mode Decomposition and its algorithms, IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado (I).]Search in Google Scholar
[[5] Rilling G., Flandrin P., Goncalves P. and Lilly. J. M., Bivariate Empirical Mode Decomposition, Signal Processing Letters (submitted).]Search in Google Scholar
[[6] Huang N. E. et al.(2003), A confidence limit for the Empirical Mode Decomposition and Hilbert spectral analysis, Proc. Royal Soc. London A, vol. 459, pp. 2317-2345.10.1098/rspa.2003.1123]Search in Google Scholar
[[7] Janosi, I. M., and R. Muller (2005), Empirical mode decomposition and correlation properties of long daily ozone records, Phys. Rev. E, 71, 056126, doi:10.1103/PhysRevE.71.056126.10.1103/PhysRevE.71.05612616089621]Search in Google Scholar
[[8] Kopsinis Y, McLaughlin S. (2009), Development of EMD-based denoising methods inspired by wavelet thresholding, IEEE Transactions on Signal Processing, 57:1351-1362.10.1109/TSP.2009.2013885]Search in Google Scholar
[[9] Kopsinis, Y., McLaughlin, S. (2009) Development of EMD based denoising methods inspired by wavelet thresholding, IEEE Transactions on Signal Processing, vol. 57, pp. 1351-1362.10.1109/TSP.2009.2013885]Open DOISearch in Google Scholar
[[10] Kay, S. (2006), Intuitive Probability and Random Processes Using MATLAB, Springer Science & Business Media, Berlin.10.1007/b104645]Search in Google Scholar
[[11] Khan, N.A., Boashash, B.(2013), Instantaneous frequency estimation of multicomponent nonstationary signals using multiview time-frequency distributions based on the adaptive fractional spectrogram, IEEE Signal Processing Letters, vol. 20 (2), pp.157-160.10.1109/LSP.2012.2236088]Open DOISearch in Google Scholar
[[12] Sejdic, E., Djurovic, I., Jiang, J., (2009), Time-frequency feature representation using energy concentration: an overview of recent advances, Digital Signal Processing, vol. 19 (1), pp. 153-183.10.1016/j.dsp.2007.12.004]Open DOISearch in Google Scholar
[[13] Stankovic, L., Dakovic, M, Thayaparan, T. (2013), Time-Frequency Signal Analysis with Applications, Artech House, Boston.]Search in Google Scholar
[[14] Tsolis, G. S., Xenos, T. D. (2011), Signal denoising using empirical mode decomposition and higher order statistics, Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 4, no. 2, pp. 91-106.]Search in Google Scholar