[1] I. Daubechies, J. F. Lu1 and H.-T. Wu, “Synchrosqueezed Wavelet Transforms: An Empirical Mode Decomposition-Like Tool,” 25 July 2010.
[2] S. Haykin and L. Fellow, “Cognitive Radio: Brain Empowered Wireless Communications,” IEEE Journal, Vol. 23, No. 2, 2005.
[3] Gianfelici, F. Biagetti, G. Crippa and P. Turchetti, “AM-FM Decomposition of Speech Signals: An Asymptotically Exact Approach Based on the Iterated Hilbert Transform,” IEEE Conference Publication, July 2005, pp. 333-338.
[4] T. Backstrom, “Parametric AM/FM Decomposition for Speech and Audio Coding,” IEEE Conference Publication, October 2009, pp. 333-336.
[5] T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Communication Surveys & Tutorials, Vol. 11, No. 1, 2009. doi:10.1109/SURV.2009.090109
[6] S. Meignen and V. Perrier, “A New Formulation for Empirical Mode Decomposition Based on Constraint Optimization,” 2007. http://wwwljk.imag.fr/membres/Valerie.Perrier/PUBLI/EMD-optim07.pdf
[7] G. Thakur, E. Brevdo, N. S. Fuˇckar and H.-T. Wu, “The Synchrosqueezing Algorithm for Time-Varying Spectral Analysis: Robustness Properties and New Paleoclimate Applications,” 2012. arXiv:1105.0010v2[math.CA]
[8] P. Flandrin, “Time-Frequency/Time-Scale Analysis,” Academic Press, San Diego, 1999.
[9] A. Graps, “An Introduction to Wavelets,” IEEE Computational Science and Engineering, Vol. 2, No. 2, 1995. doi:10.1109/99.388960
[10] M. Clausel, T. Oberlin1 and V. Perrier, “The Monogenic Synchrosqueezed Wavelet Transform: A Tool for the Decomposition/Demodulation of AM-FM Images,” 2012. http://arxiv.org/abs/1211.5082