In this work, a wavelet transform-based frequency identification method is developed to distinguish the lattice orientation of graphene.
In this paper, a wavelet transform-based frequency ratio identifying method is developed to determine the lattice orientation of graphene theoretically and experimentally.
The discrete wavelet transform (DWT) in combination with EA method is developed to extract significant features from the EEG signals.
A method was developed to normalize, transform, discretize, and add identification techniques to the data.
A numerical method is developed for the inversion of Laplace transform, and its accuracy is shown through examples.
First, an online discriminating method is developed, which alternately uses the empirical mode decomposition (EMD) / Hilbert transform (HT) and square calculation to process the measurement data.
The method is developed such that both fast Fourier transform and discrete Fourier transform algorithms can be used.
A Fourier transform-based method is developed in [15] by the current author.
An online LFO property discrimination method is developed first, which alternately uses empirical mode decomposition (EMD /Hilbert transform (HT) and square calculation to process the measurement data.
A number of hybrid methods have been developed to attempt to combine the best aspects of classical methods and wavelet and contourlet transforms.
