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The basic dissimilarity between wavelet and Fourier analysis is that the wavelet basis function can distinguish local events at different times at the same frequency.
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Then the network basis function can be formed.
Several basis functions can jointly mask one or several other basis functions.
Generating functions for the Bernstein basis functions can be defined as follows.
The most useful polarization parameter on the basis of which we can distinguish the breast cancer disease from normal is depolarization index.
Consequently, we can distinguish between two categories of functions: empirical (e.g. attributes) and analytical.
This forms the basis for future experimental testing of model assumptions and predictions that can distinguish between the different scenarios.
Hence, we can distinguish signal from noise by analyzing the SCD function.
One can distinguish polygenetic GXE research in terms of the basis used for creating multi-gene composites.
These basis functions are distinguished by the property that they fall off algebraically in momentum space and can be exactly Fourier Bessel transformed.
Roughly, we can distinguish two strategies.
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