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The joint photographic experts group (JPEG) 2000 standard is designed to update and replace the current JPEG standard; one can use wavelets instead of the discrete cosine transform (DCT) to perform decomposition of the image.
We propose to use wavelets for extracting and quantifying structural variations from the CGH data and classifying it into distinct levels or 'scales'scales
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So, it is more common to use wavelet as sparse basis in CS-based image fusion.
These agents could use wavelet analysis to improve their response performance.
It is possible to use wavelet functions to detect transient property components due to similar structures.
This chapter introduces the concept of wavelets and describes how to use wavelet-based decompositions in compression schemes.
To characterize multiple spatial gene expression patterns in embryo images, we use wavelet transforms to generate features.
Some use wavelet-based transform such as using discrete wavelet transform to denoising the spectrum (Coombes et al., 2005b; Morris et al., 2005; Randolph and Yasui, 2006) or continuous wavelet-based pattern matching to detect peaks (Du et al., 2006).
Wavelet-networks are feed-forward networks using wavelets as activation functions.
The forecasting method is accelerated by using wavelets, taking into account the sparsity of wavelet coefficients.
Wavenet is a single hidden layer feedforward neural network, which uses wavelets as its activation functions.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com