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Wavenet is a single hidden layer feedforward neural network, which uses wavelets as its activation functions.
For the recognition of the voice commands, the system uses wavelets and neural networks for feature extraction and classification, respectively.
A new wavelet-based method for solving population balance equations with simultaneous nucleation, growth and agglomeration is proposed, which uses wavelets to express the functions.
The first approach uses wavelets as a basis and results in a differential-algebraic system of equations, where the algebraic part arises from boundary conditions.
This paper uses wavelets in the detection comparison of breast cancer among the three main races in Malaysia: Chinese, Malays, and Indians followed by a system that evaluates the radiologist's findings over a period of time to gauge the radiologist's skills in confirming breast cancer cases.
Specifically, WaVPeak uses wavelets.
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First, the study uses wavelet smoothing method to eliminate noise from the images.
The detection method uses wavelet energy (WE) and zero crossing rate (ZCR) as detection parameters.
Another prominent technique uses wavelet thresholding or comparable methods.
Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation.
An alternative approach uses wavelet expansions to compare images and reprojections, demonstrating improvements in speed and robustness to noise.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com