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This algorithm utilized the relationship of the entropy with the enthalpy and the isentropic efficiency.
This algorithm utilized two toolboxes from MATLAB (multi-objective genetic algorithm toolbox and parallel computing toolbox) and MSC© NASTRAN finite element solver.
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This algorithm utilizes the signum of the input regressors at each subband during the adaptation.
Because this algorithm utilizes quantization techniques, it is called QKLMS-MDL.
This algorithm utilizes the smooth approximation technique, solving the derivative problem of L 1 norm.
This algorithm utilizes the law of total probability to form the distributed architecture, thus avoiding the increment of estimation error.
This algorithm utilizes the minimum upper bound to choose the best candidate from the LLS set with a high probability.
This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed watermark into the image.
This algorithm utilizes the underlying anycast mechanism to enable effective coordination among the nodes, thus obviating the need for any external control channel.
This algorithm utilizes a standard space database (SSDB) as a key scale to calculate the Mahalanobis distance (MD) from the center of gravity in the SSDB.
This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com