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Compared to the traditional plane-wave methods, our methods allow for two-level parallelization, which is particularly interesting for large scale parallelization.
Compared with previous bimodal emotion recognition methods, e.g., SVM, CART, Boosting, and rule-based decision fusion methods, our methods can give the better emotion recognition results.
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Compared with the traditional related methods, our method showed a better performance.
Thus, compared to earlier methods, our method provides more flexibility for solving surface modeling problems.
Compared with other detection methods, our method can achieve better performance.
In comparison with the existing methods, our method is aimed for increasing recognition accuracy and reliability.
Different from the traditional hand-engineered representation methods, our method uses CNN to automatically learn the related features of passengers.
Compared to existing methods, our method involves less invasive measurement on the skin, and is equipped with computational methods for identifying dynamic skin tension automatically.
Different from current methods, our method can solve the problem.
Although there exist some M-ary UWB methods, our method is different to these methods.
Compared to search-based SNR detection methods, our method is not limited to given SNR levels.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com