As more features are extracted, the diagnosis becomes increasingly accurate.
Firstly, vibration signals are collected and trend features are extracted.
With each successive layer, increasingly complex features are extracted from the image.
In each case, relevant hemodynamic features are extracted and their uncertainty is quantified.
SIFT features are extracted locally and consist of a vector of 128 feature values for each point in an image.
In a speech recognition system, audio is segmented into sequential chunks of speech, and features are extracted from each chunk.
After extracting the IMFs, several statistical features are extracted from the first six IMFs.
GLCM features are extracted for capturing fault pattern information underlying sound fields.
In addition, four features are extracted using the acquired signals eigenvalues.
Hence, once features are extracted for single load conditions they remain same for all load conditions.
Then, relevant features are extracted from the resulting wavelet subbands by resorting to appropriate statistical models.
