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In modern society, more and more people are suffering from stress. The accumulation of stress will result in poor health condition to people. Effectively detecting the stress of human being in time provides a helpful way for people to better manage their stress. Much work has been done on recognizing the stress level of people by extracting features from the bio-signals acquired by physiological sensors...
Considering the problem of parametric fault diagnosis of nonlinear circuits, an approach based on the wavelet filtering technology and principal components analysis (PCA) is proposed. Firstly, the output signals filtered by DB2 wavelet packet are used to detect the faults. Then, the energetic values in eight subbands are calculated as the principal components. By comparison with the eigenvalues of...
The wavelet filtering technology and principal components analysis (PCA) is used to extract the signatures of circuits in this paper. Firstly, the output signal of the analog circuits is filtered by Harr wavelet packet. Then, the energetic values in eight subbands are extracted as the principal components. By comparison with the eigenvalues of the energetic relative matrices, different states of the...
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