Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/73842
Title: Signal identification based on an eigenvector approach
Authors: Nyan, M.N. 
Tay, F.E.H. 
Seah, K.H.W. 
Keywords: Eigenvector
Multi-dimensional analysis
Signal identification
Issue Date: 2004
Source: Nyan, M.N.,Tay, F.E.H.,Seah, K.H.W. (2004). Signal identification based on an eigenvector approach. Proceedings of the Annual Southeastern Symposium on System Theory 36 : 137-140. ScholarBank@NUS Repository.
Abstract: In this paper, we propose a novel eigenvector-based signal identification algorithm for multi-dimensional signal identification. Signal patterns of 3-D accelerometer output concerning human activities are of low frequency, non-stationary and transient, and can also be termed dynamic or time-varying patterns of arbitrary length. Therefore, a matrix was formed by including features from each dimension of extracted signal pattern, and transformed eigenvectors associated with maximum eigenvalues were used as feature vectors in the identification process. Eigenvectors can preserve the identification efficiency of the feature matrix and can have the smallest number of features for robust, reliable classification in the application of multi-dimensional analysis.
Source Title: Proceedings of the Annual Southeastern Symposium on System Theory
URI: http://scholarbank.nus.edu.sg/handle/10635/73842
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