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To improve the convergence performance of the adaptive filtering and avoid excessive sub-band decomposition, sub-band decomposition method to decompose the whole band based on the autocorrelation matrix eigenvalue of the signal is proposed. Performance analysis demonstrates that, comparing with traditional algorithms, the proposed method can efficiently perform better with an equal number of sub-bands...
In this paper we proposed a linearly constrained minimum variance receiver for a space-time coded multicarrier (MC) CDMA system in frequency selective fading channels. It is shown that in the proposed receiver the channel can be blindly estimated as the eigenvector that corresponds to the maximum eigenvalue of an autocorrelation matrix, and then, efficient algorithms for subspace tracking can be used...
This paper introduces an on-line unsupervised learning neural network (NN) for adaptive feature extraction via principal component analysis (LEAP) of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS) signals. The proposed method is based on eigen-analysis of DS-SS signals. The PN sequence and the strength of the signal can be extracted by the first and second principal eigenvectors...
This paper introduces an on-line unsupervised learning neural network (NN) for adaptive feature extraction via principal component analysis (LEAP) of lower signal to noise ratios (SNR) direct sequence code-division multiple-access (DS-CDMA) signals. The proposed method is based on eigen-analysis of DS-CDMA signals. The received signal is firstly sampled and divided into non-overlapping signal vectors...
In this study, we proposed a genetic adaptive filter to removing power-line interference. In previous work, the proposed structure, which extracts the interference component from the input biomedical signal to be a reference signal of the adaptive filter to estimate power-line interference, is effective for removing interference. Since this adaptive filter with least-mean square algorithm is sensitive...
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