tailieunhanh - Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 8

Tham khảo tài liệu 'biosignal and biomedical image processing matlab-based applications muya phần 8', ngoại ngữ, anh văn thương mại phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | PCA and ICA 267 Figure Scree plot of eigenvalues from the data set of Figure . Note the shape break at N 3 indicating that there are only three independent variables in the data set of five waveforms. Hence the ICA algorithm will be requested to search for only three components. Ox Do PCA and plot Eigenvalues figure U S pc svd X 0 Use single value decomposition eigen diag S .A2 Get the eigenvalues plot eigen k Scree plot .labels and title. Q. nu_ICA input Enter the number of independent components Compute ICA W jadeR X nu_ICA Determine the mixing matrix ic W X Determine the IC s from the mixing matrix figure Plot independent components plot t ic 1 -4 k t ic 2 k t ic 3 4 k .labels and title. The original source signals are shown in Figure . These are mixed together in different proportions to produce the five signals shown in Figure . The Scree plot of the eigenvalues obtained from the five-variable data set does show a marked break at 3 suggesting that there in fact only three separate components Figure . Applying ICA to the five-variable mixture in Figure Copyright Marcel Dekker Inc. All rights reserved. Marcel Dekker Inc. 270 Madison Avenue New York New York 10016 TLFeBOOK Mixed Signals 0 1l 2 XI3 4M5WK 700500900 1D00 Time msec Figure Five signals created by mixing three different waveforms and noise. ICA was applied to this data set to recover the original signals. The results of applying ICA to this data set are seen in Figure . 268 Copyright Marcel Dekker Inc. All rights reserved. Marcel Dekker Inc. 270 Madison Avenue New York New York 10016 TLFeBOOK PCA and ICA 269 Figure The three independent components found by ICA in Example . Note that these are nearly identical to the original unmixed components. The presence of a small amount of noise does not appear to hinder the algorithm. recovers the original source signals as shown in Figure . This figure dramatically demonstrates the ability of this approach to .

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