By National Research Council, Division on Engineering and Physical Sciences, Board on Manufacturing and Engineering Design, Commission on Engineering and Technical Systems, Committee on Machine Tool Industry
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Extra resources for The U.S. Machine Tool Industry and the Defense Industrial Base
Blazer, S. Senn, P. Stadelmann, N. Felpar, and W. Fichtner. (2006, August). On the “Silicon implementation of the SPIHT algorithm for compression of ECG records,” [on line]. ch/~mwegmuel/ethz2/docs/SPIHT 6. M. Pooyan, A. Taheri, M. Moazami, I. Saboori, “Wavelet compression of ECG signal using SPIHT algorithm,” IEEE Transaction on Engineering, Computing and Technology, vol. 2, December, 2004. 7. S. Mallat. “A theory for multiresolution signaldecomposition: the wavelet representation,” IEEE Pattern Anal.
18 __________________________________________________________________ Automated Classification of Deceleration Patterns in Fetal Heart Rate Signal using Neural Networks find the optimal set of parameters the number of inputs was changing during experiments. It turned out that the best were the networks with radial basis functions (RBF) and multilayer perceptrons (MLP). The RBF networks were learned using the k-means algorithm – for obtaining weights of neurons, k-nearest neighbor algorithm – for obtaining the radii of neurons, and pseudo-invert algorithm (linear least squares optimization) – for obtaining the weights of output neuron.
Ch/~mwegmuel/ethz2/docs/SPIHT 6. M. Pooyan, A. Taheri, M. Moazami, I. Saboori, “Wavelet compression of ECG signal using SPIHT algorithm,” IEEE Transaction on Engineering, Computing and Technology, vol. 2, December, 2004. 7. S. Mallat. “A theory for multiresolution signaldecomposition: the wavelet representation,” IEEE Pattern Anal. , vol. 11, no. 7. pp. 674-693, 1989. 8. M. Misiti, Y. Misiti, G. Openheim y J. M. Poggy. “Wavelet Toolbox, User’s Guide,” The Math work, Inc. 2000. 9. J Tompkins. Biomedical digital signal processing.