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Extra info for A bad network problem for the simplex method and other minimum cost flow algorithms

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For a clear visualization, only the estimates of the first three missing samples are shown in Fig. 4. The real and imaginary parts of the estimated samples as a function of frequency are plotted in Figs. 4(b), respectively. All estimates are close to the corresponding true values, which are also indicated in Fig. 4. It is interesting to note that larger variations occur at frequencies where strong signal components are present. The results displayed so far were for one randomly picked realization of the data.

5. Besides these spectral lines, Fig. 25. The data sequence has N = 128 samples among which 51 (40%) samples are missing; the locations of the missing samples are chosen arbitrarily. 01. In Fig. 1(b), the APES algorithm is applied to the complete data and the resulting spectrum is shown. The APES spectrum will be used later as a reference for comparison purposes. The WFFT spectrum for the incomplete data is shown in Fig. 1(c), where the artifacts due to the missing data are readily observed. As expected, the WFFT spectrum has poor resolution and high sidelobes and it underestimates the true spectrum.

1(c), where the artifacts due to the missing data are readily observed. As expected, the WFFT spectrum has poor resolution and high sidelobes and it underestimates the true spectrum. Note that the WFFT spectrum will be used as the initial estimate for the GAPES and MAPES algorithms. Fig. 1(d) shows the GAPES spectrum. GAPES also underestimates the sinusoidal components and gives some artifacts. 16). Figs. 01, 51 (40%) missing samples]. (a) True spectrum, (b) complete-data APES, (c) WFFT, (d) GAPES with M = 64 and = 10−2 , (e) MAPES-EM1 with M = 64 and and (f ) MAPES-EM2 with M = 64 and = 10−3 .

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A bad network problem for the simplex method and other minimum cost flow algorithms by Zadeh N.


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