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Fig. 12 Bump solutions as a function of ϵ for a Gaussian threshold distribution.
In Fig. 6 we plot c̅ as a function of ϵ for the shifted exponential and bump distribution.
c̅ as a function of ϵ for (kappa=0.5), (h_{0}=0.3), (L=100) for the exponential distribution (top) and the bump distribution (bottom).
Optimal distribution of processors to two g-mapping RAs as a function of ϵ for different θ ( t s max = 10 ms p b max = 5 %, and Φ(n) = n / 1.5).
Parameter values are (alpha=5), (B=0.76), (beta=3), (h_{0}=0.05), (kappa=0.5) and (sigma^{2}=4) Fig. 13 Bump solutions as a function of ϵ for a Gaussian threshold distribution.
Note, however, that the correction term remains uniformly small since (lambda_{m} simkappa) for all m when (kappall1). Figure 5 shows c̅ as a function of ϵ for a Gaussian distribution of threshold values, as well as results from directly averaging realisations of (21).
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There is an optimal value for η as a function of ϵ.
In Fig. 12 we show the number of solutions of (32)–(33) as well as the number and fraction of stable solutions as a function of ϵ.
The starting precision is (epsilon=10^{-10} epsilon=10^{-10}ngth is anduncthen of ϵ as in equation (4).
Samples of medium/agent opinions as a function of time for ϵ A = 0.42, and for three different regimes represented by p new = 0.001, 0.002, 0.003 (from left to right respectively).
Figure 6 Output SINR comparison as a function of NFR for MIMO MC-CDMA systems with N = 32, SNR = 10 dB, K = 10, and Δ ϵ 1 = 0.25.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com