New PDF release: Computer: A Very Short Introduction (Very Short

By Darrel Ince

ISBN-10: 0199586594

ISBN-13: 9780199586592

What's the simple nature of the trendy desktop? How does it paintings? How has it been attainable to squeeze lots strength into more and more smaller machines? what's going to the subsequent generations of desktops glance like?

In this Very brief advent, Darrel Ince seems on the uncomplicated suggestions in the back of all desktops, the alterations in and software program that allowed pcs to turn into so small and average, the demanding situations produced via the pc revolution--especially entire new modes of cybercrime and defense matters, the net and the arrival of "cloud computing," and the promise of complete new horizons commencing up with quantum computing and computing utilizing DNA

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Extra info for Computer: A Very Short Introduction (Very Short Introductions)

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The weights used for the estimation of the correlation coefficients, variances, and noise variances for equations (6) and (17) are set to w = [1,1,1] 3 . We employ four criteria to measure the closeness of the selected localization scale with the noise-free image: (i) mean absolute difference between the pixel-values of the selected scale and the noise-free image (MMAD), (ii) the PSNR, (ii) the structural similarity index (SSIM)[23], and (iv) the correlation between the selected scale and the noise-free.

Furthermore, we experienced that coarser scale structural changes are not always detected due to a too fine sampling, which implies smoother transitions. The main difference between the proposed method and the successive local minima of the decorrelation criterion is that the local minima are better discriminated at the coarser scales, which results in selecting more relevant coarser scales. 5 Conclusions In this paper we propose a new method for the selection of a localization scale and explore the scale-space discretization capabilities of the Mr´azek and Navara’s decorrelation criterion [1].

B) Gaussian noise with σ = 30 added. (c,d,e,f) Denoising result of the iterated nonlocal means filter with the sorting criterion after 1, 2, 4, and 10 iterations (n = 100). nonlocal means filter, in particular with its iterated version, look quite satisfactory. Clearly, iterating the filter improves the regularity of the texture pattern. This can lead to interesting effects, if the filter is applied to an image that is mainly non-regular. Such a case is shown in Fig. 5. For an increasing number of iterations, the filter acts more and more coherence enhancing reminiscent of curvature motion or coherence enhancing anisotropic diffusion [14].

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Computer: A Very Short Introduction (Very Short Introductions) by Darrel Ince

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