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Convert PDF to multipage TIFF in C# .NET - Tallcomponents
NET 3.0; Created: 3/10/2010; Tags: Convert PDF Images. This article shows how to convert PDF to multipage TIFF in C# using PDFRasterizer.NET 3.0.

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Most algorithms require some xed overhead costs regardless of input. For example, an algorithm to count the number of rows in a table might require overhead to allocate space for and initialize an integer variable to be incremented for each row. When the input is large, xed overhead is likely to be insigni cant relative to the total execution cost. Comparing execution costs for large inputs provides more insight into the essence of an algorithm s computational complexity. In the metal notebook example, doubling the size of a large notebook increased the cost of materials by a factor of about 3.87, and you can check that doubling the size of an extremely large notebook increases the cost of materials by a factor of almost exactly 4.0. The relationship between hinge length (in inches) and materials cost (in dollars) for notebooks having the same proportions as a 5-by-7-inch notebook can be expressed mathematically as MaterialsCost(h) = 0.1h+0.028h2. This cost function is a quadratic polynomial. Complexity is often expressed by the relationship between input size and cost for inputs large enough that xed overhead costs don t matter. Technically, this is the asymptotic complexity. For large values of h in the preceding example, the quadratic term 0.028h2 dominates the cost, and doubling the input size approximately quadruples the cost. The single expression h2 characterizes this doubling-quadrupling behavior, and the cost in this case is said to have asymptotic order h2.



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I'm c# a developer, i always use this pdf to image converter(https://www.iditect.​com/tutorial/pdf-to-image/) to convert pdf to tiff, and this tool also ...

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The scripts below demonstrate how to render PDF to TIFF image in C# and Visual Basic .NET using Bytescout PDF Renderer SDK. C# ...

Complexity is often expressed using Big Oh notation. In Big Oh notation which uses not only the big oh symbol O but also big theta ( ), little oh (o), big omega ( ), and others the asymptotic cost in the previous example can be expressed this way: MaterialsCost(n) (n2), or the cost function is in big theta of n-squared. You can also say the cost is n-squared or grows like n-squared. For many algorithms that depend on more than the size of the input, it may be possible to express the minimum and maximum possible costs as functions of the input size. These are called the best-case complexity and worst-case complexity, respectively. It may also be possible to determine lower and upper bounds on complexity. Big Oh notation is useful in describing these various properties of complexity as well as other asymptotic properties of an algorithm s complexity.

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The .NET Framework supports encryption algorithms that can be broken down into two groups: symmetric and asymmetric. Symmetric algorithms use the same key to encrypt and decrypt data, whereas asymmetric algorithms use a public and a private key. Symmetric algorithms are usually used to encrypt large amounts of data; the processing requirements are less than for asymmetric encryption but are considered to be less secure because both the originator and the recipient of the encrypted data must possess a copy of the same key. Asymmetric encryption requires more computation, but the originator and the recipient don't have to use the same key; asymmetric encryption is often used to encrypt the keys for symmetric schemes.

I won t de ne the Big Oh notations here; the de nitions are quite technical. However, I will point out that you re more likely to hear someone mention Big Oh than Big Theta, which I used earlier. If you hear students of computer science refer to Big Oh, they are almost certainly talking about algorithmic complexity, but they could mean Big Anything because the meanings of the various notations are frequently confused. The Big Oh family of notations are generally attributed to the late-nineteenth- and early-twentieth-century number theorists Landau and Bachmann. Although they look like real-valued functions, the expressions (n2), O(n), o(log n), and so on are not real-valued functions. Instead, they are sets of functions, whence the preceding language in (n2) . Unfortunately, this notation is used in a number of confusing (some might say careless, sloppy, or wrong) ways. In particular, f = O(g) is commonly written to mean not that f equals O(g) but that f equals some element of O(g).

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