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Copper Standards for Gigabit Ethernet In the old days of Category 3 cabling, the concept of Gigabit Ethernet operation over twisted pair or any other type of copper cabling was widely ridiculed It was well known that the operation of higher-speed networks required higher frequencies to be launched down the copper pairs, and the combination of crosstalk, attenuation, and return loss clearly made such operation impractical, if not impossible At least this was true for the copper cable technology of the day However, great advances have been made in identifying the phenomena that reduce cable operating distance and frequency range As soon as these phenomena were discovered, the corresponding channel parameters that produced them became obvious, and the race was on to produce even better cable and components Initially, these advances led to the wide use of Category 5 cable, with component and link testing to 100 MHz Topologies such as Fast Ethernet actually only require fundamental frequency bandwidths to about 33 MHz (with spectral distribution to three times the fundamental), because of the complex encoding of data signals used In addition, 4-pair transmission methods, such as 100BaseT4 and 100BaseVGAnyLAN, pioneered the use of the two unused pairs to increase the data rate far above the 2-pair range Advanced cable constructions, such as Category 5e, Category 6, Category AC6,2 and even Category 7 (ISO Classes D, E, Augmented-E, and F) now allow technologists to push the limit far beyond the 100 and 155 Mbps limits that were generally accepted for ordinary Cat 5 only a few years back These and other factors have made Gigabit Ethernet signaling over copper cables practical and desirable 1000BaseT After much debate and testing, the IEEE 8023ab standard for Gigabit Ethernet over twisted-pair copper cabling was released The standard, called 1000BaseT, implements a complete gigabit data interface using Category 5e-grade, 8-pin modular (RJ-45 style) connectors and cables The distance supports the standard 100 m link and conforms to most modern structured cabling designs All four pairs are used to keep the frequency requirements very low The operating specifications are shown in Table 122 This ability to use standard hardware is extremely important in the ultimate acceptance of this technology In addition, the topology and connecting hardware requirements of Gigabit Ethernet are essentially the same as those of 100BaseTX, although all four pairs are used, as with 100BaseT4 That means that any link that can support Fast Ethernet can probably also support Gigabit Ethernet Common. download pdf file on button click in asp.net c# iTextSharp.example | C# Online Compiler | .NET Fiddle
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This is a package of C#, VB.NET Example Project for Spire.PDF for .NET. ... Forms applications without installing Adobe Acrobat or any other external libraries. Note these two rules compactly describe a recursive function that would be very difficult to represent using a decision tree or other propositional representation One way to see the representational power of first-order rules is to consider the In programs are sets of general purpose programming language PROLOG PROLOG, first-order rules such as the two shown above (rules of this form are also called H r clauses) In fact, when stated in a slightly different syntax the above rules on form a valid PROLOG program for computing the Ancestor relation In this light, a general purpose algorithm capable of learning such rule sets may be viewed as an algorithm for automatically inferring PROLOG programs from examples In this chapter we explore learning algorithms capable of learning such rules, given appropriate sets of training examples In practice, learning systems based on first-order representations have been successfully applied to problems such as learning which chemical bonds fragment in a mass spectrometer (Buchanan 1976; Lindsay 1980), learning which chemical substructures produce mutagenic activity (a property related to carcinogenicity) (Srinivasan et al 1994), and learning to design finite element meshes to analyze stresses in physical structures (Dolsak and Muggleton 1992) In each of these applications, the hypotheses that must be represented involve relational assertions that can be conveniently expressed using first-order representations, while they are very difficult to describe using propositional representations In this chapter we begin by considering algorithms that learn sets of propositional rules; that is, rules without variables Algorithms for searching the hypothesis space to learn disjunctive sets of rules are most easily understood in this setting We then consider extensions of these algorithms to learn first-order rules Two general approaches to inductive logic programming are then considered, and the fundamental relationship between inductive and deductive inference is explored c# document to pdf Converting PDF to Text in C# - CodeProject
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