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FastHenry is a program capable of computing the frequency-dependent self and mutual inductances as well as the resistances of a 3D system of conductors. The data describing the geometry of the conductors and the frequencies of interest must be provided in an input le. This le speci es every conductor in the system as a sequence of rectilinear segments connected between nodes where a node is a point in 3D space, as shown in Fig. 2.9.1. Every segment has a nite conductivity and has the shape of a parallelepiped whose height and width can be assigned. Any section of a segment can be further divided into an arbitrary number of parallel laments, that is, parallelepipeds with smaller cross sections than the original one. This is done to validate the assumption that every lament carries a uniform current. In fact, the current may no longer be uniformly distributed along the cross section of a conductor segment when the frequency increases due to the skin effect. However, if the section is divided into smaller laments, the current can be reasonably approximated as uniform in the laments. In this way, it is possible to model the high-frequency effects on the segments. Ability to specify an arbitrary discretization of the volume of the conductors affects the accuracy of the results and is better as the discretization is re ned. It is interesting to note that the computational complexity of the analysis technique used in FastHenry grows only linearly with the number of laments required to discretize the conductors. The results are provided in the form of a Maxwell impedance matrix Z R j L . The results can then be converted into equivalent SPICE-like lumped-element circuit models with a utility called MakeLCircuit provided with



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where a1 ; a2 ; ; a3n are the 3n Boolean variables corresponding to the attributes Attr1 ; Attr2 ; ; Attr3n of each object in the information system The expression fA is reduced to the set of all prime implicants of fA that determines the set of all reducts of A Step 3 The self-organizing map is created with 3n inputs (Section 381), which correspond to the attributes of the information table, and a competitive layer of N N grid of units where N is the total number of implicants present in discernibility functions of all the objects of the information table Step 4 Each implicant of the function fA is mapped to a unit in the competitive layer of the network, and high weights are given to those links that come from the attributes, which occur in the implicant expression.

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The idea behind this is that when an input pattern belonging to an object, say Oi, is applied to the inputs of the network, one of the implicants of the discernibility function of Oi will be satis ed, and the corresponding unit in the competitive layer will re and emerge as the winning unit All the implicants of an object Oi are placed in the same layer, while the implicants of different objects are placed in different layers separated by the maximum neighborhood distance In this way the initial knowledge obtained with rough set methodology is used to train the SOM This is explained with the following example Let the reduct of an object Oi be Oi : F1low ^ F2medium _ F1high ^ F2high where F low ; F medium ; and F high represent the low, medium, and high values of the corresponding features.

Since you ll probably use your phone for both work and personal needs, you need to think about your overall lifestyle when choosing one. Some people need a high degree of connectivity. Some people only use their phone occasionally. Some people want an all-in-one device to minimize weight and inconvenience. Some people choose two devices: a feature phone for easy calling and texting along with a smart phone for email and web browsing. Swisscom researcher Stefana Broadbent says research shows that mobile workers communicate on the road but do the bulk of their work back at their desks. See http://www.economist.com/printedition/ displayStory.cfm story_id=9249302 for more.

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Then the implicants are mapped to the nodes of the layer as shown in Figure 38 Here high weights H are given only to those links that come from the features present in the implicant expression Other links are given low weights 384 Experimental Results.

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