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P(t , Tj )



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No term has been coined yet for the fifth phase of computing shown in Table 14.1. The idea here is to come up with a framework and underlying infrastructure required for enabling true Services on Demand such that the consumer should be able to use any device to consume a particular service, using any transport mechanism, such as Web (HTTP), JXTA, or Jini. JXTA technology, open sourced by Sun Microsystems, provides a standard framework for building peer-to-peer (P2P) services. JXTA defines a set of open protocols that enables building P2P applications in any language, including Java. Currently, the JXTA community has provided JXTA runtime implemented for Java language. For more information on JXTA, visit the JXTA community Web site at www.jxta.org. Jini is a distributed computing technology, originally from Sun Microsystems, that enables networked devices and software services to interconnect with each other dynamically. These networked devices and software services can form communities on an ad-hoc basis without any a priori knowledge or configuration. This enables the creation of network services (software or hardware) that are highly adaptive to change. Jini technology enables the building of such adaptive networks that can be evolved over time and space. For more information on Jini, visit the Jini official Web site at wwws.sun.com/software/jini.

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where M (s) is the MGF of any of the l s with mean value d = Pd / 2 , which also represents the average SNR of the desired signal per antenna. Before proceeding with the evaluation of average BEP, we rst specify the eigenvalues for the single-interferer case. It has been shown in several places in the literature [5 8] that the eigenvalues of the covariance matrix of (11.3) are given by L L 2 PI |cI n |2 + 2 = PI I n + 2 , l=1 (11.8) l = n=1 n=1 2 , l = 2, 3, . . . , L that is, L 1 of them are constant and one of them, 1 , is a RV. Making this substitution in (11.7) gives M t | 1 (s) = M (s)

This criterion is based on looking at observations (x) in all k clusters, where m(Ci) is the mean for the cluster. When the error criterion becomes less than a predetermined value, the clustering stops. In the following example, the normalized data shown in Table 3.26 is clustered into three groups using k-means clustering. The euclidean distance is used to calculate the distance between observations. Initially, one observation is assigned to each of the three clusters. In this example, the assignment is done randomly. Observation B is assigned to cluster 1, observation C to cluster 2, and observation E to cluster 3. Next, all remaining observations are placed in the cluster closest to them. Observation B is placed into cluster 1; observations A and C are placed into cluster 2; and observations D and E are placed into cluster 3. Once the initial clusters have been populated, the mean for each cluster (centroid) is calculated, as shown in Table 3.27. Each observation is then re-examined to determine whether it belongs to a different group. In this example, this examination results in no changes to the group assignment, and hence these clusters are the nal grouping. The k-means clustering, described in this section, is dependent to some extent on the initial assignment of observations, especially if the initial assignment of

Choosing a Linux Distribution ..........................................................................................244 Linux at Work ........................................................................................................244 Other Distributions ................................................................................................245 Getting Your Own Linux Distribution ..............................................................................245 Finding Another Linux Distribution ........................................................................246 Understanding What You Need ..............................................................................247 Downloading the Distribution ................................................................................247 Burning the Distribution to CD ..............................................................................248

Average SNR per symbol (dB)

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