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VPCs also allow fast recovery in case of path failure, since an alternate path can be brought up almost instantly without interrupting the data ow, as there is no need to cycle through all the VCCs individually. In traditional PSTN terminology, VCLs are similar in function to trunks, VCCs to end-to-end connections, VPLs to trunk groups, and VPCs to routes. VCI values are unique only within a VPL, and VPI values are unique only within a physical interface. A VCC is uniquely identi ed, on a given physical link, by the paired values of VPI and VCI. Both VCLs and VPLs, and hence VCCs and VPCs, are unidirectional; ATM communication, however, is bidirectional, composed of paired unidirectional VCCs and VPCs (one per direction), both following the same path through the network. Since transmission parameters can be set up independently for each direction, a unidirectional connection can be obtained simply by setting the bandwidth in one direction to zero. One exception is ATM s support of point-to-multipoint connections. These are unidirectional connections from one source (the root) to multiple destinations (the leaves in a tree). ATM cells are issued once at the root and then replicated as necessary by the network for delivery to the leaves [6]. Possible applications are video or audio distribution. VCCs and VPCs can be established semipermanently by provisioning or dynamically via ATM signaling. In the rst case, they are called permanent virtual channel connections (PVCCs) and permanent virtual path connections (PVPCs); in the second case, they are called switched virtual channel connections (SVCCs) and switched virtual path connections (SVPCs). VPCs are most commonly setup semipermanently by network administrators. VCCs and VPCs can be established for user-to-user, user-to-network, or network-to-network connections.

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Inestimationthe goal is to determine a set of parametersas precisely as possible from noisy observations. Wewill focus on the case where the estimators are linear, that is, the estimates for the parameters are computed as linear combinations of the observations. This problem is closely related to the problem of computing the coefficients of a series expansion of a signal, as described in 3. Linear methods do not require precise knowledge of the noise statistics; only moments up to the second order are taken into account. Therefore they are optimal only under the linearity constraint, and, in general, non-linear estimators with better properties may be found. However, linear estimators constitutethe globally optimal solution as far as Gaussian processes are concerned [ 1491.

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impulse response after equalization to have minimum ISI [Luc65, Luc66, Pro01] For MMSE equalization, tap gains are adjusted to minimize the mean square of the error signal between equalized signal and the actual transmitted symbols [Pro01] The equalizer is a discrete-time lter intended to compensate for the amplitude and phase distortions of the channel One can see intuitively that for an in nite-tap equalizer, the sampled frequency response of the equalizer should be the inverse of the frequency response of the channel, which is a correct characterization of zero-forcing equalization In MMSE equalization, the tap gains are set so as to minimize both channel distortions and additive noise, and therefore the frequency-response function of the equalizer depends in part on the variance of the noise Today, MMSE is the dominant criterion used in the design of equalization algorithms, and various versions of the equalizer architectures are developed with this criterion.

This sequence is tending to the root, but it is doing so rather slowly, with alternate terms being larger than and less than the root. The second process in the problem also tends to the root, but much faster. By 8-digit calculator: 3, 3.7013 .. " 3.6055 .. " 3.70153 .. " 3.6888 .. " 3.6998 .. " 3.7015615 . "

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