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You ll find that the palettes and menus of these two types of animation are completely different from one another. In this chapter, I am going to introduce you to the Animation (Frames) palette and show you different methods of animating using this palette.

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White input data { ~ ( i of } variance is fed into the channel and the output sequence is ob) unit served in the presence of white additive noise of variance 0.01. A total of N = 600 samples { ~ ( id ) i,) } are generated and used to train an adaptive filter with M = 4 taps. The filter is trained ( by using the LMS algorithm of this chapter with step-size p = 0.01, as well as two other algorithms derived in s 11 and 14 for comparison purposes, namely, the so-called E-NLMS algorthm with step-size p = 0.2 and E = 0.001, and the RLS algorithm with X = 0.995 and the same value of E . All filters start from the same initial condition w-1 = 0. Figure 10.9 shows two typical instantaneous squared-errorc w e s for each of the algorithms over the first 200 iterations, i.e., the rows show plots of le(i)I2 versus time in two random simulations for each algorithm. Observe how the curves die out quicker for E-NLMS and RLS relative to LMS. By averaging L = 300 such curves for each algorithm, we obtain the ensemble-averaginglearning curves shown in Fig. 10.10. These curves illustrate the fact that the convergence speed increases as we move from LMS to E-NLMS to RLS.





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MESMERIC REVELATION d e t e c t e d . THE FACTS I N THE CASE O M . VALDEMAR F 1004,363 363,1095 detected

Phase 1 Estimation Not just limited to E-911 services, location proximity information in phase 1 can be applied to other applications such as location-sensitive billing, fraud detection, cellular system design, and resource management Location-sensitive billing allows cellular carriers to offer different rates depending on whether a mobile station is used at home, in an of ce, or on the road System designer can use the load information of each location to better position and tune cells, and thus improve spectrum utilization ef ciency In cellular networks, a service coverage area is divided into smaller hexagonal areas called cells Each cell is served by a basestation with a systemwide unique station ID Several basestations are then controlled by a radio network controller (RNC), a number of which in turn are managed by a mobile switching center (MSC) A mobile station is active if it is powered on.

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Mean-square stability of the filter requires la1 < 1. Actually, it is easy to see that a: > 0 in this example since a: can be written as the sum of two positive terms,

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Since the exact cell location of a mobile station is known to the network during the call, the main issue of location proximity is the location estimation of a mobile station between two consecutive calls Two major operations are involved in location proximity: location update and paging [19] The location update is performed by an active mobile station to fresh its location information state in the cellular network The paging operation is carried on by the cellular network to alarm a mobile station about upcoming events such as a coming call There is a basic tradeoff between the updating cost and paging cost The updating cost is mainly the bandwidth usage and mobile station power consumptions The paging cost is the paging traf c load and the paging delay.

Category 1 (Cat1): Used for low-speed data applications. Category 2 (Cat2): Used for medium-speed data applications. Category 3 (Cat3): Used for high-speed data applications. Category 4 (Cat4): Used to transfer high-speed data over long distances. Category 5 (Cat5): Used to transfer high-speed data over long distances. This is the most commonly used unshielded twisted-pair cabling type.

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