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By contrast, in open-loop algorithms (also known as periodic broadcasting), all multicast transmissions are prescheduled in a xed pattern irrespective of the user arrival pattern Thus, the bandwidth requirement at the media server is xed regardless of the number of users in the system In other words, open-loop algorithms in principle can serve as many concurrent users as the network will allow Unlike closed-loop algorithms where transmission schedules are dynamically determined at the media server (or a separate scheduler), in open-loop algorithms the complexity is shifted to the clients, who will need to determine which media data and when to receive media data from one or more multicast channels to sustain continuous media playback Comparing closed-loop and open-loop architectures, the performance (eg, start-up latency) of closed-loop architectures depends on the system load (ie, user arrival rate), and generally the performance deteriorates with higher system load.

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By contrast, open-loop architectures have invariant performance irrespective of the system load Consequently, at light system load closed-loop architectures can achieve better performance while open-loop architectures perform better at high system load..

$feed->enable_order_by_date(true);

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can classify N < nx prototype vectors s(i) if they are linearly independent, that is, if a1s(1) + a2s(2) + + aNs(N) = 0 implies a1 = a2 = = aN = 0 This is true in many applications, for example, for almost all image-pattern prototypes6 The desired optimal connection-weight matrix W can be computed explicitly if each classifier input x is simply one of N linearly independent prototype patterns s(i) with additive zero-mean noise W is then the Penrose pseudoinverse of the nx N pattern matrix XT whose N columns are the given nx-dimensional prototype vectors7 But the successive-approximation technique described below is simpler, and it is not restricted to additive noise 6-9 The LMS Algorithm We start with random connection weights W[i, k] and feed our one-layer network (6-17) successive noise-perturbed prototype patterns, say x = s + a * ran() To reduce the error measure (6-16) at each step, Widrow s LMS algorithm (least-mean-squares algorithm) or delta rule [8,9] repeatedly moves each connection weight W[i, k] in the negative-derivative direction by assigning

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This could just as well have been in the single-feed example as well, but if you just output a single feed you can probably rely on the fact that the latest item will come at the top, so it would be a bit redundant. 266 After that there s the foreach loop again, starting at the first item (the 0) and moving onward to the tenth (the 10), after which the loop is over and hence you get 10 items. Again, SimplePie is huge, but this is a start at least. At the time of writing, the help section in the WordPress Codex is pretty scarcely populated given that fetch_feed() was added in version 2.8, but I m sure it will be completed with more examples as time goes on. Meanwhile, turn to the SimplePie documentation for an extensive look to what you can do with this RSS parser.

W[i, k] = W[i, k] 1 lrate g/ W[i, k] (i = 1, 2, , N; k = 1, 2, , nx) (6-18) 2

This chapter has presented an overview of network multicast in general and multicast streaming in particular. The focus of Part II will be on techniques to support on-demand multicast streaming, which are classi ed into closed-loop algorithms ( 17), open-loop algorithms ( 18), and hybrid algorithms ( 19). In 20 we address the I/O issues in implementing hybrid media servers supporting both periodic and aperiodic data retrievals.

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