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time. Surprisingly, instead of having a lower reliability than FEC, we discover that we can con gure PRT to outperform FEC (achieving longer MTTF) and consume signi cantly less transmission bandwidth overhead (half that of FEC). We rst introduce the system model in the next section and then analyze the two redundant data transmission protocols.

6-16 Experiments with Pattern Classification and Vector Quantization The program in Figure 6-5 permits a wide range of experiments

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Figure 13.1 depicts the generic system model for a parallel streaming server. Let N S be the number of servers in the system. Each server (denoted by Si , i = 0, 1, . . . , N S 1) is autonomous, and equipped with its own CPU, memory, disk storage, and network interface. This ensures that any server failure will not spread to other servers. Speci cally, we assume the servers to fail independently and, when they fail, they simply stop all data transmissions. A media object such as a video stream is rst divided into xed-size data blocks of Q bytes each, denoted by {bi |i = 0, 1, . . . }. To generate redundant data we use a (N S , K) erasure correction code and compute K redundant data blocks for every (N S K ) data blocks. For example, K redundant blocks (i.e., blocks marked with P in Figure 13.1) are computed from data blocks {bi |i = 0, 1, . . . , (N S K 1)} and together they form a parity group. Each of the data/redundant blocks in a parity group will be stored in a separate server in the system. The client can recover all (N S K ) data blocks as long as any (N S K ) data/redundant blocks of a parity group are available, thus enabling it to tolerate up to K simultaneous server failures.

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With that in mind, would you risk losing that content, even if it is just for the time your hosted comment provider is experiencing downtime or other issues Other possible issues include downtime and added clutter because chances are that the comment solution won t fit seamlessly with your smashing design Also, problems experienced by the commenting system s host will hit your site as well That s not good However, there are some great advantages here too First of all, spamming is taken care of on a wider scale, and that has its advantages Both comment systems also offer various login methods, reply by e-mail, RSS feeds, as well as Twitter integration and e-mail notifications The scope of features you ll get out of the box from Disqus or IntenseDebate is something you d have to supercharge your WordPress comments with plugins to achieve.

to negligible levels (order of 10 1 percentage bit-rate variation averaged over 1 second interval) without any impact on other parts of the system.

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Figure 6-6a illustrates classification of noise-perturbed two-dimensional prototype pattern vectors represented as points s (s[1], s[2]) The experiment protocol generates N simple two-dimensional prototype patterns (N uniformly spaced points in a square) and stores them as rows of the N 2 pattern-row matrix INPUT (Section 6-5b) A DYNAMIC program segment adds approximately Gaussian noise to produce the classifier input x with

It is often said that sites using these systems get more comments, and that doesn t seem to be just the PR talk of the companies themselves So with that in mind, if you are to use a service like this, which one should you pick I have no idea; they are pretty similar Give them both a go and see which one fits your site better..

We conducted extensive benchmarking experiments to collect three performance results, namely start-up latency, aggregate bit-rate of all channels, and peak aggregate reception bit-rate to compare with the theoretical calculations In all experiments, we use the system parameters of L = 4,401, C = 284, m = 2 and b = 142 The media stream is a MPEG-1 encoded system stream multiplexing one video stream with one audio stream We conducted benchmarks for a total of 7 GCB system con gurations, with the number of media segments N ranging from 50 to 1,000 For each con guration, we obtain the performance data by averaging data collected from 20 benchmark runs The results are summarized in Table 182 We rst consider start-up latency that is measured from within the client software The results show that the experimental results agree closely with the theoretical calculations.

Vector x = INPUT# + noise * (ran() + ran() + ran())