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lexmark ocr software download x6675: Tesseract is an optical character recognition engine for various operating systems. It is free software, released under ...



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We use the LBNL Network Simulator, ns (version 2), as the basis of our simulation environment. ns is an event-driven simulator derived from Keshav's REAL network simulator supporting several avors of TCP and router packet scheduling algorithms. We have modi ed ns in order to model a bottleneck network environment where several concurrent connections are multiplexed over a shared bottleneck link. A UDP-based unreliable transport protocol was added to the existing protocol suite, and our congestion control and predictive control were implemented on top of it. An important feature of the setup is the mechanism whereby self-similar traf c conditions are induced in the network. One possibility is to have a host inject self-



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Another option for implementing an FMS is to write it in-house. In our experience, homemade FMS consists of code that one or two system administrators wrote in their copious spare time to manage failover for one or two particular pairs of servers. Since their salaries are preapproved expenses, it may seem that having them write this code is a wonderful idea, as the project looks like it is free. But who is left doing the job the administrators were hired to do while they are writing and testing this code But putting that aside, let s compare the likely results of this coding effort to our key HA design principles: Homegrown FMS is not mature code. Nobody else has tested it. You are placing your critical systems under the care of code that has never been rolled out anywhere else and has never recovered a production application or system before. Homegrown FMS is version 1.0 code. As it is new and untested, it will inevitably have bugs. The only way to discover the bugs is to use the code. Testing the code in production violates another principle. Homegrown FMS has no reference sites. It also has no USENET newsgroups to discuss problems and issues with other users, or any user





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TABLE 18.1 Snapshot of CondProb 10,000 s After Connection Has Been Established (Top, a 1:05; Bottom, a 1:95) L1 1 2 3 4 5 6 7 8 L1 1 2 3 4 5 6 7 8 L2 L2 1 0.667 0.003 0 0 0 0 0 0 1 0.155 0.043 0.023 0.020 0.012 0.017 0.008 0 2 0.333 0.568 0.126 0.035 0.003 0 0 0 2 0.116 0.058 0.049 0.058 0.039 0.058 0.042 0 3 0 0.306 0.468 0.205 0.078 0.012 0.018 0 3 0.155 0.179 0.132 0.135 0.134 0.141 0.126 0.167 4 0 0.093 0.262 0.368 0.296 0.099 0.079 0 4 0.233 0.272 0.306 0.274 0.273 0.243 0.211 0.233 5 0 0.027 0.116 0.305 0.356 0.285 0.245 0.333 5 0.165 0.257 0.273 0.286 0.307 0.325 0.322 0.233 6 0 0.003 0.023 0.077 0.205 0.418 0.443 0 6 0.078 0.128 0.161 0.167 0.183 0.166 0.195 0.300 7 0 0 0.003 0.201 0.060 0.182 0.213 0.500 7 0.087 0.054 0.054 0.055 0.044 0.044 0.088 0.067 8 0 0 0 0 0.002 0.003 0.003 0.167 8 0.097 0.008 0.003 0.004 0.008 0.007 0.008 0 E L2 j 1.3 2.6 3.4 4.2 4.9 5.7 5.8 6.5 E L2 j 3.8 4.3 4.5 4.5 4.6 4.5 4.8 4.8 E 0.769 0.384 0.294 0.238 0.204 0.175 0.172 0.153 E 0.263 0.232 0.222 0.222 0.217 0.222 0.208 0.208

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Tesseract (software) - Wikipedia
Tesseract is an optical character recognition engine for various operating systems. It is free software, released under the Apache License, Version 2.0, and development has been sponsored by Google since 2006. In 2006, Tesseract was considered one of the most accurate open-source OCR ... History · Features · Reception

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similar time series into the network. We follow a different approach based on the notion of structural causality (see Section 18.2.2) whereby we make use of the fact that a networked client=server environment, with clients interactively accessing les or objects with heavy-tailed sizes from servers across the network, leads to aggregate traf c that is self-similar [33]. Most importantly, this mechanism is robust and holds when the le transfers are mediated by transport layer protocols executing reliable ow-controlled transport (e.g., TCP) or unreliable ow-controlled transport. The separation and isolation of ``self-similar causality'' to the highest layer of the protocol stack allows us to interject different congestion control protocols in the transport layer, discern their in uence, and study their impact on network performance. This is illustrated in Fig. 18.7. Figure 18.8 shows a 2-server, n-client n ! 33 network con guration with a bottleneck link connecting gateways G1 and G2 . The link bandwidths were set at 10 Mb=s and the latency of each link was set to 5 ms. The maximum segment size was xed at 1 kB for all runs. Some of the clients engage in interactive transport of les with heavy-tailed sizes across the bottleneck link to the servers (i.e., the nomenclature of ``client'' and ``server'' are reversed here), sleeping for an exponential time between successive transfers. Others act as in nite sources (i.e., they have always data to send) executing the generic linear increase=exponential decrease feeback congestion control with and without SAC in the protocol stack trying to

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