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between them Link Lij L represents the bidirectional communication link between the processors Pi and Pj and Pi , Pj P Examples of such undirected topology graphs are omitted here, but a large number of them can be found in Section 221 The undirected graph model is a simple and intuitive representation of communication networks In task scheduling it is employed for various purposes Wu and Gajski [207] apply it to map scheduled task graphs onto physical processors, trying to balance the network traf c Other algorithms utilize it for a more accurate determination of communication costs, for example, by counting the hops of a communication (ie, the transitions between links) (eg, Coli and Palazzari [39], Sandnes and Megson [164]) Contention Awareness with Undirected Topology Graph Primarily, the undirected topology graph is the model employed in the few existing network contention aware scheduling heuristics El-Rewini and Lewis [63] propose a contention aware scheduling algorithm called MH (mapping heuristic) The target system is represented as an undirected topology graph and a table is associated with each processor This table maintains for each processor (1) the number of hops on the route, (2) the preferred outgoing link, and (3) the communication delay due to contention With this information, the communication route between processors is adapted during the execution of the list scheduling based MH However, the tables are only updated at each start and at each nish of a communication, which is a trade-off between complexity and a real view of the traf c Furthermore, such a scheduling approach is only meaningful if the target system can be forced to use the routes determined by the algorithm, which is usually not the case (Section 722) Concept of Edge Scheduling A more realistic view of the network traf c is gained when, for each communication, its start and nish times on every utilized communication link are recorded This corresponds to scheduling the edges on the links, like the nodes are scheduled on the processors, which is illustrated in the Gantt chart of Figure 71 The idea of edge scheduling emerged with the DLS algorithm proposed by Sih and Lee [169] They proposed representing the target system as an undirected topology.



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The plots depict packet drop traces at 100 second time aggregation We observe signi cant variation in packet drops across different 100 second time segments for the a 1:05 tail index case, which diminishes as a approaches 2 This is even more pronounced when the ows are open-loop controlled over UDP Given the dif culties underlying queueing analysis of nite buffer systems with selfsimilar input with respect to rst-order performance measures, the challenges facing queueing analysis for second-order measures are even greater There is, however, a special case bufferless queueing that is more amenable to tractable analysis (see 17 for a discussion of bufferless queueing in the context of predictive control).





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I recommend trying the Java OCR project on sourceforge.net. ... We have tested a few OCR engines with Java like Tesseract,Asprise, Abbyy etc ...

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Bufferless queueing, which can be viewed as an extreme form of the small buffer=large bandwidth resource provisioning strategy, derives its justi cation from (1) the high delay penalty associated with long-range dependent traf c and (2) the observation that a large number of independent ows correlated in time or not when aggregated over space (ie, ows) is approximately Gaussian by the central limit theorem Thus, for second-order stationary processes (most traf c models fall under this class) this yields a simple handle on the marginal distribution, which can be used to compute deviation probabilities the aggregate traf c exceeds a given link capacity using the tail of the Gaussian.

Real-time multimedia traf c such as video and audio can tolerate some packet loss (described by rst-order measures), but for the same packet loss rate, concentrated packet drops exert a more detrimental impact on QoS than dispersed losses (similarly for delay variations in buffered systems) A comprehensive understanding of the packet loss process and, in general, delay variation in buffered systems is needed to complement the hereto one-sided.

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limitations under the License. */. package net.sourceforge.javaocr.ocrPlugins.​OCRDemo;. import java.awt.Image;. import java.io.File;. import java.io.​IOException;.

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12 Jun 2015 ... Java OCR allows you to perform OCR and bar code recognition on images ( JPEG, PNG, TIFF, PDF, etc.) and output as plain text, xml with full ...

Fig. 21.1 Packet drop trace at bottleneck router multiplexing 32 TCP connections that transport heavy-tailed les, at 100 second time aggregation. Top row: File size distribution with shape parameter a 1:05 (left) and a 1:35 (right). Bottom row: Corresponding traces for shape parameter a 1:65 (left) and a 1:95 (right).

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Using this API in a mobile app? Try ML Kit for ... Optical Character Recognition (​OCR) ... There are two annotation features that support optical character recognition (OCR): ...... Java is a registered trademark of Oracle and/or its affiliates. Optical Character ... · Text detection requests · Detect text in a remote image

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Sep 18, 2015 · Google's Optical Character Recognition (OCR) software works for more than 248 international languages, including all the major South Asian ...












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