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As users draw in a rich ink control, the control stores the ink data in an internal data structure. Users need to be able to store and retrieve what they input into the control as needed. For example, one application of the rich ink control might be a simple notepad application. Users make handwritten notes on a page of the notepad and then save the note for later reference. The application obviously needs to be able to store and retrieve inking data in persistent storage. To demonstrate how to save and restore inking data, let s look at how the INK.EXE application saves and reads ink data files. The application stores inking data in files with the extension .INK. Saving and restoring inking data requires the two rich ink control messages IM_GETDATA and IM_SETDATA. The meanings of their parameters are shown in Tables 13.3 and 13.4. Table 13.3 The IM_GETDATA Message PARAMETER wParam lParam MEANING Integer indicating the length of the inking data to be retrieved from the rich ink control. Pointer to an array of bytes that receives the inking data from the control. Table 13.4 The IM_SETDATA Message PARAMETER wParam lParam MEANING Integer indicating the length of the data buffer that contains the rich ink control data. Pointer to an array of bytes that contains the rich ink control inking data.

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(a) Using the Viterbi decoding algorithm, find the sequence most likely to have been transmitted, and hence determine the positions of any errors which may have occurred during transmission.

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For instance, it facilitates rapid failover to a nearby neighbor node, in case the parent node of a distribution tree crashes Plus, it speeds up the parent selection process by narrowing down the list of candidate parents Furthermore, the neighbor list can, to some extend, re ect the underlying network topology, especially when manually con gured or measured via traceroute or roundtrip time Therefore, picking parents from the neighbor list helps match the application layer distribution tree with the underlying network topology, hence improving the bandwidth consumption by selecting nearby peers as parents Given a neighbor list, Joe s parent selection process would be modi ed (or rather, simpli ed) as follows: Step 1 For each node in the neighbor list (noted as Terry), if Joe is not one of Terry s ancestors, Joe measures the bandwidth from the tree root to Joe through Terry and Terry s ancestors..

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The authors sincerely thank Wayne Velicer and four anonymous reviewers for helpful comments on an earlier draft. This chapter was supported in part by NIDA grant DA 01070-28.

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With reference to the information in P4.2, use a common-size vertical analysis to determine the composition of current assets and current liabilities for Years 0003 and 0004. Discuss the results. A fire occurred in a friend s restaurant overnight on December 31, 0005, and the friend has asked for your help. Although many accounting records

1. Website of Hammer Technologies, www.hammer.com, 1999 (or http://www.empirix. com/empirix/voice network test/, 2001). 2. P.861 Recommendation, Objective Quality Measurement of Telephone-Band (300 3400 Hz) Speech Codecs, ITU-T, Geneva, 1998. 3. D. Minoli and A. Schmidt, Internet Architectures, Wiley Computer Publishing, New York, NY, USA, 1999.

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In this chapter, we discussed various aspects of time synchronization and calibration in sensor networks. We outlined the applications of physical time and discussed why existing algorithms for time synchronization have to be revisited. We also presented common classes of and techniques for synchronization, reviewed time-synchronization algorithms from the literature, and discussed evaluation strategies. Time synchronization was identi ed as a special case of calibration, and many of the observations about time synchronization could be transferred to calibration. While time synchronization for sensor networks is an established eld of research, calibration has not received that much attention yet. However, we expect that calibration becomes a more active eld as sensor networks move beyond the lab and small eld experiments. Unfortunately, calibration is a much more general and complex problem than time synchronization. Hence, it is likely that research will rst focus on more speci c calibration problems. An interesting question is, can techniques developed for time synchronization be adapted to calibration problems The case studies of time synchronization algorithms and the discussion of evaluation techniques illustrated the very real problem of evaluating and comparing synchronization algorithms. Note that these dif culties also apply to calibration and many other distributed algorithms. Hence, one of the challenges for future research is the development of methods and tools for the evaluation of time synchronization and calibration in large-scale sensor networks. Current application-oriented projects (e.g., ref. [8]) indicate that many simplifying assumptions about sensor networks (e.g., immobile nodes, xed-network topology) may not hold in practice. Hence, future work might have to revisit existing approaches for time synchronization and calibration under updated assumptions.

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