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Real-time. In general, satellite-based positioning systems are capable of providing quasi-real-time localization services. However, localization algorithms that are based on the elaboration of beacon signals are not suitable for strictly real-time applications, since, in general, they require the reception of several control packets to reduce the estimation error. On the other hand, the problem of whether a real-time application can be supported or not only becomes an issue for mobile networks of COs. In many application scenarios in which nodes are static, the localization process can be performed at the network set-up, reducing its costs and allowing the use of different types of algorithms independent of the real-time constraints of the application. Reliability. Reliability of the localization algorithms depends on the number and position of the beacon nodes, possibly the number of hops over which the localization error propagates, or the presence of malfunctioning or malicious nodes. Malicious nodes are nodes whose purpose is to compromise the correct operation of the network. Such nodes can provide, for example, incorrect ranging estimates or incorrect information on their own position to other nodes, affecting other nodes localization accuracy or the ranging estimate accuracy (DV-Hop, N-Hop TERRAIN, AHLoS). Ways to detect and lter the information provided by malfunctioning or malicious nodes have to be provided. The localization-algorithm taxonomy is summarized in the following table.
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the posterior on the remaining parameters is given by ( 1 , 2 , , , |data) ( )
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public StockTradeResult(String status, String cName, int nt, String ss) { statusMsg = status ; custName = cName ; numberTraded = nt; stockSymbol = ss; } public String getStatusMsg() { return public String getCustName() { return public int getNumberTraded() { return public String getStockSymbol() { return }
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The data summary report is used to examine preprocessed data in order to nd and correct inconsistencies in the data that can reduce the validity of statistical inference. Unlike other procedures, there are no golden standards for this step. It is a good practice to evaluate the data summary report before and after data preprocessing. Approaches used to inspect the data include the evaluation of a histogram to provide information about data distribution in one microarray, a boxplot of the whole data set to check the similarities of all data distributions, and the evaluation of correlation coef cient maps (see Fig. 4.7) to check consistency among arrays. Correlation coef cient heat maps plot the values of correlation coef cients between pairs of arrays. For a given pair of arrays, #i and #j, their expression pro les are observed as vectors and the correlation coef cient between the two vectors is plotted as two pixels in symmetrical positions (ij) and (ji) in the heat map (the magnitude of correlation coef cient is indicated by the color of the pixel). Correlation coef cients are normally expected to be high, since we assume that the majority of gene expression levels are similar in different arrays. A horizontal (and the corresponding vertical) line in a heat map represents all of the correlation coef cients between a given array and all other arrays. If a line has a near-constant color representing a very low value, we should suspect a problem with the corresponding array.
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4.8 Head-End Optical Line Terminal 115
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CAUTION: Because of the complexities that can arise, it is important at the outset to determine which type of arrangement the parents wish to have. The income tax, elder law planning, asset protection and divorce planning, estate-planning, and cash-f low consequences can be dramatically different. The legal documents that must be prepared for each type of arrangement are also completely different. For example, there are important legal differences between making a loan to a child where part (or all) of the interest is based on the appreciation in the value of the house (an equity kicker), versus an equity-sharing arrangement, where the parent actually owns an interest in the house. If you re not absolutely positive which approach is the
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CAPM-Like Models In contrast to focusing on the frequency and/or severity of operational losses, this approach would relate the volatility in share returns (and earnings and other components of the institution s valuation) to operational risk factors. Predictive Models Extending the risk indicator techniques described previously, the analyst uses discriminant analysis and similar techniques to identify factors that lead operational losses. The objective is to estimate the probability and severity of future losses. (Such techniques have been used successfully for predicting the probability of credit losses in credit card businesses.)
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# rpm -qa | less glibc-2.5.90-15 libICE-1.0.3-1.fc7
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