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It must be emphasized that the noise figure in (13.67) is valid if, out of N + 1 ports, only two ports are utilized while the other ports are match terminated. In other words, the divider must be used as a two-port device. If, however, all of the N + 1 ports are used (one input port and N output ports), then the device-generated noise becomes zero because the total output noise does not exceed the input noise. The output noise power of an N 1 combiner (N input ports, one output port) depends on its circuit configuration and the nature of the noise sources (uncorrelated or correlated). Figure 13.23 shows a combiner. A typical combiner operates perfectly (no loss of power) if the signals at the input ports are in phase. On the other hand, the noise powers from input ports do not add up if the noise sources are uncorrelated. To illustrate this we match terminate all the input ports and assume that the terminated loads are at temperature Tc . Then looking from the output port, the resultant noise temperature of the device would be Tc per the theorem of equivalent noise temperature in Section 13.7.2, making the noise power delivered to the output load kT c B. Thus one can say that each matched load at the input ports transfers only kT c B/N amount of noise power to the output port, making the total output noise power N kTc B/N = kTc B. Therefore, effectively an attenuation of uncorrelated noise occurs in a combiner. On the contrary, no such attenuation occurs if the noise sources at the input ports are correlated and in phase. This point must be carefully considered in the system analysis of an array antenna.

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, through Lagrangian methods) are more ef cient, robust, and accurate (see Nocedal and Wright 1999) Good catalogs of specialized optimization algorithms for different categories of problems include Mor and Wright (1993) and its online successor, and the e online guides in Mittelmann and Spellucci (2003) These resources can readily be accessed at <http://wwwecenorthwesternedu/OTC/> and <http://platoasuedu/guidehtml> (respectively) Whereas some specialized algorithms are designed to exploit the structure of a particular type of function, black box heuristics take the opposite approach Black box algorithms (some of which are technically heuristics) gain their sobriquet by avoiding, as much as possible, explicit assumptions about the function being optimized [For example, black box algorithms make use only of function evaluations (rather than derivatives, etc) and can be applied to any function with at least some hope of success.

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common creditor country lending the affected countries. Their term is unholy trinity for the capital in ows, surprises, and common creditors responsible for currency crises. These indicators are just a few of the many offered to predict currency crises. Kaminsky, Lizondo, and Reinhart (1998) compile an exhaustive list of indicators to predict currency and banking crises in emerging economies. These indicators are not surprising, since they arise from common macroeconomic data. Yet currency crises continue to come as a surprise. Take, for instance, the Asian u of the late 1990s. Southeast Asia was the hottest market going in the early 1990s. Their manufacturing industry was booming and creating exports; the capital account demand for their bonds was high. These strengths deteriorated as labor costs went up, and their currency pegged to the dollar became overvalued as the dollar gained strength. Eventually these pressures led to a currency devaluation that left the nancial market shattered. Why didn t anyone see it coming 7.3.8 Time Lags and Data Revisions

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] Their popularity comes from their wide applicability, and their practical success in a broad range of very dif cult problems, especially discrete (combinatoric) optimization problems, for which specialized algorithms are unavailable The most popular of the black box optimization algorithms simulated annealing, genetic algorithms, and arti cial neural networks all draw upon analogies to physical processes in their design See Aarts and Lenstra (1997) and Michalewicz and Fogel (1999) for good introductions, and Kuan and White (1994) or Gupta and Lam (1996) for speci c application to common problems such as missing data estimation and nancial prediction Simulated annealing (SA) exploits an analogy between the way in which molten metal freezes into a minimum-energy crystalline structure (the annealing process) and the search for a function optimum SA was rst modeled by Metropolis et al (1953) and popularized in Kirkpatrick et al (1983).

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raphy, con dentiality, message integrity, and authentication. However, related services such as key management need to take into consideration the limited bandwidth available in MANET environments for key distribution and revocation. In order to secure communications over a MANET, mechanisms such as IPSec or HAIPE need to be implemented. Furthermore, the exchange of data between different security domains (e.g., networks at different classi cation levels) must be carefully controlled in order to prevent undesired leakage of information from one domain to another. This is achieved by the use of crossdomain guards that are used to control the information ow between different security domains. A cross-domain guard inspects every message that traverses the boundary between different security domains in order to verify that the message conforms to a prede ned set of rules about what information is allowed to cross this boundary. Intrusion detection for MANETs is more challenging than for wireline networks, owing to the nature of the wireless medium, which cannot be physically secured from access, and due to the absence of any kind of infrastructure. This means that there is no one point at which network intrusion detection can be performed, since there is no single entry/exit point for such networks. Furthermore, given the unpredictability of the network topology and the network links, it is dif cult to perform any statistical analysis of the traf c in a MANET to detect anomalies. An intrusion detection architecture for MANETs was presented that uses a distributed, hierarchical approach to intrusion detection that is integrated with fault correlation and root cause analysis.

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