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Both of these techniques are intended to get round the problem of interfering signals that may well occur in the electrically noisy environments of modern buildings, while at the same time trying to keep the signal level of the radio LAN from further contributing to that noise In the case of DSSS, the signal is spread out across a wider bandwidth than is required to code up the data in a simple, conventional scheme; this makes it possible to recover the original signal, even if parts of the frequency spectrum are too noisy To understand the basic principles of DSSS, it helps to look at a rather simpli ed version of how the signal is coded In Figure 26, we see a steam of digital data, which can be tted into a frequency band A, say However, let us instead take the signal and transmit identical copies of.

possible graphs are shown in Figure 7.8 (upper numbers). Obviously, graph 5 would be selected, which is indeed the graph that represents a decomposition of the distribution (cf. Section 3.4.2). Note that, as in the relational and the probabilistic case, the complete graph 8 always receives or shares the best assessment, because it consists of only one clique. As a consequence, it represents no real decomposition of the distribution. Note also that in order to rank two graphs (that is, in order to decide which of them is better) we need not compute the specificity divergences explicitly, but can confine ourselves to computing the nonspecificities of the distributions corresponding to the graphs, because the nonspecificity of the original distribution is fixed. This simplifies the assessment: find the graph among those of acceptable complexity (w.r.t. maximal clique size) for which the corresponding possibility distribution is most specific. Up to now we have assumed that the multidimensional distribution, for which a decomposition is desired, is given. However, as in the probabilistic setting, this is usually not the case in applications. Instead we are given a database of sample cases. Clearly, the situation parallels the probabilistic case: it is infeasible to estimate first the joint possibility distribution from the database, so that we can proceed as indicated above. In order to derive a feasible indirect method we may reason as follows: an approximation of a given possibility distribution may be compared to this distribution by integrating the differences in the number of tuples for each -cut over the possible values of (see above). However, this formula can be

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#ifndef PLAYERSERVER_hh #define PLAYERSERVER_hh_ #include "example.hh" class PlayerServer : public virtual PlayerBOAImpl { private: char * the_player_name; public: PlayerServer(); virtual NameStr name(CORBA::Environment & env = CORBA::default_environment); virtual void set_name(char *); }; class PlayerFactoryServer:public virtual PlayerFactoryBOAImpl { virtual Player * createPlayer(NameStr name, CORBA::Environment & env = CORBA::default_environment); }; #endif ///////////////////////////////////////////////////////////// // PlayerServer.cc // 20 March 1999 ///////////////////////////////////////////////////////////// #include "PlayerServer.hh" #include <iostream.h> PlayerServer::PlayerServer(){ the_player_name=NULL; }; void PlayerServer::set_name(char * name){ if (the_player_name != NULL) delete the_player_name; the_player_name = new char [strlen(name)+1]; strcpy(the_player_name,name); cout << "Created Player object for " << name << "\n"; }; NameStr PlayerServer::name(CORBA::Environment & env = CORBA::default_environment){ char * ret = new char[strlen(the_player_name)+1]; strcpy(ret,the_player_name); return(ret); }; Player * PlayerFactoryServer::createPlayer(NameStr name, CORBA::Environment & env = CORBA::default_environment){ PlayerServer * aPlayer = new PlayerServer; aPlayer->set_name(name);

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is to prepare the solution of a strong acid (e.g. HClO4) and that of a strong base (e.g. R4NOH) and measure the potentials of the pH sensor in the two solutions. Then, pKSH can be obtained approximately from the potential difference between the two solutions (DE, mV): pKSH ! DE=59:2 logfc strong acid c strong base g 6:7

where U is the electrochemical potential which, as a rst approximation, should be taken as N Snm m0 5:3:6 U Z qV kT ln N0 where V electric potential, V N0 equilibrium metal ion concentration at reference condition, number/m3 atomic volume, m3, Snm mechanical stress, N/m2 m0 reference chemical potential, J Substituting Eq. (5.3.6) in Eq. (5.3.5), we get ! ND kT Z qE rN r Snm Ja kT N

CHAPTER SUMMARY The f irst step in understanding how to buy a house with no (or almost no) money down is to understand the importance of the down payment. This chapter has explained what the down payment is, why it is important to buyers, sellers, and lenders, and how it helps determine the most house you can afford to buy. Finally, the second critical component in the home-buying decision, the amount you can afford to spend each month on housing costs, which is integrally related to the down payment, was also reviewed.

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