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the system If Terry the Terrorist does not have to have a national ID card and provide a biometric to the USG, he won t In effect, the identity freeze melts away A secondary purpose of requiring a biometric is to enable the US government to search the biometric against a watchlist database consisting of undesirables, such as suspect terrorists and wanted criminals A major drawback to this process is that the search only works if the database contains the biometric of the undesirables However, watchlist data may be available The FBI has a start with its IAFIS Criminal Master File, and a complete set of fingerprints for many wanted criminals is on record (from recidivism) Given that every US citizen and resident would have to apply for a card, the biometric database of cardholder applicants will become very large, very quickly: there are roughly 300 million citizens This biometric database would become the world s largest biometric database A major technical challenge presents itself: As the size of the database increases, the number of matching errors also increases In a verification system, where the sample is compared only to the template corresponding to the claimed identity, false matches are rare, except in cases of forgery However, in an identification system as proposed here, each cardholding applicant will have to be biometrically enrolled and searched for a match against a portion of the database (containing hundreds of millions of records) The chance of falsely matching at least one of them will be much higher than the single-match False Match Rate A rough approximation of the False Match Rate (FMR) for the System is: FMR(SYS) = N * FMR, where N is the number of records (templates) in the database and FMR is the false match rate for a single comparison (A more precise approximation is: FMR(SYS) = 1-(1-FMRm )P*N where N is the number of records (templates) in the database, m is the number of measures for each individual enrolled, P is the penetration rate and FMR is the false match rate for a single comparison (assumed equal across all measures and users)) As explained in 17, fingerprint-based biometric systems try to resolve this dilemma by reducing N, that is by subdividing the database to be searched so that only part of it must be searched This subdivision is done by using a multistage classification process of filtering and/or binning Filtering uses external information (data not having to do with the actual fingerprint) to select the search set within the database An example of a characteristic used for filtering is the gender of the person being identified Binning is selection of potential matches based on information intrinsic to the fingerprint that can be readily detected (such as pattern types like loops, whorls, and arches) Ensemble matching is then performed With ensemble matching, a set of independent samples (for example, fingerprints taken from four different fingers) is collected from each person being identified and is binned and filtered as a group, then matched against corresponding sets in the database The net effect in many cases is to further reduce the number of potential matches to be considered (Wayman 1999, 35 48) This reduces the probability of a false match, and also.

00 00 01 Speed ratio (1/l) 02 03

means you don t need to worry about changing the design (which you may not have time for, or perhaps can t for testability reasons). You can start testing almost immediately. There s also a free version of Typemock Isolator for open source projects, so you can feel free to download this product and try it on your own. Listing 9.1 shows a couple of examples of using the new Isolator API to fake instances of classes.

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The system of Eqs (1348) and (1349) is solved with the variation-of-extremals algorithm When weight factor W3 is nonzero, the problem is nonlinear Accurate estimates of the initial costates are then needed for convergence For the rst estimate, weight factor W3 is set to zero and the algorithm converges in one iteration Factor W3 is then progressively increased in steps small enough to ensure the convergence of the algorithm until the desired value is achieved Using the same data as Sec 13583 above, results are obtained as shown in Fig 1312 The reduction in contact stress relative to the polydyne approach is seen to be relatively small This is due to three reasons The rst is the rather high value of l To observe larger differences, a value of half the value used or less would be needed The second reason is that the cam optimality criterion does not include (as required as described previously to obtain an acceptable solution) the effect of preload on contact stress Therefore, only the portion of the contact stress due to system dynamics and return spring compression during the rise is minimized Hence, contact stress is reduced less than one percent in this example The third reason is that the ten speci ed boundary conditions restrict the freedom to adjust for contact stress The residual output vibration for the polydyne cam is somewhat lower than that for the stress-optimal cam, as depicted in Fig 1313 The two residual-vibration curves are similar, implying similar cam displacement curves and supporting the conclusion that the boundary conditions are at least partially responsible 1359 Optimization of Tuned Dwell-Rise-Return-Dwell Cams 13591 General In applying optimal control theory to the design of tuned DRRD cams, we restrict the optimization to the output criterion The formulation of the previous sections for tuned DRD cams is modi ed slightly to accommodate this new class The return segment immediately following the rise renders the optimization procedure more dif cult It is not feasible to divide the optimization into separate dwell-rise and return-dwell portions Doing the optimization in such a piecewise manner leads to unacceptable distortions in the cam displacement curve 13592 Formulation of Two-Point Boundary-Value Problem Consider a modi cation for the cost given by Eq (1342) with the weight factor W3 set to zero.

[Test] public void FakeAStaticMethod() { Isolate .WhenCalled(()=>MyClass.SomeStaticMethod()) .WillThrowException(new Exception()); } [Test] public void FakeAPrivateMethodOnAClassWithAPrivateConstructor() { ClassWithPrivateConstructor c = Isolate.Fake.Instance<ClassWithPrivateConstructor>(); Isolate.NonPublic .WhenCalled(c,"SomePrivateMethod").WillReturn(3); }

+ Yi (t )[Y (t ) - Yd (t )] + V2 (t )Y 2 (t )dt where weight functions V1(t) and V2(t) are normal distributions given by vi (t ) = with t - 1 n= Sd k 10 4 Sd 10 -5 and the function Yd(t) is a polynomial given by Yd (t ) = 6t 5 - 5t 6 0 t 1 Yd (t ) = 6 S 5 - 5S 6 1 < t T where T > 1 T -t S= T -1

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