c# data matrix reader

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Here the width (narrow to broad), gives the close focus or the big picture, and the direction (internal or external), focuses inside or outside your boat. Most people will tend towards one type of concentration and it is sometimes dif cult to get your head out of the boat (2+4) when you are worried about getting the telltales ying (3), or you may nd you are worried about the boats around you (2+4) when you should be concentrating on boat speed (1+3). In a crewed boat you can of course share the responsibilities. The most important thing to switch on/off is when to concentrate 100% on boat speed as shown in gure 19.2 b. The focus outside the boat can also change: are you looking for a at patch of water (either for boat speed or to nd somewhere to tack or gybe) Or pressure Or angle of wind Which is the most important thing to look for To make it easier to concentrate on the most important things, write them down (on waterproof paper) so you can refer to them if and when needed. This information can

At this point you re probably a bit at a loss regarding the origin of the buffer, you just hashed in Listing 6.4. In such cases, it is usually best to simply trace back in the program until you find the origin of that buffer. In this case, the hashed buffer came from the calling function, at 00402300. This function is shown in Listing 6.5.

Monte Carlo Quadrature Convergence 1,00000 basic 0.10000 linear importance 0.01000 cubic importance 0.00100 linear control 0.00010 cubic control 0.00001 10 100 1000 Number of points 10000

6

00402300 00402303 00402308 00402309 0040230D 00402311 00402315 00402316 00402318 0040231A 0040231F 00402320 00402326 00402328 0040232E 00402332 00402334 00402335 00402338 0040233A 0040233D 0040233F 00402341 00402345 00402347 00402349 0040234A 0040234B 0040234C 00402352 00402354 00402355 00402357 00402359 0040235D 00402361 00402365 00402369 0040236E 00402372 00402376 0040237A 0040237F 00402387 00402388 0040238C 0040238D 0040238E

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Figure 17.8 Estimated error in Monte Carlo simulation of integration of cos2 with basic method, linear importance function, cubic importance function, linear control variate, and cubic control variate.

SUB ESP,24 MOV EAX,DS:[405020] PUSH EDI MOV EDI,SS:[ESP+2C] MOV SS:[ESP+24],EAX LEA EAX,SS:[ESP+4] PUSH EAX PUSH 0 PUSH 0 PUSH 8004 PUSH EDI CALL DS:[<&ADVAPI32.CryptCreateHash>] TEST EAX,EAX JE cryptex.004023CA MOV EDX,SS:[ESP+30] MOV EAX,EDX PUSH ESI LEA ESI,DS:[EAX+1] MOV CL,DS:[EAX] ADD EAX,1 TEST CL,CL JNZ SHORT cryptex.00402338 MOV ECX,SS:[ESP+8] PUSH 0 SUB EAX,ESI PUSH EAX PUSH EDX PUSH ECX CALL DS:[<&ADVAPI32.CryptHashData>] TEST EAX,EAX POP ESI JE SHORT cryptex.004023BF XOR EAX,EAX MOV SS:[ESP+11],EAX MOV SS:[ESP+15],EAX MOV SS:[ESP+19],EAX MOV SS:[ESP+1D],EAX MOV SS:[ESP+21],AX LEA ECX,SS:[ESP+C] LEA EDX,SS:[ESP+10] MOV SS:[ESP+23],AL MOV BYTE PTR SS:[ESP+10],0 MOV DWORD PTR SS:[ESP+C],14 PUSH EAX MOV EAX,SS:[ESP+8] PUSH ECX PUSH EDX PUSH 2

the probability of failure for a normally distributed M, but we might need to compute the probability of failure directly when the distribution is not known. Many simulations will be needed if we estimate the probability by comparing the number of failure states with the total number of simulations. Importance sampling is one way to reduce this number. Hasofer and Wang (1993) and Melchers (1993) describe the theoretical basis for importance sampling; the speci c technique follows from Melchers approach. There are two uncertain variables, c and , and they have a joint probability distribution function f (c, ), which in this case is a bivariate Normal distribution. We want an importance function of two variables that is concentrated in the region of failure. First we nd values of c and that satisfy the failure condition (M = 0 or FS = 1) and have the maximum value of the joint probability distribution function. This is actually easy to do using the solve feature in a spreadsheet or a mathematical simulation system; it is also the failure point found in the Hasofer-Lind rst-order reliability method described in 16. We then assume that the importance function is a joint Normal distribution with means at the failure point and variances equal to the variances of the original variables c and . Let us call this h(c, ). The probability of failure is pf =

00402390 00402391 00402397 00402399 0040239B 004023A0 004023A6 004023A9 004023AD 004023B2 004023B3 004023B8 004023BB 004023BD 004023BF 004023C3 004023C4 004023CA 004023CC 004023CD 004023D1 004023D6 004023D9 004023DA 004023DE 004023E2 004023E3 004023E5 004023E6 004023EB 004023EC 004023F4 004023FA 004023FE 004023FF 00402405 00402409 0040240D 0040240E 00402413 00402416

19.2a There are four different types of concentration (Nideffer 1976).

PUSH EAX CALL DS:[<&ADVAPI32.CryptGetHashParam>] TEST EAX,EAX JNZ SHORT cryptex.004023A9 PUSH cryptex.00403504 ; format = Unable to obtain MD5 hash value for file. CALL DS:[<&MSVCR71.printf>] ADD ESP,4 LEA ECX,SS:[ESP+10] PUSH cryptex.00405038 PUSH ECX CALL cryptex.00402280 ADD ESP,8 TEST EAX,EAX JNZ SHORT cryptex.004023DA MOV EDX,SS:[ESP+4] PUSH EDX CALL DS:[<&ADVAPI32.CryptDestroyHash>] XOR EAX,EAX POP EDI MOV ECX,SS:[ESP+20] CALL cryptex.004027C9 ADD ESP,24 RETN MOV ECX,SS:[ESP+4] LEA EAX,SS:[ESP+8] PUSH EAX PUSH 0 PUSH ECX PUSH 6603 PUSH EDI MOV DWORD PTR SS:[ESP+1C],0 CALL DS:[<&ADVAPI32.CryptDeriveKey>] MOV EDX,SS:[ESP+4] PUSH EDX CALL DS:[<&ADVAPI32.CryptDestroyHash>] MOV ECX,SS:[ESP+24] MOV EAX,SS:[ESP+8] POP EDI CALL cryptex.004027C9 ADD ESP,24 RETN

(17.42)

Listing 6.5 (continued)

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