A quality review of each RCT was performed by examining details of randomization, generation of random numbers, details of double-blinding procedure, information on withdrawals, and concealment of allocation [ 5].
For the generation of random numbers, the 'MATLAB5 generator' was used.
Generation of random numbers is carried out in 3 stages, namely, the seed generator, seed value modulator, and output generator.
The generation of random numbers in KMCLib now relies on the C++11 standard library implementation, and support has been added for the user to choose from a set of C++11 implemented random number generators.
The quality of RCTs was evaluated by using the following criteria: 1) randomization (the study was described as randomized); 2) double blinding (participant masking and researcher masking); 3) reporting the number of withdrawals and reasons for withdrawal; 4) allocation concealment; and 5) generation of random numbers (using computer, random numbers table, shuffled cards or tossed coins, etc).
D. P. Rosin, D. Rontani, and D. J. Gauthier, 'Ultrafast physical generation of random numbers using hybrid Boolean networks,' Phys.
The random number generation method can generate a series of random numbers y i (i = 1, 2,…, NE) distributed uniformly under {0, 1}.
As discussed in the previous section IR scene simulation [5] requires fast generation of large sequences of random numbers on the provision of a single seed.
Reconfigurable computing is increasingly being seen as an attractive solution for accelerating simulations that require fast generation of large quantities of random numbers.
The Monte Carlo method is a numerical statistical method based on the generation of a set of random numbers to compute a set of results associated with each.
