pseudorandom numbers

pseudorandom numbers

[‚sü·dō′ran·dəm ′nəm·bərz]
(computer science)
Numbers produced by a definite arithmetic process, but satisfying one or more of the standard tests for randomness.
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Earlier methods for generating pseudorandom numbers include use of block ciphers, stream ciphers, hash functions, chaos theory etc.
Then, pseudorandom numbers of these 4,000 discontinuities were generated for the five elements, namely the X-coordinate, 7-coordinate, Z-coordinate, diameter and aperture.
In the tag generation phase, the client uses a pseudorandom number generator to generate a series of pseudorandom numbers and then multiplies the video file blocks with pseudorandom numbers to obtain the tag.
The simulation is repeated 2000 times by generating new pseudorandom numbers and the simulated SMSE values of the estimators and predictors are obtained using the following equations:
It requires a unique key to generate the same set of pseudorandom numbers [49] which are used in embedding equation (9) and again in Step8 of the extraction algorithm for comparing correlation coefficients.
Reference [30] used chaotic schema with linear congruence based on pseudorandom numbers generation, that is, coupling of chaotic function with XOR operations during encryption process to achieve randomness in cipher image and large key space to resist brute-force attack.
Hyperchaotic Rossler map is used to generate a sequence of pseudorandom numbers, which are used in permutation and diffusion process.
To achieve such situation, attacker have to repeat the scenario for some appropriate j, then if it gets new IDS in next protocol run then it means that tag has accepted invalid pseudorandom numbers. Next time when a valid reader communicates with this tag, the reader will not recognize this tag and hence desynchronize with the particular tag.
When simulating extreme values, it is primarily necessary to use the program for generating pseudorandom numbers. Basic pseudorandom numbers are within the interval <0, 1> and represent a random observation from a continuous uniform distribution on this interval.
There are three types of random numbers: truly random numbers (from physical generators), pseudorandom numbers (from mathematical generators), and quasirandom numbers (special correlated sequences of numbers, used only for integration).
The MC method used in the LSM approach employs the pseudorandom numbers which show aggregation and make the convergence too slow.