Tanyer, Suleyman GokhunAtalay, Kumru DidemInam, Sitki Cagdas2024-03-202024-03-202014978-1-4799-4324-1http://hdl.handle.net/11727/11902Random number generators ( RNGs) are one of the key tools necessary for statistical analysis and optimization methods such as Monte Carlo, particle swarm optimization ( PSO) and the genetic algorithm. Various pseudo and true RNGs are available today, and they provide sufficient randomness. Unfortunately, they generate data that do not always represent the required distribution accurately, especially when the data length is small. This could possibly threaten the 'repeatability' of an academic study. A novel true RNG ( TRNG) using the method of uniform sampling ( MUS) is recently proposed. In this work, the Sun's RF emissions MUS-TRNG is comparatively tested with well known pseudo and true RNGs. It is observed that both randomness and very high goodness-of-fit qualities are possible.enginfo:eu-repo/semantics/closedAccessrandom number generatorstatistical signal processingprobabilitytest statisticsoptimizationconferenceObject216218000380446200036