Chaotical PRNG based on composition of logistic and tent maps using deep-zoom

We propose the deep-zoom analysis of composition logistic map and tent map, which are well-known discrete one-dimensional chaotic maps. The technique transforms each point a given orbit by removing first k-digits after decimal separator. found that pseudo-random qualities as number generator (PRNG) improve k parameter increases. This was evidenced fact it successfully passed randomness tests even outperformed k-logistic k-tent PRNG. These dynamic properties show application to maps, at least these two is suitable for better randomization PRNG purposes well cryptographic systems.