Noise-based Pattern Generation

Noise-Based Pattern Generation is a pattern generation method that utilizes stochastic noise as a foundational input to produce structured outputs, distinguished by its reliance on probabilistic variations to drive the formation of complex, non-repetitive patterns.

17

Publications

536

Citations

51

Authors

31

Institutions

Publications per year

2017–2026

2

Authors

51

  1. YY
    Yun Yang

    Swinburne University of Technology, Australia

    3 publications · 117 citations

    3

    h-index

  2. GZ
    Gaofeng Zhang

    Swinburne University of Technology, Australia

    3 publications · 117 citations

    3

    h-index

  3. JC
    Jinjun Chen

    Swinburne University of Technology, Australia

    2 publications · 89 citations

    2

    h-index

  4. XL
    Xiao Liu

    Deakin University, Australia

    2 publications · 45 citations

    2

    h-index

  5. AK
    Alexander Kolinichenko

    Ural Federal University, Russia

    1 publications · 13 citations

    1

    h-index

1–5 of 51

Venues

  1. 2

    h-index

  2. Journal of Mechanical Science and Technology

    1 publications · 18 citations

    1

    h-index

  3. The European Physical Journal B

    1 publications · 31 citations

    1

    h-index

  4. IEEE Transactions on Computers

    1 publications · 28 citations

    1

    h-index

  5. Journal of the Optical Society of America A

    1 publications · 48 citations

    1

    h-index

1–5