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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 6(1) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 2017-2029
S&M2237 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.2792 Published: June 10, 2020 Sensing and Controlling with Markov Process for Locally Independent Fractal Image [PDF] Li-liang Zhang, Ting-cheng Chang, and Yan-ming Mao (Received December 31, 2019; Accepted April 29, 2020) Keywords: iterated function system, Markov model, fractal image, local control
We used the Markov process to impose parameter perturbation on affine transformations to overcome the self-similarity limitations of the fractal attractor of the classic iterated function system (IFS) and construct a fractal image with irregular shapes and features. Then, a control method for the IFS system for fractal image generation was proposed. This method decomposes the original IFS into several independent local subsystems. Then, we defined a transition probability matrix of the Markov process for each of the different local subsystems, and carried out image shape operation of a perturbation function to increase the control of the fractal image. This method can construct colorful fractal images effectively through computer image generation.
Corresponding author: Li-liang Zhang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Li-liang Zhang, Ting-cheng Chang, and Yan-ming Mao, Sensing and Controlling with Markov Process for Locally Independent Fractal Image, Sens. Mater., Vol. 32, No. 6, 2020, p. 2017-2029. |
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