J
Jeng-Shyang Pan
Researcher at Shandong University of Science and Technology
Publications - 889
Citations - 14887
Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Computer science. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.
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Book ChapterDOI
Improving the Searching Capacity of Evolved Bat Algorithm by the Periodic Signal
TL;DR: In this paper, the evolved bat algorithm is improved by replacing the fixed value, which is determined by the media, with the periodic signal, the sine/cosine signal.
Book ChapterDOI
A Simple Image Encryption Algorithm Based on Logistic Map
TL;DR: An image encryption algorithm based on logistic map with substitution approach is proposed and the line chart, histogram, and pixel loss analysis are made to show the performance of the algorithm.
Journal ArticleDOI
Guest editorial: bio-inspired information hiding
Jeng-Shyang Pan,Ajith Abraham +1 more
TL;DR: This Special Issue on Bio-Inspired Information Hiding applies the analogy to biological systems to handle the challenges in the algorithm and application for information hiding, and finds that bio-inspired information hiding can be effectively hidden into multimedia contents.
Improving Swarm Intelligence Accuracy with Cosine Functions for Evolved Bat Algorithm.
TL;DR: The evolved bat algorithm is improved by replacing the fixed value, which is determined by the media, with a cosine function, the familiar trigonometric signal which exists in the natural environment is the sine/cosine signal.
Journal Article
3-D Terrains Deployment of Wireless Sensors Network by Utilizing Parallel Gases Brownian Motion Optimization
TL;DR: In this paper, a modified particle swarm optimization (GBMO) algorithm combining with the concept of parallel is proposed to improve the search and convergence efficiency of the global optimum search in WSNs.