IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1285

Special Issue Editors


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Guest Editor
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufeng, Taichung 413310, Taiwan
Interests: graph algorithms; linux system; applications for smart phones; security on IoT
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Guest Editor
Institute of Information and Decision Sciences, National Taipei University of Business, Taipei 10051, Taiwan
Interests: design and analysis of algorithms; robot interactive programming; digital exhibition design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We cordially invite you to submit your articles to the Special Issue of Mathematics entitled “IIHMSP: Intelligent Information Hiding and Multimedia Signal Processing”. This Special Issue will include papers from the IIHMSP 2025 conference. For information about the conference, please refer to https://iihmsp25.github.io. Multimedia technologies facilitate the creation of global information infrastructure for acquiring, storing, and communicating data in different forms. The proliferation of multimedia applications has raised challenges such as multimedia security and privacy, big data in multimedia, and intelligence in multimedia processing. Topics of interest for this Issue include, but are not limited to, the following: information hiding and security; multimedia signal processing and networking; bioinspired multimedia technologies and systems; and information and computer technology. We welcome authors of all related interests to contribute to this Special Issue.

Prof. Dr. Ruo-Wei Hung
Dr. Ling-Ju Hung
Guest Editors

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Keywords

  • information hiding and security
  • multimedia signal processing and networking
  • bioinspired multimedia technologies and systems
  • information and computer technology

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Published Papers (2 papers)

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Research

28 pages, 993 KB  
Article
Fair Domination on Extended Supergrid Graphs: Complexity, Linear-Time Algorithms, and ILP Formulations
by Ruo-Wei Hung
Mathematics 2026, 14(6), 947; https://doi.org/10.3390/math14060947 - 11 Mar 2026
Viewed by 207
Abstract
A dominating set of a graph is a subset of vertices such that every vertex is either contained in the set or adjacent to at least one vertex in it. A dominating set is called k-fair if each vertex not in this [...] Read more.
A dominating set of a graph is a subset of vertices such that every vertex is either contained in the set or adjacent to at least one vertex in it. A dominating set is called k-fair if each vertex not in this set is adjacent to exactly k vertices of the set. The domination and k-fair domination problems aim to find such sets of minimum cardinality. Both problems are NP-complete for general graphs, and the domination problem remains NP-complete on grid graphs, whereas the k-fair domination problem remains open on grid graphs. In this paper, we study the 1-fair and 2-fair domination problems on extended supergrid graphs, which generalize grid graphs and include both grid and supergrid graphs as subclasses. We prove that the 1-fair domination problem is NP-complete for these graph classes, even when restricted to planar graphs with maximum degree 4. On the positive side, for rectangular supergrid graphs, we present a linear-time algorithm for computing minimum 1-fair dominating sets. In addition, we formulate an integer linear programming (ILP) model to investigate the 1-fair and 2-fair dominations on small instances and introduce a restricted k-fair domination problem motivated by the experimental observations. Full article
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16 pages, 1121 KB  
Article
A Residual Control Chart Based on Convolutional Neural Network for Normal Interval-Censored Data
by Pei-Hsi Lee
Mathematics 2026, 14(3), 423; https://doi.org/10.3390/math14030423 - 26 Jan 2026
Viewed by 311
Abstract
To reduce reliability testing time, experiments are often terminated at a predetermined time, producing right-censored lifetime data. Alternatively, when test samples are inspected at fixed intervals, failures are only observed within these intervals, resulting in interval-censored lifetime data. Although quality control methods for [...] Read more.
To reduce reliability testing time, experiments are often terminated at a predetermined time, producing right-censored lifetime data. Alternatively, when test samples are inspected at fixed intervals, failures are only observed within these intervals, resulting in interval-censored lifetime data. Although quality control methods for right-censored data are well established, relatively little attention has been given to interval-censored observations. Motivated by the success of residual control charts based on convolutional neural network (CNN) for right-censored data, this study extends the chart for monitoring normally distributed interval-censored lifetime data. Simulation results based on average run length (ARL) indicate that the proposed method outperforms the traditional exponentially weighted moving average (EWMA) chart in detecting decreases in mean lifetime. The findings also highlight the practical benefits of employing high- or low-order autoregressive CNN models depending on the magnitude of process shifts. Full article
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