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Mathematics
Special Issues
Computational Intelligence in Systems, Signals and Image Processing
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Computational Intelligence in Systems, Signals and Image Processing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 701

Special Issue Editors

Dr. Jaesung Lee

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Chung-Ang University, 84 Heukseok-ro, Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea
Interests: artificial intelligence; machine learning; neural architecture design; feature engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Wangduk Seo

E-Mail Website
Guest Editor
College of Software and Management, Kyonggi University, Suwon 16227, Gyeonggi-do, Republic of Korea
Interests: machine learning; neural architecture design; feature engineering

Special Issue Information

Dear Colleagues,

This Special Issue on “Computational Intelligence in Systems, Signals and Image Processing” highlights the role of mathematical techniques in advancing computational intelligence across various fields. As system modeling, signal processing, and image analysis become increasingly complex, rigorous mathematical methods are essential for addressing these challenges.

We welcome research focused on mathematical modeling, optimization, control, and analytical methods to improve the reliability and efficiency of these applications. Topics of interest include advanced algorithms, machine learning grounded in mathematical analysis, and optimization frameworks for real-world applications in systems, signals, and image processing.

By broadening the mathematical foundations of computational intelligence, this Special Issue aims to provide researchers and industry experts with valuable insights, linking theory with practice. We encourage contributions that address both theoretical and practical needs, supporting progress in these dynamic fields.

Dr. Jaesung Lee
Dr. Wangduk Seo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational intelligence
  • deep learning
  • signal processing systems
  • image and video processing
  • multi-modal learning
  • generative models
  • biomedical signal processing
  • pattern recognition
  • real-world applications
  • mathematical modeling

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Published Papers (1 paper)

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Research

30 pages, 2843 KiB  
Article
Survey on Replay-Based Continual Learning and Empirical Validation on Feasibility in Diverse Edge Devices Using a Representative Method
by Heon-Sung Park, Hyeon-Chang Chu, Min-Kyung Sung, Chaewoon Kim, Jeongwon Lee, Dae-Won Kim and Jaesung Lee
Mathematics 2025, 13(14), 2257; https://doi.org/10.3390/math13142257 - 12 Jul 2025
Viewed by 464
Abstract
The goal of on-device continual learning is to enable models to adapt to streaming data without forgetting previously acquired knowledge, even with limited computational resources and memory constraints. Recent research has demonstrated that weighted regularization-based methods are constrained by indirect knowledge preservation and [...] Read more.
The goal of on-device continual learning is to enable models to adapt to streaming data without forgetting previously acquired knowledge, even with limited computational resources and memory constraints. Recent research has demonstrated that weighted regularization-based methods are constrained by indirect knowledge preservation and sensitive hyperparameter settings, and dynamic architecture methods are ill-suited for on-device environments due to increased resource consumption as the structure scales. In order to compensate for these limitations, replay-based continuous learning, which maintains a compact structure and stable performance, is gaining attention. The limitations of replay-based continuous learning are (1) the limited amount of historical training data that can be stored due to limited memory capacity, and (2) the computational resources of on-device systems are significantly lower than those of servers or cloud infrastructures. Consequently, designing strategies that balance the preservation of past knowledge with rapid and cost-effective updates of model parameters has become a critical consideration in on-device continual learning. This paper presents an empirical survey of replay-based continual learning studies, considering the nearest class mean classifier with replay-based sparse weight updates as a representative method for validating the feasibility of diverse edge devices. Our empirical comparison of standard benchmarks, including CIFAR-10, CIFAR-100, and TinyImageNet, deployed on devices such as Jetson Nano and Raspberry Pi, showed that the proposed representative method achieved reasonable accuracy under limited buffer sizes compared with existing replay-based techniques. A significant reduction in training time and resource consumption was observed, thereby supporting the feasibility of replay-based on-device continual learning in practice. Full article
(This article belongs to the Special Issue Computational Intelligence in Systems, Signals and Image Processing)
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