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Mathematics
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Advanced Information and Signal Processing: Models and Algorithms
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Advanced Information and Signal Processing: Models and Algorithms

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2419

Special Issue Editors

Dr. Li Zhou

E-Mail
Guest Editor
College of Computer, National University of Defense Technology, Changsha, China
Interests: signal processing; network performance analysis; information processing; wireless computing; cloud computing; deep learning
Prof. Dr. Xinchao Zhao

E-Mail Website
Guest Editor
School of Science, Beijing University of Posts and Telecommunications, Beijing, China
Interests: swarm intelligence; operations research
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 “Advanced Information and Signal Processing: Models and Algorithms”. We would like to invite authors to apply novel computational models and algorithms to study information and signal processing. Topics of interest include, but are not limited to, the following:

  • Computational intelligence in network security, computer security, and data protection;
  • Access controls, cryptography, and their applications;
  • Models in biometrics technologies;
  • Data mining and knowledge discovery;
  • Distributed and parallel computing and their applications;
  • Fuzzy and neural network systems to study information and signal processing;
  • Image processing and computer vision;
  • Information propagation and opinion spread on social networks;
  • Multimedia computing and multimedia data analysis;
  • Resource optimization for networked systems;
  • Signal processing, pattern recognition and applications;
  • Ubiquitous computing and applications.

The title of the Special Issue not only reflects the topicality of the Special Issue itself but is also associated with 2024 the 10th International Conference on Communication and Information Processing (ICCIP 2024) to take place in Lingshui, Hainan, China, on 14–17 November 2024. ICCIP is dedicated to exploring cutting-edge theories and mathematical applications within communication and information processing technologies, intending to revolutionize future developments. We invite authors to submit an extended version of their conference papers for this Special Issue.

The Special Issue is also open to submissions from authors who are interested in the topic, even if they have not attended the conference.

Dr. Li Zhou
Prof. Dr. Xinchao Zhao
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

  • information processing
  • signal processing
  • wireless communications
  • pattern recognition
  • network security
  • computer security
  • cryptography
  • biometrics
  • data mining
  • knowledge discovery
  • parallel computing
  • neural networks
  • fuzzy logic
  • image processing
  • information propagation
  • opinion spread
  • multimedia computing
  • ubiquitous computing
  • edge computing

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Published Papers (2 papers)

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37 pages, 6090 KiB  
Article
Abstract Cyclic Functional Relation and Taxonomies of Cyclic Signals Mathematical Models: Construction, Definitions and Properties
by Serhii Lupenko
Mathematics 2024, 12(19), 3084; https://doi.org/10.3390/math12193084 - 1 Oct 2024
Viewed by 708
Abstract
This work is devoted to the procedure of the construction of an abstract cyclic functional relation, which summarizes and extends the known results for a cyclically correlated random process and a cyclic (cyclically distributed) random process to the case of arbitrary cyclic functional [...] Read more.
This work is devoted to the procedure of the construction of an abstract cyclic functional relation, which summarizes and extends the known results for a cyclically correlated random process and a cyclic (cyclically distributed) random process to the case of arbitrary cyclic functional relations. Two alternative definitions of the abstract cyclic functional relation are given, and the fundamental properties of its cyclic and phase structures are presented. The theorem on the invariance of cyclicity attributes of an abstract cyclic functional relation to shifts of its argument, and which are determined by the rhythm function of this functional relation, is formulated and proved. This theorem gives the sufficient and necessary conditions that the rhythm function of an abstract cyclic functional relation must satisfy. By specifying the range of values and attributes of the cyclicity of an abstract cyclic functional relation, the definitions of important classes of cyclic functional relations are formulated. A deductive approach to building a wide system of taxonomies of classes of deterministic, stochastic, fuzzy and interval cyclic functional relations as potential mathematical models of cyclic signals is demonstrated. A comparative analysis of an abstract cyclic functional relation with the known mathematical models of cyclic signals was carried out. The results obtained in the article significantly expand and systematize the mathematical tools of the description of cyclic signals and are the basis for the development of effective model-based technologies for processing and computer simulation of signals with a cyclic space-time structure. Full article
(This article belongs to the Special Issue Advanced Information and Signal Processing: Models and Algorithms)
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17 pages, 1215 KiB  
Article
C-KAN: A New Approach for Integrating Convolutional Layers with Kolmogorov–Arnold Networks for Time-Series Forecasting
by Ioannis E. Livieris
Mathematics 2024, 12(19), 3022; https://doi.org/10.3390/math12193022 - 27 Sep 2024
Viewed by 1130
Abstract
Time-series forecasting represents of one of the most challenging and widely studied research areas in both academic and industrial communities. Despite the recent advancements in deep learning, the prediction of future time-series values remains a considerable endeavor due to the complexity and dynamic [...] Read more.
Time-series forecasting represents of one of the most challenging and widely studied research areas in both academic and industrial communities. Despite the recent advancements in deep learning, the prediction of future time-series values remains a considerable endeavor due to the complexity and dynamic nature of time-series data. In this work, a new prediction model is proposed, named C-KAN, for multi-step forecasting, which is based on integrating convolutional layers with Kolmogorov–Arnold network architecture. The proposed model’s advantages are (i) the utilization of convolutional layers for learning the behavior and internal representation of time-series input data; (ii) activation at the edges of the Kolmogorov–Arnold network for potentially altering training dynamics; and (iii) modular non-linearity for allowing the differentiated treatment of features and potentially more precise control over inputs’ influence on outputs. Furthermore, the proposed model is trained using the DILATE loss function, which ensures that it is able to effectively deal with the dynamics and high volatility of non-stationary time-series data. The numerical experiments and statistical analysis were conducted on five challenging non-stationary time-series datasets, and provide strong evidence that C-KAN constitutes an efficient and accurate model, well suited for time-series forecasting tasks. Full article
(This article belongs to the Special Issue Advanced Information and Signal Processing: Models and Algorithms)
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