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Mathematics
Special Issues
Mathematical Innovations and Contributions within Communication and Information Processing
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Mathematical Innovations and Contributions within Communication and Information Processing

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3211

Special Issue Editors

Prof. Dr. Li Guo

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Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: digital signal processing; blockchain technology; intelligent medical image processing; precision medical big data analysis
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
Dr. Jesus Requena-Carrión

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Guest Editor
School of Electronics Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Interests: statistical data and signal processing; modeling and simulation; biomedical engineering; machine learning project management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xingquan Zuo

E-Mail Website
Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: intelligent optimization; data mining; artificial intelligence; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The primary objective of this Special Issue aligns with the proceedings of the ‘2023 9th International Conference on Communication and Information Processing (ICCIP 2023),’ a pivotal event aimed at addressing unprecedented advancements in the mathematical aspects of Communication and Information Processing: Theories and Applications. Held in Lingshui, Hainan, China, in December 14–16, 2023, the conference's website, http://www.iccip.org/, serves as a repository of invaluable information.

ICCIP 2023 is dedicated to exploring cutting-edge theories and mathematical applications within communication and information processing technologies, intending to revolutionize future developments.

Featuring world-class plenary speakers, technical symposiums, and specialized tracks, ICCIP 2023 invites original mathematical papers for inclusion in its proceedings. This international conference provides a pivotal platform for researchers to converge and explore mathematical complexities across diverse areas within this field.

Moreover, this Special Issue warmly invites manuscripts that complement the conference's themes and keywords, specifically focusing on mathematical aspects not previously presented at ICCIP 2023. Such contributions are encouraged to further the mathematical underpinnings of Communication and Information Processing: Theories and Applications.

Prof. Dr. Li Guo
Prof. Dr. Xinchao Zhao
Dr. Jesus Requena-Carrión
Prof. Dr. Xingquan Zuo
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

  • block chain and cryptography
  • cloud computing and scheduling optimization
  • computational intelligence and grid computing
  • computer crime prevention and detection
  • computer security
  • data mining and big data analysis
  • data stream processing in mobile/sensor networks
  • distributed and parallel applications
  • evolutionary learning and optimization
  • fuzzy and neural network systems
  • image processing and signal processing
  • information and data management
  • information and network security
  • mobile and social network programming
  • multimedia computing
  • mathematical optimization and swarm intelligence
  • software engineering
  • ubiquitous computing, services and applications
  • web services modeling and web composition
  • wireless communications optimization

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

11 pages, 282 KiB  
Article
New Constructions of One-Coincidence Sequence Sets over Integer Rings
by Jin-Ho Chung, Daehan Ahn and Daehwan Kim
Mathematics 2024, 12(21), 3316; https://doi.org/10.3390/math12213316 - 22 Oct 2024
Viewed by 531
Abstract
In this paper, we introduce new constructions of one-coincidence frequency-hopping sequence (OC-FHS) sets over integer rings. These OC-FHSs are designed to minimize interference in frequency-hopping multiple access (FHMA) systems, which are widely used in various communication applications. By leveraging the properties of primitive [...] Read more.
In this paper, we introduce new constructions of one-coincidence frequency-hopping sequence (OC-FHS) sets over integer rings. These OC-FHSs are designed to minimize interference in frequency-hopping multiple access (FHMA) systems, which are widely used in various communication applications. By leveraging the properties of primitive elements in integer ring Zpn, we develop OC-FHS sets with lengths mpn−1 for m dividing (p−1), along with constructions with composite lengths based on linear functions. The proposed OC-FHS sets include parameters not previously addressed in the literature and encompass some known cases as special cases. Full article
(This article belongs to the Special Issue Mathematical Innovations and Contributions within Communication and Information Processing)
13 pages, 5095 KiB  
Article
Vehicle Re-Identification Method Based on Multi-Task Learning in Foggy Scenarios
by Wenchao Gao, Yifan Chen, Chuanrui Cui and Chi Tian
Mathematics 2024, 12(14), 2247; https://doi.org/10.3390/math12142247 - 19 Jul 2024
Cited by 1 | Viewed by 680
Abstract
Vehicle re-identification employs computer vision to determine the presence of specific vehicles in images or video sequences, often using vehicle appearance for identification due to the challenge of capturing complete license plate information. Addressing the performance issues caused by fog, such as image [...] Read more.
Vehicle re-identification employs computer vision to determine the presence of specific vehicles in images or video sequences, often using vehicle appearance for identification due to the challenge of capturing complete license plate information. Addressing the performance issues caused by fog, such as image blur and loss of key positional information, this paper introduces a multi-task learning framework incorporating a multi-scale fusion defogging method (MsF). This method effectively mitigates image blur to produce clearer images, which are then processed by the re-identification branch. Additionally, a phase attention mechanism is introduced to adaptively preserve crucial details. Utilizing advanced artificial intelligence techniques and deep learning algorithms, the framework is evaluated on both synthetic and real datasets, showing significant improvements in mean average precision (mAP)—an increase of 2.5% to 87.8% on the synthetic dataset and 1.4% to 84.1% on the real dataset. These enhancements demonstrate the method’s superior performance over the semi-supervised joint defogging learning (SJDL) model, particularly under challenging foggy conditions, thus enhancing vehicle re-identification accuracy and deepening the understanding of applying multi-task learning frameworks in adverse visual environments. Full article
(This article belongs to the Special Issue Mathematical Innovations and Contributions within Communication and Information Processing)
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18 pages, 2822 KiB  
Article
Learning the Hybrid Nonlocal Self-Similarity Prior for Image Restoration
by Wei Yuan, Han Liu, Lili Liang and Wenqing Wang
Mathematics 2024, 12(9), 1412; https://doi.org/10.3390/math12091412 - 6 May 2024
Cited by 1 | Viewed by 934
Abstract
As an immensely important characteristic of natural images, the nonlocal self-similarity (NSS) prior has demonstrated great promise in a variety of inverse problems. Unfortunately, most current methods utilize either the internal or the external NSS prior learned from the degraded image or training [...] Read more.
As an immensely important characteristic of natural images, the nonlocal self-similarity (NSS) prior has demonstrated great promise in a variety of inverse problems. Unfortunately, most current methods utilize either the internal or the external NSS prior learned from the degraded image or training images. The former is inevitably disturbed by degradation, while the latter is not adapted to the image to be restored. To mitigate such problems, this work proposes to learn a hybrid NSS prior from both internal images and external training images and employs it in image restoration tasks. To achieve our aims, we first learn internal and external NSS priors from the measured image and high-quality image sets, respectively. Then, with the learned priors, an efficient method, involving only singular value decomposition (SVD) and a simple weighting method, is developed to learn the HNSS prior for patch groups. Subsequently, taking the learned HNSS prior as the dictionary, we formulate a structural sparse representation model with adaptive regularization parameters called HNSS-SSR for image restoration, and a general and efficient image restoration algorithm is developed via an alternating minimization strategy. The experimental results indicate that the proposed HNSS-SSR-based restoration method exceeds many existing competition algorithms in PSNR and SSIM values. Full article
(This article belongs to the Special Issue Mathematical Innovations and Contributions within Communication and Information Processing)
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