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Recent Advances in Parallel Computing and Big Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1377

Special Issue Editors

Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
Interests: big data; artificial intelligence; high-performance computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
Interests: data science; time series modeling; artificial intelligence platforms

E-Mail Website
Guest Editor Assistant
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
Interests: software engineering; cloud computing; artificial intelligence platforms

Special Issue Information

Dear Colleagues,

We are pleased to announce a forthcoming Special Issue titled "Recent Advances in Parallel Computing and Big Data". In the era of advanced computing, the synergy between parallel computing and big data has become pivotal for facilitating research and applications across diverse scientific and industrial domains. This Special Issue aims to highlight significant developments in parallel computing technologies that effectively tackle the challenges in big data analysis, artificial intelligence models and large-scale parallel applications. By focusing on scalable and efficient solutions, we explore how these technologies are critical in various domains.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Innovative parallel algorithms for big data processing;
  • Numerical methods and parallel algorithms for the advanced modeling and simulation;
  • Use of hardware accelerators (GPUs, FPGA) and heterogeneous hardware in computational science;
  • Scalability and performance studies on large-scale systems;
  • Architectures and frameworks for integrating big data and parallel computing;
  • Machine learning and artificial intelligence approaches;
  • Advances in distributed databases and file systems for handling big data;
  • Challenges and solutions in data-intensive environments for parallel computing systems

We invite researchers to submit their original research articles, comprehensive review papers and short communications that push the frontiers of knowledge in parallel computing and big data. All submissions will undergo a rigorous peer-review process, ensuring that only high-quality research is published.

Prof. Dr. Jue Wang
Guest Editor

Dr. Meng Wan
Prof. Dr. Rongqiang Cao
Guest Editor Assistants

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. Applied Sciences 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 2400 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

  • big data
  • high-performance computing
  • data science
  • parallel computing

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Published Papers (1 paper)

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Research

20 pages, 2075 KiB  
Article
MCIDN: Deblurring Network for Metal Corrosion Images
by Jiaxiang Wang, Meng Wan, Pufen Zhang, Sijie Chang, Hao Du, Peng Shi, Hongying Yu, Dongbai Sun, Jue Wang and Yangang Wang
Appl. Sci. 2024, 14(24), 11565; https://doi.org/10.3390/app142411565 - 11 Dec 2024
Viewed by 625
Abstract
The analysis of corrosion images is crucial in materials science, where acquiring clear images is fundamental for subsequent analysis. The goal of deblurring metal corrosion images is to reconstruct clear images from degraded ones. To the best of our knowledge, this study introduces [...] Read more.
The analysis of corrosion images is crucial in materials science, where acquiring clear images is fundamental for subsequent analysis. The goal of deblurring metal corrosion images is to reconstruct clear images from degraded ones. To the best of our knowledge, this study introduces the first paired blurry-sharp image dataset specifically designed for the metal corrosion domain, filling a critical gap in the existing research. This innovative approach effectively addresses the unique challenges associated with deblurring metal corrosion images. We propose a novel metal corrosion images deblurring network (MCIDN) that employs a dual-domain attention mechanism, integrating both spatial and frequency domains to enhance image clarity. This innovative approach effectively addresses the unique challenges associated with deblurring metal corrosion images. While self-attention is widely used in visual tasks, its quadratic complexity often leads to high computational costs. To address this issue, we introduce a new spatial channel attention module (SCAM) that employs dynamic group convolutions to achieve self-attention, effectively integrating information from local regions and enhancing representation learning capabilities. Recognizing the critical role of frequency components in image restoration, we develop a frequency channel attention module (FCAM) that selectively focuses on different frequency components of images, thereby enhancing deblurring performance. These two attention modules are seamlessly integrated into our network. Compared to existing methods, our approach demonstrates superior performance on datasets of blurry metal corrosion images, achieving a peak signal-to-noise ratio (PSNR) of 32.8645 dB and a structural similarity (SSIM) of 0.9768. These metrics indicate that our method provides clearer and more detailed reconstructions, significantly enhancing the image quality. Full article
(This article belongs to the Special Issue Recent Advances in Parallel Computing and Big Data)
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