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Electronics
Special Issues
Parallel and Distributed Computing Systems: Current Trends and Future Prospects
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Parallel and Distributed Computing Systems: Current Trends and Future Prospects

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 833

Special Issue Editors

Prof. Dr. Dazhao Cheng

E-Mail Website1 Website2
Guest Editor
The School of Computer Science, Wuhan University, Wuhan 430072, China
Interests: parallel and cloud computing; distributed computing; big data platform; artificial intelligence architecture
Special Issues, Collections and Topics in MDPI journals
Dr. Chuang Hu

E-Mail Website
Guest Editor
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China
Interests: distributed computing; edge computing; federated learning; data analysis
Dr. Fang He

E-Mail Website
Guest Editor
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
Interests: distributed computing; data analysis; edge learning

Special Issue Information

Dear Colleagues,

The rapid evolution of computing technologies, driven by the exponential growth of data-intensive applications, artificial intelligence, IoT, and cloud-edge ecosystems, has positioned parallel and distributed computing systems as critical enablers of modern computational efficiency and scalability. This Special Issue seeks to explore and highlight the latest advances and emerging challenges in parallel and distributed computing systems. It aims to address challenges in architecture design, algorithm optimization, resource management, fault tolerance, energy efficiency, and security while fostering discussions on novel applications in areas such as machine learning, edge computing, blockchain, and quantum-inspired distributed systems. Topics of interest include, but are not limited to, multi-core and many-core processors, high-performance computing, cloud and edge computing, distributed algorithms, scalability, energy-efficient solutions, and heterogeneous architectures. This Special Issue welcomes original research articles, comprehensive review papers, and case studies that address both fundamental principles and real-world implementations.

Prof. Dr. Dazhao Cheng
Dr. Chuang Hu
Dr. Fang He
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. Electronics 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

  • parallel computing systems
  • AI/ML integration in distributed systems
  • scalability and optimization
  • energy-efficient computing
  • scalability and optimization

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Published Papers (1 paper)

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Research

22 pages, 7476 KB  
Article
Neural Network for Robotic Control and Security in Resistant Settings
by Kubra Kose, Nuri Alperen Kose and Fan Liang
Electronics 2025, 14(18), 3618; https://doi.org/10.3390/electronics14183618 - 12 Sep 2025
Viewed by 577
Abstract
As the industrial automation landscape advances, the integration of sophisticated perception and manipulation technologies into robotic systems has become crucial for enhancing operational efficiency and precision. This paper presents a significant enhancement to a robotic system by incorporating the Mask R-CNN deep learning [...] Read more.
As the industrial automation landscape advances, the integration of sophisticated perception and manipulation technologies into robotic systems has become crucial for enhancing operational efficiency and precision. This paper presents a significant enhancement to a robotic system by incorporating the Mask R-CNN deep learning algorithm and the Intel® RealSense™ D435 camera with the UFactory xArm 5 robotic arm. The Mask R-CNN algorithm, known for its powerful object detection and segmentation capabilities, combined with the depth-sensing features of the D435, enables the robotic system to perform complex tasks with high accuracy. This integration facilitates the detection, manipulation, and precise placement of single objects, achieving 98% detection accuracy, 98% gripping accuracy, and 100% transport accuracy, resulting in a peak manipulation accuracy of 99%. Experimental evaluations demonstrate a 20% improvement in manipulation success rates with the incorporation of depth data, reflecting significant enhancements in operational flexibility and efficiency. Additionally, the system was evaluated under adversarial conditions where structured noise was introduced to test its stability, leading to only a minor reduction in performance. Furthermore, this study delves into cybersecurity concerns pertinent to robotic systems, addressing vulnerabilities such as physical attacks, network breaches, and operating system exploits. The study also addresses specific threats, including sabotage and service disruptions, and emphasizes the importance of implementing comprehensive cybersecurity measures to protect advanced robotic systems in manufacturing environments. To ensure truly robust, secure, and reliable robotic operations in industrial environments, this paper highlights the critical role of international cybersecurity standards and safety standards for the physical protection of industrial robot applications and their human operators. Full article
(This article belongs to the Special Issue Parallel and Distributed Computing Systems: Current Trends and Future Prospects)
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