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Electronics
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AI in Information Processing and Real-Time Communication
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AI in Information Processing and Real-Time Communication

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 1407

Special Issue Editors

Prof. Dr. Yulin Wang

E-Mail Website
Guest Editor
School of Computer Science, Wuhan University, Wuhan 430072, China
Interests: Image and video information processing; computer vision; information hiding; information security; Internet of Things (IoT) technologies; code cloning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cheng-Ying Yang

E-Mail Website
Guest Editor
Department of Computer Science, University of Taipei, Taipei 10066, Taiwan
Interests: communication system; signal processing; information security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fournier-Viger Philippe

E-Mail Website
Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Interests: data analysis; data mining; machine learning; artificial intelligence; big data; applications

Special Issue Information

Dear Colleagues,

Information intelligent processing technology is a cutting-edge and challenging research direction in the field of signal and information technology. It is based on artificial intelligence theory and focuses on the intelligence of information processing, including computer intelligence (intelligent processing of text, images, speech, and other information), communication intelligence, and control information intelligence. Deep learning is an artificial intelligence technology that can be used for tasks such as feature extraction, classification, and regression of signals. The main advantage of deep learning is that it can automatically learn features without the need for manual intervention. Deep learning has achieved significant results in fields such as image, speech, and natural language processing.

In traditional Ethernet, real-time data transmission is often limited due to non-deterministic factors such as collisions, delays, and jitter in the network. Time-sensitive networks (TSNs) are a transformative force in the field of real-time communication. By seamlessly integrating with existing Ethernet infrastructure and introducing a set of standards, TSN enables industries to embrace the era of instant connectivity, paving the way for innovative applications and the future of transmitting time-sensitive data with unparalleled accuracy.

The scope of this Special Issue includes, but is not limited to, the theory and methods of image, speech, and video processing based on artificial intelligence; application-oriented multimedia processing; big data and intelligent processing; application-oriented speech processing; time-sensitive network-related research content including theories and methods of time synchronization, clock synchronization, data scheduling, network configuration, traffic scheduling, simulation and verification of networks and reliability, as well as application-oriented deterministic transmission.

Prof. Dr. Yulin Wang
Prof. Dr. Cheng-Ying Yang
Prof. Dr. Fournier-Viger Philippe
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

  • artificial and computational intelligence
  • advanced image and video processing
  • information systems and technology
  • computational science and technology
  • information and knowledge
  • signal and speech processing
  • communications and networking
  • computer network communications
  • internet technology
  • network and information security techniques
  • modeling and optimization
  • design system and algorithm
  • network and information security techniques
  • networking strategy
  • real-time communication
  • time-sensitive network
  • deterministic network
  • network scheduling
  • bandwidth utilization

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Published Papers (2 papers)

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Research

17 pages, 1642 KiB  
Article
Leveraging Time-Critical Computation and AI Techniques for Task Offloading in Internet of Vehicles Network Applications
by Peifeng Liang, Wenhe Chen, Honghui Fan and Hongjin Zhu
Electronics 2024, 13(16), 3334; https://doi.org/10.3390/electronics13163334 - 22 Aug 2024
Viewed by 461
Abstract
Vehicular fog computing (VFC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Moreover, more and more Artificial Intelligence (AI) technologies are applied in task offloading on [...] Read more.
Vehicular fog computing (VFC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Moreover, more and more Artificial Intelligence (AI) technologies are applied in task offloading on the Internet of Vehicles (IoV). Focusing on the problems of computing latency and energy consumption, in this paper, we propose an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for an IoV network, which ensures reliable low-latency communication, efficient task offloading in the IoV network by using a Software-Defined Vehicular-based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in the IoV layer to implement the task allocation of each vehicle. By applying AI technologies such as reinforcement learning (RL), Markov decision process, and deep learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay. Full article
(This article belongs to the Special Issue AI in Information Processing and Real-Time Communication)
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12 pages, 2934 KiB  
Article
Streamlined Deep Learning Models for Move Prediction in Go-Game
by Ying-Chih Lin and Yu-Chen Huang
Electronics 2024, 13(15), 3093; https://doi.org/10.3390/electronics13153093 - 5 Aug 2024
Viewed by 519
Abstract
Due to the complexity of search space and move evaluation, the game of Go has been a long-standing challenge for artificial intelligence (AI) to achieve a high level of proficiency. It was not until DeepMind proposed the deep neural network and tree search [...] Read more.
Due to the complexity of search space and move evaluation, the game of Go has been a long-standing challenge for artificial intelligence (AI) to achieve a high level of proficiency. It was not until DeepMind proposed the deep neural network and tree search algorithm AlphaGo in 2014 that an efficient learning algorithm was developed, marking a significant milestone in AI technology. In light of the key technologies in AI Computer Go, this work examines move prediction across different Go rankings and sophisticatedly develops two deep learning models by combining and extending the feature extraction methods of AlphaGo. Specifically, effective modules for neural networks are proposed to guide learning through complicated Go situations based on the Inception module in GoogLeNet and the Convolutional Block Attention Module (CBAM). Subsequently, the two models are combined by ensemble learning to improve generalization, and these streamlined models significantly reduce the number of model parameters to the scale of one hundred thousand. Experimental results show that our models achieve prediction accuracies of 46.9% and 50.8% on two different Go datasets, outperforming conventional models by significant margins. This work not only advances AI development in the Go-game but also offers an innovative approach to related studies. Full article
(This article belongs to the Special Issue AI in Information Processing and Real-Time Communication)
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