Human-Centered Artificial Intelligence for Human-Computer Interaction, Signal Processing, and Unmanned Systems

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

Deadline for manuscript submissions: 15 March 2026 | Viewed by 1014

Special Issue Editors


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Guest Editor
1. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325000, China
2. Faculty of Industrial Design Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: human-computer interaction; ubiquitous computing; medical informatics

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Guest Editor
School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Interests: pattern recognition; machine learning; signal processing

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Guest Editor
Institute of Unmanned Systems, Beihang University, Beijing 100191, China
Interests: command and control of UAVs; image processing; pattern recognition

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Guest Editor
The College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325000, China
Interests: swarm intelligence; optimization; integer programming; medical data processing

Special Issue Information

Dear Colleagues,

Human-centered artificial intelligence (HCAI) is an increasingly vital research domain dedicated to designing AI technologies that respect human values, prioritize user needs, and enhance overall well-being. Concurrently, signal processing provides essential techniques for acquiring, analyzing, and interpreting complex datasets that underpin a multitude of technological advancements. Unmanned systems—including aerial, terrestrial, and underwater platforms—have become indispensable in applications such as environmental monitoring, defense, logistics, and infrastructure inspection. The integration of HCAI, advanced signal processing methodologies, and the dynamic evolution of unmanned systems holds the promise of transformative breakthroughs in usability, performance, and ethical governance. By embedding human-centered and user-centric principles into design and development practices, these technologies can foster trust, reliability, and meaningful impact across diverse sectors.

This Special Issue seeks to compile and showcase cutting-edge research and comprehensive review articles at the nexus of human-centered artificial intelligence, signal processing, and unmanned systems, with a particular focus on human–computer interaction (HCI). We invite contributions that present innovative approaches and interdisciplinary frameworks to advance autonomous systems, enhance user experiences, and address the ethical implications of algorithmic decision-making. Aligned with the scope of Electronics—which encompasses advanced electronics, computing, and interdisciplinary research—this Special Issue will highlight theoretical advancements, practical implementations, and forward-looking analyses that facilitate the user-oriented design and operation of AI-driven unmanned systems.

Potential topics for this Special Issue include, but are not limited to, the following:
•    AI and machine learning frameworks;
•    Signal processing and data analytics;
•    User interface and interaction design;
•    Autonomous system control and navigation;
•    Usability and performance evaluation;
•    Decision support and situational awareness.

In this Special Issue, both original research articles and comprehensive review papers are invited. We encourage submissions that introduce theoretical advancements, present innovative practical implementations, or offer critical discussions on future directions within the realms of HCAI, signal processing, and unmanned systems.

We look forward to receiving your contributions.

Dr. Chaofan Wang
Dr. Duona Zhang
Dr. Wenrui Ding
Dr. Yi Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • human-centered artificial intelligence (HCAI)
  • human-computer interaction (HCI)
  • signal processing
  • unmanned systems
  • machine learning
  • ethical AI
  • autonomous systems
  • user-centered design

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Published Papers (1 paper)

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Research

18 pages, 9486 KB  
Article
MCCSAN: Automatic Modulation Classification via Multiscale Complex Convolution and Spatiotemporal Attention Network
by Songchen Xu, Duona Zhang, Yuanyao Lu, Zhe Xing and Weikai Ma
Electronics 2025, 14(16), 3192; https://doi.org/10.3390/electronics14163192 - 11 Aug 2025
Cited by 1 | Viewed by 643
Abstract
Automatic Modulation Classification (AMC) is vital for adaptive wireless communication, yet it faces challenges in complex environments, including insufficient feature extraction, feature redundancy, and high interclass similarity among modulation schemes. To address these limitations, this paper proposes the Multiscale Complex Convolution Spatiotemporal Attention [...] Read more.
Automatic Modulation Classification (AMC) is vital for adaptive wireless communication, yet it faces challenges in complex environments, including insufficient feature extraction, feature redundancy, and high interclass similarity among modulation schemes. To address these limitations, this paper proposes the Multiscale Complex Convolution Spatiotemporal Attention Network (MCCSAN). In this work, we propose three key innovations tailored for AMC tasks: a multiscale complex convolutional module that directly processes raw I/Q sequences, preserving critical phase and amplitude information while extracting diverse signal features. A spatiotemporal attention mechanism dynamically weights temporal steps and feature channels to suppress redundancy and enhance discriminative feature focus. A combined loss function integrating cross-entropy and center loss improves intraclass compactness and interclass separability. Evaluated on the RML2018.01A dataset and RML2016.10A across SNR levels from −6 dB to 12 dB, MCCSAN achieves a state-of-the-art classification accuracy of 97.03% (peak) and an average accuracy improvement of 3.98% over leading methods. The study confirms that integrating complex-domain processing with spatiotemporal attention significantly enhances AMC performance. Full article
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