Submission Deadline: 31 July 2026 View: 351 Submit to Special Issue
Prof. Dr. Hai Zhao
Email: zhaoh@mail.neu.edu.cn
Affiliation: School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China
Research Interests: Artificial Intelligence, Internet of Things (IoT), Internet of Medical Things (IoMT), edge intelligence
Prof. Dr. Gwanggil Jeon
Email: gjeon@inu.ac.kr
Affiliation: Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, South Korea
Research Interests: signal&image processing, IoT, healthcare, sustainable cities and society, embedded system
Prof. Dr. Khursheed Aurangzeb
Email: kaurangzeb@ksu.edu.sa
Affiliation: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
Research Interests: machine learning, smart grids, data science, healthcare, embedded systems
In complex systems, signals may come from different sources. For example, the physiological signals in medical systems may be electroencephalogram, electromyography, photoplethysmography, or CT image; the autonomous or auxiliary driving system may utilize (infrared) images from cameras and cloud data from LiDAR; the Industrial Internet of Things (IIoTs) may adopt the data from various sensors, etc. Traditional learning models may fail to process these complex signals.
The complex signals from different sources follow a mixture of distributions. In some complex systems, it is difficult to grasp the information from several aspects, which requires fusing the information from different views. Even if complex signals are from the same source, they may still follow a mixture of distributions, which differs from the assumption in the traditional learning model. Furthermore, the signals for learning may follow a different distribution in real applications, or there are not enough data for learning, which may need to reuse the learning model from existing or historical data. These challenges strongly affect the signal processing in complex systems. This special issue will focus on handling these challenges to enhance hypercomplex signal processing and make complex systems smarter.
This special issue will provide a chance to connect researchers and practitioners to share their state-of-the-art discoveries and findings about hypercomplex signal processing in complex systems, such as hypercomplex signal processing in the Internet of Things (IoT), hypercomplex signal processing in traffic and environment systems, hypercomplex signal processing in social networks, and hypercomplex signal processing in information systems.
Topics of Interest:
This special issue specifically encourages high-quality submissions about hypercomplex signal processing in complex systems. The topics of interest include, but are not limited to, the following
· Advanced theory of hypercomplex signal processing
· Advances of hypercomplex signal processing in the information system.
· Advances of hypercomplex signal processing for social networks
· Advances of hypercomplex signal processing in Wireless Sensor Networks
· Advances of hypercomplex signal processing in the Internet of Things
· Advances of hypercomplex signal processing for natural language processing in the intelligent system
· Advances of hypercomplex signal processing in the reasoning system
· Advances of hypercomplex signal processing for assisted diagnosis in the Internet of Medical Things
· Advances of hypercomplex signal processing for assisted driving in the Internet of Vehicles
· AI technology in hypercomplex signal processing
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