Call for Papers: Intelligent Signal Processing for Complex Industrial Systems

Aim

Artificial Intelligence computing solutions have been widely used for signal processing. With 5G/6G, IoT, and cloud system development, there are increasing amount of data generated, communicated and computed, hence the needs for intelligent signal processing solutions. The advancement of modern communication and computing technologies enables new networked complex industrial systems. Intelligent signal processing solutions may have applications in a wide range of domains, such as: mobile sensor networks, multi-area power systems, robotics, intelligent building, smart city, submarine autonomous systems, intelligent transportation systems, modern manufacturing system, industry 4.0 technology, et al. For the design and implementation of complex industrial systems, there are challenges related to signal collection, communication, storage, integration, and processing due to resource constraints and limits of involved cyber-physical systems. Therefore, it is important to understand such constraints and limits in order to effectively deploy modern complex industrial systems. Implementation constraints and limits render fundamental problems regarding the design of complex industrial systems.

Many AI computing solutions, such as for pattern recognition, optimization, or decision support, are NP-hard. The theoretical basis, such as computing complexity research for pattern recognition, optimization, or decision support solutions is required for intelligent signal processing for complex industrial systems, such as to evaluate their usability in real world. Complexity research for intelligent signal processing in the constrained industrial applications is important to meet the increasing expectations of modern manufacturing.

This special issue focuses on the intelligent signal processing for complex industrial systems. The complex signal processing solutions can be researched to meet industrial constraints for specific real world applications with limitations of cyber physical devices. Relevant qualitative and quantitative approximation solutions can be researched together with exact solutions. Both theoretical and experimental studies are important to research intelligent signal processing for complex industrial systems.

This special issue aims to attract latest research results and the latest solutions for intelligent signal processing for complex industrial systems. Both theory focused and application driven studies are welcome, especially papers with good technical depth on the intelligent signal processing for complex industrial systems.

Scope

Potential topics include but are not limited to the following:

  • Complexity analysis for signal pattern recognition solutions
  • Optimal signal processing solutions in industry under limited resources
  • Intelligent signal processing for industry decision support
  • Performance of intelligent signal processing for complex industrial systems 
  • Low complexity signal processing for distributed cooperation
  • Optimal signal processing for mobile sensor networks
  • Optimal signal processing for intelligent transportations
  • Complexity analysis for signal processing in smart city solutions

Paper Submission
Authors need to directly log into the JSPS submission system and find the article type for this special issue. Submissions site: https://www.editorialmanager.com/vlsi/default.aspx

The special issue is open to all the researchers, developers and industry experts who wish to contribute by submitting a relevant original technical manuscript.


Tenative Schedule

Manuscript submission deadline: 28 October, 2021

First round review results: 16 December, 2021
Submission deadline for revisions: 28 January, 2022
Second round review results: 16 March, 2022
Final manuscript due for accepted papers: 28 April, 2022
 

Corresponding Guest Editor

Dr. Xiaochun Cheng

Middlesex University, London, UK 

Email: xiaochun.cheng@gmail.com


Dr. Xiaochun Cheng (SM04) received the B.Eng. degree in computer engineering and the Ph.D. degree in computer science in 1992 and 1996, respectively. He has been the Computer Science Project Coordinator in Middlesex University since 2012. He is currently a member of IEEE SMC Technical Committee on Enterprise Information Systems, IEEE SMC Technical Committee on Computational Intelligence, IEEE SMC Technical Committee on Cognitive Computing, IEEE SMC Technical Committee on Intelligent Internet Systems, IEEE Communications Society Communications and Information Security Technical Committee, BCS Information Security Specialist Group, BCS Cybercrime Forensics Specialist Group, and BCS Artificial Intelligence Specialist Group.  One project was funded with 16 Million Euro. 3 his papers are in the 2019, 2020 top 1% of the academic field by Data from Essential Science Indicators. He won 3 times national competitions and National Science and Technology Advance Award.