Intelligent Sensing and Computing Methods for Cyber-Physical Systems

Dear Colleagues,

The cyber-physical system (CPS) has been coming into view and will be increasingly applied in our daily lives and in business process management. The emerging CPSs must be robust and responsive for their implementation in coordinated, distributed, and connected ways. It is expected that future CPSs will far exceed today’s systems on a variety of characteristics, such as capability, adaptability, resiliency, safety, security, and usability. With the rapid development of computing and sensing technologies (e.g., ubiquitous wireless sensor networks), the amount of data from dissimilar sensors and social media has increased tremendously. Conventional data fusion algorithms such as registration, association, and fusion are not effective for massive datasets. New research opportunities and challenges for content analysis on CPS networks have arisen. Making sense of these volumes of Big Data requires cutting-edge tools that can analyze and extract useful knowledge from vast and diverse data streams. How to integrate and analyze the data? How to retrieve knowledge from Big Data? How to share knowledge among smart things? How to ensure security and protect privacy? These are some of the questions in the long list of challenges that need to be addressed in future CPSs. This is opening a vast space of opportunities to extend the current networks, communications, and computer applications to more pervasive and mobile applications.

Topics of interest include but are not restricted to:

• New methods for intelligent computing in combination with deep learning;
• New learning methods with intelligent computing concepts for established deep learning architectures and structure;
• Faster and more robust intelligent computing methods for learning of deep models;
• Complexity reduction with intelligent computing methods and transformation of deep learning models;
• Evolutionary and intelligent computing-based optimization and tuning of deep learning models;
• Evolving and intelligent computing techniques for deep learning systems (expanding and pruning layers, components etc. on the fly);
• Metaheuristics aspects and intelligent computing algorithms in deep learning for improved convergence;
• Hybrid learning schemes with intelligent computing (deterministic with heuristics-based, mimetics);
• Interpretability aspects with intelligent computing for a better understanding of deep learning models;
• Intelligent computing methods for non-established deep learning models (deep SVMs, deep fuzzy models, deep clustering techniques, etc.).

Dr. Zheng Xu
Prof. Dr. Jemal Abawajy
Guest Editors

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