Recent Advances on Signal Processing and Deep Learning for Public Security Applications

Dear Colleagues,

In recent years, with the rapid growth of signal processing and deep learning technologies, a lot of new algorithms like Local tangent space alignment, Spectral Clustering, Compressive Sensing, Restricted Boltzmann Machine and Long Short-Term Memory, have all been applied into the public security fields, both in model system evolution, design novel applications, and optimize system performance. For instance:

1. Social security monitoring, warning and control: Personnel identification features accurate identification, integrated feature perception and risk control for people, vehicles and objects, super high-rise building fire prevention and control.
2. Production security and the prevention and control of major accident: Production process failure judgment and special equipment failure early diagnosis and accurate life prediction.
3. Withstand natural disasters, emergencies and crisis management capabilities for towns: Urban high-rise building operation and maintenance security protection and city underground integrated pipe security and security.
4. Scientific and technological strength that supports Impartial justice and justice for the people: Knowledge-Centric wisdom judicial operation support system and Cross-sectoral, multi-service judicial synergy.
5. Various engineering or biomedical systems that demand advanced signal processing and intelligent approaches for enhance its performance and security.

However, it introduces many technological challenges, such as how to intelligently analyze, mine and understand the fusion information inside from such big data, and how to utilize the mined insights to design novel applications and optimize the legacy systems. This Special Issue aims at providing a forum to discuss the recent advances on signal processing, deep learning, and intelligent algorithm for public security and other engineering applications. Papers dealing with integration of techniques and/or with newly developed techniques are welcome.   

Prof. Wen-Hsiang Hsieh
Prof. Jia-Shing Sheu
Dr. Ling-Zhong Guo
Prof. Carlo Meno
Guest Editors

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