Advances in Deep Learning for Intelligent Sensing Systems

Dear Colleagues,

Recent advances in deep learning techniques have led to significant progress in sensing systems. Today, intelligent sensing can benefit sensing processes in many research and application fields. For example, inherent and complex time-series or spatiotemporal correlations among sensing data can be exploited using deep learning to characterize the studied objectives of interest. In many real-world sensing scenarios, the high-level features within sensing data imply underlying or unknown interactions leading to useful information or knowledge, which may outperform traditional analytical tools when learning and interpreting studied problems.

The Special Issue focuses on advanced deep learning methods, e.g., deep neural networks, to address a broad view of problems about sensor, sensing, and sensory issues in intelligent sensing systems. Contributions are encouraged to design and develop novel deep learning frameworks, particularly concentrating on their effectiveness, intelligence, and reliability in solving challenging sensing issues or achieving superior sensing performance. This Special Issue is dedicated to both theoretical innovations and real-world applications with field implementation and experiments. The topics of this issue include but are not limited to the following:

• Data acquisition, analysis, and decision making in sensors, robotics, and intelligent systems;
• Deep neural networks, deep attention mechanisms, and other representative learning techniques;
• Classification, regression, interpretation, and prediction for intelligent sensing;
• Machine condition monitoring, environmental monitoring, structural health monitoring, and other monitoring programs;
• Comparison studies between traditional methods and advanced deep learning methods in sensing problems.

Dr. Min Xia
Prof. Dr. Teng Li
Prof. Dr. Clarence de Silva
Guest Editors

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