Deep Learning and Machine Learning for Unmanned Equipment

Dear Colleagues,

Multiple sensors have been applied in unmanned equipment—for example, intelligent vision and autonomous navigation—for which sensor data fusion is the key issue. Many theories, methods and techniques have been developed in recent years. Deep learning and meta-heuristic optimization may promote applications of machine learning for image and speech recognition. However, it is necessary that these methods are further studied for data fusion and analysis. This Special Issue discusses the relative design and analysis of learning networks and algorithms of deep learning for multisensor data fusion with applications of unmanned equipment. The topics of interest for publications include but not limited to:

• Multisensor fusion theory.
• Deep learning networks for multisensor
• Machine learning for multisensor
• Sensor signal processing for unmanned equipment.

Prof. Jeng-Shyang Pan
Prof. Jun-Bao Li
Dr. Meng Li
Prof. Shi-Huang Chen
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• sensor fusion
• deep learning
• machine learning
• unmanned equipment