With the development of mathematical algorithms like intelligent optimization and deep learning, the data measured from different kinds of sensors can be processed with high efficiency and precision. As parameterized reflections of the real-world events, signals, images, etc., which are measured by sensors can be properly processed to interpret valuable information for both military and civilian uses. For example, human biological signals can be processed for disease diagnosis. Images and videos can be well handled for object location, recognition, and tracking.

However, it is still a challenging problem to develop proper mathematical models and find suitable mathematical algorithms to represent and analyze the data streams from different fields. Recent advances in intelligent optimization and deep learning provide some insights for high-level data processing. The intelligence optimizations like swarm intelligence and group optimization can be used to model data from different sensors. Deep learning models including CNN, LSTM, and GAN can be employed to interpret different kinds of data to find potential information.

This Special Issue welcomes original research and review articles with a focus on applying advanced mathematical algorithms such as intelligent optimization and deep learning in data processing from multiple sensors.

Potential topics include but are not limited to the following:

• Newly proposed mathematical algorithms
• Intelligent optimization-driven deep learning
• Intelligent optimization for data representation
• Deep learning for signal/image/video processing
• Intelligent optimization and deep learning in the Internet of Things (IoT)
• Intelligent optimization and deep learning for multi-sensor data fusion
• Deep learning for data forecasting
• Intelligent algorithms in sports, education, culture, etc
• Intelligent optimization and deep learning for pattern recognition
• System design based on intelligent optimization and deep learning