The modern society is covered and manifested by various kinds of sensors. The measurements from these sensors are mainly described by signals, images, videos, etc. Proper interpretation of these measurements can greatly improve the efficiency of industrial production and daily life. At the early stage, the signals or images were mainly processed and explained by human labor, which was low in both efficiency and precision. With the development of computational intelligence algorithms like machine learning, deep learning, etc., the sensor measurements can be automatically handled with little manual intervention. Hence, volumes of signals and images can be properly processed for different kinds of uses.
It should be noted that the signals and images are diverse and rich, e.g., radar signal, biomedical signal, optical image, medical image. And they may have different characteristics. In this sense, different computational intelligence algorithms should be developed or employed for different kinds of signals or images. The recent developments in machine learning and deep learning provided a rich set of tools for signal and image processing such as convolution neural networks (CNN), deep generative models, and deep belief networks. Application of such novel computational intelligence methods to signal and image processing can help make accurate and fast interpretations.
Potential topics of interest include but are not limited to the following:
• Computational intelligence for signal and image presentation
• Deep learning for signal and image processing
• Deep learning for video processing
• Computational intelligence for medical uses including biomedical signals and medical images
• Deep learning vs traditional machine learning comparative analysis of signals and images
• Computational intelligence for big data
• Computational intelligence for IOT
• Computational intelligence for system design based on signals, images, and videos
• Computational intelligence for real-time analysis of data from sports, fitness, etc.
The modern society is covered and manifested by various kinds of sensors. The measurements from these sensors are mainly described by signals, images, videos, etc. Proper interpretation of these measurements can greatly improve the efficiency of industrial production and daily life. At the early stage, the signals or images were mainly processed and explained by human labor, which was low in both efficiency and precision. With the development of computational intelligence algorithms like machine learning, deep learning, etc., the sensor measurements can be automatically handled with little manual intervention. Hence, volumes of signals and images can be properly processed for different kinds of uses.
It should be noted that the signals and images are diverse and rich, e.g., radar signal, biomedical signal, optical image, medical image. And they may have different characteristics. In this sense, different computational intelligence algorithms should be developed or employed for different kinds of signals or images. The recent developments in machine learning and deep learning provided a rich set of tools for signal and image processing such as convolution neural networks (CNN), deep generative models, and deep belief networks. Application of such novel computational intelligence methods to signal and image processing can help make accurate and fast interpretations.
Potential topics of interest include but are not limited to the following:
• Computational intelligence for signal and image presentation
• Deep learning for signal and image processing
• Deep learning for video processing
• Computational intelligence for medical uses including biomedical signals and medical images
• Deep learning vs traditional machine learning comparative analysis of signals and images
• Computational intelligence for big data
• Computational intelligence for IOT
• Computational intelligence for system design based on signals, images, and videos
• Computational intelligence for real-time analysis of data from sports, fitness, etc.