Acquiring and analyzing body information is the first step to sensing and understanding the body. The signals obtained from our body often hold the characteristics of high dimension, non-linearity, and low signal-to-noise ratio. Therefore, high-dimensional sense and non-linear signal processing (HDS-NLSP) play a considerable role in many fields such as biomedicine and robotics. In recent years, the explosive growth of medical Bigdata and the explosive development of deep learning provide new opportunities and tools for the Internet of Things (IoT) based on signal processing and real-time supervision, monitoring, and diagnosis of disease. In many emerging practical applications, such as intelligent medical systems/sub-health monitoring systems, it is necessary to capture and process large-scale, high-dimensional, non-linear, and multimodal data in a high-precision and real-time manner. To address these challenges, people urgently need to develop new high-performance IoT technology and signal processing technology and use the latest progress in deep learning to find the features behind high-dimensional and non-linear data and predict the state.
This Research Topic is dedicated to the deep learning technology of high-dimensional IoT and signal processing. Its purpose is to emphasize the new research results and development, open problems, and promising new directions related to system design, theory, algorithm, and application. This special issue will include high-quality innovation contributions in this emerging field, including but not limited to:
1. HDS-IoT (EEG, MEG, MRI, CT, X-ray, Video) and its brain-machine interface technology.
2. Design and development of low-cost brain-computer interface equipment.
3. Signal super-resolution technology and its application.
4. Development of a high-dimensional data reconstruction algorithm based on deep learning.
5. Theoretical analysis and interpretability of deep learning method for high-dimensional non-linear data.
6. Multimodal data fusion algorithm based on deep learning (machine learning).
7. Medical signal analysis and processing.
8. Intelligent sensing and human-machine interface.
Acquiring and analyzing body information is the first step to sensing and understanding the body. The signals obtained from our body often hold the characteristics of high dimension, non-linearity, and low signal-to-noise ratio. Therefore, high-dimensional sense and non-linear signal processing (HDS-NLSP) play a considerable role in many fields such as biomedicine and robotics. In recent years, the explosive growth of medical Bigdata and the explosive development of deep learning provide new opportunities and tools for the Internet of Things (IoT) based on signal processing and real-time supervision, monitoring, and diagnosis of disease. In many emerging practical applications, such as intelligent medical systems/sub-health monitoring systems, it is necessary to capture and process large-scale, high-dimensional, non-linear, and multimodal data in a high-precision and real-time manner. To address these challenges, people urgently need to develop new high-performance IoT technology and signal processing technology and use the latest progress in deep learning to find the features behind high-dimensional and non-linear data and predict the state.
This Research Topic is dedicated to the deep learning technology of high-dimensional IoT and signal processing. Its purpose is to emphasize the new research results and development, open problems, and promising new directions related to system design, theory, algorithm, and application. This special issue will include high-quality innovation contributions in this emerging field, including but not limited to:
1. HDS-IoT (EEG, MEG, MRI, CT, X-ray, Video) and its brain-machine interface technology.
2. Design and development of low-cost brain-computer interface equipment.
3. Signal super-resolution technology and its application.
4. Development of a high-dimensional data reconstruction algorithm based on deep learning.
5. Theoretical analysis and interpretability of deep learning method for high-dimensional non-linear data.
6. Multimodal data fusion algorithm based on deep learning (machine learning).
7. Medical signal analysis and processing.
8. Intelligent sensing and human-machine interface.
