Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities
MS Seyfioğlu, AM Özbayoğlu… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Radar-based activity recognition is a problem that has been of great interest due to
applications such as border control and security, pedestrian identification for automotive …
applications such as border control and security, pedestrian identification for automotive …
Device-free wireless sensing for human detection: The deep learning perspective
Currently, developments in wireless sensing technologies have shown that wireless signals
can be employed to transmit information between wireless communication devices and are …
can be employed to transmit information between wireless communication devices and are …
Review and analysis of patients' body language from an artificial intelligence perspective
Body language is a nonverbal communication process consisting of movements, postures,
gestures, and expressions of the body or body parts. Body language expresses human …
gestures, and expressions of the body or body parts. Body language expresses human …
On learning disentangled representations for gait recognition
Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of
the existing gait recognition methods take silhouettes or articulated body models as gait …
the existing gait recognition methods take silhouettes or articulated body models as gait …
[HTML][HTML] Study on deep learning in radar
W Jun, Z Tong, L Peng, W Shaoming - 雷达学报, 2018 - radars.ac.cn
Electromagnetic waves are transmitted by radars and reflected by different objects, and
radar signal processing is highly significant as its analyses can lead to the acquisition of …
radar signal processing is highly significant as its analyses can lead to the acquisition of …
Personnel recognition and gait classification based on multistatic micro-Doppler signatures using deep convolutional neural networks
Z Chen, G Li, F Fioranelli… - IEEE Geoscience and …, 2018 - ieeexplore.ieee.org
In this letter, we propose two methods for personnel recognition and gait classification using
deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler …
deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler …
Practical classification of different moving targets using automotive radar and deep neural networks
A Angelov, A Robertson… - IET Radar, Sonar & …, 2018 - Wiley Online Library
In this work, the authors present results for classification of different classes of targets (car,
single and multiple people, bicycle) using automotive radar data and different neural …
single and multiple people, bicycle) using automotive radar data and different neural …
[HTML][HTML] 深度学习在雷达中的研究综述
王俊, 郑彤, 雷鹏, 魏少明 - 雷达学报, 2018 - radars.ac.cn
王俊(1972–), 男, 教授. 现于北京航空航天大学电子信息工程学院从事科研教学工作. 1995
年于西北工业大学获通信工程专业工学学士学位, 1998 年, 2001 年于北京航空航天大学分别获 …
年于西北工业大学获通信工程专业工学学士学位, 1998 年, 2001 年于北京航空航天大学分别获 …
DNN transfer learning from diversified micro-Doppler for motion classification
Recently, deep neural networks (DNNs) have been the subject of intense research for the
classification of radio frequency signals, such as synthetic aperture radar imagery or micro …
classification of radio frequency signals, such as synthetic aperture radar imagery or micro …
Deep neural network initialization methods for micro-Doppler classification with low training sample support
MS Seyfioğlu, SZ Gürbüz - IEEE Geoscience and Remote …, 2017 - ieeexplore.ieee.org
Deep neural networks (DNNs) require large-scale labeled data sets to prevent overfitting
while having good generalization. In radar applications, however, acquiring a measured …
while having good generalization. In radar applications, however, acquiring a measured …