Human Activity Recognition Based on Frequency-Modulated Continuous Wave and DenseNet

With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread attention and become a hot research area. FMCW signals reflected by target activity can be collected, and human activity can be recognized based on the measurements. This paper focused on human activity recognition based on FMCW and DenseNet. We collected point clouds from FMCW and analyzed them to recognize human activity because different activities could lead to unique point cloud features. We built and trained the neural network to implement human activities using a FMCW signal. Firstly, this paper presented recent reviews about human activity recognition using wireless signals. Then, it introduced the basic concepts of FMCW radar and described the fundamental principles of the system using FMCW radar. We also provided the system framework, experiment scenario, and DenseNet neural network structure. Finally, we presented the experimental results and analyzed the accuracy of different neural network models. The system achieved recognition accuracy of 100 percent for five activities using the DenseNet. We concluded the paper by discussing the current issues and future research directions.

蛇婆子地上部分化学成分研究（英文）

运用多种色谱和波谱方法对蛇婆子地上部分的化学成分进行研究,从中分离鉴定了16个化合物,包括5个萜类(1–5),4个香豆素(6–9),6个黄酮(10–15)和1个其它类化合物(16)。除了化合物12和15外,其余14个化合物均为首次从蛇婆子中分离得到,其中香豆素类化合物为首次报道从蛇婆子中分离得到。对分离得到的16个化合物进行了体外NO生成抑制活性筛选,结果显示化合物10, 13和14有弱的NO生成抑制活性。