With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors we...With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.展开更多
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 at...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.展开更多
Aminopeptidase N(APN/CD13), a Zn<sup>2+</sup>-dependent ectopeptidase localized on the cell surface, is widely considered to influence the invasion of tumor cells. We found that boroleucine and dino-leucin...Aminopeptidase N(APN/CD13), a Zn<sup>2+</sup>-dependent ectopeptidase localized on the cell surface, is widely considered to influence the invasion of tumor cells. We found that boroleucine and dino-leucine borate exhibited a strong inhibitory effect on the enzyme activity of aminopeptidase N. The tested assay indicated that both compounds had an anti-proliferative effect on triple-negative breast cancer cells. Wound healing assay, migration test and matrigel-coated transwell assay showed that both boroleucine and dino-leucine borate inhibited the migration and invasion of breast cancer cells. Immunoblot analysis showed that both compounds down-regulated the expression of matrix metalloproteinase-2/9. In the capillary tube formation assay of human umbilical vein endothelial cells (HUVECs), dino-leucine borate showed better antiangiogenic activity than ubenimex even at a low concentration (10 μM). Moreover, compared with ubenimex, the anti-metastatic activity of dino-leucine borate in vivo was similar to or even better than that of ubenimex in the H22 pulmonary metastasis mouse model. In this paper, we found the novel APN inhibitors to markedly suppress the enzyme activity of APN and inhibit the migration and invasion of tumor cells in vitro and in vivo.展开更多
文摘With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.
文摘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.
文摘Aminopeptidase N(APN/CD13), a Zn<sup>2+</sup>-dependent ectopeptidase localized on the cell surface, is widely considered to influence the invasion of tumor cells. We found that boroleucine and dino-leucine borate exhibited a strong inhibitory effect on the enzyme activity of aminopeptidase N. The tested assay indicated that both compounds had an anti-proliferative effect on triple-negative breast cancer cells. Wound healing assay, migration test and matrigel-coated transwell assay showed that both boroleucine and dino-leucine borate inhibited the migration and invasion of breast cancer cells. Immunoblot analysis showed that both compounds down-regulated the expression of matrix metalloproteinase-2/9. In the capillary tube formation assay of human umbilical vein endothelial cells (HUVECs), dino-leucine borate showed better antiangiogenic activity than ubenimex even at a low concentration (10 μM). Moreover, compared with ubenimex, the anti-metastatic activity of dino-leucine borate in vivo was similar to or even better than that of ubenimex in the H22 pulmonary metastasis mouse model. In this paper, we found the novel APN inhibitors to markedly suppress the enzyme activity of APN and inhibit the migration and invasion of tumor cells in vitro and in vivo.
