Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
针对传统的IP欺骗攻击缓解方法存在运算开销大、缺乏灵活性等问题,提出了一种基于动态限制策略的软件定义网络(software defined network,SDN)中IP欺骗攻击缓解方法。首先,利用Packet-In消息中三元组信息回溯攻击路径,定位IP欺骗攻击源...针对传统的IP欺骗攻击缓解方法存在运算开销大、缺乏灵活性等问题,提出了一种基于动态限制策略的软件定义网络(software defined network,SDN)中IP欺骗攻击缓解方法。首先,利用Packet-In消息中三元组信息回溯攻击路径,定位IP欺骗攻击源头主机;然后,由控制器制定动态限制策略对连接攻击源头主机的交换机端口的新流转发功能进行限制,待限制期满再恢复其转发新流的功能,限制期的大小随着被检测为攻击源的次数而增长。研究结果表明:这种动态的限制策略可阻隔攻击流进入SDN网络,从而有效避免SDN交换机、控制器以及链路过载;由于在限制期间无需再对这些限制的交换机端口进行实时监测,该方法在应对长时攻击时较传统方法具有更高的缓解效率和更少的资源消耗。展开更多
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
文摘针对传统的IP欺骗攻击缓解方法存在运算开销大、缺乏灵活性等问题,提出了一种基于动态限制策略的软件定义网络(software defined network,SDN)中IP欺骗攻击缓解方法。首先,利用Packet-In消息中三元组信息回溯攻击路径,定位IP欺骗攻击源头主机;然后,由控制器制定动态限制策略对连接攻击源头主机的交换机端口的新流转发功能进行限制,待限制期满再恢复其转发新流的功能,限制期的大小随着被检测为攻击源的次数而增长。研究结果表明:这种动态的限制策略可阻隔攻击流进入SDN网络,从而有效避免SDN交换机、控制器以及链路过载;由于在限制期间无需再对这些限制的交换机端口进行实时监测,该方法在应对长时攻击时较传统方法具有更高的缓解效率和更少的资源消耗。
