Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control prot...Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields （APF） which could guarantee the connectivity maintenance, col ision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that al robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.展开更多
The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilizati...The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilization of MANETs in real life seems limited due to the lack of protocols for the automatic creation and evolution of ad hoc networks. Recently, a novel P2P protocol named Wi-Fi Direct has been proposed and standardized by the Wi-Fi Alliance to facilitate nearby devices’ interconnection. Wi-Fi Direct provides high-performance direct communication among devices, includes different energy management mechanisms, and is now available in most Android mobile devices. However, the current implementation of Wi-Fi Direct on Android has several limitations, making the Wi-Fi Direct network only be a one-hop ad-hoc network. This paper aims to develop a new framework for multi-hop ad hoc networking using Wi-Fi Direct in Android smart devices. The framework includes a connection establishment protocol and a group management protocol. Simulations validate the proposed framework on the OMNeT++ simulator. We analyzed the framework by varying transmission range, number of hops, and buffer size. The results indicate that the framework provides an eventual 100% packet delivery for different transmission ranges and hop count values. The buffer size has enough space for all packets. However, as buffer size decreases, the packet delivery decreases proportionally.展开更多
针对眼底图像中存在大量不规则、噪声干扰严重、边界模糊、分割难度较大的细小血管的问题,提出一种基于多方向特征和连通性检测的眼底图像分割方法MDF_Net&CD(Multi-Directional Features neural Network and Connectivity Detecti...针对眼底图像中存在大量不规则、噪声干扰严重、边界模糊、分割难度较大的细小血管的问题,提出一种基于多方向特征和连通性检测的眼底图像分割方法MDF_Net&CD(Multi-Directional Features neural Network and Connectivity Detection)。设计了一个以像素点不同方向特征向量为输入的深度神经网络模型MDF_Net(Multi-Directional Features neural Network),利用MDF_Net对眼底图像进行初步分割;提出连通性检测算法,根据血管的几何特征,对MDF_Net的初步分割结果进一步修订。在公开的眼底图像数据集上,将MDF_Net&CD与近期有代表性的分割方法进行实验对比,结果表明MDF_Net&CD各项评估指标均衡,敏感度,F1值和准确率优于其他方法。该方法能有效捕捉像素点的细节特征,对不规则、噪声干扰严重、边界模糊的细小血管有较好分割效果。展开更多
将网络信息的概念引入到神经科学当中对于研究脑功能机制有着积极的作用。然而人脑网络的复杂性对于理解有一定的困难。该文基于有向传递函数(Directed Transfer Function,DTF)的方法估计得到功能连接模式,进一步提出了信息流增益的计...将网络信息的概念引入到神经科学当中对于研究脑功能机制有着积极的作用。然而人脑网络的复杂性对于理解有一定的困难。该文基于有向传递函数(Directed Transfer Function,DTF)的方法估计得到功能连接模式,进一步提出了信息流增益的计算方法,用以评价特定脑区在全脑信息传输过程中的作用。该方法将流入信息和流出信息结合,具有浓缩两者信息的优点,简化了脑复杂网络的辨识度,并且提高了结果的显示标度。仿真运算和自发、诱发脑电数据的结果都显示出通过计算分析信息流增益可以比较理想地得到各个脑区对全脑信息流的贡献。结果证明信息流增益方法为进一步理解大脑认知机制提供了可能。展开更多
基金supported by the National Natural Science Foundation of China(61175112)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(G61321002)+3 种基金the Projects of Major International(Regional)Joint Research Program(61120106010)the Beijing Education Committee Cooperation Building Foundationthe Program for Changjiang Scholars and Innovative Research Team in University(IRT1208)the ChangJiang Scholars Program and the Beijing Outstanding Ph.D.Program Mentor Grant(20131000704)
文摘Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields （APF） which could guarantee the connectivity maintenance, col ision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that al robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.
文摘The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilization of MANETs in real life seems limited due to the lack of protocols for the automatic creation and evolution of ad hoc networks. Recently, a novel P2P protocol named Wi-Fi Direct has been proposed and standardized by the Wi-Fi Alliance to facilitate nearby devices’ interconnection. Wi-Fi Direct provides high-performance direct communication among devices, includes different energy management mechanisms, and is now available in most Android mobile devices. However, the current implementation of Wi-Fi Direct on Android has several limitations, making the Wi-Fi Direct network only be a one-hop ad-hoc network. This paper aims to develop a new framework for multi-hop ad hoc networking using Wi-Fi Direct in Android smart devices. The framework includes a connection establishment protocol and a group management protocol. Simulations validate the proposed framework on the OMNeT++ simulator. We analyzed the framework by varying transmission range, number of hops, and buffer size. The results indicate that the framework provides an eventual 100% packet delivery for different transmission ranges and hop count values. The buffer size has enough space for all packets. However, as buffer size decreases, the packet delivery decreases proportionally.
文摘针对眼底图像中存在大量不规则、噪声干扰严重、边界模糊、分割难度较大的细小血管的问题,提出一种基于多方向特征和连通性检测的眼底图像分割方法MDF_Net&CD(Multi-Directional Features neural Network and Connectivity Detection)。设计了一个以像素点不同方向特征向量为输入的深度神经网络模型MDF_Net(Multi-Directional Features neural Network),利用MDF_Net对眼底图像进行初步分割;提出连通性检测算法,根据血管的几何特征,对MDF_Net的初步分割结果进一步修订。在公开的眼底图像数据集上,将MDF_Net&CD与近期有代表性的分割方法进行实验对比,结果表明MDF_Net&CD各项评估指标均衡,敏感度,F1值和准确率优于其他方法。该方法能有效捕捉像素点的细节特征,对不规则、噪声干扰严重、边界模糊的细小血管有较好分割效果。
文摘诸如交通网络、供水网络、电信网络、燃气网络等在人们的生活中极其重要,但是这些网络容易受到自然和人为等因素的影响导致失效,进而降低其连通性。为研究其连通性问题,改进SCM（sequential compounding method）实现了考虑点和线可靠性的有向无环网络连通性的计算方法。该算法是一种快速可靠性评价算法,其结果是近似的,适用于分析可分解为点—线—点结构的网络,特别适用于有一定统计规律的网络。算法主要由两种运算组成,即＂与＂合并和＂或＂合并,通过这两种运算将网络化简直到合并为一个点为止。计算八种典型的网络,并将结果与文献和MCS（Monte Carlo simulations）比较,结果表明,提出的算法与MCS相比计算得到的连通性略有不同,误差在-6.2%~4.6%;但是计算时间差别很大,大约是MCS的1.2%~9.2%。
文摘将网络信息的概念引入到神经科学当中对于研究脑功能机制有着积极的作用。然而人脑网络的复杂性对于理解有一定的困难。该文基于有向传递函数(Directed Transfer Function,DTF)的方法估计得到功能连接模式,进一步提出了信息流增益的计算方法,用以评价特定脑区在全脑信息传输过程中的作用。该方法将流入信息和流出信息结合,具有浓缩两者信息的优点,简化了脑复杂网络的辨识度,并且提高了结果的显示标度。仿真运算和自发、诱发脑电数据的结果都显示出通过计算分析信息流增益可以比较理想地得到各个脑区对全脑信息流的贡献。结果证明信息流增益方法为进一步理解大脑认知机制提供了可能。
