We investigate the similarities and differences among three queue rules,the first-in-first-out(FIFO)rule,last-in-firstout(LIFO)rule and random-in-random-out(RIRO)rule,on dynamical networks with limited buffer size.In ...We investigate the similarities and differences among three queue rules,the first-in-first-out(FIFO)rule,last-in-firstout(LIFO)rule and random-in-random-out(RIRO)rule,on dynamical networks with limited buffer size.In our network model,nodes move at each time step.Packets are transmitted by an adaptive routing strategy,combining Euclidean distance and node load by a tunable parameter.Because of this routing strategy,at the initial stage of increasing buffer size,the network density will increase,and the packet loss rate will decrease.Packet loss and traffic congestion occur by these three rules,but nodes keep unblocked and lose no packet in a larger buffer size range on the RIRO rule networks.If packets are lost and traffic congestion occurs,different dynamic characteristics are shown by these three queue rules.Moreover,a phenomenon similar to Braess’paradox is also found by the LIFO rule and the RIRO rule.展开更多
当前医学图像的特征匹配主要依靠像素灰度来完成,但是像素灰度对空间信息不敏感,当匹配图像之间存在灰度信息不均衡以及噪声干扰时,将导致误匹配率较高,对此,本文提出了一种基于移动队列规则耦合角度约束的医学图像匹配算法.首先,利用...当前医学图像的特征匹配主要依靠像素灰度来完成,但是像素灰度对空间信息不敏感,当匹配图像之间存在灰度信息不均衡以及噪声干扰时,将导致误匹配率较高,对此,本文提出了一种基于移动队列规则耦合角度约束的医学图像匹配算法.首先,利用高斯金字塔模型对源图像进行滤波预处理,以减少源图像中存在的噪声等干扰;再利用Harris算子对预处理后的源图像进行特征检测,获取图像的特征点;然后,利用SURF(Speed Up Robust Feature)特征描述子,获取特征点对应的特征描述子.并通过尺度空间理论获取特征点集,通过将特征点集进行排序来形成队列,从而设计移动队列规则,完成特征点的匹配;最后,通过求取匹配特征点间的夹角,形成角度约束模型,对匹配特征点进行提纯,剔除伪匹配特征点,使得匹配准确度得以提升.从仿真实验结果与分析可见,在对医学图像进行匹配时,本文所提出的方法具有匹配精度高、鲁棒性能好等特点.展开更多
We first consider an infinite-buffer single server queue where arrivals occur according to a batch Markovian arrival process （BMAP）. The server serves customers in batches of maximum size ＇b＇ with a minimum thresh...We first consider an infinite-buffer single server queue where arrivals occur according to a batch Markovian arrival process （BMAP）. The server serves customers in batches of maximum size ＇b＇ with a minimum threshold size ＇a＇. The service time of each batch follows general distribution independent of each other as well as the arrival process. The proposed analysis is based on the use of matrix-analytic procedure to obtain queue-length distribution at a post-departure epoch. Next we obtain queue-length distributions at various other epochs such as, pre-arrival, arbitrary and pre-service using relations with post-departure epoch. Later we also obtain the system-length distributions at post-departure and arbitrary epochs using queue-length distribution at post-departure epoch. Some important performance measures, like mean queue-lengths and mean waiting times have been obtained Total expected cost function per trait time is also derived to determine the locally optimal values of a and b. Secondly, we perform similar analysis for the corresponding infinite-buffer single server queue where arrivals occur according to a BMAP and service process in this case follows a non-renewal one, namely, Markovian service process （MSP）.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.71801066 and 71431003)the Fundamental Research Funds for the Central Universities of China(Grant Nos.PA2019GDQT0020 and JZ2017HGTB0186)
文摘We investigate the similarities and differences among three queue rules,the first-in-first-out(FIFO)rule,last-in-firstout(LIFO)rule and random-in-random-out(RIRO)rule,on dynamical networks with limited buffer size.In our network model,nodes move at each time step.Packets are transmitted by an adaptive routing strategy,combining Euclidean distance and node load by a tunable parameter.Because of this routing strategy,at the initial stage of increasing buffer size,the network density will increase,and the packet loss rate will decrease.Packet loss and traffic congestion occur by these three rules,but nodes keep unblocked and lose no packet in a larger buffer size range on the RIRO rule networks.If packets are lost and traffic congestion occurs,different dynamic characteristics are shown by these three queue rules.Moreover,a phenomenon similar to Braess’paradox is also found by the LIFO rule and the RIRO rule.
文摘当前医学图像的特征匹配主要依靠像素灰度来完成,但是像素灰度对空间信息不敏感,当匹配图像之间存在灰度信息不均衡以及噪声干扰时,将导致误匹配率较高,对此,本文提出了一种基于移动队列规则耦合角度约束的医学图像匹配算法.首先,利用高斯金字塔模型对源图像进行滤波预处理,以减少源图像中存在的噪声等干扰;再利用Harris算子对预处理后的源图像进行特征检测,获取图像的特征点;然后,利用SURF(Speed Up Robust Feature)特征描述子,获取特征点对应的特征描述子.并通过尺度空间理论获取特征点集,通过将特征点集进行排序来形成队列,从而设计移动队列规则,完成特征点的匹配;最后,通过求取匹配特征点间的夹角,形成角度约束模型,对匹配特征点进行提纯,剔除伪匹配特征点,使得匹配准确度得以提升.从仿真实验结果与分析可见,在对医学图像进行匹配时,本文所提出的方法具有匹配精度高、鲁棒性能好等特点.
基金partial financial support from the Department of Science and Technology,New Delhi,India under the research grant SR/FTP/MS-003/2012
文摘We first consider an infinite-buffer single server queue where arrivals occur according to a batch Markovian arrival process （BMAP）. The server serves customers in batches of maximum size ＇b＇ with a minimum threshold size ＇a＇. The service time of each batch follows general distribution independent of each other as well as the arrival process. The proposed analysis is based on the use of matrix-analytic procedure to obtain queue-length distribution at a post-departure epoch. Next we obtain queue-length distributions at various other epochs such as, pre-arrival, arbitrary and pre-service using relations with post-departure epoch. Later we also obtain the system-length distributions at post-departure and arbitrary epochs using queue-length distribution at post-departure epoch. Some important performance measures, like mean queue-lengths and mean waiting times have been obtained Total expected cost function per trait time is also derived to determine the locally optimal values of a and b. Secondly, we perform similar analysis for the corresponding infinite-buffer single server queue where arrivals occur according to a BMAP and service process in this case follows a non-renewal one, namely, Markovian service process （MSP）.
