The feature of finite state Markov channel probability distribution is discussed on condition that original I/O are known. The probability is called posterior condition probability. It is also proved by Bayes formula ...The feature of finite state Markov channel probability distribution is discussed on condition that original I/O are known. The probability is called posterior condition probability. It is also proved by Bayes formula that posterior condition probability forms stationary Markov sequence if channel input is independently and identically distributed. On the contrary, Markov property of posterior condition probability isn’t kept if the input isn’t independently and identically distributed and a numerical example is utilized to explain this case. The properties of posterior condition probability will aid the study of the numerical calculated recurrence formula of finite state Markov channel capacity.展开更多
Network Coding (NC) is confirmed to be power and bandwidth efficient technique, because of the less number of transmitted packets over the network. Wireless Sensor Network (WSN) is usually power limited network applic...Network Coding (NC) is confirmed to be power and bandwidth efficient technique, because of the less number of transmitted packets over the network. Wireless Sensor Network (WSN) is usually power limited network application, and in many scenarios it is power and bandwidth limited application. The proposed scenario in this paper applies the advantages of NC over WSN to obtain such power and bandwidth efficient WSN. To take the advantages of NC over the one of the most needed applications i.e., WSN, we come up to what this paper is discussing. We consider a WSN (or its cluster) that consists of M nodes that transmit equal-length information packets to a common destination node D over wireless Rayleigh block-fading channel where the instantaneous SNR is assumed to be constant over a single packet transmission period. Finite-State packet level Markov chain (FSMC) model is applied to give the channel more practical aspect. The simulation results showed that applying NC over the WSN cluster improved the channel bandwidth significantly by decreasing the number of the Automatic Repeat Request (ARQ), resulting in improving the power consumption significantly. The results are collected for different transmission distances to evaluate the behavior to the proposed scenario with regard to the bath losses effect.展开更多
Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct...Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the "curse of dimensionality" and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient.展开更多
计算机网络缓存侧信道能够间接体现计算机内部状态以及数据传输情况,其受攻击时,用户端信息数据存在泄露风险,因此提出一种基于马尔科夫的计算机网络缓存侧信道攻击检测方法。构建隐马尔科夫模型,对计算机网络缓存侧信道状态改变的概率...计算机网络缓存侧信道能够间接体现计算机内部状态以及数据传输情况,其受攻击时,用户端信息数据存在泄露风险,因此提出一种基于马尔科夫的计算机网络缓存侧信道攻击检测方法。构建隐马尔科夫模型,对计算机网络缓存侧信道状态改变的概率进行计算。通过Baum‐Welch算法估计隐马尔科夫模型最优参数,并计算缓存侧信道状态观测序列输出概率。比较缓存侧信道观测序列输出概率与设定的阈值,判断该序列为计算机网络缓存侧信道攻击信号的可能性,并引入平均信息熵判断计算机缓存侧信道状态是否存在异常,完成计算机网络缓存侧信道攻击检测。通过实验验证得出,该方法用于计算机网络缓存侧信道攻击检测的准确率高,误报率低,在遭受DDoS攻击(Distributed denial of service)时的检测时间较短,对计算机网络缓存侧信道攻击的防御与保护产生了积极影响。展开更多
文摘The feature of finite state Markov channel probability distribution is discussed on condition that original I/O are known. The probability is called posterior condition probability. It is also proved by Bayes formula that posterior condition probability forms stationary Markov sequence if channel input is independently and identically distributed. On the contrary, Markov property of posterior condition probability isn’t kept if the input isn’t independently and identically distributed and a numerical example is utilized to explain this case. The properties of posterior condition probability will aid the study of the numerical calculated recurrence formula of finite state Markov channel capacity.
文摘Network Coding (NC) is confirmed to be power and bandwidth efficient technique, because of the less number of transmitted packets over the network. Wireless Sensor Network (WSN) is usually power limited network application, and in many scenarios it is power and bandwidth limited application. The proposed scenario in this paper applies the advantages of NC over WSN to obtain such power and bandwidth efficient WSN. To take the advantages of NC over the one of the most needed applications i.e., WSN, we come up to what this paper is discussing. We consider a WSN (or its cluster) that consists of M nodes that transmit equal-length information packets to a common destination node D over wireless Rayleigh block-fading channel where the instantaneous SNR is assumed to be constant over a single packet transmission period. Finite-State packet level Markov chain (FSMC) model is applied to give the channel more practical aspect. The simulation results showed that applying NC over the WSN cluster improved the channel bandwidth significantly by decreasing the number of the Automatic Repeat Request (ARQ), resulting in improving the power consumption significantly. The results are collected for different transmission distances to evaluate the behavior to the proposed scenario with regard to the bath losses effect.
文摘Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the "curse of dimensionality" and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient.
文摘计算机网络缓存侧信道能够间接体现计算机内部状态以及数据传输情况,其受攻击时,用户端信息数据存在泄露风险,因此提出一种基于马尔科夫的计算机网络缓存侧信道攻击检测方法。构建隐马尔科夫模型,对计算机网络缓存侧信道状态改变的概率进行计算。通过Baum‐Welch算法估计隐马尔科夫模型最优参数,并计算缓存侧信道状态观测序列输出概率。比较缓存侧信道观测序列输出概率与设定的阈值,判断该序列为计算机网络缓存侧信道攻击信号的可能性,并引入平均信息熵判断计算机缓存侧信道状态是否存在异常,完成计算机网络缓存侧信道攻击检测。通过实验验证得出,该方法用于计算机网络缓存侧信道攻击检测的准确率高,误报率低,在遭受DDoS攻击(Distributed denial of service)时的检测时间较短,对计算机网络缓存侧信道攻击的防御与保护产生了积极影响。
