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sliding window methods
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     This article provides a brief description of RSA public key cryptography, an analysis and compare of all kinds of present existed modular exponentiation in RSA public key cryptography, a colligation of the fastest accelerating software algorithm-VLNW sliding window methods and hardware mapping fast Montgomery modular multiplication algorithm that can improve the implementary efficiency of RSA public key cryptography for achieving the novel algorithm-Mnexp algorithm.
     本文简单介绍了RSA公钥密码体制,分析比较RSA公钥密码中已有的模幂运算方法,将得到的最快软件加速算法VLNW滑动窗口法和硬件映射最快的Montgomery模乘算法综合,得到改进后的Mnexp算法能有效提高RSA公钥密码的实现效率。
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  相似匹配句对
     The Window
     窗(英文)
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     The Window
     窗口(英文)
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     Join algorithms of compound sliding window
     复合滑动窗口连接算法
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     The security of sliding window data transfer
     滑动窗口数据传输的安全问题
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     SLIDING CURVES
     滑动曲线
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  sliding window methods
The best addition chain reached by the ant system is compared to the one used in the m-ary and sliding window methods as well as with the best addition chain evolved by genetic algorithms.
      


Objective: To evaluate the efficiency of sliding window technique in extracting and analyzing somatosensory evoked potentials (SEP) from multichannel electroencephalogram (EEG) data. Methods: A time window of certain window size was moved along the time dimension of data sets. Values within the window were averaged for each trial, and then compared with a preset control window. The probability of randomly appeared significance resulting from repeated statistical comparison...

Objective: To evaluate the efficiency of sliding window technique in extracting and analyzing somatosensory evoked potentials (SEP) from multichannel electroencephalogram (EEG) data. Methods: A time window of certain window size was moved along the time dimension of data sets. Values within the window were averaged for each trial, and then compared with a preset control window. The probability of randomly appeared significance resulting from repeated statistical comparison was calculated utilizing simulated EEG data sets. Cluster size (number of successive significant data points with given individual significance threshold) was determined to keep the general alpha value under 0.05. To test this procedure, multichannel EEG signals were recorded and analyzed from fourteen healthy right handed volunteers, with painful and non painful electrical stimuli delivered to the right middle fingers. Results: Cluster size increased in parallel with window size and individual statistical threshold. The major SEP components of real EEG data, as well as the difference between pain and non pain SEPs, were demonstrated to be significant with the sliding window method. Conclusion: Sliding window method is an effective tool for the analysis of SEP data.

目的 :评价滑行窗口技术分析脑电诱发电位的能力。方法 :将具有一定宽度的时间窗口延时间轴滑行 ,计算该窗口内的脑电电位平均值 ,再与对照窗口进行统计比较 ,以检验诱发电位是否具有统计显著性。利用该方法分析随机产生的模拟数据 ,计算在指定单次检验阈值下 ,多次统计比较导致显著性差异点连续出现的几率 ,以确定可使整体α值小于 0 .0 5的cluster大小。为检验该方法的有效性 ,在 14名健康右利手志愿者右手中指给予痛或非痛电刺激 ,记录EEG信号并采用上述技术加以分析。结果 :在整体α值确定的前提下 ,作为显著性判据的cluster大小随单次检验阈值与窗宽的增加而增大。依据上述方法分析真实EEG数据 ,确定了体感与痛觉诱发电位波形中具有统计学意义的成分 ,以及两种波形之间的显著性差异。结论 :滑行窗口技术可有效地用于分析脑电诱发电位。

This paper explores a method for detecting and tracking small and faint targets in high-cluster environments. Based on the technology of conventional radar digital detection, the new method spreads the pointed targets to lines or planes. In view of the moving target properties and dimension changing of spread target, stochastic noise and high clutter are eliminated by the rules of the targets movements and their substantialized dimensions. The precise detection and stable tracking of targets are...

This paper explores a method for detecting and tracking small and faint targets in high-cluster environments. Based on the technology of conventional radar digital detection, the new method spreads the pointed targets to lines or planes. In view of the moving target properties and dimension changing of spread target, stochastic noise and high clutter are eliminated by the rules of the targets movements and their substantialized dimensions. The precise detection and stable tracking of targets are carried out by the sliding-window method and m/N detection criterion together with track-matching algorithm. A simulation experiment has confirmed the feasibility of the method.

研究了一种在较强杂波背景下对弱小目标进行有效检测和跟踪的方法 ,该方法基于常规雷达的数字式检测技术 ,将原来的点目标扩展为一个波束范围内的线目标甚至面目标 ;利用目标的运动特性和目标扩展后的维数变化去除大面积杂波和随机噪声 ,通过滑窗法和m/N检验准则 ,结合航迹匹配算法实现目标的准确判定和跟踪 .并结合仿真验证了该方法的可行性 .验证的结果说明 ,该方法算法简便 ,抑制杂波能力强 ,是常规雷达在杂波背景中进行目标跟踪检测的一条可行的方法 .

The canonical re-coding and sliding window techniques are often used in computation of scalar multiplication k·P on elliptic curves for reducing the average number of required operation.In this paper,scalar multiplication with canonical re-coding and sliding window techniques is analyzed by modeling the window partition process of canonical re-coding expression of k as Markov-chain,the average performance of scalar multiplication under different parameters are given and the optimal...

The canonical re-coding and sliding window techniques are often used in computation of scalar multiplication k·P on elliptic curves for reducing the average number of required operation.In this paper,scalar multiplication with canonical re-coding and sliding window techniques is analyzed by modeling the window partition process of canonical re-coding expression of k as Markov-chain,the average performance of scalar multiplication under different parameters are given and the optimal window sizes are computed.Finally,the comparison shows that scalar multiplication with canonical re-coding and sliding window techniques requires 10.32~17.32% fewer operations than m-ary method,and 4.53~8.40% fewer operations than simple sliding window method.

在椭圆曲线密码系统中 ,采用规范重编码、滑动窗口等优化技术可以有效提高椭圆曲线上点的标量乘法k·P的运算性能 ,但在实现中 ,需要对不同优化技术的算法性能进行定量分析 ,才能确定标量乘法的最优实现 .本文运用Markov链对标量k规范重编码表示的滑动窗口划分过程进行了建模 ,提出了一种对椭圆曲线标量乘法的平均算法性能进行定量分析的方法 ,并运用该方法分析了不同参数下标量乘法运算的平均性能 ,计算了滑动窗口的最优窗口大小 .最后 ,通过比较说明 ,采用规范重编码和滑动窗口技术的椭圆曲线标量乘法的运算开销比用m ary法少 10 32~ 17 32 % ,比单纯采用滑动窗口法也要少 4 5 3~ 8 4 0 % .

 
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