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An audio wave is a one-dimensional acoustic wave.

声波是一维的声学''。

The prototype can generate 2-D and 1-D style layout with pins/terminals on and inside the boundary of the cell, meanwhile area and timing being optimized.

系统可生成二维和一维的标准单元版图,并允许引脚端出现在单元内部,版图比较紧凑,同时性能得到优化。

In the layout analysis, we chose a bottom-up algorithm based on nearest neighbor connect-strength and line confidence to segment the image area, table area and text area; In the printed Chinese character recognition module, we calculate the degree of incorporative difference to match the character, also define a refusal class which could orientate the mathematical expression automatically; Put the expression into the formula processing module, then we chose the character segmentation method based on connectivity and the template match method to recognize the character in the mathematical expression and at last we used the structure analysis based on the character to transform the two dimension formula into one dimension Word EQ expression.

在版面分析中,采用基于最近邻连接强度和行列可信度的自底向上的版面分析算法,分割出图像区域、表格区域和文本区域;在汉字识别模块中,采用回溯切分方法切分出字符段,计算合并差异度与特征字典比较,通过引入汉字的拒识类,从而实现了公式的定位;将定位后的数学公式送入公式识别器,在公式识别器中采用基于连通域搜索的字符分割方法和模板匹配方法对字符识别,对于识别出的字符,再采用基于特征字符的结构分析方法,从而将二维的数学公式转化为一维的Word EO域语句。

In the meantime, we got several achievements during the process:Firstly, a sequence encryption algorithm of real-time video in use of one dimension piece-wise linear chaotic map is carried out. This algorithm makes use of Inter' s SSE2 special dictates and makes nonlinear change of chaotic sequences.As a result, the algorithm not only encrypts faster, but also resists several cipher analysis methods. Secondly, this paper presents a block encryption algorithm of real-time video based on chaotic neural network which makes use of numerical transient chaotic neural network to produce chaotic sequences and uses these sequences to do encryption.

同时,在实现系统的过程中,取得了以下研究成果:(1)提出了一种利用一维的分段线性混沌映射来对实时视频进行序列加密的算法,该算法利用Inter的SSE2专用指令集来实现,并对产生的混沌序列进行了有效的非线性变换,从而使该算法不仅加密速度快,并且能有效的抗多种密码分析;(2)提出了一种利用混沌神经网络对实时视频进行分组加密的算法。

We have done some exploratory work from several aspects to generalize the Brownian motor to the more real situation: The model which molecular motor has internal degree of freedom; The track on which the Brownian particle moves cannot be completely ordered and contain certain amount of disorder, the model is rocking ratchets with stochastic potentials; A two-dimensional ratchet model with a non-conservative force to realize the coupling between the mechanical and chemical processes.

目前大多数的研究工作是把分子马达抽象为一维的点粒子,我们在推广布朗马达到更真实的方向上开展了以下几个侧面的研究分子马达动力学行为的探索性工作:分子马达具有内部结构的电偶极子模型;考虑分子马达的轨道不规则、不光滑的摇摆棘轮模型;提出一个具有非保守脉冲力场的二维棘轮模型。

Thus, the problem is essentially one dimensional—as would be the case in an air-track simulation.

因此,这一问题基本上是一维的,与气流路径的模拟情况可能是一样。

Information in ecology IS NOT one dimensional.

信息在生态学里从来就不是一维的

Variable velocity feedback method is proposed to achieve complete synchronization of spatiotemporal chaos in one-dimensional complex Ginzburg-Landau equation system.

一维的复Ginzburg-Landau方程系统中,提出了时空混沌同步的一种广义反馈方法,称为变量速率反馈方法。

Thus, the lane keeping and collision avoidance can be achieved. In the laser scanner subsystem, the estimation of relative speed between two cars is achieved by applying the 1-dimensional Kalman filter to obtain a reliable D/V curve.

在雷射扫瞄器方面,本文以一维的Kalman filter针对相对速度做估测,并利用相对距离与相对速度之间的关系,提出一 D/V 曲线碰撞预先警告时间。

Throughout comparing the above estimating methods, we have the following results: the estimators of the moments of the errors does not depend on the random effects, and that of the random effects does not depend on the errors, and then the corresponding asymptotic variances are very simple and optimal; when the random effects are multivariate, we can not construct different estimating equations for the random effects and errors respectively, which results that the asymptotic covariances of estimation are very complex and then the estimating efficiency is bad.

比较上述两种估计法,我们发现:当随机效应是一维的时侯,误差的各阶矩的估计不依赖不可观测的随机效应,随机效应的估计也不依赖误差,因此,估计的渐近方差结构特别简单也是最优的;而当随机效应是多维的,因为随机效应的协变量的影响,我们没有办法针对随机效应和误差的各阶矩分别建立估计方程,这导致所得的估计的渐近方差或者协方差矩阵特别复杂,从而估计的效果不是很好。

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