过抽样四相混沌序列的构成及其扩频应用

结合Chebyshev源映射和通信原理中抽样理论,产生了过抽样混沌映射(OSCM),证明了OSCM也具备混沌特性.并提出利用最大平衡差函数考察序列平衡性.通过和源映射序列广义相关函数的对比,分析了自相关、互相关旁瓣的最大值和平均值,进而分析了四相OSCM序列的相关性.仿真结果表明:针对现行移动通信扩频系统地址码,四相ChebyshevOSCM序列具有良好的自相关和互相关性,可以增强系统的保密性和提升系统容量,是CDMA移动通信扩频系统地址码的优选方案之一.     

过抽样M通道小波星图去噪方法研究

小波图像去噪已经成为目前图像去噪的主要方法之一,在分析了小波变换的基本理论和小波变换的多尺度分析基础上,根据多尺度小波变换的多分辨特性,提出了过抽样M通道小波变换去噪方法,并将此方法用于星图降噪处理中,收到良好的效果.     

双无回答层抽样的三重抽样比率估计量

无回答在抽样调查中经常出现,无回答层再抽样是解决无回答的常用方法.当辅助变量总体均值未知时,本文讨论了双无回答层抽样的三重抽样方法,给出了三重抽样的分层汉森-赫维茨估计量和比率估计量,以及它们的方差和估计方差.给出满足事前给定总调查费用约束的三重抽样过程的最优设计参数,以及比率估计量的方差估计.给定总调查成本,三重抽样的分层汉森—赫维茨估计量与比率估计量进行模拟比较,演示比率估计量的优良性.     

辅助信息下的逆抽样设计算法

通过将逆抽样设计视为一种特殊的二重抽样,建立了二重抽样和为回归估计的二重抽样的一般形式,得到了逆抽样设计算法下的回归估计.模拟分析的结果表明,以回归估计的形式引入较为合适的辅助信息,能够在估计精度上对逆抽样设计算法做出改进.     

裂区设计连续调查的条件影响及重抽样核实调整

条件的影响是有匹配样本的连续调查特有的计量误差来源。本文在变异计量误差模型之下，研究了未调整时条件影响对两期裂区设计连续调查中采用的组合差估计量的统计影响，同时提出了重抽样核实调查方法，得到了调整后的无偏估计量，计算了调整后估计量的方差。讨论了调整后估计量准确度比未调整估计量准确度高的条件。     

分层三阶段及以上抽样的自加权抽样设计

大型抽样调查总是采用分层多阶段抽样.分层多阶段抽样若采用自加权的抽样设计,则总体总量的估计量形式简单,易于计算.本文提出了分层三阶段及以上抽样的自加权抽样设计方法.     

基于二相抽样的PPS二阶抽样

研究初级单元大小未知时基于二相抽样的PPS二阶抽样，给出总体总值的估计量，估计量的方差及方差的估计量。     

产品检验中的抽样个数

设某批产品 (总体 )的次品率为 p,对总体提出假设H0 :p≤ p0 ,　 H1:p >p1其中 0 相似文献   

谈不放回抽样与放回抽样的区别

高中数学新教材中增加了概率论的内容 ,在有关的课外资料中经常出现 (或隐含 )“不放回”与“放回”这类问题 ,本文就此谈一下它们的区别 .不放回抽样与放回抽样的区别主要体现在以下四个方面 :(1)不放回抽样是指每次抽出样品不放回 ,下次再抽样时 ,样品结构发生变化 ,总数比前次少一 ;而放回抽样是指每次抽出的样品放回 ,下次再抽样时 ,样品结构和总数保持不变 .(2 )不放回抽样各次抽取不是相互独立的 ;而放回抽样各次抽取是相互独立的 .(3)对不放回抽样来说 :事件A =“不放回地逐个取k个样品”与事件B =“一次任取k个样品“的概率相等 ,…     

Stein两阶段抽样问题中第一阶段最优抽样量研究

当精度和可靠度给定时，Stein（1945）提出了两阶段抽样方法，构造了同时满足一定可靠度与精度的区间估计．本文则利用数值计算方法，进一步给出了此两阶段抽样中最优的第一阶段抽样量．     

Oversampling generates super-wavelets

We show that the second oversampling theorem for affine systems generates super-wavelets. These are frames generated by an affine structure on the space .

     

Convex Variational Formulation with Smooth Coupling for Multicomponent Signal Decomposition and Recovery

A convex variational formulation is proposed to solve multicomponent signal processing problems in Hilbert spaces. The cost function consists of a separable term, in which each component is modeled through its own potential, and of a coupling term, in which constraints on linear transformations of the components are penalized with smooth functionals. An algorithm with guaranteed weak convergence to a solution to the problem is provided. Various multicomponent signal decomposition and recovery applications are discussed.     

Required Number of Iterations for Sparse Signal Recovery via Orthogonal Least Squares

In countless applications, we need to reconstruct a $K$-sparse signal $\mathbf{x}\in\mathbb{R}^n$ from noisy measurements $\mathbf{y}=\mathbf{\Phi}\mathbf{x}+\mathbf{v}$, where $\mathbf{\Phi}\in\mathbb{R}^{m\times n}$ is a sensing matrix and $\mathbf{v}\in\mathbb{R}^m$ is a noise vector. Orthogonal least squares (OLS), which selects at each step the column that results in the most significant decrease in the residual power, is one of the most popular sparse recovery algorithms. In this paper, we investigate the number of iterations required for recovering $\mathbf{x}$ with the OLS algorithm. We show that OLS provides a stable reconstruction of all $K$-sparse signals $\mathbf{x}$ in $\lceil2.8K\rceil$ iterations provided that $\mathbf{\Phi}$ satisfies the restricted isometry property (RIP). Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.     

城市交通信号实时控制问题

以车辆平均延误时间最小为目标,建立单交叉口和线状区域的多交叉口信号实时配置的优化模型,结合外点罚函数法和模式搜索法求解,解决函数的不可微问题,算法简单可行.     

Dualization of Signal Recovery Problems

In convex optimization, duality theory can sometimes lead to simpler solution methods than those resulting from direct primal analysis. In this paper, this principle is applied to a class of composite variational problems arising in particular in signal recovery. These problems are not easily amenable to solution by current methods but they feature Fenchel–Moreau–Rockafellar dual problems that can be solved by forward-backward splitting. The proposed algorithm produces simultaneously a sequence converging weakly to a dual solution, and a sequence converging strongly to the primal solution. Our framework is shown to capture and extend several existing duality-based signal recovery methods and to be applicable to a variety of new problems beyond their scope.     

多元Besov-Wiener类的平均宽度和最优恢复

本文得到了Besov－Wiener类的平均σ-K宽度，平均σ-L宽度，最优恢复的弱渐进结果．     

高维带宽有限随机信号从平均过采样的指数阶逼近

本文中我们主要考虑利用有限的平均过采样值来重构高维带宽有限随机信号.我们给出了一个能够达到指数阶衰减逼近能力的重构算法.对于一般型和乘积型的采样测度,我们分别给出了对应的重构算法和指数阶衰减的重构误差估计.     

The High Order Block RIP Condition for Signal Recovery

In this paper, we consider the recovery of block sparse signals, whose nonzero entries appear in blocks (or clusters) rather than spread arbitrarily throughout the signal, from incomplete linear measurements. A high order sufficient condition based on block RIP is obtained to guarantee the stable recovery of all block sparse signals in the presence of noise, and robust recovery when signals are not exactly block sparse via mixed $l_2/l_1$ minimization. Moreover, a concrete example is established to ensure the condition is sharp. The significance of the results presented in this paper lies in the fact that recovery may be possible under more general conditions by exploiting the block structure of the sparsity pattern instead of the conventional sparsity pattern.     

一类指数信号与正弦信号相乘的不定积分注记

针对两类不定积分I_1=∫e~(ax)cosbxdx和I_2=∫e~(ax)sinbxdx的分部积分常规求法,从指数信号与正弦信号相乘及复指数信号的角度进行分析,并给出一些特别的形变统一及记忆方法.     

带有信号因子的试验设计及在测砷中的应用

本文用带有信号因子的内、外表试验设计的分析方法优化了用分光光度计测微量砷的试验条件。得到了优化条件下的理想工作曲线 ,将此方法用于细菌在难浸金矿石的预处理中微量砷的测定 ,结果满意