有时延及时延差别约束的最小代价组播路由问题

本文把有时延、时延差别约束的组播路由问题提到优化的层次上,提出了有时延、时延差别约束的最小代价组播路由优化问题,并证明此问题是NP－complete问题。继而提出了一种基于动态罚函数法的启发式遗传算法以及解该问题,并分析了算法的复杂度。仿真表明,本文算法是有效的、稳定的。在满足两种约束的情况下,能够使网络代价优化。     

时延和时延抖动约束的低费用多播路由算法

为了有效支持交互式实时组播业务,不仅要考虑时延约束,而且要考虑时延抖动约束,同时还需高效管理网络资源,以降低多播费用。本文提出了一种新的时延和时延抖动约束的低费用我播路由启发式算法,仿真结果表明该算法复杂度较低,时延抖动较小,又降低了网络费用,是一种快速有效的多播路由算法。     

精确时延模型下满足时延约束的缓冲器数目最小化的算法

提出了在精确时延模型下,满足时延约束的缓冲器数目最小化的算法.给出一个两端线网,该算法可以求出满足时延约束的最小缓冲器数目.运用高阶时延模型计算互连线的时延,运用基于查找表的非线性时延模型计算缓冲器的时延.实验结果证明此算法有效地优化了缓冲器插入数目和线网的时延,在二者之间取得了较好的折中.算法的运行时间也是令人满意的.     

精确时延模型下满足时延约束的缓冲器数目最小化的算法

提出了在精确时延模型下,满足时延约束的缓冲器数目最小化的算法.给出一个两端线网,该算法可以求出满足时延约束的最小缓冲器数目.运用高阶时延模型计算互连线的时延,运用基于查找表的非线性时延模型计算缓冲器的时延.实验结果证明此算法有效地优化了缓冲器插入数目和线网的时延,在二者之间取得了较好的折中.算法的运行时间也是令人满意的     

一种时延和时延抖动受约束的启发式多播路由算法

多播路由算法在组播应用中是至关重要的,对视频会议等交互式实时组播业务来说,不仅要考虑时延约束,而且要考虑时延抖动约束。本文提出了一种基于最短时延路径的时延和时延抖动约束的启发式算法,仿真结果表明该算法复杂度较低,而且性能也较好,在算法复杂度和性能之间达到了很好的折中。     

基于可用带宽满足时延和时延差别 约束的实时多播路由算法

实时多播路由中具有可加性的代价(Cost)不能确切反映网络本质特性,尤其不能反映路径带宽的凹性(Concave).已有基于代价的算法不能很好适应多播应用,需要新的模型和算法.本文采用可用带宽代替代价作为主要度量,并满足实时多播中二个重要约束度量:时延和时延差别.同时基于此三个度量,本文提出二种新的具有多项式复杂性的实时多播路由算法并比较其性能.新算法通过分析得到每路径时延和二约束之间的关系,有效降低涉及时延和时延差别此类问题的复杂性.新算法采用度量反映实时多播本质特性而具有实际推广性.     

改进的低时延语音增强算法

针对目前语音增强存在较大时延的问题,提出一种低时延语音增强算法。在传统的先验信噪比估计和噪声估计的基础上,对判决引导算法进行低时延的改进,并提出了一种基于语音存在概率和语音激活检测相结合的噪声估计方法,本文的算法采用对数MMSE估计器结合语音存在概率。采用ITU-T P.826 PESQ、分段信噪比、总信噪比和对数谱失真对该算法进行了测试,并与其他几种算法进行了对比,实验结果表明,该算法有效降低了时延,可以很好的跟踪非平稳噪声,在信噪比较低的情况小可以取得很好的增强效果,且音乐噪声和残留背景噪声也可以得到很好的抑制。     

一种基于多QoS约束的改进算法

在现有CSPF（约束最短路径优先）算法的基础上,提出了一种基于多QoS（服务质量）约束的改进算法,详细论述算法的设计思路和算法流程,分析了算法计算的时间复杂度。改进的算法主要是加入了时延约束机制,从选择最小时延路径集入手,考虑了关键链路的重定位问题,选择时延最小路径集中的瓶颈链路作为关键链路,希望在消耗较少网络资源的同...     

采用精确时延模型基于路径的缓冲器插入时延优化

提出了一种基于路径的缓冲器插入时延优化算法 ,算法采用高阶模型估计连线时延 ,用基于查表的非线性时延模型估计门延迟 .在基于路径的时延分析基础上 ,提出了缓冲器插入的时延优化启发式算法 .工业测试实例实验表明 ,该算法能够有效地优化电路时延 ,满足时延约束     

采用精确时延模型基于路径的缓冲器插入时延优化

提出了一种基于路径的缓冲器插入时延优化算法,算法采用高阶模型估计连线时延,用基于查表的非线性时延模型估计门延迟.在基于路径的时延分析基础上,提出了缓冲器插入的时延优化启发式算法.工业测试实例实验表明,该算法能够有效地优化电路时延,满足时延约束.     

Optimal simultaneous detection and estimation under a false alarmconstraint

This paper addresses the problem of finite sample simultaneous detection and estimation which arises when estimation of signal parameters is desired but signal presence is uncertain. In general, a joint detection and estimation algorithm cannot simultaneously achieve optimal detection and optimal estimation performance. We develop a multihypothesis testing framework for studying the tradeoffs between detection and parameter estimation (classification) for a finite discrete parameter set. Our multihypothesis testing problem is based on the worst case detection and worst case classification error probabilities of the class of joint detection and classification algorithms which are subject to a false alarm constraint. This framework leads to the evaluation of greatest lower bounds on the worst case decision error probabilities and a construction of decision rules which achieve these lower bounds. For illustration, we apply these methods to signal detection, order selection, and signal classification for a multicomponent signal in noise model. For two or fewer signals, an SNR of 3 dB and signal space dimension of N=10 numerical results are obtained which establish the existence of fundamental tradeoffs between three performance criteria: probability of signal detection, probability of correct order selection, and probability of correct classification. Furthermore, based on numerical performance comparisons between our optimal decision rule and other suboptimal penalty function methods, we observe that Rissanen's (1978) order selection penalty method is nearly min-max optimal in some nonasymptotic regimes     

Dynamic scheduling and analysis of real time systems with multiprocessors

This research work considers a scenario of cloud computing job-shop scheduling problems. We consider m realtime jobs with various lengths and n machines with different computational speeds and costs. Each job has a deadline to be met, and the profit of processing a packet of a job differs from other jobs. Moreover, considered deadlines are either hard or soft and a penalty is applied if a deadline is missed where the penalty is considered as an exponential function of time. The scheduling problem has been formulated as a mixed integer non-linear programming problem whose objective is to maximize net-profit. The formulated problem is computationally hard and not solvable in deterministic polynomial time. This research work proposes an algorithm named the Tube-tap algorithm as a solution to this scheduling optimization problem. Extensive simulation shows that the proposed algorithm outperforms existing solutions in terms of maximizing net-profit and preserving deadlines.     

基于参数优化的再入制导律设计

本文介绍了一种基于参数优化解决再入制导的方法。由于再入飞行过程中存在各种约束,再入制导问题可以看做是一个对给定的目标函数进行优化的最优控制问题。但是直接采用经典最优控制理论的变分法运算量巨大且难以得到解析解。本文把再入制导问题的所有约束都转化成一个目标函数,然后将动力学方程中的控制量进行有限维数的参数化,使再入制导变成了一个有限维的参数优化问题。采用Powell非梯度式优化算法对此问题进行求解。仿真结果表明,在经过26次迭代,301次目标函数计算,目标函数收敛到精度范围,实现了制导的目的。     

有效保持细节特征的图像椒盐噪声滤除方法

 论文结合几种去噪方法,提出一种统一的图像去噪模型.该模型通过一个统一的目标函数将图像去噪问题转化为最优化问题,目标函数的构造主要包括估计残差惩罚函数、局部权函数及正则化项三个方面.随后基于此模型提出一种新的去除椒盐噪声的非线性滤波方法,其中估计残差惩罚函数采用L1范数形式,局部权函数采用自适应高斯核函数,正则化项则利用图像的小波域稀疏性作为先验约束来构造.由于充分融合了图像的全局和局部统计特性,因而在抑制噪声的同时能够更好地保持图像边缘等细节特征,相关去噪实验结果证实了本文方法的有效性.     

免疫计算在求解约束优化问题中的应用

介绍了约束条件的函数优化的主要问题及处理约束的解决方法，针对一般性非线性规划问题，采用免疫算法来进行处理。具体方法是：首先引入罚函数构造新的目标函数，而后运用免疫算法进行求解。通过测试表明，算法收到较好的效果。     

抗冲激干扰的稀疏惩罚约束遗漏最小均方算法

当被识别系统是稀疏系统时,传统的遗漏最小均方( LLMS )自适应算法收敛性能较差,特别在非高斯噪声环境中,该算法性能进一步恶化甚至算法不平稳收敛。为了解决因信道的稀疏性使算法收敛变慢的问题,对LLMS算法的代价函数分别利用加权詛1-norm和加权零吸引两种稀疏惩罚项进行改进;为了优化算法的抗冲激干扰的性能,利用符号函数对已改进的算法迭代式作进一步改进。同时,将提出的两个算法运用于非高斯噪声环境下的稀疏系统识别,仿真结果显示提出的算法性能优于现存的同类稀疏算法。     

Increasing the Robustness of Boosting Algorithms within the Linear-programming Framework

AdaBoost has been successfully used in many signal classification systems. However, it has been observed that on highly noisy data AdaBoost easily leads to overfitting, which seriously constrains its applicability. In this paper, we address this problem by proposing a new regularized boosting algorithm LP_norm2-AdaBoost (LPNA). This algorithm arises from a close connection between AdaBoost and linear programming. In the algorithm, skewness of the data distribution is controlled during the training to prevent outliers from spoiling decision boundaries. To this end, a smooth convex penalty function (l ₂ norm) is introduced in the objective function of a minimax problem. A stabilized column generation technique is used to transform the optimization problem into a simple linear programming problem. The effectiveness of the proposed algorithm is demonstrated through experiments on many diverse datasets.     

Robust and Fuzzy Spherical Clustering by a Penalty Parameter Approach

A spherical clustering algorithm that provides robustness against noise and outliers is proposed. It is formulated as a constrained nonlinear optimization problem inspired by the idea of using minimum radii spheres of support vector clustering. An augmented cost function obtained by the penalty parameter approach is minimized by a stable coupled gradient network. Minimizing the first term in the cost forces spheres to include all the data while the second term is responsible for having small radii spheres. The third term added to the cost via a time-varying penalty parameter forces each datum to be assigned to the clusters with unity-sum membership values. It has been observed from the applications performed on the artificial and IRIS data sets that suitably chosen penalty parameters create tradeoffs among the cost terms providing fuzziness and robustness.     

基于变量节点更新的交替方向乘子法 LDPC惩罚译码算法

基于交替方向乘子法(ADMM)的改进惩罚函数LDPC译码算法能够提升译码性能,但其所需优化参数过多且性能提升有限。针对该问题,将该算法与其它带有惩罚函数的译码算法比较后发现,两者的不同之处仅在于译码算法中变量节点的更新规则不同。因此,该文通过构造一种新的变量节点的更新方法去减少优化参数数目并提升译码性能。实验仿真表明,相较于原有算法,该文所提算法有效减少了所需优化的参数数目,此外,所提算法的平均迭代次数更少且能实现约0.1 dB的性能提升。     

Tomographic fluorescence imaging in tissue phantoms: a novel reconstruction algorithm and imaging geometry

A novel image reconstruction algorithm has been developed and demonstrated for fluorescence-enhanced frequency-domain photon migration (FDPM) tomography from measurements of area illumination with modulated excitation light and area collection of emitted fluorescence light using a gain modulated image-intensified charge-coupled device (ICCD) camera. The image reconstruction problem was formulated as a nonlinear least-squares-type simple bounds constrained optimization problem based upon the penalty/modified barrier function (PMBF) method and the coupled diffusion equations. The simple bounds constraints are included in the objective function of the PMBF method and the gradient-based truncated Newton method with trust region is used to minimize the function for the large-scale problem (39919 unknowns, 2973 measurements). Three-dimensional (3-D) images of fluorescence absorption coefficients were reconstructed using the algorithm from experimental reflectance measurements under conditions of perfect and imperfect distribution of fluorophore within a single target. To our knowledge, this is the first time that targets have been reconstructed in three-dimensions from reflectance measurements with a clinically relevant phantom.