基于可靠性及噪声约束的电源／ 地线优化设计

电源/地线(P/G)拓扑结构的优化设计是超大规模集成电路(VLSl)中直接影响芯片性能的一个非常重要的问题.通过对考虑可靠性及噪声的各项约束条件的分析,提出了一种时间复杂度为O(N2m)基于最小代价生成树(MST)和改进的Prim算法的快速构造算法.实验结果表明,该算法在满足同样的性能约束条件下能有效地减小布线面积.     

基于信源路由的时延受限点到点路由算法

本文研究了网络路由中的一个NPC问题:时延受限最小代价路由问题.文中提出了一个理论框架,并给出了多个简单有效的启发式算法,在满足给定时延约束条件可行路径存在时,算法总能找到满足约束条件的代价优化路径.文中提出的启发式算法复杂性为O(|V|²)且在线复杂性为O(|V|).仿真显示算法取得了良好的平均代价性能.最后将模型扩展到多QoS限制条件下的路由问题.     

基于主动网络的多约束路由探测算法

本文提出了一种基于主动网络的多约束路由探测算法MAR(Multi-constrained Active Routing).与传统路由算法相比,MAR不仅能灵活地提供多种约束条件的服务质量保证,而且充分发挥了主动网络的可编程和自定制能力,允许用户根据业务要求和网络状况自定制约束条件和相关参数.通过仿真MAR算法在各种网络条件下的性能表明,MAR算法能够提高多约束条件寻路的成功率.     

基于蚁群算法和遗传算法融合的QoS组播路由问题求解

包含延迟、延迟抖动、带宽、丢包率和最小花费等约束条件在内的服务质量(QoS)组播路由问题,是一个NP完备问题,传统方法很难求得全局最优解.本文将遗传算法和蚁群算法融合,提出了用遗传蚁群算法(GAACS)求解QoS组播路由问题的解决方案.仿真实验表明,采用新算法比遗传算法具有更好的性能.     

基于免疫算法的带模糊需求舰艇保障调度问题

免疫算法是模仿生物体高度进化,复杂的免疫系统仿生的一种智能化启发式算法.带模糊需求的舰艇调度问题(NVOSSPFD)是在基本的舰艇调度问题(NVOSSPFD)上增加了模糊需求约束条件的一种变化形式,是一个典型的NP(Non-deterministic Polyncmial)难题.通过引用一种新的编码方法、交叉和变异概率的自适应机制,构造一个免疫算法来求解NVOSSPFD.并将求解结果与遗传算法比较.比较结果表明,该算法对于求解NVOSSPFD问题具有较好的性能.     

基于QoS保证的广播/组播OFDM系统资源分配策略

该文提出了一种基于服务质量(QoS)保证的次优广播/组播OFDM系统资源分配算法。算法在最优分配原则的基础上采用不同广播/组播业务目标速率作为约束条件,并以业务组中最差接收用户传输速率作为搜索目标,使得业务组中全部用户达到QoS要求,算法的公平性及有效性得到提高;同时采用时频格作为分配算法中的基本资源单位,以降低分配算法的复杂度。仿真结果表明,改进分配策略的系统吞吐量性能接近最优广播/组播分配算法,提高了广播/组播系统资源分配的公平性。     

GS算法在多输入多输出雷达信号集设计中的应用

为了高效地设计出性能良好的多输入多输出(Multiple Input Multiple Output,MIMO)雷达正交相位编码信号集,提出一种改进的Gerchberg-Saxton(GS)算法,将最小化信号集旁瓣能量的代价函数转化为适合求解的极小化问题;将原始GS算法扩展至多维,并改进其物面和频谱面的约束条件,以适应文中优化问题的求解;利用改进的GS算法迭代优化设计出信号集.仿真实验表明:该算法可以有效改善所得信号集的积分旁瓣和相关性能.     

基于初始尺度变换的SIFT匹配算法

直接使用检测到的SIFT(Scale-Invariant Feature Transformation)特征点进行特征点匹配,匹配性能仍然有待提升.提出了改进的SIFT匹配算法,利用匹配特征点的尺度比直方图,估计出近似的图像尺度比k,然后将空间分辨率较高的图像初始尺度增大到k倍,再次提取特征点进行匹配.实验结果表明,相比于其它用尺度约束条件提升性能的匹配算法,基于初始尺度变化的SIFT匹配算法在处理结构型图像时性能得到了很大的提升.     

CoMP-JT中预编码与功率联合分配算法

在CoMP(协作多点发送/接收)联合传输资源分配中,研究发现在单天线功率约束条件下,传统的迫零波束赋形预编码技术会降低平均天线发射功率利用率。为了进一步提升系统吞吐量,文章提出一种预编码与功率联合分配算法,该算法采用一种预编码迭代计算方式,提高了天线发射功率利用率。仿真结果表明,所提算法与传统的预编码相比性能有较大提高。     

基于ε等级约束差分进化的多径估计算法

本文针对基于扩展Kalman滤波（EKF）的多径估计算法需要对非线性观测方程进行线性化.对初值比较敏感,造成估计性能下降的问题,提出了基于智能优化的多径估计算法.该算法将估计误差的二阶矩作为目标函数,将瞬时误差作为约束条件,同时考虑多径参数的先验信息,实现了将多径估计问题转化为具有约束条件的优化问题.然后,利用一种智能优化算法来解决该优化问题.本文采用了ε等级约束差分进化（εCRDE）算法来解决有约束条件的优化问题,并对该算法进行改进,使改进后的εCRDE算法可以实现多径参数的迭代估计.仿真结果表明,与EKF算法相比,在单一多径和2路多径情况下,基于改进εCRDE的多径估计算法都具有更好的估计性能.     

Projected least-squares algorithms for constrained FIR filter design

Constrained finite-impulse response (FIR) filter design with time- and frequency-domain linear constraints can be generally transformed into a, or a series of, constrained least-squares problems, which can be generally reformulated as positive definite quadratic programming (QP) problems. This paper presents a novel algorithm referred to as a projected least-squares (PLS) algorithm for the positive definite QP problems. The PLS algorithm essentially projects the unconstrained (least-squares) minimization solution successively onto the boundaries of active constraints that are identified by an active-set strategy. The PLS algorithm has been applied to the constrained least-squares design of FIR filters directly, and to the constrained Chebyshev design of FIR filters in an iterative fashion. The PLS algorithm is compared with the most widely used interior-point methods and an active-set method through design examples of low-pass filters with specified passband and stopband ripples, Nyquist filter constraints and step response constraints. All these examples demonstrate the high efficiency of the PLS algorithm.     

Constrained eigenfilter design without specified transition bands

The design of a finite-impulse response (FIR) filter with constraints in the frequency domain and/or time domain is considered. We further consider the design specification without explicitly specified transition band bandedges. An iterative algorithm without transition band specification is proposed to design FIR filters with various design constraints. We suggest the possible design tradeoff between transition band bandwidth and the ripple size of the filter. The proposed algorithm can be used to design filters with an optimal tradeoff from the design specification. The eigenfilter formulation further allows the propose algorithm to incorporate time-domain constraints simultaneously. Various design examples are presented to illustrate the versatility of the digital filter obtained by the propose algorithm. Although we have not proven the convergence of the proposed algorithm, it is found to converge efficiently in all the simulations.     

Constrained Chebyshev design of FIR filters

In many filter-design problems, additional constraints are often imposed on the optimal filter in the sense of, say, minimal Chebyshev error norm. Based on the characteristic properties of the optimal filter for the Chebyshev design with frequency equation constraints, a modified Remez (MRemez) algorithm is proposed in this paper. The central problem of this paper is the constrained Chebyshev design of finite-impulse response filters with equation and inequality constraints in the frequency domain. By converting the problem into a series of Chebyshev design problems with equation constraints, an iterative MRemez algorithm which uses the MRemez algorithm as the computational core of the iteration is proposed, and the convergence of the algorithm is obtained. Design examples demonstrate the effectiveness and the fast convergence of the MRemez algorithm and the iterative MRemez algorithm.     

Theory and Design of Linear-Phase Minimax FIR Filters with Mixed Constraints in the Frequency Domain

The alternation theorem is the core of efficient approximation algorithms for the minimax design of finite-impulse response (FIR) filters. In this paper, an extended alternation theorem with additional mixed constraints, i.e., equality-and-inequality constraints, is obtained. Then, an efficient multiple-exchange algorithm based on the extended theorem is presented for designing linear-phase FIR filters with frequency mixed constraints in the minimax sense. Further, convergence of the algorithm is established. Several design examples and comparisons with existing techniques are presented, and the simulation results show that the proposed algorithm is numerically more efficient and guaranteed to converge to the optimal solution.     

Digital Laguerre filter design with maximum passband-to-stopband energy ratio subject to peak and Group delay constraints

In this paper, we formulate a general design of transversal filter structures with maximum relative passband-to-stopband energy ratio subject to complex frequency response constraints in the passband and the stopband as well as additional constraints such as constraints. These constraints are important for applications where the suppression of noise at certain frequencies are important. Additional constraints are introduced allowing approximately linear phase and constant group delay in the passband. For a given set of basis functions, the design problem can be formulated as a semi-infinite quadratic optimization problem in the filter coefficients, which are the decision variables to be optimized. In this paper, we focus on the design of digital Laguerre filter and digital finite impulse response (FIR) filter structures. A modified bridging algorithm is developed for searching for the optimum pole of the Laguerre filters. Design examples are given to demonstrate the effectiveness of the proposed algorithm.     

PLS Algorithms for Equality-Constrained Linear-Phase FIR Filter Design

The least-squares design of linear-phase finite impulse response (FIR) filters with equality constraints in the time and frequency domains can be formulated as an equality-constrained quadratic programming (QP) problem. This paper presents an effective and robust novel algorithm, the projected least-squares (PLS) algorithm, for equality-constrained QP problems. The algorithm eventually projects an unconstrained minimization solution successively onto the feasible hyperplane of the problem. An additional term is added into the Hessian matrix of the cost function, thus ensuring the positive definiteness of the Hessian matrices during the iterative procedure for the design of constrained minimax filters. To demonstrate its effectiveness and robustness, the PLS algorithm is applied to design optimal linear-phase Nyquist filters that may satisfy some frequency domain constraints besides the zero-crossing impulse response.     

Joint optimization of data network design and facility selection

The authors describe a data network design model based on a mixed integer/linear programming (MILP) formulation that does not, as do most other approaches, separate link capacity and facility selection from routing and topological design; it fully integrates these processes to capture the important couplings that exist between them. The performance constraints are incorporated into the model in such a way that they are linear, but lead to the same grade of service for a balanced network as nonlinear average network delay constraints. It is shown that this formulation leads to a natural decomposition of the optimal design problem into two subproblems solvable sequentially. In the absence of capacity allocation constraints, the capacity and flow assignment problem is solved optimally and efficiently as part of the overall design process. Moreover, the model leads directly to the solution of multifacility design problems. A fast link reduction algorithm that efficiently designs single or multifacility networks and yields robust local extrema is presented. This algorithm is based on a special-purpose monotonic greedy drop heuristic procedure. An important application of this model is the design of multifacility networks     

Equiripple FIR filter design by the FFT algorithm

The fast Fourier transform (FFT) algorithm has been used in a variety of applications in signal and image processing. In this article, a simple procedure for designing finite-extent impulse response (FIR) discrete-time filters using the FFT algorithm is described. The zero-phase (or linear phase) FIR filter design problem is formulated to alternately satisfy the frequency domain constraints on the magnitude response bounds and time domain constraints on the impulse response support. The design scheme is iterative in which each iteration requires two FFT computations. The resultant filter is an equiripple approximation to the desired frequency response. The main advantage of the FFT-based design method is its implementational simplicity and versatility. Furthermore, the way the algorithm works is intuitive and any additional constraint can be incorporated in the iterations, as long as the convexity property of the overall operations is preserved. In one-dimensional cases, the most widely used equiripple FIR filter design algorithm is the Parks-McClellan algorithm (1972). This algorithm is based on linear programming, and it is computationally efficient. However, it cannot be generalized to higher dimensions. Extension of our design method to higher dimensions is straightforward. In this case two multidimensional FFT computations are needed in each iteration     

Optimization of image coding algorithms and architectures usinggenetic algorithms

This paper addresses the application of genetic algorithm (GA)-based optimization techniques to problems in image and video coding, demonstrating the success of GAs when used to solve real design problems with both performance and implementation constraints. Issues considered include problem representation, problem complexity, and fitness evaluation methods. For offline problems, such as the design of two-dimensional filters and filter banks, GAs are shown to be capable of producing results superior to conventional approaches. In the case of problems with real-time constraints, such as motion estimation, fractal search and vector quantization codebook design, GAs can provide solutions superior to those reported using conventional techniques with comparable implementation complexity. The use of GAs to jointly optimize algorithm performance in the context of a selected implementation strategy is emphasized throughout and several design examples are included     

Optimal design of finite precision FIR filters using linearprogramming with reduced constraints

An algorithm for the design of optimal one-dimensional (1-D) and two-dimensional (2-D) FIR filters over a discrete coefficient space is proposed. The algorithm is based on the observation that the equiripple frequencies of a subproblem (SP) in the branch and bound (BaB) algorithm are closely related to those of neighboring SPs. By using the relationship among the SPs, the proposed algorithm reduces the number of constraints required for solving each SP. Thus, the overall computational load for the design of FIR filters with discrete coefficients is significantly alleviated, compared with the conventional BaB algorithm