SOC设计中的浮动压焊块电源/地线网络设计和优化

随着VLSI工艺技术的发展和芯片规模的不断增加，尤其是在SOC设计中，原有的那种供电压焊块只能位于芯片边缘的确定益的模式已经不能够满足整个电路性能的需要。在很多情况下，依靠在电源线的拓扑结构确定后的线宽优化，还是无法保证在有限的布线资源下为电路提供可靠的高性能的供电需求。由此，出现了在芯片边缘上浮动放置压焊块及在芯片的顶部放置供电压焊块阵列的方法。文中提出了一种用于SOC设计中新的基于树型结构的浮动压焊块的电源／地线网络优化算法，经过MCNC电路实例测试后得到明显的优化结果。     

Active set truncated-Newton algorithm for simultaneous optimization of distillation column

An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.     

Design of pole-assignment controller for systems with algebraic-equation constraint

In this paper we have studied the control of linear systems with algebraic-equation constraint. A pole-assignment controller with two-layer structure for such constrained systems has been presented. The objective of the controller is to assign the desired poles to constrained system and simultaneously force the corresponding closed-loop system to satisfy the constraint. The conditions for the existence of pole-assignment controller are discussed in different cases. The implementation algorithm of pole-assignment controller is also presented. An example is given to illustrate the validity of the method in this paper.     

New approaches for error estimation and adaptivity for 2D potential boundary element methods

This work presents two new error estimation approaches for the BEM applied to 2D potential problems. The first approach involves a local error estimator based on a gradient recovery procedure in which the error function is generated from differences between smoothed and non‐smoothed rates of change of boundary variables in the local tangential direction. The second approach involves the external problem formulation and gives both local and global measures of error, depending on a choice of the external evaluation point. These approaches are post‐processing procedures. Both estimators show consistency with mesh refinement and give similar qualitative results. The error estimator using the gradient recovery approach is more general, as this formulation does not rely on an ‘optimal’ choice of an external parameter. This work presents also the use of a local error estimator in an adaptive mesh refinement procedure. This r‐refinement approach is based on the minimization of the standard deviation of the local error estimate. A non‐linear programming procedure using a feasible‐point method is employed using Lagrange multipliers and a set of active constraints. The optimization procedure produces finer meshes close to a singularity and results that are consistent with the problem physics. Copyright © 2002 John Wiley & Sons, Ltd.     

Identification environment and robust forecasting for nonlinear time series

In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.     

一种基于RSA安全可行的（t，n）门限签名方案*

分析了大多数RSA门限群签名方案存在的缺陷,RSA的模数是n,φ(n)是秘密参数,参加部分签名的成员无法知晓,求Lagrange相关系数存在困难,也为建立门限RSA密码体制带来困难.提出一种新的方案以克服上述困难,通过求ai无须在求Lagrange相关系数时进行求逆运算,使该方案在保证安全的前提下变得可行实用.     

Finite-time coordination in multiagent systems using sliding mode control approach

Finite-time stability in dynamical systems theory involves systems whose trajectories converge to an equilibrium state in finite time. In this paper, we use the notion of finite-time stability to apply it to the problem of coordinated motion in multiagent systems. Specifically, we consider a group of agents described by fully actuated Euler–Lagrange dynamics along with a leader agent with an objective to reach and maintain a desired formation characterized by steady-state distances between the neighboring agents in finite time. We use graph theoretic notions to characterize communication topology in the network determined by the information flow directions and captured by the graph Laplacian matrix. Furthermore, using sliding mode control approach, we design decentralized control inputs for individual agents that use only data from the neighboring agents which directly communicate their state information to the current agent in order to drive the current agent to the desired steady state. Sliding mode control is known to drive the system states to the sliding surface in finite time. The key feature of our approach is in the design of non-smooth sliding surfaces such that, while on the sliding surface, the error states converge to the origin in finite time, thus ensuring finite-time coordination among the agents in the network. In addition, we discuss the case of switching communication topologies in multiagent systems. Finally, we show the efficacy of our theoretical results using an example of a multiagent system involving planar double integrator agents.     

基于RFID的虚拟标签算法研究与改进

针对室内定位算法VIRE的定位精度,特别是邻近边界目标的定位精度较低的问题,以RFID标签定位为研究基础,从改变VIRE算法中虚拟标签的插值方式和在边界上加入虚拟标签两个方面进行研究和改进。使用拉格朗日插值代替VIRE算法中的线性插值方式,使虚拟标签的数据更加接近实际环境值;在边界上加入虚拟参考标签能增加已知标签数,防止在计算待定位标签坐标时引入误差标签。实验结果表明,标签定位精度有了明显提高,整体能提高了35%,邻近边界标签定位精度提高较大,可达到50%以上。     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.     

基于重加权快速交替方向法的频谱感知

在认知无线电网络中,由于深衰落和低信噪比的影响,单个认知用户的宽带频谱检测性能较差,且算法复杂度较高。针对该问题,提出一种基于重加权快速交替方向法的频谱感知算法。利用目标函数的凸性,通过求导简化辅助变量的更新过程。对目标函数进行线性化处理,增加一个二次项,使待估变量更新时部分项线性化的增广拉格朗日函数成为严格凸函数,并使用迭代软阈值算法进行求解。在目标项中增加大权值抑制信号中的非零元素,获得接近于最小?0范数的解。实验结果表明,该算法能有效提高低信噪比环境下的检测概率和检测速度。