Five-step phase-shifting algorithms with unknown values of phase shift

The presented work offers new algorithms for phase evaluation in interferometric measurements. Several phase-shifting algorithms with an arbitrary but constant phase-shift between captured intensity frames are proposed. These phase calculation algorithms need to measure five frames of the intensity of the interference field. The algorithms are similarly derived as so called Carré algorithm. The phase evaluation process then does not depend on the linear phase shift errors. Furthermore, the detailed analysis of the algorithms with respect to most important factors, which affect interferometric measurements, is carried out. It is also studied the dependency of the evaluation algorithms on the phase shift values, and the proposed phase calculation algorithms are compared with respect to the resulting phase errors. The influence of most important factors in the measurement and evaluation process was simulated as systematic and random errors using a proposed mathematical model.     

无人机地面运动控制与仿真

为了解决无人机纠偏刹车系统中实际存在的问题,对无人机地面运动进行了研究,采用前轮转弯、方向舵、差动刹车联合使用模式,以无人机的侧偏角、侧偏距为反馈信号,设计了无人机地面运动纠偏控制律,通过遗传算法的全局优化过程实现其纠偏参数寻优,在Simulink平台建立了无人机纠偏数学模型;试验结果表明,建立的无人机地面运动纠偏数学模型正确有效,能够模拟出无人机地面运动的真实情况;基于遗传算法的控制策略能够实现一定范围内的无人机地面运动纠偏,提高跑道的利用率。     

数字图像散斑相关技术的蚁群优化方法

基于蚁群优化方法提出新的数字图像散斑相关算法。该方法模仿了真实蚂蚁从其巢到食物找到最短路径的方式,通过对蚁群优化方法改进,减少迭代次数并改善解的质量。将新的数字图像散斑相关算法应用到计算机模拟的散斑图像和实验获得的散斑图像中,并与广泛使用的Newton-Raphson算法进行了比较。实验结果展示了新算法的精度、可行性和有效性。当数量级为0.01像素,误差离散均方根小于0.002像素。     

基于过抽样Gabor变换的核磁共振FID信号增强算法

基于作者先前提出的过抽样实值离散Gabor变换,本文提出了一有效的算法用于核磁共振自由感应衰减（NMR FID）信号的减噪. 由于NMR FID信号在时域中是一短暂的振荡衰减信号,使得变换后的NMR FID信号能量在时频域中集中在少数变换系数上,而噪声则遍布在整个变换系数上,因此通过对变换系数幅度进行阈值限制方法可达到明显地增强NMR FID信号的目的. 文中在理论和模拟实验上分析表明,过抽样Gabor变换比临界抽样Gabor变换更适宜于NMR FID信号的减噪,因为在过抽样条件下比在临界抽样条件下的综合窗及其对应的分析窗,无论是在时域中还是在频域中都可具有更好的局域分布集中性,同时,Gabor变换在过抽样条件下也比在临界抽样条件下具有更高的时频精度.     

Partial match retrieval

The study of partial match file designs is continued. This study is concerned with a class of file designs which properly contains theABD designs of Rivest. A new family of partial match files is presented, the worst case performance is determined, and the implementation of these files is discussed.     

Function-space quasi-Newton algorithms for optimal control problems with bounded controls and singular arcs

Two existing function-space quasi-Newton algorithms, the Davidon algorithm and the projected gradient algorithm, are modified so that they may handle directly control-variable inequality constraints. A third quasi-Newton-type algorithm, developed by Broyden, is extended to optimal control problems. The Broyden algorithm is further modified so that it may handle directly control-variable inequality constraints. From a computational viewpoint, dyadic operator implementation of quasi-Newton methods is shown to be superior to the integral kernel representation. The quasi-Newton methods, along with the steepest descent method and two conjugate gradient algorithms, are simulated on three relatively simple (yet representative) bounded control problems, two of which possess singular subarcs. Overall, the Broyden algorithm was found to be superior. The most notable result of the simulations was the clear superiority of the Broyden and Davidon algorithms in producing a sharp singular control subarc.This research was supported by the National Science Foundation under Grant Nos. GK-30115 and ENG 74-21618 and by the National Aeronautics and Space Administration under Contract No. NAS 9-12872.     

Decentralized robust control for a class of uncertain large-scale interconnected nonlinear dynamical systems

The problem of the decentralized robust control for a class of large-scale interconnected nonlinear dynamical systems with input interconnection and external interconnection perturbations is considered. Based on the stabilizability of each nominal isolated subsystem (i.e., the isolated subsystem in the absence of interconnection perturbations), a class of decentralized local state feedback controllers is proposed, and some sufficient conditions are derived by making use of the Lyapunov stability criterion such that uncertain large-scale interconnected systems can be stabilized asymptotically by these decentralized state feedback controllers. For large-scale systems with only input interconnection perturbations, such decentralized controllers become a class of decentralized stabilizing state feedback controllers. That is, the decentralized stability of such large-scale systems can be guaranteed always by using the decentralized state feedback controllers proposed in the paper. Finally, a numerical example is given to demonstrate the validity of the results.     

A class of polynomial variable metric algorithms for linear optimization

In the paper, the behaviour of interior point algorithms is analyzed by using a variable metric method approach. A class of polynomial variable metric algorithms is given achieving O ((n/β)L) iterations for solving a canonical form linear optimization problem with respect to a wide class of Riemannian metrics, wheren is the number of dimensions and β a fixed value. It is shown that the vector fields of several interior point algorithms for linear optimization is the negative Riemannian gradient vector field of a linear a potential or a logarithmic barrier function for suitable Riemannian metrics. Research Partially supported by the Hungarian National Research Foundation, Grant Nos. OTKA-T016413 and OTKA-2116.     

Heuristic methods for evolutionary computation techniques

Evolutionary computation techniques, which are based on a powerful principle of evolution—survival of the fittest, constitute an interesting category of heuristic search. In other words, evolutionary techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strife for survival.Any evolutionary algorithm applied to a particular problem must address the issue of genetic representation of solutions to the problem and genetic operators that would alter the genetic composition of offspring during the reproduction process. However, additional heuristics should be incorporated in the algorithm as well; some of these heuristic rules provide guidelines for evaluating (feasible and infeasible) individuals in the population. This paper surveys such heuristics and discusses their merits and drawbacks.An abridged version of this paper appears in the volume entitled META-HEURISTICS: Theory & Application, edited by Ibrahim H. Osman and James P. Kelly, to be published by Kluwer Academic Publishers in March 1996.     

Measurement scheduling for recursive team estimation

We consider a decentralized LQG measurement scheduling problem in which every measurement is costly, no communication between observers is permitted, and the observers' estimation errors are coupled quadratically. This setup, motivated by considerations from organization theory, models measurement scheduling problems in which cost, bandwidth, or security constraints necessitate that estimates be decentralized, although their errors are coupled. We show that, unlike the centralized case, in the decentralized case the problem of optimizing the time integral of the measurement cost and the quadratic estimation error is fundamentally stochastic, and we characterize the -optimal open-loop schedules as chattering solutions of a deterministic Lagrange optimal control problem. Using a numerical example, we describe also how this deterministic optimal control problem can be solved by nonlinear programming.This research was supported in part by ARPA Grant N00174-91-C-0116 and NSF Grant NCR-92-04419.