一种稳健的有意义图像水印算法

提出了一种稳健的有意义图像的水印算法，通过双正交小波变换，将有意义的水印信息全部嵌入到低频系数上，并给出了低频系数的选择策略以提高水印的安全性。实验结果证实了所提出的水印算法具有很好的不可见性，对JPEG有损压缩、噪声、剪切等攻击具有良好的稳健性。     

图像小波域的鲁棒隐形水印算法

提出一种小波域透明图像水印算法，算法将一个二值图像内嵌到原始图像经过小波多分辨率分解后的低频子带上，并利用人眼视觉掩蔽特性对水印的添加强度进行自适应调节以增强水印的鲁棒性和保证水印的不可感知性。实验结果表明：该算法较好地保持了图像的质量，并且对常用的图像处理有较强的稳健性。     

BLDC位置伺服系统的离散变结构控制

针对离散变结构控制系统的抖振，在离散趋近率的基础上，提出了一种扰动动态补偿的离散趋近率，设计了离散变结构控制(DVSC)策略，研究了变结构控制系统的鲁棒性能，应用于无刷直流电动机(BLDC)位置伺服系统。理论推导与仿真实验表明：新的离散变结构控制策略可以明显改善系统的品质，增强系统的鲁棒性。     

An output-based adaptive iterative learning controller for high relative degree uncertain linear systems

In this paper, we derive an output tracking error model based on signals filtered from plant input and output, and then present a new output-based adaptive iterative learning controller for repeatable linear systems with unknown parameters, high relative degree, initial resetting error, input disturbance and output noise. The proposed controller solves the important robustness issues without assuming the bounds of uncertainties to be sufficiently small and can be applied to high relative degree plants without using output differentiation. Control parameters are updated between successive iterations so as to compensate for unknown system parameters and uncertainties. It is shown that the internal signals inside closed-loop learning system remain bounded and the output tracking error will asymptotically converge to a profile tunable by some design parameters. Furthermore, the learning speed is easily improved if the learning gain is increased.     

Robust model predictive control using tubes

A form of feedback model predictive control (MPC) that overcomes disadvantages of conventional MPC but which has manageable computational complexity is presented. The optimal control problem, solved on-line, yields a ‘tube’ and an associated piecewise affine control law that maintains the controlled trajectories in the tube despite uncertainty; computational complexity is linear (rather than exponential) in horizon length. Asymptotic stability of the controlled system is established.     

Iterative learning of model reference adaptive controller for uncertain nonlinear systems with only output measurement

In this paper, a model reference adaptive control strategy is used to design an iterative learning controller for a class of repeatable nonlinear systems with uncertain parameters, high relative degree, initial output resetting error, input disturbance and output noise. The class of nonlinear systems should satisfy some differential geometric conditions such that the plant can be transformed via a state transformation into an output feedback canonical form. A suitable error model is derived based on signals filtered from plant input and output. The learning controller compensates for the unknown parameters, uncertainties and nonlinearity via projection type adaptation laws which update control parameters along the iteration domain. It is shown that the internal signals remain bounded for all iterations. The output tracking error will converge to a profile which can be tuned by design parameters and the learning speed is improved if the learning gain is large.     

Decentralized overlapping control of a formation of unmanned aerial vehicles

Decentralized overlapping feedback laws are designed for a formation of unmanned aerial vehicles. The dynamic model of the formation with an information structure constraint in which each vehicle, except the leader, only detects the vehicle directly in front of it, is treated as an interconnected system with overlapping subsystems. Using the mathematical framework of the inclusion principle, the interconnected system is expanded into a higher dimensional space in which the subsystems appear to be disjoint. Then, at each subsystem, a static state feedback controller is designed to robustly stabilize the perturbed nominal dynamics of the subsystem. The design procedure is based on the application of convex optimization tools involving linear matrix inequalities. As a final step, the decentralized controllers are contracted back to the original interconnected system for implementation.     

Connectivity-based multiple-circle fitting

This paper proposes a connectivity-based method for circle fitting. The use of pixel connectivity effectively avoids false circle detection, improves the robustness against noise and significantly reduces the computational load. The desired circular models are extracted by searching for meaningful circular arcs. The algorithm does not require a good initial guess, and is effective for extracting an a priori unknown number of circles even when the number of outliers exceeds 50%. The experimental results demonstrate that the proposed method performs well in detecting multiple intersecting or occluded circles.     

函数再现机构的模糊稳健优化设计方法

针对函数再现机构的综合问题 ,分析了可控因素与不可控因素对机构运动精度的影响 ,探讨了机构运动精度设计目标的模糊特性 ,提出了模糊稳健设计准则 ,给出了模糊稳健优化设计数学建模方法。设计实例和理论分析表明 ,该方法能使机构运动精度和各尺寸的制造公差得到合理匹配 ,具有工程实用价值     

Cooperative schemes for path establishment in mobile ad-hoc networks under shadow-fading

The performance of routing protocols for multi-hop path-establishment in Mobile Ad-hoc NETworks (MANET) is examined when the individual radio links undergo shadow fading of parameterized severity. Such propagation modeling is typical of ground-level networking along with node mobility. The metrics of main interest are the probability of having a reliable multi-hop path plus the temporal statistics of such availability under a Markovian model. Such availability is an indicator for the suitability of the network to carry delay-sensitive applications (push-to-talk voice, streaming video) in uninterrupted manner. It is shown that high levels of diversity are necessary in such adverse environments, as obtained either by multiple independent paths or via concurrent cooperative transmission. Analytical and simulation comparisons of typical routing options are presented on the above metrics, plus on the required nodal engagement for supporting such diversity.