基于自适应云模型的多模态脑部图像融合方法

多模态医学图像融合通过提取并综合不同模态的医学图像信息,获得对病灶部位更加清晰、全面、准确、可靠的图像描述,为医生对疾病的诊断和合理治疗方案的制定提供可靠的依据。云模型理论是认知科学研究的新成果,具有兼顾随机性和模糊性的优点,在图像融合中的应用较少。借助云模型理论将来自不同模态的MRI(核磁共振成像)脑部图像、MRI与PET(正电子发射断层成像)、MRI与SPECT(单光子发射断层成像)脑部图像进行融合。首先,根据脑部图像自身的灰度直方图特征,对灰度直方图进行拟合;然后,由拟合曲线的谷值点划分区间并通过逆向云发生器自适应地生成云模型;最后,设计云推理规则,得到融合后的图像。实验结果表明,相比传统融合方法,所提方法融合后的图像脑部特征更清晰,激活区域更明显,在主观融合效果与客观评价指标方面均有很大的提高。     

基于改进的背景模型的图像识别算法研究

当图像亮度不均匀、对比度低时,提取图像前景较困难。为此,提出一种图像分割方法,结合正弦基函数和绝对值距离测度构建背景模型,依据优化理论和迭代法求解背景模型,通过比较背景模型中各像素点亮度与实际图像中各像素点亮度来判别各像素点是背景还是前景。为应对图像亮度不均匀的情况,在图像分割前对图像进行分块,在分块图像中依据背景模型或相邻分块背景相似度进行图像分割。实验结果表明,在普适性方面,相对于经典的模糊C均值法和OTSU法,该方法的分割误差小,尤其是对亮度不均匀和对比度低的图像;在掌纹图像分割应用方面,与迭代线跟踪法和模糊粗糙集法相比,该方法的错误率低、信噪比高、处理时间短。最后将提出的分割算法应用在人脸识别上,实验结果表明了该算法的先进性。     

State transformation-based dynamic visual servoing for an unmanned aerial vehicle

In this paper, we propose a visual servoing control for a quadrotor unmanned aerial vehicle (UAV) which is based on a state transformation technique. The UAV is equipped with a single downwards facing camera, and the motion control objective is the regulation of relative displacement and yaw to a stationary visual target located on the ground. The state transformation is defined by a system of partial differential equations (PDEs) which eliminate roll and pitch rate dependence in the transformed image feature kinematics. A method for computing the general solutions of these PDEs is given, and we show a particular solution reduces to an established virtual camera approach. We treat point and line cases and introduce image moment features defined in the virtual camera image plane. Robustness of the control design is improved by accounting for attitude measurement bias, and uncertainty in thrust gain, mass, and image feature depth. The asymptotic stability of the closed-loop is proven. The method is based on a simple proportional-integral-derivative (PID) structure which can be readily implemented on-board. Experimental results show improved performance relative to previous work.     

SDP-based approximation of stabilising solutions for periodic matrix Riccati differential equations

Numerically finding stabilising feedback control laws for linear systems of periodic differential equations is a nontrivial task with no known reliable solutions. The most successful method requires solving matrix differential Riccati equations with periodic coefficients. All previously proposed techniques for solving such equations involve numerical integration of unstable differential equations and consequently fail whenever the period is too large or the coefficients vary too much. Here, a new method for numerical computation of stabilising solutions for matrix differential Riccati equations with periodic coefficients is proposed. Our approach does not involve numerical solution of any differential equations. The approximation for a stabilising solution is found in the form of a trigonometric polynomial, matrix coefficients of which are found solving a specially constructed finite-dimensional semidefinite programming (SDP) problem. This problem is obtained using maximality property of the stabilising solution of the Riccati equation for the associated Riccati inequality and sampling technique. Our previously published numerical comparisons with other methods shows that for a class of problems only this technique provides a working solution. Asymptotic convergence of the computed approximations to the stabilising solution is proved below under the assumption that certain combinations of the key parameters are sufficiently large. Although the rate of convergence is not analysed, it appeared to be exponential in our numerical studies.     

Supervisory control based on minimal cuts and Petri net sub-controllers coordination

This paper addresses the synthesis of Petri net (PN) controller for the forbidden state transition problem with a new utilisation of the theory of regions. Moreover, as any method of control synthesis based on a reachability graph, the theory of regions suffers from the combinatorial explosion problem. The proposed work minimises the number of equations in the linear system of theory of regions and therefore one can reduce the computation time. In this paper, two different approaches are proposed to select minimal cuts in the reachability graph in order to synthesise a PN controller. Thanks to a switch from one cut to another, one can activate and deactivate the corresponding?PNcontroller. An application is implemented in a flexible manufacturing system to illustrate the present method. Finally, comparison with previous works with experimental results in obtaining a maximally permissive controller is presented.     

An interactive approach based on a discrete differential evolution algorithm for a class of integer bilevel programming problems

This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn–Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.     

Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of unmanned aerial vehicles (UAVs), modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi–Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly, feedback gains are synthesised using a parallel distributed compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law, the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as linear matrix inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.     

Guarding against online threats: why entrepreneurs take protective measures

Information technology is becoming increasingly important for entrepreneurs. Protecting their technical infrastructure and stored data is, therefore, also becoming more vital. Nevertheless, research into the safety and security of entrepreneurs in general, and online threats targeted at entrepreneurs in particular, is still limited. This study investigates to what extent self-employed entrepreneurs protect themselves against online threats, and what motivates them to do so. Based on secondary analyses on data collected from 1622 Dutch entrepreneurs, we observe that the majority implement technical and personal coping measures. By adopting protection motivation theory as a theoretical basis for our study, we found that coping appraisal explains the adoption of protective measures. Entrepreneurs are likely to implement protective measures when they believe a measure is effective, when they are capable of using internet technology, when their attitude towards online protection is positive and when they believe they are responsible for their own online security. Although the secondary analysis provides some limitations, this study offers new insights into the usage of protective measures and the motivation for taking them. These insights can help to improve information security campaigns.