Optimization of bits allocation and path planning with trajectory constraint in UAV-enabled mobile edge computing system

In this paper, an Unmanned Aerial Vehicle (UAV) enabled Mobile Edge Computing (MEC) system is studied, in which UAV acts as server to offer computing offloading service to the Mobile Users (MUs) with limited computing capability and energy budget. We aim to minimize the total energy consumption of MUs by jointly optimizing the bit allocation for uplink, computing at the UAV and downlink, along with the UAV trajectory in a unified framework. To this end, a trajectory constraint model is employed to avoid sudden changes of velocity and acceleration during flying. Due to high-order information in use, we lead to a more reasonable nonconvex optimization problem than prior arts. An Alternating Direction Method of Multipliers (ADMM) method is introduced to solve the optimization problem, which is decomposed into a set of easy sub-problems, to meet the requirement on the efficiency in edge computing. Numerical results demonstrate that our approach leads a smoother UAV trajectory, significantly save the energy consumption for UAV during flying.     

刀刃法在轨MTF测量性能分析

文章主要研究采用刀刃法测量在轨遥感器的MTF。刀刃法MTF测量准确程度如何，需要有一个定量的客观的评价，文章用仿真的方法对MTF测量精度进行了检验，对于噪声对MTF的影响进行了研究。     

基于边缘模型的图像后处理算法的FPGA实现

在传统图像后处理算法中,性能和复杂度往往难以兼顾。从FPGA硬件实现出发,在原有的基于边缘模型的数字图像后处理算法基础上,提出一种新的基于模型匹配的重建模型算法,并且根据边缘的宽度和边缘退化的主要形式建立两个边缘重建模型。仿真实验结果表明,改进的算法不仅满足工程实时性要求,而且一定程度上抑制了高倍压缩后的数字图像中出现的振铃效应和模糊效应,起到了改善图像主观和客观质量的作用。     

叶轮机非定常气动设计的缘线匹配(II)数值研究

缘线匹配通过考虑多叶排叶轮机相邻叶排间前排叶片尾缘线与后排叶片前缘线空间相对位置匹配关系来进一步提升叶轮机性能,有潜力使叶轮机非定常设计走向工程实际。以一单级跨声轴流压气机和一单级轴流透平为例,尝试用基元流动展向积分方法和全优化方法改善其性能,从而示例应用缘线匹配的具体方法及前景。结果表明,缘线匹配为叶轮机非定常设计提供了可操作的自由度。     

在边缘弯矩和局部线性荷载共同作用下圆板的塑性极限分析

本文应用广义阶梯函数对承受边缘弯矩和局部线性分布荷载的筒支圆板进行塑性极限分析，文中考虑了局部线性分布荷载的４种可能分布形式，给出了简支圆板在Ｔｒｅｓｃａ屈服条件下边缘弯矩和线性荷载所满足的关系式。     

遗传算法设计梯度子实现优化的图象边界检测

计算机图象处理技术可广泛应用各种图象测试、模式识别和计算机视觉等领域，而图象边界检测是计算机图象处理的最基本和最低层的步骤之一。由于噪声的干扰和图象光照不均匀等因素的影响，目前的图象边界检测方法还不能有效地检测出各种不同模式的边界。本文提出了一种基于遗传算法和灰度梯试算子的图象边界检测法，通过对样本图象的训练，设计对训练样本模式最优的灰度梯度算子，增加了对噪声的抗干扰能力，并且使被检测出的边界更准确。     

Aerodynamics of smart flap under ground effect

Aerodynamic characteristics of two-dimensional smart flap under the ground effect have been assessed by a numerical simulation. In this process, a pressure-based implicit procedure to solve Navier–Stokes equations on a nonorthogonal mesh with collocated finite volume formulation is used. The boundedness criteria for this procedure are determined from the Normalized Variable Diagram (NVD) scheme. The procedure incorporates the k–ε eddy–viscosity turbulence model. Cantilever beam with uniformly varying load with roller support at the free end is considered for the configuration of the smart flap. The method is first validated against experimental data. Then, the algorithm is applied for turbulent aerodynamic flows around airfoil with smart and conventional flaps for different attack angle, flap angle and ground clearance where the results of two flaps are compared. The comparisons show that the quality of the solution is considerable.     

基于改进Canny的激光主动成像图像边缘检测算法研究

针对传统Canny边缘检测算法存在的不足,结合激光主动成像末制导的实际特点,提出了一种改进的Canny边缘检测算法.该算法在Canny边缘检测算法的基础上,采用同态滤波和提升小波变换级联的方法代替传统的高斯滤波器;采用3×3邻域的权值梯度计算方法代替原有的2×2邻域差分运算;采用Otsu自适应阈值计算方法,使算法可根据图像自身特点选择最合适的阈值.实验证明:该算法提升了传统Canny算子的抗干扰能力和鲁棒性,能够较好地保存边缘信息,准确检测出激光主动成像图像的边缘,具有较强的自适应性.     

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.