基于形态特征提取的图像匹配搜索技术研究

传统的图像搜索方法一般是由图像处理软件自动抽取图像的颜色、形状、纹理等特征,并以此建立特征索引库,进而由用户输入要查找的物品图像,从而找出与之具有相近特征的图像。而文中给出了从数学形态学的角度来提取图像的关键形态特征,然后建立海量物品图片的形态细化骨架库,并以此简化图像搜索的关键内容,降低数据库存储量,提高匹配效率以及准确性的具体方法。     

VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10–20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.     

Skeleton pruning as trade-off between skeleton simplicity and reconstruction error

The position and orientation of moving platform mainly depends on global positioning system and inertial navigation system in the field of low-altitude surveying, mapping and remote sensing and land-based mobile mapping system. However, GPS signal is unavailable in the application of deep space exploration and indoor robot control. In such circumstances, image-based methods are very important for self-position and orientation of moving platform. Therefore, this paper firstly introduces state of the art development of the image-based self-position and orientation method (ISPOM) for moving platform from the following aspects: 1) A comparison among major image-based methods (i.e., visual odometry, structure from motion, simultaneous localization and mapping) for position and orientation; 2) types of moving platform; 3) integration schemes of image sensor with other sensors; 4) calculation methodology and quantity of image sensors. Then, the paper proposes a new scheme of ISPOM for mobile robot — depending merely on image sensors. It takes the advantages of both monocular vision and stereo vision, and estimates the relative position and orientation of moving platform with high precision and high frequency. In a word, ISPOM will gradually speed from research to application, as well as play a vital role in deep space exploration and indoor robot control.     

Fair relay selection in decode-and-forward cooperation based on outage priority

The opportunistic cooperation schemes,where only the "best" relay is selected to forward the message,have been widely investigated recently for their good performance in terms of outage probability.However,the unfair selections of relays may cause unbalance power consumptions among relays,which reduces the lifetime of energy constrained networks.In this paper,we introduce a novel concept of outage priority based fairness(OPF),aiming at improving the selection fairness among relays appropriately without outage performance deterioration.Then,a cooperation scheme is proposed to meet this concept,and corresponding theoretical analysis is also provided.Afterward,based on OPF,the achievable upper bound of the fairness is derived,and an optimal cross-layer designed scheme is also provided to achieve the bound.Numerical simulations are carried out finally,which not only validate the theoretical analysis,but also show that taking advantages of the proposed schemes,the fairness among all relays,as well as the network lifetime,can be greatly improved without any loss of outage performance,especially in high SNR regime.     

A Stochastic Vehicle Routing Problem with Travel Time Uncertainty: Trade-Off Between Cost and Customer Service

On-time shipment delivery is critical for just-in-time production and quick response logistics. Due to uncertainties in travel and service times, on-time arrival probability of vehicles at customer locations can not be ensured. Therefore, on-time shipment delivery is a challenging job for carriers in congested road networks. In this paper, such on-time shipment delivery problems are formulated as a stochastic vehicle routing problem with soft time windows under travel and service time uncertainties. A new stochastic programming model is proposed to minimize carrier’s total cost, while guaranteeing a minimum on-time arrival probability at each customer location. The aim of this model is to find a good trade-off between carrier’s total cost and customer service level. To solve the proposed model, an iterated tabu search heuristic algorithm was developed, incorporating a route reduction mechanism. A discrete approximation method is proposed for generating arrival time distributions of vehicles in the presence of time windows. Several numerical examples were conducted to demonstrate the applicability of the proposed model and solution algorithm.     

层次分析法在太湖典型区域污染物总量分配中的应用

在公平性、有效性、可持续性等原则的指导下,考虑水污染物总量分配的社会、经济、环境等整体效益,兼顾各区域间的实际差异,以及各项评价指标的相对重要性,用层次分析法构造水污染物排放总量分配的层次结构模型,把求得的各分区相对于区域允许排污总量这一总目标的权重之比,作为各分区允许排污量之比,然后按此比例在各分区间进行排污总量分配。将该方法应用于太湖流域典型区域(常州市武进区)水污染物排放总量分配。结果表明:本文提出的分配方法,可以克服等比例分配的不公平性,并且兼顾各分区间的实际差异,是一种较理想的分配方法,为制定科学合理的总量方案提供了一种新思路。     

一种新的等高线质量评价方法

分析了传统等高线质量评价相关模型,提出了基于信息量的等高线精度评价方法和基于形状指数的等高线光滑度评价方法,并给出了具体的量化计算过程;最后通过实验验证了该评价方法的可行性。     

基于云模型的图像区域分割方法

基于区域的图像分割方法由于其高效、稳健的特点成为自动或半自动图像分割方法的研究热点之一。针对区域分割方法中存在的不确定性问题,提出了一种基于云模型的区域分割方法。首先以云变换为基础确定了区域生长过程中的生长准则,然后以逆向云算法实现分割区域由定量的像素集合到定性的云概念的转换过程,最后以云综合算法为基础将相邻区域进行合并,实现了基于区域的不确定性图像分割。两组图像分割实验表明该方法可以准确地分割出目标,并优于传统的图像分割算法。     

一种基于运动矢量分析的Mean shift目标跟踪算法

Mean shift算法作为一种非参密度估计算法,目前已被广泛应用于视频运动目标的跟踪。该算法具有运算效率快,对目标变形、旋转不敏感,在部分遮挡的情况下有一定鲁棒性等特点,但该算法在运动目标速度过快的情况下,由于没有考虑利用目标的运动方向和速度信息,因此在跟踪快速运动目标时容易造成跟踪丢失。针对此问题,提出了一种基于运动矢量分析与Mean shift跟踪算法相结合的新方法,即首先对视频编码过程中产生的运动矢量进行概率统计分析,以获取目标运动方向与运动速度的估计值,再以此修正Mean shift运动候选区域的中心位置,使每次搜索开始时,候选中心位置更接近实际目标中心位置。通过与传统的Mean shift算法的跟踪实验比较可见,新算法不仅提高了快速运动目标跟踪的精度,而且减少了算法的搜索迭代次数,从而提高了运算效率。该算法可适用于智能视频监控设备中的视频编码与目标跟踪同时计算的情况,实验结果表明,该算法是有效可行的。     

VANET无线链路质量的测量与分析

目前对移动自组织网络的研究很少涉及现实环境里的测量问题。该文对不同环境下无线链路丢包率、信号强度等参数进行了测量和分析。结果表明,环境对链路质量有较大影响,在郊区环境下,丢包率较低,链路质量较好,信号强度反应链路质量的能力有限,连续丢包概率的变化与丢包率平均值的变化基本一致。分析结果对预测无线链路质量和设计VANET网络协议具有重要意义。