基于改进的MSR算法在软件视频会议系统上的应用

视频会议是一种应用广泛的会议方式,由于光照、视频采集设备品质、网络带宽等外界复杂因素的影响,常常导致视频会议视频图像质量下降。在多尺度Retinex滤波(MSR)算法的基础上,使用双边滤波获取原始图像的高低频信息,然后采用颜色约束方法和增益补偿方法增强经过双边滤波处理后的高频反射图像。通过主观体验和客观评价,改进算法增强了视频图像,同时保留了边缘细节,应用于软件视频会议系统,提高了视频图像的清晰度,改善了视觉效果。     

市政工程给水管道施工质量控制

在各个城市市政工程给水管道的施工中,相关部门在质量监测和管理等方面的工作中仍然存在不足之处。因此,如何解决市政工程给水管道施工中出现的各种故障,是相关施工人员值得思考的问题。     

SQL Server2008数据库存储过程的应用

在应用程序开发过程中,如何避免客户机执行大量的数据访问工作,提高客户端的工作效率,是程序设计者需要考虑的问题。本文介绍了SQL Server 2008中存储过程的含义及优点,并通过具体实例向读者介绍存储过程的具体应用,从而在系统开发时能够灵活运用存储过程提高效率。     

项目教学在高职院校程序设计课程中的应用——以《C#程序设计》为例

程序设计课程是高职院校计算机应用和软件技术专业的必修课程。主要培养学生软件设计的基本技能,熟悉软件开发所需的软件开发工具,了解软件开发方法和开发过程。在课堂教学中,如何使理论讲解和动手实践相互结合,使学生在做中学,学中做。提高学生学习效率,提升教学质量,本文《C#程序设计》课程控件知识点为例,以项目化的教学方式贯穿整个课堂教学过程。经过教学的实践说明,课堂得到了有效的利用,学生自主学习和主动学习的意识被激发。     

TMS320C6678的LPI雷达信号检测模型设计

本文围绕低截获概率雷达信号处理的特点和实时性的要求,设计了基于FPGA和DSP传统组合模式的LPI雷达信号检测处理平台。在一款基于Keystone构架的多核DSP处理板上,创新性地运用并行关联流水线体系结构和多层任务调度分配信号处理机制,构建了针对LPI雷达信号从预处理到检测结果输出的一整套信号处理模型。经过仿真实验验证,该信号处理模型适合在低信噪比条件下对LPI雷达信号进行检测,且系统资源分配合理占用逻辑资源少,具有实时检测LPI雷达信号的潜力。     

基于多源测量与属性混合信息的分类识别方法

充分利用多传感器系统提供的多源异类信息,将观测数据处于数据级的属性和处于特征级的属性混合作为描述目标的特征矢量;对特征矢量进行了主成分分析,在此基础上转换至三维直角坐标系寻找最优分类平面进行分类识别;采用"一对一"策略解决多类分类问题;通过仿真实验验证了该方法在加入不同百分比高斯白噪声环境下的有效性,并与BP神经网络识别方法在同等条件下作了对比实验,突出了论文所提方法正确识别率高、识别速度快和稳定性高的优越性。     

基于云服务的计算化学社区

中国科学院超算环境是由中国科学院统筹规划建设的“院总中心-分中心-所级中心”三层结构的超级计算环境,环境的资源聚合使用网格中间件 SCE,并使用 API 接口对外提供服务。依托超算环境建设的科学计算学云服务社区采用软件即服务的模式,使用浏览器/服务器结构来提供专业交互式的计算化学科学计算服务,以“降低使用门槛、提高使用效率,助跑计算化学用户的科学研究工作”为目标,为广大科研用户提供“一站式”计算的 Web 服务模式和异构资源协同工作模式。本文同时介绍了社区的建设应用情况以及环境的运行、监控情况。     

An incentive game based evolutionary model for crowd sensing networks

Crowd sensing networks can be used for large scale sensing of the physical world or other information service by leveraging the available sensors on the phones. The collector hopes to collect as much as sensed data at relatively low cost. However, the sensing participants want to earn much money at low cost. This paper examines the evolutionary process among participants sensing networks and proposes an evolutionary game model to depict collaborative game phenomenon in the crowd sensing networks based on the principles of game theory in economics. A effectively incentive mechanism is established through corrected the penalty function of the game model accordance with the cooperation rates of the participant, and corrected the game times in accordance with it’s payoff. The collector controls the process of game by adjusting the price function. We find that the proposed incentive game based evolutionary model can help decision makers simulate evolutionary process under various scenarios. The crowd sensing networks structure significantly influence cooperation ratio and the total number of participant involved in the game, and the distribution of population with different game strategy. Through evolutionary game model, the manager can select an optimal price to facilitate the system reach equilibrium state quickly, and get the number of participants involved in the game. The incentive game based evolutionary model in crowd sensing networks provides valuable decision-making support to managers.     

An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

With shrinking technology, the increase in variability of process, voltage, and temperature (PVT) parameters significantly impacts the yield analysis and optimization for chip designs. Previous yield estimation algorithms have been limited to predicting either timing or power yield. However, neglecting the correlation between power and delay will result in significant yield loss. Most of these approaches also suffer from high computational complexity and long runtime. We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic (CAA) and the adaptive weighted sum (AWS) method. Both power and timing yield are set as objective functions in this framework. The two objectives are optimized simultaneously to maintain the correlation between them. The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations. Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function (CDF) bounds. Finally, the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions. Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.     

A <Emphasis Type="Italic">K</Emphasis> self-adaptive SDN controller placement for wide area networks

As a novel architecture, software-defined networking (SDN) is viewed as the key technology of future networking. The core idea of SDN is to decouple the control plane and the data plane, enabling centralized, flexible, and programmable network control. Although local area networks like data center networks have benefited from SDN, it is still a problem to deploy SDN in wide area networks (WANs) or large-scale networks. Existing works show that multiple controllers are required in WANs with each covering one small SDN domain. However, the problems of SDN domain partition and controller placement should be further addressed. Therefore, we propose the spectral clustering based partition and placement algorithms, by which we can partition a large network into several small SDN domains efficiently and effectively. In our algorithms, the matrix perturbation theory and eigengap are used to discover the stability of SDN domains and decide the optimal number of SDN domains automatically. To evaluate our algorithms, we develop a new experimental framework with the Internet2 topology and other available WAN topologies. The results show the effectiveness of our algorithm for the SDN domain partition and controller placement problems.