监控系统与视频会议系统整合平台设计与实现

为解决传统视频监控与视频会议系统相互独立、处理突发事件时二者不能互通的问题,提出了一种视频监控与视频会议整合平台的设计与实现方案,该方案将每个监控摄像头对应为视频会议中的一个视频终端,在视频会议发起时或会议中,可以将一个或多个实时监控图像调入视频会议终端中进行浏览和分析,提高了应急指挥能力和对突发事件的处置效率．利用可视电话和MCU（micro controller unit,多点控制单元）对该平台进行实验验证,验证结果表明,视频效果良好,实现了监控系统和视频会议系统的整合,满足了应用需求．     

校际间教育投入分析——以广州市为例

教育投入均衡配置关注全体学生均衡共享财政教育资源,校际间教育投入均衡配置应是教育均衡发展的重要内容。当前,我国校际间财政教育投入存在明显差距。2009年广州市9所高中教育投入数据显示：在高中生均教育经费支出上,省示范高中的经费投入既比市示范高中低,也比区示范高中低。原来广州为实现各校的均衡发展,已在校际间教育投入上有所作为。学习广州经验,各地区在均衡校际间教育投入可采取的对策有：保证合理的教育投入;增加对薄弱学校的投入与改造;加大对民办学校的财政支持;省级统筹拨付学校经费。     

236例儿科抗菌素使用对比分析

目的为了解儿科抗菌素应用的变化及现状,探讨改善儿科抗菌素滥用趋势的途径。方法分别随机抽取时间跨度10年两组儿科住院病历,对其诊断,用药指征、用药时间、用药种类、用药量进行对比分析。结果住院病例全部使用抗菌素、X线、实验室检查支持使用抗菌素分别为75%和47.1%,抗菌素越用越新,用量偏大。结论必须建立系统的到位的干预措施,强化继续教育,加强知识更新和病原微生物的临床研究。     

IP组播技术在远程监控中的应用

讨论了IP组播技术在Winsock中的实现 ,结合远程视频监控系统的开发 ,从视频的采集、发送、接收和控制四个方面 ,阐述了使用IP组播技术在数码监控系统中视频远程传输的设计思想和实现方法     

钢筋拉伸的测量分析研究

通过对大量钢材性能指标参数的数据取样，利用逐步逼近方法和组合分析的方法，对钢筋拉伸的质量做出测量与分析的研究。并采用钢筋拉伸的测量分析系统——钢筋拉伸单击板，绘制出相应试件的曲线图像与之标准模型的曲线图像相互比较，看其是否达到国家质量监督检验检疫总局发布的各项标准。钢筋拉伸单击板的方法是目前我国钢材检测技术中的创新方法，具有先进水平。     

Numerical simulation of radar surveillance for the ship target in oceanic clutters

A fast iterative approach of generalized forward-backward method with the spectrum acceleration algorithm (GFBM/SAA) is developed for solving electromagnetic field integral equation and is applied to numerical simulation of radar surveillance of the ship target in oceanic clutters. Randomly rough surface is realized by the Monte Carlo method using the Pierson-Moskowitz spectrum. Numerical results of bistatic and back-scattering from the ship target and oceanic clutters demonstrate the functional dependencies upon the situation of radar surveillance such as the incidence and observation angles, radar altitude, ship RCS and other oceanic conditions.     

Background Subtraction and Frame Difference Based Moving Obiect Detection for Real-Time Surveillance

A new real-time algorithm is proposed in this paper for detecting moving object in color image sequences taken from stationary cameras. This algorithm combines a temporal difference with an adaptive background subtraction where the combination is novel. When changes occur, the background is automatically adapted to suit the new conditions. For the background model, a new model is proposed with each frame decomposed into regions and the model is based not only upon single pixel but also on the characteristic of a region. The hybrid presentation includes a model for single pixel information and a model for the pixel's neighboring area information.This new model of background can both improve the accuracy of segmentation due to that spatial information is taken into account and saliently speed up the processing procedure because portion of neighboring pixel can be selected into modeling. The algorithm was successfully used in a video surveillance system and the experiment result shows it can obtain a clearer foreground than the single frame difference or background subtraction method.     

一种基于超像素的局部判别式跟踪算法

针对目标在复杂环境下容易受到外界干扰而发生漂移的问题,提出了一种基于超像素的局部判别式跟踪方法.首先,对视频序列前10帧的目标区域进行分割,得到超像素,并利用kmeans方法对其进行聚类以构造初始字典;其次,通过训练样本集来训练线性分类器;然后,为了减少目标发生漂移的可能性,将初始训练的分类器与更新后的分类器线性加权之和定义为似然函数;最后,在粒子滤波的框架下,将似然函数值最大的粒子作为跟踪的结果,每运行U帧更新一次字典和分类器参数,以捕获目标表观的变化.仿真结果表明,所提算法在目标发生遮挡、光照变化的复杂环境下仍然能够跟踪目标.     

Fisher’s linear discriminant ratio based threshold for moving human detection in thermal video

In video surveillance, the moving human detection in thermal video is a critical phase that filters out redundant information to extract relevant information. The moving object detection is applied on thermal video because it penetrate challenging problems such as dynamic issues of background and illumination variation. In this work, we have proposed a new background subtraction method using Fisher’s linear discriminant ratio based threshold. This threshold is investigated automatically during run-time for each pixel of every sequential frame. Automatically means to avoid the involvement of external source such as programmer or user for threshold selection. This threshold provides better pixel classification at run-time. This method handles problems generated due to multiple behavior of background more accurately using Fisher’s ratio. It maximizes the separation between object pixel and the background pixel. To check the efficacy, the performance of this work is observed in terms of various parameters depicted in analysis. The experimental results and their analysis demonstrated better performance of proposed method against considered peer methods.     

Penalized Fast Subset Scanning

We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic. However, PFSS also allows incorporation of prior information about each data element’s probability of inclusion, which was not previously possible within the subset scan framework. PFSS builds on two main results: first, we prove that a large class of likelihood ratio statistics satisfy a property that allows additional, element-specific penalty terms to be included while maintaining efficient computation. Second, we prove that the penalized statistic can be maximized exactly by evaluating only O(N) subsets. As a concrete example of the PFSS framework, we incorporate “soft” constraints on spatial proximity into the spatial event detection task, enabling more accurate detection of irregularly shaped spatial clusters of varying sparsity. To do so, we develop a distance-based penalty function that rewards spatial compactness and penalizes spatially dispersed clusters. This approach was evaluated on the task of detecting simulated anthrax bio-attacks, using real-world Emergency Department data from a major U.S. city. PFSS demonstrated increased detection power and spatial accuracy as compared to competing methods while maintaining efficient computation.