A New Algorithm for the Establishing Data Association Between a Camera and a 2-D LIDAR

In this paper,we propose a new algorithm to establish the data association between a camera and a 2-D Light Detection And Ranging sensor （LIDAR）.In contrast to the previous works,where data association is established by calibrating the intrinsic parameters of the camera and the extrinsic parameters of the camera and the LIDAR,we formulate the map between laser points and pixels as a 2-D homography.The line-point correspondence is employed to construct geometric constraint on the homography matrix.This enables checkerboard to be not essential and any object with straight boundary can be an effective target.The calculation of the 2-D homography matrix consists of a linear least-squares solution of a homogeneous system followed by a nonlinear minimization of the geometric error in the image plane.Since the measurement quality impacts on the accuracy of the result,we investigate the equivalent constraint and show that placing the calibration target nearby the 2-D LIDAR will provide sufficient constraints to calculate the 2-D homography matrix.Simulation and experimental results validate that the proposed algorithm is robust and accurate.Compared with the previous works,which require two calibration processes and special calibration targets such as checkerboard,our method is more flexible and easier to perform.     

三网融合驱动的全媒体技术复合型人才培养探索

培养适应全媒体时代的合格人才是我国高校数字媒体专业的基本任务。文章对照三网融合社会大环境对数字媒体人才的实际需求,以数字媒体技术专业为切入点,分析了目前国内高校数字媒体专业培养模式存在的突出问题,并分别从课程体系规划及教材建设、科学教学方式探索、学生实践创新能力培养和考核评价体系改革等四个方面提出了建议和改进措施。希望为我国数字媒体及其相关专业的人才培养探索行之有效的新途径。     

基于AHP的产城融合评价研究

通过阐述我国产城融合发展中存在的问题,筛选出影响产城融合的指标因素,构建了包括经济发展、城市化水平和配套设施3个指标在内的评价体系.利用AHP方法进行定性和定量分析,确定了指标的权重,同时建立了产城融合评价模型,可以明确城市发展阶段,为促进城市产城融合建设提供了一些思路.     

Mining potential social relationship with active learning in LBSN

Rapid development of local-based social network ( LBSN ) makes it more convenient for re-searchers to carry out studies related to social network.Mining potential social relationship in LBSN is the most important one.Traditionally, researchers use topological relation of social network or tel-ecommunication network to mine potential social relationship.But the effect is unsatisfactory as the network can not provide complete information of topological relation.In this work, a new model called PSRMAL is proposed for mining potential social relationships with LBSN.With the model, better performance is obtained and guaranteed, and experiments verify the effectiveness.     

融合干扰素rPoIFN-α/β联合黄芪多糖抗病毒活性研究

以融合干扰素rPoIFN-α/β和黄芪多糖(APS)作为抗病毒药物,采用MTT法与细胞病变(CPE)抑制试验评价了二者联用抗PRRSV,CSFV和PRV 3种猪源性病毒的效果.结果显示,融合干扰素rPoIFN-α/β和黄芪多糖(APS)联合使用对PRRSV,CSFV和PRV 3种猪源性病毒抑制作用远远高于单独使用,体现出显著的协同抗病毒效应,为融合干扰素与黄芪多糖的联合用药防控病毒性疾病提供了理论基础.     

An Efficient Multidimensional Fusion Algorithm for IoT Data Based on Partitioning

The Internet of Things (IoT) implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions (attributes), i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.     

考虑电磁激励的车用永磁同步电机转子扭振特性

研究了车用永磁同步电机转子系统在电磁激励与机械激励作用下的非线性扭振特性.建立了电磁激励作用下的转子系统机电耦合扭振振动模型,利用多尺度法求解了振动方程,确定了电机-转子系统扭振的稳态解及其稳定性.分别研究了永磁同步电机内功率因数角、极对数、电机运行的磁饱和系数以及转子系统的扭转刚度等对转子系统扭转振动特性的影响.研究结果表明:合理地控制和设计永磁同步电机的电磁与机械参数,可以避开车用永磁同步电机转子系统的扭转振动系统可能出现的跳跃及分岔等不稳定现象.     

基于惯性测量器件的无线步态分析平台

步态定量测量方法应用于许多领域,如临床医学、双足机器人控制等.采用惯性测量单元结合无线传感器网络建立了一个步态分析平台,将两个无线惯性测量单元传感器节点分别绑定在左右双侧脚踝,以同时采集双脚运动过程中的加速度和角速度信号,并将其通过无线方式发送到远程终端.通过模式识别、时间序列分析、阈值检测和零速修正等多种数据融合方法计算步态参数,并通过融合双足传感器数据得到双支撑相、双脚步行周期等重要的双足步态参数,其中双支撑相参数对人体日常动作的识别有重要意义.实验结果显示该研究具有较高的计算精度.     

基于特征融合的视频异常事件检测方法

为有效对视频数据进行降维并去除特征集合中的冗余信息, 以提高异常事件的检测效率, 从特征提取和选择的角度提出了融合特征区分度和相关性的视频异常事件检测方法。利用视频数据的时空邻域信息进行特征提取。通过分析特征的判别力和相关性进行特征选择, 从而去除特征集合中的冗余信息, 提高异常事件检测的效率和准确性。实验结果表明, 该方法的检测准确率都优于其他传统方法, 能有效地对场景中发生异常事件的区域进行准确定位。     

一种基于皮层变换的图像融合方法

提出一种基于皮层变换的图像融合方法. 该方法模拟生物视觉皮层中简单细胞的响应特性,将单幅图像映射成一系列分辨力及方向各异的子图像,子图像中每个像素代表了视觉皮层中某个神经元的响应. 通过简单的展开运算和加法操作就可实现逆变换. 仿真实验表明,较之于基于最大绝对系数和最大方向对比度的小波变换融合方法而言,该方法在交叉熵和对比度意义上性能更佳. 对于某些源图像而言,其主观效果亦更好.