梭式窑火焰数字图像的边缘检测初探

本文介绍了几种典型的边缘检测算法,对它们各自的特点进行了比较。对梭式窑内的一幅原始图像,可用边缘检测算法提取火焰图像的边缘。     

Evaporative light scattering mass detection for high-performance liquid chromatographic analysis of sucrose polyester blends in cooking oils

A high-performance liquid chromatographic method is described to determine the sucrose polyester (SPE) content in seven blends of cooking oils. Four gel-permeation chromatography (GPC) columns were used in series with an evaporative light scattering mass detector to separate the SPE from the acylglycerols in the final chromatogram. The SPE fraction was collected off the GPC column and injected onto a reverse-phase C-18 column for quantitation with sucrose octaacetate as an internal standard and a gradient of nonaqueous solvents as mobile phase. The chromatograms were interference-free, with only two sharp peaks appearing. The standards were linear from 500 to 5000 μg/mL with a correlation coefficient of r=0.999. The mean percent recovery (n=9) and standard deviation were 102±6.7. The detector could detect amounts as low as 5 μg SPE.     

墙地砖边缘检测中的小波去噪

在墙地砖的边缘检测过程中,噪声的引入是不可避免的,有效地消除噪声而不影响检测结果是检测过程中需要解决的关键问题。本文采用了小波去噪方法—对图像进行去噪后再进行边缘检测的方法,其检测结果较好地满足了检测要求。     

Cloud based intelligent system for delivering health care as a service

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity.     

Fall detection for multiple pedestrians using depth image processing technique

A fall detection method based on depth image analysis is proposed in this paper. As different from the conventional methods, if the pedestrians are partially overlapped or partially occluded, the proposed method is still able to detect fall events and has the following advantages: (1) single or multiple pedestrian detection; (2) recognition of human and non-human objects; (3) compensation for illumination, which is applicable in scenarios using indoor light sources of different colors; (4) using the central line of a human silhouette to obtain the pedestrian tilt angle; and (5) avoiding misrecognition of a squat or stoop as a fall. According to the experimental results, the precision of the proposed fall detection method is 94.31% and the recall is 85.57%. The proposed method is verified to be robust and specifically suitable for applying in family homes, corridors and other public places.     

Detecting symmetries of rational plane and space curves

This paper addresses the problem of determining the symmetries of a plane or space curve defined by a rational parametrization. We provide effective methods to compute the involution and rotation symmetries for the planar case. As for space curves, our method finds the involutions in all cases, and all the rotation symmetries in the particular case of Pythagorean-hodograph curves. Our algorithms solve these problems without converting to implicit form. Instead, we make use of a relationship between two proper parametrizations of the same curve, which leads to algorithms that involve only univariate polynomials. These algorithms have been implemented and tested in the Sage system.     

Detecting criminal organizations in mobile phone networks

The study of criminal networks using traces from heterogeneous communication media is acquiring increasing importance in nowadays society. The usage of communication media such as mobile phones and online social networks leaves digital traces in the form of metadata that can be used for this type of analysis. The goal of this work is twofold: first we provide a theoretical framework for the problem of detecting and characterizing criminal organizations in networks reconstructed from phone call records. Then, we introduce an expert system to support law enforcement agencies in the task of unveiling the underlying structure of criminal networks hidden in communication data. This platform allows for statistical network analysis, community detection and visual exploration of mobile phone network data. It enables forensic investigators to deeply understand hierarchies within criminal organizations, discovering members who play central role and provide connection among sub-groups. Our work concludes illustrating the adoption of our computational framework for a real-word criminal investigation.     

基于多决策树的RFID入侵检测模型

为了能够有效对RFID系统的已知攻击和未知攻击进行检测和预警,在分析大量RFID安全协议的基础上,提出了利用构建多决策树的方法对RFID系统的入侵行为进行检测。通过分析,得到所构造出的多决策树能够发现针对RFID系统的攻击之间的关系,并且具有可扩展性和高效性,使之能够适用于各种实际的RFID系统的应用。     

基于边缘信息车流量检测方法的研究

针对边缘检测中阈值很难确定导致不能准确提取背景边缘信息的问题,提出一种改进的基于边缘信息车流量检测方法。该方法预先对路况边缘图像作削顶处理获取一个恰当的阈值,从而可以准确地提取背景边缘信息;且在车辆计数过程中,结合固定窗法和浮动窗法二者的优点,设计一种新的浮动窗法进行车辆计数。实验结果表明,该方法在实时性、检测速度、正确率等方面得到很大的改进,能够在不同环境下进行正确有效地车流量检测。