基于DHT的P2P资源定位的研究

文章对DHT的P2P资源定位进行了分析和比较,并对Chord模型进行了改进。提出了一个新的模型TL—Chord。该模型将自治域中的超级节点上的缓存表的资源定位机制与Chord的资源定位相结合,缩短了搜索路径长度,缩小了路由时间。     

一种大场景下的棋盘靶标角点自动定位算法

针对棋盘图案,提出一种亚像素精度的角点自动定位算法．该算法采用由粗到精的分层次检测策略,首先通过投影算法自动定位靶标图像中棋盘子图像的大致位置;其次在棋盘子图像中进行Hough变换,并根据角度投影图和幅值投影图进行棋盘角点的初步定位;最后在初步定位的角点邻域内通过高斯灰度插值和Harris算法得到最终的亚像素精度的角点位置．此外,本文还利用Hough变换提取的直线对角点进行自动排序．实验结果表明,该算法自动化程度较高,能够在大场景中自动定位靶标及其角点位置,而且精度较高,适用于大场景环境下畸变较小的摄像机的标定．     

嵌入式网络连通检测器的实现

介绍了以太网连通错误管理（CFM）的技术背景,指出纯软件开发的以太网的CFM模块具有局限性,并无法满足协议多变的特点。该模块是在SOPC系统上实现IEEE802．1ag协议中连通性检测功能。系统采用Altera公司提供的DE2开发板作为实现平台,实现了能以毫秒级速度发送及分级告警的嵌入式网络连通检测器。用两个开发板模拟一个小型的网络,通过PC机来对开发板进行操作和控制,利用对开发板上的LCD显示屏及LED显示灯进行监测,验证连通检测器的功能及性能。     

化工设备故障诊断仿真模型设计及计算分析

通过化工设备的结构模型、功能模型建立仿真模型,在对仿真模型分析的基础上得到症状与故障之间的数量关系并进行分析计算,推断出潜在的故障模式,为设计化工设备故障诊断专家系统奠定基础。     

智能车辆定位系统设计

车辆定位与导航系统是智能交通系统中当前需求较为迫切、应用比较广泛的一个重要的应用系统,是国际上公认的解决城市交通问题的有效途径之一。本文通过分析给出了智能车辆定位与导航系统的组成,并详细分析了核心技术。最后,给出了系统的硬件平台。     

医院信息系统(HIS)系统备份与容错设计

根据医院的业务量,设计了该院的信息系统的备份与容错设计方案,采用系统冷备份与双机热备份的容错设计。运行情况表明,能充分保证医院的关键数据的安全、完整。     

Vision-guidance of agricultural vehicles

A vision guidance system has been designed, built and commissioned which steers a tractor relative to the rows of a crop such as cotton. It was required to be insensitive to additional visual noise from weeds, while tolerating the fading out of one or more rows in a barren patch of the field. The system integrates data from several crop rows, testing for image quality. At the same time, the data processing requirements have been limited by the use of frame-sequential strategies to reduce the image space which must be processed. The design has been developed to the stage where six evaluation prototypes have been installed to test farmer-acceptance. The present prototypes employ a 486 PC motherboard embedded in a custom housing, together with a 68HC11 microcomputer to which the task of closing the steering servo loop is delegated. The system shows great promise for cost effective commercial exploitation.Experimental results are reported and further sensing systems are outlined for performing related guidance tasks when vision is inappropriate.     

Novel Metrics for Mutation Analysis

A measure of the “goodness” or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault detection ratio of the system. Because it is difficult to identify the system’s potential flaws precisely. In mutation testing, the Fault Detection Ratio (FDR) metric is currently used to express the adequacy of a test suite. However, there are some issues with this metric. If both test suites have the same defect detection rate, the smaller of the two tests is preferred. The test case (TC) is affected by the same issue. The smaller two test cases with identical performance are assumed to have superior performance. Another difficulty involves time. The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic. Our study developed three metrics to address these issues: , , and In this context, most used test generation tools were examined and tested using the developed metrics. Thanks to the metrics we have developed, the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system.     

Control allocation technology based on fault diagnosis for the unmanned aerial vehicle system subject to physical constraints and fault reconfiguration mismatch

The problem of the system robustness subject to physical constraints and mismatched fault reconstruction is discussed in this paper. In order to facilitate the design, a four-rotor unmanned aerial vehicle (UAV) system model was selected for research. First, the control allocation model of the nonlinear UAV system with disturbances is shown in the paper. Secondly, a weighted pseudo-inverse method based on adaptive weights is proposed, which reduces the impact of physical constraints on the system. After that, a dynamic weight control allocation method based on the fault efficiency matrix is designed. The weight matrix can dynamically adjust the control distribution law according to the fault estimation value provided by the observer. Then, a dynamic adaptive control allocation method for faults and physical constraints is carried out by combining adaptive weights and dynamic weights. Finally, a simulation example is presented to further illustrate the effectiveness of the algorithm proposed in this paper.     

Automated construction of Wi-Fi-based indoor logical location predictor using crowd-sourced photos with Wi-Fi signals

Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes.