An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

一种减少测距误差的传感器网络定位算法

接收信号强度(RSSI)常被作为无线传感器网络定位算法的测距技术,通常,获取的RSSI信号序列中同时存在随机误差和粗差两种误差,利用以往常用的算法都难以消除混合误差对RSSI测量统计数据的影响;在分析两种误差特征的基础上,提出了一种基于统计中值的加权定位算法,算法在去除粗差的基础上,能在一定程度上平滑了随机误差,算法不仅提高了定位的精度,同时提高了节点的覆盖率;理论分析和仿真结果都表明算法具有较高的定位精度和覆盖率,最优情况下定位精度较原先算法能够提高6.99%,而不可定位节点比例下降8.05%。     

基于MATLAB的复合数字水印认证系统的设计与应用

为实现多媒体图像的知识产权保护,进行有效的防篡改认证,设计了复合数字水印认证系统。利用该系统可以对单个或批量的多媒体图像嵌入鲁棒性水印及脆弱性水印。仿真实验证明,多媒体数字图像嵌入复合数字水印后和原始图像几呼一样,水印隐蔽效果好;当图像被篡改后,用该系统进行水印认证,可以清晰看到版权标志信息是否被篡改以及定位显示篡改的内容。     

结合语句执行补集的程序错误定位

基于频谱的错误定位方法一般利用覆盖信息为每条语句度量出错的可能,即可疑度,通过逐条检查按可疑度值降序排列的语句序列来确定错误语句.针对已有的方法大多只考虑覆盖信息中语句执行信息的问题,分析了语句执行补集对错误定位的积极影响,进一步提出了在语句执行信息基础上结合语句执行补集的错误定位方法.实验结果表明,与其他方法相比,所...     

Why Teachers Must Learn: Student Innovation as a Driving Factor in the Future of the Web

User innovation is the key to the development and vitality of technology. As Huatong Sun wrote, “expanding the scope of localization practices and linking user localization efforts” to design cycles—including pedagogical design—will help bridge the gap between what teachers/designers create and what skills users/students need and want. This article theorizes the role of modern composition students and teachers as co-constructors of productive spaces for learning critical inquiry based on students’ statuses as digital natives. It focuses on a class of first-year composition students who reshaped an assignment to fit their own needs within the physical classroom and also enacted a shift to a virtual classroom. While doing so, they provided ways for each class member to individualize space within that digital environment, and they focused their project on examining the ways in which social networking forums colonize their daily lives. This article argues that by letting student innovation drive pedagogical practice—just as social media creators let user innovation drive the digital structures they produce—composition teachers can be assured of having a text for critique that blurs the lines between “private” student underlife and “public” classroom practice and legitimizes the creation of student-produced learning spaces. Using critical and cyberfeminist theories as lenses, this article draws conclusions about the future of computer usage in composition pedagogy based on students’ abilities to re-appropriate physical and digital classroom space.     

模糊化的无线传感节点自适应定位算法

节点定位是无线传感网络的研究基础,具有重要的研究价值和研究意义.现有定位算法主要分为测距相关的算法(range-based)与测距无关的算法(range-free)两类.前者通常使用未经处理的、包含环境噪声的测距信号,导致其定位稳定性较差;后者常使用节点间的连通性信息,导致其定位精度相对较低.针对该现状,本文提出一种基于模糊处理的节点自适应定位算法(fuzzy localization, FL).FL算法首先收集有关测距信息,然后对测距信息进行模糊化处理,最后采用自适应算法计算节点位置.FL算法对测距信息的模糊化处理,减少了环境噪声对定位的影响,提高了节点定位的稳定性;同时细化了通讯半径内的距离估计,提高了节点的定位精度;且自适应算法能对模糊化过程进行自适应控制,取得有效模糊化参数.实验结果表明,与DV-Hop算法和Spring算法相比,FL算法减少了约31%和6%的定位误差,且定位稳定性较好.     

水下无线传感器网络的研究进展

随着世界各国对海洋权益的日益重视、发展海洋经济热潮的兴起和陆地无线传感器网络研究的迅速发展,水下无线传感器网络的研究已经成为新的研究热点.首先对水下传感器网络通信技术、传感器网络节点、网络体系结构和水下声学传播特征作了介绍;进而重点对物理层、数据链路层、网络层、传输层、跨层设计、网络信息处理技术、水下定位和仿真平台8个方面的研究进展作了系统的综述;最后介绍了国内外主要研究机构的情况,并讨论了水下无线传感器网络研究存在的一些问题和需要进一步研究的方向.     

基于视觉的移动机器人同时定位与建图研究进展

同时定位与建图(SLAM)是实现移动机器人真正自治的必要前提, 视觉传感器由于能够提供丰富的环境信息而在SLAM研究中受到重视, 本文从视觉传感器配置方式、视觉特征提取方法、视觉SLAM实现机制、地图表示类型以及环境对视觉SLAM的影响五个方面综述基于视觉传感器的同时定位与建图研究的发展现状, 对已有的典型视觉SLAM方法进行分析和比较, 并展望了未来的发展趋势.     

一种基于移动坐标系的新型节点定位算法

提出一种与距离无关的基于移动坐标系的分布式定位算法,以移动锚节点为原点建立坐标系,未知节点以邻居锚节点信息在临时坐标系中确定自身位置区域,通过在不同坐标系下确定的位置区域取公共集不断缩小自身未知区域,最后利用质心算法求出节点位置坐标。仿真结果表明:新算法拥有较高的定位精度和适度的开销。     

基于传播算子法的某型飞机油箱冲击定位

研究利用阵列信号处理技术实现冲击定位的新方法,通过小波变换提取冲击响应信号某一窄带频率成分,运用传播算子法实现冲击源波达方向估计;根据Lamb波传播特性,用小波变换求出某一中心频率下的对称模式和反对称模式的Lamb波到达同一传感器的时间差,结合对称模式和反对称模式Lamb的速度差就可以估计出冲击源到达传感器的距离,从而实现冲击定位.在航空油箱上的实验表明:该方法能快速、精确地识别出冲击源的位置.