SRS: Spatial-tagged radio-mapping system combining LiDAR and mobile-phone data for indoor location-based services

One of the most challenging issues in radio received signal strength (RSS)-based localization systems is the generation and distribution of a radio map with a coordinate system linked with spatial information in a large indoor space. This study proposes a novel spatial-tagged radio-mapping system (SRS) that effectively combines the heterogeneous properties of LiDAR and mobile phones to simultaneously perform both spatial and radio mappings. The SRS consists of synchronization, localization, and map building processes, and enables real-time spatial and radio mapping. In the synchronization process, the distance range, motion data, and radio signals obtained through the LiDAR and mobile phone are collected in nodal units according to the sensing time. In the localization process, a feature variance filter is used to control the number of features generated from LiDAR and estimate the positions at which the nodes are generated in real time according to the motion data and radio signals. In map building, the estimated positions of the nodes are used to extract spatial and radio maps by using a unified location coordinate system. To ensure mobility, the SRS is manufactured in the form of a backpack supporting LiDAR and a mobile phone; the usefulness of the system is experimentally verified. The experiments are performed in a large indoor shopping mall with a complex structure. The experimental results demonstrated that a common coordinate system could be used to build spatial and radio maps with high accuracy and efficiency in real time. In addition, the field applicability of the SRS to location-based services is experimentally verified by applying the constructed radio map to well-known fingerprinting algorithms using the heterogeneous mobile phones.     

A review of phosphorescent and fluorescent phosphors for fingerprint detection

Currently, the efficient detection of fingerprints is essential for the crime investigations. Revealing fingerprints is commonly achieved with fluorescent organic compounds but they are not efficient for fingerprint detection on porous or reflective surfaces. In order to solve the problem of collecting fingerprints on porous/reflective surfaces, inorganic phosphors have been employed, since they have characteristics of variable color emission, afterglow, high chemical stability and nano-size, which allow the fingerprint detection on any porous or non-porous surfaces. Due to these last properties, this review presents a summary about the use of phosphorescent and fluorescent phosphors for the detection of latent fingerprints. First, we discussed the main physical and chemical characteristics of the fingerprints which permit their detection and collection from any surface. After this, we presented the main morphological, structural and luminescent properties of the phosphorescent and fluorescent phosphors that allow their use for fingerprint detection. Later, we demonstrated with pictures of fingerprints (with and without light emission from the phosphors deposited on them) that both, phosphorescent and fluorescent phosphors can be used to visualize fingerprints with high resolution and high contrast without interference of the background surface, which is ideal for its collection and registration in the Automated Fingerprint Identification System (AFIS). We believe that this review could be useful to understand how to select an appropriate phosphorescent or fluorescent material for fingerprint detection depending on the type of surface (porous or non-porous, reflective or not reflective) where the fingerprint is deposited.     

基于星座图对射频指纹识别方法的研究

针对星座图的射频(RF)指纹识别方案中,低信噪比环境下识别准确率低的问题,提出一种基于欧式距离与幅度距离的二维识别算法来进行RF指纹识别。该方案通过对星座图进行优化处理,可从优化后的星座图中提取识别性能更好的RF指纹,再通过二维识别算法来提高识别准确率。仿真结果表明:与仅用欧式距离作为判断依据的方法相比,所提出的二维识别算法的识别准确率最高可提升8%,在设备容量为50组的情况下识别准确率为77.8%,并且从优化后的星座图中所提取的RF指纹具有更好的唯一性和稳定性。     

指纹考勤管理软件的研究与实现

针对企、事业单位原有的考勤系统，如打卡钟、磁卡、IC卡等考勤方式存在代打卡、卡丢失等不足和缺陷的实际，结合指纹识别技术，建立一套考勤、薪资、人事管理于一体的软件系统。给出了系统的体系结构和功能实现，能使企业考勤管理更加简便快捷、客观公正。并提出了进一步的发展方向和展望。     

网上考试系统中指纹验证模块的研究与算法实现

网上考试中运用指纹认证方法,可以有效地杜绝代考现象的发生.文中探讨了网上指纹验证过程中的算法实现,综合运用图像处理和模式识别技术,研究了指纹识别的理论和技术,建立了一对一的指纹识别系统.整个系统框架采用浏览器/服务器(B/S)结构,用户在客户端提交指纹图像,而服务器端则完成指纹图像的预处理、匹配算法等,再将识别信息反馈给客户端.     

自适应指纹预处理算法

基于大量的实验提出了一套行之有效的指纹处理算法，主要包括使用基于方向图的自适应旋转滤波器、与自适应增强滤波同时进行的自适应阀值边缘处理、自适应阀值二值化，很好地增强了指纹图像的清晰度，同时起到了很好的滤波作用，为可靠、准确地实现指纹自动识别系统奠定了最可靠的基础．     

利用茚三酮或DFO加强显现热敏纸张上的潜在指纹

本文介绍一种新技术——利用茚三酮或DFO加强显现热敏纸张上的潜在指纹。大部分热敏纸张的热敏剂表面,使用DFO或茚三酮石油醚溶液(NPB)显影时将会变黑。显现的指纹和背景之间反差很小。新技术介绍了在指纹和热敏层之间,用丙酮水洗,可以去掉黑色污斑。其它新的化学试剂漂洗,显现的指纹有清晰的纹线和很高的反差。大量使用这种技术,可以使试剂优化,成本降低,短时间内能处理大量检材。工作试剂包括商用、不挥发、含氮的有机化合物,也使用象NPB这样的浸透性试剂。     

基于非下采样Contourlet变换的指纹图像超分辨率重建

非下采样Contourlet变换(Nonsubsampled contourlet transform,NSCT)采用非抽样金字塔结构和非抽样方向滤波器组构成,具有Contourlet变换所不具备的平移不变性、较高冗余度等优良特性,而且能够克服伪吉布斯现象。图像经过非下采样Contourlet变换后分解成多尺度、多方向的细节信息,这些细节信息代表了图像不同频带不同方向的特征,这就简化了系数之间的关系。基于学习的超分辨率重建算法具有整体的预测性,将非下采样Contourlet变换和基于学习的算法相结合,在一定程度上提高训练精度。针对指纹图像的实验证明该算法具有良好的性能,重建的图像纹理性细节信息较好,基本保持了原指纹图像的特征点,更接近于原始的高分辨率图像。     

基于指纹识别的虚拟硬盘研究

为防止机密数据不被窃取或篡改,本文提出一种基于指纹身份识别的虚拟硬盘文件加密存储方法。该方法是在Windows设备驱动程序的基础上,利用指纹识别技术和虚拟硬盘驱动程序,实现用户身份的唯一认证和文件的安全性管理。实验证明,该方法是一种安全、高效、实用的加密存储方法。     

基于ARM的QNMV指纹识别系统的研究

目前指纹识别技术正向着嵌入式和小型化的方向发展,搭建了基于ARM的指纹识别系统的硬件平台并对硬件进行了优化,研究了指纹识别的软件开发环境,给出了指纹识别系统的软件实现方框图和详细的指纹识别算法流程图;详细论述了全新的基于QNMV结构向量的指纹匹配算法,给出了算法优化后的一些测试数据,将指纹匹配算法与其他常见的算法进行了数据比对后,发现系统具有较好的性能指标:具有较低的误识率(FAR)和拒识率(FRR)、识别率(MRI)有了较大的提高、有效地缩短了匹配耗费时间(Time-Match);系统具有安全可靠、易于扩展、性价比高等优点。