Recent developments in laser scanning technologies have provided innovative solutions for acquiring three-dimensional (3D) point clouds about road corridors and its environments. Unlike traditional field surveying, satellite imagery, and aerial photography, laser scanning systems offer unique solutions for collecting dense point clouds with millimeter accuracy and in a reasonable time. The data acquired by laser scanning systems empower modeling road geometry and delineating road design parameters such as slope, superelevation, and vertical and horizontal alignments. These geometric parameters have several geospatial applications such as road safety management. The purpose of this book is to promote the core understanding of suitable geospatial tools and techniques for modeling of road traffic accidents by the state-of-the-art artificial intelligence (AI) approaches such as neural networks (NNs) and deep learning (DL) using traffic information and road geometry delineated from laser scanning data. Data collection and management in databases play a major role in modeling and developing predictive tools. Therefore, the first two chapters of this book introduce laser scanning technology with creative explanation and graphical illustrations and review the recent methods of extracting geometric road parameters. The third and fourth chapters present an optimization of support vector machine and ensemble tree methods as well as novel hierarchical object-based methods for extracting road geometry from laser scanning point clouds. Information about historical traffic accidents and their circumstances, traffic (volume, type of vehicles), road features (grade, superelevation, curve radius, lane width, speed limit, etc.) pertains to what is observed to exist on road segments or road intersections. Soft computing models such as neural networks are advanced modeling methods that can be related to traffic and road features to the historical accidents and generates regression equations that can be used in various phases of road safety management cycle. The regression equations produced by NN can identify unsafe road segments, estimate how much safety has changed following a change in design, and quantify the effects of road geometric features and traffic information on road safety. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.

采集方式： Cyberware三维扫描仪 发布单位： 斯坦福大学 The Stanford 3D Scanning Repository ,可用于点云配准、表面重建

本人自己翻译的Burp Suite 专业版的中文手册，陆续更新，请期待

这是一篇机器人线激光标定论文，可供参考。主要原理：利用已知半径的标准球面作为标定对象（参考工具），计算线激光传感器与机器人末端之间的变换矩阵。

图书扫描应用 描述 这是一个Android应用程序，可以扫描ISBN代码以搜索书籍。 这是Udacity的Android Nandodegree程序的一部分，是在Super Duo项目的基础上构建的。 技术细节 使用Google Mobile Vision API实施条形码扫描功能

主要介绍了Python EOL while scanning string literal问题解决方法,本文总结出是数据库数据出现问题导致这个问题,需要的朋友可以参考下

合成人类生成的姿势数据增强 该知识库是的硕士论文的一部分，是在开发的。 给定的管道可以使用通过任何深度相机扫描的点云（推荐：Intel RealSense D435i），并根据点云的颜色信息的可用性实现迭代最近点（ICP）的两种不同变体。 整个过程如下图所示。 接触： 表中的内容 输出 后期处理 引文 执照 致谢 接触 参考 资料收集程序 出于本项目的目的，请从深度相机收集点云或扫描数据。 主要算法接受.pcd或.ply格式的点云，并且可以接受2到5000之间的任意数量的点云。点云（扫描）的数量取决于最终注册的点云分辨率的要求。 硬件 在创建管道的实验中，我们使用了Intel RealSense D435i ，它是Intel的深度感测相机。 英特尔实感:trade_mark:D4xx深度摄像头可以每秒高达90帧的速度传输实时深度（即测距数据）和色彩数据，生成深度数据的所有处理均由嵌入式D4专用集成电路在板

Nmap, or Network Mapper, is a free, open source tool that is available under the GNU General Public License as published by the Free Software Foundation. It is most often used by network administrators and IT security professionals to scan corporate networks, looking for live hosts, specific services, or specific operating systems. Part of the beauty of Nmap is its ability to create IP packets from scratch and send them out utilizing unique methodologies to perform the above-mentioned types of scans and more. This book provides comprehensive coverage of all Nmap features, including detailed, real-world case studies. . Understand Network Scanning Master networking and protocol fundamentals, network scanning techniques, common network scanning tools, along with network scanning and policies. . Get Inside Nmap Use Nmap in the enterprise, secure Nmap, optimize Nmap, and master advanced Nmap scanning techniques. . Install, Configure, and Optimize Nmap Deploy Nmap on Windows, Linux, Mac OS X, and install from source. . Take Control of Nmap with the Zenmap GUI Run Zenmap, manage Zenmap scans, build commands with the Zenmap command wizard, manage Zenmap profiles, and manage Zenmap results. . Run Nmap in the Enterprise Start Nmap scanning, discover hosts, port scan, detecting operating systems, and detect service and application versions . Raise those Fingerprints Understand the mechanics of Nmap OS fingerprinting, Nmap OS fingerprint scan as an administrative tool, and detect and evade the OS fingerprint scan. . "Tool" around with Nmap Learn about Nmap add-on and helper tools: NDiff--Nmap diff, RNmap--Remote Nmap, Bilbo, Nmap-parser. . Analyze Real-World Nmap Scans Follow along with the authors to analyze real-world Nmap scans. . Master Advanced Nmap Scanning Techniques Torque Nmap for TCP scan flags customization, packet fragmentation, IP and MAC address spoofing, adding decoy scan source IP addresses, add random data to sent packets, manipulate time-to-live fields, and send packets with bogus TCP or UDP checksums.

Nmap Cookbook The Fat-free Guide to Network Scanning.pdf

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