Trajectory Planning for Automatic Machines and Robots的勘误 机器人轨迹规划的权威书籍，做机器人算法的必看 学习这一本就够了

Autonomous Cyber Deception: Reasoning, Adaptive Planning, and Evaluation of HoneyThings By 作者: Ehab Al-Shaer – Jinpeng Wei – Kevin W. Hamlen – Cliff Wang ISBN-10 书号: 3030021092 ISBN-13 书号: 9783030021092 Edition 版本: 1st ed. 2019 出版日期: 2019-01-02 pages 页数: (235 ) $84.99 This textbook surveys the knowledge base in automated and resilient cyber deception. It features four major parts: cyber deception reasoning frameworks, dynamic decision-making for cyber deception, network-based deception, and malware deception. An important distinguishing characteristic of this book is its inclusion of student exercises at the end of each chapter. Exercises include technical problems, short-answer discussion questions, or hands-on lab exercises, organized at a range of difficulties from easy to advanced,. This is a useful textbook for a wide range of classes and degree levels within the security arena and other related topics. It’s also suitable for researchers and practitioners with a variety of cyber security backgrounds from novice to experienced. Cover Front Matter Part I. Cyber Deception Reasoning Frameworks 1. Using Deep Learning to Generate Relational HoneyData 2. Towards Intelligent Cyber Deception Systems 3. Honeypot Deception Tactics Part II. Dynamic Decision-Making for Cyber Deception 4. Modeling and Analysis of Deception Games Based on Hypergame Theory 5. Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception Part III. Network-Based Deception 6. CONCEAL:A Strategy Composition for Resilient Cyber Deception: Framework, Metrics, and Deployment 7. WetShifter:A Comprehensive Multi-Dimensional Network Obfuscation and Deception Solution 8. Deception-Enhanced Threat Sensing for Resilient Intrusion Detection 9. HONEYSCOPE: IoT Device Protection with Deceptive Network Views Part IV. Malware Deception 10. gExtractor: Automated Extraction of Malware Deception Parameters for Autononous Cyber Deception 11. Malware Deception with Automatic Analysis and Generation of HoneyResource

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Demand and Supply Planning with SAP APO.pdf

This book takes a pragmatic approach to deploying state-of-the-art optical networking equipment in metro-core and backbone networks. The book is oriented towards practical implementation of optical network design. Algorithms and methodologies related to routing, regeneration, wavelength assignment, sub rate-traffic grooming and protection are presented, with an emphasis on optical-bypass-enabled (or all-optical) networks. The author has emphasized the economics of optical networking, with a full chapter of economic studies that offer guidelines as to when and how optical-bypass technology should be deployed. This new edition contains: new chapter on dynamic optical networking and a new chapter on flexible/elastic optical networks. Expanded coverage of new physical-layer technology (e.g., coherent detection) and its impact on network design and enhanced coverage of ROADM architectures and properties, including colorless, directionless, contentionless and gridless. Covers ‘hot’ topics, such as Software Defined Networking and energy efficiency, algorithmic advancements and techniques, especially in the area of impairment-aware routing and wavelength assignment. Provides more illustrative examples of concepts are provided, using three reference networks (the topology files for the networks are provided on a web site, for further studies by the reader). Also exercises have been added at the end of the chapters to enhance the book’s utility as a course textbook.

Computational Motion Planning 第2+3+4周编程作业 对应博客链接：https://blog.csdn.net/qq_39537898/article/details/104400702

本书是第二版，2017的书。 《Web容量规划的艺术》由John Allspaw(F订ickr的工程运营经理)撰写，结合了他个人在F1ickr成长过程中的许多经历和很多其他产业中同行的洞察力。在衡量增长、预测趋势、成本效益等方面，他们的经验都会给你一些可靠并有效的指导。 网站的成功是以使用和增长来衡量的，而且网站类公司的成败(生死)是依赖于他们是否有能力来衡量决定他们的基础结构，从而适应不断增长的需求。作者通过自身实践给你提供所需要的相关知识和工具，来帮助你预知一些有威胁性的瓶颈问题和突然的网络增长，从而测量、部署并提前设计好网站应用的基本架构。

局部RRT路径规划matlab代码运动计划 Python 用于几种路径规划算法的Python代码位于文件夹中。 让我们来看几个例子。 为了熟悉人工势能场（APF）算法，请执行以下操作： jupyter-notebook python_src/adaptive_formation/GradientBasedPlanning.ipynb 基于实时势场的具有动态或静态障碍物的机器人编队避障方法。 python python_src/adaptive_formation/gradient_interactive.py 快速探索随机树（RRT）算法的路线图和路径构建： python python_src/rrts/main_rrt2D.py 在3D环境中： python python_src/rrts/3D/rrt3D.py 这里的RRT节点和边缘用蓝色表示，从树中检索到的路径是绿色，而橙色曲线是缩短的轨迹。 分层计划器（RRT + APF） 以RRT作为全局路径构造器和APF的分层计划器的示例负责局部轨迹的创建。 该算法不仅提供给自我车辆，而且还提供给一组机器人。 用于基于RRT + APF算

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GridWorld（计划和强化学习） 包含用于规划的算法：策略迭代和价值迭代。 还包含强化学习算法：蒙特卡洛学习，Sarsa（lambda）和Q学习。 在GridWorld问题的上下文中使用这些方法，在该问题中，代理的目标是找到到达终端状态的最快路径。 game.py文件包含Grid World环境。 可以运行其他文件来执行强化学习或计划（取决于所选文件）。