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说明：  This classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system.

说明：  Introduction to BeiDou navigation satellite system

Electro Magnetic Theory Tutorial Questions & solutions

ansys 用于分析纤维增强复合材料的专用模块ACP。非常好的内部培训资料。(ansys fiber reinforced composites for analysis of specific modules ACP. Very good internal training materials.)

自己写的文章，总结了s函数的使用方法，并且实现了风速与风机的模拟，希望对相关的人有启示(Write your own article, summed up the s function to use, and to achieve a simulated wind speed and fan, I hope the relevant people have inspiration)

微波与卫星通信课件(ppt)第一章节第二章第三章第四章第五章第六章第七章 (Microwave and satellite communications Courseware (ppt) Chapter II the first chapter Chapter III Chapter IV Chapter V Chapter VI Chapter VII)

C C++ interview Questions and Answers

《小白学SAS》配套材料适读人群 ：SAS用户，统计、数据分析领域的相关工作人员(sas course, Readable population: SAS users, relevant staff in the field of statistics and data analysis)

本书是数学通信领域的一本经典教材，通过对概率论及随机过程的复习，详细介绍了数字和模拟信源编码，数字调制信号和窄带信号与系统的特征、加性高斯白噪声中数字通信的调制和最佳调制与检测方法、基于最大似然准则的载波相位估计和定时同步的方法、不同信道模型的信道容量及随机编码、带限信道的信号设计、受到符号干扰恶化信号的解调与检测问题、自适应信道均衡、多信道与多载波调制、扩展频谱信号和系统、衰落信道上的数字通信。本书适合通信工程相关专业的高年级本科生、研究生及工程技术人员阅读。()

说明：  统计学习是计算机及其应用领域的一门重要的学科本书全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、K近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归、与最大熵模型、支持向量机、提升方法、EM算法、隐马尔可夫模型和条件随机场等。(Statistical learning is an important subject in the field of computer and application of this book comprehensively systematically introduces the main methods of statistical learning, especially the supervision method of study, including the perception, K neighbor method, naive bayesian method, decision tree, logistic regression, and the maximum entropy model, support vector machine (SVM), promotion methods, the EM algorithm, the hidden markov model and conditional random field, etc.)