参考.pdf

Matlab教程，本教程主要讲述了如何从初学者变成精通Matlab的大师(Matlab teaching program)

These are Mathematics sample papers for IIT india.

提出一种基于粒子群优化算法的图像矢量量化码书设计算法. 该算法引入粒子群的全局搜索策略 , 结合矢量量化码书设计方法 , 增加了算法解的随机性和多样性.(Proposed based on particle swarm optimization algorithm for image vector quantization codebook design algorithm. The introduction of particle swarm algorithm global search strategy, combined with vector quantization codebook design methods, solutions to increase randomness of the algorithm and diversity.)

matlab GUI编辑的很好讲解，对初学者很有帮助的文档(matlab GUI，helpful to the newer)

对槽内多边形区域是否松动出槽外的松动检测，本程序运用harris角点检测的方法进行处理，(Polygonal area is loose on the tank outside the groove loose detection, the use of this program harris corner detection method for processing,)

ieee papers on image compression

本文的环境为opencv3.0+vs13，使用库函数简单的实现了背景差方法下的车辆检测。PS：但检测的效果并不理想，所以改用haar+adaboost的方法去做检测了，介绍里提及使用背景差实现了检测和跟踪，基本的思路都体现在注释里了较为简单，任何提高识别率的方法还不吝赐教！

xilinx的时序约束实验，通过阅读本文档，你可以用全局时序约束来轻松提高已有的项目的系统时钟频率，同时你还可以用映射后静态时序报告以及布局布线后静态时序报告来分析你的设计性能(Xilinx timing constraints of the experiment, by reading this document, you can use the overall timing constraints to easily enhance existing projects the system clock frequency, at the same time you can also use static timing report after mapping, as well as after placement and routing static timing analysis report to you design performance)

研究了不确定同时取送货车辆路径问题（VRPSPD），考虑运行环境的不确定性，顾客时间窗口要求和对顾客同时进行取货和送货服务的情况，以运作成本最低和顾客满意度最高为决策目标，构建不确定 VRPSPD 数学模型。模型中，引入模糊随机理论来描述决策环境中的双重不确定性，假定顾客需求量（送货量）和取货量是模糊随机变量。随后，提出基于模糊随机算子的改进粒子群算法对模型进行求解。(This article studies the vehicle routing problem with simultaneous pickup and delivery under a fuzzy random environment. More specifically, to adapt the mathematical model and improve the algorithm performance, this approach makes improvements and modifications in encoding and decoding, multi-objective handling, and particle updating. The encoding and decoding are suitable, multiple fitness functions are used to deal with the multi-objectives, and the improved update function can help overcome the shortcomings of the basic particle swarm optimization.)

Optimization Techniques