d2

a code which export a letter/array to serial port

利用模板对图像进行增强，主要用于对道路的增强处理，所用模板是适应予道路增强的，当然可以自己根据自己需要进行修改(use templates to enhance the images, mainly for the increase of road, template is used to meet the increasing road course according to their own need for amendment)

粒子群优化算法（PSO）是一种基于种群的随机优化技术，由Eberhart和Kennedy于1995年提出。粒子群算法模仿昆虫、兽群、鸟群和鱼群等的群集行为，这些群体按照一种合作的方式寻找食物，群体中的每个成员通过学习它自身的经验和其他成员的经验来不断改变其搜索模式。 (Particle swarm optimization algorithm (PSO) is a population based stochastic optimization technique, put forward by Eberhart and Kennedy in 1995. Particle swarm optimization (pso) algorithm on insects, herds, flocks and schools of fish swarm behavior, and these groups to find food in a cooperative way, each member of the group itself by learning experience and the experience of the other members to continuously change its search pattern.)

信号检测中采用Bayes准则求最佳判决门限的影响因素观察(Signal detection using Bayes factors observed criteria for the best decision threshold)

steepest descent/bfgs with polynomial line search

RD算法作为SAR处理中最重要的处理算法，在远场近似的条件下，可将二维处理分离为两个一维处理，降低了运算量，成像效果良好。(RD算法作为SAR处理中最重要的处理算法，在远场近似的条件下，可将二维处理分离为两个一维处理，降低了运算量，成像效果良好。 RD algorithm is the most important processing algorithm in processing SAR, in the far field approximation, the two-dimensional separation into two one-dimensional processing, reduces the amount of computation, good imaging effect. )

Image classification using RBFSVM and Manhattan distance. It detects face and eye. Face detection using Viola jones.

基于均匀直线阵列的DOA估计，采用多重信号分类算法对信号源方位进行估计。(Based on uniform linear array DOA estimation, using multiple signal classification algorithm to estimate the signal source direction.)

matlab7图书里面的所有matlab代码，对初学者很有用，可以用来来纳西基本操作，熟悉基本的子程序，或供工程人员进行功能仿真是很的参考(Book inside matlab7 all matlab code, very useful for beginners and can be used to Naxi basic operation, familiar with the basic subroutine, or by project team members to conduct functional simulation is the reference)

本文介绍了基于MATLAB的语音识别系统，包括对语音信号的特征提取，包括语音信号的特征提取，快速傅立叶变换，离散余弦转换，线性预测分析，梅尔频率倒谱系数以及高斯混合模型。(This paper aims at development and performance analysis of a speaker dependent speech recognition system using MATLAB® . The issues that were considered are 1) Can Matlab, be effectively used to complete the aforementioned task, 2) Accuracy of the Gaussian Mixture Model used for parametric modelling, 3) Performance analysis of the system, 4) Performance of the Gaussian Mixture Model as a parametric modelling technique as compared to other modelling technique and 5) Can a Matlab® based Speech recognition system be ported to a real world environment for recording and performing complex voice commands. The aforementioned system is designed to recognize isolated utterances of digits 0-9. The system is developed such that it can easily be extended to multisyllabic words as well.)