02.TDMonitor

关于铁路轨道激光平顺度检查仪的使用说明，对仪器功能、使用范围和注意事项进行了详细说明。(On the railway track laser smoothness tester instructions, the instrument features, application scope and matters needing attention are discussed in detail.)

LDA知识的讲解，浅显易懂，便于学习，还有对LDA模型的建立的说明以运用(LDA knowledge to explain, easy to understand, easy to learn, as well as the establishment of LDA model description to use)

Simulation Digital communication by using matlab

matlab simulink 模糊控制器模型(matlab simulink fuzzy controller model)

说明：  这篇文章是关于标准空间二次曲面拟合的研究,对于齿轮研究者有很好的参考价值,希望大家喜欢(This article is about the standard space quadric fitting studies, researchers for the gear have a good reference value, I hope you like)

通过M atlab 模拟了随机产生的一定概率分布的信号序列, 叠加高斯噪声后利用最大后验概率(MAP)检 测到的误码率, 做出BER~ SNR曲线, 并与理论计算得到的曲线进行比较, 两者吻合.(M atlab simulated by a certain probability distribution of random signal sequence, use of superposition of Gaussian noise is the maximum a posteriori (MAP) detection to the error rate, making BER ~ SNR curves obtained with the theoretical curve of the combined fit.)

基于小波变换的分开—合并图像分割matlab．采用多尺度小波变换系数作为四分树结构的分开一合并法图像分割的一致性度量 从而在大的图像块中。。去除噪声的影响，在小的图像块中，以小波变换的局部极大值精确定位边缘，根据边缘信息进行分开一合并法图像分割 ．实验表明．算法得到满意的结果 ． (Separately based on wavelet transform- merge image segmentation Matlab. Multi-scale wavelet transform coefficients as the quadtree structure separate consolidation method of image segmentation consistency measure large image block. . To remove the effects of noise, in small image blocks, the local maxima of the wavelet transform precise positioning edge, to separate a combined method for image segmentation according to the edge information. Experiments show. Algorithm to obtain satisfactory results.)

汽车8自由度simulink模型，可乙乙进行各种汽车动力学控制及其仿真研究工作 (Automotive 8 degrees of freedom simulink model, B B a variety of vehicle dynamics control and simulation study of)

Serial com communication

说明：  基于非负矩阵分解（NMF）的人脸特征提取算法，NMF基本思想是找到一个线性子空间W，使的构成子空间的基本图像的像素点都是正值，而且人脸图像在子空间上的投影系数也是正数(Non-negative Matrix Factorization (NMF) of facial feature extraction algorithm, NMF basic idea is to find a linear sub-space W, so that the composition of sub-space of the basic image pixels are positive, and face image in the sub-space projection coefficient is positive)