WiMAX-Simulink

short introduction for gauss programming to time sereis analysis

图像模板匹配计算，逐象素移动计算当前模板数据，提取图像特征点(image template matching, pixel-by-mobile current template data, image feature extraction point)

说明：   有一个长度为整数L(1<=L<=10000)的马路，可以想象成数轴上长度为L的一个线段，起点是坐标原点，在每个整数坐标点有一棵树，即在0,1,2，...，L共L+1个位置上有L+1棵树。 现在要移走一些树，移走的树的区间用一对数字表示，如 100 200表示移走从100到200之间（包括端点）所有的树。 可能有M(1<=M<=100)个区间，区间之间可能有重叠。现在要求移走所有区间的树之后剩下的树的个数。 输入： 两个整数L(1<=L<=10000)和M(1<=M<=100)。 接下来有M组整数，每组有一对数字。 输出： 可能有多组输入数据，对于每组输入数据，输出一个数，表示移走所有区间的树之后剩下的树的个数。(Has a length of integer L (1 &lt = L &lt = 10000) of the road, one can imagine a number line segment of length L, the starting point is the coordinate origin, each integer coordinate points in a tree, i.e. 0,1 , 2, ..., L total has L+1 L+1 positions on the tree. Now to remove some trees, tree removed by a range of digitally represented, such as 100 200 represents removed (inclusive) between 100 and 200 all the trees. There may be M (1 &lt = M &lt = 100) intervals, there may be overlap between the intervals. Now, after the number of trees require the removal of all remaining sections of the tree. Input: two integer L (1 &lt = L &lt = 10000) and M (1 &lt = M &lt = 100). Then there is an integer M groups, each with a pair of numbers. Output: there may be multiple sets of input data, for each set of input data, the output of a number indicating the number of the removal of all remaining sections of the tree after tree.)

对线性时变模型和非线性模型的预测控制算法进行了仿真分析，设计了两种模型下的模型预测控制的实现(Simulation analysis of linear time-varying model and nonlinear model predictive control algorithm)

光纤光栅特性分析，对光纤光栅的反涉率特性进行了详细的研究 (Optical fiber Bragg grating characteristic analysis of fiber grating, the characteristics of the involvement of the detailed study)

语音信号处理实基于MATLAB的语音信号时域特征分析 要求： 按所学相关语音处理得的知识，通过网上学习、资料查阅，自己设计程序，给出某一语音信号的短时过零率、短时能量、短时自相关特征的分析结果，并借助时域分析方法检测所分析语音信号的基音周期(yu yin xin hao chu li)

该程序是在flac下运行的，没找到flac，所以用matlab表示了，供岩土力学研究方向学习(The program is run under flac, did not find flac, so use matlab shows for rock mechanics research study)

induction motor line start simulation in matlab

Bit error rate of MIMO using Zero forcing and MMSE Equalizer

说明：  BP_Adaboost模型即把BP神经网络作为弱分类器，反复训练BP神经网络预测样本输出，通过Adaboost算法得到多个BP神经网络弱分类器组成的强分类器，利用此方法设计出强分类器。(The BP_Adaboost model uses the BP neural network as a weak classifier, repeatedly trains the BP neural network to predict the sample output, and uses the Adaboost algorithm to obtain a strong classifier composed of multiple BP neural network weak classifiers. This method is used to design a strong classifier.)