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LDPC在BPSK考虑AWGN信道下，采用软判决和积算法的仿真。这是第一个(LDPC in AWGN channel BPSK considering the introduction of soft-decision and the plot of the simulation algorithm. This is the first)

一个关于FUZZY kmeans算法的matlab源程序 有带聚类分析结果(Kmeans algorithm on matlab source are the result of cluster analysis with)

说明：  使用matlab仿真雷达系统设计的经典好书，附书中全部源码(Matlab simulation of radar system design, with all the source code)

ode45 function- the matlab code of ode function

研究LTE系统中半静态调度引入优先级后，用户吞吐量与SNR的关系(After studying the LTE system, the introduction of the semi-static scheduling priority, and user throughput and the relationship between SNRoutput of the converted character encoding. If the input aAb, output cCg)

training a neural networks using error back propagation algorithm

findEnergy.m 计算出图像的梯度图 findSeamImg.m 计算出所有可能的竖直缝 findSeam.m找出能量最小的一条竖直缝 removalMap.m将需要删除的N条竖直缝存储起来并按照索引逐条删除 SeamCut.m将选中的缝一条条的删除 SeamPlot.m在图中画出要删除的缝 Testseam.m调用以上函数的主程序(FINDENERGY creates an gradient img from a given RGB or grayscale image. FINDSEAMIMG finds the seam map from which the optimal (vertical running) seam can be calculated. FINDSEAM returns a column vector of coordinates for the pixels to be removed (the seam). SEAMPLOT takes as input an image and the SeamVector array and produces an image with the seam line superimposed upon the input image, x, for display purposes. SEAMCUT takes as input a RGB or grayscale image and SeamVector array to find the pixels contained in the seam, and to remove them from the image. REMOVALMAP takes a given image and finds the ordered set of (vertical) seams that are removed from an image and returns them in an array, where the Nth column in the array corresponds to the Nth seam to be removed.)

it is matlab file of load flow analysis by the help of guass siedal method in power system

基于最小二乘支持向量机理论,结合某风电场实测风速数据,建立了最小二乘支持向量机风速预测模型。对该风电场的风速进行了提前1h的预测,其预测的平均绝对百分比误差仅为8.55 ,预测效果比较理想。同时将文中的风速预测模型与神经网络理论、支持向量机(support vector machine,SVM)理论建立的风速预测模型进行了比较。仿真结果表明,文中所提模型在预测精度和运算速度上皆优于其他模型。 (Based on least squares support vector machine theory, combined with a wind farm measured wind speed data, the establishment of a wind vector machine forecasting model of least squares support. The velocity of the wind farm were predicted in advance 1h, the mean absolute percentage error of only 8.55 of its forecast, forecast effect is ideal. While the text of the wind speed forecasting models and neural networks, support vector machines (support vector machine, SVM) wind speed prediction models were compared with established theories. Simulation results show that our proposed model on prediction accuracy and computing speed are superior to other models.)

Codes used to beamform with a horizontal line array