shenjinwangluoyuce

用振幅为0.8的方波进行傅里叶分析，并求当K不同值时的合成信号。(By amplitude of 0.8 square wave to Fourier analysis, and pray for different value when K synthetic signal. )

极化SAR分类方向的很好的入门PPT资料，对各种相干、非相干分解方法以及监督、非监督分类方法做了简单的阐述(polarimetric SAR decomposition and image classification )

说明：  画二维和三维箭头矢量，matlab工具包。直接调用。 This functin is used to draw 2D、3D-arrows. The size of arrow head is decided by the minimum distance between start and stop.(Two-dimensional and three-dimensional vector drawing arrows, matlab toolkit. Direct calls. This functin is used to draw 2D, 3D-arrows. The size of arrow head is decided by the minimum distance between start and stop.)

matlab输入成形器zvzvdzvdd(matlab input shaper zvzvdzvdd)

计算偶极子天线几何尺寸以及各个偶极子之间的参数(Calculation of the dipole antenna geometric size)

mimo系统基本仿真 mse算法下错误率最小(mimo system)

风力发电工具箱，里面有风速模型、风力机模型、发电机模型等等，搞风力发电的用起来比较方便 (Wind-PSMG model box)

Matlab 工具，各类积分，二次定积分，gquad2dggen等(Matlab tool, all kinds of points, the second definite integral, gquad2dggen etc.)

传统的K-medoids聚类算法的聚类结果随初始中心点的 不同而波动，且计算复杂度较高不适宜处理大规模数据集； 快速K-medoids聚类算法通过选择合适的初始聚类中心改进 了传统K-medoids聚类算法，但是快速K-medoids聚类算法 的初始聚类中心有可能位于同一类簇。为了克服传统的K- medoids聚类算法和快速K-medoids聚类算法的缺陷，提出 一种基于粒计算的K-medoids聚类算法。(The traditional K-medoids clustering algorithm clustering results with different initial center points and volatility, and high computational complexity is not suitable for processing large data sets; K-medoids clustering algorithm by choosing proper initial cluster centers to improve the traditional K-medoids clustering algorithm, but the initial cluster center of K-medoids clustering algorithm can be located in the same cluster. In order to overcome the shortcomings of the traditional K- medoids clustering algorithm and the fast K-medoids clustering algorithm, a K-medoids clustering algorithm based on granular computing is proposed.)

Design a linear phase minimum order FIR Band Stop filter to stop the frequency band 0.45π to 0.55π with atleast 60dB attenuation at the edges. Further -3dB frequencies are required to be at 0.3π and 0.7π. 1. Plot the frequency response of the psuedo magnitude function using (a)rectangular window, (b)Hann window and (c) Hamming window. 2. Plot the frequency response for Rectangular and Hamming window for a length of 1024 to demonstrate the fact that the increase in the length, doesn t reduce undershoot and overshoot in Rectangular window.