fusion

首先分析了瑞利衰落信道统计特性，并在此基础上给出了clarke模型，然后提出仿真方法，包括成形滤波法、jakes仿真模型及其多种改进方法，接着阐述了上述仿真模型的软件实现过程，并利用matlab进行了计算机仿真，然后给出衰落信号的仿真信号和分析结果，仿真结果证明了提出的仿真模型与理论曲线匹配度高，可以很好地模拟无线衰落信道(First analyze the statistical properties of the Rayleigh fading channel, and clarke model is given on this basis, and then asked the simulation methods, including the forming of the filter method, the the jakes simulation model and a variety of improvement, then described the simulation model of the software implementationprocess, and using matlab computer simulation, and then gives the simulated signals and analysis results of the fading signals, simulation results show the proposed simulation model with the theoretical curve to match high, can simulate the wireless fading channel)

用matlab的m文件仿真FM过程，从调制到白噪声到解调(Simulation using matlab m file FM process, from modulated white noise to the demodulator)

说明：  用于求解配送路径优化问题，即vrp问题，算法是蚁群算法，很有价值(It is used to solve the distribution path optimization problem, namely VRP Problem. The algorithm is ant colony algorithm, which is very valuable)

matlab变成实现prony算法，自动提取符合信号中分量的频率、幅值、相位、衰减系数等参数，可以实现数据拟合(matlab become realized prony algorithms automatically extract the parameters frequency, amplitude, phase, in line with the signal attenuation coefficient component, you can achieve data fitting)

一种确定多目标无功规划补偿点的方法，具有实用性。(A goal to identify many points of reactive power compensation planning method is practical.)

在matlab的环境下证明傅里叶切片定理(At the matlab environment Fourier slice theorem proved)

matlab 实现 Reed Muller 编码及误码率曲线的绘制(matlab implementation Reed Muller code and BER Curve)

带有偏振模色散的光纤系统中基于lms算法的前馈均衡补偿系统(Optical fiber with polarization mode dispersion system lms algorithm based on feed-forward compensation system balance)

基于hht的大地电磁信号资料处理理MATLAB方法实现可直接使用。 (Based on hht magnetotelluric data processing management MATLAB method can be used directly.)

- K means algorithm is performed with different initial centroids in order to get the best clustering. - The total cost is calculated by summing the distance of each point to its cluster centre and then summing over all the clusters.Based on the minimum overall cost achieved during each iteration of iterKMeans the pixel assignment to their respective clusters are made and final compressed image is obtained. This algorithm will run slower as the number of clusters , size of the image and number of iterations increase.