hechengkongjingjiance

电力系统状态估计快速解耦法（PQ分解：有功和无功的分解；雅克比矩阵常数化）(Fast decoupling method for power system state estimation (PQ decomposition decomposition; active and reactive Jacobi matrix))

book to learn Visual c++ and logiciel

高成编写的《matlab接口技术与应用》中例题的源代码(High into the preparation of the " matlab interface technology and applications" in the source code examples)

图1为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列，其均值为零；参考信号；信道具有脉冲响应： 式中用来控制信道的幅度失真(W = 2～4, 如取W = 2.9,3.1,3.3,3.5等)，且信道受到均值为零、方差(相当于信噪比为30dB)的高斯白噪声的干扰。试比较基于下列几种算法的自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况,在同一坐标下画出其学习曲线): 1）横向/格-梯型结构LMS算法 2）横向/格-梯型结构RLS算法 并分析其结果。(Figure 1 is a band-limited signal caused balanced horizontal or lattice distortion adaptive equalizer (FIR system in which the lateral length M = 11), the system input is the value of a random sequence of ± 1, mean zero reference signal channel has impulse response: Wherein the amplitude of the distortion for the control channel (W = 2 ~ 4, and so as to take W = 2.9,3.1,3.3,3.5), and subjected to a channel with zero mean and variance (equivalent SNR is 30dB) Gaussian white noise. Compare the following types of algorithm-based adaptive equalizer in a different channel distortion, the convergence of different noise interference (corresponding to each situation, draw their learning curve in the same coordinate): 1) landscape/grid- LMS algorithm ladder structure 2) landscape/grid- RLS algorithm ladder structure And analyze the results.)

justenjoy it and have a nice day lamigo monkeys is theNO1 in cpbl im loveing it

实现快速傅里叶变换，自编函数FFT.而不需使用matlab自带的FFT函数(it can realize the fast fuleir tansform)

德国低干扰谱，以及公路C级路面的路面不平度的c++实现。一些相关的资料，便于初学者理解。(German low interference spectrum, as well as C-level road highway pavement roughness achieved. Some relevant information, easy for beginners to understand.)

对于OFDM初学者很有帮助，是很好的学习程序哦，可以学会用matlab仿真(the study of )

此程序实现一个模拟的雷达PPI显示界面，能显示雷达站的相关信息(this program produces a interpace about radar PPI, it can display radar station information)

Image evaluation (mean, clarity, information entropy, standard deviation)