资源列表
kalman
- 关于卡尔曼滤波器的程序，程序可直接运行，有效果图，PPT解释说明，供初学者学习。-On the Kalman filter process, the program can be run directly, there are effective plans, PPT explanations for beginners to learn.
Raytracing
- 射线追踪，比较基本的源代码，根据自己的需要调整后使用-Ray tracing,the basic source code, adjusted it before use according to your needs
gv3MQAM_type2_ofdm_cp_zf
- 4 16 64 QAM plots for simulated and theoretical results for OFDM system with addition of Cyclic prefix and a zero-Forcing equalizer.
ma yi sparse representation classification
- ma yi sparse representation classification .EXTENDED YALE B database.recognition rate 95 。-ma yi sparse representation classification. recognition rate 95 .
IV_mohu1
- 案例解说MATLAB典型控制应用[田敏][程序源代码]，找了很久才找到，希望对初学者有帮助！-Typical control applications MATLAB Case Commentary [Tian Min] [source code], looking for a long time to find, I hope to help beginners!
waveletmvdr
- 对含窄带噪声的语音信号进行小波去噪，并汇出LP谱和MVDR谱-LP and MVDR based wavelet
PRQMF
- 用Matlab实现完全重构双通道滤波器组，用matlab自带函数库实现-Using Matlab to achieve perfect reconstruction two-channel filter banks, the library comes with a matlab implementation
s223_5_mutilate_LevelPWM_10b
- 二极管箝位五电平电路建模与仿真的mdl文件，本人所作作业，绝对可以运行。-Diode-clamped five-level circuit modeling and simulation mdl file, I made the job can definitely run.
jizuobiao
- 本程序采用MATLAB对IEEE标准测试系统算例进行编程，并给出了9,14,30，39,57等系统的数据，将数据直接导入潮流主程序编程进行计算分析-The program uses MATLAB to IEEE standard test systems for programming examples, and gives 9,14,30,39,57 other system data, trend data directly into the main program for calcula
fft
- 设计一个按照时间抽取的基2快速傅里叶变换（基2FFT-DIT）。输入倒位序，输出自然顺序。-Design is taken in accordance with the time radix-2 fast Fourier transform the (base 2FFT-DIT). Enter the inversion sequence, the output of the natural order.
fft
- fft算法的具体实现，应用基2法实现，有比较好的实验结果。-fft concrete realization of the algorithm, the application base 2 method achieved a relatively good results.
F_LMS
- matlab实现系统辨识 采用LMS（最小均方误差）估计 随机信号均值为0方差为1，加入高斯白噪声-matlab for system identification using LMS (least mean square error) estimation of random signal with mean 0 variance 1, by adding Gaussian white noise