sr-matlab

SPIHT algorithm development in matlab.

在程序中用一般的MUSIC方法估计了信号的频谱，从两图中可看出在信号各频率处，频谱的峰值较大，故能明显的与噪声相区别，可在一定程度上避免在信号幅度较小时信号被噪声淹没的情况。(MUSIC method general procedures used to estimate the spectrum of the signal, it can be seen from the two graphs in the signal at each frequency of the spectrum peak larger, and it could be significantly distinguished from noise in the signal can be avoided to some extent a lesser extent, the signal is noise submerged.)

speech compression using rabiner algorithm

永磁同步电机滑模控制，滑模观测器，仿真，很经典，(pmsm smo)

对脉冲多普勒雷达调频噪声进行仿真，你会发现非常又有有用。(FM noise to simulate the radar you will find that it is very useful.thank you to use it.thank you very much.you must be glad to find it.)

用mathlab7.0编码，实现分布式电源接入对配电网电压，网损影响仿真，并以IEEE33节点为例说明。代码可直接运行(Mathlab7.0 coding to achieve the access of distributed power distribution network voltage loss affect the simulation, and IEEE33 node example. Code can be run directly)

用粒子群算法解决0/1背包问题，解决在背包体积有限的情况下，如何最大化的实现有效资源分配(Solve knapsack problem with particle swarm algorithm to solve the limited volume of the backpack, how to maximize the efficient allocation of resources to achieve)

SLAM 地图构建与定位算法 其中含有卡尔曼滤波和粒子滤波器的程序(the principal of pso)

针对PID控制模型，使用模糊控制方法对PID的参数自适应优化，属于基于模糊自适应PID控制算法程序(For the control of the PID model, using the parameter optimization of adaptive fuzzy control method for PID, which belongs to the fuzzy adaptive PID control algorithm program based on)

Wavelet denoising For using this code need to use signal toolbox and general toolbox in your matlab In the first part of this assignment, we asked to obtain a (black-and-white) digital image of size 512 by 512 and then generate noisy image by adding a Gaussian noise but under the condition of having SNR=20dB by select the suitable value of variance for Gaussian noise formula. Second step is performing wavelet denoising using the hard thresholding (Use the db 6 for four levels) in the condition of finding the optimal thresholding value of T in terms of the SNR obtained. It means that, we should find the highest SNR value by finding the suitable value for threshold. Then we asked to do the same process but this time using soft thresholding. Finally for the last part of question one, we should compare the results of the obtained SNR with the recommendations of 3*sigma for the hard thresholding and 3/2*sigma for the soft thresholding.