MIMOjishuxiangjie

用matlab程序模拟了多层平板波导的导波情况 包括TE模和TM模 该程序可进一步扩展(Matlab procedure used to simulate the multi-layered slab waveguide, including the guided wave TE mode and TM mode of the program can be further expanded)

MATLABfunction y=c6optm2(x) assignin( base , Z1 ,x(1)) assignin( base , P1 ,x(2)) assignin( base , Z2 ,x(3)) assignin( base , P2 ,x(4)) assignin( base , K ,x(5))

pso 粒子群优化算法 以简单的函数为例，展示基本算法(PSO particle swarm optimization algorithm with simple function as an example, show the basic algorithm )

The process to segmentation of images. Where it to identify the object an image.

说明：  利用matlab进行图像配准,医学图像配准是目前图形处理中的重要问题(image registration by matlab，which is a key problem in the image prcessing field)

利用Kalman工具箱实现对目标跟踪中的航迹分析(use Toolbox to achieve the goal of tracking Track Analysis)

递归最小均方误差中，权矢量随迭代次数收敛曲线(recursive least squares )

skin detraction Channing the skin color to another color

Simulink block diagram of simple Antilock Braking System control

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.