KalmanTrack

16QAM在SG中的模型，对于学习system generator 很实用的(this 16QAM application in SG,and it s very useful for you to study system generator)

灰色预测模型是一种模糊预测算法，可以对信息不完全的或不知发展规律的变量进行预测(Gray prediction model is a fuzzy prediction algorithm, you can not know or incomplete information to predict the development of the law of variable)

这是用matlab实现的卷积码源代码加最终的结果图(This is matlab convolution code with the source code plus the final results of Figure)

卫星完好性监测的程序，工程中经常那个用，这是我在学习中自己编的，希望对大家有用(satellite navigation system )

em算法运用于有散射体的信道模型下实现参数估计（los距离）(em algorithm applied to a scattering channel model to achieve parameter estimation (los distance))

我们提出了一种新的方法来实现全光 微波使用光纤布拉格光栅阵列滤波 相同的反射率。确定了过滤器攻丝系数 通过一个宽带光源的光谱分布和 光栅的中心波长。由于光纤布拉格 光栅有相同的反射率，其特征 高均匀度的光栅是可能的，从而简化了 光纤布拉格光栅的制作，降低了实现 错误。此外，该宽带光的光谱分布 源可以控制使用的光学过滤器，可用于 控制滤波器系数的滤波器的旁瓣抑制。一个 微波滤波器采用四个光纤布拉格光栅的实验 实施。 (We propose a novel approach to realizing all-optical microwave filtering using an array of fiber Bragg gratings with identical reflectivities. The filter tapping coefficients are determined by the spectrum profile of a broadband light source and the central wavelengths of the gratings. Since the fiber Bragg gratings have identical reflectivities, the characteristics of the gratings with high uniformity are possible, which simplifies the fiber Bragg grating fabrication and reduces the implementation error. In addition, the spectrum profile of the broadband light source can be controlled using an optical filter, which could be used to control the filter coefficients to suppress the filter sidelobes. A microwave filter using four fiber Bragg gratings is experimentally implemented.)

Cryptage et decryptage Cesaraffine

particle swarm optimization toolbox for matlab.粒子群优化的工具箱(particle swarm optimization toolbox for m atlab. Particle Swarm Optimization Toolbox)

This algorithm solves the otimization problem min_x | y - A x |_2^2 + lambda Phi(x) and has applications in compressed sensing, image restoration and reconstruction, sparse regression, and several other problems.

信任函数理论，也称为D-S证据理论，是经典概率理论的扩展。特别适应于决策级信息融合。(Belief function theory, also known as DS evidence theory is an extension of classical probability theory. Especially adapted to Decision Level Information Fusion.)