P_ZF_SIC

介绍lms算法的基本原理，用matalb编程，并给出了运行结果(Introduce the basic principles of LMS algorithm, using matalb programming, and give the results)

第一步是计算输人信号单边功率谱密度（ESD）。使用快 ％速傅里叶（FFr）算法将信号从时域转换到频域。因为FFr算法的输出是离散谱，而这 ％里我们需要的是连续谱，因此需要引人不同的比例因子来实现从离散谱到连续谱的转换。 ％在第二步中，我们利用迭代算法计算出相对于特定阂值的ESD的最高和最低频率，从而 ％估算出被检测信号所占用的带宽。这种算法同时适用于基带信号和已调制信号。最后， ％在第三步中，我们给出了输出图形的原代码。(The first step is calculating the input signal unilateral power spectral density (ESD). The use of fast Fourier speed (FFr) algorithm for the signal conversion from time domain to frequency domain. FFR algorithm because the output is a discrete spectrum, which we need is a continuous spectrum, so the need for food of different scale factor to achieve from the discrete spectrum to continuous spectrum conversion. In the second step, we use iterative algorithm to calculate a specific threshold in relation to the ESD of the highest and lowest frequency, thus estimated to be occupied by the signal detection bandwidth. This algorithm applies to both baseband signal and modulation signals. Finally, in the third step, we give the graphical output of the original code.)

在matlab下利用空间均值法、中值法、最大值法和最小值法实现数字图像的平滑处理(Use of space under the matlab mean, median method, the maximum and minimum method method of smoothing digital images)

Navier Stokes Equation

进一步加深对卡尔曼滤波算法的理解，了解卡尔曼滤波器算法的基本特点，掌握卡尔曼滤波算法应用研究的基本步骤和方法(Kalman filter algorithm to further deepen the understanding and knowledge of the basic characteristics of the Kalman filter algorithm, Kalman filter algorithm is applied to master the basic steps and methods of research)

calculates the bentley sweep algorithm between two trapezoidal membership functions

使用高斯消元法，在GF（2）域上计算矩阵的逆矩阵，比MATLAB自带方法快！(Use Gaussian elimination in GF (2) matrix inverse matrix calculation on the domain, faster than MATLAB own way!)

Poisson equation solver

truetime 安装包及其说明，作为matlab的工具箱，用来处理网络问题(TrueTime installation package and basic introduction, as the Matlab toolbox, used for real-time simulation network)

Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene.[2][3] The concept of optical flow was first studied in the 1940s and ultimately published by American psychologist James J. Gibson[4] as part of his theory of affordance. Optical flow techniques such as motion detection, object segmentation, time-to-collision and focus of expansion calculations, motion compensated encoding, and stereo disparity measurement utilize this motion of the objects surfaces and edges.[5][6]