RA0-estimation

simulation of environnement mobile

CI fuzzy logic application on aircraft landing program

说明：  基于matlab的MCMA算法仿真程序，给出均衡前后均衡效果图(Based on the MCMA algorithm matlab simulation program is given before and after the balance of the effect of a balanced graph)

NSD检测边缘算子，可用于一维信号二维图像，检测阶跃边缘(NSD edge detection operator, the signal can be used for one-dimensional two-dimensional image, the edge detection step)

三相单目标配电网重构MATLAB程序，可直接使用，注释详细，便于初学者学习(Three-phase single-target distribution network reconstruction MATLAB program, can be used directly, detailed annotations for beginners to learn)

people tracking after performing background subtraction and find the centroid of the blobs use for video suvelliance

Read_Me: Instructions for setting up matlab speech processing exercises.

椭圆滤波器C程序和matlab的仿真m文件，根据matlab仿真设计椭圆滤波器参数，再编写C程序(C program of ellipsefilter)

基于LMS的自适应信道均衡，训练序列采用QPSK信号，从星座图可直观的观察结果(LMS-based adaptive channel equalization, the training sequence using QPSK signal constellation can be observed from the observation)

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)