estimate-the-fundamental-matrix

ns下的dd协议代码 可以进行wsn路由协议的仿真(the code of directed diffusion)

说明：  关于两个目标函数的，利用matlab遗传算法工具箱的优化程序。(The two objective function, using genetic algorithm matlab optimization toolbox procedures.)

MAtlab code to do depth conversion of horizons in seismic interpretation

Blind Equalizer 的演算法主要是利用CMA及 LMS 的配合，当CMA将EYE打开，使讯号趋近于正确值，就切换到LMS，利用Slicer的输出当作training sequence来调整Equalizer的系数，而Carrier Recovery 的部份，则是将phase error track出来(Blind Equalizer algorithm is the use of the CMA and LMS tie, When the CMA will EYE open, signaling close to the correct value, it switched to the LMS. SLICER use of the output as training sequence to adjust Equalize The coefficient r, and some of Carrier Recovery, the type of track out phase error)

solar cell photovoltaik pv mat lab bir diyodlu model m-file

主要完成CRC的编解码 16bit 可用于多种运算 可用于实际的仿真系统(CRC completed the main 16bit codec can be used for a variety of computing can be used for the actual simulation system)

两个新的特征参数在数字调制识别中的应用，两个新的特征参数在数字调制识别中的应用(Two new parameters in digital modulation recognition, two new parameters in digital modulation recognition)

matlab时频联合分析工具箱，包括平滑伪维格那-维利分布SPWVD等很多时频工具(matlabtfr)

Calculation modes of ATquartz using mathieu function

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.)