高等应用数学问题的MATLAB求解

this code can segment class using kmeans

说明：  遗传算法的小程序，希望能够对大家有用处！(genetic algorithm for small procedures, we hope to be able to be useful!)

二维MUSIC算法仿真，经典的角度估计算法之一，阵列信号处理必备程序之一。完整，详细，可运行(2D_DOA_MUSIC_Algorithm , array signal processing)

用matlab实现的基于LSB的数字水印算法。算法可以在BMP灰度图像中嵌入水印，水印信息为二值图像，并将其扩展到与原始图像同样大小，进行重复嵌入，提高鲁棒性。同时算法中给出了PSNR值以评价水印图像的效果，(Using matlab realize the number of LSB-based watermarking algorithm. Algorithm can be embedded BMP gray image watermark, watermark information for the binary image, and its expansion to the original image the same size, the repetition of embedding, improve robustness. At the same time algorithm PSNR values are given to evaluate the effect of watermark images,)

读串口信息,在matlab 环境下读出串口内容(read serial)

作为第四版的升级，此程序用于计算和绘制均匀线阵源的方向图(This program is an updated and combined version of the programs ANTGAIN ARRAYPATGAIN in the 4th edition of Kraus s Electromagnetics. This program computes and plots the field pattern of a uniform linear array of sources. )

直流电机双闭环调速系统，亲测可用,欢迎下载(DC motor double closed loop speed regulation system, pro test available)

levle set method to handle image in c-v model which is improved by original method

Local vote decision fusion is a recently proposed method of target detection for a wireless sensor network in which individual sensors combine their decisions with those of their neighbors and report to a fusion center only if there is a majority in favor of presence. The fusion center reaches afinal decision about presence or absence according to whether the number of positive reports exceeds a threshold. This has been shown to give a higher target detection rate, for a specified false al

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.)