smooth_spectrum

说明：  使用matlab绘制图形各种基本操作技术总结，非常实用。推荐给大家作为学习必备资料。(Matlab draw graphics using a variety of basic operations technical summary, very useful. Recommend it to everyone as a learning essential information.)

TDMA forward link channel simulation in SR500 system wireless

Economic load dispatch problem is allocating loads to plants for minimum cost while meeting the constraints. It is formulated as an optimization problem of minimizing the total fuel cost of all committed plant while meeting the demand and losses .The variants of the problems are numerous which model the objective and the constraints in different ways. The basic economic dispatch problem can described mathematically as a minimization of problem of minimizing the total fuel cost of all committed plants subject to the constraints

常见的滤波学习 中值滤波、均值滤波等 图像的解码(matlab )

滑模变结构控制的基础理论，很适合初学者使用和学习(The sliding mode variable structure control of the basic theory, it is suitable for beginners to use and learn)

基于SVM的快速极小化SMO化算法程序，程序清晰易懂，易理解，可读性强，希望对同行有所帮助 (SVM-based fast minimization algorithm SMO program, the program clear and easy to understand, easy to understand, readable, and want to help peers)

说明：  test1: a simple example for optical flow: 1 take the camera man picture for example 2 move it with subpixels (interpolate to get the result) 3 optical flow to estimate the motion test2-7 simple examples for picture rotation and translations

课程设计做的车牌识别，运行licence_recognise.m选择实例图片，按空格单步，直到最后识别(Curriculum design license plate recognition, run licence_recognise.m select instances pictures, press the space single step, until the final identification)

一种基于稀疏变量的欠定盲分离算法，很好用，大家试一下。(Underdetermined blind separation algorithm based on sparse variables)

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点，如无法获得概率输出，需要估计一个误 差参数C，以及必须使用Mercer 核函数等。相关向量机算法，克服了SVM 上述缺点，RVM 能获得与SVM 相比拟的推 广性能，并且更为稀疏。在此基础上，文中介绍了一种RVM 回归用于分类的新分类方法，用RVRC 来表示。并通过 实验证明了它的可行性。(Support vector machines for classification and regression techniques. Due to its many shortcomings, such as the probability that the output can not be obtained, need to estimate an error parameter C, and the need to use Mercer kernel function and so on. Relevance vector machine algorithm to overcome these shortcomings of SVM, RVM and SVM can be obtained comparable to promote performance and more sparse. On this basis, the paper introduces a RVM regression was used to classify new classification method, RVRC to represent. And through experiments proved its feasibility.)