MATLAB

matlab 基础教程的课件 ，第二章，对初学者有一定作用。(matlab Essentials courseware, and the second chapter, there is a role for beginners.)

说明：  matlab SIMULINK Arabic tutorial part 5

image processing it will process the image

拟牛顿法和最速下降法(Steepest Descent Methods)一样只要求每一步迭代时知道目标函数的梯度。通过测量梯度的变化，构造一个目标函数的模型使之足以产生超线性收敛性。这类方法大大优于最速下降法，尤其对于困难的问题。另外，因为拟牛顿法不需要二阶导数的信息，所以有时比牛顿法(Newton s Method)更为有效。如今，优化软件中包含了大量的拟牛顿算法用来解决无约束，约束，和大规模的优化问题。(The quasi-Newton method and the Steepest Descent Methods only require that each step iterations know the gradient of the objective function. By measuring the change of the gradient, constructing a model of the objective function is sufficient to produce superlinear convergence. This method is much better than the steepest descent method, especially for difficult problems. In addition, because the quasi-Newton method does not require information on the second derivative, it is sometimes more effective than the Newton s Method. Today, the optimization software contains a large number of quasi-Newton algorithm used to solve the unconstrained, constraint, and large-scale optimization problems.)

Mechanism Design Slider crank Mechanism

读取图像 显示图像 将图像矩阵类型转换为double 傅里叶变换 离散余弦变换(Read image shows the image matrix type image is converted to double Fourier transform discrete cosine transform)

FIMM自适应交互多模型的机动目标跟踪，FIMM_PDA.m为主程序(FIMM adaptive interacting multiple model target tracking, FIMM_PDA.m-based program)

非平稳的大规模多输入多输出的最小二乘信道估计(Non-stationary channel fast fading radio channel least squares estimation)

卷积码的仿真，其误码率实验值与理论值比较，调用函数，可以方便在以后的算法应用。(Simulation of a convolutional code, bit error rate value is compared with the theoretical value experiment, the calling function, the algorithm can be easily applied in the future.)

支持向量机(SVM)是数据挖掘中的一个新方法，能非常成功地处理回归问题(时间序列分析)和模式识别(分类问题、判别分析)等诸多问题，并可推广于预测和综合评价等领域，因此可应用于理科、工科和管理等多种学科。目前国际上支持向量机在理论研究和实际应用两方面都正处于飞速发展阶段。它广泛的应用于统计分类以及回归分析中. 支持向量机属于一般化线性分类器.他们也可以认为是提克洛夫规则化（Tikhonov Regularization）方法的一个特例.这族分类器的特点是他们能够同时最小化经验误差与最大化几何边缘区.因此支持向量机也被称为最大边缘区分类器. (Support vector machine, the source code)