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显示图像，可同时显示多福图像，并可用title标注说明(subplot)

产生真实运动轨迹的程序-系统在建模的时候，可以用一个确定的状态方程表述出，不存在有随机量。(To have a real trajectory of the process- system in the modeling, they can identify with a state equation formulation that does not exist there is a random quantity.)

这本电子书是Stephen J. Chapman著，邢树军译，内容简单，讲解详细，并且附有常见的编程错误和好的编程习惯，比较适合初学者。(This eBook is Stephen J. Chapman a, Xing Shujun translation, is simple to explain in detail, and with a common programming errors and good programming habits are more suitable for beginners.)

单脉冲雷达信号的波形的产生和其模糊函数的计算，以及模糊函数的图形。(Single-pulse radar signal waveform generation and the calculation of its ambiguity function, and the ambiguity function of the graphics.)

Programa que calcula as curvas de I x v e potencia de PV solar

拉普拉斯特征映射，采用热核构造权重，是一种基于流行学习的非线性降维技术，可用于图像分割提高聚类的性能(Laplacian Eigenmap is a kind of nonlinear dimensionality reduction technique which based on manifold study, it choose the weights W using the heat kernel and it can be used for image segmentation to promote clustering performance.)

低通和高通滤波程序，是常用的图像预处理程序(lowpass filter program and highpass filter program)

Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量 activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量 getlabel.m:根据树和测试样例，得到根据该树得到的类标 getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加) RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树， RandomCreateTree_d_all.m:根据所有样本点的割点建造树 randomdata.m：给定属性取值，造数据 randomselect.m:从数据中随机选择一部分作为 showTree.m:显示树的结构 test.m:给出树，测试样例，得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)

DS证据，功能：融合x,y两行向量(经典Dempster-Shafer组合公式)(DS evidence features: integration of x, y two row vectors (classical Dempster-Shafer combination formula))

利用GNSS 进行高精度导航定位的前提是正确固定载波相位观测值的整周模糊度。由Galileo 系统的4 个载波观测值可以 形成诸多有良好特性的组合观测值,利用这些组合观测值,结合MCAR 方法,可以有效地确定整周模糊度。(The use of high-precision GNSS navigation and positioning the premise is correct carrier phase observations fixed integer ambiguity. May consist of four carriers observations Galileo system There are many good characteristics to form a combination of observations using a combination of these observations, combined with MCAR method can effectively determine the integer ambiguity.)