demo_3dFDTD[1]

说明：  以MATLAB为工具使用canny方法对图像进行边缘检测(cannyjiance)

海上地震勘探中的多次波的一种——鬼波的模拟及其各种响应机理研究(Multiples of a marine seismic exploration- the ghost wave simulation and its various response mechanism )

在matlab中用delaunay函数实现三角剖分，在一个三角形区域内，实现简单的均匀的delaunay剖分。(In matlab function implementation using delaunay triangulation, in the area of ​ ​ a triangle, a simple uniform delaunay triangulation.)

this code has an amplitude modulation with plots.

matlab procedures for variables

Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 [21, 13] and, since then, many diverse variants of the basic principle have been reported in the literature. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions.

matlab 卷积码的仿真，很好很实用 经过了测试的(matlab simulation of convolutional codes)

同步日期和时间的例子。(date and time synchronization example.)

计算三维模型检索结果的PR曲线（precision recall curve）(compute precision recall curve for 3D model retrieval results )

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)