LMSsuanfashixian

计算机网络数据链路选择性重传实验。 　　设计一个滑动窗口协议，在仿真环境下编程实现有噪音信道环境下两站点之间无差错双工通信。信道模型为8000bps 全双工卫星信道，信道传播时延270毫秒，信道误码率为，信道提供字节流传输服务，网络层分组长度固定为256字节。(Computer network data link selective retransmission experiment. Design of a sliding window protocol in simulation programming environment there is no error duplex communication channel between the noisy environment the two sites. Channel model is 8000bps full-duplex satellite channel, the channel propagation delay 270 ms, channel bit error rate, channel provides a byte stream transmission services, network layer packet length is fixed at 256 bytes.)

plot radius velocity and pressure vs. time during ultrasonic cavitation. the main parameter is Mechanical index

说明：  蝙蝠算法求解最优化问题，本代码是用于求解目标函数(Bat algorithm solving objective function)

MATLAB数学建模工具箱，实用工具集，对在做建模的朋友有帮助(MATLAB toolbox of mathematical modeling, practical tools for doing modeling friends help)

matlab门限模型，这是一个用matlab做门限模型的一个好的程序。(matlab threshold model, which is a threshold model using matlab to do a good program.)

Cascade Training GUI Specify Ground Truth

智能车辆的控制技术 这里介绍了智能车辆主动避让和超车等的simulink仿真例子(Intelligent vehicle control technology of intelligent vehicle initiative to avoid overtaking simulink simulation example)

阈值隐藏图像，图像通过阈值方式，把小图像隐藏到大图当中，实现信息隐藏，用于二值图像的信息隐藏(Threshold hidden image, the image by the threshold method, the small images to enlarge them hidden, to achieve information hiding for binary image information hiding)

说明：  DE Algorithm engine for LP and ILP

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)