b_V_TM

Matlab 符号运算是通过集成在 Matlab 中的符号数学工具箱（Symbolic Math Toolbox） 来实现的。和别的工具箱有所不同，该工具箱不是基于矩阵的数值分析，而是使用字符串来 进行符号分析与运算。实际上，Matlab 中的符号数学工具箱是建立在 Maple 基础上的，当 进行 Matlab 符号运算时，它就请求 Maple 软件去计算并将结果返回给 Matlab。(Matlab symbolic operation is integrated in the Matlab Symbolic Math Toolbox (Symbolic Math Toolbox) to achieve. And different from other toolbox, the toolbox is not based on numerical analysis matrix, but use a string for symbolic analysis and calculation. In fact, Matlab' s Symbolic Math Toolbox is built based on Maple, Matlab symbolic computation to the time when it requests Maple software to calculate and return results to Matlab.)

matlab code from friend shortest path (dijkstra)

Partial integration with system SyncManager we tell our EAS SyncManager to start a calendar sync when we get the signal from the system SyncManager.

小车位移的模拟仿真实验.其中pidoff.mdl 为无pid的时候小车的位移仿真曲线 pidon.mdl 为有pid的时候小车的位移仿真曲线(Trolley displacement simulation experiment)

说明：  多目标粒子群算法，两个目标的粒子群算法，得到的图时帕累托前沿(Particle swarm optimization algorithm for two targets)

viterbi译码算法，可以对卷积码进行解码。通信类的朋友知道的。。。(viterbi)

给出了信号压缩通常的变换-量化-编码函数。DHT函数为离散DHT变换，是FFT变换的近似，其正反变换相同，克服了FFT复系数不利于存储的缺点。LLMAX函数给出了某x序列在固定比特下的最佳量化输出。熵编码函数为霍夫曼编码。(Gives the signal compression usually transform- quantitative- coding function. DHT DHT discrete transform function, the FFT transform approximation, the same positive and negative transformation, overcoming complex FFT coefficient is not conducive to storage shortcomings. LLMAX function x is given a fixed bit sequence in the optimum quantized output. Entropy coding function as Huffman coding.)

程序根据国外大学iheartmath改编而成,实现绘制动态三维心并可自由修改文字内容,但是同大多数三维心一样由于数学表达式不完美而存在部分凹陷的缺陷(Procedures adapted from foreign universities iheartmath to achieve to draw dynamic three-dimensional heart and the freedom to modify the text, but with most of the three-dimensional heart defects imperfect mathematical expression exists partially sunken)

考虑到传统的多示例多标签框架忽略了标签相关性，或者是将标签相关性作为全局概念知识加以利用，我们将局部标签相关性引入到MIML一SVM多示例多标签学习框架中来，得到了一种新的多示例多标签学习算法MIML LC SVM(Taking into account the traditional multi-sample multi-label framework ignores the label correlation or correlation between the label to be used as a global concept of knowledge, we will bow} local label correlation MIML into a multi-instance multi-label SVM learning framework in the past, get a new multi-instance multi-label learning algorithm MIML LC SVM)

说明：  电子工业出版社出版的《射频电路设计》，一个外国人写的，很经典的教材，这是同该书配套的光盘程序，对每一章的例题都用matlab实现。(Publishing House of Electronics Industry, )