xiaobofenjiechonggou

QFDTD90: A General 3D-FDTD Electromagnetic Simulator

Quade test for non parametric two way ANalysis Of VAriance

matlab simulink model of an uninterrupted power supply

This Script Comparison MIMO scheme.And Compares the 1X1 2X2 3X3 4X4

说明：  非常好用的程序，里面包括EMD分解和HHT谱分析的程序，可以很好地处理非线性数据(Very useful procedure, which include EMD and HHT spectrum decomposition process, can well handle nonlinear data)

利用发动机进气温度、转速等信息通过神经网络模拟得到排气温度等信息(Neural network simulation)

最优化方法中的非线性规划的powell方法的matlab实现，可支持多维的参数，自己修改即可。(optimization of the nonlinear programming method of Matlab Powell realized, support multidimensional parameter can change themselves.)

matlab实现的蚁群算法（ACA）例程，解决TSP旅行商问题，可以在图中进行动态显示(ant colony algorithm matlab realized (ACA) routines to solve the traveling salesman problem TSP, can be dynamically shown in the figure)

以前使用matlab和vc混合编程都使用的是先将m程序用mcc转化成dll，再在vc中调用 的方法。这种方法对比较复杂的m文件很方便，但是对一些小的程序，如曲线可视化、计算矩阵特征值等，对这些只需要一两行代码的小程序，再使用dll就得不偿失了。经过试验后，发现可以直接在C++程序中调用matlab c/c++ math & graphic library的函数。(before using Matlab and vc mixed programming using the first procedure used mcc m into dl l, then vc call method. This method of complex documents m very easy, but for some minor procedures, such as curve visualization, matrix eigenvalue calculation, and so on, these are just two lines need a small procedure code, reuse dll on the loss outweighs the gain. After testing, can be found directly in the C Programming Calling Matlab c/c math)

：该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift 跟踪算法，提出了基于多 特征的Mean Shift 人脸跟踪算法以解决Mean shift 跟踪算法的鲁棒性问题。通过对LTP 纹理特征的分析、研究， 提出了一个LTP 关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP 纹理模型。在此基础上，提出一 种基于LTP 关键纹理特征和肤色特征的Mean Shift 人脸跟踪算法，有效地解决了Mean Shift 算法的鲁棒性问题。 为进一步提高对快速运动目标的跟踪速度和跟踪性能，该文引入了卡尔曼滤波器对目标进行预测。实验结果表明， 该文的算法在目标定位的准确性和跟踪性能上比Mean Shift 算法均有明显的提高。(: In this paper, the texture characteristics of the local ternary patterns Local Ternary Patterns (LTP) Mean Shift tracking algorithm proposed Mean Shift face tracking algorithm based on multiple features in order to solve the Mean shift tracking algorithm robustness. Texture characteristics of LTP analysis, research, a key LTP texture model, not only enhanced the key goal of texture information, but also simplifies the the LTP texture model. On this basis, based on the LTP key texture features and color characteristics Mean Shift face tracking algorithm, effectively solved the robustness of the Mean Shift algorithm. To further enhance the fast-moving target tracking speed and tracking performance, this paper introduces the Kalman filter to predict the target. The experimental results show that the algorithm of the text in the target positioning accuracy and tracking performance than the Mean Shift algorithm to significantly improve.)