cic_coe_cut

说明：  BP神经网络实现数据分类，实现语音特征信号的分类(BP network realize voice feature classification )

：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.)

角点是图像的重要特征，一般指曲率大的边缘点，该代大戏是用MATLAB编写的角点检测算子。(corner image is an important characteristic of large curvature generally refers to the brink, The show is substituting prepared using MATLAB corner detection operator.)

《MATLAB图像处理实例详解》源代码 第9 章( MATLAB image processing example explanation Source Code Chapter)

An Introduction to Structural Optimization

Simulink Model of Buck Converter, Use it with any Motor.

此段程序为matlab程序，用熵值法计算城市化综合指数(shang method to calculate urbanization composite index)

power system analysis book

空时二维自适应处理Matlab程序，采用线性调频信号仿真杂波(Space-Time Adaptive Processing Matlab program, using the linear FM signal simulation clutter)

This code is about red object track. This code is done in MATLAB.