communication-system-simulation

本课件对Matlab初学者是个不错的中文教程，详细介绍了Matlab文件操作、运算基础、程序设计，绘图功能，数值计算，数据处理方法与多项式，符号计算及图形界面设计。(The courseware Matlab is a good Chinese language beginners tutorial, detailing the Matlab file operations, basic computing, programming, graphics, numerical calculation, data processing method and polynomials, symbolic computation and graphical design.)

基于灰度投影的人脸特征定位--matlab(Based on gray-scale projection of facial feature location- matlab)

采用神经网络实现手写识别的一种方法，建立Bp神经网络，采用快速训练方法，可快速完成一类相关手写字体的模式识别，识别率较高，当字体变化较大识别率降低时，可重新训练具有较强的适应性。实验证实本方法较好实现了手写字符识别，但也存在识别速度较慢，有时训练不收敛等缺点(Handwriting recognition using neural network is a way to establish Bp neural network, using fast training methods, and can be related to the completion of a class of handwriting pattern recognition, recognition rate higher recognition rate when the font changed greatly reduced, they can re- Training has strong adaptability. Experiments confirmed that this method achieved a better handwritten character recognition, but there are also identified slow, and sometimes non-convergence of the training deficiencies)

说明：  改进的广义预测控制，源码程序，适用于非最小相位系统的C(JGPC)

根据输入的调制方式，对输入序列MOD_IN进行调制，分别采用BPSK, QPSK, !6QAM, 64QAM，完成对星座图的映射(According to the modulation input, the input sequence MOD_IN modulation, using BPSK. QPSK,! 6QAM, 64QAM, the completion of the mapping constellation)

Gardner 方法时间同步，时间同步非常重要的方法(Gardner way for time-sychronizing)

利用空间采用数据和慢时间数据采用空时二维处理是阵列天线雷达常用的处理方法。该程序仿真了基本的stap方法(Use of space data and slow-time data using space-time two-dimensional processing array antenna radar approach. The program simulates basic stap methods)

：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.)

shearlet lab toolbox,shearlet的matlaB工具箱，这个是1.1版本的(shearlet lab toolbox, shearlet of matlaB toolbox, this is version 1.1)

根据读取到的已知控制点的坐标，计算在该区域内的平面转换五参数，该五参数不同于当前使用较多的四参数(According to the known control point to read the coordinates to calculate the plane in the region converted five parameters, the five parameters to use more different from the current four-parameter)