Ica-to-face

analog communication lab experiments and source codes using MATLAB

自适应遗传算法的应用举例，包含程序，数据对比图这类(Adaptive Genetic Algorithm Application examples, including programs, data comparison chart such)

说明：  Download the file porsch.tiff. Write an M-file that get the image and does the following tasks: i. Smooth the image with a 3×3 filter mask.

通信系统的自适应均衡器，能够恢复失真的PSK信号(Adaptive equalizer communication system, a PSK signal to recover distorted)

sobel算子进行边缘监测，附有源代码和效果图(sobel operator edge monitoring, with the source code and effect diagram)

对文献《Simulation Models With Correct Statistical Properties for Rayleigh Fading Channels》提出的Rayleigh信道模型的仿真，仿真了信道的自相关，互相关，以及四阶统计量。仿真结果与文献一致。目前PDF的仿真的效果不好，还未奉上。关于LCR,AFD也没有做。但是总体说来，目前的仿真结果与文献是完全符合的。对应的文献也放到压缩包了。(This matlab code for paper, <Simulation Models With Correct Statistical Properties for Rayleigh Fading Channels>.This papar is very popular in channel estimation simulation.The paper is also upload with the code.)

在网上找到的代码 在网上那个找到的系统辨识与自适应控制程序 大家分享(Online to find the code online that found in the system identification and adaptive control procedures to share)

这是一个单相并网逆变器滑模变结构控制，采用滑模变结构控制方法对其进行稳定性控制，并实现有源阻尼控制。(This is a single-phase grid-connected inverter sliding mode variable structure control, using sliding mode variable structure control method to its stability control, and to achieve active damping control.)

：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.)

implementation of support vector machine