convex_letterV

用Matlab实现的简单的二维主成分分析算法，最后以点阵图输出结果(Using Matlab realize a simple two-dimensional principal component analysis algorithm, the final output to bitmap)

clustering network theroy

信号包络，提出信号包络，便于观察信号波动过程，非常有用(Signal envelope, put forward the signal envelope, easy to observe the process of signal fluctuations, very useful)

说明：  局部均值分解+样本熵的MATLAB程序，包含仿真例程。(LMD)

计算光波在波长、入射角、金属膜厚度不同时的反射率(Calculated at the wavelength of light, angle of incidence, the thickness of the metal film is not the same reflectivity)

基于FPGA的快速傅里叶变换。使用ISE软件以及MATLAB验证。(FFT based on FPGA)

BPSK modulation with Hard decision type of coding.

MATLAB CODES OF MODAL ANALYSIS OF MULTI-STORIED STRUCTURE, RESPONSE SPECTRUM, TIME HISTORY ANALYSIS USING CENTRAL DIFFERENCE METHOD FOR ELCENTRO GROUND MOTION

Find all java objects contained within a java container or Matlab GUI handle If no output parameter is specified, then an interactive GUI window will be displayed with a tree-view of all container components, their properties and callbacks.

：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.)