mfcc

不考虑压缩的REYNOLDS方程的求解简单程序(Not consider compression REYNOLDS equations simple procedure)

4RRR运动学相关程序，添加了多种解决方案，也是一个不断变化的图形用户界面，对几乎所有的模拟参数允许使用。 (4RRR kinematics procedures, adding a variety of solutions, but also a changing of the graphical user interface, almost all of the simulation parameters allowed.)

Matlab平台下用SuSan算法检测眼睛角点(Matlab platform with SuSan algorithm to detect the eye corner)

粗糙集约简的matlab算法,包含了基本的约简算法(Rough Set Theory and matlab algorithm, including the basic reduction algorithm)

说明：  主要是matlab7.0在数字信号处理中的应用，仅供初学者看，本人是菜鸟级人物。(Mainly matlab7.0 in digital signal processing applications, only a beginner, I was the rookie-level characters.)

通过虚拟阵元进行DOA估计，计算目标和海洋回波的功率谱密度,可实现对二维数据的聚类。( Conducted through virtual array DOA estimation, Calculating a target and ocean echo power spectral density, Can realize the two-dimensional data clustering.)

无迹卡尔曼滤波的matlab编程，运行无障碍，给想要学习该算法的你(Unscented Kalman filter matlab program, run barrier to the algorithm you want to learn)

数据挖掘领域中的一种算法－ML-KNN是一种改进的最近邻算法(Data Mining in the area of an algorithm-ML-KNN is an improved nearest neighbor algorithm)

通过运用13折线法实现信号的压缩，使压缩更为的简单(13 fold line achieved through the use of signal compression, so compression is more simple)

：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.)