mang

根据空时分组编码的特性及缺陷,考虑使用差分空时编码,该程序实现差分空时分组编码的性能仿真,是根据Space–Time Block Coding for Wireless一书的性能仿真(Space-time block coding according to the characteristics and defects, to consider the use of differential space-time coding, the program realize differential space-time block coding performance simulation is based on Space-Time Block Coding for Wireless book one of the performance simulation)

本文件是因子分析的源代码，代码精要有注释。(Factor analysis of source code)

两自由度非线性比例前馈四轮转向模型，测汽车质心侧偏角和横摆角速度(Two degrees of freedom nonlinear feedforward four-wheel steering model, auto measuring centroid side-slip Angle and yawing angular velocity )

matlab 卡尔曼滤波实际例子，附带详细注释(Kalman filter matlab practical examples, with detailed notes)

基于ARDUINO的四旋翼飞行器，实现自动控制(Arduino for quadx)

可以产生正弦信号和白噪声时域信号波形，以及混杂噪声的正弦波波形，信噪比可调(the sinusoidal signal can be generated when the white noise domain waveform, sinusoidal waveform, and the noise mixed SNR adjustable)

Obscurance Invariance in Template Matching

利用人工势场法进行移动机器人的路径规划的matlab程序。包含具体的讲解。(The use of artificial potential field method for mobile robot path planning matlab procedures. Contains specific explanation.)

本程序实现基于Haralick灰度共生矩阵的区域增长在matlab下实现(Implementation of this program based on Haralick GLCM achieved under the regional growth in matlab)

ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。 基本思想： 在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。 (Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach. Basic idea: The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions. After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour. Literature: - Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I)