DesignForQpsk

运用李亚普诺夫指数分析混沌系列并进行预测(lyapunov index analysis and chaos series predict)

c均值聚类算法小论文，包括原理、实验及结论(papers, including c-means clustering algorithm principle experiments and conclusions)

详细介绍了matlab中声声音录制、读写与播放的语句描述 (Described in detail the description of Matlab acoustic sound recording the statement, read and write and play)

SAR 仿真程序,包含基础知识介绍,信号模型,回波仿真等(SAR simulater)

实现对一个信号源的doa估计，非常有用，对初学者很用帮助(Achieve a signal source doa estimation, very useful)

Gradient information serves several purposes. It can relate the structure of objects in an image, identify features of interest for recognition/classification directly or provide the basis of further processing for various computer vision tasks. For example, "edges," noted by regions of high gradient magnitude, are central to the task of identifying defects in circuitry. A sample "edge-detected" image using the Image Processing Toolbox for MATLAB is shown where locations marked by white are those points that are indicative of high gradient magnitude, which can also be described as regions of high pixel contrast.

基于PWM的直流电机的闭环调速控制系统设计(PWM based closed loop DC motor speed control system design)

y_compare= 1.0954 1.1832 1.2649 1.3416 1.4142 1.4833 1.5492 1.6125 1.6734 1.7321 1.0954 1.1832 1.2649 1.3416 1.4142 1.4832 1.5492 1.6125 1.6733 1.7321 var_y=2.1742e-010 精度再上一个层次(三阶代数精度) (y_compare = 1.0954 1.1832 1.2649 1.3416 1. 4142 1.4833 1.5492 1.6125 1.6734 1.7321 1.0954 1.1832 1.2649 1.3416 1.4142 1.4832 1.5492 1.61 25 1.6733 1.7321 var_y = 2.1742e-010 precision on a level again ( Third-order algebraic precision))

利用matlab归一化算法提取边缘后配准目标图像，并能框出目标物(get edge )

广义最小二乘的算法结构，广义最小二乘递推的每一步用两步递推算法，先把噪声参数和模型参数均给个初值，然后第一步先假设噪声参数已知，用最小二乘估计出模型参数，第二步再用最新的模型参数来估计噪声参数，如此循环直到辨识精度达到指定要求或者可用数据用完为止(Generalized least squares algorithm structure, generalized recursive least squares every step of the two-step recursive algorithm, first noise parameters and model parameters to the initial value, then the first step is to assume that the noise parameters are known, with the smallestsquares to estimate the model parameters, and the second step, and then the latest model parameters to estimate the noise parameters, and so on until the identification accuracy specified requirements or available data is used up)