Statistical-decision

关于图像的二值化三种算法程序，实现图像的二值化(With regard to the image binarization process three kinds of algorithms to achieve the image binarization)

说明：  粒子群算法matlab实现方法！！！！！(PSO)

Rayleigh in transmitbeamforming

由于Kalman 滤波算法将被估计的信号看作在白噪声作用下——个随机线性系统的输出， 并且其输入输出关系是由状态方程和输出方程在时间域内给出的， 因此这种滤波方法(Because of Kalman filtering algorithm will be estimated as the signal in white noise-a random under the action of linear systems, and its output input and output relationship is by the state equation and output equation in time domain are within the, so the filtering method )

Useful selection of Filters for Signal Processing with Matlab

用MATLAB实现AsyLnCPSO算法，程序没错误，可以直接运行，非常适合刚学PSO者(Implementation of AsyLnCPSO algorithm with MATLAB, the program no error, can be directly run, very suitable for just learning PSO )

压缩文件包含两个matlab程序，m_sequence.m是子函数，功能是根据本原多项式生成对应的m序列，m_sequence_nature.m是主函数，调用m_sequence.m生成m序列并验证m序列的特性。(Zip file contains two matlab program, m_sequence.m a subroutine, function is a polynomial to generate the corresponding m-sequence according to the primitive, m_sequence_nature.m is the main function, call m_sequence.m m sequence generation and validation features m sequences.)

随着新的数学工具小波分析的实用化为基于NN负荷预测模型性能的改善提供了理论依据对于电力系统负荷非线性时间序列的辨识在预测方法研究中应给予重视在本文所用的基于小波原理和NN融合的预测原理是具有强的非线性时间序列的辩能力由研究和仿真表明它能有效提高预测的精度(with new mathematical tools wavelet analysis based on NN into practical load forecasting model to improve the performance of the rationale On the basis for the power system load nonlinear time series forecasting methods in literacy research should give importance to the use of paper Based on wavelet theory and NN fusion principle is the forecast of strong nonlinear time series by the dialectical capacity of research and simulation shows it can effectively improve the accuracy of forecasts)

This paper presents a new image-based visual servoing (IBVS) controller named augmented IBVS for a 6-DOF manipulator. The main idea of this controller is that it produces acceleration as the controlling command. A proportional-derivative controller is developed to provide the robot with the controlling command. This controller can achieve a smoother and more linear feature trajectory in the image space and decrease the risk that the features leave the field of view. The developed control method also enhances the camera trajectory in 3-D space. The stability of the proposed method is fully investigated by using the Lyapunov method and the perturbed systems theory. Experimental tests are performed on a 6-DOF robotic system to validate the effectiveness of the proposed controller. The performance of the controller is compared with that of a conventional IBVS.

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