Mittag_Leffler

全数字接收机中符号同步的文档，方法实用，容易实现~~(Symbol synchronization in digital receivers documentation, methods, practical and easy to implement ~ ~)

This code can crosscorrelate seismic data togther.

说明：  Optimization Example

power factor correction using matlab

采用配电网各个节点对电压灵敏度的不同计算分布式光伏最大接入容量(Each node uses a different calculation of the distribution network of distributed PV voltage sensitivity maximum access capacity)

在未知方向发射信号，由接收阵元接收。对接收的信号使用MUSIC算法处理，得到发射信号方位(process the signals received using MUSIC to get the DOA)

针对二维熵图像分割方法在求取最佳阈值时存在计算量大及微粒群算法容易陷 入局部最优且速度较慢等等问题, 提出了基于混沌粒子群优化算法的二维熵图像分割方法。 该方法考虑了图像中像素点灰度􀀂 􀀂 􀀂 邻域灰度均值对作为阈值对图像进行分割 利用混沌运 动随机性、遍历性和初值敏感性, 将混沌粒子群优化算法与阈值法相结合在二维空间作全局搜 索。实验结果表明了基于混沌粒子群优化算法的二维熵图像分割法用于阈值寻优减少了搜索 时间, 提高了收敛率。(Calculation of its large capacity and particle swarm algorithm is easy to fall to strike the best threshold for the two-dimensional entropy image segmentation method Into the local optimum and slower, the proposed two-dimensional entropy image segmentation method based on chaotic particle swarm optimization algorithm. The method takes into account the image pixel the gray 􀀂 􀀂 􀀂 neighborhood average gray value as a threshold for image segmentation chaotic transport Dynamic randomness, ergodicity and initial value sensitivity of chaotic particle swarm optimization algorithm with the threshold method in two-dimensional space for the global search On request. Experimental results show that the entropy method of image segmentation method based on chaotic particle swarm optimization algorithm for threshold optimization to reduce the search Time and improve the convergence rate.)

不同光纤长度色散对光波的影响分析.对于50km，100km 和150km光纤进行了色散分析。(Different fiber length dispersion impact on optcal signal)

说明：  偏最小二乘法的原始程序，粒子群优化算法对支持向量机SVM的优化。对初学者有帮助。套用模型，可以改PSO-SVR。(Partial least squares method of the original program, particle swarm optimization algorithm for support vector machine SVM optimization. It is helpful for beginners. Applying the model, PSO-SVR can be changed.)

The aim of the assignment is to understand the concept of the image enhancement stage We need to know the technique to improve the image or better input for the image processing technique as in the assignment. In the first part of the assignment is the segmentation using thresholding technique. Before we threshold the image, we need to prepare the better image for input such as histogram equalization to stretch the intensity value of the image before converting to binary image. In the second part of the assignment is about frequency domain. We need to transform the spatial image to frequency image using Fourier transform of the image before using the filter.