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一个EMD应用的事例，源码解压即可使用，文档内附有解释说明，方便易懂，希望对大家有所帮助(A EMD application examples, source code extract can be used, the document included with explanations, easy to understand, we want to help)

WOLF Searches steepest descent method

说明：  计算分数阶李雅普诺夫指数的Matlab代码(Matlab code for Lyapunov exponents of fractional order)

IEEE802.15.4a超宽带信道模型的源代码，是最新的标准超宽带信道模型(IEEE802.15.4a ultra-wideband channel model of the source code, is the latest standard of ultra-wideband channel model)

自适应滤波算法的MATLAB代码:run_lms_mvdr(Adaptive filtering algorithm MATLAB code: run_lms_mvdr)

will implement either Jacobi, Gauss-Seidel or SOR iterative techniques to solve the problem

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.)

视频中运动人捕捉，MATLAB程序，实验视频需要自己录制，程序已验证(walking body)

使用matlab实现输入信号的小波消噪和自适应滤波(The input signal using matlab Wavelet Denoising and Adaptive Filtering)

matlab宝典，matlab经典教材，讲解深入透彻；PDF文件，文字和图片都非常清晰，并有索引。(Collection matlab, matlab classic text, explain the depth and thorough PDF files, text and pictures are very clear, and index.)