Robust Point Set Registration Using Gaussian Mixture-MATLAB Development

This submission includes useful MATLAB functions for speaker recognition using adapted GMM. The implementation details for steps (i)-(iii) can be found in [1]. The fourth function, gmm2sv.m, connects the means (i.e., centers) of the GMM. The cascade means of the adapted GMM are referred to as the GMM supervector (GSV), which is used in the GMM-SVM based speaker recognition system. More information about the GMM-SVM based speaker recognition system can be found in [2]. These codes require the Netlab toolbox. You can access it at: Netlab Toolbox. References: [1] DA Reynolds, TF Quatieri, and RB Dunn, “Speaker Verification Using Adapted Gaussian Mixture Models,” Digital Signal Processing, Vol. 10, pp. 19–41, 2000. [2] Campbell, W. M.; Sturim, D. E.; Reynolds, D. A.; “Support Vector Machines Using GMM Supervectors for Speaker Verification,” Signal Processing Letters.

[使用优化工具箱的二维互信息匹配]这是新墨西哥大学的Kateryna Artyushkova编码的IP工具箱用户使用互信息更新的自动图像配准。对象函数'image_registr_MI.m'最初是由人编码并由我修改的。我使用优化工具箱为完整的图像配准添加了一个比例因子。因此，运行此程序需要优化工具箱。zip文件包含三个文件：- opti_MI_scaling.m %主要代码- image_registr_MI.m %对象函数- image.mat %图像矩阵。在“image.mat”中，IM1和IM2仅用于示例目的。- IM1：230 X 230 MRI 8位图像- IM2：512 X 512 CT 8位图像要运行此代码，请输入以下命令： x0=[50; -15; 0.5];[x, fval]=fminsear

MATLAB development - Mandelbrot set with Parallel Computing Toolbox. This example demonstrates how to fully leverage the Mandelbrot set using parallel computing techniques in MATLAB to improve performance for large-scale computations. Parallel processing allows the algorithm to run faster by utilizing multiple cores to handle different parts of the set simultaneously, speeding up the visualization and exploration of fractals.

MATLAB Development - Using the GNewton Method to iterate a variable. The programme iterates given values of a function that intersects the x-axis.

DFT的Matlab源代码示例，用于通过DFT实现任意一维函数与高斯核之间的卷积。该代码利用卷积定理，简化计算过程。

这是高斯核平滑估计函数的局部线性版本： http : //www.mathworks.com/matlabcentral/fileexchange/loadFile.do? objectId=19195&objectType= FILE局部线性估计器改进了在数据收集过程中处理区域边缘的回归表现。

C. Qian, X. Fu, N.D. Sidiropoulos, L. Huang, and J. Xie explore phase retrieval in the presence of outliers in their paper published in the IEEE Transactions on Signal Processing. Typically, phase retrieval algorithms perform well under Gaussian noise; however, their performance severely degrades with significant data corruption. This study investigates heavy-tailed phase retrieval techniques, proposing p-norm estimators (0 < p>imprecise alternating optimization are introduced to tackle the resulting optimization problem. Notably, the core minimization step can be interpreted as iterative reweighted least squares and gradient descent. The authors discuss the convergence properties of the algorithms and derive the Cramer-Rao Bound (CRB). Simulations demonstrate that the proposed algorithms are effective and close to optimal.

Matlab Development: Robot Target Tracking Control Using Fuzzy Logic. This project involves using fuzzy logic with MatlabhW2K16 to develop a two-degree-of-freedom robotic arm for precise target tracking using image processing techniques.

----此脚本说明了如何使用NMF提取和弦音乐中的鼓部分。它利用了Mathworks文件交换中可用的NMF和Signal类。该技术的主要流程是： 计算不同频段的起始点。 将整个信号建模为NMF，对应于鼓的分量的H被初始化。 对信号进行过滤。 对于小文件（大约30秒），此代码应该可以正常工作。将此脚本用于研究目的时，请提供相应的参考：@article{LiutkusGPSS,author = {Liutkus, A. and Badeau, R. and Richard, G.},journal = {IEEE Transactions on Signal Processing},title = {Gaussian Processes for Underdetermined Source Separation},year = {2011},month = {July},volume = {59},number = {7},pages = {3155-3167},doi = {10.1109/TSP.2011.2117402}}