1D_DFT_Convolution_Using_Gaussian_Kernel

This function implements linear 1D convolution using the overlap-and-add method. It is fully optimized, and the main loop avoids memory allocation. The function automatically computes the best DFT window for performance. It supports three output modes: Full, Same, and Valid, which align with MATLAB's conv() function. The package also includes a frequency-domain implementation and performance comparisons with two other methods.

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。

DFT的matlab源代码标题日期作者使用FFT测试DEM中的高频噪声2017年1月本·普林顿（purinton@uni-potsdam.de）代码迁移到PYTHON :) 注意：截至2020年5月，此分析已迁移到Python：如果要使用MATLAB TM，请继续下面的操作。使用2D DFT进行DEM噪声分析的Matlab代码。该MATLAB TM函数用于栅格化地形数据（DEM）的频谱分析，以分析高频，低波长噪声，如以下所示： Purinton，B.，and Bookhagen，B .:验证中部安第斯高原南部的数字高程模型（DEM）和地貌指标，《地球表面动力学》，2017年。）运行它在分析之前，用户必须下载并设置一些必需功能的路径： T. Perron的2DSpecTools软件包可用。有关此光谱分析程序的背景以及引起该分析的论文，请参见：Perron，JT，Kirchner，JW和Dietrich，WE：景观中特征空间尺度和非分形结构的光谱特征，地球物理研究杂志，113，2008年。 W.Schwangh

该包包含稳健点集的MATLAB代码，基于ICCV'05论文中描述的配准算法：“冰健和Baba C. Vemuri，一种使用高斯混合的点集配准鲁棒算法。”软件包可从以下网址免费下载：http://www.cise.ufl.edu/research/cvgmi/Software.php#gmmreg

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.

MATLAB编程：TYPE2GAUSSIAN3D的开发。用于可视化类型2的MF功能。

本实验基于 MATLAB 的 GUI 展示 动态卷积 和 圆周卷积 的过程，帮助学生理解数字信号处理的基本概念。通过交互式界面，用户可以直观地观察卷积的变化及其在信号处理中的应用。

The PlotClusters function is used for visualizing clustering data, such as the output from k-means, in 2D or 3D. The inputs include: Data: An m×d matrix, where m is the number of data points and d is the number of dimensions. IDX: An m×1 vector that associates each data point with a cluster. Optional inputs:- Centers: A c×d matrix representing the c cluster centers. If not provided, the function will compute them.- Colors: A c×3 matrix generated using the hsv command, where the number of colors must be at least equal to the number of clusters. The function uses plot or plot3 for visualizing the clusters in 2D or 3D, respectively. Note: This function has been tested only on MATLAB version 2008a on Windows but should work for all versions.

This MATLAB code demonstrates the use of two mirrors to perform 3D reconstruction. The process involves capturing reflected images from the mirrors and using these images to reconstruct a 3D object. The mirrors' positioning plays a crucial role in accurate depth perception and image synthesis for the final model.