PSF-based Matlab Code for LightField Deconvolution

基于遗传神经网络的图像分割MATLAB源码，非常经典！

Course Information Course Title: Matlab Scheduling Algorithm Simulation Code - CSC417 / CSC2549: Physics-Based Animation Instructor: Professor [Name] (Contact via email) Office Hours: Tuesdays 5:00 PM - 6:00 PM via Zoom (Link will be sent to registered students) Teaching Assistant (TA): Vismay Modi, Honglin Chen Course Description:This course aims to introduce students to the fundamental mathematical and algorithmic techniques required for effective numerical simulation of physical phenomena, such as rigid bodies, deformable bodies, and fluids. The focus is on developing algorithms that produce visually compelling representations of physical systems. Topics include the mathematics for describing the motion of physical objects, discretization techniques, and efficient numerical methods for solving discrete equations. Prerequisites:- C/C++ programming- Linear algebra- Calculus- Numerical methods Students should be familiar with basic linear algebra, geometry, and vector calculus. Basic programming skills in C++ are assumed. (Strongly recommended: Multivariable calculus.) Useful Resources: Please refer to the course materials for recommended readings and additional resources. Zoom Office Hours:- Tuesday: 4:00 PM - 5:00 PM- Wednesday: 2:00 PM - 3:00 PM(Links will be sent via email to registered students) Discussion Board: Access course discussion board for assignments and discussions. Summary CSC417 provides the theoretical and practical foundation for developing physics-based animation algorithms using Matlab. Students will learn to simulate and represent complex physical systems like rigid bodies, deformable bodies, and fluids. The course emphasizes the application of numerical methods to solve physical equations for real-time simulations.

This MATLAB code provides an implementation of the Particle Swarm Algorithm (PSO) to optimize the layout of charging piles. It includes detailed functions and algorithms for solving the charging pile layout problem by considering factors like distance, capacity, and distribution efficiency. The code aims to find an optimal positioning solution for charging stations using the Particle Swarm Optimization method. This ZIP file contains all the necessary scripts and documentation to execute the layout optimization task in MATLAB.

OFDM传输、信道估计、PN序列、RS码实现、比较

The Conservative Value Method MATLAB code Distribution_Planning_Lot_sizing_Decomposition.m is used for the Lagrangian Relaxation Method and decomposition algorithms applied to high-speed railway (CSHR) catering services. These programs are coded based on the following works: the time-varying demand and pedestrian congestion-based high-speed railway catering distribution planning problem and the batch-based model and decomposition algorithm developed by the Beijing Jiaotong University Research Team under the guidance of Professor Nie Lei. All these codes were written by Dr. Wu Xin. For any inquiries, please contact him. Your feedback is important to us, and the code will continue to be updated and improved in the future. The code includes three main parts: Main Program: main.m is the key component that initiates all related algorithms. The CPLEX solver used in the file can decompose the mixed-integer programming model into submodels. Therefore, the program will work only if the CPLEX interface is correctly installed in the MATLAB environment. Convex Program: A program that solves a series of single-variable convex maximization submodels. The submodels can be solved using the fmincon function in MATLAB's optimization toolbox. HCEA Functions: All files prefixed with HCEA_ embed the Convex Group Method (Frank Wolfe algorithm) as part of the Hybrid Cross-Entropy Algorithm (HCEA). The implementation of HCEA can be used to compare with the proposed decomposition method. Default settings are provided for various configurations.

Total variation regularized least squares deconvolution is one of the standard problems in image processing. This package uses the concept of Augmented Lagrangian [1] to implement the state-of-the-art algorithm, which can be viewed as a variant of the widely known Alternating Direction Method of Multipliers (ADMM). The deconvtv user interface is similar to the current MATLAB deconvolution tools, including deconvwnr, deconvlucy, and deconvreg: out = deconvtv(img, psf, mu, opt); deconvtv supports direct spatiotemporal processing for image and video deconvolution problems. Its applications include, but are not limited to: image and video deblurring, image and video denoising, depth data enhancement, thermal air turbulence stabilization, and multi-view synthesis. For more information and citations, please refer to: [1] SH Chan, R. Khoshabeh, KB Gibson, PE Gill, and TQ Nguyen, \"Augmented Lagrangian Method for Total Variation Video Restoration\", IEEE Trans. Image.

ORIX是一个开放源代码的Python库，专用于分析方向和晶体对称性。该软件包定义了用于分析取向的对象和函数，这些取向表示为四元数或3D旋转矢量，详细说明了晶体的对称性。其功能主要建立在软件包的基础上，并受其启发。如果将ORIX的分析结果用于已发表的论文，请务必引用相关的预印本。此外，您可以在存储库中找到演示。ORIX的发布遵循GPL v3许可。

The vad function MATLAB code for pyBK implements speaker differentiation on a list of audio files by performing speaker binarization (speech segmentation and clustering in multi-speaker scenarios). The system utilizes a binary key background model (KBM), which is trained on conference data, eliminating the need for external training datasets. This results in a system that is easy to operate and adjust for speaker differentiation tasks. Additionally, the implementation includes useful features for the speaker digitization system pipeline. The code was developed and tested in Python 3.6 using conda, relying on common packages for audio processing, feature extraction, and speech activity detection. Installation steps:1. $ conda create -n pyBK python=3.62. $ source activate pyBK3. $ conda install numpy4. $ conda install -c conda-forge librosa5. $ pip install webrtcvad6. $ git clone h...

本项目包含用于带深度学习的监控视频中的人体分割的官方培训和测试代码（多媒体工具和应用程序，2020年）。请参阅技术细节，视频演示已提供。该实现基于MATLAB R2018a构建，因此需要安装深度学习工具箱。请注意，本教程假定您的根文件夹为/human-segmentation/，如使用其他目录，请相应修改命令。 文件结构 您的文件结构应如下所示：/human-segmentation/dataset/imageDataset/train/test/val/pixelLabelDataset/train/test/val/myColorMap.mpixelLabelColorbar.mpreprocessImage.msemanticseg_newImage.mtestMySegnet.mtrainMySegnet.m 使用方法 运行trainMySegnet.m以训练网络，运行testMySegnet.m以进行测试。