DFT MATLAB Source Code and OpenCV.js WeChat Mini-Program Library

Iris Recognition with Python In this project, we explore Iris Recognition using Python by implementing MATLAB-inspired code. The libraries used are critical for visualizing and processing iris images. matplotlib.pyplot: This module, part of the matplotlib library, mimics MATLAB's global style. It simplifies the transition for users familiar with MATLAB by integrating functions and classes from NumPy and matplotlib. In our project, pyplot is used to plot images and define circles representing the pupil and iris edges. It's also useful for displaying the processed images during the recognition process. OpenCV: OpenCV is an open-source computer vision library that provides more than 2500 optimized algorithms for applications like face recognition, object detection, and behavior classification in videos. In this project, OpenCV is primarily used for Hough Circle Detection. This technique helps identify the center and boundaries of the pupil and iris, crucial for accurate iris recognition. By using these libraries together, we can create a functional and efficient iris recognition system that processes and identifies the unique features of the iris for biometric applications.

Goertzel算法是数字信号处理（DSP）中的一项技术，为有效评估离散傅里叶变换（DFT）的各个项提供了一种方法。我创建了一个具有多个频率f0，f1和f2的正弦波，并加了一些白噪声。之后，我将Goertzel功能应用于嘈杂的信号，计算了每个频率的检测概率，并绘制了平均概率与检测到的所有频率的关系图。

DFT MATLAB Source Code is an exciting and powerful open-source package designed for scientific developers and users who need modern and adaptive platforms for research. It provides a wide range of functionalities that make it easy to perform Discrete Fourier Transform (DFT) operations with flexibility and efficiency. This package is ideal for anyone looking to implement or research DFT in a MATLAB environment, offering ease of use and robust capabilities. Whether for educational purposes, simulations, or advanced scientific applications, it is a highly adaptable tool to suit various needs.

DFT的Matlab源代码简单线性代数C标头（SLACH），是一个为使用C89设计的低耦合的线性代数库，便于可移植性。SLACH主要包含以下部分：基础、运算、QR分解、LU分解、SVD分解和快速傅立叶变换（FFT）。除base外，其他部分均依赖于base，没有复杂的依赖关系。用户若仅需使用库，无需了解内部数据结构，便可通过数组获得结果。如需实现算法，程序可轻松传输。程序中清晰区分了内部和外部，使用了众多包装函数。用法：SLACH的接口为：float* src size_t row，size_t col或size_t len，float* dest size_t height，size_t width或size_t len。其他参数表示在src上使用该功能并将结果保存在dest中。base中声明并定义了基本数据结构：向量和矩阵，可在其他应用中轻松使用。base还定义了一些实用工具：安全的malloc和free、打印功能及随机数生成。

MBeautifier: MBeautifier是MATLAB源代码格式化程序，beautifier。它可以直接在MATLAB编辑器中使用，并且是可配置的。

AP聚类算法的源代码，基于MATLAB程序，有较详细解说。此代码实现了基于聚类的方法，通过图的结构和相似度计算，进行有效的数据分组。

Fingerprint Recognition Source Code - MATLAB Version This source code implements fingerprint recognition using MATLAB. The code includes several stages such as image preprocessing, feature extraction, and matching. The provided fingerprint images are used as input for the system. Main Features: Image Preprocessing: Enhances fingerprint image quality for better recognition accuracy. Feature Extraction: Identifies distinctive features of the fingerprint like minutiae points. Matching Algorithm: Compares fingerprints to find similarities. Included in the package are fingerprint images used for testing the algorithm. Image Example: The fingerprint images provided show the original and processed versions of the input data, helping users visualize the recognition process.

Here is the Chaos Optimization Algorithm implementation in MATLAB. This source code allows you to utilize chaotic optimization techniques to solve various optimization problems. It involves generating chaotic sequences and using them to find the optimal solutions more effectively than traditional methods. The code is designed to work with multiple test functions and can be customized for specific optimization tasks.

ESP-DNN: Graph Convolutional Deep Neural Network for Predicting Electrostatic Potential Surfaces from DFT Calculations This repository contains trained models and code designed for generating ligands and proteins, creating electrostatic potential (ESP) surfaces that closely resemble DFT-quality molecular surfaces. The PQR files generated by our model include atomic charges and dipole-like atomic features, such as lone pairs, σ-conjugation, and p-orbitals. To generate ligand PQR files, a graph convolutional deep neural network (DNN) model was trained on about 100,000 molecules with ESP surfaces derived from DFT calculations. For proteins, parameterized charges of amino acids were used, ensuring compatibility with the ligand ESP surfaces generated by the DNN model. For more detailed methods and validation information, refer to the full documentation. System Requirements The program can only run on 64-bit Linux operating systems. Installation Instructions To run ESP-DNN, you will need to:1. Clone this repository.2. Set up Python and required dependencies.3. (Optional) Install additional packages. The package has been developed and tested with Python 2.7 and the following third-party libraries:- rdkit == 2018.09.3- keras == 2.2.4- tensorflow == 1.10.0- num