KNN MATLAB Source Code for Near-Infrared Data Processing

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.

This is the MATLAB source code for CPSid, which is a data-driven discovery framework for cyber-physical systems (CPS). The testing platform is Windows 10, and the code was implemented in MATLAB 2017a. For versions MATLAB 2018a and later, when using the slr function to identify transition logic, you should set the algorithm to trust-region. You can replace the code in line 103 of the function slr_learning_l1 with: option = optimset('Gradobj', 'on', 'Hessian', 'on', 'MaxIter', WMaxIter, 'Display', WDisplay, 'Algorithm', 'trust-region'); Note that the CVX toolbox is required to run the code. Reference: Yuan, Y., Tang, X., Zhou, W., et al. Data-driven discovery of cyber-physical systems. Nat Commun 10, 4894 (2019).

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。

MATLAB小波图像边缘检测源码，实现了利用小波对图像边缘检测。含实验报告。

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.

在使用Matlab拟合工具箱处理试验数据时，首先需要导入数据。可以使用以下代码示例： load('data.mat'); % 导入数据 x = data(:,1); % 自变量 y = data(:,2); % 因变量 接下来，使用fit函数来进行拟合。例如，若要拟合一个线性模型： ft = fit(x, y, 'poly1'); % 线性拟合 通过plot函数可以可视化拟合结果： plot(ft, x, y); % 绘制拟合曲线与原始数据 使用Matlab拟合工具箱的优势在于其图形界面友好，适合初学者。此外，工具箱支持多种拟合类型，如多项式拟合、指数拟合等，使得数据处理更加灵活。