Generating Adversarial Networks in MATLAB Counting Images

红细胞计数，Matlab源代码。用于计算图像中红细胞的数目。

本程序可以进行人脸检测，并且用矩形框出人脸位置。适合初学者使用，是个比较好的工具。

Development of Computer Networks (Understanding) Remote Terminal Connection Stage: The early stage of computer networks, where terminals were connected to a central mainframe for data access. Computer-to-Computer Network Stage: This stage marked the beginning of direct communication between computers, laying the foundation for modern networking. Computer Network Interconnection Stage: The evolution of networking where multiple networks were interconnected, forming the internet. Information Superhighway Stage: The current phase, characterized by high-speed data transfer and advanced networking technologies, forming the backbone of global communications. Content Slides

脑血管系统是一个复杂的血管网络，为大脑提供重要的营养和氧气。这种系统易于遭受出血、感染、血栓等损伤，常常需要进行脑部手术。然而，手术时通常无法直接进入手术地点，因此必须寻找替代入口点和路径。提出的系统利用MRA图像上的图像处理和路径查找技术，帮助医生/外科医生找到脑血管系统中两点之间的最短距离。论文链接：ACM Paper

This document describes histogram equalization for grayscale images in Matlab. The goal of this process is to enhance the contrast of the image by spreading out the most frequent intensity values, thereby improving the overall visual quality of the image.

Oracle安装和删除的详细图片说明如下： 1. 下载Oracle安装包 2. 运行安装程序 3. 配置数据库设置 4. 完成安装，查看安装结果 5. 删除Oracle时需注意的步骤 6. 清理残留文件。

偏亮图像与直方图的结合在图像增强中起着重要作用。通过调整图像的亮度和对比度，可以显著提高视觉效果。直方图是分析和处理图像的重要工具，它能够展示图像像素值的分布情况，帮助我们识别图像的亮度特征。利用MATLAB进行仿真，可以实现对偏亮图像的有效增强，通过调整直方图的形状来改善图像的视觉质量。

本代码库提供了一种去噪声的深度卷积神经网络（DCNN）用于数字化胸部断层合成图像（DBT）的对抗训练。该存储库与以下论文相关：M. Gao, JA Fessler和H.-P. Chan，‘具有对抗训练的深层卷积神经网络对数字化乳房断层合成图像进行降噪’，IEEE医学影像交易，2021年。训练数据通过模拟软件生成，乳房幻影来自GE的基于Matlab的私有CatSim，模拟GE Pristina DBT系统。虽然有开源软件，但未包含所有模块，可以使用VICTRE软件包生成PV。使用自有的SART算法进行DBT重构，若无侦察算法，可尝试VICTRE中的FBP算法。代码要求：Python 2.7和TensorFlow 1.4.1。要部署降噪器，请运行：python deploy_dngan.py。我们提供了5片重建的VICTRE幻影DBT图像（异型，带有某些MC）作为测试部署代码的示例。

Normalization Issues In neural network training, normalization is crucial to ensure consistent model performance and faster convergence. Below are key normalization methods: Normalization Method One E and E’ Distance metric (d) adjustments Normalization is used to transform input data, enhancing the efficiency of the network by bringing diverse features into a common scale. This approach helps in minimizing gradient issues, ensuring stable and accelerated training progress.