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Posts
C/C++ 工程冗余头文件检查工具 include-what-you-use 的 Linux 构建（OpenEuler/CentOS）
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Include-what-you-use (iwyu) 是 Google 推出，基于 Clang 的 C/C++ 工程冗余头文件检查工具。本文介绍在 Linux 上构建 iwyu 的过程，以 OpenEuler（属于 RHEL/CentOS 系）为例。
Shell 自动补全 + 终端美化汇总：zsh、cmd 和 powershell
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zsh 是一个兼容 bash 的 shell, 还提供了强大的自动补全功能，并拥有丰富的主题和插件支持。Oh-my-zsh 项目提供了很多漂亮的主题，其中我个人比较喜欢的是 powerlevel10k。由于它实在美观，了解到美化 windows 终端的 oh-my-posh 后，我也把 cmd 和 powershell 一并美化成了 powerlevel10k 的风格，并通过 Clink 实现了自动补全功能。具体的操作步骤在此记录。
WSL 代理配置
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介绍使用 Clash for Windows 配置 WSL 代理的两种方法。
C++ Primer (5th Edition) 读书笔记（更新中）
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阅读 C++ Primer，更加深入地了解 C++ 语言。
OpenMP 并行编程笔记
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学习并行计算课程做的笔记，较为翔实地梳理了 OpenMP 并行编程的核心概念与基本实践方法，涵盖并行域、共享结构、任务调度、同步机制等内容，以兹查阅。
代码随想录笔记（更新中）
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代码随想录通关记录~ 涵盖数组、链表、哈希表、字符串、栈与队列、二叉树、图论等数据结构与回溯、动态规划、贪心等算法的 LeetCode & KamaCoder 精选 AC 题解。
U-Net：从入门到理解——学习总结与参考资料推荐
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本科导师给的一篇经典图像分割论文，第一次了解该方向，查了两天资料基本读懂了文章，写了个 ppt 总结，在此做一个小小的记录。 我把读文章时查阅学习过的网站链接都整理在了文尾，有学习需要的可以参考。相信我，即使你是 CNN 小白，读了这些资料也能两天以内读懂图像分割经典入门之作。
逻辑回归实现糖尿病预测（GD、SGD、K 折）
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本实验使用逻辑回归，根据数据集中包含的某些诊断测量值来预测对象是否患有糖尿病。将数据预处理后，先使用两种方法实现逻辑回归：梯度下降和调用库函数LogisticRegression。作为延伸，还使用了随机梯度下降和 K 折交叉验证进行效果对比。
K-Means 算法实现消费者聚类
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本实验自行实现了 K-Means 算法和聚类性能指标评估函数（误差平方和 SSE、 轮廓系数 SC、 Calinski-Harabasz 指数等），并对消费者数据集 Mall_Customers.csv 进行数据处理和聚类分析。
【算法导论4-3 i】T(n)=T(n-2)+1/lg n 的渐近界与对数积分
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在学习《算法导论》时遇到了一个关于递推式 \(T(n)=T(n-2)+1/\lg n\) 渐近行为的练习题，网上解答不甚全面，于是自己探究了一番。本文通过构造积分与求和的关系，结合对数积分函数 \(\mathrm{li}(x)\) 的性质，使用严格的算法复杂度分析方法证明了 \(T(n)=\Theta(n/\lg n)\) 的结论。
线性判别函数权向量的（修正）感知器算法【python实现】
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本文详细介绍了线性判别函数权向量的（修正）感知器算法，并提供了修正与未修正两个版本的 Python 实现代码。该算法适用于 \(\omega_i/\omega_j\) 两类判别问题，通过迭代调整权向量实现样本分类。
C Primer Plus (6th Edition) 读书笔记（更新中）
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整理了一些在已经系统学习过 C 语言之后拜读 C Primer Plus 全书后学到的一些未曾注意的新细节。内容涵盖数据类型、运算符、流程控制、函数、数组与指针、字符串处理、存储类别等核心知识点，包含翔实的实用代码示例。
clock() 函数：计算程序段的运行时间
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本文介绍了 C/C++ 中 clock() 函数的使用方法，通过计算程序段的运行时间来比较两种多项式求值算法的效率。文章详细讲解了 clock() 的基本用法和模板，并通过实际案例进行对比分析。
portfolio
Portfolio item number 1
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Short description of portfolio item number 1
Portfolio item number 2
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Short description of portfolio item number 2
publications
Exploring Deep Learning-Based Techniques for 3D Medical CT Image Segmentation
Published in , 2024
Abstract: In recent years, novel medical imaging technologies, epitomized by CT imaging, have emerged and been widely used, becoming a significant auxiliary means for clinical diagnosis and treatment. Since the images generated by CT scans are three-dimensional with large data volume, in order to help clinicians locate the lesion area faster and make accurate diagnoses, it is an exigent need for automatic and precise segmentation of key targets within medical images through computer assistance. With the maturation of deep learning technologies and further enhancement of computing power, segmentation methods based on convolutional neural networks have become the mainstream choice in the field of medical image segmentation, due to their potent capacity for context extraction. However, medical CT images often exhibit noise, motion artifacts, and uneven contrast among other issues, which leads to a notable difference from natural images. Some existing segmentation methods designed for natural scenarios often have poor results if directly applied to medical images. Besides, pixel-wise annotation for medical images requires considerable time and manpower costs. It is usually costly to obtain large-scale, high-quality annotated datasets, especially for 3D data. Given the above status quo, 3D medical image segmentation tasks face numerous challenges. This thesis conducts research on exploring deep learning-based techniques for 3D medical CT image segmentation. First, a comprehensive analysis of prevalent 3D medical image segmentation methods is undertaken. Afterwards, replications are carried out on both 3D segmentation models for small-scale datasets (such as nn-UNet and UNETR) and unified 3D segmentation models for large-scale datasets (such as SAMMed3D) respectively, with experiments being performed on three publicly available medical CT image segmentation datasets: the MSD spleen dataset, BTCV dataset, and WORD dataset. Lastly, enhancements for existing 3D medical CT image segmentation methods are proposed.
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.