Fudan University thesis (fduthesis)

% 复旦大学学位论文 / Fudan University thesis on fduthesis.cls. Build with XeLaTeX and biber.
% type = doctor | master | bachelor. For a thesis written in English use the class
% fduthesis-en instead (same keys, no abstract* environment).
\documentclass[type=master]{fduthesis}

% cjk-font = fandol uses the Fandol fonts in TeX Live; switch to cjk-font = windows when the
% Windows fonts are available. bib-backend = biblatex loads biblatex with the GB/T 7714-2015 style.
\fdusetup{
  style = {
    font         = times,
    cjk-font     = fandol,
    bib-backend  = biblatex,
    bib-style    = numerical,
    bib-resource = {references.bib},
    hyperlink    = color,
  },
  info = {
    title       = {Your paper},
    title*      = {Your paper},
    author      = {作者姓名},
    author*     = {Author Name},
    supervisor  = {导师姓名 教授},
    department  = {某某学院},
    major       = {计算机科学与技术},
    student-id  = {00000000000},
    degree      = academic,
    date        = {2027 年 6 月},
    instructors = {导师姓名 教授, 委员姓名 副教授},
    keywords    = {信息论, 熵, 学位论文},
    keywords*   = {information theory, entropy, thesis},
    clc         = {TP301},
  },
}

\usepackage{tikz}

% The class prints the university name as an image file at the top of the cover. This sample
% sets the name in type instead; delete the three lines below to print the image.
\ExplSyntaxOn
\cs_set_protected:Npn \__fdu_cover_logo: { \zihao { 1 } \bfseries 复旦大学 }
\ExplSyntaxOff

\begin{document}

\frontmatter

\begin{abstract}
  本文研究离散信源的熵。摘要应概括研究的问题、方法、结果和结论。
\end{abstract}

\begin{abstract*}
  What the problem was, what you did, what you found, and why it matters, in a few short
  paragraphs. Most examiners read the abstract and the conclusions first.
\end{abstract*}

\tableofcontents
\listoffigures
\listoftables

\mainmatter

\chapter{引言}
\label{chap:introduction}

本章说明论文研究的问题及其意义。信息论的基本概念见文献~\cite{shannon1948}。
This chapter states the problem and the plan of the thesis.

\section{研究内容}

列出本文要回答的两到三个问题。

\chapter{方法}
\label{chap:method}

\section{模型}

设离散信源有 $n$ 个符号，其概率为 $p_1, \dots, p_n$，则信源的熵为
\begin{equation}
  H = -\sum_{i=1}^{n} p_i \log_2 p_i ,
  \label{eq:entropy}
\end{equation}
当各符号等概率时熵最大。

\section{数据}

表~\ref{tab:data} 列出了实验所用的三个数据集。

\begin{table}[htbp]
  \centering
  \caption{实验数据集}
  \label{tab:data}
  \begin{tabular}{lrr}
    \hline
    数据集 & 样本数 & 符号数 \\
    \hline
    A & 1200 & 4 \\
    B & 3500 & 8 \\
    C & 9800 & 16 \\
    \hline
  \end{tabular}
\end{table}

\chapter{结果与结论}
\label{chap:results}

图~\ref{fig:entropy} 给出二元信源的熵随概率 $p$ 的变化，在 $p = 0.5$ 时取最大值 1 比特，与式~(\ref{eq:entropy}) 一致。

\begin{figure}[htbp]
  \centering
  \begin{tikzpicture}[x=6cm,y=3cm]
    \draw[->] (0,0) -- (1.1,0) node[right] {$p$};
    \draw[->] (0,0) -- (0,1.15) node[above] {$H(p)$};
    \foreach \t in {0.5,1} \draw (\t,0) -- (\t,-0.03) node[below] {\t};
    \draw[thick,domain=0.01:0.99,samples=60] plot (\x, {-\x*log2(\x)-(1-\x)*log2(1-\x)});
  \end{tikzpicture}
  \caption{二元信源的熵}
  \label{fig:entropy}
\end{figure}

\printbibliography

\backmatter

\begin{acknowledgements}
  感谢导师的悉心指导，感谢所有给予帮助的老师和同学。
\end{acknowledgements}

\end{document}

@article{shannon1948,
  author  = {Shannon, C. E.},
  title   = {A Mathematical Theory of Communication},
  journal = {Bell System Technical Journal},
  volume  = {27},
  number  = {3},
  pages   = {379--423},
  year    = {1948},
  doi     = {10.1002/j.1538-7305.1948.tb01338.x}
}

% Add your own entries here, then cite them with cite{key}.