LaTex撰写算法笔记并管理参考文献

  第一步   首先得下载LaTeXStudio这个集成环境,按照提示安装。

第二步  写Tex文件

代码如下：

\documentclass[journal,onecolumn]{IEEEtran}
\usepackage{amsmath,graphicx}
\usepackage{CJK}
\usepackage{algorithm} %//format of the algorithm
\usepackage{algorithmic} %//format of the algorithm
\usepackage{ctex}
% correct bad hyphenation here
\hyphenation{op-tical net-works semi-conduc-tor}

%%重定义算法包require的文字显示
\renewcommand{\algorithmicrequire}{\textbf{Input:}}
\renewcommand{\algorithmicensure}{\textbf{Output:}}
\newcommand{\upcite}[1]{\textsuperscript{\textsuperscript{\cite{#1}}}}
\begin{document}
\title{语音端点检测算法 \upcite{Texton}}
\author{黄sir}
% The paper headers
%\markboth{Journal of \LaTeX\ Class Files,~Vol.~6, No.~1, January~2007}%
%{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Journals}

\maketitle
\hfill
\today

\begin{abstract}
\boldmath
The abstract goes here.
\end{abstract}
%\begin{IEEEkeywords}
%IEEEtran, journal, \LaTeX, paper, template.
%\end{IEEEkeywords}

\section{测试算法流程}

\begin{algorithm}[htb] %算法的开始
\caption{测试算法流程.} %算法的标题
\label{alg:segmentation} %给算法一个标签，这样方便在文中对算法的引用
\begin{algorithmic}[1] %这个1 表示每一行都显示数字
\REQUIRE %算法的输入参数：Input
~~\\由boost算法得到的shape filter的训练结果
\\测试图像I
\\类别集合
\ENSURE ~~\ %算法的输出：Output
\\测试图像各个点所属的类别\upcite{16bitmcuspeech}

\STATE \textbf{计算shape filter的响应}
\\对一幅图片的每个像素，计算对700个shape filter的响应（每个shape filter对各个类别均产生一个输出值！）
\\共有21类
\\对每一像素点，共有700*21个响应，加和每一类的700个响应，得到该点是该类的概率

\STATE \textbf{找最大后验概率}
\\对每一点，找到后验概率最大的类，即为该点的类别

\end{algorithmic}
\end{algorithm}

%\subsection{Subsection Heading Here}
%\subsubsection{Subsubsection Heading Here}
%Subsubsection text here.
\bibliographystyle{IEEEtran}
\bibliography{myBib}

\end{document}

第三步、制作bib文件,这部分可以和Endnote结合起来做，具体步骤参考网上

@article {Texton,
   author = {Shotton, Jamie and Winn, John and Rother, Carsten and Criminisi, Antonio},
   affiliation = {University of Cambridge Machine Intelligence Laboratory Trumpington Street Cambridge CB2 1PZ UK},
   title = {TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context},
   journal = {International Journal of Computer Vision},
   publisher = {Springer Netherlands},
   issn = {0920-5691},
   keyword = {Computer Science},
   pages = {2-23},
   volume = {81},
   issue = {1},
   url = {http://dx.doi.org/10.1007/s11263-007-0109-1},
   note = {10.1007/s11263-007-0109-1},
   year = {2009}
}

@article{16bitmcuspeech ,
language = {English},
copyright = {Compilation and indexing terms, Copyright 2014 Elsevier Inc.},
copyright = {Compendex},
title = {Making machines understand us in reverberant rooms: Robustness against reverberation for automatic speech recognition},
journal = {IEEE Signal Processing Magazine},
author = {Yoshioka, Takuya and Sehr, Armin and Delcroix, Marc and Kinoshita, Keisuke and Maas, Roland and Nakatani, Tomohiro and Kellermann, Walter},
volume = {29},
number = {6},
year = {2012},
pages = {114 - 126},
issn = {10535888},
address = {445 Hoes Lane / P.O. Box 1331, Piscataway, NJ 08855-1331, United States},
abstract = {Speech recognition technology has left the research laboratory and is increasingly coming into practical use, enabling a wide spectrum of innovative and exciting voice-driven applications that are radically changing our way of accessing digital services and information. Most of today's applications still require a microphone located near the talker. However, almost all of these applications would benefit from distant-talking speech capturing, where talkers are able to speak at some distance from the microphones without the encumbrance of handheld or body-worn equipment [1]. For example, applications such as meeting speech recognition, automatic annotation of consumer-generated videos, speech-to-speech translation in teleconferencing, and hands-free interfaces for controlling consumer-products, like interactive TV, will greatly benefit from distant-talking operation. Furthermore, for a number of unexplored but important applications, distant microphones are a prerequisite. This means that distant talking speech recognition technology is essential for extending the availability of speech recognizers as well as enhancing the convenience of existing speech recognition applications. © 2012 IEEE.},
key = {Reverberation},
keywords = {Information services;Microphones;Research laboratories;Speech recognition;},
note = {Automatic annotation;Automatic speech recognition;Digital services;Handhelds;Hands-free;Interactive TV;Reverberant room;Speech recognition technology;Speech recognizer;Speech-to-speech translation;Wide spectrum;},
URL = {http://dx.doi.org/10.1109/MSP.2012.2205029},
}

附件，可能会因为缺少IEEEtran.cls这个文件导致你的LaTeX无法编译完成。

这个文件在IEEEtran.cls里

template.tex,myBib.bib,IEEEtran.cls这三个文件放在一个文件夹下就可编译通过，并生产PDF。效果图如下：