Latex Writing algorithm notes and managing references

  The first step is to download latexstudio This integrated environment, follow the prompts to install.

Second Step write Tex file

The code is as follows:

\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 h Ere\hyphenation{op-tical Net-works semi-conduc-tor}%% redefine algorithm package require text display \renewcommand{\algorithmicrequire}{\ Textbf{input:}}\renewcommand{\algorithmicensure}{\textbf{output:}}\newcommand{\upcite}[1]{\textsuperscript{\ textsuperscript{\cite{#1}}}}\begin{document}\title{Speech endpoint detection algorithm \upcite{texton}}\author{yellow 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}\boldmaththe abstract goes here.\end{abstract}%\ Begin{ieeekeywords}%ieeetran, Journal, \latex, paper, template.%\ end{ieeekeywords}\section{test algorithm flow}\BEGIN{ALGORITHM}[HTB]% algorithm start \caption{test algorithm flow.}% algorithm title \label{alg:segmentation}% Give the algorithm a label so that it is convenient to reference the algorithm in the text \begin{aLGORITHMIC}[1]% This 1 means that each row shows the input parameters of the digital \require% algorithm: input~~\\ training result of shape filter obtained by the boost algorithm \ Test Image i\\ category set \ensure ~~\ Output of the algorithm: output\\ test the category of each point belonging to the image \upcite{16bitmcuspeech}\state \textbf{calculates the response of the shape filter}\\ to each pixel of a picture, calculates to 700 shape Filter response (Each shape filter produces an output value for each category!) \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 700*21 21 Algorithmic}\end{algorithm}%\subsection{subsection Heading here}%\subsubsection{subsubsection Heading Here}% Subsubsection text Here.\bibliographystyle{ieeetran}\bibliography{mybib}\end{document}

The third step, the production of bib files, this part can be combined with endnote to do, specific steps to refer to the online

@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 F    or 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}, key Word = {Computer science}, pages = {2-23}, volume = {Bayi}, issue = {1}, url = {http://dx.doi.org/10.1007/s11263-007 -0109-1}, note = {10.1007/s11263-007-0109-1}, Year = {{}}} @article {16bitmcuspeech, language = {English},copyright =  {Compilation and indexing terms, Copyright Elsevier inc.},copyright = {Compendex},title = {Making machines understand US in reverberant rooms:robustness against reverberation for automatic speech recognition},journal = {IEEE Signal proces Sing Magazine},author ={Yoshioka, Takuya and Sehr, Armin and Delcroix, Marc and Kinoshita, Keisuke and Maas, Roland and Nakatani, Tomohiro and Ke Llermann, Walter},volume = {29},number = {6},year = {2012},pages = {114-126},issn = {10535888},address = {445 Hoes Lane  /P.O. Box 1331, Piscataway, NJ 08855-1331, and states},abstract = {Speech Recognition technology have left the Laboratory and is increasingly coming to practical use, enabling a wide spectrum of innovative and exciting voice-drive n applications that is radically changing our 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 is able to s Peak 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, Li Ke Interactive TV, would greatly benefit from distant-talking operation. Furthermore, for a number of unexplored but important applications, distant microphones is a prerequisite. This means, distant talking speech recognition technology are essential for extending the availability of speech Recogn Izers as well as enhancing the convenience of existing speech recognition applications. © Ieee.},key = {Reverberation},keywords = {Information services; microphones; Laboratories; Speech recognition;},note = {Automatic annotation; Automatic Speech recognition;digital Services; handhelds; Hands-free;interactive TV; reverberant; Speech recognition technology; Speech recognizer; Speech-to-speech translation;  Wide Spectrum;},url = {http://dx.doi.org/10.1109/MSP.2012.2205029},}

Attachments, this file may cause your latex to fail to compile because of a missing ieeetran.cls.

This file is in Ieeetran.cls.

Template.tex,mybib.bib,ieeetran.cls These three files are placed in a folder and can be compiled and produced in PDF. As follows:

Latex Writing algorithm notes and managing references