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我的一门光纤课的大作业，利用latex编写的，双栏的，摘要，reference齐全，包括图片的插入等都有。我自认为是很好的latex中文论文的模板。没有不必要的宏包和乱七八糟的注释。

Visual C++课程设计案例精编（附书代码）作者:夏崇镨 任海军 余健第1章 Visual C++概述 第2章 MFC基础案例代码3~11章，代码全，已经删除运行结果，资源小。3GPA计算器 4聊天程序 5通讯录 6餐厅管理系统7商品库存管理系统 8旅馆管理系统 9图书馆管理系统10本科生信息管理系统 11出租车管理系统

模糊C均值 FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。模糊C均值 算法是普通C均值算法的改进，普通C均值算法对于数据的划分是硬性的，而FCM则是一种柔性的模糊划分。1973年，Jim Bezdek博士(现在是美国西佛罗里达大学退休教授，模糊数学领域泰斗)提出了该算法，作为早期硬C均值聚类（HCM）方法的一种改进。深圳电信培训中心（中通信息培训中心）徐海蛟博士上课电子资料。

这是用matlab编写的一款用于实现点云的网格化连接的源码，可以实现物体三维框架的搭建，效果很不错。

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26TDOA定位的Chan算法MATLAB源代码，内附代码，内容详尽，可直接运行

一本不错的mathcad书籍,在家看看吧常用数学软件丛书M ahead2000实用教程郑桂水等编著F荤原北京田书在版编目(CIP)数据Mathcad200实用教程/郑桂水等编著.北京国防工业出版社,2000.9(常用数学软件从书)ISBN7-118-02320Ⅰ.M...Ⅱ.郑、Ⅲ.数值计算一应用软件,Methed12000Ⅳ.0245中国版本图书馆CP数据核字(20)第26933号囤·聿宫照社出版发行北京市海淀区紫竹院南路23号〔邸政圳码1K44河飞酸乌.厂印刚新华书店经兽开本787×1002116印张16填39千字2000年9月第1版2000年9月北京郸1次印刷印效:1—4000册定价:24.00元本节如有印骏错误我杜负资调换〕前言Mathod是Mt五So公司推出的一个交互式的数学文字软件。该软件定位于向大教师学生工程人员提供一个兼备文字处理、数学和图形能力的集成工作环境,以使他们能方便地准备教案、完成作业和准备科学分析报告。 Mathcad在对待效值计算、符号分析、文子处埋图形能力的开发上,不以专业水准为追求,而尽力集各种功能为一体。需讲究精度、速度、算法稳定性的数值计算问题和需终复杂推理的符号运算问趣,都不是Mathcad所致力解决的目标。Mathcad有三大“面向大众”的特点:1. Mathcad的数学式同平常文稿中的书写格式一样,因而形象、直观便于阅读。用户可以按习惯的标准书写格式输人数学公式方程组和矩阵计算机能直接给出或数字或符号或图形的结果,用户无须考虑方法以及中间步骤。2. Mathcad生成的“电子书籍”中的指令、函数、图形都是“活的”,指令中的任何参数的变化都会使相应的结果发生改变3.灵活的“便笺”式的文字处理功能:可以输人文本,对方程、公式或图形进行注释和说明,从而生成各种报告和科学论文Mathcad可以处理各种数值运算,如数值的代数运算、复数运算、矢量和矩阵运算解方程与方程组徽分和积分的数值解、插值运算、微分方程求解等。 Mathcad在加人软件包自带的Mapd插件后还能进行符号运算,如因式分解符号徽积分、解符号方程级数与变换等。 Methed还可以绘制二维和二维图形,通过对图形结果的分析,使用户对问题的理解更加形象。 Mathcad还可以制作动画,所制作的动可以脱离 Mathcad而直接在Windows里播放。除了上述的数值运算、符号运算、图形和动画功能外, Mathcad还可以进行文字输人,以便生成论文和报告本书包括12章内容安排如下;第1章和第2章介绍 Mathcad2000的用途及基本用法。第3章介绍如何利用 Mathcad进行基本的算术与代数运算。第4章介绍 Mairead的矢量和矩阵运算。第5章介绍如何利用 Mathcad求解方程和方程组,以及如何利用 Mathcad进行规划求解等第6章介绍 Mathcad的符号运算功能。第7章和第8章介绍 Mathcad的图形功能,包括二维图形和三维图形。第9章介绍 Mathcad分析数据的功能。第10章介绍微分方程的求解。第11章介绍Mthd的编程功能。个+2Ⅳ第12章介绍如何利用 Mathcad制作动画Mathcad的功能十分强人,本书只能对读者起一个人门和引路的作用,如果要使用好Mathcad,发搁它的强大功能,需要读者多加练习,提高技巧。由于作者的水半有限,时间仓促,书中定有不少的疏漏和欠妥之处,恳请读者批评指除∫面卜著名的編著者以外,参加本书编著工作的同志还有:黄建森、林振宇、许宁林章庆藤小香、袁军冯曙红刘浪、张越胜、黄重阳、粱祥龙杨旺平、张晓等人,在此一并表示感谢。编著者内容简介本书介绍了 Mathcad的基础慨念和操作,循序渐进地讨论 Mathcad2000的使用方法,以便读砻能在较短时间内学会使用 Mathcad2000本书共分12章,内容包括: Mathcad的基本概念; Mathcad数值运算,如数犢的代数运算、复数运算、矢量和矩阵运算、解方程与方程维、微分和积分的数值锵捕值运算微分方程求解等; M athenai的符号运算,如因式分解、符号微积分解符号方程,级数与变换等; Mathcad的图形功能,包括绘制维图形、三维图形和动画.另外附录A介绍了 M ahead的内部数,附录R介绍了 athecate的错i信息,附录C介绍 M alcali的键盘操作。本书图文并茂,示例丰富,易学易用c可作为相关专业高校教材,或计算机培训班教材,也适合于非计算机专业的各类研究设计人员、计算机揲作人员及大 Mathcad用户参考。目录第1章 Mathcad筒介…1.1什么是 Mathcad………2 Mathcad的用途1.3运行环竞…………第2章 Mathcad入门會會■■■俨口■■自血自『會■自自會自【曾十自4會■忄曾血嚯會曾早中1!會自?甲2,1 Mathcad主窗口…,…"…":·日a2.2基本概念和基本操作2.2.1M盘 thad二作表…h●◆如■■●ψ■■pψφd■ψφb4d↓db中‘b4如d命ph甲↓2.2.2变量……56223函数………………2.2.4建立数学式上』L■县·』↓晶■■_↓b■↓晶昌■22.5編辑数学式2,2,6范围变量血山2.2.7区域操作………………,,…………2.3文本…b啬申咖4如bb4如4中吾b自··吾B吾吾日·B·吾山.·‘b山4442.3.1入文本13232在文本中插入数学区域……2.33摑入超级链接唱d■唱p山d24 Mathcad主菜单……1524.1File莱单………l524.2lit菜单●中·幽162.4.3View菜单24.4Ⅰnert菜单■■■q●幽PP中■}曾俨申■自P罾冒晋■■■冒俨『■鲁■■4b■192.4.5 Furmat菜单…,…………2024,6Math菜单……中d如d●p242.4菜单27248 Window菜单与Help萸堂292.5工具栏……292.5.1 Standard工具拦…………P平冒■■■冒冒P………2925.2 Formatting工具烂甲F口号■25.3Math工具栏292,5.4 Calculator l具誉甲『冒4昏『甲■個冒bb■Ⅵ2.5,5 Evaluation工具栏■■■↓p■冒司P■;↓■冒P中tP◆可中曾·F·自·_■■_口口·m■·■■·■■■·■■日。■日■302.5.6 Calculus工具拦32.5.7 Boolean工具栏32.5.8 Greek.具栏……………………………32第3章算术与代效算333.1基4代数运算……………333.1.1四则运算及乘方开方运算P日+F:::P日4■■@Pqp■司●即…33.1.2范围变量口d■■■■d…………………………353.2两数运算……:353.2.1函数射输入…353.2.2三角函数及反三角函数…………………■中中4鲁P自中………53.2.3对数函数与指数函数…373.2.4双曲函数与反双曲涵数……3.2.5整数函数……·"…………*………………………………383.26随机数生成数…自■自■■■自唱自■■自■■■·b■■自自■■自b■自白■·聊4●即幽“血d咖●p申电咖_电即D申司自■看38327求整函数3.28条件函数…393.3微分与积分…………………………………………………………………………403,3,1分运算………………3.32积分运算……∴……·423.4复数运算■b■口■bh■d■bh■■■h■■hdh■■b■■■■hbd晶最b■■凸■b山■晶bb■■山■昌bb山凸-433.4.1复数实郣和虚部画口■口■口■■■■433.4.2复数福角与复数的嫫…343复数四则运算与乘方开方运算■■■■■口■■■■■■■■■344复数的函数运算………………………………………………4534.5共轭复数4534.6复数的傲积分运算453.5使用单位…■■罪■二晶b■■晶晶↓晶■■备‘■■↓昌■■L■■晶晶晶■■↓q甲L4635.1对一个变应用单位…352带单位计算…4735.3自定义单位自···即中自中P··中中中4号·中中宁中會自‘T『日TP『日日35.4有关单位的选项↓L■晶■■■晶↓L■■郾福昌■■↓■■·晶昌b↓「●』轟bb中↓鼻b·ψb山↓山古一+b山·=—48第4章矢量与矩阵4.1矩阵工具栏……;r………………∴………504.2创建矢量和矩阵………………514.2.1直接输入矢量或矩阵…4.2.2通过表达式生成矩阵………………■■■一■■■■■■…………524.2.3输入输出数据又件■………524.2.4通过表格输入矩阵…53Ⅶ4.3矢量运算◆日‘日a+b日.··a·日日目p日导日日电●p■p中■自■血4.3.1矢量的加减运算及数乘……………………………………………544.3.2矢量的数量积b曲p血p@■…………554.3.3矢量的向量积……………………………………………………………"…554.3.4矢量的和…554.4矩阵运算P■血……554.4.1矩阵加减和数乘■■d■■■↓b●d■b■h●bψd谭b如p·q。pdb口·P■554.4.2矩阵的转置与矩阵相乘甲·■口■p■■■■d4.4.3方阵行列式值、逆炬阵和乘方………564,5矩阵的合并与提取子矩阵…………………………………………574.5.1先阵元素.·4·日日·日·4·日“·日44中◆4中卓!■!甲■■■昏■■■晋■■命如bd如看最bd+p■口■●pm司4.5.2矩阵合并…4.5.3提取子矩阵………………………"….……-5846矩阵的特征值与特征矢量………警冒肀■噜鲁會中q■■■■q晋■■唱■h昌■凸西』晶■■■如■ ta p■4.6,1求短阵的所有特征值…■·中中鲁4.6.2求矩阵的所有特征矢量……594.6.3求对应于某一特征值的特征矢量594.6.4疒义特征值和特征矢量昏■■q■■噜哂↓■■■↓■■聊命L64.7其他矩阵函数……………4.7.1特殊矩阵…………·申暗D日■·日日q日↓日日日日日日%吾↓↓4↓.“晶4a=-a+日b平m日t鲁曾中曾會64.7.2矩阵的特性函数4.8矩阵的分解4.8.1矩阵的奇异值分解晶b山↓b■■4.8.2矩阵的QR分解……………………………………………………634.8.3矩阵的LU分解484对称正定炬阵的 Cholesky分解……r654.9矢量和矩阵的排序冒會P會中·自4冒『■■k■山4七晶db…………654.9.1求最大和最小元素值4.92矢量排序……■口◆·■■中■■■·『■自■4即493炬阵排序……■■■·■日■會卓-·鲁·■■■■■■即日_●4.10矩阵的矢量化运算1■t曾自』會■■d■■■■算5章方程与规划求解……685.1解一元方程…………………■口■申···D■··争■■···■■··■■·曹鲁P◆血qt翟■■■↓■■■晋bdd古db5.1.1解一元項式方程■自『自血血冒伽自由司即看●·甲甲司血曲血曲■餐5.12求解其他一元方程………………………………695.13求解精度…………………·詈『中自詈會◆■斷自■■斷■自■■即■■q噌·■口·阜咱嘈啁◆φ◆嘻p申p■昏▲■■■昏baad705.I4求解一组带参数的方程………7l52线性方程组…………:753非线性方程组血d血身b●…72

凸优化理论在信号处理以及通信系统中的应用 比较经典的通信系统凸优化入门教程ContentsList of contributorspage IxPrefaceAutomatic code generation for real- time convex optimizationJacob Mattingley and stephen Boyd1.1 Introduction1.2 Solvers and specification languages61. 3 Examples121. 4 Algorithm considerations1.5 Code generation261.6 CVXMOD: a preliminary implementation281.7 Numerical examples291. 8 Summary, conclusions, and implicationsAcknowledgments35ReferencesGradient-based algorithms with applications to signal-recoveryproblemsAmir beck and marc teboulle2.1 Introduction422.2 The general optimization model432.3 Building gradient-based schemes462. 4 Convergence results for the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for l1-based regularization problems672.7 TV-based restoration problems2. 8 The source-localization problem772.9 Bibliographic notes83References85ContentsGraphical models of autoregressive processes89Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe3.1 Introduction893.2 Autoregressive processes923.3 Autoregressive graphical models983. 4 Numerical examples1043.5 Conclusion113Acknowledgments114References114SDP relaxation of homogeneous quadratic optimization: approximationbounds and applicationsZhi-Quan Luo and Tsung-Hui Chang4.1 Introduction1174.2 Nonconvex QCQPs and sDP relaxation1184.3 SDP relaxation for separable homogeneous QCQPs1234.4 SDP relaxation for maximization homogeneous QCQPs1374.5 SDP relaxation for fractional QCQPs1434.6 More applications of SDP relaxation1564.7 Summary and discussion161Acknowledgments162References162Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems166Anthony Man-Cho So and Yinyu Ye5.1 Introduction1665.2 Problem formulation1695.3 Analysis of the SDr detector for the MPsK constellations1725.4 Extension to the Qam constellations1795.5 Concluding remarks182Acknowledgments182References189Semidefinite programming matrix decomposition, and radar code design192Yongwei Huang, Antonio De Maio, and Shuzhong Zhang6.1 Introduction and notation1926.2 Matrix rank-1 decomposition1946.3 Semidefinite programming2006.4 Quadratically constrained quadratic programming andts sdp relaxation201Contents6.5 Polynomially solvable QCQP problems2036.6 The radar code-design problem2086.7 Performance measures for code design2116.8 Optimal code design2146.9 Performance analysis2186.10 Conclusions223References226Convex analysis for non-negative blind source separation withapplication in imaging22Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang7.1 Introduction2297.2 Problem statement2317.3 Review of some concepts in convex analysis2367.4 Non-negative, blind source-Separation criterion via CAMNS2387.5 Systematic linear-programming method for CAMNS2457.6 Alternating volume-maximization heuristics for CAMNS2487.7 Numerical results2527.8 Summary and discussion257Acknowledgments263References263Optimization techniques in modern sampling theory266Tomer Michaeli and yonina c. eldar8.1 Introduction2668.2 Notation and mathematical preliminaries2688.3 Sampling and reconstruction setup2708.4 Optimization methods2788.5 Subspace priors2808.6 Smoothness priors2908.7 Comparison of the various scenarios3008.8 Sampling with noise3028. 9 Conclusions310Acknowledgments311References311Robust broadband adaptive beamforming using convex optimizationMichael Rubsamen, Amr El-Keyi, Alex B Gershman, and Thia Kirubarajan9.1 Introduction3159.2 Background3179.3 Robust broadband beamformers3219.4 Simulations330Contents9.5 Conclusions337Acknowledgments337References337Cooperative distributed multi-agent optimization340Angelia Nedic and asuman ozdaglar10.1 Introduction and motivation34010.2 Distributed-optimization methods using dual decomposition34310.3 Distributed-optimization methods using consensus algorithms35810.4 Extensions37210.5 Future work37810.6 Conclusions38010.7 Problems381References384Competitive optimization of cognitive radio MIMO systems via game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. They start by rederiving the gradient method from several different perspectives and suggest a modification that overcomes the slow convergence of the algorithmThey then apply the developed framework to different image-processing problems suchas e1-based regularization, TV-based denoising, and Tv-based deblurring, as well ascommunication applications like source localizationIn Chapter 3, Songsiri, Dahl, and Vandenberghe consider graphical models for autore-gressive processes. They take a parametric approach for maximum-likelihood andmaximum-entropy estimation of autoregressive models with conditional independenceconstraints, which translates into a sparsity pattern on the inverse of the spectral-densitymatrix. These constraints turn out to be nonconvex. To treat them the authors proposea relaxation which in some cases is an exact reformulation of the original problem. Theproposed methodology allows the selection of graphical models by fitting autoregressiveprocesses to different topologies and is illustrated in different applicationsThe following three chapters deal with optimization problems closely related to SDPand relaxation techniquesIn Chapter 4, Luo and Chang consider the SDP relaxation for several classes ofquadratic-optimization problems such as separable quadratically constrained quadraticprograms(QCQPs)and fractional QCQPs, with applications in communications and signal processing. They identify cases for which the relaxation is tight as well as classes ofquadratic-optimization problems whose relaxation provides a guaranteed, finite worstcase approximation performance. Numerical simulations are carried out to assess theefficacy of the SDP-relaxation approach

这是我学习isight的第一个例子，还不错哦。例子当中的ansys的建模过程有点繁琐，我费了很大的功夫。关于集成部分比较清楚，如果想学习isight集成ansys，还是不错的。

TIN triangle delaunay 构造三角网的经典算法，是由乔纳森教授编写的原创算法，