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东北大学数值分析试验
东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验东北大学数值分析试验
- 2020-11-30下载
- 积分：1
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凸优化在信号处理与通信中的应用Convex Optimization in Signal Processing and Communications
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
- 2020-12-10下载
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MODBUS--ASCII方式实现（完整版）
此程序是用C语言编写的一个51的程序。作用是对MODBUS协议进行了实现，用的是ASCII方式，简明易懂。共实现的1读线圈，2写单个线圈，3写多个线圈，4读寄存器，5写单个寄存器，6写多个寄存器这几个基本的功能，简明易懂，是一个绝佳的MODBUS协议程序。传输基于最普通的串口通信，严格按照协议说明。程序中还做和详尽的注解，本人水平有限，难免出错，贻笑大方，献丑了。多多指教。。。。。。
- 2020-11-28下载
- 积分：1
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白帽子讲web安全 吴瀚清
讲述关于网络安全相关书籍，对于向我这样想了解黑客到底如何利用网站漏洞对站点进行攻击，有很多丰富的实例。很适合互联网的开发者，按照不同的漏洞类型进行了分类。 针对每个漏洞类型，阐述了基本概念、业界的惨痛教训、如何防范的基本技巧，不局限于某种语言，某种浏览器。同时对于互联网公司进行安全方面的维护和运营也阐述了相关的可以使用的经验。
- 2020-06-04下载
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学生成绩管理系统-课程设计
摘 要高校学生成绩管理工作是高等教育中的一个极为重要的环节，是院校学生管理的基础。面对种类繁多的数据和报表，手工处理方式已经很难跟上现代化管理的步伐，传统的文件管理方式进行成绩管理，效率很低，耗时费力，容易出错，安全性也存在问题。特别是在查询上，由于文件过多，带来很多不便。随着计算机及通讯技术的飞速发展，高等教育对教务管理工作提出了更高的要求。尽快改变传统的管理模式，运用现代化手段进行科学管理。本设计研究的是基于J2EE的高校成绩管理系统的设计与实现。本系统是基于J2EE开发的成绩管理系统，弥补了人工管理的不足，提高了一定的效率。主要功能包括教师对学生成绩的记录，生成总评成绩，成绩单的
- 2020-11-27下载
- 积分：1
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基于XY2-100协议的振镜控制转换板的设计与实现
基于XY2-100协议的振镜控制转换板的设计与实现,介绍了XY2-100协议，以及如何使用。《自动化与仪器仪表》2014年12期(总第182期)结束语2」李志洪计算机在办公自动化中的应用才智,2008(02)经济社会的发展促进办公自动化的发展,大数据时代使得[3]杨庆万计算机网络技术与办公自动化[广东科技,20071办公宰自动化对于计算机技术的使用提出越来越高的要求,企4宁长明,刘洪刚应用计算机网络系统实现办公自动化自动化技事业单位和办公人员都有责任共同努力,促进办公室自动化术与应用,2001(04)智能化,提高办公效率。办公室工作人员才是系统的主体部5余小惠计算机网络与办公自动化引进与咨询000分,要保持对新计算机技术的敏感性和可接受性。[6]唐世梅计算机在办公自动化中的应用[科技信息科学教研),2007(22)参考文献[7王民川.计算机在办公系统中综合运用的探索J中小企业管理与科技(下旬刊,2010(07)[l]龙玲.高校办公自动化应用教学的推广价值探究[小科技信息,2010[8]贺铿.大力推进统计系统办公自动化建设U中国统计,2001(08)(03)(上接第4页)图10表明输入控制量与光斑位置变化成线性关系,说明了输出数字量的准确性,振镜控制转换板可以有效地对振镜进行控制。5结束语本文在分析XY2-100协议的数据结构及接口定义的基础激光器振镜控制R32ARM上,结合DSP控制技术设计了振镜控制转换板,实现了对振镜振镜2204转换被的控制。从XY2-100协议入手,设计了DsPF2812的硬件电图9测试实验系统路,制作了振镜控制转换板。软件上通过对XY2-100的数据分扫描平面上的坐标值与两振镜转角的关系式为:析设计了软件流程,解决了协议的转换和与ARM11嵌入式系统通信的问题。通过实验表明设计的振镜转换板卡完成了协议y=d tane(1)转换的功能,与外界通信良好,实现了对振镜的X、Y轴方向的控制,取得了良好的控制效果。综上所述,这种高性能的振(Id 2ty+e)*tan日(2)镜控制转换板的设计具有重婆的应用价值。其中d为Y轴振镜到标记面的距离,X、Y轴振镜转角分别为,e为两个转镜之间的距离为l8mm。在距离坐标平面的距离参考文献d为235mm时,当Y轴输入控制量为0时,对应的坐标为]陈苗海中国激光加工产业现状和发展前景门激光与红外200434(2600mm,100mm),当输入的控制量为2时,对应的坐标为(1):73~77.(260.0mm,260.0mm),将坐标代人公式(1)计算得Y轴扫描[2]潘涌骆公序折射式扫描系统设计及应用应用激光,20123范围约为(-28°,+28°)。同理X轴扫描坐标范围:(260.0mm,[3] Newson Engineering. XY2-100 technical datasheet[EB/OL]. (2007-0331).http://www.new.be/rhotho1350mm),(l350mm,135.0mm),395.0mm,135.0mm)代人公式[4]柳宁基于MCU和DSP的运动控制研究硬件平台设计叮微计算(2,计算X轴扫描范围约为(-28°,+28°)。振镜控制转换板输机信息2006、22)11-2出的是20位的数据信号,其中起到控制转角作用的是16位的5]TMs320F28 x Product Data Sheet. Texas Instruments. SPRS74.2003数字量,范围是0-2的数字量,扫描范围约为28°,输入每隔6]TPs767D318 Product Data Sheet , Texas Instruments sLvS209H2004096个控制量振镜对应转角约为3.5°,在(-28,0)的范围[7]TMs32060003 oard Design for JTAG.Texas Instruments. SPRA584C,测试每转过35°与Y轴对应靶标上的位置。2002Y/mm[8]尹勇欧光军关荣锋DSP集成开发环境CCS使用指南[M北京:北京航空航天大学出版社,2003年聊入数字量50010000150002000250003000035000图10输出控制量与光斑在Y轴的位置万方数据
- 2020-12-03下载
- 积分：1
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基于RSSI利用KNN位置指纹法的室内定位（卡尔曼滤波）
本人将RSSI室内定位的matlab仿真分为8步（具体看代码文件夹中的readme.text），readme.text是代码使用教程，代码有很多注释，可结合我博客中的原理来理解，具体可看博客 https://blog.csdn.net/gjh13/article/details/80388532
- 2020-12-06下载
- 积分：1
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思岚激光雷达小车程序stm32程序
思岚的激光雷达底盘小车stm32程序，拥有超声波，激光雷达，红外，自动回充，碰撞检测的代码(注此代码需要用IAR编译器，不用KEIL)
- 2020-07-01下载
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labview音频处理
产品音频信号测试系统 LV8.6 - 副本.vi
- 2021-05-06下载
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国科大高级软工18-19秋季复习题
国科大研究生课程-高级软件工程期末复习题，18-19秋季学期。
- 2020-12-07下载
- 积分：1
