超级玛丽完整素材

可用于情绪语音识别，中文语料库

蒙特卡洛源程序用来模拟不同的水质环境下所进行的水下通信

很全的proteus库文件 解决很多元件没有库文件给用户给来的麻烦 有430的库

数字调制解调技术的MATLAB与FPGA实现-Altera/Verilog 光盘源码

论文，高速光纤通信与数字信号处理。相干光接收，频偏估计，相位估计，算法。Y175785独创性(或创新性)声明本人声明所呈交的论文是本人在导师指导下进行的研究工作及取得的研究成果。尽我所知,除了文中特别加以标注和致谢中所罗列的内容以外,论文中不包含其他人已经发表或撰写过的研究成果,也不包含为获得北京邮电大学或其他教育机构的学位或证书而使用过的材料。与我一同工作的同志对本研究所做的任何贡献均已在论文中作了明确的说明并表示了谢意。申请学位论文与套料若有不实之处,本人承担一切相关责任。本人签名:-/b日期:200列2关于论文使用授权的说明学位论文作者完全了解北京邮电大学有关保留和使用学位论文的规定,即:研究生在校攻读学位期间论文工作的知认产权单位属北京邮电大学学校有权保留并向国家有关部门或机构送交论文的复印件和磁盘,允许学位论文被查阅和借阅:学校可以公布学位论文的全部或部分内容,可以允许采用影印、缩印或其它复制手段保存、汇编学位论文。本人签名:日期:20°3.2导师签名:日期:100Gb/ s PM-QPSK相干光接收机载波频偏估计和相位恢复算法的研究摘要通信网络中高速率业务的不断发展,对现有的城域网络及省际、国际骨干通信网络的传输带宽提出了更高、更迫切的要求。从目前主流的1040Gbp光传输技术向100Gbs演进成为光传输技术的发展趋势。近年来大量研究表明,相位调制及相干接收是最具前景的100Gbs光传输方式。其中,采用相干接收技术的偏振复用QPSK( PM-QPSK传输系统最被业界认可。该系统的符号速率比比特率降低4倍,因此有较高的光谱利用率,且收发机结构相对简单实现相对容易。此外,信道中的各种损伤,如色散、载波频偏、相位偏移等导致的信号损伤,都能在接收机中通过电域的数字信号处理(DSP)来灵活地补偿。对调相信号,载波与本振间的频率和相位偏移会使信号产生较大的相位失真,频偏估计和相位恢复成为相干接收机中两个重要的功能模块。本文在国家863计划课题“1000b/s相干光传输关键技术研究(2009AA01Z221)”资助下,对上述系统接收机载波频偏和相位恢复算法展开了深入研究,主要内容如下:1.研究了 PM-QPSK的系统组成结构,重点研究了接收机DSP各组成模块的功能,并设计实现了相干接收机后端Maab仿真平台。2.详细分析了载波频偏估计算法,设计了算法并行处理实施方案以解决现有硬件处理速率不足够高的问题,并仿真验证了方案的可行性。设计了四次方频偏估计算法的并行结构;提出了基于误码性能反馈的 BA-PADE( BER-Aided pre-decision- based Angle DifferentialEstimator)算法,解决了传统PADE要求初始频偏设置与真实频偏接近的问题;提出了基于PADE的并行处理算法一一分组PADE( Grouped-PADE)。所设计方案均通过系统仿真验证了可行性。3.详细分析了载波相位恢复算法,为采用现有FPGA或DSP实现l00Gb/级信号处理,设计了载波相位恢复并行处理方案并仿真验证了方案的可行性。设计了基于Ⅴ iterbj- Viterbi的优化算法及其并行处理结构,将其同频偏估计并行算法联合进行了二进制定点仿真分析,仿真结果表明并行处理方案可显著降低硬件处理速率要求。关键词光传输相干接收频偏相位恢复THE RESEARCH OF FREQUENCY OFFSET ESTIMATIONAND PHASE RECOVERY ALGORITHMFOR 100Gbs OPTICAL COHERENT PM-QPSK RECEIVERABStRcTThe rapid development of high bit-rate services in communicationnetworks has instantly demanded a much higher bandwidth of coretransmission links in WAN, inter-province and international networks.The upgrade from the existing 10G/40G optical transmission to 100G hasbeen a trend. The research in recent years indicates that systems withphase modulation and coherent detection are the most promising, ofwhich PM-QPsk gets most recognitionThe PM-QPsK lowers the symbol rate as 1/4 of bit rate whichprovide high spectrum efficiency, and the transceiver structure ofPM-QPSK is simpler and so is easier to realize. Besides, with digitalalgorithms, the electrical Digital Signal Processing(DSP)in thee receivercan flexibly compensate the channel distortion caused by dispersion,carrier frequency offset and phase distortion. Since the phase distortioncaused by frequency and phase offset between lo and carrier is one ofthe main distortions in PM-QPSK, frequency offset compensation andphase recovery act as two of the core modules.With the support of National 863 Project"Research of the KeyTechnologies of 100Gb/s Optical Coherent Transmission Systems?", thisthesis mainly focuses on the research of the digital algorithms of carrierfrequency offset compensation and carrier phase recovery in the receiverof PM-QPSK system and the main contents are as follows1. Investigation of PM-QPSK structure, mainly on the receiver DSPstructure, including function of the several modules in this partDesigning and implementation of the Matlab simulation platform for theback-end of PM-QpsK receiver.2. Analysis on the carrier frequency offset estimation algorithmsdesigning of the parallel structure of the algorithms in order to break therestriction of hardware speed, with feasibility testified by simulationDesigning of the parallel structure of the 4 power method Designing ofBA-PADE (BER-Aided Pre-decision-based Angle Differential Estimator)based on BER feed-back to break the exact initialization restriction oftraditional PADE. Designing of a parallel operation scheme based onPADE, namely Grouped-PADE. Feasibility of both the forementionedscheme testified by simulation.3. Analysis on the carrier phase recovery algorithms, designing ofthe parallel operation scheme in order to realize phase recovery in100Gb/s PM-QPSK with current FPGA or DSP, with feasibility testifiedby simulation. Designing of the optimized and the parallel structure ofViterbi- Viterbi (V-V)method, and binary fixed-point simulation ofparallel v-v together with frequency offset algorithm, the result provedthat the scheme could observably lower the request to the hardwareoperation speedKEYWORDS optical transmission coherent detectionfrequency offset phase recovery

研究生 模式识别 结业的论文，我自己的，也通过了，大家参考一下。

【实例简介】应用MATLAB（或C）语言编写一幅灰度图像直方图统计程序

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。

基于matlab编写的人工免疫算法求解TSP问题，AIS.m为程序的主入口