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华清远见内部高清文档资料
华清远见嵌入式文档资料，嵌入式linux驱动程序详解，详细，清晰，初学和入门者值得拥有的资料
- 2021-05-06下载
- 积分：1
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实验一 信息熵与图像熵计算
一、实验目的：1．复习MATLAB 的基本命令，熟悉MATLAB 下的基本函数。 2．复习信息熵基本定义, 能够自学图像熵定义和基本概念。 二、实验内容与原理：实验内容：1．能够写出MATLAB 源代码，求信源的信息熵。 2．根据图像熵基本知识，综合设计出MATLAB 程序，求出给定图像的图像熵。
- 2020-11-30下载
- 积分：1
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MATLAB控制工程工具箱技术手册
本书基于MATLAB 6．5正式版(Release 13)，为读者提供了使用MATLAB的实践性指导。本书主要介绍了MATLAB中与控制工程相关的6个基础工具箱：系统辨识工具箱、控制系统工具箱、鲁棒控制工具箱、模型预测控制工具箱、模糊逻辑工具箱和非线性控制设计模块，同时提供了MATLAB中的一些基础知识。在讲解6个工具箱的过程中，本书还讲解了一些工程应用方面的背景知识，并对每个函数的功能、语法和参数做了详细的说明，对许多重要的函数都给出了具体的示例程序。本书可以作为高等院校控制工程专业本科生、研究生教材使用，也可作为广大科研工程技术人员的参考用书。第1章 MATLAB基础1．1 M
- 2020-12-06下载
- 积分：1
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BP神经网络 手写体
matlab代码，里面含有一个BP网络完整代码，以及mnist.mat数据集
- 2020-06-18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
- 积分：1
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海洋区域地质图ARCGIS样式库
《海洋区域地质图数据库建设规范》之附录C 及辅助材料《海洋区域地质图ArcGIS图例样式》所列要素图例样式的ESRI Style 样式库文件,
- 2021-05-06下载
- 积分：1
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FOC矢量控制参考代码
采用SVPWM和FOC矢量控制实现电机控制
- 2020-11-28下载
- 积分：1
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LittlevGL中英文档
LittlevGL是一个免费的开源图形库，提供了创建嵌入式GUI所需要的一切易于使用的图形元素，具有漂亮的视觉效果和低内存占用。具有功能强大的单元块，对输入设备支持较完善，同时具有低内存，采用C编写具有比较好的兼容性。中英文档是对LittlevGL文档的简单翻译，有利于快速了解LittlevGL各功能模块的应用。EXamde5.4 Button(v btn)............58Overview58Style usage...............59Notes59Example595.5 Button matrix(lv btnm)61Overview61Style usage.................,..................62Notes62Example625.6 Calendar( calendar)-……64OverviewStyle usage……看着翻Example655.7 Chart( ly chart)…67Overview..67Style usage.......:.:::::·::::::68Example…685.8 Check box lv cb)70Overview70Style usage…70Example705.9 Container(lv cont72Overview72Style usage……72NoExample735.10 Drop down list(lv ddlist)74Overview74Style usage ...74Example,…:::..:.:..:::::.aa是着着·目1着着,非着·着755.11 Gauge(Iv gauge7676Style usage76Example775.12mage(img)….,,,,,7878Style usage着看E着NotesExample795.13 Image button(| imgbtn).…………………281verview81Style usage…...,.,NotesExample,…815.14 Keyboard(Nkb)…83Style usageExample5.15List(|ist)…着着着D·音着着垂8585Style usage.........................85Notes86EXame865.16LED(l|ed)..,OverviewStyle usage....................89Notes89Example5.17Line(line)…91OverviewStyle usage.................,..................91NotesExample....................5.18 Line meter( Imeter)……93Overview..93Style usage…看着翻93Notes93Example945.19 Label (lv label)..96Overview96Style usage………………Notes97Example975.20 Message box(Iv mbox).......,,99OverviewStyle99Not100mp1005.21 Page(v page)........::::.:.:::102Overview…………,…102Style usage103Notes103Example1045.22 Preloader(lv preload):.::::::..105Overview105Style105Example1055.23 Roller(lv roller).......106Overview.…106Style usage…106ExampleE着1075.24 Slider (lv slider).108Overvie108Style usage108Notes108Example1095.25 Spinbox(Iv_ spinbox).111Overview……4111Style usage Notes111notes111Example....1115.26 Switch(lSW)…..112Overview.…4112Style usage…112Notes112Example1135.27 Tab view(Iv tabview)114Overview114Style usage…..114Notes…115Example1155.28 Text area(lvta)…116Overview116Style usage117Notes117Example1175.29 Window(lv window)119Overvie.119Style usage119Notes120camp内容表Welcome portingButton matrix (lv btnm欢迎移植Calendar (lv calendar)PC simulator ObjectsPC模拟器项目Chart(lv chart)Styles风格Check box (1v cb)Input devicesContainer(1v cont输入设备Drop down list(lv ddlist)Colors颜色Gauge(lv gauge)Fontmage (lv lmg字体Image button(lv imgbtn)Drawing绘画Keyboard (lv kb)List(lv listAnimations动画Led(lv led)Coding Style guideLine (1v line)Line meter(lv 1meter)Object typesArc(lv arc)Label(lv labe1)Bar (lv bar)Message box(1v mbox)Base object (lv obj)Page(lv pageButton(lv btn)Preloader(lv preload)Switch(lv sw)Roller (lv rollerTab view (lv tabview)Slider (lv sliderText area (lv ta)inbox (lv spinboXWindow (lv window)云图■xD62:ChecK Doeleica一Cekba check bpsELittleygl是一个免费的开源图形库,提供了创建嵌入式GU所需要的一切易于使用的图形儿素,只有漂亮的视觉效果和低内存占用。关键特性Powerful building blocks buttons, charts, lists, sliders, images, etc功能强大的单元块按钮、图表、列表、幻灯片、图片等Advanced graphics with animations, anti-aliasing, opacity, smooth scrolling先进的图形动画,反锯齿透明度,平滑滚动Various input devices touch pad, mouse, keyboard, encoder, buttons, etc各种输入设备触摸板、鼠标、键盘、编码器、按钮等。Multi-language support with UTF-8 encoding支持UTF-8编码的多语言Fully customizable graphical elements全可定制的图形元素Hardware independent to use with any microcontroller or display硬件独立于任何微控制器或显示器Scalable to operate with little memory (50 kB Flash, 10 kB RAM)可伸缩用于少量内存(50 kb flash,10 kb ram)的操作OS, External memory and GPU supported but not required支持O5、外部存储器和GPU,但也不强求Single frame buffer operation even with advances graphical effects即使有先进的图像特效,也可以进行单帧缓冲Written inc for maximal compatibility用C编写,从而具有最大的兼容性Simulator to develop on PC without embedded hardware没有嵌入式硬件的情况下也可以基于PC模拟器开发Tutorials, examples, themes for rapid development为快速丌发而准备的教程、例了及主题5
- 2020-12-03下载
- 积分：1
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imtoken安卓客户端源码 含btc eos eth实现
imtoken安卓客户端源码 含btc eos eth 转账 创建地址 开源免费
- 2020-12-02下载
- 积分：1
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病毒测试（熊猫烧香杀毒测试）
熊猫烧香+磁碟机病毒样本！！可以用来测试你的杀毒软件！！！！
- 2020-11-28下载
- 积分：1
