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java web投票系统demo
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- 2021-04-06 14:39:03下载
- 积分：1
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基于MATLAB复调制ZOOM-FFT算法的分析和实现
基于MATLAB复调制ZOOM-FFT算法的分析和实现2006年第4期舰船电子工程121滤波;使用函数来实现傅立叶变换次复数乘法。设数字滤波器的阶数为K,滤波器系数离线生成,则滤波需要DNK次复数乘法,则总4 Matlab仿真和验证的运算量为为验证上述算法及分析过程的正确性,在MatZFFTNloN+2N+DN·K(3)中产生一个正弦组合信号3随着细化倍数的增加,基带FFT和ZFFT的运算量x(t)=30cos(2m110t)+30cos(2x11145t)都会大幅度增加;zFF只有当细化频带较窄(此时+25cos(2x112.3t)+48cos(2m113.8t)无需数字滤波)或长序列的情况下,与基带FT相+50co(2x114.5t)比才具有运算量上的优势。分别利用基带FT和ZT对其进行谱分析ZFT算法存在自身的局限性,其存在的问题仿真条件:f=2048H,F点数N=1024,细化倍数D=50。基带FFT的频率分辨率4f=2H,历如下:(1)需要存放中间数据的内存空间巨大限制ZF的频率分辨率△f=0.04H。仿真结果如图了最大细化倍数2和图3所示。(2)采用具有线性相位的FIR数字滤波器实igure(n现抗混叠滤波,由于有限阶滤波器的吉布斯效应( Gibbs effect),滤波器截止频率处的频谱不可避免020040060080010001200会出现局部失真。(3)细化倍数越高,重釆样的选抽比越高,则细化带宽越窄。当需要细化的带宽较大时,必须进5行多次细化,这势必会增加计算量。Figure(4)频率成分调整较复杂。将FT和谱分析105110115130得到的频率成分调整到所选频带的频率成分式较Frequency(Hz复杂的过程,特别是为了避免低通抗混滤波器的边图3FF幅值频谱缘误差造成的频率混叠为了比较频率细化的效果,对图中谱线作了归化处理。图2中fgme(a)为原始信号,fgme(c)6小结为基带FYT处理后的幅值谱线,fgre(d)为移频后ZFT算法的关键在于利用傅立叶变换的移频基带FFT处理后的幅值谱线。由此图可以看出,基特性将感兴趣的高频段频率移至频谱原点,降低采带FFT的几个谱峰叠加为一个谱峰,各频率成分不可分辨。图3中fge(g)为重新采样后F处理样率重新釆样,从而获取较高的频率分辨率。它对后的幅值谱线,gure(h)为频率调整到实际频率处于获得某些特殊频段而不是整个带宽的信号细微的幅值谱线。此图中,因频率分辨率降低了D倍谱结构十分有用。该算法在实际工程技术中有较zF的幅值谱线中5条谱线清晰可见,说明ZF广泛的应用效果明显。参考文獻5ZF运算量和局限性讨论[1]胡广书.数字信号处理-理论、算法与实现[M]北京:清华大学出版社,1997当采用时域抽取FFT算法时,N点DT的复数[2] Vinay K ingle, John g proakis.数字信号处理及其乘法次数为l2N,复数加法次数为NN。为MATLAB实现[M].北京:电子工业出版社,1998[3]赵霞,熊小伏,郭珂.用细化频谱技术分析断路器简单起见,仅比较两种算法的复数乘法次数。操动机构振动信号[J.电力系统自动化,2003,(12):37设频率分辨率4f=fN,细化倍数D=△/404」f。要获得4/的分辨率,基带FFT的运算量为[4]丁康,谢明,张彼德等.基于复解析带通滤波器的FrTdN)lo复调制细化谱分析原理和方法[J.振动工程学报,2001,62(D14(1):30~35采用ZF算法,在复调制时只计算重采样的[5]宗孔德.多抽样率信号处理[M].北京:清华大学点,需N次复数乘法。同样,调制系数的计算也需N出版社,19基于 MATLAB复调制Z00M-FT算法的分析和实现旧WANFANG DATA文献链接作者:王力,张冰,徐伟, Wang li, Zhang bing, Xu Wei作者单位:王力,张冰, Wang Li, Zhang bing(江苏科技大学,镇江,212003),徐伟, Xu Wei(船舶系统工程部,北京,100036)刊名:舰船电子工程英文刊名SHIP ELECTRONIC ENGINEERING年,卷(期)2006,26(4)被引用次数:次参考文献(5条)1.宗孔德多抽样率信号处理19962.丁康;谢明;张彼德基于复解析带通滤波器的复调制细化谱分析原理和方法[期刊论文]振动工程学报2001(013.赵霞;熊小伏;郭珂用细化频谱技术分析断路器操动机构振动信号[期刊论文]电力系统自动化2003(12)4.陈怀琛数字信号处理教程- MATLAB释义与实现19985.胡广书数字信号处理一理论、算法与实现1997本文读者也读过(6条江波.唐普英基于复调制的ZooⅷFFT算法在局部频谱细化中的研究与实现[期刊论文]-大众科技2010(7)2.丁康.谢明.张彼德.赵玲.张晓飞. Ding Kang. Xie ming. Zhang bide. Zhao ling. ZHANG Xiaofei基于复解析带通滤波器的复调制细化谱分析原理和方法[期刊论文]-振动工程学报2001,14(1)3.罗利春. LUo Lic- hun zoom-FFT的改进、频谱反演与时-频局部化特性[期刊论文]-电子学报2006,34(1)4.戴振华.纪海林.徐运涛.DAⅠZhen-hua. JI Hai-1in.ⅫUYun-taoZ00MFFT算法在数字音频分析仪中的实现[期刊论文]-兵工自动化2007,26(10)5.黄镔.许婧.高峰.束洪春Z0OM-FFT在水电机组振动信号分析中的应用[期刊论文]-昆明理工大学学报(理工版)2002,27(5)6.王卫江改进的自适应Zoom-FFT算法研究[期刊论文]一电子技术应用2006,32(7)证文献(10条1.程兆刚.唐力伟.张淑琴.曹洪娜基于复调制Z0OM-FFT算法下阻尼比识别的研究[期刊论文]计算机与数字工程2012(1)2.刘树强.罗天.王宁.潘栋基于 Labview的异步电机转子断条检测[期刊论文]电子设计工程2011(3)3.王文森.邱宏安高精度超声流量检测系统设计[期刊论文]电声技术2011(2)4刘树强.罗天.谭兴文基于 Labview的笼型异步电动机转子断条故障在线检测系统[期刊论文]西南大学学报:自然科学版2011(9)5.王乐.苏小敏.杜林.李春化复白噪声中复正弦波频率估计方法硏究[期刊论文]火控雷达技术2011(36.周红霞.江佩勤.伍洲基于嵌入式系统的ZFFT移频轨道检测算法[期刊论文]通信技术2010(37.焦玮琦.陈特放基于局部频谱细化的轨道移频信号高精度检测[期刊论文]机车电传动2009(28.史瑞根.姚金杰基于 Labview的数字变频FFT设计[期刊论文]现代电子技术2009(7)9武中奇.杨世武丌FT算法在铁路移频信号分析中的应用及其DSP实现[期刊论文]铁道通信信号2008(7)10.时献江.张春喜.邵俊鹏异步电机断条故障诊断的细化包络方法[期刊论文]电机与控制学报2008(2)本文链接http://d.g.wanfangdata.com.cn/periodicaljcdzgc200604033.aspx
- 2020-12-08下载
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人工智能选股-华泰金工研报合集1-7
人工智能选股框架及经典算法简介华泰人工智能系列之一人工智能和机器学习并不神秘人工智能和机器学习方法并不神秘，其本质是以数理模型为核心工具，结合控制论、认知心理学等其它学科的研究成果，最终由计算机系统模拟人类的感知、推理、学习、决策等功能。理解常用的机器学习算法，有助于澄清对人工智能的种种误解和偏见，帮助我们更清晰地认识人工智能的长处和局限，从而更合理、有效地将人工智能运用于投资领域。
- 2020-12-11下载
- 积分：1
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电子秤proteus仿真+程序资料
本资源为基于51单片机的电子秤设计，里面有程序及电路图以及proteus工程，打开就能用，给大家参考之用
- 2020-12-11下载
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基于xilinx FPGA FM解调系统
基于xilinx A7系列FPGA实现FM信号调制解调通信系统，包含ADDA驱动，数字下变频，FM调制解调等模块。
- 2021-05-06下载
- 积分：1
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Intel(R) Parallel Studio XE 2016 下载地址及License
Intel(R) Parallel Studio XE 2016 下载地址及License,仅供学习参考用，请支持正版
- 2020-12-12下载
- 积分：1
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遥感影像镶嵌-黑边去除-提取有效区域矢量
遥感影像镶嵌-黑边去除-无效区域去除方法，提取有效区域矢量范围
- 2020-12-06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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PID控制PWM调节直流电机速度.doc
【实例简介】PID控制技术在运动控制领域中的应用
- 2021-11-28 00:37:08下载
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车牌识别数据集-自己辛苦收集的-已经标注
就看不惯要5分的人，辛辛苦苦攒的分根本不够用，呜呜呜，我好不容易搜集的哦，我只要2分，不满意的话。。。。。。。我也没办法了
- 2021-03-24 10:39:16下载
- 积分：1
