ns2使用教程与具体操作实例

详细介绍了模糊控制实现轨迹跟踪的方法步骤。仿真结果表明，该模糊控制器具有较好的收敛性和稳定性，能够满足实际轨迹跟踪的需要。

心电信号的预处理滤波器的设计（包括数据文件和程序） 包括了FIR滤波器和IIR滤波器以及整系数滤波器，可以有效滤除基线漂移与工频干扰

正交频分复用(OFDM: Orthogonal Frequency Division Multiplexing)是一种多载波调制技术，早在20世纪60年代就已经提出了OFDM的概念，不过由于实现复杂度高，大家并不怎么关注，之后随着DFT(离散傅立叶变化)、FFT(快速傅立叶变换)的提出以及DSP芯片技术的发展，极大减少了OFDM实现复杂度和成本，OFDM逐步在通信领域得到了广泛的应用，并且成为了高速移动通信中的主流技术。OFDM使用相互重叠但正交的窄带传输数据，相比传统的多载波系统具有更高的频谱利用率。3gpp选择OFDM作为LTE下行数据传输制式。由于OFDM信号是多个子载波信号的叠加，所

使用CC2591作为CC2530的功放, CC2591 PAThe absolute maximum ratings and operating conditions listed in the CC2530 datasheet [1]and the CC2591 datasheet [4] must be followed at all times. Stress exceeding one or more ofthese limiting values may cause permanent damage to any of the devicesNote that these characteristics are only valid when using the recommended register settingspresented in Section 4.6 and in Chapter 8, and the CC2530 - EM reference designOperating Frequency240524835MHzOperating Supply Voltage2036VOperating Temperature-40CTC=25C, VDD=3.0V, f=2440 MHz if nothing else is stated. All parameters are measuredon the CC2530-Cc2591EM reference design [11] with a 50 Q2 loadReceive CurrentWait for sync, -90 dBm input levelWait for sync, -50 dBm input level24mATXPOWER OXE5166mATXPOWER OXD5149mATXPOWER OXC5138mATXPOWER OXB5127mATransmit currentTXPOWER OXA5115ATXPOWER = 0X95100mATXPOWER = 0X8594ATXPOWE=0×75mATXPOWE=0×6579APower Down Current PM2UAISTRUMENTSPage 3 of 19SWRA308ATC=25C, Vdd=3.0V, f= 2440 MHz if nothing else is stated. All parameters are measuredon the CC2530-CC2591 EM reference design with a 50 Q2 loadReceive Sensitivity HGM 1 %PER, IEEE 802. 15.4[6] requires -85 dBm-988dBmReceive Sensitivity LGM1 PER, IEEE 802. 15.4 [6] requires -85 dBm-90.4dBmSaturationlEEE 802.15. 4 [6] requires-20 dBm10dBmWanted signal 3 db above the sensitivity levelIEEE 802.15.4 modulated interferer at ieee 802.15.4 channelsInterferer Rejection+5 MHz from wanted signal, IEEE 802. 15. 4 [6] requires 0 dBdB+10 MHz from wanted signal, IEEE 802. 15. 4 [6] requires 30 dB49dB+20 MHz from wanted signal wanted signal at- 82d BmdBdue to in the external lna and the offset in cc2530 the rssi readouts from cc2530CC2591 is different from rssi offset values for a standalone cc2530 design the offsetvalues are shown in table 4.4High Gain Mode79LoW Gain mode67Real rssi Register value-Rssl offsetISTRUMENTSPage 4 of 19SWRA308ATc=25C, Vdd=3.0V, f=2440 MHz if nothing else is stated All parameters are measuredon the CC2530-CC2591 EM reference design with a 50 Q2 load Radiated measurements aredone with the kit antennaRadiated Emissionwith TXPOWer Oxe5Conducted 2. RF (FCC restricted band)-462|dBmConducted 3. RF(FCC restricted band46.5 dBmComplies withFCC 15.247. SeeChapter 7 for moredetails about regulatoryRadiated 2.RF(FCC restricted band)42.2dBmrequirements andcomplianceIEEE 802.15.4[6]requires max.35%%Measured as defined by IEEE 802.15. 4 6TXPOWER OxE5. f= EEE 802.15. 4 channels13TXPOWER= OXD5. f= EEE 802.15.4 channelsTXPOWER= OXC5 f= EEE 802.15.4 channelsMax error∨ ectorTXPOWER OxB5 f= IEEE 802.15. 4 channelsMagnitude(EVM)TXPOWER OxA5. f= IEEE 802.15.4 channelsTXPOWER 0X95. f= IEEE 802. 15.4 channels643333%%%%%%%TXPOWER= 0x85. f= iEEE 802. 15.4 channelsTXPOWER =0x75 f= IEEE 802. 15.4 channels%TXPOWER= 065. f= iEEE 802. 15.4 channelsThe RF output power of the CC2530- CC2591 EM is controlled by the 7-bit value in theCC2530 TXPOWER register. Table 4.6 shows the typical output power and currentconsumption for the recommended power settings The results are given for Tc= 25 C, Vdd3.0V and f= 2440 MHz, and are measured on the cC2530-CC2591 EM reference designwith a 50 Q2 load. For recommendations for the remaining CC2530 registers, see Chapter 8 oruse the settings given by SmartRF StudioOXE520166OxD519149OxC18138OxB517127OxA5161150x95141000x8513940X75860x651079Note that the recommended power settings given in Table 4.6 are a subset of all the possibleTXPOWER register settings. However, using other settings than those recommended mightINSTRUMENTSPage 5 of 19SWRA308Aresult in suboptimal performance in areas like current consumption, EVM, and spuriousemissionTc=25C, Vdd=3.0V, f=2440 MHz if nothing else is stated All parameters are measuredon the CC2530-CC2591EM reference design with a 50 32 load2221-2V201918171611121314151617181920212223242526251510OxE5OxC5OxA50X850x65540-30-20-1001020304050607080ISTRUMENTSPage 6 of 19SWRA308A98Avg 3.6VAva 3vAvg 2V110111213141516171819202122232425261023.6V-1062V-110-40-30-20-100102030405060708070604020-Wanted signal at:-82 dBm10ISTRUMENTSPage 7 of 19SWRA308ACC2530-CC2591EM High Gain ModeC C2530-CC2591EM Low Gain Mode- CC2530EM40000-100110100908070-60-50-40-30-20-100The IEEE standard 802.15. 4 [8] requires the transmitted spectral power to be less than thelimits specified in table 4.7If-fc>3.5 MHz-20 dB-30 dBmThe results below are given for Tc=25 C, Vdd=3.0V and f= 2440 MHz, and are measuredon the CC2530-CC259 1EM reference design with a 50 Q loadIEEE absoluteChannel 182432.52435243752442524452447.5ISTRUMENTSPage 8 of 19SWRA308AOnly a few external components are required for the CC2530-CC2591 reference design. Atypical application circuit is shown below in Figure 5.1. Note that the application circuit figuredoes not show how the board layout should be done. The board layout will greatly influencethe RF performance of the CC2530-CC2591EM. TI provides a compact CC2530CC2591 EM reference design that it is highly recommended to follow. The layout, stack-upand schematic for the CC2591 need to be copied exactly to obtain good performance. Notethat the reference design also includes bill of materials with manufacturers and part numbersL102 L10=TI INF inductorVDD13cc2530LA 1RF PANTCC2591 RF NFNPA EN(P1 1)i工工I NA FNP:1HGM ENPO 7)T:1Proper power supply decoupling must be used for optimum performance. In Figure 5.1, onlythe decoupling components for the CC2591 are shown. This is because, in addition todecoupling, the parallel capacitors C11, C101, and C131 together with, L101, L102, TL11TL101 and TL131 also work as RF loads. These therefore ensure the optimal performancefrom the CC2591. C161 decouples the AvDD blAs power.The placement and size of the decoupling components, the power supply filtering and thePCB transmission lines are very important to achieve the best performance Details about theimportance of copying the CC2530-CC2591EM reference design exactly and potentialconsequences of changes are explained in chapter 6The RF input/output of CC2530 is high impedance and differential. The CC2591 includes abalun and a matching network in addition to the PA, LNa and RF switches which makes theinterface to the CC2530 seamless. Only a few components between the CC2530 andCC2591 necessary for RF matching For situation with extreme mismatch(VSWR 6: 1 till 12: 1out-of-band as shown in Figure 6.2) it is recommended to include all the components asshown in Figure 5.1ISTRUMENTSPage 9 of 19SWRA308ANote that the PCB transmission lines that connect the two devices also are part of the RFmatching. It is therefore important to copy the distance between the devices, the transmissionlines and the stack-up of the PCB according to the reference design to ensure optimumperformanceThe network between the CC2591 and the antenna(L111, C112, C111 C113 and L112matches the CC2591 to a 50 2 load and provides filtering to pass regulatory demands. C111also works as a dc-blockR151 is a bias resistor the bias resistor is used to set an accurate bias current for internaluse in the cc2591The TI reference design contains two antenna options. As default, the Sma connector isconnected to the output of CC2591 through a 0 Q2 resistor. This resistor can be soldered offand rotated 90 clockwise in order to connect to the PCB antenna, which is a planar invertedF antenna(PIFA). Note that all testing and characterization has been done using the SMAconnector. The PCB antenna has only been functionally tested by establishing a link betweentwo EMs. Please refer to the antenna selection guide [6] and the Inverted F antenna designnote [7 for further details on the antenna solutionsISTRUMENTSPage 10 of 19SWRA308A

这是一份关于MATLAB基于几何图形法的障碍物地图构建算法的源代码，是我总结的自己关于线性规划的构图方法的理解，里面包含四个matlab的函数，可以直接调试，没有主函数，直接运行需要自己提供输入参数，具体参数代码注释的很详细，不理解的可以看我写的博客，link:https://blog.csdn.net/SimileciWH/article/details/83958617

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

网上使用HAL串口时很多都没有实现不定长数据的接收，要么是限定数据长度，要么是加“0x0a 0x0d”来控制接收完成，找了很久都没有真正找到一个能用的，在很多通信中不可能是定义的，或是加上“0x0a 0x0d”来实现的，由于项目需要，自己阅读STM32 HAL的文档，写出了一个Demo程序，可以实现回显功能(就是通过给STM32发送不定长的数据，可以实现一模一样接收发送的数据），在STM32F429IGT6上验证过，跑了一天没有出现丢失数据的问题，使用DMA发送与接收方式，可以释放CPU部分运算资源，程序中的UART_RX_BUF_SIZE定义为128，就是一次最大接收为128，我在项目中通过

双目标定代码。解决方案Opencv已配置好。里面有说明文档和实验图片。可以直接运行，也可以根据自己的需要重新拍摄图像进行双目标定。

用c++模拟直线插补和圆弧插补实例一http://blog.csdn.net/z893345329/article/details/25922857

canny边缘检测程序c++语言实现，vc工程