基于cordic算法的吉文斯迭代的定点QR分解在FPGA的实现

关于Rayleigh和AWGN信道的BPSK仿真

本文章对扩频、跳频做了详细介绍，并介绍了跳频通信的实现及其MATLAB仿真，

改进的遗传算法解决排课问题，讲的很详细，需要的同学可以下载来看看。

1000W逆变器的原理图和PCB图，根据这个PCB图制成电路板可以直接使用。亲测有效。

其中点云数据是斯坦福兔子，做三维点云重构后的人可以好好看看，只是简单读入程序，作为初学者比较好些，进一步的研究还在后边，有机会的话会继续上传，比如说delauny三角网格划分，还想进一步研究snake曲线

一份很好的脑部图像处理的程序

案例分析的电子书《数值计算与工程仿真》专刊一 FLUENT HELP算例精选中文版(→)目录前言..2目录FLUENT经典算例翻译之一算例1介绍如何使用 Fluent算例4非定常可压缩流动模型.52算例5辐射与自然对流模拟·:······99FLUENT经典算例翻译之二算例13使用非预混燃烧模型151算例15蒸发性液体喷雾建模.∴214算例18使用混合物多相模型和欧拉多相模型..∴252算例21使用欧拉多相粒子传热模型垂··D垂垂垂。看垂垂垂看D看垂·D4。垂278ww.myCFDcn《数值计算与工程仿真》专刊一 FLUENT HELP算例精选中文版(→)算例1介绍如何使用 Fluent引言此向导通过图例说明了一个发生在混合弯管处的两维湍流流动和传热问题的求解方法和过程。这种混合弯管的结构常见于发电厂和化工厂的管道系统中。正确计算出弯管内流体交汇处附近的流场和温度场分布,对于设计合适的入口管道位置具有重要意义通过此向导,用户可学会以下内容●在 Fluent中输入网格文件使用混合单位制去定义儿何体和流体的属性设置强制对流的湍流流动的流体物性和边界条件迭代计算并使用残差监视器监测计算过程及其收敛性●使用隔离求解器进行求解使用等势图检察流场和温度场●运用二阶离散化方法重新计算以获得更佳的温度分布对网格进行温度梯度自适应,进一步求解更佳的温度场分布前提条件在学习此向导之前,假设用户还没有使用 Fluent的给验,不过,已经学习过用户指南第一章中的简单算例,并且熟悉 Fluent的界面及其指南中的规约可题描述问题如图1-1所小。一股温度为26℃的冷流体流入大管道,在弯管处与另股温度为40℃热流体混合。管道的长度单位为英寸,而流体的属性和边界条件则使用国际单位。入口管道的雷诺数为2.03×105,因此,选择湍流流动模型。4ww.myCFDcn《数值计算与工程仿真》专刊一 FLUENT HELP算例精选中文版(→)P =100 kg/viscosity8x14FConductivity: k=0.537 Y4 miK2SpC Ic Heat 9=4216 Jkg-k6酽.2mT1121m图图1-1问题说明准备工作1.从 Fluent的文件光盘中拷贝文件 elbow/ elbow, msh到电脑的 Fluent作日录中对于Unⅸx系统,当把文件光盘放入电脑光驱后,可以在以下目录找到这个文件:/ edrom/uent61/help/ tuttles述 cdrom为电脑的光驱目录对于 windows系统,当把文件光盘放入电脑光驱后,可以在以下冂录找到个文件cdrom: fluent 6.1 help tutfiles上述 cdrom为电脑的光驱目录2.启动 Fluent,选择2D求解器。ww.myCFDcn《数值计算与工程仿真》专刊一 FLUENT HELP算例精选中文版(→)第1步:与网格相关的操作读取网格文件 elbow, mshFilc→Read+}CascSelect fileFiterh,于d,SH,GRdirectoriesFileshome user tutorial/elbow. msh/home userautorialaCase Filehome /user/tutorialoKFiterCancela)在 Files项中点击选中 elbow. msh,然后点击OK完成操作。注意当 Fluent读取网格文件的同时,信息会不断显示在反馈窗口内,报告网格转化的过程。当读取网格文件完毕, Fluent的反馈窗凵会显示共读取了918个三角形的流体单元,以及许多带着不同分区标识符的边界面。2.网格检查。Grid→} Check6ww.myCFDcn《数值计算与工程仿真》专刊一 FLUENT HELP算例精选中文版(→)F1uent的信息反馈窗口会显示如下信息:Grid checkDomain extentscoordinate:min(m)=0.0000009+00,max(m)=6.400001e+01y- coordinate:min(m)=-4.538534e+00,max(m)=6.400000e+01Volume slatisticsminimum volume (m3): 2.782193c-01maximum volume (m3):3.926232e+00total volume (m3):1.682930e+03Face area statisticsminimum face area (m2):8.015718e-01maximum face area(m2):4. 118252e+00Checking number of nodes per cellChecking number of faces per cellChecking thread pointers.Checking number of cells per faceChecking face cellsChecking bridge facesChecking right-handed cellsChecking face handednessChecking element type consistencyChecking boundary typesChecking face pairs.Checking periodic boundaries.Checking node countChecking nosolve cell countChecking nosolve face countChecking face childrenChecking cell childrenChecking storageww.myCFDcn《数值计算与工程仿真》专刊一 FLUENT HELP算例精选中文版(→)Done注意网格检查结東后,信息反馈窗∏会以默认的SI单位制给出网格在ⅹ轴和Y轴上的最大和最小值,并将报告出网格的賦它特性。网格检查还会报告出有关网格的任何错误。需要特别注意的是,确保最小体积不能是负值,否则 Fluent无法进行计算。在SI单位制中,默认单位是m,若想改变单位制,使用 in ches:可以打开 Scale grid对话框。3.平滑(或者交换)网格Grid→} Smooth/swap…Smooth/Swap GridsmoothSwap InfoMethodNumber SwappedskewnessMinimum skewnesscumber visitedNumher of lerationsSmoothCloseFluent读取网格文件后,平滑三角形或四边形网格是一个良好的习惯,那样能确保使用质量铰好的网格进行计算。a)点击按钮 Smooth,再点击按钮Swap,重复上述操作,直到 Fluent报告没有需要交换的面为止。若 Fluent再无法通过交换改善网格质量,则没有平面可被交换了。b)点击 Close关闭对话框。ww.myCFDcn《数值计算与工程仿真》专刊一 FLUENT HELP算例精选中文版(→)4.更改网格的长度单位Grid→ scalea)在 Units conversion(单位转换)项的 Grid was created ln(网格长度单位)的右侧下拉列表中选择In(代表选择了英寸b)点击 Scale按钮,更改长度单位。在 Domain extents栏中采用了默认的SI单位制,长度单位为mc)点击按钮 Change length Units,设定 inches(英寸)为此次计算采用的长度单位确保Xmax(in)和Ymax(in)中数值为64英尺。(如图1.1)Scale gridScale factorsUnit a conversion00254rid Was Created In inY0+0254Change Length UnitsDomain extentsXmax [in]E4400001Ymin (in534Ymax(in) 64scaleUnscaleCloseHeld)计算采川的长度单位已被吏改为 inches(英寸),此时便能正确反映网格的几何尺寸注意此算例的求解过程中,除了长度外,其它单位均采用SI制。一般来说,没有必要对其它单位进行改动。按照上述的操作,长度单位已被确定为 inches若用户想采用别的单位制作为长度单位,如mm,可以在 Define的下拉菜单中打开 Set units对话框,进行更改。ww.myCFDcn《数值计算与工程仿真》专刊一 FLUENT HELP算例精选中文版(→)5.显示网格。(图1.2)Display→Grid,,Grid DisplOptionsEdge TypeSurfaces三彐p Mores今Atermal-3pressure-outlet-7H Edgesv feature velocity-inlet-5p facesy outlineuelocily-inlet-rall-4Partitionswall-iiShrink factor到终安Surface Types且彐Surface eame patternclip-surfHatchfanOutline InteriorDIsplayColorsCloseHelpa)确保在 surfaces项屮的所有表面都被选屮,然后点击 Display。ww.myCFDcn

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

RK3229 的多mic阵列麦方案，RK原厂提供，对于做多MIC客户用途比较大RaCkChpP瑞芯微电子福州瑞芯微电子股份有限公司目录目录3概述■1■■1■■1■1■■1■■1■1面■■■■■■4产品版本4适用对象41-概述,:■:■":■::■::::::::::::.::.::.::.::.::::":::::::::::::::::::1.1麦克风阵列EK廾发平台简介1.2麦克风阵列EVK框图.13麦克风阵列E∨K组件2麦克风阵列E硎K介绍..………82.1整体效果图82.2结构与接口示意图3麦克风阵列子板模块简述n113.16+0+2麦克风阵列方案说明113.2内、外圈园周阵列麦■重着国面重着国面重着国面日重面重面重面自重面自■重■,面■重■面■重■,面重■,面■113.3 Codec.3.4PA133.5 Loopback回采电路,,,,,,,,,133.6排针∴144注意事项∴.….154.1注意事项15Copyright 2015 @Fuzhou Rockchip Electronics Co., LtdRaCkChpP瑞芯微电子福州瑞芯微电子股份有限公司前言概述本文档主要介纽RK麦克风阵列EVK基本功能特点和硬件特性、硬件配置以及使用方法,旨在帮助相关开发人员更快、更准确地使用该EVK,进行麦克风阵列冫案的应用开发E∨K由RK3229主板与麦克风阵列子板共两部分组成,本文档着重介绍麦克风阵列相关内容,即麦克风阵列」板及其与RK3229主板相关联部分。涉及RK3229主板的其他功能,可参考《RK3229BOX开发板用户使用指南》以获取更详细信息。产品版本本文档对应的产品版本如下:品名称版本描述RK SDK BOX RK3229 Discrete PowerV1.0RK3229主板DDR3P416DD6V1020160120LⅩFRK BOX EVB MICARRAYTESTBOARDV1.06+0+2麦克风阵列子板RK3229V1020160901适用对象本文档主要适用于以下人员:技术支持工程师单板硬件开发工程师频算法工程师嵌入式软件开发工稈师测试工程师Copyright 2015 @Fuzhou Rockchip Electronics Co., LtdRaCkChpP瑞芯微电子福州瑞芯微电子股份有限公司缩略语缩略语指文档中常用的词组简称:内部整合电路(两线式串行通讯总I2CInter-Integrated Circuit线)I2SInter-IC Sound集成电路内置音频总线PAPowerAmplifier此处特指音频功率放大器RKRockchip lectronics Co. Ltd瑞芯微电子有限公司Copyright 2015 @Fuzhou Rockchip Electronics Co., LtdRaCkChpP瑞芯微电子福州瑞芯微电子股份有限公司1概述1.1麦克风阵列EVK开发平台简介RK3229芯片是一款包括NeOn和FPU协处理器在内的四核 ARM Cortex-A7处理器,主频1.5GHz;集成∫32 bits ddr3/DDR3L/ LPDDR2/ LPDDR3控制器,提供了高性能、髙分辨率应用程序所需要的内存带宽。芯片能胜任高分辨率(4K/60Hz)显示、多通道(8+2)音频处理和主流应用。芯片内置3路I2S接口,I2S1用于HDMI输出的音频处理,另外2路I2S0与I2S2可供用户自由分配。其中,I2S2支持同吋2通道输入与2通道输出,I2S0则可最扃支持至8通道输入或8通道输出。麦克风阵列E∨K是基于6+0+2名麦克风方案(6+0+2介绍见3.1章节)的硬件参考设计,同时RK3229主板将芯片资源都作了引出,方便客户验证、调试与二次开发,只需要简单修改参考设计的模块电路,就可以完成产品的硬件设计12麦克风阵列EvK框图RK3229主板使用12∨/2A适配器供电,通过UART串口进行调试,验证各功能模块。开发板配有HDMI输出, SPDIF输出,ⅥWIFI+BT模组, Etherne接口,USB接口,TF卡,红外接收以IR,CVBS视频接口等,非常有利于芯片方案的深入研发与快速产品化。主板与麦克风阵列子板通过10x2的2.54mm排线连接,音频接口包括5V电溟、1组I2S1信号、1组I2C信号与2个通用GPIO。详细资源使用情况见下图:Copyright 2015 @Fuzhou Rockchip Electronics Co., LtdRaCkChpP瑞芯微电子福州瑞芯微电子股份有限公司口RockchipRK3229Serug OARI2wskur Reset nimrod4x二C/D2XLDOPowerUSB HUST2J图1RK3229主板系统框图工C3Vf∞rMICaD5V王CC⊥K工CM工c5图2麦克风阵列子板系统框图13麦克风阵列EvK组件麦克风阵列EWK主要包括以下物品:RK3229主板麦克风阵列子板电源适配器,规格:输入100VAC240VAC,50;输出12VDC,2A2.54m20针排线Copyright 2015 @Fuzhou Rockchip Electronics Co., LtdRaCkChpP瑞芯微电子福州瑞芯微电子股份有限公司2麦克风阵列EW介绍2.1整体效果图麦克风阵列EWK整体实物图如下ppDJoZl (l esnw最w箱x思图1麦克风阵列EVK实物图Copyright 2015 @Fuzhou Rockchip Electronics Co., LtdRaCkChpP瑞芯微电子福州瑞芯微电子股份有限公司g88888图2麦克风阵列子板实物图-正面、背面2.2结构与接口示意图麦克风阵列了板PB布局如图所示外园麦克风、6个直径Rcm闪园麦先风、6个直径5cmCodecPAT排针Codec(6PA2喇刺叭翰出2RK BOX EVB NICARRAY TESTBOARD RK3226_2018%ad图5麦克风阵列子板PCB正面图Copyright 2015 @Fuzhou Rockchip Electronics Co., LtdRaCkChpP瑞芯微电子福州瑞芯微电子股份有限公司各模块及接∏说明如卜表表1麦克风阵列子板说明编号说明措述1选项1-外圈麦克风直径80mm6个数字麦克风2选项2-内圈麦克风直径50mm6个数字麦克风选3odec每个 Codec支持2路输出与2路输入PAD类音频功放芯片456喇叭输出2个喇叭输出排针与RK3229主板连接的排针Copyright 2015 @Fuzhou Rockchip Electronics Co., Ltd10

AD9361寄存器自动配置工具软件, 能自动生成寄存器配置，非常好用