java 学生成绩查询系统

张澄波的综合孔径雷达比较老的版本,但是很基础,逻辑简单明了

matlab语言，学习可用，主要是雷达信号分析时计算信号的脉冲描述字

基于GIS模型的林火蔓延计算机仿真 地理信息系统东北林业大学学报第36卷表4地形阻尼系数蔓延算法就显得较粗糙。边界外延算法计算量大,主要体现下山火上山火在排序、査找方面,可以通过减少这方面的计算量而达到提高坡度/(°)阻尼系数K2坡度(°)阻尼系数K2速度的目的。边界外延算法与迷宫算法的结果完全一样,但42~0.0738~421750从编程的角度看,迷宫算法较易实现,而且速度不是很慢,所0.1333~371010以在文中采用迷宫算法。28~30.3223~2722~-180.4618~22NWNNE-17~-130.63火点E0.830.90SESE-2~21.003林火蔓延模拟算法分析与选择图2林火的蔓延方向31边界插值算法4林火蔓延的计算机仿真与实现边界插值算法基于栅格数据,它对于火场边界的计算是林火蔓延的计算机仿真主要分为4个步骤:第一、建立林通过插值的方法来实现,它只计算从初始火点出发的8个方火蔓延的空间背景数据库,其空间背景数据库包括可燃物类向,森林起火后,火源点向8个方向蔓延,分别为正东、正南、型数据、坡度数据、高程数据,并且它们要以栅格数据形式存正西、正北和东南、东北、西南、西北。设火点的位置为行列号储,因为文中研究的林火蔓延仿真基于棚格数据结构;第二、(讠),各方向上只计算从火源点出发的该方向上的栅格,其把王正非的数学林火蔓延模型转换为计算机模型;第三、根据余栅格不计算。当各方向上的计算时间超过给定的林火蔓延输入的起火点的位置坐标,蔓延时间、风速及方向,在背景数时间时,就停止计算,并记录下各方向的栅格行列号,再反映据库之上对火场扩展进行动态模拟显示;第四、计算林火的蔓到具体地形上,则可得到火场状况的直观表示,再依据这8个延面积及火线周长等数据。具体流程如图3所示。终结点进行插值运算,形成封闭的火场边界。空间背景数据库32边界外延算法坡边界外延算法从火蔓延所具备的两个特性(时间和空间)出燃度程发来考虑,在假设没有二次燃烧等情况下,林火表现为从已燃区物向未燃区延烧的性质,它反映为林火在地理位置上的变化和时间的延续上,林火燃烧的路径总是遵循在诸多可到达的路径中选择林火蔓延模型最快到达的那一条,因此,它的路径并不一定是空间上的最短路(王正非林火蔓延模型)径。边界外延算法乜是基于栅格数据的蔓延算法,实施此算法的步骤是:记录每次加入一新的起燃栅格后形成的林火边界,在可像元起火特征计算机与数学知识蔓延边界相邻栅格集合中,搜索其各方向上所需时间最短的栅火点坐标蔓延时间格,并以此栅格作为下一个引燃栅格;对此栅格进行林火边界的风速风句林火蔓延计算机模拟判断,同时对该栅格的加入所导致的原有边界集合的变动作调整,然后再进入下一步循环,直至满足模拟时间为止。火场扩展图统计和计算火场动态模拟33迷宫算法图3火场蔓延的计算机仿真流程迷宫算法以栅格数据为基础,以火点为起点,从正东起沿41试验区空间背景数据库的建立顺时针方向,其八方邻位可表示为:E、 SE S SW、W、N、NNE。每一点向外扩散有8个方向的选择,如图2所示。从正釆用栅格数据进行林火蔓延模拟仿真,即将G丶中所输东开始,沿顺时针方向检测,每探测到某一方向,计算累积时入的矢量的图形数据(地形图、林相图等)转换成栅格数据,间∑t。若∑t小于给定的扩展蔓延时间T,且该方位没有走因此、模拟的过程就是对所有的栅格点处理的过程,每个栅格点所代表的长和宽(分辨率)越小,模拟的精度越高。考虑到过或是原先累积时间大于∑t就沿此方向走一步,并记下所数据的处理及计算机计算的速度,每一图形数据的栅格化都走的路径和方位,存放在堆栈或库中,同时将累积时间修改为∑t累积时间可用二维数组存放,初值置为零。如果探测到釆用30m×30m栅格精度。试验区背景数据库的建立主要某一方位四周的∑t值均大于或等于T,则退回一步重新检测需要3种专题数据分别为高程数据、坡度数据、可燃物类型数据。其中:前两种专题数据是利用现有的帽儿山数字等高线下一个方位,当初始火点四周的8个方位都已检测完毕,则蔓数据,通过 a rcv iew的3扩展模块生成;可燃物类型数据通延过程结束。最终二维数组中所存放的累积时间∑t均小于过帽儿山数字林相图进行再分类获得,并将可燃物类型数据转化是满足条件的像元集合,它们所形成的集合反映在图像当中为(RD栅格形式存储。文中所应用到的数据如表5所示。就是模拟的火场表5栅格数据34算法的选择名称数据格式数据类型描述边界插值算法不考虑蔓延过程中每个起燃栅格向8个邻 V ege tation ESR IGR D NTEGER可燃物类型数据格蔓延的可能,因而计算较简单,但是此算法人为地简化了林ElevationESR IGR DNTEGER高程数据火蔓延的复杂性,对于大范围的地形变化较复杂的地区,林火ShESR I GRⅢN TEGER坡度数据C1994-2011ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp:/www.cnki.net第9期毛学刚等:基于G模型的林火蔓延计算机仿真4142林火蔓延速度的计算难,因为不管用何种方法,都回避不了对可燃物、气象和地形在进行蔓延计算前,需婁先形成速度图文件,在林火蔓延的等因素的考虑,因而,火场蔓延模型的建立非常重要。文中结计算机仿真中最基夲的是林火的蔓延速度,通过速度图文件,可合帽儿山的实际情况建立了火行为的蔓延模型,并以Ⅴ isual以直观地得到可燃物蔓延速度的情况。形成速度图,具体来说,C++60为主要可视化开发厂具,采用先进的COM技术,成是对通过GS获得的各种薮据(可燃物类型数据、髙程数据和坡功地实现了基于G丶模型的林火蔓延计算机仿真度数椐)逐点进行处理,通过栅格的行列号得到栅格图像上与行目前的林火蔓延模型都是针对林火的始发阶段,今后对列号相对应的点的属性值,如可燃物类型、高程、坡度等信息,然森林大火和特大火的模型硏究将成为重点。林火蔓延模拟的后根据所选王正非蔓延模型并考虑实时参数,如发生火灾时的风研究也将向三维、立体的方向发展。随着对林火研究的深入,向、风速等信息计算出该点的林火蔓延速度,并将此速度值赋给也出现了一些新的林火研究方法,如突变理论、元胞自动该点这样形成的点集合在蔓延模拟中用作中间调用数据,称为机模拟林火蔓延、统计物理学方面的渗透理论、分形理速度图。速度图的生成由Ⅴ ialc++60编程实现。论2等,它们与计算机技术的结合必将对林火蔓延的研究做43林火蔓延动态显示出贡献。林火蔓延的动态显示是林火蔓延仿真中最关键的一步,参考文献主要利用Ⅴ isual c++60编程实现,其主要过程为:第一、从背景数据库中读入可燃物类型和高程数据;第二、根据输入的「1]黄作维.基于G和RS的林火行为预测研究[J西北林学院学报,20621(3):94-97风向、风速参数,利用王正非林火蔓延模型计算岀林火的蔓延「2」朱煌武,朱霁平,谢庆胜,等.基于地理信息系统的森林火灾救速度;第三、将计算结果在空间背景薮据库上输出。动态模拟辅助决策系统的研究[J.自然灾害学报,19998(1):60-70不同风向、风速的结果,如图4所示。[3 PerryG M, Sparrow A D, Ow ens I F AGs- supported modelbr the simu lation of the spatial stru cture of w iH land fire Cass barsin New Zealnd[ J]. Jou n al of a pp lied E coby, 1999 36(4)50-5184] Weber rα唐世敏.野火蔓延的数学模型[J力学与实践,199214(2):1-12I5 Rothemelr c. a m athem atical m odel for p red ct ng fire spreadw iH land fuels[R]. USDA: Forest Service R esearch Paper 1972113-115[6]毛贤敏.风和地形对林火蔓延速度的作用[小应用气象学报,1993,4(1):100-1047]温广玉,刘勇.林火蔓延模型的数学应用[J东北林业大学学报,19422(2):31-368]郑焕能,胡海清.东北东部山地可燃物类型的研究[J.森林防火,1990(4):10-13「9]钟茂华,范维澄,王清安.林火蔓延突变形态的模拟实验研究[J.自然科展,200010(4):330-353图4不同风速和风向的林火蔓延10]邬伦.地理信息系统一原理、方法和应用[M]北京:科学出版社,2000241-2465结论与展望「1]姚树人,火灾模型和辅助决策系统的现代发展[J.森林防火,1994(12):59-62从林火蔓延的硏究看,目前要做到精确的预测还比较困12]薄颖生,韩恩贤,韩刚,等.森林火灾自相似性质及其在林火管理中的应用研究初探[小森林防火,1997(3):20)-22perenne la l( Rannunau hus repe").I:wha"为adma[21李根前,黄宝龙,唐德瑞,等毛乌素沙地中国沙棘无性系生长(上接32页)10 Lovett Dous tL Popu at in dynam cs and spec ia ization格局与生物量分配[J西北农林科技大学学报:自然科学版m e2001,29(2):51-55con trast ing hab ita ts[J. Joumal ofe cology,1981.69743-7552李根前,赵粉侠,李秀寨,等.毛乌素沙地中国沙棘种群数量动11 Lovett Dous tL Popu ht on dynam is and chn al specialization in a态研究[J.林业科学,2004,40(1):180-184canal perens ial( Ranuna hus repens).l: responses to light and[23]贺斌,李根前,高海银,等.不同土壤水分条件下中国沙棘克险nu trient supply[ J]. Joumal of e co logy, 1987, 75 555-568生长的对比研究[J.云南大学学报:自然科学版,200729[12]王昱生,盖晓春.羊草无性系植物种群觅养生长格局与资源分(1):101-107配的研究[J植物生态学报,1995194:293-30124]贺斌,李根前,李周岐,等.木本克隆植物中国沙棘种群数量与13] Luo XG, DongM. A rch itec tu re p last city in response to soil mo is结构对土壤水分的响应[小西北农林科技大学学报:自然科hu re n the s tlon ife rou s herb Duchesnea ind iaa[ J. A cta Botan ica学版,2007,35(3):183-187Sinica200244(1):97-10025]赵成义,宋郁东,王玉潮,等.几种荒漠植物地上生物量估算的[14]刘庆,钟章成无性系植物种群生态学研究进展及有关概念初步研究[J应用生态学报,200415(1):49-52生态学杂志,19514(3):40-4「26刘佩勇,张庆灵,杨允菲松嫩平原朝鲜碱茅无性系种群构件生物5]刘庆,钟章成斑苦竹无性奈生长与水分供应及其适应对策的量结构及相关模型分析[小应用生态学报,20415(4):543-548研究[J.植物生态学报,199620(3):245-254I 27] A lpert P. N itrogen sharing am ong ram ets ncreases clonal grow th i[16]岳春雷,汪奎宏,何奇江,等.不同氮素条件下雷竹克隆生长的Frag aria chiloe sis[ J. Ecog y, 1991 72: 69-80比较研究[J.竹子研究汇刊,200221(1:38-40I 28 Evans JP. The effect of bcal resou Ice a vailab ility and clonal inte-[17]杨在娟,岳春雷,汪奎宏.光照强度对竹无性系生长的影响gration on ram et fun ct ina I m orphobgy in H ydroco le bonarien sisJ.0 eco log ia199289265-276I18」陈玉福,董鸣.毛乌素沙地根茎灌木羊柴的基株特征和不同生境中29董鸣.异质性生境屮的植物克隆生长:风险分摊[J植物生态的分株种群特征[J植物生态学报,2024(1):40-45学报,199620(6):543-54819]李根前,黄宝龙,唐德瑞,等.毛乌素沙地中国沙棘无性系生长[30] Pan JJ Price Js Fimess and evoht in n canal plan ts the mpact调节[J.应用生态学报,200,12(5):682-686of canal grow th[ J]. E volt inary Ecobgy 2002 15 583-600[》0]李根前,黄宝龙,唐德瑞,等.毛乌素沙地中国沙棘无性系种群311 Schm i b same ecological and ev oht in ary consequen ces ofm od年龄结构动态与遗传后果研究[J.应用生态学报,2001,12u ar org an iat in and chn al grow th in plants[ J]. Evoh tionaryC153432613chinaAcademicJournalElectronicPublishingHouseAimnrigntsreservedhttp://www.cnki.net

火力分配的遗传算法matlab程序，测试通过，适于专业学习

STM32库函数开发实战指南 作 者 刘火良，杨森 配套野火STM32开发板，原装正版，大小115M，完整版！

包含外部有效性指标和内部有效性指标，Rand index、 Adjusted Rand index、 Mirkin index、Hubert indexSilhouette、 Davies-Bouldin、Calinski-Harabasz、Krzanowski-Lai、Hartigan、weighted inter- to intra-cluster ratio、Homogeneity Separation

求雷达成像点目标积分旁瓣比，峰值旁瓣比，3DB带宽

改进模拟退火算法求解TSP问题也就是常说的旅行商问题

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

深入理解CNN，包括CNN的过程显示和前向后向推倒，以及CNN的应用举例。