Theexpressionsofpulsecharacteristicssuchasoutputenergy,peakpower,andpulsewidthareobtainedbysolvingthecoupledrateequationsdescribingtheoperationofGaAssemiconductorsaturableabsorberQ-switchedlasers.ThekeyparametersofanoptimallycoupledGaAssaturableabsorberQ-swi

原始对偶内点法计算最优潮流；
matlab程序；
optimalpowerflow

随机给出一个页面执行序列，如：1,5,3,4,2,1,3,4,5,7,9,……。
要求计算以下几种置换算法的缺页数、缺页率和命中率。
最佳置换算法OPT（Optimal）先进先出算法FIFO（FirstInFirstOut）最近最少使用算法LRU（LeastRecentlyUsed）

vs2015c++opencv3.3.1实现Interactivegraphcutsforoptimalboundary®ionsegmentationofobjectsinN-Dimagescode

nmanydataanalysistasks,oneisoftenconfrontedwithveryhighdimensionaldata.Featureselectiontechniquesaredesignedtofindtherelevantfeaturesubsetoftheoriginalfeatureswhichcanfacilitateclustering,classificationandretrieval.Thefeatureselectionproblemisessentiallyacombinatorialoptimizationproblemwhichiscomputationallyexpensive.Traditionalfeatureselectionmethodsaddressthisissuebyselectingthetoprankedfeaturesbasedoncertainscorescomputedindependentlyforeachfeature.Theseapproachesneglectthepossiblecorrelationbetweendifferentfeaturesandthuscannotproduceanoptimalfeaturesubset.InspiredfromtherecentdevelopmentsonmanifoldlearningandL1-regularizedmodelsforsubsetselection,weproposehereanewapproach,called{\emMulti-Cluster/ClassFeatureSelection}(MCFS),forfeatureselection.Specifically,weselectthosefeaturessuchthatthemulti-cluster/classstructureofthedatacanbebestpreserved.Thecorrespondingoptimizationproblemcanbeefficientlysolvedsinceitonlyinvolvesasparseeigen-problemandaL1-regularizedleastsquaresproblem.ItisimportanttonotethatMCFScanbeappliedinsuperised,unsupervisedandsemi-supervisedcases.Ifyoufindthesealgoirthmsuseful,weappreciateitverymuchifyoucanciteourfollowingworks:PapersDengCai,ChiyuanZhang,XiaofeiHe,"UnsupervisedFeatureSelectionforMulti-clusterData",16thACMSIGKDDConferenceonKnowledgeDiscoveryandDataMining(KDD'10),July2010.BibtexsourceXiaofeiHe,DengCai,andParthaNiyogi,"LaplacianScoreforFeatureSelection",AdvancesinNeuralInformationProcessingSystems18(NIPS'05),Vancouver,Canada,2005Bibtexsource

高清PDF电子书，关于卡尔曼滤波和小波的，经典书籍，第四版了Kalmanfilteringisanoptimalstateestimationprocessappliedtoadynamicsystemthatinvolvesrandomperturbations.Moreprecisely,theKalmanfiltergivesalinear,unbiased,andminimumerrorvariancerecursivealgorithmtooptimallyestimatetheunknownstateofadynamicsystemfromnoisydatatakenatdiscretereal-time.Ithasbeenwidelyusedinmanyareasofindustrialandgovernmentapplicationssuchasvideoandlasertrackingsystems,satellitenavigation,ballisticmissiletrajectoryestimation,radar,andfirecontrol.Withtherecentdevelopmentofhigh-speedcomputers,theKalmanfilterhasbecomemoreusefulevenforverycomplicatedreal-timeapplications.Inspiteofitsimportance,themathematicaltheoryofKalmanfilteringanditsimplicationsarenotwellunderstoodevenamongmanyappliedmathematiciansandengineers.Infact,mostpractitionersarejusttoldwhatthefilteringalgorithmsarewithoutknowingwhytheyworksowell.Oneofthemainobjectivesofthistextistodisclosethismysterybypresentingafairlythoroughdiscussionofitsmathe-maticaltheoryandapplicationstovariouselementaryreal-timeproblems

英文版高级数据结构PPT，内容包括：Amortizedcomplexity(Web)Externalsorting&tournamenttrees(Sections7.10.1,7.10.2,and5.8)Buffering(Section7.10.3)Rungeneration&optimalmergepatterns(Huffmantrees)(Sections7.10.4and7.10.5)Priorityqueuesandmerging(Section5.6)Leftisttrees,BinomialheapsandFibonacciheaps(Sections9.2,9.3,and9.4)Pairingheaps(Section9.5)Doubleendedpriorityqueues(Sections9.6and9.7,Web)Staticanddynamicweightedbinarysearchtrees(Section10.1)AVL-trees(Section10.2)Red-blacktrees(Section10.3)Splaytrees(Section10.4)B-,B+andB*-trees(Sections11.1-11.3)Triesanddigitalsearchtrees(Sections12.1-12.3)Triesandpacketforwarding(Section12.5)Suffixtrees(Section12.4)Bloomfilters(Section8.4)Segmenttrees(readings)IntervaltreesPrioritysearchtrees(readings)k-dtrees(readings)Quadandocttrees(readings)BSPtreesR-trees

ConstructOptimalBinarySearchTreebyUsingGreedyAlgorithm

栈数组队列等数据结构intQueueEmpty(pagequeue*q){if(q-＞count==0)return1;elsereturn0;}intQueueFull(pagequeue*q){if(q-＞count==num)return1;elsereturn0;}等等...

Author:BrainD.O.AndersonandJohnB.Moore

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。