《TheGraphSLAMalgorithmwithapplicationstolarge-scalemappingofurbanstructures》

pdf版的数据结构必读书Datastructure+Algorithm=ProgrammingThetoolsandtechniquestodesignandimplementlarge-scalecomputersystems:DataabstractionAlgorithmspecificationPerformanceanalysisPerformancemeasurement

文章作者写的matlab源代码，该文章发表在DigitalSignalProcessing:Ke-KunHuang,HuiLiu,Chuan-XianRen,Yu-FengYuandZhao-RongLai.Remotesensingimagecompressionbasedonbinarytreeandoptimizedtruncation.DigitalSignalProcessing,vol.64,pp.96-106,2017.(http://dx.doi.org/10.1016/j.dsp.2017.02.008)遥感图像数据非常广泛，因此需要通过空间设备上的低复杂度算法进行压缩。
具有自适应扫描顺序（BTCA）的二叉树编码是一个的有效算法。
然而，对于大规模遥感图像，BTCA需要大量的内存，而且不能随机存取。
在本文中，我们提出了一种基于BTCA的新的编码方法。
小波图像首先划分为几个块，并由BTCA单独编码的。
根据BTCA的属性，仔细选择每个块的有效截断点，以优化速率失真的比例，从而获得更高的压缩比、更低的内存要求和随机访问性能。
由于没有任何熵编码，所提出的方法简单快速，非常适合于空间设备。
对三个遥感图像集进行实验，结果表明它可以显着提高PSNR、SSIM和VIF，以及主观视觉体验。
Theremotesensingimagedataissovastthatitrequirescompressionbylow-complexityalgorithmonspace-borneequipment.Binarytreecodingwithadaptivescanningorder(BTCA)isaneffectivealgorithmforthemission.However,forlarge-scaleremotesensingimages,BTCArequiresalotofmemory,anddoesnotproviderandomaccessproperty.Inthispaper,weproposeanewcodingmethodbasedonBTCAandoptimizetruncation.ThewaveletimageisfirstdividedintoseveralblockswhichareencodedindividuallybyBTCA.AccordingthepropertyofBTCA,weselectthevalidtruncationpointsforeachblockcarefullytooptimizetheratioofrate-distortion,sothatahighercompressionratio,lowermemoryrequirementandrandomaccesspropertyareattained.Withoutanyentropycoding,theproposedmethodissimpleandfast,whichisverysuitableforspace-borneequipment.Experimentsareconductedonthreeremotesensingimagesets,andtheresultsshowthatitcansignificantlyimprovePSNR,SSIMandVIF,aswellassubjectivevisualexperience.

ApacheSparkisaunifiedanalyticsengineforlarge-scaledataprocessing.Itprovideshigh-levelAPIsinJava,Scala,PythonandR,andanoptimizedenginethatsupportsgeneralexecutiongraphs.Italsosupportsarichsetofhigher-leveltoolsincludingSparkSQLforSQLandstructureddataproc

本书是EricEvans对于他自己写的《规模驱动方案-软件中间繁杂性应答之道》的一本字典式的参考书，可用于快捷查找《规模驱动方案》中的诸多不雅点及其扼要评释。
书是英文版的，2015年3月修订版，我已经加了目录，便捷巨匠参阅。
AcknowledgementsDefinitionsPatternLanguageOverviewI.PuttingtheModeltoWorkBoundedContextUbiquitousLanguageContinuousIntegrationModel-DrivenDesignHands-onModelersRefactoringTowardDeeperInsightII.BuildingBlocksofaModel-DrivenDesignLayeredArchitectureEntitiesValueObjectsDomainEvents*ServicesModulesAggregatesRepositoriesFactoriesIII.SuppleDesignIntention-RevealingInterfacesSide-Effect-FreeFunctionsAssertionsStandaloneClassesClosureofOperationsDeclarativeDesigniiiDrawingonEstablishedFormalismsConceptualContoursIV.ContextMappingforStrategicDesignContextMapPartnership*SharedKernelCustomer/SupplierDevelopmentConformistAnticorruptionLayerOpen-hostServicePublishedLanguageSeparateWaysBigBallofMud*V.DistillationforStrategicDesignCoreDomainGenericSubdomainsDomainVisionStatementHighlightedCoreCohesiveMechanismsSegregatedCoreAbstractCoreVI.Large-scaleStructureforStrategicDesignEvolvingOrderSystemMetaphorResponsibilityLayersKnowledgeLevelPluggableComponentFramework

Energy-awareResourceManagementandGreenEnergyUseforLarge-scaleDatacenters:ASurvey

论文EfficientLarge-ScaleStereoMatching对于应源代码

ContentsPrefacexiiiLimitsofLiabilityandDisclaimerofWarrantyofSoftwarexv1TheWirelessChannel:PropagationandFading11.1Large-ScaleFading41.1.1GeneralPathLossModel41.1.2Okumura/HataModel81.1.3IEEE802.16dModel101.2Small-ScaleFading151.2.1ParametersforSmall-ScaleFading151.2.2Time-Dispersivevs.Frequency-DispersiveFading161.2.3StatisticalCharacterizationandGenerationofFadingChannel192SISOChannelModels252.1IndoorChannelModels252.1.1GeneralIndoorChannelModels262.1.2IEEE802.11ChannelModel282.1.3Saleh-Valenzuela(S-V)ChannelModel302.1.4UWBChannelModel352.2OutdoorChannelModels402.2.1FWGNModel412.2.2JakesModel502.2.3Ray-BasedChannelModel542.2.4Frequency-SelectiveFadingChannelModel612.2.5SUIChannelModel653MIMOChannelModels713.1StatisticalMIMOModel713.1.1SpatialCorrelation733.1.2PASModel763.2I-METRAMIMOChannelModel843.2.1StatisticalModelofCorrelatedMIMOFadingChannel843.2.2GenerationofCorrelatedMIMOChannelCoefficients883.2.3I-METRAMIMOChannelModel903.2.43GPPMIMOChannelModel943.3SC妹妹IMOChannelModel973.3.1SCMLink-LevelChannelParameters983.3.2SCMLink-LevelChannelModeling1023.3.3SpatialCorrelationofRay-BasedChannelModel1054IntroductiontoOFDM1114.1Single-Carriervs.Multi-CarrierTransmission1114.1.1Single-CarrierTransmission1114.1.2Multi-CarrierTransmission1154.1.3Single-Carriervs.Multi-CarrierTransmission1204.2BasicPrincipleofOFDM1214.2.1OFD妹妹odulationandDemodulation1214.2.2OFDMGuardInterval1264.2.3OFDMGuardBand1324.2.4BERofOFDMScheme1364.2.5Water-FillingAlgorithmforFrequency-DomainLinkAdaptation1394.3CodedOFDM1424.4OFDMA:MultipleAccessExtensionsofOFDM1434.4.1ResourceAllocation–SubchannelAllocationTypes1454.4.2ResourceAllocation–Subchannelization1464.5Duplexing1505SynchronizationforOFDM1535.1Ef

Cisco-IPMulticast,VolumeII.2018AdvancedMulticastConceptsandLarge-ScaleMulticastDesign

国外经典的关于网络测量的册本，在sprint骨干网上做过实验

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。