PDF-Figure2.(a)Thewavefrontsensorprovidesuswithalow-orderesti-mateofthewav

Author : alida-meadow | Published Date : 2015-10-29

101117212008101296Page22 everwerealizedthatthelattercanbecomputedfromtheresidualwavefrontsensorWFSsignaldrivingtheAOsystem4seeFigure2Bypostprocessingthedatatocomputethespecklephaseswecan

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Figure2.(a)Thewavefrontsensorprovidesuswithalow-orderesti-mateofthewav: Transcript

101117212008101296Page22 everwerealizedthatthelattercanbecomputedfromtheresidualwavefrontsensorWFSsignaldrivingtheAOsystem4seeFigure2Bypostprocessingthedatatocomputethespecklephaseswecan. 4Chap.2KaleidoscopicTilings Figure2.1Agenus2surface-spherewithtwohandles Figure2.2Icosahedraltiling-topview 2.1Tilingsonsurfaces5 Figure2.32-4-4tilingonatorus Figure2.43-3-3tilingonatorusRemark2.1Tili gorithm1:Algorithmusedbyarobottoperformrepartitioncoverage. epart-Coverage(:VoronoicellofrobotOutput:Repartitionedcoverageregionforrobotperformboundarycoverageinanddeterminesetofblockedpatchescomprisi 1Weremarkthattherunningtimesmentionedaboveboundthenumberofarithmeticoperationsperformedbythealgorithmsandnotthebitcomplexity. extendstohigherdimensionsinastraightforwardmanner,atthecostofincreasingthe ImageNetPascalVOC Figure3:Illustrationofdifferentdatasetstatisticsbetweenthesource(ImageNet)andtarget(PascalVOC)tasks.PascalVOCdatadisplaysobjectsembeddedincomplexscenes,atvariousscales(right),andinco Figure1:C-MACsynchronizationactivitydiagram. Figure2:C-MACtransmissionactivitydiagram.ThemaincongurationpointsofC-MACare:Physicallayer:Thesecongurationparametersarede-nedbytheunderlyingradiotransc {z }unaryX(i;j)2EwTpp(yi;yj)| {z }pairwise:(2)Ourunarypotentialsexploitappearance,edgesaswellastemporalinformation(intheformofhomography),whileourpairwisepotentialsencodespatialsmoothnessinthe1Dcurv CrypticandConspicuousColorPatterns Figure2: Figure3:DetailpreservationisexhibitedusingGreenCoordinates(ontheright),wherethedetailsadheretothesurfacedeformationandrotateaccordingly.Inthemiddle,theMVCresultisdepictedwherethedetailsmaintaintheiror Figure2.The95%statisticalparsimonynetworkobtainedforeachlocusfor(A)east Figure2.IllustrationhowdenseinterestpointscanbecomputedusingaLaplacian-of-Gaussiansscalepyramid.Localmaximaaresearchedwithinlargerneighbourhoodsthanthe333neighbourhoodsusedbystandardinterestpointdet Figure2.Exampleprogramforillustratingdynamicdeterminacyanalysis.Somekeydeterminacyfactsaregivenincomments.WepresentaprototypeimplementationoftheanalysisforJavaScript.(Section4).Wereportontwocasestud Figure2.CDFsofrequestfrequencyandaverageservicetimesforseveralapplicationsonanNvidiaGTX670GPU.Alargepercentageofarrivingrequestsareshortandsubmittedinshortintervals(“back-to-back”).Overviewo InternationalPacificResearchCenter,SchoolofOceanandEarthScienceandTechnology,UniversityofHawaii,Honolulu,Hawaii,USA.PhysicalOceanographyResearchDivision,ScrippsInstitutionofOceanography,UniversityofCa 3AbstractInthisreport,wewilldiscusstheworkdonefordesigningandimplementingaVisualSudokuSolverappforiOS.Theapp'scapabilitiesincluderecognizingasudokupuzzleonthephone'scameraandoverlayingtheimagewiththec

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