LEADER 00975nam0-22003371i-450- 001 990003852590403321 005 20090929120634.0 035 $a000385259 035 $aFED01000385259 035 $a(Aleph)000385259FED01 035 $a000385259 100 $a20030910d1949----km-y0itay50------ba 101 0 $aeng 102 $aGB 200 1 $a<>Theory of Fluctuations in Contemporary Economic Thought$fby Sidney D. Merlin. 210 $aNew York$cAMS Press$d1968. 215 $a168 p.$d23 cm 225 1 $aStudies in history, economics and public law$v556 610 0 $aCicli economici$aTeoria 676 $aD/0 676 $aF/5 700 1$aMerlin,$bSidney D.$0146011 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003852590403321 952 $aF/5 MER$b041838$fSES 952 $aE3.24$b01299$fDECTS 959 $aSES 959 $aDECTS 996 $aTheory of Fluctuations in Contemporary Economic Thought$9515403 997 $aUNINA LEADER 02312oam 2200637I 450 001 9910709889603321 005 20180720142936.0 035 $a(CKB)5470000002473357 035 $a(OCoLC)1035552049$z(OCoLC)1035213542 035 $a(EXLCZ)995470000002473357 100 $a20180514d2015 ua 0 101 0 $aeng 135 $aurnn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBait and capture $eholding on to molecules of interest /$fprepared by Brian M. Kemp with contributions from Misa Winters. Cara Monroe, and Jodi Lynn Barta 205 $aRevised July 2015. 210 1$a[Pullman, Wash.] :$c[Department of Anthropology, Washington State University] ;$a[Washington, D.C.] :$cNational Criminal Justice Reference Service,$d2015. 215 $a1 online resource (95 pages) $cillustrations (some color) 300 $a"Original submitted to the NIJ January 2015, revised July 2015." 300 $a"Document Number: 251646" -- Grant transmittal document. 300 $a"Date Received: April 2018" -- Grant transmittal document. 320 $aIncludes bibliographical references. 517 $aBait and capture 606 $aDNA fingerprinting 606 $aDNA, Fossil 606 $aForensic genetics$xTechnique 606 $aPolymerase chain reaction 606 $aDNA fingerprinting$2fast 606 $aDNA, Fossil$2fast 606 $aForensic genetics$xTechnique$2fast 606 $aPolymerase chain reaction$2fast 615 0$aDNA fingerprinting. 615 0$aDNA, Fossil. 615 0$aForensic genetics$xTechnique. 615 0$aPolymerase chain reaction. 615 7$aDNA fingerprinting. 615 7$aDNA, Fossil. 615 7$aForensic genetics$xTechnique. 615 7$aPolymerase chain reaction. 700 $aKemp$b Brian Matthew$01412188 702 $aWinters$b Misa 702 $aMonroe$b Cara 702 $aBarta$b Jodi Lynn 712 02$aNational Criminal Justice Reference Service (U.S.), 712 02$aWashington State University.$bDepartment of Anthropology. 801 0$bVVJ 801 1$bVVJ 801 2$bZCY 801 2$bOCLCF 801 2$bGPO 906 $aBOOK 912 $a9910709889603321 996 $aBait and capture$93505047 997 $aUNINA LEADER 06223nam 2201585z- 450 001 9910557360703321 005 20220111 035 $a(CKB)5400000000042285 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76767 035 $a(oapen)doab76767 035 $a(EXLCZ)995400000000042285 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (454 p.) 311 08$a3-0365-1268-3 311 08$a3-0365-1269-1 330 $aRecent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue "Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments" present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woz?niak, Silesian University of Technology, Poland - 606 $aInformation technology industries$2bicssc 610 $a3D convolutional neural networks 610 $a3D imaging 610 $aactivity measure 610 $aadvanced driver assistance system (ADAS) 610 $aanchor box 610 $aartefacts 610 $aartificial bee colony 610 $aatrous convolution 610 $aaugmented reality 610 $aautomatic design 610 $abenchmark 610 $abio-inspired techniques 610 $abrain hemorrhage 610 $acascade classifier 610 $acascaded center-ness 610 $acitrus 610 $aCNN 610 $acomplex search request 610 $acomputer vision 610 $acontinuous casting 610 $aconvolution neural networks (CNNs) 610 $aconvolutional neural network 610 $aconvolutional neural networks 610 $across-scale 610 $aCT brain 610 $aCT images 610 $adata acquisition 610 $adeep learning 610 $adeep sort 610 $adefect detection 610 $adeformable localization 610 $adrone detection 610 $aevidence chains 610 $aevolving connectionist systems 610 $afabric defect 610 $afeature extraction 610 $afeature fusion 610 $afew shot learning 610 $afocal loss 610 $agenerative adversarial network 610 $agrow-when-required neural network 610 $ahand gesture recognition 610 $ahepatic cancer 610 $ahigh-speed trains 610 $ahuman-robot interaction 610 $aHungarian algorithm 610 $ahunting 610 $aimage analysis 610 $aimage processing 610 $aimage recognition 610 $aindustrial environments 610 $ainformation retriever sensor 610 $aInSAR 610 $amachine learning 610 $amarine systems 610 $amixed kernels 610 $amulti-hop reasoning 610 $amulti-scale 610 $amulti-sensor fusion 610 $an/a 610 $anearest neighbor filtering 610 $aneural network 610 $anon-stationary 610 $aobject detection 610 $aobject detector 610 $aobject tracking 610 $aone-class classifier 610 $aoptical flows 610 $aparameter efficiency 610 $apests and diseases identification 610 $apixel convolution 610 $apose estimation 610 $areinforcement learning 610 $aRFI 610 $asemantic segmentation 610 $aship classification 610 $aship radiated noise 610 $aspatial pooling 610 $aspatiotemporal interest points 610 $asports scene 610 $asuperalloy tool 610 $asurface defects 610 $asurface electromyography (sEMG) 610 $aSVM 610 $asynthetic images 610 $athree-dimensional (3D) vision 610 $athresholding 610 $atool wear monitoring 610 $aTraffic sign detection and tracking (TSDR) 610 $aUAV detection 610 $aUAV imagery 610 $aunderwater acoustics 610 $aunmanned aerial vehicles 610 $avehicle detection 610 $avehicular traffic congestion 610 $avehicular traffic flow classification 610 $avehicular traffic flow detection 610 $avideo classification 610 $avideo surveillance 610 $avisual detection 610 $avisual inspection 610 $avisual question answering 610 $aYolo 610 $aYOLOv2 615 7$aInformation technology industries 700 $aWoz?niak$b Marcin$4edt$01303762 702 $aWoz?niak$b Marcin$4oth 906 $aBOOK 912 $a9910557360703321 996 $aAdvanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments$93027189 997 $aUNINA