LEADER 00893nam0-22002771i-450 001 990006010650403321 005 20210520091822.0 035 $a000601065 100 $a20000112d1984----km-y0itay50------ba 101 0 $ager 102 $aDE$aUS 105 $ay---n---001yy 200 1 $aEuropäisches Gesellschaftsrecht$etexte und Materialen zur Rechtsangleichung nebst Einfünrung und Bibliographie$fMarcus Lutter 205 $a2. neubearbeitete Aufl. 210 $aBerlin$aNew York$cWalter de Gruyter$d1984 215 $aXXXI, 67 p.$d22 cm 225 1 $aZeitschrift für Unternehmens- und Gesellschaftsrecht$v1 676 $a346.06 700 1$aLutter,$bMarcus$0227847 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006010650403321 952 $aVIII H 591 (1)$b119896 $$fFGBC 959 $aFGBC 996 $aEuropäisches Gesellschaftsrecht$9581982 997 $aUNINA LEADER 03687nam 2200877z- 450 001 9910576879503321 005 20220621 035 $a(CKB)5720000000008380 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84496 035 $a(oapen)doab84496 035 $a(oapen)84496 035 $a(EXLCZ)995720000000008380 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEdge Computing for Internet of Things 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (186 p.) 311 08$a3-0365-4276-0 311 08$a3-0365-4275-2 330 $aThe Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond. 606 $aEnergy industries and utilities$2bicssc 606 $aHistory of engineering and technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $aassociation rules 610 $acache memory 610 $acomputational offloading 610 $aconvolutional neural network 610 $adata processing 610 $adew computing 610 $adistributed collaboration 610 $adynamic offloading 610 $aedge computing 610 $aedge node 610 $aedge security 610 $afailure recovery 610 $afog access points 610 $afog computing 610 $afog service orchestration 610 $aFP-Growth algorithm 610 $afrequency pattern analysis 610 $afunctional programming 610 $agateways 610 $ahierarchical edge computing 610 $aInternet of Things 610 $aInternet of Things (IoT) 610 $aIoT 610 $ajob scheduling 610 $amobile edge computing 610 $amobile edge server placement 610 $amultiagent RL 610 $an/a 610 $aoffloading computation 610 $aorchestration 610 $aportable optical fiber spectrometers 610 $aproactive caching 610 $arapid response strategy 610 $ascheduling heuristics 610 $aservice placement 610 $asmartphone 610 $aspectral classification 610 $aWSN 615 7$aEnergy industries and utilities 615 7$aHistory of engineering and technology 615 7$aTechnology: general issues 700 $aLee$b Kevin$4edt$0390621 702 $aMan$b Ka Lok$4edt 702 $aLee$b Kevin$4oth 702 $aMan$b Ka Lok$4oth 906 $aBOOK 912 $a9910576879503321 996 $aEdge Computing for Internet of Things$93027017 997 $aUNINA LEADER 03714nam 2200829z- 450 001 9910674007403321 005 20210211 010 $a3-03921-951-0 035 $a(CKB)4100000011302321 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/53451 035 $a(oapen)doab53451 035 $a(oapen)53451 035 $a(EXLCZ)994100000011302321 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMicrowave Imaging and Electromagnetic Inverse Scattering Problems 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (170 p.) 311 08$a3-03921-950-2 330 $aMicrowave imaging techniques allow for the development of systems that are able to inspect, identify, and characterize in a noninvasive fashion under different scenarios, ranging from biomedical to subsurface diagnostics as well as from surveillance and security applications to nondestructive evaluation. Such great opportunities, though, are actually severely limited by difficulties arising from the solution of the underlying inverse scattering problem. As a result, ongoing research efforts in this area are devoted to developing inversion strategies and experimental apparatus so that they are as reliable and accurate as possible with respect to reconstruction capabilities and resolution performance, respectively. The intent of this Special Issue is to present the experiences of leading scientists in the electromagnetic inverse scattering community, as well as to serve as an assessment tool for people who are new to the area of microwave imaging and electromagnetic inverse scattering problems. 606 $aHistory of engineering and technology$2bicssc 610 $a3D 610 $a5G communication 610 $aadjoint inversion methods 610 $aantenna array 610 $aantenna testing 610 $aarray diagnosis 610 $aBayesian compressive sensing (BCS) 610 $abreast cancer 610 $abreast imaging 610 $acompressed sensing 610 $acontraction integral equation for inversion (CIE-I) 610 $acontrast source inversion (CSI) 610 $acontrast-source inversion 610 $adiscontinuous Galerkin method (DGM) 610 $aelectrical-property tomography 610 $aelectromagnetic inverse scattering 610 $aelectromagnetic inverse scattering problems 610 $afinite-difference methods 610 $aimage-based approach 610 $aimaging 610 $ainverse obstacles problem 610 $ainverse problems 610 $ainverse scattering 610 $ainverse source problem 610 $ajoint sparsity 610 $aKolmogorov-Smirnov (K-S) test 610 $alinear sampling method 610 $amagnetic resonance imaging 610 $amicrowave imaging 610 $amicrowave imaging profilometry 610 $amicrowave plasma diagnostics 610 $anear-field measurements 610 $anonlinear optimization 610 $anonlinear problem 610 $aorthogonality sampling method 610 $aradar-based breast imaging 610 $arank minimization 610 $aRCS estimation 610 $astopping criteria 610 $atomography 615 7$aHistory of engineering and technology 700 $aDi Donato$b Loreto$4auth$01338907 702 $aMorabito$b Andrea$4auth 906 $aBOOK 912 $a9910674007403321 996 $aMicrowave Imaging and Electromagnetic Inverse Scattering Problems$93059171 997 $aUNINA