LEADER 01064cam0-2200349---450- 001 990005822840403321 005 20140924125808.0 010 $a84-249-1462-7 035 $a000582284 035 $aFED01000582284 035 $a(Aleph)000582284FED01 035 $a000582284 100 $a19990604d1991----km-y0itay50------ba 101 0 $aspa 102 $aES 105 $ay-------001yy 200 1 $aDichos de los siete sabios de Grecia$esentencias morales en verso$fedición, estudio y materiales por Alvaro Galmés de Fuentes 210 $aMadrid$cGredos$d1991 215 $a182 p.$d22 cm 225 1 $aColección de literatura española Aljamiado-Morisca$v8 225 1 $aLiteratura gnómica 610 0 $aProverbi spagnoli 676 $a398.9 676 $a861.008 702 1$aGalmés de Fuentes,$bAlvaro$f<1924-2003> 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005822840403321 952 $a398.9 GAL 1$bDip.f.m.5719$fFLFBC 959 $aFLFBC 996 $aDichos de los siete sabios de Grecia$9567411 997 $aUNINA LEADER 01020nam0-22003371i-450- 001 990007063720403321 005 20041206133242.0 010 $a88-06-15625-X 035 $a000706372 035 $aFED01000706372 035 $a(Aleph)000706372FED01 035 $a000706372 100 $a20020319d2000----km-y0itay50------ba 101 $aita 105 $ay-------001yy 200 1 $aSegreto di Stato$ela verità da Gladio al caso Moro$fGiovanni Fasanella e Claudio Sestieri con Giovanni Pellegrino 210 $aTorino$cEinaudi$dc2000 215 $a250 p.$d20 cm 225 1 $a<>struzzi$v523 610 0 $aStoria contemporanea$aItalia$aTerrorismo$aStragi 676 $a945.09$v21$zita 700 1$aFasanella,$bGiovanni$0259712 702 1$aPellegrino,$bGiovanni 702 1$aSestieri,$bClaudio 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007063720403321 952 $a945.09 FAS 1$bBibl.42107$fFLFBC 959 $aFLFBC 996 $aSegreto di Stato$9706034 997 $aUNINA LEADER 01068nam0-2200337---450- 001 990001074600203316 005 20020531143149.0 010 $a0-486-65241-6 035 $a000107460 035 $aUSA01000107460 035 $a(ALEPH)000107460USA01 035 $a000107460 100 $a20020503d1986----km|y0engy0103----ba 200 1 $aNumerical methods for scientists and engineers$fR.W.Hamming 205 $a2. ed 210 $aNew York$cDover Publ.$d[1986] 215 $aIX, 721 p.$cill.$d22 cm 300 $aOriginariamente pubbl.: New York: McGraw-Hill, 1973 606 0 $aAnalisi numerica$xElaborazione dei dati 676 $a519.4 700 1$aHAMMING,$bRichard Wesley$013873 801 0$aIT$bsalbc$gISBD 912 $a990001074600203316 951 $a519.4 HAM$b16776 Ing.$c519$d00081815 959 $aBK 969 $aTEC 979 $aSTELLA$b10$c20020503$lUSA01$h1256 979 $aJOHNNY$b90$c20020531$lUSA01$h1431 979 $aPATRY$b90$c20040406$lUSA01$h1714 996 $aNumerical methods for scientists and engineers$933561 997 $aUNISA LEADER 01682nas 2200529-a 450 001 9910139279803321 005 20240413014845.0 035 $a(CKB)2480000000004075 035 $a(CONSER)sn-99039629- 035 $a(EXLCZ)992480000000004075 100 $a19990422b19992015 --- a 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aJBR-BTR $eorgane de la Société royale belge de radiologie (SRBR) = orgaan van de Koninklijke Belgische Vereniging voor Radiologie (KBVR) 210 $aBruxelles $cSociété royale belge de radiologie$d1999- 215 $a1 online resource 300 $aTitle from cover. 311 08$aPrint version: JBR-BTR : 1780-2393 (DLC)sn 99039629 (OCoLC)41640418 517 3 $aJBR 517 3 $aBTR 517 1 $aJournal belge de radiologie 517 1 $aBelgisch tijdschrift voor radiologie 517 1 $aBTR-JBR 517 1 $aBelgian journal of radiology 531 $aJBR-BTR 606 $aRadiology$vPeriodicals 606 $aMedical radiology$vPeriodicals 606 $aDiagnostic Imaging 606 $aRadiology 606 $aRadiology$2fast$3(OCoLC)fst01088271 606 $aMedical radiology$2fast$3(OCoLC)fst01088281 608 $aPeriodical. 608 $aPeriodicals.$2fast 615 0$aRadiology 615 0$aMedical radiology 615 12$aDiagnostic Imaging. 615 22$aRadiology. 615 7$aRadiology. 615 7$aMedical radiology. 712 02$aSocie?te? royale belge de radiologie. 906 $aJOURNAL 912 $a9910139279803321 920 $aexl_impl conversion 996 $aJBR-BTR$92238678 997 $aUNINA LEADER 05165nam 22006495 450 001 9910502972903321 005 20251113180725.0 010 $a3-030-77939-4 024 7 $a10.1007/978-3-030-77939-9 035 $a(CKB)5360000000049950 035 $a(MiAaPQ)EBC6739112 035 $a(Au-PeEL)EBL6739112 035 $a(OCoLC)1273410337 035 $a(PPN)258296704 035 $a(DE-He213)978-3-030-77939-9 035 $a(EXLCZ)995360000000049950 100 $a20211001d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning for Unmanned Systems /$fedited by Anis Koubaa, Ahmad Taher Azar 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (731 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v984 311 08$a3-030-77938-6 327 $aDeep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review -- Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment -- Reactive Obstacle Avoidance Method for a UAV -- Guaranteed Performances for Learning-Based Control Systems using Robust Control Theory -- A cascaded deep Neural Network for Position Estimation of Industrial Robots -- Managing Deep Learning Uncertainty for Autonomous Systems -- Uncertainty-Aware Autonomous Mobile Robot Navigation with Deep Reinforcement Learning -- Deep Reinforcement Learning for Autonomous Mobile Networks in Micro-Grids -- Reinforcement learning for Autonomous Morphing Control and Cooperative Operations of UAV Cluster -- Image-Based Identification of Animal Breeds Using Deep Learning. 330 $aThis book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v984 606 $aControl engineering 606 $aRobotics 606 $aAutomation 606 $aComputational intelligence 606 $aEngineering$xData processing 606 $aControl, Robotics, Automation 606 $aComputational Intelligence 606 $aData Engineering 615 0$aControl engineering. 615 0$aRobotics. 615 0$aAutomation. 615 0$aComputational intelligence. 615 0$aEngineering$xData processing. 615 14$aControl, Robotics, Automation. 615 24$aComputational Intelligence. 615 24$aData Engineering. 676 $a006.31 702 $aKouba?a$b Anis 702 $aAzar$b Ahmad Taher 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910502972903321 996 $aDeep learning for unmanned systems$92894694 997 $aUNINA