01176nam0 22002771i 450 SUN001971720050228120000.088-203-2894-120040713d2001 |0itac50 baitaIT|||| |||||Fondamenti di navigazione aereanozioni preliminari, bussola magnetica, strumenti a capsula, azione del vento, elementi di astronomia nautica, progettazione di un volo a vistaVincenzo Nastro, Gabriella MessinaMilanoU. Hoepli2001226 p.ill.24 cm.MilanoSUNL000284629.1325121Nastro, VincenzoSUNV015905630214Messina, GabriellaSUNV015906630215HoepliSUNV000715650ITSOL20181109RICASUN0019717UFFICIO DI BIBLIOTECA DEI DIPARTIMENTI DI INGEGNERIA05 CONS H II 011 05 4322 UFFICIO DI BIBLIOTECA DEI DIPARTIMENTI DI INGEGNERIAIT-CE01004322CONS H II 011caFondamenti di navigazione aerea38310UNICAMPANIA04123nam 2201033z- 450 991057688160332120231214133058.0(CKB)5720000000008359(oapen)https://directory.doabooks.org/handle/20.500.12854/84504(EXLCZ)99572000000000835920202206d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierRecent Advances in Embedded Computing, Intelligence and ApplicationsBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (188 p.)3-0365-4246-9 3-0365-4245-0 The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems.Information technology industriesbicsschigh-level synthesisHLSSDSoCsupport vector machinesSVMcode refactoringZynqZedBoardextreme edgeembedded edge computinginternet of things deploymenthardware designIoT securityContiki-NGtrustabilityembedded systemscollaborative filteringrecommender systemsparallelismreconfigurable hardwareneuroevolutionblock-based neural networkdynamic and partial reconfigurationscalabilityreinforcement learningembedded systemartificial intelligencehardware accelerationneuromorphic processorpower consumptionharsh environmentfog computingedge computingcloud computingIoT gatewayLoRaWiFilow power consumptionlow latencyflexiblesmart portquantisationevolutionary algorithmneural networkFPGAMovidius VPU2D graphics acceleratorline-drawingBresenham’s algorithmalpha-blendinganti-aliasingfield-programmable gate arraydeep learningperformance estimationGaussian processInformation technology industriesPortilla Jorgeedt1300063Otero AndresedtMujica GabrieledtPortilla JorgeothOtero AndresothMujica GabrielothBOOK9910576881603321Recent Advances in Embedded Computing, Intelligence and Applications3025391UNINA