LEADER 04123nam 2201033z- 450 001 9910576881603321 005 20231214133058.0 035 $a(CKB)5720000000008359 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84504 035 $a(EXLCZ)995720000000008359 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Advances in Embedded Computing, Intelligence and Applications 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (188 p.) 311 $a3-0365-4246-9 311 $a3-0365-4245-0 330 $aThe 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. 606 $aInformation technology industries$2bicssc 610 $ahigh-level synthesis 610 $aHLS 610 $aSDSoC 610 $asupport vector machines 610 $aSVM 610 $acode refactoring 610 $aZynq 610 $aZedBoard 610 $aextreme edge 610 $aembedded edge computing 610 $ainternet of things deployment 610 $ahardware design 610 $aIoT security 610 $aContiki-NG 610 $atrustability 610 $aembedded systems 610 $acollaborative filtering 610 $arecommender systems 610 $aparallelism 610 $areconfigurable hardware 610 $aneuroevolution 610 $ablock-based neural network 610 $adynamic and partial reconfiguration 610 $ascalability 610 $areinforcement learning 610 $aembedded system 610 $aartificial intelligence 610 $ahardware acceleration 610 $aneuromorphic processor 610 $apower consumption 610 $aharsh environment 610 $afog computing 610 $aedge computing 610 $acloud computing 610 $aIoT gateway 610 $aLoRa 610 $aWiFi 610 $alow power consumption 610 $alow latency 610 $aflexible 610 $asmart port 610 $aquantisation 610 $aevolutionary algorithm 610 $aneural network 610 $aFPGA 610 $aMovidius VPU 610 $a2D graphics accelerator 610 $aline-drawing 610 $aBresenham?s algorithm 610 $aalpha-blending 610 $aanti-aliasing 610 $afield-programmable gate array 610 $adeep learning 610 $aperformance estimation 610 $aGaussian process 615 7$aInformation technology industries 700 $aPortilla$b Jorge$4edt$01300063 702 $aOtero$b Andres$4edt 702 $aMujica$b Gabriel$4edt 702 $aPortilla$b Jorge$4oth 702 $aOtero$b Andres$4oth 702 $aMujica$b Gabriel$4oth 906 $aBOOK 912 $a9910576881603321 996 $aRecent Advances in Embedded Computing, Intelligence and Applications$93025391 997 $aUNINA