Recent Advances in Embedded Computing, Intelligence and Applications
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...
Saved in:
| HerausgeberIn: | |
|---|---|
| Sonstige: | |
| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (188 p.) |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 03958nam-a2200997z--4500 | ||
|---|---|---|---|
| 001 | 993544776904498 | ||
| 005 | 20231214133058.0 | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 202206s2022 xx |||||o ||| 0|eng d | ||
| 035 | |a (CKB)5720000000008359 | ||
| 035 | |a (oapen)https://directory.doabooks.org/handle/20.500.12854/84504 | ||
| 035 | |a (EXLCZ)995720000000008359 | ||
| 041 | 0 | |a eng | |
| 100 | 1 | |a Portilla, Jorge |4 edt | |
| 245 | 1 | 0 | |a Recent Advances in Embedded Computing, Intelligence and Applications |
| 260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
| 300 | |a 1 electronic resource (188 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 520 | |a 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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Information technology industries |2 bicssc | |
| 653 | |a high-level synthesis | ||
| 653 | |a HLS | ||
| 653 | |a SDSoC | ||
| 653 | |a support vector machines | ||
| 653 | |a SVM | ||
| 653 | |a code refactoring | ||
| 653 | |a Zynq | ||
| 653 | |a ZedBoard | ||
| 653 | |a extreme edge | ||
| 653 | |a embedded edge computing | ||
| 653 | |a internet of things deployment | ||
| 653 | |a hardware design | ||
| 653 | |a IoT security | ||
| 653 | |a Contiki-NG | ||
| 653 | |a trustability | ||
| 653 | |a embedded systems | ||
| 653 | |a collaborative filtering | ||
| 653 | |a recommender systems | ||
| 653 | |a parallelism | ||
| 653 | |a reconfigurable hardware | ||
| 653 | |a neuroevolution | ||
| 653 | |a block-based neural network | ||
| 653 | |a dynamic and partial reconfiguration | ||
| 653 | |a scalability | ||
| 653 | |a reinforcement learning | ||
| 653 | |a embedded system | ||
| 653 | |a artificial intelligence | ||
| 653 | |a hardware acceleration | ||
| 653 | |a neuromorphic processor | ||
| 653 | |a power consumption | ||
| 653 | |a harsh environment | ||
| 653 | |a fog computing | ||
| 653 | |a edge computing | ||
| 653 | |a cloud computing | ||
| 653 | |a IoT gateway | ||
| 653 | |a LoRa | ||
| 653 | |a WiFi | ||
| 653 | |a low power consumption | ||
| 653 | |a low latency | ||
| 653 | |a flexible | ||
| 653 | |a smart port | ||
| 653 | |a quantisation | ||
| 653 | |a evolutionary algorithm | ||
| 653 | |a neural network | ||
| 653 | |a FPGA | ||
| 653 | |a Movidius VPU | ||
| 653 | |a 2D graphics accelerator | ||
| 653 | |a line-drawing | ||
| 653 | |a Bresenham’s algorithm | ||
| 653 | |a alpha-blending | ||
| 653 | |a anti-aliasing | ||
| 653 | |a field-programmable gate array | ||
| 653 | |a deep learning | ||
| 653 | |a performance estimation | ||
| 653 | |a Gaussian process | ||
| 776 | |z 3-0365-4246-9 | ||
| 776 | |z 3-0365-4245-0 | ||
| 700 | 1 | |a Otero, Andres |4 edt | |
| 700 | 1 | |a Mujica, Gabriel |4 edt | |
| 700 | 1 | |a Portilla, Jorge |4 oth | |
| 700 | 1 | |a Otero, Andres |4 oth | |
| 700 | 1 | |a Mujica, Gabriel |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:42:12 Europe/Vienna |f system |c marc21 |a 2022-07-02 22:45:44 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5337725630004498&Force_direct=true |Z 5337725630004498 |b Available |8 5337725630004498 | ||