Real-time task scheduling and network device security for complex embedded systems based on deep learning networks

2020 ◽  
Vol 79 ◽  
pp. 103282 ◽  
Author(s):  
Junyan Zhou
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Learning Networks ◽  
Time Task ◽  
Network Device

scholarly journals Real-Time Task Scheduling for Energy-Aware Embedded Systems

2005 ◽  
Author(s):  
Vishnu Swaminathan ◽  
Krishnendu Chakrabarty
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Energy Aware ◽  
Time Task

Lifetime-aware real-time task scheduling on fault-tolerant mixed-criticality embedded systems

2019 ◽  
Vol 100 ◽  
pp. 165-175 ◽  
Author(s):  
Kun Cao ◽  
Guo Xu ◽  
Junlong Zhou ◽  
Mingsong Chen ◽  
Tongquan Wei ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Fault Tolerant ◽  
Time Task ◽  
Mixed Criticality

scholarly journals Real-time task scheduling for energy-aware embedded systems

2001 ◽  
Vol 338 (6) ◽  
pp. 729-750 ◽  
Author(s):  
Vishnu Swaminathan ◽  
Krishnendu Chakrabarty
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Energy Aware ◽  
Time Task

Energy Efficient Real-Time Task Scheduling for Embedded Systems with Hybrid Main Memory

2015 ◽  
Vol 84 (1) ◽  
pp. 69-89 ◽  
Author(s):  
Zhiyong Zhang ◽  
Zhiping Jia ◽  
Peng Liu ◽  
Lei Ju
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Energy Efficient ◽  
Main Memory ◽  
Time Task

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

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