This paper focuses on a usually ignored issue, the autocon guration modelling and decision in the context of reconfigurable embedded systems. We propose an original and generic method based on control theory including stability analysis. Our approach addresses the question of local vs global reconguration decision at hardware, software and RTOS levels. We tackle the run-time uncertainty conditions with a learning system model that balances tradeoffs between accuracy and complexity using sensors and run-time light estimators based on signal processing theory. Then we figure out how our method can be applied to a smart camera
Data-driven approaches like reinforcement learning (RL) allow a model-free, self-adaptive controller...
Cyber-physical systems (CPS) integrate physical processes with computing and communication to autono...
Conventional control theory has been used in many application domains with great success in the past...
This paper focuses on a usually ignored issue, the autocon guration modelling and decision in the co...
This paper focuses on a usually ignored issue, the auto-configuration modelling and decision in the ...
We observe that upcoming strongly recongurable embedded systems will be soon available for complex m...
We observe that upcoming strongly recongurable embedded systems will be soon available for complex m...
We consider the problem of power-aware Quality of Service (QoS) control for soft real-time embedded ...
The complexity of thermal systems for future electric vehicles is increasing to maximize range, prol...
This paper presents our solution for specifying and imple-menting self-adaptivness within an OS-base...
This article presents our methodology for implementing self-adaptivness within an OS-based and re-co...
Operational cycle control is an attractive field of research which can lead to improvements in t...
International audienceIn hybrid systems on chip (SoC) the algorithms must share limited resources cl...
Traditional feedback control methods are often model-based and the mathematical system models need t...
In this paper we show that stochastic learning automata based feedback control switching strategy ca...
Data-driven approaches like reinforcement learning (RL) allow a model-free, self-adaptive controller...
Cyber-physical systems (CPS) integrate physical processes with computing and communication to autono...
Conventional control theory has been used in many application domains with great success in the past...
This paper focuses on a usually ignored issue, the autocon guration modelling and decision in the co...
This paper focuses on a usually ignored issue, the auto-configuration modelling and decision in the ...
We observe that upcoming strongly recongurable embedded systems will be soon available for complex m...
We observe that upcoming strongly recongurable embedded systems will be soon available for complex m...
We consider the problem of power-aware Quality of Service (QoS) control for soft real-time embedded ...
The complexity of thermal systems for future electric vehicles is increasing to maximize range, prol...
This paper presents our solution for specifying and imple-menting self-adaptivness within an OS-base...
This article presents our methodology for implementing self-adaptivness within an OS-based and re-co...
Operational cycle control is an attractive field of research which can lead to improvements in t...
International audienceIn hybrid systems on chip (SoC) the algorithms must share limited resources cl...
Traditional feedback control methods are often model-based and the mathematical system models need t...
In this paper we show that stochastic learning automata based feedback control switching strategy ca...
Data-driven approaches like reinforcement learning (RL) allow a model-free, self-adaptive controller...
Cyber-physical systems (CPS) integrate physical processes with computing and communication to autono...
Conventional control theory has been used in many application domains with great success in the past...