Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from freque...Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.展开更多
FlexRay is a vehicular communication protocol designed to meet growing requirements in hard real time automotive systems and to support time triggered as well as event triggered paradigms. Thus, there has been a lot o...FlexRay is a vehicular communication protocol designed to meet growing requirements in hard real time automotive systems and to support time triggered as well as event triggered paradigms. Thus, there has been a lot of recent interest in timing analysis techniques in order to provide bounds for the message communication times on FlexRay. In this paper, we present an approach to compute the WCRT （worst case response time） for periodic and sporadic tasks, within a FlexRay node, responsible for sending messages on the FlexRay SS （static segment） and DS （dynamic segment）. On the other hand, we propose a scheduling table for messages transmitted over the FlexRay SS. An interesting innovation would be the use of a scheduling algorithm performed on a FlexRay node to guarantee the arrival of the right data on the right time and to ensure that every task meets its deadline. As application, we will use the extended SAE （society of automotive engineers） benchmark for the FlexRay network to identify the static and dynamic tasks, and calculate the response time, based on a hybrid scheduling model to further prove that the deadline of the SAE benchmark applications is insured.展开更多
基金This work was supportedin part by the National Natural Science Foundation of China(No.60271025,No.30370395)in part by the Science and Technology Depart ment of Shaanxi Province(No.2003K10-G24).
文摘Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.
文摘FlexRay is a vehicular communication protocol designed to meet growing requirements in hard real time automotive systems and to support time triggered as well as event triggered paradigms. Thus, there has been a lot of recent interest in timing analysis techniques in order to provide bounds for the message communication times on FlexRay. In this paper, we present an approach to compute the WCRT （worst case response time） for periodic and sporadic tasks, within a FlexRay node, responsible for sending messages on the FlexRay SS （static segment） and DS （dynamic segment）. On the other hand, we propose a scheduling table for messages transmitted over the FlexRay SS. An interesting innovation would be the use of a scheduling algorithm performed on a FlexRay node to guarantee the arrival of the right data on the right time and to ensure that every task meets its deadline. As application, we will use the extended SAE （society of automotive engineers） benchmark for the FlexRay network to identify the static and dynamic tasks, and calculate the response time, based on a hybrid scheduling model to further prove that the deadline of the SAE benchmark applications is insured.
