论述基于安卓(Android)智能手持设备的智能遥控系统的设计与实现。实现了对智能电视的遥控。提出并实现了新颖的基于近场通信(Near Field Communication,NFC)和二维码的智能遥控器与受控设备之间的初始化绑定配对方法,以实现安全便捷的...论述基于安卓(Android)智能手持设备的智能遥控系统的设计与实现。实现了对智能电视的遥控。提出并实现了新颖的基于近场通信(Near Field Communication,NFC)和二维码的智能遥控器与受控设备之间的初始化绑定配对方法,以实现安全便捷的通信连接。该设计可移植到智能空调等智能家居设备中,以实现多功能的智能家居设备遥控。展开更多
This paper presents the multi-step Q-learning(MQL)algorithm as an autonomic approach to thejoint radio resource management(JRRM)among heterogeneous radio access technologies(RATs)in theB3G environment.Through the'...This paper presents the multi-step Q-learning(MQL)algorithm as an autonomic approach to thejoint radio resource management(JRRM)among heterogeneous radio access technologies(RATs)in theB3G environment.Through the'trial-and-error'on-line learning process,the JRRM controller can con-verge to the optimized admission control policy.The JRRM controller learns to give the best allocation foreach session in terms of both the access RAT and the service bandwidth.Simulation results show that theproposed algorithm realizes the autonomy of JRRM and achieves well trade-off between the spectrum utilityand the blocking probability comparing to the load-balancing algorithm and the utility-maximizing algo-rithm.Besides,the proposed algorithm has better online performances and convergence speed than theone-step Q-learning(QL)algorithm.Therefore,the user statisfaction degree could be improved also.展开更多
文摘论述基于安卓(Android)智能手持设备的智能遥控系统的设计与实现。实现了对智能电视的遥控。提出并实现了新颖的基于近场通信(Near Field Communication,NFC)和二维码的智能遥控器与受控设备之间的初始化绑定配对方法,以实现安全便捷的通信连接。该设计可移植到智能空调等智能家居设备中,以实现多功能的智能家居设备遥控。
基金the National Natural Science Foundation of China(No.60632030)the National High Technology Research and Development Program of China(No.2006AA01Z276)
文摘This paper presents the multi-step Q-learning(MQL)algorithm as an autonomic approach to thejoint radio resource management(JRRM)among heterogeneous radio access technologies(RATs)in theB3G environment.Through the'trial-and-error'on-line learning process,the JRRM controller can con-verge to the optimized admission control policy.The JRRM controller learns to give the best allocation foreach session in terms of both the access RAT and the service bandwidth.Simulation results show that theproposed algorithm realizes the autonomy of JRRM and achieves well trade-off between the spectrum utilityand the blocking probability comparing to the load-balancing algorithm and the utility-maximizing algo-rithm.Besides,the proposed algorithm has better online performances and convergence speed than theone-step Q-learning(QL)algorithm.Therefore,the user statisfaction degree could be improved also.
