An open architecture for converged Internet of Things (IoT) is proposed in this paper.By using this model,the various and huge amount of data can be converted into converged data and then encapsulated as service packa...An open architecture for converged Internet of Things (IoT) is proposed in this paper.By using this model,the various and huge amount of data can be converted into converged data and then encapsulated as service packages.Moreover,a Representational State Transfer (REST) platform has been implemented in the lab environment.The merit of this architecture is the enhancement of data efficiency.Experimental results are provided to show the benefits of the proposed architecture.展开更多
With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued no...With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.展开更多
基金supported by the Special Funds for Key Program of the China No.2009ZX01039-002-001-07,No.2010ZX03005-001-03Ministry of Education infrastructure construction project(2-5-2)
文摘An open architecture for converged Internet of Things (IoT) is proposed in this paper.By using this model,the various and huge amount of data can be converted into converged data and then encapsulated as service packages.Moreover,a Representational State Transfer (REST) platform has been implemented in the lab environment.The merit of this architecture is the enhancement of data efficiency.Experimental results are provided to show the benefits of the proposed architecture.
基金supported by the National Natural Science Foundation of China(6100115361271415+4 种基金6140149961531015)the Fundamental Research Funds for the Central Universities(3102014JCQ010103102014ZD0041)the Opening Research Foundation of State Key Laboratory of Underwater Information Processing and Control(9140C231002130C23085)
文摘With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.
