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Adaptive Resource Allocation Algorithm for Internet of Things with Bandwidth Constraint 被引量:1
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作者 李征 刘开华 +1 位作者 苏育挺 马永涛 《Transactions of Tianjin University》 EI CAS 2012年第4期253-258,共6页
In order to improve the transmission accuracy and efficiency of sensing and actuating signals in Internet of Things (loT) and ensure the system stability, an adaptive resource allocation algorithm is proposed, which... In order to improve the transmission accuracy and efficiency of sensing and actuating signals in Internet of Things (loT) and ensure the system stability, an adaptive resource allocation algorithm is proposed, which dynami- cally assigns the network bandwidth and priority among components according to their signals' frequency domain characteristics. A remote sensed and controlled unmanned ground vehicle (UGV) path tracking test-bed was devel- oped and multiple UGV's tracking error signals were measured in the simulation for performance evaluation. Results show that with the same network bandwidth constraints, the proposed algorithm can reduce,, the accumulated and maximum errors of UGV path tracking by over 60% compared with the conventional static algorithm. 展开更多
关键词 Intemet of Things bandwidth constraint adaptive resource allocation sampling rate scheduling
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SELF-CANCELLATION OF MODULES HAVING THE FINITE EXCHANGE PROPERTY 被引量:2
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作者 CHENHUANYIN 《Chinese Annals of Mathematics,Series B》 SCIE CSCD 2005年第1期111-118,共8页
Self-cancellation of modules having the finite exchange property is introduced. If a right R-module M has the finite exchange property, it is shown that M has selfcancellation if and only if EndR(M) is a strongly sepa... Self-cancellation of modules having the finite exchange property is introduced. If a right R-module M has the finite exchange property, it is shown that M has selfcancellation if and only if EndR(M) is a strongly separative ring. Using this result,some new characterizations of strong separativity are obtained. 展开更多
关键词 Self-cancellation Strong separativity
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Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface 被引量:1
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作者 Bang-hua YANG Liang-fei HE Lin LIN Qian WANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2015年第6期486-496,共11页
Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interferenc... Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces (BCIs), a method named spatial constraint independent component analysis based recursive least squares (SCICA-RLS) is proposed. The method consists of two stages. In the first stage, independent component analysis (ICA) is used to decompose multiple EEG channels into an equal number of independent components (ICs). Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition (EMD) is employed to remove any cerebral activity from the identified ocular ICs to obtain exact altifact ICs. In the second stage, first, SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs (SC-ICs). Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference, which avoids the need for parallel EOG recordings. In addition, the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects, the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG, respectively. In addition, the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS, respectively, which demonstrates a better and faster OA reduction. 展开更多
关键词 Ocular artifacts Electroencephalogram (EEG) Electrooculogram (EOG) Brain-computer interface (BCI) Spatialconstraint independent component analysis based recursive least squares (SCICA-RLS)
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