A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detectio...A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detection and control state(CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.展开更多
A framework is presented for robustly estimating the location of a mobile robot in urban areas based on images extracted from a monocular onboard camera, given a 2D map with building outlines with neither 3D geometric...A framework is presented for robustly estimating the location of a mobile robot in urban areas based on images extracted from a monocular onboard camera, given a 2D map with building outlines with neither 3D geometric information nor appearance data. The proposed method firstly reconstructs a set of vertical planes by sampling and clustering vertical lines from the image with random sample consensus (RANSAC), using the derived 1D homographies to inform the planar model. Then, an optimal autonomous localization algorithm based on the 2D building boundary map is proposed. The physical experiments are carried out to validate the robustness and accuracy of our localization approach.展开更多
基金National Natural Science Foundation of China(90820305,60775040)
文摘A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detection and control state(CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.
基金National Nature Science Foundation of China(60905061)
文摘A framework is presented for robustly estimating the location of a mobile robot in urban areas based on images extracted from a monocular onboard camera, given a 2D map with building outlines with neither 3D geometric information nor appearance data. The proposed method firstly reconstructs a set of vertical planes by sampling and clustering vertical lines from the image with random sample consensus (RANSAC), using the derived 1D homographies to inform the planar model. Then, an optimal autonomous localization algorithm based on the 2D building boundary map is proposed. The physical experiments are carried out to validate the robustness and accuracy of our localization approach.
