Accurate decoding of electroencephalogram (EEG) signals in the shortest possible time is essential for the realization of a high-performance brain-computer interface (BCI) system based on the steady-state visual evoked potential (SSVEP). However, the degradation of decoding performance of short-length EEG signals is often unavoidable due to the reduced information, which hinders the development of BCI systems in real-world applications. In this paper, we propose a relaxed matching knowledge distillation (RMKD) method to transfer both feature-level and logit-level knowledge in a relaxed manner to improve the decoding performance of short-length EEG signals.
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September 2016
This article concentrates on open-source implementation on flying object detection in cluttered scenes. It is of significance for ground stereo-aided autonomous landing of unmanned aerial vehicles. The ground stereo vision guidance system is presented with details on system architecture and workflow.
View Article and Find Full Text PDFThis paper presents a new algorithm for extrinsically calibrating a multi-sensor system including multiple cameras and a 2D laser scanner. On the basis of the camera pose estimation using AprilTag, we design an AprilTag array as the calibration target and employ a nonlinear optimization to calculate the single-camera extrinsic parameters when multiple tags are in the field of view of the camera. The extrinsic parameters of camera-camera and laser-camera are then calibrated, respectively.
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