Temporal-spatial cross attention network for recognizing imagined characters.

Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features of the signals in a sequential, parallel, or single-feature manner. However, there has been limited research on the cross-relationships between temporal and spatial features, despite the inherent association between channels and sampling points in Brain-Computer Interface (BCI) signal acquisition, which holds significant information about brain activity.

1D-CNN-based audio tampering detection using ENF signals.

The extensive adoption of digital audio recording has revolutionized its application in digital forensics, particularly in civil litigation and criminal prosecution. Electric network frequency (ENF) has emerged as a reliable technique in the field of audio forensics. However, the absence of comprehensive ENF reference datasets limits current ENF-based methods.

Beyond Photometric Consistency: Geometry-Based Occlusion-Aware Unsupervised Light Field Disparity Estimation.

Although learning-based light field disparity estimation has achieved great progress in the most recent years, the performance of unsupervised light field learning is still hindered by occlusions and noises. By analyzing the overall strategy underlying the unsupervised methodology and the light field geometry implied in epipolar plane images (EPIs), we look beyond the photometric consistency assumption, and design an occlusion-aware unsupervised framework to deal with the situations of photometric consistency conflict. Specifically, we present a geometry-based light field occlusion modeling, which predicts a group of visibility masks and occlusion maps, respectively, by forward warping and backward EPI-line tracing.

Link-Correlation-Aware Opportunistic Routing in Low-Duty-Cycle Wireless Networks.

In low-duty-cycle wireless networks with unreliable and correlated links, Opportunistic Routing (OR) is extremely costly because of the unaligned working schedules of nodes within a common candidate forwarder set. In this work, we propose a novel polynomial-time node scheduling scheme considering link correlation for OR in low-duty-cycle wireless networks (LDC-COR), which significantly improves the performance by assigning nodes with low correlation to a common group and scheduling the nodes within this group to wake up simultaneously for forwarding packets in a common cycle. By taking account of both link correlation and link quality, the performance of the expected transmission count (ETX) is improved by adopting the LDC-COR protocol.