Zobrazeno 1 - 10
of 665
pro vyhledávání: '"Li, Yueyang"'
Autor:
Chen, Hongyu, Zeng, Weiming, Chen, Chengcheng, Cai, Luhui, Wang, Fei, Wang, Lei, Zhang, Wei, Li, Yueyang, Yan, Hongjie, Siok, Wai Ting, Wang, Nizhuan
In the fields of affective computing (AC) and brain-machine interface (BMI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches
Externí odkaz:
http://arxiv.org/abs/2410.00166
In future 6G networks, anti-jamming will become a critical challenge, particularly with the development of intelligent jammers that can initiate malicious interference, posing a significant security threat to communication transmission. Additionally,
Externí odkaz:
http://arxiv.org/abs/2409.14418
Autor:
Li, Yueyang, Zeng, Weiming, Dong, Wenhao, Han, Di, Chen, Lei, Chen, Hongyu, Yan, Hongjie, Siok, Wai Ting, Wang, Nizhuan
Single-channel electroencephalogram (EEG) is a cost-effective, comfortable, and non-invasive method for monitoring brain activity, widely adopted by researchers, consumers, and clinicians. The increasing number and proportion of articles on single-ch
Externí odkaz:
http://arxiv.org/abs/2407.14850
Autor:
Li, Yueyang, Zeng, Weiming, Dong, Wenhao, Cai, Luhui, Wang, Lei, Chen, Hongyu, Yan, Hongjie, Bian, Lingbin, Wang, Nizhuan
Background: Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ s
Externí odkaz:
http://arxiv.org/abs/2407.03217
Autor:
Chen, Hongyu, Zeng, Weiming, Cai, Luhui, Wang, Lei, Lu, Jia, Li, Yueyang, Yan, Hongjie, Siok, Wai Ting, Wang, Nizhuan
High-precision acquisition of dense-channel electroencephalogram (EEG) signals is often impeded by the costliness and lack of portability of equipment. In contrast, generating dense-channel EEG signals effectively from sparse channels shows promise a
Externí odkaz:
http://arxiv.org/abs/2406.15269
Autor:
Cai, Luhui, Zeng, Weiming, Chen, Hongyu, Zhang, Hua, Li, Yueyang, Yan, Hongjie, Bian, Lingbin, Wang, Nizhuan
Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data. However, the effectiveness of GDL based methods heavily depends on th
Externí odkaz:
http://arxiv.org/abs/2406.14455
Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, but multi-modal industrial anomaly detection based on 3D point c
Externí odkaz:
http://arxiv.org/abs/2311.06797
In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial
Externí odkaz:
http://arxiv.org/abs/2311.06794
Point-level weakly-supervised temporal action localization (PWTAL) aims to localize actions with only a single timestamp annotation for each action instance. Existing methods tend to mine dense pseudo labels to alleviate the label sparsity, but overl
Externí odkaz:
http://arxiv.org/abs/2309.09060
The replay attack detection problem is studied from a new perspective based on parity space method in this paper. The proposed detection methods have the ability to distinguish system fault and replay attack, handle both input and output data replay,
Externí odkaz:
http://arxiv.org/abs/2306.02020