A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning
F Li, F He, F Wang, D Zhang, Y **a, X Li - Applied Sciences, 2020 - mdpi.com
Left and right hand motor imagery electroencephalogram (MI-EEG) signals are widely used
in brain-computer interface (BCI) systems to identify a participant intent in controlling …
in brain-computer interface (BCI) systems to identify a participant intent in controlling …
Deep learning in the wavelet domain
This paper examines the possibility of, and the possible advantages to learning the filters of
convolutional neural networks (CNNs) for image analysis in the wavelet domain. We are …
convolutional neural networks (CNNs) for image analysis in the wavelet domain. We are …
Multi-path learnable wavelet neural network for image classification
Despite the remarkable success of deep learning in pattern recognition, deep network
models face the problem of training a large number of parameters. In this paper, we propose …
models face the problem of training a large number of parameters. In this paper, we propose …
[HTML][HTML] Enhancing Motor Imagery Classification in Brain–Computer Interfaces Using Deep Learning and Continuous Wavelet Transform
In brain–computer interface (BCI) systems, motor imagery (MI) electroencephalogram (EEG)
is widely used to interpret the human brain. However, MI classification is challenging due to …
is widely used to interpret the human brain. However, MI classification is challenging due to …
Visual defect recognition with stationary wavelet transform based neural networks
Q Cui, Y Li, H Bian, J Kong, Y Dong - Digital Signal Processing, 2025 - Elsevier
The comprehensive, intelligent development of the manufacturing industry poses new
requirements and challenges for the quality testing of industrial products. To tackle these …
requirements and challenges for the quality testing of industrial products. To tackle these …
Application of the WNN‐Based SCG Optimization Algorithm for Predicting Soft Soil Foundation Engineering Settlement
G Li, C Han, H Mei, S Chen - Scientific Programming, 2021 - Wiley Online Library
Settlement prediction in soft soil foundation engineering is a newer technique. Predicting
soft soil settling has long been one of the most challenging techniques due to difficulties in …
soft soil settling has long been one of the most challenging techniques due to difficulties in …
Parameterized wavelets for convolutional neural networks
Convolutional neural networks (CNNs) have become the prominent type of machine
learning approach for visual pattern recognition but suffer from the tuning of a large number …
learning approach for visual pattern recognition but suffer from the tuning of a large number …
[PDF][PDF] 변형 WASPP 를 이용한 Segmentation 성능 개선 연구
김종식， 강대성 - 한국정보기술학회논문지, 2020 - ki-it.com
요 약본 논문에서는 DeepLab V3 알고리즘의 핵심인 ASPP (Atrous Spatial Pyramid Pooling)
를 대신하기 위해 선행 연구한 WASPP (Wavelet Atrous Spatial Pyramid Pooling) 의 mIOU …
를 대신하기 위해 선행 연구한 WASPP (Wavelet Atrous Spatial Pyramid Pooling) 의 mIOU …
[CITATION][C] Modified ASPP (Atrous Spatial Pyramid Pooling) 를 활용한 Semantic Segmentation 성능 향상
김종식， 강대성 - 대한전자공학회 학술대회, 2020 - dbpia.co.kr
In this paper, the wavelet pooling convolution was used instead of ASPP (Atrous Spatial
Pyramid Pooling), which is the core of the DeepLab v3 algorithm. Semantic segmentation …
Pyramid Pooling), which is the core of the DeepLab v3 algorithm. Semantic segmentation …