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Nils Hütten, Miguel Alves Gomes, Florian Hölken, Karlo Andricevic, Richard Meyes and Tobias Meisen
Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is ther...
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Jih-Ching Chiu, Guan-Yi Lee, Chih-Yang Hsieh and Qing-You Lin
In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era,...
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Yong Liu, Xiaohui Yan, Wenying Du, Tianqi Zhang, Xiaopeng Bai and Ruichuan Nan
The current work proposes a novel super-resolution convolutional transposed network (SRCTN) deep learning architecture for downscaling daily climatic variables. The algorithm was established based on a super-resolution convolutional neural network with t...
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Alvaro A. Teran-Quezada, Victor Lopez-Cabrera, Jose Carlos Rangel and Javier E. Sanchez-Galan
Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long?short-term memory (LSTM) have become a means for providing solutions to problem...
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Ancilon Leuch Alencar, Marcelo Dornbusch Lopes, Anita Maria da Rocha Fernandes, Julio Cesar Santos dos Anjos, Juan Francisco De Paz Santana and Valderi Reis Quietinho Leithardt
In the current era of social media, the proliferation of images sourced from unreliable origins underscores the pressing need for robust methods to detect forged content, particularly amidst the rapid evolution of image manipulation technologies. Existin...
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Ching-Lung Fan
The emergence of deep learning-based classification methods has led to considerable advancements and remarkable performance in image recognition. This study introduces the Multiscale Feature Convolutional Neural Network (MSFCNN) for the extraction of com...
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Shaoyan Zuo, Dazhi Wang, Xiao Wang, Liujia Suo, Shuaiwu Liu, Yongqing Zhao and Dewang Liu
In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (????
H
s
) and wave period (????
T
s
) from X-band marine radar images. Since the shore-based radar image in this study is in...
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Diya Wang, Yonglin Zhang, Lixin Wu, Yupeng Tai, Haibin Wang, Jun Wang, Fabrice Meriaudeau and Fan Yang
In recent years, the study of deep learning techniques for underwater acoustic channel estimation has gained widespread attention. However, existing neural network channel estimation methods often overfit to training dataset noise levels, leading to dimi...
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Che-Hao Chang, Jason Lin, Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang
Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybri...
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Oscar Leonardo García-Navarrete, Oscar Santamaria, Pablo Martín-Ramos, Miguel Ángel Valenzuela-Mahecha and Luis Manuel Navas-Gracia
Corn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of...
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