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Abstract: This paper presents De-Conformer, an advanced image dehazing framework that addresses critical limitations in existing approaches. While CNN-based methods often fail to preserve background ...

Abstract: Accurate monitoring of water resources is essential for disaster risk reduction and sustainable development amid global climate change. At present, various methods based on convolutional ...

Abstract: This paper investigates the necessity for denoising in Synthetic Aperture Radar (SAR) dataset, emphasizing the comparison of three distinct neural networks: U-Net, ResNet, and DeepDeblur.

Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes data at 12.5 GHz using light rather than electricity. Its integrated ...

Abstract: Structural health monitoring is crucial for safeguarding critical infrastructure and requires the use of traceable methods. Hence, using explainable machine learning (ML) becomes ...

Abstract: Convolutional neural networks (CNNs) have been foundational in deep learning architectures for image processing, and recently, Transformer networks have emerged, bringing further ...

Abstract: Enterprises often maintain large volumes of scanned documents and images that contain critical data vital for driving organizational growth and success. In the finance sector, for example, ...

Abstract: Fiber Bragg Grating (FBG) sensing systems have demonstrated strong potential for distributed vibration monitoring, yet recognizing mixed intrusion events remains challenging due to the ...

Abstract: A quasi-static small signal model is vitally important to bridge the gap in device circuit co-design. To the best of our knowledge, for the first time in this paper, we proposed the ...