Jie Luo

EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification

Hanhui Deng, Xuemei Li, Jie Luo, Di Wu

Electrocardiogram is a useful diagnostic signal that can detect cardiac abnormalities by measuring the electrical activity generated by the heart. Due to its rapid, non-invasive, and richly informative characteristics, ECG has many emerging applications. In this paper, we study novel deep learning …

Point cloud registration algorithm for enhanced feature constraints on the body-in-white sheet metal parts

Jie Luo, Lizhao Yin, Yuwei Lu

Abstract To address the low detection accuracy and high inspection-mold cost of the traditional sheet-metal part detection methods, a fast and accurate non-contact 3D measurement system was designed. For the registration difficulty due to unclear regional features of sheet-metal parts, an ICP …

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Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment

Yao Luo, Wenhan Chen, Zhenhua Su, Xiaoqi Shi et autres

High-quality nuclear magnetic resonance (NMR) spectra can be rapidly acquired by combining non-uniform sampling techniques (NUS) with reconstruction algorithms. However, current deep learning (DL) based reconstruction methods focus only on single-domain reconstruction (time or frequency domain), leading to drawbacks like peak loss …

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Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network

Feng Wu, Xiulin Geng, Xiaoyu He, Miao Hu et autres

Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited perceptual …

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JTF-Net: A joint time-frequency domain deep learning network for accurate reconstruction and reference-free assessment of NMR Spectra

Yao Luo, Zhenhua Su, Wenhan Chen, Xiaoqi Shi et autres

High-quality nuclear magnetic resonance (NMR) spectra can be rapidly acquired by combining non-uniform sampling techniques (NUS) with reconstruction algorithms. However, current deep learning (DL) methods are constrained to single-domain reconstruction (either in the time domain or frequency domain), leading to limitations like …

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Fixed pattern noise removal for solar images using a Self-Supervised Destriping Network

Jie Luo, Rui Wang, Mengwei Ban, Xudong Nan

With the development of Complementary metal oxide semi-conductor (CMOS) fabrication process, CMOS Image Sensor (CIS) is widely used in solar high-resolution observations. However, the images often suffer from obvious fixed pattern noise (FPN). In this paper, we present a innovative technique framework …

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Reversible adversarial steganography for security enhancement

Jie Luo, Peisong He, Jiayong Liu, Hongxia Wang et autres

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Content-adaptive Adversarial Embedding for Image Steganography Using Deep Reinforcement Learning

Jie Luo, Peisong He, Jiayong Liu, Hongxia Wang et autres

Recently, adversarial perturbations have been used to reassign cost which can enhance the security of steganography, called as adversarial embedding. However, existing methods selected costs to be modified by self-defined rules which were hard to achieve the optimal security against steganalyzers. In …

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Carbon Emissions Reduction of Neural Network by Discrete Rank Pruning

Songwen Pei, Jie Luo, Sheng Liang, Haonan Ding et autres

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Deep Learning Methodology for Obtaining Ultraclean Pure Shift Proton Nuclear Magnetic Resonance Spectra

Zhengxian Yang, Xiaoxu Zheng, Xinjing Gao, Qing Treitler Zeng et autres

Nuclear magnetic resonance (NMR) is one of the most powerful analytical techniques. In order to obtain high-quality NMR spectra, a real-time Zangger-Sterk (ZS) pulse sequence is employed to collect low-quality pure shift NMR data with high efficiency. Then, a neural network named …

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Constructing Immunized Stego-Image for Secure Steganography via Artificial Immune System

Wanjie Li, Yijing Chen, Sani M. Abdullahi, Jie Luo

Adaptive image steganography is the process of embedding secret messages into undetectable regions of a cover image through the design of a distortion function by a steganographer. Since the state-of-the-art steganalyzers are mainly based on image residual analysis, it is reasonable to …

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Improving security for image steganography using content-adaptive adversarial perturbations

Jie Luo, Peisong He, Jiayong Liu, Hongxia Wang et autres

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