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Multi-Scale Fusion and Decomposition network for Single Image Deraining

IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

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Radiative cooling for passive thermal management towards sustainable carbon neutrality

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National science Review 2023年第1期10卷 87-97页

作者： Jun Liang Jiawei Wu Jun Guo Huagen Li Xianjun Zhou Sheng Liang Cheng-Wei Qiu Guangming Tao Wuhan National Laboratory for Optoelectronics and Optics Valley Laboratory Huazhong University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore MOE Key Laboratory of Thermo-Fluid Science and Engineering School of Energy and Power Engineering Xi'an Jiaotong University Key Laboratory of Education Ministry on Luminescence and Optical Information Technology National Physical Experiment Teaching Demonstration CenterDepartment of PhysicsSchool of Physical Science and Engineering Beijing Jiaotong University State Key Laboratory of Material Processing and Die &Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology

Photonic structures at the wavelength scale offer innovative energy solutions for a wide range of applications,from high-efficiency photovoltaics to passive cooling,thus reshaping the global energy *** cooling based on structural and material design presents new opportunities for sustainable carbon neutrality as a zero-energy,ecologically friendly cooling *** this review,in addition to introducing the fundamentals of the basic theory of radiative cooling technology,typical radiative cooling materials alongside their cooling effects over recent years are summarized and the current research status of radiative cooling materials is outlined and ***,technical challenges and potential advancements for radiative cooling are forecast with an outline of future application scenarios and development *** the future,radiative cooling is expected to make a significant contribution to global energy saving and emission reduction.

关键词： photonic structures radiative cooling energy saving and emission reduction carbon neutrality

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Joint IoT Device Selection and Health-Aware Beamforming Design for MIMO-WPT

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IEEE Transactions on Mobile Computing 2024年第9期23卷 8822-8838页

作者： Wang, Chih-Hang Shi, Yishuo Yang, De-Nian Chen, Wei-Yu Chen, Wen-Tsuen Academia Sinica Institute of Information Science Taipei115201 Taiwan Wenzhou University Department of Statistics and Information Science College of Mathematics and Physics Wenzhou325035 China Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taipei115201 Taiwan University of Southern California Department of Electrical and Computer Engineering Los AngelesCA90007 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Wireless power transfer (WPT) has emerged to enhance the robustness of the energy harvesting Internet of Things (EH-IoT), whereas beamforming has been leveraged to significantly boost the efficiency of far-field WPT. Nevertheless, potential negative impacts due to high electromagnetic fields (EMF) exposure for radiation-susceptible users have not been thoroughly considered in the design of WPT for EH-IoT with IoT application-level requirements (e.g., coverage). In this article, we explore the health-aware beamforming and IoT selection problem under the EH and human safety constraints. First, we formulate a new optimization problem Health-Aware Beamforming and IoT Selection (HABIS) and prove the NP-hardness. Second, we design an approximation algorithm, named Minimum Radiation Exposure and Maximum IoT Coverage (MREMIC), to exploit the EH-health dependency (EHHD) graph for properly addressing the trade-off between EH efficiency and potential EMF radiation exposure to human bodies. We also discover the optimal health-aware beamforming to minimize the total radiation energy absorption of humans. Simulation results show that MREMIC can effectively charge IoT devices and significantly outperforms existing EH approaches by more than 200% regarding human safety. © 2002-2012 IEEE.

关键词： Energy harvesting

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期12卷 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

关键词： pretrained model diabetes continuous glucose monitoring glucose dynamics

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A novel firmware secure transmission scheme based on SM2 threshold key agreement algorithm

A novel firmware secure transmission scheme based on SM2 thr...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Wang, Kun Gao, Shaofeng Chen, Xinze Fu, Yong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510677685

During the process of embedded firmware upgrade, to protect the security of the firmware during transmission, this paper proposes an improved firmware transmission scheme based on the combination of the SM2 threshold key agreement algorithm. Between the server, client, and downloader, the SM2 key agreement algorithm is used to generate session key pairs to encrypt the firmware information, and the method of reinforcing and verifying re-transmission is adopted to ensure data security. In order to improve the security of the algorithm, a threshold algorithm is used in the key agreement process. The server acts as a trusted center to execute the threshold algorithm and divide the main secret private keys dA and dB. During the phase of key exchange, at least t+1 sub-secrets are required to recover the complete main secret. This paper successfully implements and verifies the feasibility of this scheme. © 2024 SPIE.

关键词： Firmware

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Lightweight Malicious Encrypted Traffic Detection Method Based on KL Image Compression 9

Lightweight Malicious Encrypted Traffic Detection Method Bas...

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Wen, Yuecheng Li, Xiaotong Lv, Haiqing Wang, Menglu Zuo, Wenbo Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Timely and accurate detection of and response to potentially malicious encrypted traffic is crucial in cybersecurity strategies. Convolutional neural network-based approaches that convert traffic loads into images for analysis have made significant progress in malicious encrypted traffic detection, but they suffer from the challenges of large number of participants, large network size, and high demand for computational resources, especially when edge computing devices are limited. Therefore, there is an urgent need for a lightweight, efficient and accurate malicious encrypted traffic detection model. This paper proposes a malicious encrypted traffic detection model whose core idea is to utilize KL image compression technique. The model first converts the raw traffic data into grayscale images and applies KL transform to compress the images efficiently. Subsequently, a convolutional neural network model with lightweight design is used to classify the compressed image for fast and accurate malicious encrypted traffic detection. After validating on both the public traffic dataset and the lab's self-collected dataset, the results indicate that our method requires just 0.0216 seconds to process a single input sample, achieving an average classification accuracy of 98%. These findings highlight its practicality and excellence in addressing the real-world challenges of malicious encrypted traffic. © 2024 IEEE.

关键词： Cybersecurity

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Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7

Distributed acoustic sensing signal event recognition and lo...

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2023 International Congress on Ultrasonics, ICU Beijing 2023

作者： Zhang, Hongyu Wang, Chang Zhang, Faxiang Jiang, Shaodong Sun, Zhihui Wang, Xiaodong Duan, Zhenhui Gao, Fengxia Liu, Zhaoying Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection (Φ -OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multiscene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial-temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and *** have shown that the proposed method achieves an mAP@.5 (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%,and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Light reflection

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ICM-Assistant: Instruction-tuning Multimodal Large Language Models for Rule-based Explainable Image Content Moderation 39

ICM-Assistant: Instruction-tuning Multimodal Large Language ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Mengyang Zhao, Yuzhi Cao, Jialun Xu, Mingjie Jiang, Zhongming Wang, Xuehui Li, Qinbin Hu, Guangneng Qin, Shengchao Fu, Chi-Wing Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Huawei Hong Kong Research Center Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Huawei 2012 Laboratories China Artificial Intelligence Institute Shanghai Jiao Tong University China School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Xidian University China Guangzhou Institute of Technology Xidian University China ICTT and ISN Laboratory Xidian University China

ISBN: (纸本)157735897X

Controversial contents largely inundate the Internet, infringing various cultural norms and child protection standards. Traditional Image Content Moderation (ICM) models fall short in producing precise moderation decisions for diverse standards, while recent multimodal large language models (MLLMs), when adopted to general rule-based ICM, often produce classification and explanation results that are inconsistent with human moderators. Aiming at flexible, explainable, and accurate ICM, we design a novel rule-based dataset generation pipeline, decomposing concise human-defined rules and leveraging well-designed multi-stage prompts to enrich short explicit image annotations. Our ICM-Instruct dataset includes detailed moderation explanation and moderation Q-A pairs. Built upon it, we create our ICM-Assistant model in the framework of rule-based ICM, making it readily applicable in real practice. Our ICM-Assistant model demonstrates exceptional performance and flexibility. Specifically, it significantly outperforms existing approaches on various sources, improving both the moderation classification (36.8% on average) and moderation explanation quality (26.6% on average) consistently over existing MLLMs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Salient Co-Speech Gesture Synthesizing with Discrete Motion Representation 48

Salient Co-Speech Gesture Synthesizing with Discrete Motion ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ye, Zijie Jia, Jia Wu, Haozhe Huang, Shuo Sun, Shikun Xing, Junliang Tsinghua University Department of Computer Science and Technology Beijing100084 China Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9781728163277

Synthesizing co-speech gestures is challenging because the mapping from speech to gesticulation is inherently non-deterministic. When giving talks, people conduct not only gentle and rhythmic motions but also abrupt and salient gesticulations. Most previous research efforts, however, ignore this nature of co-speech gestures and synthesize deterministic results, producing over-smoothed movements with limited expressiveness. To address this issue, we propose a new co-speech gesture generation approach that produces high-quality salient gesticulations. Specifically, we build a discrete motion representation (DMR) space to bridge the speech-gesture mapping and the gesture generation stages. The incorporation of DMR enables random sampling in motion space and avoids the over-smooth problem in speech-gesture mapping. Based on DMR, we devise a novel multi-modal co-speech gesture synthesis model with temporal attention (MCGT). MCGT explicitly models DMR's categorical distribution conditioned on the speech context, which captures complex context patterns and produces more salient gesticulations in sync with the context. In addition, we construct a new benchmark for evaluating salient motion quality in co-speech gestures, containing a large-scale co-speech gesture dataset with salient gesticulations. We also introduce a new metric, referred to as salient motion similarity, to evaluate the salient motion quality. Experiments demonstrate superior results from our approach over several competing baselines. © 2023 IEEE.

关键词： Co-speech gesture Discrete motion representation Temporal attention

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Design of a garbage classification system based on deep transfer learning

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 225-232页

作者： Zucai, Tang Luping, Wang Miaoyan, Qu Aitong, Sheng Nianwang, Huai Department of Automation University of Shenyang Aerospace Liaoning Shenyang China Engineering Training Center University of Shenyang Aerospace Liaoning Shenyang China Department of computer science and technology University of Shenyang Aerospace Liaoning Shenyang China

In light of the present challenges related to the insufficient autonomous identification and classification of garbage, along with the management and movement of garbage bins, a novel intelligent garbage classification system utilizing deep learning is suggested. This system is designed to autonomously identify types of garbage and to finalize the mobile garbage classification system for packaging. An analysis of the three-dimensional structure of the garbage bin and a dynamic examination of the motion system of the three-wheeled chassis are performed. The Raspberry Pi 3B and STM32 microcontrollers are employed for management, while the Inception v3 network structure algorithm is carefully adjusted and enhanced to facilitate garbage image recognition and classification. The outcomes of experimental research demonstrate that the fine-tuned and optimized Inception v3 network structure algorithm effectively facilitates the identification of garbage image recognition, and the model demonstrates superior performance in recognizing and categorizing images. This investigation offers a reliable approach for enhancing the efficiency and accuracy of garbage classification, thereby contributing to environmental protection and sustainable development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Transfer learning

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