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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Chen, Hanchuan Liu, Yichao Sun, Fei Taiyuan University of Technology Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Electronic Information and Optical Engineering Taiyuan030024 China

ISBN: (纸本)9798350375909

In previous study, many single-function static/quasi-static magnetic devices, such as cloak and concentrator, have been designed base on transformation optics. However, few have explored devices capable of simultaneously generating two different functions on the static/quasi-static magnetic field. In this study, we propose a dual-function quasi-static magnetic device, which can create a concealed region and concentrated region for quasi-static magnetic field simultaneously. The dual-function quasi-static magnetic device is designed via twice extreme spatial stretching transformation and magnetic null medium, which can be realized by using arrays of superconductors and ferromagnetic materials in specific arrangement. The simulated results verify the performance of the proposed dual-function quasi-static magnetic device, which can simultaneously achieve cloaking and concentrating effect for the magnetic field from DC to 1000 Hz. The quasi-static magnetic field can be enhanced 9 times than the background magnetic field when the side lengths of the internal square pillar and external square pillar of the proposed structure are selected as a = 10 mm and b = 90 mm, respectively. Moreover, the concentrating ratio obtained from theoretical calculation fits well with the results obtained through numerical simulation. The dual-function quasi-static magnetic device designed in this study may have essential application in medical testing for special occasions. © 2024 IEEE.

关键词： Wigglers

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Portable fluorogenic probe for monitoring of volatile amine vapour and food spoilage

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Chinese Chemical Letters 2024年第3期35卷 257-261页

作者： Jian-Hao Zhao Wen-Xing Xu Bin Li Wei Xu Wu-Kun Zhang Ming-Shuai Yuan Hui-Zi Li Qing-Guo He Xiang Ma Jian-Gong Cheng Yan-Yan Fu State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Center of Materials Science and Optoelectronics Engineering University of the Chinese Academy of SciencesBeijing 100039China School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center Frontiers Science Center for Materiobiology and Dynamic Chemistry School of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghai 200237China

There are urgent needs of volatile amine gas sensors with high-performance in food quality control,disease monitoring and environmental ***-film fluorescent probe is suitable for amine vapour sensing due to its high sensitivity,high selectivity,and no polluting ***,a novel fluorescent probe based on indacenodithiophene structure withπconjugated system was designed and *** experimental results show that the films prepared by this material exhibit rapid and distinct fluorescence quenching after being exposed to saturated vapours of primary amine,secondary amine and tertiary amine represented by n-propylamine,diethylamine and trimethylamine,*** quenching of fluorescence is 84%,87%and 96%,respectively,within 10 *** detection mechanism of probe for primary amine is based on specific chemical reaction,while the detection mechanism for secondary amine and tertiary amine is intramolecular charge *** experiments show that the detection limit of the fluorescent probe for trimethylamine,an important marker of food spoilage,could reach 4.610 ***-site detection based on spoilage of small yellow croaker suggests the material possesses the potential for food freshness *** simple fluorogenic probe is an original approach to simplify real-time visual monitoring of volatile amine vapour.

关键词： Fluorescent probe Volatile amine Gas sensing Trimethylamine Food spoilage

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A Cross-Scale Multi-Branch Feature Fusion Based Method for Smoke and Fire Detection in Warehouses 24

A Cross-Scale Multi-Branch Feature Fusion Based Method for S...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Bai, Yanbing Liu, Pengyu Liu, Weilong Deng, Yiyang Kong, Lili Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University Of Technology Beijing China Information Technology Co. Ltd Dingzhou China Beijing-Dublin International College at BJUT Beijing University Of Technology Beijing China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

To address the challenges of leakage, misdetection, and low accuracy caused by environmental complexity, object occlusion, and insufficient lighting in smoke and fire detection within warehouse scenarios, this paper proposes a cross-scale multi-branch feature fusion network for smoke and fire detection, named SF-YOLO. Firstly, a multi-branch feature enhancement module, Res2Net-C, is proposed to introduce an attention mechanism using a multi-branch feature fusion network, which not only extends the sensory field of the network, but also enhances the ability of extracting effective information, while effectively suppressing the interference of irrelevant information. It effectively improves detection performance under complex environmental conditions and in the presence of object occlusion. Secondly, a cross-scale feature fusion network structure, MSFPN, is proposed, which achieves effective complementation and fusion of high-level and low-level information by establishing hopping connections between features at different scales, and significantly improves the recognition accuracy of the type and location of the smoke and fire in low-light conditions. In addition, the added small target detection layer further improves the network's ability to detect small targets. Finally, in order to better adapt to pyrotechnic targets with variable morphology, EIoU is adopted as the bounding box loss function. Test results on a self-built dataset show that SF-YOLO achieves a mean accuracy rate (mAP) of 87.5% in the task of detecting smoke and fire under multiple environmental factors in a warehouse scenario, proving its effectiveness and usefulness. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Electromagnetic-acoustic Beam Compressor by Double-physical-fields Null Medium

Electromagnetic-acoustic Beam Compressor by Double-physical-...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Sun, Ran Sun, Fei Liu, Yichao Zhang, Zeyu Yang, Chao Taiyuan University of Technology Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Electronic Information and Optical Engineering Taiyuan030024 China

ISBN: (纸本)9798350375909

In this study, double-physical-fields null medium, i.e., a subwavelength air channels surrounded by a carefully arranged metal plates, is utilized to design an electromagnetic-acoustic beam compressor capable of simultaneously compressing electromagnetic waves and acoustic waves. Numerical simulation results verify its ability to simultaneously produce the expected compression ratios for both electromagnetic waves and acoustic waves. Moreover, the compression ratio precisely matches the geometric size ratio of the input and output planes of the compressor. This work may have potential applications in electromagnetic-acoustic coupling systems and electromagnetic-acoustic processing/detection. © 2024 IEEE.

关键词： Acoustic waves

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A Coarse to Fine Detection Method for Prohibited Object in X-ray Images Based on Progressive Transformer Decoder 24

A Coarse to Fine Detection Method for Prohibited Object in X...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Chunjie Du, Lina Gao, Zan Zhuo, Li Wang, Meng Shandong Jinan China School of Computer Science and Technology Shandong Jianzhu University Shandong Jinan China Faculty of Information Technology Beijing University Of Technology Beijing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798400706868

Currently, Transformer-based prohibited object detection methods in X-ray images appear constantly, but there are still some shortcomings such as poor performance and high computational complexity for prohibited object detection with heavily occlusion. Therefore, a coarse to fine detection method for prohibited object in X-ray images based on progressive Transformer decoder is proposed in this paper. Firstly, a coarse to fine framework is proposed, which includes two stages: coarse detection and fine detection. Through adaptive inference in stages, the computational efficiency of the model is effectively improved. Then, a position and class object queries method is proposed, which improves the convergence speed and detection accuracy of the model by fusing the position and class information of prohibited object with object queries. Finally, a progressive Transformer decoder is proposed, which distinguishes high and low score queries by decreasing confidence thresholds, so that high-score queries are not affected by low-score queries in the decoding stage, and the model can focus more on decoding low-score queries, which usually correspond to prohibited object with severe occlusion. The experimental results on three public benchmark datasets (SIXray, OPIXray, HiXray) demonstrate that compared with the baseline DETR, the proposed method achieves the state-of-the-art detection accuracy with a 21.6% reduction in model computational complexity. Especially for prohibited objects with heavily occlusion, accurate detection can be carried out. © 2024 ACM.

关键词： Decoding

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Adaptive Intrusion Detection in Cyber-Physical systems using Reinforcement Learning-Based Autoencoders 2

Adaptive Intrusion Detection in Cyber-Physical Systems using...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection system (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Combining Statistical and Semantic Features for Trajectory Point Classification 7th

Combining Statistical and Semantic Features for Trajectory P...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Xu, Jian Xu, Xin Ruan, Guoqing Science and Technology on Information System Engineering Laboratory Nanjing Research Institute of Electronic Engineering Nanjing210023 China

ISBN: (纸本)9789811992964

Trajectory point classification can be described as a supervised sequence labeling problem, in which a model is trained by labeling data to predict the category of unknown points and identify key events in the trajectory. Due to the difficulty of labeling trajectory point, a large amount of trajectory data is either unlabeled or labeled in an imbalanced way. To make matters worse, traditional trajectory point classification methods are generally constrained to utilize the statistical features of the labeled data and the semantic features as well as the large amount of unlabeled data have not been well studied yet. For this reason, the performance of traditional trajectory point classification methods is far from satisfactory. To solve this problem, we transfer existing language model knowledge to construct the semantic features and construct a trajectory point classification model by combining both the motion features and semantic features. The simulation results show that, compared with the traditional methods, our method has improved the accuracy of trajectory point classification by three and seven percentage points in the classification of circular and turning movements respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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DMSS:An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

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Big Data Mining and Analytics 2024年第3期7卷 577-589页

作者： Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190Chinaand with University of Chinese Academy of SciencesBeijing 100049China Syneron Technology Guangzhou 510000China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Insitute of Computing Technology Chinese Academy of SciencesBeijing 100190China with Western Institute of Computing Technology Chongqing 400000China

Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis,as well as for gaining insight into various biological *** this study,we introduce Deep MS Simulator(DMSS),a novel attention-based model tailored for forecasting theoretical spectra in mass *** has undergone rigorous validation through a series of experiments,consistently demonstrating superior performance compared to current methods in forecasting theoretical *** superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein *** findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.

关键词： mass spectrometry proteomics machine learning deep learning

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