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Heralded optical entanglement distribution via lossy quantum channels: a comparative study

Optics Express 2025年第6期33卷 12459-12474页

作者： Zo, Wan Chin, Seungbeom Kim, Yong-Su Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Okinawa Institute of Science and Technology Graduate University Okinawa904-0495 Japan Division of Quantum Information Korea University of Science and Technology KIST School Seoul02792 Korea Republic of

Quantum entanglement serves as a foundational resource for various quantum technologies. In optical systems, entanglement distribution relies on the indistinguishability and spatial overlap of photons. Heralded schemes play a crucial role in ensuring the reliability of entanglement generation by detecting ancillary photons to signal the creation of desired entangled states. However, photon losses in quantum channels remain a significant challenge, limiting the distance and capacity of entanglement distributions. This study suggests three heralded schemes for distributing multipartite Greenberger-Horne-Zeilinger (GHZ) states via lossy quantum channels. These schemes differ both qualitatively in their network architecture (centralized or decentralized) and photon source requirements (Bell states or single-photons), and quantitatively in their success probabilities and heralding efficiency. Through comprehensive analysis incorporating both practical implementation considerations and theoretical performance metrics, we find that each scheme offers distinct advantages depending on the number of parties, channel distance, and security requirements. The decentralized scheme proves particularly advantageous for networks requiring balanced information distribution, while centralized schemes may offer better performance for smaller networks. This analysis provides insights into designing resilient heralded circuits for quantum information processing over lossy channels, considering both architectural constraints and performance requirements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Quantum channel

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Bi2F-YOLO: a novel framework for underwater object detection based on YOLOv7

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Intelligent Marine Technology and Systems 2025年第1期3卷 1-12页

作者： Liu, Xiaopeng Zhao, Keke Liu, Cong Chen, Long College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China School of Science Nanjing University of Science and Technology Nanjing China Department of Medical Physics and Biomedical Engineering University College London London UK

Underwater object detection faces significant challenges, including ambiguity and occlusion, which greatly undermine the accuracy of traditional algorithms. To address these issues, we propose Bi2F-YOLO, an algorithm, specifically designed for underwater environments. Bi2F-YOLO integrates the BiFormer module into the YOLOv7 backbone, utilizing Bi-Level routing attention (BRA) to focus on key features such as object edges and textures. This effectively addresses the problem of object ambiguity. In the detection head, we replace the conventional ELAN component with the FasterNet module. This update enhances detection efficiency and accuracy through the use of partial convolution (PConv), which redistributes the convolution kernel weights based on the sparsity of the input feature map. By doing so, it prevents the dilution of critical underwater object features caused by interference from irrelevant data. This effectively resolves the occlusion problem in underwater target detection while simultaneously reducing model parameters and computational costs. The experimental results show that Bi2F-YOLO achieves 87.3% $${{mAP}}_{50}$$ and 86.8% $${{AP}}_{50}$$ on the RUOD dataset. Compared to existing methods, it reduces model parameters by 14.3% fewer parameters (Params) and lowers computational costs by 5.5%, highlighting its efficiency and accuracy in underwater object detection.

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Low-Complexity Channel Estimation for RIS-Assisted ISAC System

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China Communications 2025年第4期22卷 296-308页

作者： Chen Zhen Li Jianqing Zhang Haijun Zhang Wei The Institute of Microelectronics University of MacaoMacao 999078China School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing 100083China Henan Academy of Science Applied Physics Institute Co. LtdZhengzhou 450008China

Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless ***,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability *** address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is *** approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift *** previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC *** effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.

关键词： channel estimation element grouping integrated sensing and communication(ISAC) RIS

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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A technical assessment of latent diffusion for Alzheimer's Disease progression

A technical assessment of latent diffusion for Alzheimer's D...

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Medical Imaging 2025: Image Processing

作者： McMaster, Elyssa Puglisi, Lemuel Gao, Chenyu Krishnan, Aravind R. Saunders, Adam M. Ravi, Daniele Beason-Held, Lori L. Resnick, Susan M. Zuo, Lianrui Moyer, Daniel Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science University of Catania Catania Italy School of Physics Engineering and CS University of Hertfordshire United Kingdom Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD United States Department of Computer Science Vanderbilt University NashvilleTN United States

ISBN: (纸本)9781510685901

Latent Diffusion Models (LDMs) introduce exciting opportunities in medical imaging, from disease progression prediction to interpolation to generate entire datasets of rare data. The stochastic nature of generative models makes it challenging to validate their outputs and assess their robustness across diverse datasets. BrLP, a state-of-the-art LDM for T1-weighted (T1w) images that incorporates auxiliary brain volume information, has been evaluated on Alzheimer's Disease (AD) progression, and achieves structural similarity index (SSIM) of 0.91 ± 0.03. In this work, we conducted a pilot study of the BrLP model using the Baltimore Longitudinal Study of Aging (BLSA) dataset. Our objectives are to (1) evaluate the model performance on an external dataset using pretrained image-based and brain image-based metrics such as mean squared error (MSE), similarity index, and mean absolute error (MAE) between conditional and unconditional brain regions;and (2) determine if a harmonization step, in addition to the proposed model's preprocessing steps, is required to improve performance. We found that the BrLP is robust to T1w imaging scanner effects, and harmonization is not required. However, there exists a bias toward a younger population compared to the BLSA cohort (BrLP reported 0.91 ± 0.03 SSIM in a cohort with age range 74 ±7 years;SSIM in our experiment is 0.91 ± 0.03 in cognitively normal subjects with age range 79.47 years ± 7.35 and AD cohort 0.90 ± 0.012 with age range 83.73 years ± 6.01). Interestingly, when the model's input was changed to simulate AD progression instead of normal aging, a higher SSIM of 0.91 ± 0.0012 was achieved compared to a ground truth non-AD scan, suggesting a potential mismatch. However, the resulting lower conditional volume regions function as expected. The model's architecture shows promise for longitudinal T1w imaging studies. © 2025 SPIE.

关键词： Neurodegenerative diseases

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Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks 2

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Subramani, Ponnusamy Selvam, M. Panneer Sworna Kokila, M.L. Vijayalakshmi, R. Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Physics Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India School of Computing SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks. © 2025 IEEE.

关键词： Deep learning

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic engineering and Renewable Energy Systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Multi-Scale Cascaded with Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 717-731页

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： Image fusion

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Statistics of 3rd-order modal dispersion in mode-division multiplexing optical communication systems

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Applied Optics 2025年第16期64卷 4660-4669页

作者： Xinran Zhu Fengtao Sun Xiaoxiao Liu MingHua Cao HeFu Li Zhenshan Yang Xia Zhang School of Physics Science and Information Engineering Liaocheng University Liaocheng Shandong 252000 China School of Computer and Communication Lanzhou University of Technology Lanzhou Gansu 730050 China yangzhenshan@***

Based on the group-delay matrix and its frequency derivatives, we study the statistics of modal dispersion (MD) up to 3rd order in randomly perturbed fibers. Particularly, we numerically simulate and analyze the statistical properties of quantities that characterize various aspects of the MD in a randomly perturbed six-mode fiber of sufficiently large length $L$ . The results show that, under proper normalization by the group-delay difference, the probability density functions (PDFs) of all 1st-, 2nd-, and 3rd-order MD are essentially independent of $L$ . Furthermore, we find that the 2nd-order and 3rd-order MD effects become significant at roughly the same signal spectral width, and the frequency variation of the principal modes dominates over that of the group delays in both the 2nd- and 3rd-order MD. The correlations among MD of different orders are also investigated by simulating the bivariate joint PDFs of relevant quantities.

关键词： Bit error rate Fiber optic communications Modal dispersion Polarization mode dispersion Signal processing Signal recovery

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Understanding Deep Learning RF Wireless Side-Channel Attacks: Perspectives on Noisy Scenarios 25

Understanding Deep Learning RF Wireless Side-Channel Attacks...

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Proceedings of the 2025 4th International Conference on Cryptography, Network Security and Communication Technology

作者： Bijia Cao Yuanyuan Chen Xiaoxia Wang Qixiang Ouyang Huanyu Wang Junnian Wang School of Physics and Electronics Hunan University of Science and Technology Xiangtan Hunan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China

ISBN: (纸本)9798400712623

Recently developed Radio Frequency Wireless Side-Channel Attacks (RF-WSCAs) have become a significant threat to the widespread deployment of IoT edge devices as they eliminate the need for physical proximity to the target. By analyzing side-channel emissions from mixed-signal chips (unintentionally coupled with radio transmission signals), attackers can compromise cryptographic implementations over long distances. Existing work demonstrates the feasibility of extracting AES keys from Bluetooth devices at a distance of 15-metres using CNN models trained on traces. However, they may overestimate the threat of RF WSCAs by ignoring the effects caused by ambient noise and interference. In this study, we conduct a detailed examination of the extent to which environmental noise and interference can affect the attack efficiency of RF-WSCAs. We first proposed a 20 input CNN architecture that achieved similar attack results to the SOTA model. Then, we conducted a comprehensive analysis of RF-WSCAs from the perspective of noisy scenarios.

关键词： AES

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