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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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Advanced technologies in InGaN micro-LED fabrication to mitigate the sidewall effect

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光：科学与应用（英文版） 2025年第2期14卷 307-332页

作者： Zhiyuan Liu Haicheng Cao Xiao Tang Tingang Liu Yi Lu Zixian Jiang Na Xiao Xiaohang Li Advanced Semiconductor Laboratory Electrical and Computer Engineering ProgramCEMSE DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The size of InGaN micro-LEDs is continuously decreasing to meet the demands of various emerging applications,especially in tiny micro-displays such as AR/***,the conventional pixel definition based on plasma etching significantly damages the mesa sidewalls,leading to a severe reduction in efficiency as the micro-LED size *** seriously impedes the development and application of *** this work,we comprehensively explain the origin of micro-LED sidewall effects and corresponding physical ***,we systematically review recent progress in micro-LED fabrication aiming at suppressing sidewall ***,we discuss advancements in micro-LED fabrication with"damage-free"techniques,which hold the potential to fundamentally address the issue of plasma damage in the micro-LED *** believe this review will deepen the understanding of micro-LED sidewall effects and provide a better insight into the latest associated fabrication technologies for high-efficient InGaN micro-LEDs.

关键词： technologies micro-led advanced ingan effect fabrication mitigate sidewall

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INN: Experimental Evaluation of Machine Learning Based Obesity and Overweight Prediction Using Intensive Neural Networks

INN: Experimental Evaluation of Machine Learning Based Obesi...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Vijayalakshmi, C. Kalist, V. Charulatha, G. Giri, J. Sabri, M.O. Ulaganathan, M.S. B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai600048 India Sathyabama Institute of Science and Technology Department of Ece Chennai India Selvam College of Technology Department of Biomedical Engineering Namakkal India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Economic Business Information Technology Department Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia Sri Sai Ram Institute of Technology Department of Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331543617

Obesity and overweight are significant public health concerns worldwide, contributing to a range of chronic diseases, including cardiovascular disease, diabetes, and certain cancers. This study presents an advanced predictive model for obesity and overweight detection using an Intensive Neural Network (INN) integrated with Support Vector Machine (SVM). The model is designed to address the complex, non-linear relationships between various factors contributing to obesity, such as age, gender, height, weight, BMI, physical activity levels, dietary habits, and genetic factors. A dataset consisting of 28,765 manually collected data points was used to train and validate the model. Through meticulous data preprocessing, including handling missing values, noise reduction, feature scaling, and feature selection, the data was prepared for optimal model performance. The proposed INN + SVM model achieved an outstanding accuracy of 94.76%, outperforming nine existing models, including Convolutional Neural Network (90.47%) and Deep Neural Network (89.12%). Additional metrics such as Precision (93.22%), Recall (92.88%), and F1-Score (93.05%) further validate the model's superiority. The integration of SVM with INN enhances the model's ability to classify difficult cases, resulting in a robust and reliable tool for predicting obesity and overweight conditions. The results suggest that this hybrid approach holds significant potential for improving early detection and intervention strategies in healthcare. © 2024 IEEE.

关键词： Deep Learning Healthcare Intensive Neural Network Machine Learning Obesity Prediction Overweight Detection Support Vector Machine

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Adaptive optics two photon microscopy enables in vivo longitudinal visualization of the glaucoma progression with subcellular resolution in the mouse model 34

Adaptive optics two photon microscopy enables in vivo longit...

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Ophthalmic Technologies XXXIV 2024

作者： Fu, Yiming Pham, Binh Minh He, Sicong Qin, Zhongya Xie, Ting Qu, Jianan Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Division of Life Science Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Biomedical Engineering Southern University of Science and Technology Guangdong Province Shenzhen China

ISBN: (纸本)9781510669079

Glaucoma is characterized by the irreversible retinal ganglion cells (RGCs) loss and has received great research and clinical attention due to its complex mechanism and loss of effective treatment. Longitudinal in vivo study on glaucoma in mouse model can be a promising approach to unveil the mechanism of glaucoma. In this paper, we use a silicon oil (SO) induced glaucoma model on mouse and the adaptive optics two photon microscope system to evaluate whether the aberration from both mouse eye and SO can be corrected and then longitudinal imaging with subcellular resolution on the mouse retina can be achieved. © 2024 SPIE.

关键词： Adaptive optics

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Predicting compressive strength of sustainable concrete using advanced AI models: DLNN, RF, and MARS

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Asian Journal of Civil engineering 2025年第5期26卷 1939-1954页

作者： Wagh, Monali Waghmare, Charuta Gudadhe, Amit Thakur, Nisha Mohammed, Salah J. Algburi, Sameer Majdi, Hasan Sh. Ansari, Khalid Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Computer Science and Design Division Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India Department of Civil Engineering Bajaj Institute of Technology Wardha India Civil Engineering Department Dijlah University College Baghdad Iraq Al-Kitab University Kirkuk 36015 Iraq Department of Chemical Engineering and Petroleum Industries Al-Mustaqbal University College Babylon 51001 Iraq

Recycled aggregate is becoming a sustainable construction resource that minimizes the carbon footprint in concrete structures. To predict the behavior of environmentally friendly (EnF) concrete in sustainable construction, it is necessary to predict the compressive strength using recycled materials accurately. The current research highlights the performance of the Deep Learning Neural Network (DLNN), Random Forests (RFs), and Multivariate Adaptive Regression Splines (MARS) are evaluated and extensive analysis of data segmentation was performed by splitting the dataset used in this study into 75–25% as well as 80–20% training/testing scenarios using Extreme Gradient Boosting (XG Boost), a quantitative measurement of the effect of data segmentation on model efficiency. The combination of AI models with Extreme Gradient Boosting (XG Boost) was employed to ascertain the governing variables on the CS prediction. Numerous statistical models developed were used to compare the effectiveness of these given models showing the best performance of the DLNN model based on the least RMSE (2.93). The results found that more variables should be added to the prediction problem for better prediction accuracy and the data split of 80–20% was the best choice. Based on the high accuracy of models, the results demonstrated that over the other established models, the DLNN model surpasses them in the analysis of concrete behavior and is useful for future applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Compressive strength prediction Deep learning neural network Multivariate adaptive regression splines Random forests Recycled aggregate XG Boost

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Intelligent Traffic Management System using Multi-Agent Reinforcement Learning

Intelligent Traffic Management System using Multi-Agent Rein...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Dalaali Shaheen Getzi Jeba Leelipushpam Paulraj Immanuel Johnraja Jebadurai Stewart Kirubakaran Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of CSE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This paper presents a decentralized multi-agent system for intelligent traffic management in urban environments, where each agent represents a traffic light controller at an intersection. The proposed system leverages a combination of Distributed W-Learning and Deep Q-Networks (DQN) to optimize traffic flow. Distributed W-Learning enables agents to prioritize decisions based on multiple performance policies, while DQN enhances their ability to handle complex state-action mappings through neural network approximations. By utilizing locally available traffic data, each agent adapts dynamically to diverse traffic conditions. The integration of these machine learning techniques ensures a fully decentralized and self-organizing approach, minimizing congestion, improving traffic efficiency, and addressing multiple objectives simultaneously. Simulation experiments are conducted using SUMO (Simulation of Urban MObility) to evaluate the system's performance in realistic traffic scenarios. This study highlights the potential of combining Distributed W-Learning and DQN in multi-agent reinforcement learning to revolutionize traffic management systems and achieve scalable, adaptive, and efficient urban traffic control.

关键词： Adaptation models Renewable energy sources System performance Urban areas Reinforcement learning Traffic control Real-time systems Vehicle dynamics Traffic congestion Optimization

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Advanced Underwater Imagery Enhancement using CycleGAN for Marine Research and Photography

Advanced Underwater Imagery Enhancement using CycleGAN for M...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Abhishek Sarsiha Stewart Kirubakaran S Anitha J Santhiya P Ebenezer V Shobana M Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Sciences and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering SNS College of Technology Coimbatore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

This paper proposes the Underwater Image Enhancement using CycleGAN generative adversarial network (GAN). It can be seen that underwater images encounter kinds of color distortion, low contrast, and haziness due to the interaction of light with water. CycleGAN helps our system to overcome the challenge of fine tuning raw underwater images to better quality images in terms of color and contrast. The goal of this method is to serve those in marine biology, underwater photography, and research by delivering superior visual data for enhanced identification and representation purposes. We mention below that we have compared the outcome with existing approaches, judged by standard image quality measures. Consequently, the results show the possible application of CycleGAN for achieving better image quality with minimized preprocessing time.

关键词： Photography Image quality Pervasive computing Visualization Image color analysis Social networking (online) Generative adversarial networks Image enhancement Tuning Standards

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Adaptive Coordination with Graph Convolution Networks and Grey Wolf Optimization for Next-Gen 5G Network Slicing

Adaptive Coordination with Graph Convolution Networks and Gr...

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2024 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2024

作者： Kowsalya, M. Ra, In-Ho Naveenraj, K.S. Thruvenkadam, S. Park, Seok-Woo School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computer & Software Kunsan National University Gunsan Korea Republic of Jio Platform Limited Internet Protocol Division Mumbai India

ISBN: (纸本)9798350352788

The need for flexible and effective 5G communication networks has prompted studies into novel approaches for network slicing, a fundamental paradigm that permits network service personalization for a range of applications. In this research, we provide a unique technique that combines Graph Convolution Networks (GCN) and Grey Wolf Optimization (GWO) strategies to improve the adaptive configuration of 5G network slices. GCN, a software capable of recording detailed network interactions, and GWO, well-known for its metaheuristic optimization, combine to offer a strong foundation for tackling dynamic network slicing challenges. We optimize GWO settings via rigorous testing and demonstrate the effectiveness of the integrated strategy by achieving gains in important performance measures including latency, throughput, and resource use. The investigation not only advances the continuing development of 5G networks but also highlights how cutting-edge machine learning and optimization techniques can completely transform network slicing approaches. © 2024 IEEE.

关键词： 5G mobile communication systems

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A Review of Mooring Monitoring Methods for Anomalies Detection in Thin Mooring Lines: Challenges and Future Realms 4

A Review of Mooring Monitoring Methods for Anomalies Detecti...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Hashmani, Manzoor Ahmed Khatri, Tarwan Kumar Koh, Tieng Wei Rahim, Lukman Ab. Taib, Hasmi Abdullah, Nasir Department of Computer and Information Sciences Perak Seri Iskandar Malaysia Perak Malaysia Floating Production Facilities Project Delivery & Technology Division Civil and Structural Section Engineering Department Kuala Lumpur Malaysia Metocean Engineering Kuala Limpur Malaysia

ISBN: (纸本)9798350395914

Mooring systems originate in the form of polyester ropes, chains, and steel wire that are employed for the purpose of offshore station keeping and have been shaped in bulk based on the defined criteria of the mooring design principles. But, still, there have been many incidents, including financial losses, loss of human life, injuries of crew members during onboard mooring operations, catastrophic failure, and more due to the failure in mooring lines during offshore mooring operations, which reveals that there is a strong need to improve the reliability of the mooring systems and a way for the betterment of mooring integrity management, inspection, and monitoring of mooring systems that may aid in bettering the reliability. In this paper, an extensive review is conducted to examine conventional mooring monitoring methods with challenges and future directions for anomaly detection in mooring lines. Additionally, this research study also focuses on furnishing insights into the strengths, limitations, and effectiveness of conventional mooring monitoring techniques that have been employed to detect the failure in mooring lines and dig into the new research advancement and emerging trends that extend the potential solution for the bettering of mooring monitoring in the detection of anomalies in mooring lines by assuring the reliability, safety, scalability, and optimality in the performance of marine structures and offshore mooring operations. © 2024 IEEE.

关键词： Mooring

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An Improved Transfer Learning-Based Model with Data Augmentation for Brain Tumor Detection 6

An Improved Transfer Learning-Based Model with Data Augmenta...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Prayogo, Rizal Dwi Hamid, Nur Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350344349

Current advances in deep learning have brought various breakthroughs in processing medical data. However, dealing with a limited number of medical datasets remains a challenge in deep learning and often leads to overfitting. To solve this research gap, here we show a new approach to improve the performance of a transfer learning-based model for brain tumor detection from 253 brain magnetic resonance imaging (MRI) sample images. The concept of transfer learning has been applied using a pretrained Inception V3combined with data augmentation. Modified layers using dropout and regularization have been additionally utilized to deal with overfitting. The proposed method shows an increase in accuracy of 6.430%, a precision of 5.531 %, a recall of 10.545%, and an F1-score of 8.040% compared to the baseline method. We show that our proposed method has been able to effectively enhance performance and reduce overfitting, even with a small number of datasets. Moreover, our proposed method outperforms state-of-The-Art brain tumor detection. © 2024 IEEE.

关键词： Tumors

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