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Fabrication-Tolerant High-Speed 5-Bit Silicon Optical True Time Delay Line in the O-Band

Journal of Lightwave Technology 2025年第9期43卷 4358-4366页

作者： Ni, Ziheng Lu, Liangjun Wang, Yixuan Liu, Yuanbin Chen, Jianping Zhou, Linjie Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Key Lab of Navigation and Location Services Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

Fast reconfiguration time on the order of nanoseconds is crucial for microwave photonic beamformers, particularly in 5G/6G wireless communication systems. Most previously reported integrated optical true time delay lines (OTTDLs) exhibit a slow response time of tens of microseconds. In this study, we demonstrate an O-band high-speed 5-bit silicon OTTDL based on fabrication-tolerant electro-optic push-pull optical switches, which exhibit lower phase deviation and reduced insertion loss compared to conventional designs. The rising and falling times are 9 ns and 11 ns for the electro-optic switching, respectively. The insertion loss and crosstalk of our fabrication-tolerant high-speed switch element are 0.66 dB (0.74 dB) and −25.09 dB (−27.41 dB) at the bar (cross) state, respectively, at the wavelength of 1310 nm. For the 5-bit OTTDL, the initial phase variation is only ∼0.10π, which is one-sixth of that observed in the conventional design. Additionally, our proposed OTTDL features a low fiber-to-fiber insertion loss of 8.72 dB and a delay tuning range of 0-313.9 ps. The high-performance, fabrication-tolerant OTTDLs hold significant promise for future applications in fast beamforming. © 1983-2012 IEEE.

关键词： Fiber to the x

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Seeing the light: Hyperspectral retinal imaging as a surrogate marker for early stage Alzheimer's disease

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ACTA OPHTHALMOLOGICA 2025年第S284期103卷

作者： Ghesquiere, Michiel Christinaki, Eirini Van Eijgen, Jan Vandenbulcke, Mathieu Vandenberghe, Rik De Vos, Maarten Stalmans, Ingeborg Department of Neurosciences – Research Group Ophthalmology KU Leuven Leuven Belgium Department of Neurosciences – Research Group Psychiatry KU Leuven Leuven Belgium Department of Neurosciences – Research Group Experimental Neurology KU Leuven Leuven Department of Electrical Engineering (ESAT) – Dynamical Systems Signal Processing and Data Analytics (STADIUS) KU Leuven Leuven Belgium

Aims/Purpose: Alzheimer's disease (AD), the leading cause of dementia, affects millions, underscoring the need for early diagnostic methods. The retina, which can exhibit AD-related pathological processes, offers a promising diagnostic site. This study aims to utilize Hyperspectral Retinal Imaging (HSRI) to differentiate between positive (+) and negative (-) PET-amyloid beta (Aβ) status. Within this cohort, a subset is Aβ+ while remaining cognitively healthy, indicating preclinical amyloid accumulation. Unlike most research targeting later symptomatic stages, our approach could facilitate interventions before clinically apparent neurodegeneration occurs. Methods: data was used from 37 Aβ- and 25 Aβ+ individuals of which 8 with mild cognitive impairment (MCI). HSRI were captured using an easy-to-use hyperspectral snapshot camera, covering a spectral range of 460–630 nm. Images were taken from primary, inferior, and superior views of the retina, with further regions of interest (ROI) defined in the primary view. After preprocessing, these spectra were used to train machine learning classifiers [1]. Results: The best classifier performance was observed using XGBoost with the superior spectra (AUC = 0.72), and using a Linear Discriminant Analysis classifier in superior quadrants (S1 AUC = 0.74, S2 AUC = 0.68). Lower performance in other ROI underscored the variability in amyloid deposition across the retina, correlating with previous findings that retinal Aβ deposits in AD patients are frequently concentrated in the superior quadrants. Conclusions: We were able to achieve similar performance of previous research in AD patients [1], but in a preclinical cohort of cognitively normal participants up to MCI patients. Consistent with the literature, the superior retinal regions seem most discriminative for Aβ status. Further exploration of the specificity of the HSRI signal will pave the way for early AD detection, potentially improving outcomes through earlier therapeutic i

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Synchronization in prefabricated off-site fit-out with graduation intelligent manufacturing system

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Automation in Construction 2025年 176卷

作者： Ding, Haoran Zhong, Ray Y. Huang, George Q. HKU Laboratory for Physical Internet Department of Data and Systems Engineering The University of Hong Kong Hong Kong Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong

Prefabricated Off-site Fit-out (POF) plays a critical but often overlooked role in Modular Prefabricated Construction (MPC). Due to fragmented and asymmetric information, inconsistent production and intralogistics operations, and production uncertainty, the POF faces significant synchronization challenges. To address these challenges, this paper proposes the Graduation Intelligent Manufacturing System (GiMS), inspired by the university graduation ceremonies. GiMS creates a smart environment that supports POF real-time decision-making under uncertainties. A ticket-based synchronization mechanism is used to coordinate production and intralogistics operations dynamically. Numerical analysis verified that GiMS significantly improves synchronization performance and reduces earliness and tardiness across various uncertainty scenarios. The experiment results confirmed its effectiveness in optimizing POF processes. This paper presents a synchronization framework explicitly designed for POF, providing practical insights for applying smart manufacturing solutions in modular construction environments. © 2025

关键词： Forming

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Performance Evaluation of Intrusion Detection systems on the TON_IoT datasets Using a Feature Selection Method 24

Performance Evaluation of Intrusion Detection Systems on the...

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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Maseno, Elijah Mwandata Wang, Zenghui Sun, Yanxia Department of Computing and Information Technology Mama Ngina University College Gatundu Nairobi Kenya Department of Electrical and Smart Systems Engineering Centre for Augmented Intelligence and Data Science University of South Africa Florida Gauteng South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400718182

As Internet of Things (IoT) technology develops so quickly, security issues with IoT devices have come to light. IoT is an array of intelligent devices connected via a network to provide various services. The amount of data generated by these devices has an impact on how well the current intrusion detection systems (IDS) function. The generated dataset consists of irrelevant features which reduces the performance of IDS, making IoT ecosystem vulnerable to cyberattacks. The researchers have suggested the feature reduction technique as a potential solution to the current problem. The proposed method seeks to reduce the feature count by removing the redundant feature subset. Several machine learning methods have been successfully implemented in this discipline. This study proposed the application of hybrid feature reduction technique. The research combined Convolutional neural network (CNN) and Long short-term memory (LSTM);CNN extracted local features and decreased dimensionality, while LSTM identified long-term relationships in the data. SVM and Random Forest classifiers models were used to classify the chosen feature subset. This study employed the TON_IoT datasets, an up-to-date dataset, to evaluate the model. During data preprocessing, the study applied SMOTETomek data pre-processing technique to address class imbalance in the dataset. With the decreased feature subset, the classification models fared reasonably well;RF had a 98% accuracy rate while SVM had a 91% accuracy rate, showcasing the suggested methodology’s potential for creating efficient IDS. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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ERBM: A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

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Computers, Materials and Continua 2025年第3期83.0卷 5155-5179页

作者： Mehmmod, Arshad Batool, Komal Sajid, Ahthsham Alam, Muhammad Mansoor Khan, Inam Ullah Department of Information Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad 46000 Pakistan Faculty of Computing Riphah International University Islamabad 46000 Pakistan Faculty of Computing and Informatics Multimedia University Cyberjaya 63100 Malaysia

Traditional rule-based Intrusion Detection systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic, where three membership functions are created to classify all the network traffic features as low, medium, or high to remain situationally aware of the environment. Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty. Also, for further classification, machine learning classifiers such as Decision Tree (DT), Random Forest (RF), and Neural Networks (NN) learn complex ways of attacks and make the detection process more precise. A thorough performance evaluation using different metrics, including accuracy, precision, recall, F1 Score, detection rate, and false-positive rate, verifies the supremacy of ERBM over classical IDS. Under extensive experiments, the ERBM enables a remarkable detection rate of 99% with considerably fewer false positives than the conventional models. Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions, the ERBM system provides a unique, scalable, data-driven approach to IoT intrusion detection. This research presents a major enhancement initiative in the context of rule-based IDS, introducing improvements in accuracy to evolving IoT threats. Copyright © 2025 The Authors.

关键词： fuzzy prediction intrusions IoT Rule based

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Frequency-Domain Refinement of Vision Transformers for Robust Medical Image Segmentation Under Degradation

Frequency-Domain Refinement of Vision Transformers for Robus...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Karimijarbigloo, Sanaz Kolahi, Sina Ghorbani Azad, Reza Bagci, Ulas Merhof, Dorit Tarbiat Modares University Department of Industrial and Systems Engineering Tehran Iran Northwestern University Department of Radiology Chicago United States University of Regensburg Faculty of Informatics and Data Science Regensburg Germany Fraunhofer Institute for Digital Medicine Mevis Bremen Germany

ISBN: (纸本)9798331510831

Medical image segmentation is crucial for precise diagnosis, treatment planning, and disease monitoring in clinical settings. While convolutional neural networks (CNNs) have achieved remarkable success, they struggle with modeling long-range dependencies. Vision Transformers (ViTs) address this limitation by leveraging self-attention mechanisms to capture global contextual information. However, ViTs often fall short in local feature description, which is crucial for precise segmentation. To address this issue, we reformulate self-attention in the frequency domain to enhance both local and global feature representation. Our approach, the Enhanced Wave Vision Transformer (EW-ViT), incorporates wavelet decomposition within the self-attention block to adaptively refine feature representation in low and high-frequency components. We also introduce the Prompt-Guided High-Frequency Refiner (PGHFR) module to handle image degradation, which mainly affects high-frequency components. This module uses implicit prompts to encode degradation-specific information and adjust high-frequency representations accordingly. Additionally, we apply a contrastive learning strategy to maintain feature consistency and ensure robustness against noise, leading to state-of-the-art (SOTA) performance in medical image segmentation, especially under various conditions of degradation. Source code is available at GitHub. © 2025 IEEE.

关键词： Convolutional neural networks

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High PM_2.5 episodes in Seoul, Korea, associated with Siberian High intensity

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Climate Dynamics 2025年第5期63卷 1-20页

作者： Kim, Ka-Young Ho, Chang-Hoi Oh, Hye-Ryun Choi, Woosuk Kim, Jinwon Cheung, Hung Ming School of Earth and Environmental Sciences Seoul National University Seoul South Korea Department of Climate and Energy Systems Engineering Ewha Womans University Seoul South Korea Department of Earth Science Education Kyungpook National University Daegu South Korea Department of Artificial Intelligence and Data Science Sejong University Seoul South Korea

The Siberian High (SH) dominates the wintertime atmospheric circulation over East Asia which strongly modulates the concentration of particulate matter ≤ 2.5 μm in diameter (PM_2.5) in the region. While the SH exhibits considerable day-to-day variations, previous studies have focused on its seasonal and interannual effects rather than its short-term influence on PM_2.5 concentrations. This study investigates the link between the evolution of atmospheric circulation associated with the changes in the SH intensity and the occurrence of high PM_2.5 concentrations (≥ 35 µg m⁻³) in Seoul, Korea. Using a hierarchical clustering method, the synoptic patterns associated with 94 high-PM_2.5 episodes in Seoul during the winters of 2008–2022 are classified into two groups based on the intensity of SH: 49 episodes with strong SH and 45 episodes with weak SH. Each SH pattern can induce high-PM_2.5 episodes over distinct daily timescales through different dynamical and thermodynamical processes in the upper and lower troposphere. The strong SH is transformed into a migratory anticyclone over Korea, reducing wind speeds in the lower troposphere. These conditions suppress atmospheric ventilation and enhance dynamic stability, providing a favorable environment for rapid accumulation of PM_2.5 near the surface within two days. During the weak SH events, the anticyclonic flow in the upper troposphere increases air temperatures in the mid-to-lower troposphere, resulting in increased low-tropospheric thermal stability. Air pollutants cannot disperse into the upper atmosphere and gradually accumulate to high PM_2.5 levels over a period of four days. This study highlights that the development of atmospheric circulation in both strong and weak SH patterns plays an important role in the air quality degradation in Seoul through different temporal characteristics. These SH-driven atmospheric circulations provide

关键词： Air pollution Atmospheric circulation PM2.5 Siberian High

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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Computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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