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Breaking demodulation limitations:AWG and Deep Learning in Dynamic Spectral Reconstruction for Differential FBG Accelerometers

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IEEE Sensors Journal 2025年第11期25卷 19210-19222页

作者： Yang, Qian Liu, Lei Niu, Ning Quan, ChenYi Ren, SuFen Chen, ShengChao Zhang, JiaLin Yan, YuMeng Xiang, LongTeng Wang, YiPing Liao, ChangRui He, Jun Wang, GuanJun School of Information and Communication Engineering Hainan University College of Electronic Science and Technology China Henan Mechanical and Electrical Vocational College School of Electrical Engineering Henan451100 China Hainan University College of Electronic Science and Technology School of Information and Communication Engineering Haikou570228 China University of Technology Sydney School of Computer Science Faculty of Engineering and Information Technology SydneyNSW Australia Hainan University College of Electronic Science and Technology Haikou570228 China Shenzhen University School of Physics and Optoelectronic Engineering Shenzhen518060 China

Conventional fiber Bragg grating (FBG) accelerometer demodulation often suffers from high environmental sensitivity, complexity, and cost. To address these issues, this paper presents two arrayed waveguide grating (AWG)-based dynamic spectral reconstruction models. First, a novel differential FBG accelerometer with an equal-intensity triangular cantilever beam and a symmetric string-like design enhances sensitivity and mitigates temperature interference. A power ratio-nonlinear logarithmic regression (PR-NLR) model derived from static calibration then enables spectral reconstruction and wavelength demodulation across varying accelerations, achieving 4-82pm accuracy, slightly below that of high-speed demodulators (HSD). Next, by integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), attention mechanisms, and a generative adversarial network (GAN) for data augmentation, prediction errors drop to 1-26pm at 20Hz and 0.2-21pm at 40Hz-a reduction of at least 60% over PR-NLR. This outcome underscores the advantages of deep learning (DL) in managing complex spectral shifts. By overcoming frequency-only demodulation limits, the proposed AWG approach achieves precise wavelength demodulation through dynamic spectral reconstruction. Its compact design, low manufacturing cost, and robust performance facilitate scalable deployment in structural health monitoring, particularly for bridge vibration analysis and industrial machinery diagnostics. © 2001-2012 IEEE.

关键词： Arrayed waveguide gratings

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd Science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad 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 Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： Data quality

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The Role of Bio Informatics in Identifying Cancer Informatics 2

The Role of Bio Informatics in Identifying Cancer Informatic...

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

作者： Gole, Harshada Daronde, Subodh Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Deparment of Computer Science and Medical Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Biomedical Enginering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

With the advent of bioinformatics, cancer research has entered a new age wherein vast amounts of complex biological data can be interpreted to create powerful treatments. This approach has exceptionally improved the identification of biomarkers, deciphering hidden patterns contained within data, and developing personalized treatment modalities by incorporating bioinformatics-based strategies with artificial intelligence and machine learning. Advances in bioinformatics will soon give insight into the cancer process at cellular levels and offer a better understanding of tumor heterogeneity and evolution. Increased reliance on bioinformatics has directly affected the development of newer immunotherapy forms. In the last year, there have been 13 new FDA-approved immunotherapy treatments for 10 different drugs and 11 types of cancers specific to each one. The future of bioinformatics in cancer research is bright, and the field is likely to be further revolutionized with the continued progress in artificial intelligence, machine learning, and single-cell analysis techniques © 2025 IEEE.

关键词： Bioinformatics

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Multi-Scale Regularization and Bounding Box Refinement in Semi-Supervised Object Detection Framework 3

Multi-Scale Regularization and Bounding Box Refinement in Se...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Raman, Ramakrishnan Rajan, Karthick Tlajiya, Falguni Paliwal, Manisha Pune India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Parul University Faculty of Engineering and Technology Department of Electrical Engineering Vadodara India Pune India

ISBN: (纸本)9798331508456

Semi-supervised object detection (SSOD) has emerged as a critical approach to bridge the gap between the extensive labeled data requirements of supervised methods and the abundance of unlabeled data available. However, existing SSOD techniques often suffer from two significant limitations: inconsistent performance across scales and inaccuracies in object localization. To address these challenges, this paper introduces a novel framework that integrates Multi-Scale Regularization (MSR) and Bounding Box Refinement (BBR). The proposed MSR mechanism enforces consistency in predictions across different feature map scales, ensuring robust pseudo-labeling and mitigating scale-related variations. Simultaneously, BBR employs a two-stage optimization process to enhance the precision of bounding box predictions, correcting spatial errors in initial detections. The combined framework leverages both labeled and unlabeled datasets effectively, resulting in improved detection performance. Extensive experiments conducted on benchmark datasets such as COCO and Pascal VOC demonstrate the efficacy of the proposed approach, showing significant improvements in mean Average Precision (mAP) compared to existing state-of-the-art SSOD methods. Ablation studies further highlight the individual contributions of MSR and BBR to the overall performance gains. This study establishes a new benchmark in SSOD, paving the way for more accurate and efficient object detection systems that rely on minimal labeled data while leveraging abundant unlabeled data. © 2025 IEEE.

关键词： Semi-supervised learning

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Evaluating the Efficiency of Dual-Zone Registration System with Timer Mechanism in Wireless Networks 3

Evaluating the Efficiency of Dual-Zone Registration System w...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Raman, Ramakrishnan Vinoth, T. Barve, Amit Tiwari, Vinita Pune India Chennai Institute of Technology Department of Mechatronics Engineering Kundrathur Chennai India Paruluniversity Faculty of Engineering and Technology Department of Computer Science and Engineering Vadodara India Pune India

ISBN: (纸本)9798331508456

The increasing demand for efficient communication in wireless networks necessitates optimized registration systems that ensure smooth connectivity and scalability. This paper introduces a novel Dual-Zone Registration System with a Dynamic Timer Mechanism designed to enhance registration efficiency in wireless networks. The system classifies devices into two zones: a primary zone for immediate registration and a secondary zone for devices requiring delayed registration. Leveraging a dynamic timer mechanism, the system regulates registration attempts to reduce congestion and optimize resource allocation. Simulations reveal that the proposed system significantly improves key performance metrics, including registration success rate, average registration time, packet loss, and network load. The dual-zone system achieved a 95% registration success rate, reducing packet loss to 3% compared to 12% in centralized systems and 8% in simple timer-based systems. Average registration time decreased to 6.3 seconds, outperforming centralized (12.5 seconds) and simple timer-based systems (9.8 seconds). Additionally, the system maintained a lower network load of 45 requests per second, showcasing its scalability and efficiency. By dynamically adjusting registration delays based on real-time network conditions, the proposed system ensures efficient resource utilization and balanced load distribution, making it a promising solution for large-scale wireless networks. These findings validate the effectiveness of the dual-zone approach in improving the scalability, reliability, and overall performance of modern wireless networks. © 2025 IEEE.

关键词： Resource allocation

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Simulation of a 100 MW Grid-Connected Solar Power Plant and MPPT Control Using the PSO-P&O Technique 11th

Simulation of a 100 MW Grid-Connected Solar Power Plant and ...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Bruk, Betelhem Dereje Shewarega, Fekadu Belay, Birhanu Hailu Yenealem, Mezigebu Getinet Negash, Dereje Shiferaw Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia University Duisburg-Essen Essen Germany Faculty of Computing Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia School of Electrical and computer Engineering Addis Ababa Institute of Technology Addis Ababa Ethiopia

ISBN: (纸本)9783031641503

Among the most prominent sources of alternative power in recent years is solar energy. Thus, photovoltaic (PV) systems need to be made more efficient and reliable. A PV panel’s conversion capacity is between 23% and 24% when it converts solar energy to electricity. However, the average conversion efficiency for most PV panels was around 15–20%. Identifying the maximum power point (MPP) on PV panels is one way to increase conversion efficiency. MPPT system control is presented and discussed in this paper for a 100 MW system in Metehara, Ethiopia. The aim of this paper is to present a novel hybrid approach for maximum power point tracking (MPPT). A particle swarm optimization (PSO) algorithm and a P&O MPPT technique are combined in this method. In order to achieve the MPP with improved speed and accuracy, the algorithm goes through a series of stages. MATLAB/Simulink was used to model the proposed algorithm under different operating conditions. Through comprehensive simulations across diverse input conditions, the proposed approach’s MPP tracking performance is assessed. When compared with systems lacking MPPT techniques, the hybrid approach demonstrates an average efficiency of 99.76%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATLAB

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A New ZVS Interleaved Flyback Converter 12

A New ZVS Interleaved Flyback Converter

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Mojaveri, Shokouh Shabani Delshad, Majid Heydarian-Forushani, Ehsan Branch Islamic Azad University Faculty of Engineering Isfahan Iran Qom University of Technology Department of Electrical and Computer Engineering Qom Iran

ISBN: (纸本)9798331525132

This paper presents a new auxiliary circuit for interleaved flyback converters to create soft switching conditions in the element in addition the semiconductor in the converter structure prevents the voltage spike across the switches. Also, due to the use of the ripple cancellation technique, the input current has decreased. The proposed interleaved flyback converter has been completely analyzed, a 100W laboratory sample has been developed to confirm its performance, and practical results have been provided to verify the proper performance of the circuit. © 2025 IEEE.

关键词： Flyback converter Interleaved Ripple cancellation Zero voltage switching

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The Role of AI in Modern Classroom Management 2

The Role of AI in Modern Classroom Management

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

作者： Gayaki, Unnati Daronde, Subodh Datta Meghe Institute of Higher Education and Research Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Biomedical Engineering Faculty of Engineering and Technology Maharashtra Wardha India

ISBN: (纸本)9798331505745

Artificial Intelligence is changing the way classrooms are managed, encouraging new ideas, making things easier, and helping students learn in their own way. Adaptive platforms, smart tutoring systems, and tools like I-MINDS customize learning experiences to fit different needs while making it easier for teachers. It takes care of jobs like grading, tracking attendance, and analyzing behavior, allowing teachers to concentrate on important thinking skills and emotional growth. AI also enhances teamwork through group tools, promotes fairness, and assists in real-time decision-making. Technologies like VR, AR, and MR allow people to learn through experience, especially with hard subjects. Incorporating AI learning into school curricula readies students for a future driven by AI. Despite issues with data privacy, biased algorithms, and ethical questions, AI is making learning environments inclusive, dynamic, and just. New technology, combined with ways that are focused on people, is how AI seeks to transform education into a holistic and flexible system for learners and teachers alike. © 2025 IEEE.

关键词： Decision making

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Effective Node Deployment in Wireless Sensor Networks Using Reinforcement Learning 14

Effective Node Deployment in Wireless Sensor Networks Using ...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Teja, Panduranga Ravi Vishwakarma, Anish Kumar Ranjan, Rakesh Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Science UPES Uttarakhand Dehradun India

ISBN: (纸本)9798331531935

Wireless Sensor Networks (WSNs) are vital for applications like environmental monitoring and smart cities, but effective node deployment remains challenging. Traditional methods often fail in dynamic scenarios. This paper proposes a Reinforcement Learning (RL)-based framework to optimize WSN node deployment, maximizing coverage while minimizing energy use and extending network lifetime. The RL model adapts to environmental changes and varying network conditions, ensuring robust performance in static, dynamic, and high-density environments. Experimental results show the RL approach outperforms conventional methods, achieving better coverage, lower energy consumption, and improved network longevity. The study demonstrates RL’s adaptability and effectiveness in addressing WSN deployment challenges, enabling intelligent, self-optimizing networks for dynamic real-world applications. © 2025 IEEE.

关键词： Reinforcement learning

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Enhancing structural health monitoring with AI-ML algorithms: a focus on crack detection and prediction

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Asian Journal of Civil engineering 2025年第5期26卷 1907-1918页

作者： Bader, Ahmad Shtayat, Amir Al-Mistarehi, Bara’ Communication and Computer Department Faculty of Engineering Jadara University Irbid Jordan Department of City Planning and Design Jordan University of Science and Technology Irbid 22110 Jordan Department of Civil Engineering Jordan University of Science and Technology Irbid Jordan

SHM is a very important process in terms of the safety and durability of infrastructure. Traditional SHM often faces problems detecting minor structural defects and handling large datasets. Therefore, certain more advanced approaches are called for. The paper discussed the applications of AI and ML algorithms, such as CatBoost and the African Vultures Optimization Algorithm, for such challenges. The research is based on a unique dataset of 8,541 rows and diverse features, developing a predictive framework that enhances crack detection and forecast capabilities. The approach mainly deals with heterogeneous data using the CatBoost algorithm, given its capability for high-accuracy predictions, while AVOA optimizes feature selection, reduces the computational cost, and guarantees no loss in model performance. This methodology has resulted in a significant enhancement of the prediction accuracy, which states the importance of AI-ML integration in SHM. The key results demonstrate the effectiveness of the model in detecting structural anomalies and crack propagation to enable proactive maintenance strategies. This study’s contributions have gone toward advancing SHM with scalable and efficient AI-ML frameworks, enabling real-time monitoring for better infrastructure management. Such development might have a transforming potential to cut down on maintenance costs and enhance operational safety, thus further encouraging sustainable infrastructure systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： African vultures optimization algorithm CatBoost Crack Detection Feature selection Machine learning Structural health monitoring

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